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River RestorationManaging the Uncertainty in Restoring Physical Habitat

EditorsStephen Darby

School of Geography, University of Southampton, UK

and

David SearSchool of Geography, University of Southampton, UK

River Restoration

River RestorationManaging the Uncertainty in Restoring Physical Habitat

EditorsStephen Darby

School of Geography, University of Southampton, UK

and

David SearSchool of Geography, University of Southampton, UK

Copyright © 2008 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777

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Library of Congress Cataloging-in-Publication Data

Darby, Stephen.River restoration : managing the uncertainty in restoring physical habitat / Stephen Darby and David Sear. p. cm. Includes bibliographical references and index. ISBN 978-0-470-86706-8 (cloth)1. Stream restoration. 2. Riparian restoration. I. Sear, David (David A.) II. Title. TC409.D28 2008 627′.12–dc22 2007030210

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN-13 978-0-470-86706-8 (HB)

Typeset in 9/11 pt Times by SNP Best-set Typesetter Ltd., Hong KongPrinted and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire

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Contents

Preface vii

List of Contributors xi

Section I Introduction: The Nature and Signifi cance of Uncertainty in River Restoration

1 Uncertainty in River Restoration 3 J. Lemons and R. Victor

2 Sources of Uncertainty in River Restoration Research 15 W. L. Graf

3 The Scope of Uncertainties in River Restoration 21 J.M. Wheaton, S.E. Darby and D.A. Sear

Section II Planning and Designing Restoration Projects

4 Planning River Restoration Projects: Social and Cultural Dimensions 43 G.M. Kondolf and C-N. Yang

5 Conceptual and Mathematical Modelling in River Restoration: Do We Have Unreasonable Confi dence? 61

M. Stewardson and I. Rutherfurd

6 Uncertainty in Riparian and Floodplain Restoration 79 F.M.R. Hughes, T. Moss and K.S. Richards

7 Hydrological and Hydraulic Aspects of Restoration Uncertainty for Ecological Purposes 105 N.J. Clifford, M.C. Acreman and D.J. Booker

8 Uncertainty Surrounding the Ecological Targets and Response of River and Stream Restoration 139 M.R. Perrow, E.R. Skeate, D. Leeming, J. England and M.L. Tomlinson

Section III The Construction and Post-Construction Phases

9 Constructing Restoration Schemes: Uncertainty, Challenges and Opportunities 167 J. Mant, R. Richardson and M. Janes

vi Contents

10 Measures of Success: Uncertainty and Defi ning the Outcomes of River Restoration Schemes 187 K. Skinner, F.D. Shields, Jr and S. Harrison

11 Methods for Evaluating the Geomorphological Performance of Naturalized Rivers: Examples from the Chicago Metropolitan Area 209

B.L. Rhoads, M.H. Garcia, J. Rodriguez, F. Bombardelli, J. Abad and M. Daniels

12 Uncertainty and the Management of Restoration Projects: The Construction and Early Post-Construction Phases 229

A. Brookes and H. Dangerfi eld

Section IV Uncertainty and Sustainability: Restoration in the Long Term

13 The Sustainability of Restored Rivers: Catchment-Scale Perspectives on Long Term Response 253 K.J. Gregory and P.W. Downs

14 Uncertainty and The Sustainable Management of Restored Rivers 287 M.D. Newson and M.J. Clark

Index 303

Preface

exceptions (Wissmar and Bisson, 2003), has not yet devoted consideration to identifying associated uncertain-ties, let alone seeking to quantify, manage, or – where appropriate (see below) – constrain them. Rather, the dis-cipline has instead tended to focus on management responses (e.g. post-project appraisal, adaptive manage-ment strategies) that only implicitly confront assumed sources of variability and uncertainty. Our concern is that a collective disciplinary failure to recognise, communicate and deal appropriately with uncertainties might, at some time in the future, undermine institutional and public con-fi dence in river restoration. In a fi rst attempt to address these issues, we (together with Dr Andrew Collison and Dr Sean Bennett) convened a special session on Uncer-tainty in River Restoration at the 2002 Fall Meeting of the American Geophysical Union (AGU) in San Francisco, California. While recognising that no single volume can ever cover all aspects of such a multi-faceted discipline as river restoration, the positive response to the topic at that AGU symposium prompted us to seek to explore it further in this volume. All the chapters for this book were, there-fore, specially commissioned in an attempt to provide a coherent narrative structure that offers a rational theoreti-cal analysis of the uncertain basis of restoration, while simultaneously providing practical guidance on managing the implications of that uncertainty.

The resulting book is structured into four main sections. Each offers a range of case studies in an attempt to ensure a wide geographic coverage. Likewise, the authorship is drawn from a range of countries and disciplines, in an attempt to bring a range of perspectives to the table. Section I comprises three chapters that review the nature and signifi cance of uncertainty in river restoration, provid-ing a context for the remainder of the book. In Chapter 1 Lemons and Victor focus on the specifi c nature of scien-tifi c uncertainty in restoration, while Graf (Chapter 2) expands on this theme, identifying a series of sources of

For many years scientists and river practitioners have recognised the severity and extent to which aquatic eco-systems have been degraded by a variety of human distur-bances and activities (Gregory and Park, 1974; Sear and Arnell, 2006). In turn, realisation of the widespread nature of the problem has more recently elicited a surge of inter-est in the possibility of undertaking corrective interven-tions, such as fl ow restoration and channel modifi cations, to restore or rehabilitate lost and/or damaged ecosystem functions (Brookes and Shields, 1996; Wissmar and Bisson, 2003). Indeed, there is now a substantial volume of literature on the broad topic of river restoration, much of which suggests that, to be sustainable, river restoration projects should be designed to recreate functional charac-teristics within a context of physical (i.e. geomorphic) stability. It is true that the emphasis on stable channel design may refl ect the traditional disciplines of many of the river engineers who have now turned their attentions to restoration. Whatever the provenance and merits of this approach, a focus on stable channel design requires the application of geomorphic and engineering design tools (models) that are for the most part either entirely empirical or empirically calibrated. As a result, different results are obtained when different models are applied to the same problem. Furthermore, the data required to apply morpho-logical models to restoration design are often absent, incomplete, or subject to measurement error. Finally, even when a restoration design is completed, it is usually not possible to predict the precise sequence of fl ood events. In the long term further variability is introduced by cli-matic or catchment changes (e.g. in land use), or unantici-pated social or cultural changes, all of which might shift the basic premise(s) of the design. It is evident that the designers and managers of stream restoration projects are inevitably confronted with uncertainty.

Despite, or perhaps because of, this challenging situa-tion the restoration literature, albeit with some notable

viii Preface

uncertainty in theory, research and communication. In Chapter 3 Wheaton et al. synthesise and extend these analyses, presenting a classifi cation that suggests uncer-tainty fundamentally arises either through limited knowl-edge or through natural system variability. This is an important distinction, not least because it helps to dis-criminate between those sources of uncertainty (limited knowledge) which should, where possible, be constrained (e.g. by scientifi c progress) from those sources (e.g. natural variability) that should be embraced to promote healthy system functioning. The management implications associ-ated with each form of uncertainty are therefore distinct, but recognition that embracing certain types of uncertainty may be both necessary and desirable to assure sustain-ability is a theme that runs throughout many of the con-tributions herein.

The book is subsequently structured to address the dis-crete stages in the life span of a typical restoration project, covering the planning and design activities associated with the pre-construction phase (Section II), the construction phase itself (Section III) and the long term post-construc-tion phase (Section IV). Section II (Chapters 4 to 8) pres-ents contributions covering various aspects of planning and design associated with restoration projects. In Chapter 4 Kondolf and Yang’s review reminds us that restoration is fundamentally a social and cultural process, with vari-ability in cultural values acting as a signifi cant contributor of uncertainty. Presenting an Australian case study, where the aim was to restore fl ows capable of fl ushing fi ne sedi-ment from river gravels, Stewardson et al. (Chapter 5) identify the limits in our understanding of hydrological, hydraulic and geomorphic processes and how these con-strain our ability to model river system dynamics. In terms of the uncertainty classifi cation discussed in Chapter 3, their focus is essentially on quantifying the magnitude of uncertainty due to limited knowledge. Their results (that the magnitude of designed fl ushing fl ows is subject to uncertainty estimates approximately twice that of the fl ow itself) reinforce the earlier suggestion that restoration is indeed an uncertain discipline. Whether this really means that we should have ‘unreasonable confi dence’ in restora-tion, as suggested by their provocative sub-title, is a theme that is continued throughout the book.

In contrast to the focus on uncertainty due to limited knowledge expounded in Chapter 5, Chapter 6 (Hughes et al.) reviews some of the diffi culties associated with extending restoration into complex riparian and fl oodplain habitats, emphasising that in these systems uncertainty (in this case in the form of physical diversity and variability) is necessary to underpin the successful restoration of forest fl oodplain ecosystems. In Chapter 7, Clifford et al.’s comprehensive review of how the restoration of fl ow

hydrology and hydraulics can be used to enhance aquatic habitats also recognises the importance of restoring natural variability, and provides recommendations on how such variability can be interpreted in modelling investigations. The theme of uncertainty associated with ecological targets is explored further in Chapter 8 (Perrow et al.), where the paradox that uncertainty is often viewed as a pejorative term is again highlighted, even if it is uncer-tainty (in the form of natural variability) that is the key mechanism for sustaining healthy ecosystems. How uncer-tainty due to the lack of understanding of a discipline (ecology) by river managers has led to a lack of using available science within restoration process is also highlighted.

Section III (Chapters 9 to 12) addresses the construction phase of a restoration project, which is defi ned in this book as extending up to one or two years after completion of the project. The contributions in this section are written primarily by river practitioners, who employ their collec-tive experience to offer a range of perspectives on uncer-tainties encountered during this key stage of restoration. Mant et al. (Chapter 9) review the diffi culties encountered during construction and note that strong teamwork skills are required to ensure that the design concepts provided by geomorphologists and ecologists are correctly trans-lated into practice by those responsible for construction. A diffi culty here is that restoration is seen as a relatively new facet of civil engineering, such that contractors may not always have the experience necessary to recognise that variability, rather than uniformity (their experience to date), is often necessary. To this end it is essential that designers inform the workforce of the specifi c require-ments of the river restoration project, while project man-agers must also take responsibility for monitoring construction as it progresses. This points to the importance of ensuring that the constructed project does indeed conform to the design specifi cations, raising the issue of evaluating project outcomes.

This subject is the theme of the next three chapters. Skinner et al. (Chapter 10) review post-project appraisals with reference to both physical and ecological measures of success, whilst Rhoads et al. (Chapter 11) focus on methods for evaluating the geomorphological performance of restored rivers, providing examples from heavily urban-ised catchments in Illinois in the USA. Both contributions emphasise the key need for both pre and post-project moni-toring, even if only to a minimum standard. This is viewed as necessary to verify that projects are constructed accord-ing to their design, as well as an integral tool of adaptive management that can make project adjustments in the face of uncertainties introduced by variable post-project condi-tions. This theme is further explored by Brookes and

Preface ix

Dangerfi eld (Chapter 12), who propose that managers should adopt continuous improvement as an overall opera-tional philosophy for restoring rivers. The term continuous improvement is widely documented in human resource, organisational management and environmental manage-ment literature, and is taken to be a philosophy of learning during the construction and post-construction phases (and making adaptations to a particular project as necessary) for the benefi t of the continuing work and future practice. This appears to be a robust approach that has the potential, over time, to reduce uncertainties associated with limited knowledge while simultaneously providing a framework for adaptive response to uncertainties associated with natural variability.

Section IV (Chapters 13 and 14) addresses the challenge of the need to assure the long term sustainability of restora-tion projects in the face of uncertain futures. There is a clear recognition that as the time scales over which project outcomes should be considered increase, there is a con-comitant need to address increased spatial scales. Specifi -cally, there is a need to consider how catchment-scale processes (which infl uence the fl uxes of water and sedi-ment supplied to restoration reaches) are to be sustained in the long term. Clearly, as spatial and temporal scales increase, then so do the uncertainties particularly, but not exclusively so, those associated with increases in spatial and temporal variability. In Chapter 13 Downs and Gregory bring a hydromorphological perspective to these issues, suggesting that the bounds of these uncertainties can be evaluated with reference to long term (palaeo)hydrological and geomorphological evidence of past catchment response – in effect advocating a more precise defi nition of the uncertainty due to natural variability.

The fi nal chapter (Chapter 14; Newson & Clark) pro-vides an apt conclusion. Recognising that uncertainty (in the form of natural variability) is both endemic and neces-sary, it is noted that there is a confl ict between the precau-tionary principle – a cornerstone of sustainable thinking – and uncertainty. Newson and Clark recognise that all restorations have outcomes that are to some extent unpre-dictable, and the precautionary principle thus becomes a recipe for inaction. Uncertainty is therefore simultane-ously necessary for, but also a barrier to, sustainability. They attempt to resolve this particular problem by identi-fying management and restoration opportunities that are sustainable despite being uncertain, noting that in practical terms it is to adaptive management that we most often turn for a way forward, reinforcing a series of conclusions from earlier chapters.

Do we have unreasonable confi dence in restoration, based on the state of the art, or are we happy to boldly go

with the uncertain ebb and fl ow of natural variability? Perhaps the way forward is through a clearer and more transparent approach to communicating uncertainty, such that all participants – and especially stakeholders – under-stand that while every effort can and should be made to identify, and where possible reduce, scientifi c and meth-odological uncertainties, uncertainty due to natural vari-ability is both welcome and necessary to sustain healthy aquatic ecosystems. This implies a concerted approach on two fronts: scientists can continue to refi ne the knowledge base (and managers need to recognise the value of this research for their (adaptive) management practices), while managers must work harder to build and accommodate variability into projects. Uncertainty in river restoration is endemic but clearly offers opportunities, not just as a rationale for further research, but fundamentally for more sustainably managed restoration projects. Just as life goes on with little confi dence or ability to predict the future, so restoration must continue to evolve and adopt an approach that is consistent with the uncertain functioning of riverine ecosystems.

In closing this preface, we would like to acknowledge those who have made signifi cant contributions during the production of this book. Firstly, we would like to thank those numerous professionals who provided detailed peer reviews of each chapter, often working to tight deadlines. The anonymous nature of peer review means that we are unable to identify them here, but you know who you are! Finally, Tim Aspden and the staff of the Cartographic Unit at the School of Geography, University of Southampton, provided guidance on, and help with, the production of much of the artwork. A fi nal acknowledgment must go to our families who have put up with longer hours than normal (or natural!) in the drive for completion. Stephen Darby and David Sear December 2007

REFERENCES

Brookes A, Shields FD. 1996. River Channel Restoration: Guiding Principles for Sustainable Projects. John Wiley & Sons Ltd: Chichester, UK.

Gregory KJ, Park CC. 1974. Adjustment of river channel capacity downstream from a reservoir. Water Resources Research 10: 840–873.

Sear DA, Arnell NW. 2006. The application of palaeohydrology to river management. Catena 66: 169–183.

Wissmar RC, Bisson PA (Eds). 2003c. Strategies for Restoring River Ecosystems: Sources of Variability and Uncertainty in Natural and Managed Systems. American Fisheries Society: Bethesda, Maryland, USA.

List of Contributors

Dr Melinda Daniels, Department of Geography, Kansas State University, USA ([email protected]).

Dr Stephen Darby, School of Geography, University of Southampton, Highfi eld, Southampton SO17 1BJ, UK ([email protected]).

Dr Peter Downs, Stillwater Sciences, 2855 Telegraph Avenue, #400, Berkeley, California 94705 USA ([email protected]).

Dr Judy England, Environment Agency, Apollo Court, 2 Bishop’s Square, St. Albans Road West, Hatfi eld AL10 9EX, UK.

Professor Marcelo Garcia, Department of Civil and Envi-ronmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA ([email protected]).

Professor William Graf, Department of Geography, Uni-versity of South Carolina, Columbia, South Carolina 29208, USA ([email protected]).

Professor Ken Gregory, School of Geography, University of Southampton, Highfi eld, Southampton SO17 1BJ, UK ([email protected]).

Dr Simon Harrison, Department of Zoology, Ecology and Plant Sciences, University College Cork, Lee Maltings, Prospect Row, Cork, Ireland ([email protected]).

Jorge Abad, Department of Civil and Environmental Engi-neering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

Dr Mike Acreman, Water Resources & Environment Divi-sion, Centre for Ecology & Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK ([email protected]).

Professor Fabian Bombadelli, Department of Civil and Environmental Engineering, University of California at Davis, Davis, California 95616, USA ([email protected]).

Dr Douglas Booker, National Institute of Water and Atmospheric Research, 10 Kyle St., Riccarton, Christ-church, 8011, New Zealand ([email protected]).

Dr Andrew Brookes, Jacobs (UK), School Green, Shin-fi eld, Reading RG2 9HL, UK ([email protected]).

Professor Mike Clark, School of Geography, University of Southampton, Highfi eld, Southampton SO17 1BJ, UK ([email protected]).

Professor Nick Clifford, School of Geography, The Uni-versity of Nottingham, University Park, Nottingham NG7 2RD, UK ([email protected]).

Dr Helen Dangerfi eld, Royal Haskoning, 4 Dean’s Yard, London, SW19 3NL, UK (Helen.dangerfi [email protected]).

xii List of Contributors

Dr Francine Hughes, Department of Life Sciences, Anglia Ruskin University, East Road, Cambridge CB1 1PT, UK ([email protected]).

Martin Janes, River Restoration Centre Manager, The River Restoration Centre, Building 53, Cranfi eld Univer-sity, Cranfi eld, Bedfordshire MK43 0AL, UK ([email protected]).

Professor Mat Kondolf, Department of Landscape Archi-tecture and Environmental Planning, University of Cali-fornia, Berkeley, California 94720-2000, USA ([email protected]).

David Leeming, Consultant Ecologist, Spindlewood, 45 West End, Ashwell, Hertfordshire SG7 5QY, UK.

Professor John Lemons, Department of Environmental Studies, University of New England,11 Hills Beach Road, Biddeford, Maine 04005, USA ([email protected]).

Dr Jenny Mant, The River Restoration Centre, Building 53, Cranfi eld University, Cranfi eld, Bedfordshire MK43 0AL, UK ([email protected]).

Dr Tim Moss, Institute for Regional Development and Structural Planning (IRS), Flakenstrasse 28–31, 15537 Erkner, Germany. ([email protected]).

Professor Malcolm Newson, Department of Geography, University of Newcastle-Upon-Tyne, Newcastle-Upon-Tyne NE1 7RU, UK ([email protected]).

Dr Martin Perrow, ECON, Ecological Consultancy, Norwich Research Park, Colney Lane, Norwich, Norfolk NR4 7UH, UK ([email protected]).

Professor Bruce Rhoads, Department of Geography, Uni-versity of Illinois at Urbana-Champaign, Room 220 Dav-enport Hall, 607 South Mathews Avenue, Urbana, Illinois 61801-3671, USA ([email protected]).

Professor Keith Richards, Department of Geography, Uni-versity of Cambridge, Downing Place, Cambridge CB2 3EN, UK ([email protected]).

Dr Roy Richardson, Scottish Environment Protection Agency, Burnbrae, Mossilee Road, Galashiels TD1 1NF, UK.

Dr Jose Rodriguez, Faculty of Engineering and Built Envi-ronment, University of Newcastle, Newcastle, New South Wales 2308, Australia ([email protected]).

Dr Ian Rutherfurd, Geography Program, School of Resource Management, University of Melbourne, Mel-bourne, Victoria 3010 Australia ([email protected]).

Professor David Sear, School of Geography, University of Southampton, Highfi eld, Southampton SO17 1BJ, UK ([email protected]).

Dr F. Doug Shields Jr, Water Quality and Ecology Research Unit, National Sedimentation Laboratory, USDA Agricultural Research Service, National Sedimentation Laboratory, PO Box 1157, Oxford, Mississippi, USA ([email protected]).

Eleanor R. Skeate, ECON, Ecological Consultancy, Norwich Research Park, Colney Lane, Norwich, Norfolk NR4 7UH, UK ([email protected]).

Dr Kevin Skinner, Principal Geomorphologist, Jacobs Babtie, School Green, Shinfi eld, Reading RG2 9HL UK ([email protected]).

Dr Michael Stewardson, Department of Civil and Envi-ronmental Engineering and eWater CRC, University of Melbourne, Melbourne, Victoria, 3010 Australia ([email protected]).

Mark L. Tomlinson ECON, Ecological Consultancy, Norwich Research Park, Colney Lane, Norwich, Norfolk NR4 7UH, UK ([email protected]).

Professor Reginald Victor, Centre for Environmental Studies and Research, c/o Department of Biology, Sultan Qaboos University, PO Box 36, Al-Khod, PC 123, Muscat Sultanate of Oman ([email protected]).

Joseph M. Wheaton, Institute of Geography and Earth Sciences, University of Wales, Aberystwyth SY23 3DB, UK ([email protected]).

Dr Chia-Ning Yang, Department of Landscape Ar -chitecture, California State Polytechnic University, Pomona, California 91768, USA ([email protected]).

SECTION I

Introduction: The Nature and Signifi cance of Uncertainty in River Restoration

River Restoration: Managing the Uncertainty in Restoring Physical Habitat Edited by Stephen Darby and David Sear© 2008 John Wiley & Sons, Ltd

1

Uncertainty in River Restoration

John Lemons1 and Reginald Victor2

1Department of Environmental Studies, University of New England, USA2Center for Environmental Studies and Research, Department of Biology, Sultan Qaboos University, Sultanate of Oman

some West African small rivers entire fi sh communities had changed due to impoundment and the ecological per-turbations extended for considerable distances downstream (Victor and Tetteh, 1988; Victor and Meye, 1994; Victor and Onomivbori, 1996). Gopal (2003) describes how rivers in arid and semi-arid regions in Asia are being degraded due to overexploitation of natural resources, salinization, pollution and introduction of exotic species.

Just as rivers have undergone alteration, so too have there been efforts to restore them in order to provide ben-efi ts to the environment and/or human health, as this book attests (see also MacMahon and Holl, 2001). Obviously, scientifi c research contributes to river restoration by: pro-viding reliable and needed explanatory or heuristic knowl-edge and understanding of restoration problems; helping to identify and defi ne new research needs and directions through the acquisition of factual information; and inform-ing policy and decision making (Caldwell, 1996).

A major premise of this book is that to be sustainable, river restoration projects need to effectively recreate a rivers’ functional characteristics taking into account the dynamic geomorphic characteristics. While many restora-tion projects have benefi ted environmental and/or human health, understudied sources of uncertainty limit confi -dence in predicting the outcomes of restoration activities and programs. Specifi c examples of uncertainty in river restoration discussed in this book include those inherent in: river management processes; the planning and design phases of restoration projects; hydraulic and hydrological aspects of restoration; water quantity issues; identifying appropriate ecological characteristics and predicting their

1.1 INTRODUCTION

As we are well aware, rivers fundamentally shape the planet and human life. Both ancient and modern societies have developed and fl ourished in the proximity of rivers and this trend has continued till modern times. Nienhuis and Leuven (2001) summarize how humans have spatially and temporally altered rivers over a 6000-year period by various anthropogenic activities. For example, intensive use of European rivers started over 500 years ago leading to the loss of their ecological integrity (Smits et al., 2000). Some rivers were altered for navigation, fl ood control, agriculture and reclamation of land for urban develop-ment, while most were used as chutes for waste disposal including sewage, thermal effl uents and both nontoxic and toxic chemicals; some rivers were also routinely dredged to facilitate the transport and storage of timber, while others were heavily fi shed (Ward and Stanford, 1979; De Wall et al., 1995; Eiseltova and Biggs, 1995).

Large river systems (stream order >8) all over the world have been extensively dammed for hydroelectric power, recreation, fl ood control and to divert water to support agriculture. Impacts of large dams include the loss of fi sheries and the ecological collapse of the entire river regime (Balon and Coche, 1974; Rzoska, 1976; Obeng, 1981). Extensive series of levees built along large rivers have caused major losses of ecosystem structure and func-tion. Along the Mississippi River, the largest river in North America, levees threaten federal plans to protect endan-gered species (EPA, 2004). The effects of impounding small rivers (stream order 4–8) are even more drastic. In

4 River Restoration: Managing the Uncertainty in Restoring Physical Habitat

responses in restoration designs; and the construction and post-construction phases of restoration projects. The sources of uncertainties include: lack of scientifi c and other information; limitations of analytical methods and tools; complexities of river systems; and needs to make value-laden judgments at all stages of river res-toration problem identifi cation, analysis and solution implementation.

Beginning in and since the early 1990s some philoso-phers, scientists and public policy experts concluded that the sources and implications of scientifi c and other uncer-tainty in environmental problem solving, including resto-ration, have been understudied and, as a consequence, not suffi ciently taken into account by researchers, public policy makers and decision makers (Mayo and Hollander, 1991; Cranor, 1993; Shrader-Frechette and McCoy, 1993; Funtowicz and Ravetz, 1995; Lemons and Brown, 1995; Lemons, 1996; EEA, 2001; Kriebel et al., 2001; Tickner, 2002, 2003).

In agreeing with this conclusion, the objective in this chapter is therefore to fi rst discuss various broad views about scientifi c uncertainty and indicate how and why these need to be taken into greater account by scientists, policy makers and decision makers. (Other chapters address uncertainty and analyze in more concrete detail how it interacts with the specifi c theories and practices of river restoration.). Discussion then focuses on what might constitute ‘good’ science when science is used to inform policy and decision making under conditions of scientifi c uncertainty. Value-laden sources and implications of uncertainty in river restoration are then discussed because they are both important but understudied. Discussion of the value-laden sources and implications of uncertainty is followed with: a brief discussion of some of the practical and policy implications of uncertainty in river restoration, and, fi nally, a brief case study of river restoration in order to communicate our views with a practical example. For reasons of brevity the case study communicates views about some, but not all, aspects of uncertainty in river restoration.

Parenthetically, here it is necessary to comment on defi -nitions of ‘restoration’ when used in the context of river restoration. The fi eld of restoration ecology suffers from a lack of conceptual clarity concerning its meaning, goals and objectives. Since about the mid-1980s, the fi eld of river restoration has increasingly evolved in an attempt to better meet societies’ needs to more effectively repair damage to rivers (e.g., Cairns and Heckman, 1996; Karr and Chu, 1999; Cairns, 2001). The Society of Wetlands Scientists (SWS, 2000) defi ned restoration as ‘actions taken in a converted or degraded natural wetland that result in the re-establishment of ecological processes,

function, and biotic/abiotic linkages and lead to a persis-tent, resilient system integrated within its landscape.’ In 2002, the Society for Ecological Restoration (SER, 2002) defi ned restoration as the ‘. . . process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed.’ Regardless of these defi nitions, the goals and objectives of river restoration are not clear.

Rolston (1988) believes that where possible ecosystems should be returned to their ‘natural’ or ‘original’ condition. Westra (1995) argues that restoration should focus on restoring ecosystems’ abilities to continue their ongoing change and development unconstrained by human inter-ruptions past or present. The United States National Research Council (NRC, 1999) defi ned restoration as ‘the return of an ecosystem to a close approximation of its condition prior to disturbance.’ This defi nition was expanded by Cairns (2001), who asserted that the goal of restoration should be devoted to ‘returning damaged eco-systems to a condition that is structurally and functionally similar to the predisturbance state.’

Alternatively, others involved in the fi eld of restoration ecology provide defi nitions for restoration that more explicitly focus on historical, social, cultural, political, aesthetic and moral aspects. For example, Sweeney (2000) argues that restoration should focus on the value-laden social and ethical perspectives regarding what constitutes a ‘restored’ ecosystem. Some others maintain that conser-vation and, by implication, restoration goals should take into account the views and practices of rural and indige-nous people who depend on the ecosystems for their physical and cultural subsistence, and should also include scientifi c and nonscientifi c considerations (Gomez-Pompa and Kaus, 1992; Westra, 1995; Light and Higgs, 1996; Higgs, 1997; Chauhan, 2003). Regier (1995) proposes an abstract defi nition for restoration that is dependent on what people believe as fostering a state of ‘well-being.’

Obviously, lack of conceptual clarity about restoration introduces an element of uncertainty into restoration problem solving. In this chapter, while being mindful of the unresolved problems of conceptual clarity regarding ‘restoration’ other sources and implications of uncertainty and their relevance to river restoration are focused upon.

1.2 BROAD PHILOSOPHICAL VIEWS ABOUT SCIENTIFIC UNCERTAINTY

During the 19th century there was a high degree of confi -dence in the methods and tools of science and technology to increase understanding of the natural world and enable robust predictions of its future states. This confi dence in science contributed to beliefs that ‘nature’ could be con-trolled and rendered useful to humankind (Latour, 1988).

Uncertainty in River Restoration 5

Contributing to these beliefs were philosophers and scien-tists (so-called ‘logical positivists’) who proposed that an important goal of science should focus on formulating hypotheses and conducting observations to test them, developing an understanding of processes and linkages among variables, and developing conclusions and predic-tions about which there is a high degree of confi dence. More specifi cally, the logical positivistic view of science assumes that: knowledge is founded on experience; con-cepts and generalizations only represent the particulars from which they have been abstracted; meaning is grounded in observation; the sciences are unifi ed accord-ing to the methodology of the natural sciences and the ideal pursued in knowledge is the form of mathematically formulated universal science deducible from the smallest number of possible axioms; and values are not facts grounded in observation and therefore cannot be included as a part of scientifi c knowledge. One the one hand, while logical positivism has infl uenced the thinking of modern scientists public policy makers, and decision makers, on the other it does not enjoy wide support from contempo-rary scientifi c philosophers (Hull, 1974).

Scientists typically are conservative insofar as they pro-visionally reject a null hypothesis only if the probability of making a type I error is fi ve percent or less (Cranor, 1993; Lemons et al., 1997). This scientifi c conservatism is consistent with the logical positivist goal of developing conclusions about which there is a high degree of confi -dence. With respect to the use of science as a basis for public policy and decision making, there are those who hold that scientifi c methods and tools are capable of yield-ing information about which there is a high degree of sci-entifi c confi dence and, therefore, it is this information and not more speculative information that should be used as the basis for policy and decision making (Peters, 1991; Sunstein, 2002). This latter view is a component of the fi eld of environmental and human health risk assessment, which has developed to help inform public policy and decision makers about the risks from threats from both natural phenomena and human activities, including assess-ing whether to undertake some river restoration projects. Components of risk analysis include: identifying the sequence of events through which exposure to risk could occur; determining the number and kinds of people or environmental resources exposed to the risk; determining the adverse effects of exposure to the risks; and commu-nicating risk assessment fi ndings to decision makers and the public. Although risk assessors acknowledge scientifi c uncertainty, they often hold that scientifi c methods and tools can identify the risks and enable the calculation of the probabilities of their occurrence, including the bound-ing of the probabilities with confi dence limits. For in-

depth discussions on the role of scientifi c information in policy and decision making, see Peters (1991), Shrader-Frechette, (1994), Caldwell (1996), Lemons (1996), and Kaiser and Storvik (2003).

Historically, logical positivism and its outgrowths also have infl uenced the thinking of some scientists and policy makers in other ways by inculcating the view that ‘good’ science is objective insofar as it is not biased by the values of the scientists. Accordingly, this view holds that the proper role of science in policy and decision making is to provide factual information to decision makers, and that any controversies about the factual information should be left to members of the scientifi c community competent in evaluating the scientifi c bases of the controversies (Shrader-Frechette, 1982). Consequently, the conclusions of scientifi c analyses do not become a part of broader public policy debates such as those that might pertain to such issues as what level of risk is acceptable. Practically speaking, proponents of this view believe that the scien-tifi c and technical problems of managing large scale and complex problems are enormous and that the public cannot be expected to grasp the many scientifi c and technical issues inherent in understanding and resolving the prob-lems. Further, the fundamental differences people have about how problems should be handled generate endless debate and controversy. This implies that while people and local governmental representatives with different interests may review and comment on scientifi c and technical documents, they would not be brought into the actual decisionmaking process regarding the complex scientifi c dimensions of problems (Lemons et al., 1997).

Despite the high degree of confi dence held by some people in scientifi c methods, confi dence in the power of science to understand and predict natural phenomena has been undermined by general relativity theories, quantum theories and chaos theories (Brown, 1987). Rorty (1979) notes that there is no evidence that science develops better and more accurate ‘mirrors’ with which to view nature. In his classic work, Kuhn (1962) describes how on the one hand the level of confi dence in models used by members of the scientifi c community increases with evidence that supports the underlying hypotheses of the models, and on the other the scientists’ use of the models cannot be expected to produce consistently better and cumulatively more truthful descriptions of the way the world works. According to Kuhn, the reason is because predictive suc-cesses of scientifi c theories do not guarantee their meta-physical accuracy because ‘paradigm shifts’ subsequently change scientists’ views of nature. Other critics have pointed out that so-called scientifi c truths of historical periods are social constructs infl uenced by the dominant cultural and political powers of those periods (Briggs and


Peat, 1982; Funtowizc and Ravetz, 1995). Some postmod-ern critics argue that Western science has been permeated by a variety of biases (e.g., ‘free market’ economics and industrialism, racism, religion, patriarchy) that while serving powerful interests have not led to the generation and use of more ‘objective’ or value-free scientifi c knowledge (Sirageldin, 2002).

More practically speaking, scientifi c institutions as well as individual scientists increasingly hold the view that scientifi c uncertainty regarding environment and human health problems is so pervasive and value laden that many conclusions about the problems cannot be made with a high degree of scientifi c confi dence (Cranor, 1993; Shrader-Frechette and McCoy, 1993; Lemons and Brown, 1995; Lemons, 1996; EEA, 2001; Kriebel et al., 2001; Tickner, 2002, 2003). This view is based on empirical studies focusing on: exposure to radiation from nuclear facilities and nuclear waste; managing large-scale ecosys-tems such as the Florida Everglades, agricultural lands, marine and freshwater oil spills; biodiversity protection and management of biological reserves; ocean dumping of sewage sludge; sulfur dioxide and protection of human lungs to remote lake restoration; antifouling paints on ships (e.g. tributyltin); estuarine eutrophication; protec-tion and management of marine fi sheries; extrapolating from toxicological responses in laboratory systems to both human health and to the responses of natural systems; management of fresh water resources; benzene in occupa-tional settings; the use and health impacts of asbestos; risks from polychlorinated biphenyls (PCBs); halocarbons and the ozone layer; diethylstilbestrol (DES) and long-term consequences of prenatal exposure; human health effects of lead in the environment; methyl tertiary-butyl ether (MBTE) in petrol as a substitute for lead; chemical contamination in the Great Lakes; hormones as growth promoters in animals used for food; and global climate change.

1.3 WHAT IS ‘GOOD’ SCIENCE UNDER CONDITIONS OF UNCERTAINTY?

Here, the question discussed is: What is ‘good’ science when science is used in trying to solve river restoration problems under conditions of scientifi c uncertainty?

A traditional and commonly accepted goal of science is that the probabilities of adding speculative information to the body of scientifi c knowledge should be minimal (Hull, 1974; Peters, 1991). For this reason, scientists typically are conservative insofar as they provisionally reject a null hypothesis if there is a fi ve percent or less chance of rejecting it when it is true; this criterion is known as a normal standard of scientifi c proof or so-called ‘ninety-

fi ve percent confi dence rule.’ With respect to the science used to inform certain types of river restoration policies and decisions, an example of a null hypothesis is that there is no effect on rivers or their resources from existing or proposed human activities. A type I error is to accept a false positive result, that is, to conclude that there is harm to rivers or their resources when in fact there is none. A type II error is to accept a false negative result, that is, to conclude there is no harm when in fact there is.

Many environmental laws and regulations place the burden of proof for demonstrating harm to the environ-ment or human health on government regulatory agencies or others attempting to demonstrate harm from develop-ment activities and, often, the standard that is used to meet the burden of proof test is the normal standard of scientifi c proof (Brown, 1995). When this standard is adopted as a basis for environmental decisions the scientifi c uncer-tainty that pervades many environmental problems means that the burden of proof usually will not be met, despite the fact that some information or even the weight of evi-dence might indicate the existence of harm to the environ-ment or human health. Consequently, in public policy and decision making if the data show that some factor or per-turbation has had an effect on the environment or human health but, say, only at the 70–90% confi dence level the null hypothesis that there is no effect from the factor or perturbation is accepted. In such cases there is a tendency by decision makers and others to assume not only that there was not enough evidence to reject the null hypothesis but that there was no effect when, in fact, the experimental design or test could have been too weak or the data too variable or too close for an effect to be demonstrated even if there had been one (a type II error).

Minimizing a type II error requires the statistical power of a research design or hypothesis test to be calculated. In contrast to confi dence, which is designed to minimize type I error, power depends on the magnitude of the hypothe-sized change to be detected, the sample variance, the number of replicates and the signifi cance value. The power of a test is the probability of rejecting a null hypothesis when it is in fact false and should be rejected. The larger the detected change, the larger is the power. In situations where the detected changes are relatively small, statistical power is increased by increased sampling size but this involves additional costs, research facilities and time. Analysis of variance in assessing threats to environmental and human health problems shows that the number of samples required to yield a power of 0.95 increases rapidly if changes smaller than 50% of the standard deviation are to be detected (Cranor, 1993). If the sample size stays the same the probability of a type I error is increased if the probability of a type II error is decreased. A practical


problem in river restoration is that a desired emphasis on avoiding type II error must be balanced against other opportunities to use limited scientifi c resources to address other environmental and human health problems.

Decisions about water management in the Klamath Basin along the California and Oregon border in the United States show some of the types of consequences that can happen when the law or decision makers require the use of scientifi c information that meets the normal stan-dard of scientifi c proof. In decades long disputes about water management in the basin, federal biologists have been trying to save three species of endangered fi sh by calling for diversions of water from irrigation into the basin to reduce the frequency of fi sh kills during low water periods (over 30 000 Chinook salmon died during a fi sh kill in 2002) (Service, 2003). As would be expected, a recommendation to reduce the amount of water available for irrigation met with strong opposition by ranchers and farmers in the basin. However, failure of the biologists to meet normal scientifi c standards of proof demonstrating that releasing more water into the basin would help the fi sh has been cited by the United States Department of Interior (DOI) in its recent refusal to restrict the amount of water farmers can remove from waterways in the basin (NRC, 2004). It is important to understand that the DOI was not criticizing the scientists for doing poor science; rather, it concluded that the normal standard of proof was not met. The DOI noted that factors such as nutrient runoff from natural sources as well as farms and ranches, algae blooms and dams that restrict access to fi shes’ spawning grounds complicate and in fact might preclude demon-strating the relation of water fl ow into the basin and the health of the fi sh populations with a higher degree of scientifi c confi dence.

The question of how to protect endangered species in the Klamath Basin and manage water resources raises a fundamental dilemma that those involved in river restora-tion have to confront. On the one hand, traditional scien-tifi c norms call for making conclusions on information about which there is a high degree of confi dence. In the Klamath Basin example, adhering to traditional scientifi c norms constrains decisions to protect endangered fi sh under conditions of uncertainty but, at the same time, in the absence of decisions to protect endangered fi sh the threats continue. In this type of situation, when science is used for public policy and decision making, scientists might wish to consider whether and to what extent they should be more comfortable with making conclusions based on the weight of evidence rather than based solely or primarily on high levels of confi dence, especially since public policy decisions are not based simply upon proba-bilistic considerations but rather involve making discrete

and explicit choices among specifi c alternatives, including those with political, economic and ethical ramifi cations (Bella et al., 1994; Lemons et al., 1997). Admittedly, this could create a tension between doing ‘good’ science as traditionally defi ned because scientists would be making more speculative conclusions; however, in their attempt to make science rigorous in the sense of not wanting to add speculation to the body of scientifi c knowledge as required by the scientifi c profession the regulatory questions for which the studies are done may be frustrated.

1.4 VALUE-LADEN DIMENSIONS OF SCIENCE AND UNCERTAINTY

In addition to the policy and management problems that arise from the use of traditional scientifi c norms for making conclusions in river restoration, other value-laden dimensions of science and policy both contribute to uncer-tainty and raise complicated questions about how it should be handled in public policy.

Westra and Lemons (1995) and Lemons (1996) contain papers analyzing both philosophical and scientifi c con-cepts used to inform ecological restoration science and practice. The concepts are diverse and include basing res-toration on: ecosystems’ abilities to function successfully in a way deemed satisfactory by society; ecosystems’ abilities to maintain a balanced, integrated, adaptive community of organisms having species composition, diversity and functional organization comparable to that of ‘natural’ habits of the region; ecosystems’ abilities to regenerate themselves and withstand anthropogenic stress; and ecosystems’ abilities to approach optimum capacity for ecological succession development options. One problem with all these defi nitions is that they are incom-plete, general and qualitative insofar as they fail to provide precise principles that would make them operational.

In his analysis of value-laden issues in restoration for ecological as opposed to primarily or exclusively economic development goals, Cairns (2003) focuses on several types of problems. Firstly, some restoration proj-ects are carried out on habitats different in kind from those altered or destroyed. For example, an upland forest may be destroyed in order to partially restore river systems and wetlands that once occupied a particular lowland area. Despite the fact that restoration of rivers and/or wetlands has ecological value, sacrifi cing a relatively undamaged habitat to restore another kind may cause unanticipated ecological change or harm. Secondly, with few exceptions most river and other ecological restoration projects are done to support the anthropocentric commodity or utilitar-ian values they offer humans and this poses confl icts with restoration goals for nonanthropcentric reasons. Thirdly,


river restoration has uncertain outcomes because of unpre-dictable events like fl oods or droughts, and because of the limitations of the methods and tools of science to predict long-term outcomes. Fourthly, restoration efforts focusing on single species or ecosystem attributes might eliminate those species that had initially colonized disturbed areas and were at the same time able to tolerate anthropocentric stress. However, restoration projects might result in the displacement of species tolerant to human activities with those less tolerant, at least in the short term. Fifthly, eco-logical restoration often takes place with species that tolerate anthropocentric stress and the ultimate succession processes and states will be human dominated or depen-dent. Most likely, a return to indigenous species would require continual intervention by researchers and environmental decision makers on behalf of their re-establishment. While science is not determinative to how the issues are resolved, robust scientifi c information is needed to help inform satisfactory policy judgments.

Mayo and Hollander (1991), Cranor (1993), Shrader-Frechette and McCoy (1993) and Lemons and Brown (1995) analyzed how and why numerous value-laden judgments, evaluations, assumptions and inferences are embedded in scientifi c methods pertaining to the study and management of ecosystems, including geohydrologi-cal and other water resources. For example, people have to decide the ecosystem parameters that are more impor-tant to base judgments on, often with little or no empirical information available. Assumptions have to be made, often without direct empirical evidence, whether ecosystem parameters should be considered independently or syner-gistically, and whether threshold values for environmental or health impacts exist and, if so, what such values should be. In addition, a lack of empirical data cannot be sepa-rated entirely from practical limitations imposed on envi-ronmental scientists. Decision makers require information in a relatively short period and at reasonable cost. These factors constrain the focus of most restoration studies to the short term, relatively small spatial areas and measure-ment of a relatively small number of samples and param-eters. Further, the above commentators conclude that many of the value-laden dimensions of scientifi c method-ology and information not only are not fully recognized by scientists, policy and decision makers, but that the failure to suffi ciently recognize the value-laden dimen-sions of science casts serious doubts about even the best and most thorough scientifi c and technical studies used to inform decisions about problems such as river restoration. In other words, unless the value-laden dimensions of sci-entifi c studies are disclosed the positions of decision makers will appear to be justifi ed on value-neutral scien-tifi c reasoning and will appear to be more certain than

warranted when, in fact, the positions will be based, in part, on often controversial and confl icting values of sci-entists and decision makers (see also Fleck, 1979).

One of the most common ways in which value issues are hidden in public policy concerning issues such as river restoration develops out of the expectation that technical analysts can isolate and apply the facts under dispute in a manner consistent with policy directives or legislative mandates. This separation of facts and values is highly problematic. For example, consider the use of safety factors in river water quality regulations as a means of extra protection for human or environmental health. Implicit in the choice of safety factors is an asymmetric cost function with health costs rising more steeply than costs for over-treatment. Implicit in the magnitude of a safety factor are signifi cant uncertainties in health impacts and a steeper cost function for health effects from under-treatment than for over-treatment. When these issues remain implicit in the use of safety factors (as they typi-cally are) the real issues of knowledge and uncertainty are obscured for decision makers and the public. Often, these issues remain implicit or hidden because safety factors and cost factors are described in quantitative terms per-taining to risks or cost–benefi t calculations. This increases the likelihood of the misuse of conclusions by decision makers who do not understand the basis for deriving safety factors (Brown, 1987).

1.5 PRACTICAL AND POLICY ASPECTS OF UNCERTAINTY

Cairns (2001) analyzed how most complex environmental problems transcend the capabilities of any single disci-pline but at the same time and all too often research teams are not suffi ciently interdisciplinary to deal adequately with the problems. In addition, problem solving often does not provide a balanced mix of academicians, public policy and decision makers, representatives from private industry or business and nongovernmental organizations. As a result, the framing of problems and their solution is too often fragmented and ineffectual and biased towards one or a few disciplinary approaches or stakeholder groups (Nienhuis and Leuven, 2001; Benyamine, 2002).

Some scientists and policy makers involved in environ-mental problem solving have argued for synthesizing analyses and alternatives to solutions of environmental resource problems (Lubchenco et al., 1991; Bella et al., 1994; Lemons and Brown, 1995; Caldwell, 1996). In prac-tice, at least three levels of synthesis may be identifi ed. The fi rst is conceptual synthesis and occurs when the diverse and often disparate elements of a problem situation are pulled together intuitively, then tested and integrated


to form a coherent research design. Following analysis of the problem and identifi cation of its causes and conse-quences, a second level of synthesis involves delineation of the fi ndings of the scientifi c research. A third level of synthesis can occur when research fi ndings are evaluated and consolidated in deciding a course of action by decision makers.

Despite the need for greater synthesis of research methods and information, synthesis itself introduces addi-tional value-laden dimensions and uncertainties into envi-ronmental problem solving. Caldwell (1996) and Brown (1995) discuss how decision makers must synthesize a policy (in part) from the scientifi c information available even when the information often is incomplete. When science is used to inform policy decisions such decisions also include economic, legal, administrative and cultural parameters and, therefore, are based on human values and judgments. Benyamine (2002) discusses how dis-agreements about scientifi c theories that are used as a basis for informing public policy and decision making become entangled with economic, legal and ideological issues. Sometimes, the disagreements remain largely con-fi ned to the scientifi c community, while at other times the public knows about them. When scientists and/or decision makers know the underlying theoretical bases for dis-agreements, this knowledge can infl uence the scientifi c arguments about the disagreements. However, some con-fl icting arguments and their underlying theoretical support can be under recognized or little understood by the non-scientifi c communities as well as by scientists whose spe-cialized fi elds are outside the discipline where debates about theories are taking place. When this happens, con-fl icting scientifi c arguments will not have much infl uence on the disagreements.

There is debate within the scientifi c and public policy communities regarding approaches to deal with uncertain-ties (Bradshaw and Borchers, 2000). For example, one approach might be to attempt to increase scientifi c confi -dence by increasing scientifi c confi rmation of hypotheses. In this way, scientists can decrease uncertainty suffi ciently to allow more precise estimates of risk for policy and decision makers. A second approach might be to increase the knowledge of sources of uncertainty by enhancing education and communication between scientists, policy and decision makers and the general public. A benefi t of this approach is that when scientists and decision makers are involved with the public there is greater opportunity for consensus building and less risk of legal challenges from disaffected stakeholders. A third approach might be to foster the view that scientifi c uncertainty should be regarded in public policy and decision making as it is within the scientifi c community, namely, as information

for hypothesis building and testing. Consequently, calls for faster and more ‘certain’ scientifi c conclusions to inform public policy and decision making would be tem-pered with a better understanding of the limitations and capabilities of science to provide information about which there is a high degree of confi dence.

Still another approach might be for society to require procedural rules for making decisions under conditions of scientifi c uncertainty to take into account confl icting points of view, possible consequences to welfare, as well as various ethical and legal obligations such as those involving free informed consent and due process (Shrader-Frechette, 1996). This approach could include greater use of the precautionary principle by helping to ensure that when there is substantial scientifi c uncertainty about the risks and benefi ts of a proposed activity, policy decisions should be made in a way that errs on the side of caution with respect to the environment and the health of the public (Kriebel et al., 2001; Tickner, 2003).

1.6 CASE STUDY OF SCIENTIFIC UNCERTAINTY IN RIVER RESTORATION

The example discussed here is based on ecological studies conducted from 1980–1989 in a small (4th order), black water West African river, the River Ikpoba fl owing through Benin City, Southern Nigeria (Victor and Dickson, 1985; Victor and Ogbeibu, 1985, 1986, 1991; Victor and Tetteh, 1988; Ogbeibu and Victor, 1989; Victor and Brown, 1990; Victor and Meye, 1994; Victor and Onomivbori, 1996; Victor, 1998). The stretch of river studied was affected by a variety of urban perturbations such as damming, water extraction, point and nonpoint source pollution, sand dredging and agriculture. As a result of government policies and directives mandating river clean-up activities, there was a rare opportunity to study river restoration by recovery processes. Scientifi c results of this study were published in the series of publications listed above and provide one of the bases of our focus on uncertainties associated with the restoration process.

The fi rst logical step was to investigate recovery pro-cesses. Geomorphologic changes of the river channel and the entire riparian corridor infl uenced by urban develop-ment could not be reversed (e.g. the presence of a dam, water extraction for human consumption) and therefore complete restoration would not be possible. Removal of human infl uences where possible would permit recovery, but the rates limiting recovery in different sections would not only depend on the type of infl uence (e.g. sand extrac-tion, car washing), but would also be complicated by natural events such as fl oods. Thus the optimum threshold for the recovery process in this study at various sections


of the river continuum was unpredictable and uncertain. Other signifi cant uncertainties were: the role of early recolonizing species affecting the trajectory of recovery; the successional sequence of species re-establishing; and the establishment of appropriate abiotic conditions and the establishment of previously non-existing non-native species like the water hyacinth.

The next group of uncertainties was related to the analy-sis and synthesis of data. Removal of a particular human infl uence (e.g. discharge of untreated sewage) in one section signifi cantly increased the presence of a parameter, say i (P < 0.05), showing that this parameter was a good indicator of recovery. But the same parameter did not increase signifi cantly in an adjacent section with a similar problem (P > 0.05) showing its uncertain predictive status. Graphical examination of associations between specifi c human infl uences (e.g. removal of detergent contamina-tion) and biological parameters like taxa richness and abundance showed positive relationships, but statistically these relationships as evaluated by Pearson’s r or Spear-man’s rs were not signifi cant (P > 0.05). Thus, correlation matrices generated for evaluating relationships between the removal of perturbation infl uences and the recovery of both biotic and abiotic parameters were diffi cult to inter-pret. Interpretation using traditional statistical norms and acceptable levels of signifi cance were ecologically and rationally highly problematic.

Further uncertainties arose while considering the tem-poral and spatial scale of the recovery process. The recov-ery process was happening in an urban setting with a new land use matrix, far different from pristine or semipristine natural conditions that previously existed. Therefore, com-parison of the restored river sections to that of ‘undis-turbed’ sections upstream was not valid and new baseline standards had to be established for future monitoring. Even these were extremely site specifi c with very limited potential for use in other sections of the study stretch. Because of the uncertainties involved, the scale needed for managing temporal and spatial variability in restoration was not apparent. ‘Rules of thumb’ based on value judg-ments had to be made to evaluate recovery in specifi c sections of the river stretch with specifi c types of perturba-tions. The magnitude of uncertainties involved render the combination of tools used here (e.g. sampling duration, sampling frequencies, choice of methods, size of samples, analytical models) inadequate to evaluate recovery pro-cesses in other rivers of similar stream order, larger rivers with higher stream order and even the same river 100 km downstream where its stream order is >8.

Implementation and analysis of monitoring were also wrought with uncertainties. For example, fi ve different sections of the river stretch were monitored for restoration

by recovery. Each section was characterized by its own set of physical and biological parameters that were good indicators of recovery at the time of the study. Due to limitations of funding, personnel and the required cost effectiveness of the monitoring program, proposals had to identify common parameters that would monitor the overall health of the study stretch in the long term. As discussed earlier, uncertainties associated with the analy-sis and synthesis of data did not permit the ready identi-fi cation of common parameters. Even if there was an agreement on using different sets of parameters for differ-ent sections of the stretch, there was no certainty that these parameters (e.g. BOD, nitrate–N, fi sh diversity) will con-tinue to serve as good indicators of recovery in the long term. It was also possible that a parameter considered trivial and not included in the monitoring program (e.g. dissolved organic matter, haptobenthos) may become important in the long term, which in itself cannot be defi ned clearly. ‘Long term’ in this case at least did not refer to an indefi nite period and envisaged monitoring programs were not relatively open-ended, as often is the case in countries with limited resources. Policy and deci-sion makers considered what seemed to be a comprehen-sive proposal for monitoring in the view of scientists as not being practical.

Policy questions concerning river restoration in the geopolitical context were plagued with more uncertainties than scientifi c questions. The political climate of the study area at that time was unstable and government changed hands frequently. For example, one government down-graded the priority given to environmental issues, such as river restoration, by the previous government if personal interests and political expediency demanded it. Assuming no change in policies with change in governments, there were uncertainties concerning funding tools that would ensure the long term success of restoration, design of legislation to accommodate river restoration without com-promising sustainable development and coordination of policies and legislation to devise strategies for river resto-ration in a broader context of the administrative region (e.g. district, state, country). The management of restored or recovered river as a water resource for domestic use, agriculture, fi sheries and recreation was not considered intentionally. For scientifi c uncertainty concerning water resources management, see Canter (1996).

1.7 CONCLUSION

Scientifi c and other uncertainty is pervasive in environ-mental problem solving, and river restoration is no excep-tion. When the traditional scientifi c standard of proof is used as a basis for river restoration decisions, the scientifi c


uncertainty that pervades many restoration problems means that the standard usually will not be met, despite the fact that some information or even the weight of evi-dence might indicate the existence of harm and therefore the need for restoration. A high degree of confi dence in river restoration science, as in other sciences, unfortu-nately seems to hinge on conventional statistical decision rules such as when, for example, river monitoring during restoration strives to detect human-infl uenced factors that caused deviations from baseline conditions. The major concern here will be ecological change and not how large or small the P-values are (Yoccoz, 1991; Stewart-Oaten, 1996). Most statistical decision rules are too simplistic and misleading insofar as their assumptions that lack of statis-tical signifi cance means lack of environmental signifi -cance (Karr and Chu, 1999). According to Yoccoz (1991), Kriebel et al. (2001), and Lemons et al. (1997) ecologists tend to over-use tests of signifi cance and restoration ecolo-gists are no exception to this rule. Karr and Chu (1999) suggest that it would be wiser to decide what is ecologi-cally relevant fi rst and then use hypothesis testing to detect ecologically relevant effects; the use of other statistical tools such as power analysis and decision theory also is recommended (Hilborn, 1997).

Cairns and Heckman (1996) state that restoration ecology in general ‘is a bridge between the social and natural sciences.’ In this chapter it has been shown that it is impossible to separate scientifi c and policy questions in restoration ecology and this, in and of itself, introduces uncertainty into what otherwise might be viewed as value–neutral or ‘objective’ scientifi c conclusions.

As discussed more generally in this chapter and shown more specifi cally in the case study section, scientifi c research is both value-laden and is used to support politi-cally-driven river restoration policies and decision making (see also Shrader-Frechette, 1994). For example, historical or descriptive research is intended to reveal or explain the dynamics of a given policy and to explore its origin and evolution. Prescriptive or advocacy research defends a conclusion or possibly even a preconceived policy, and also is characterized by publicized disputes among, e.g., scientists. Decision-informing or predictive research typi-cally is fi nanced by grants or contracts leading to conclu-sions supportive of a predetermined policy preference, sponsor bias, or predilections within a research peer group. Consequently, the focus of this research does not attempt to analyze all feasible alternative policy choices and the probable consequences. Because the focus of this research is on applicability for a particular policy its fi ndings are presented in the form of propositions upon which deci-sions can be made. The effi cacy of the policy towards which the research is focused depends on the validity,

reliability and persuasiveness of the research and the extent of political public receptivity.

It is important to clearly distinguish between the use of methods and tools of science to understand the phenom-ena of nature and the acquisition of scientifi c information about a restoration issue and the setting of policy; but in practice, there is not always an unambiguous demarcation. Policy makers set agendas that determine the questions that are asked of scientists; scientists formulate hypothe-ses in ways limited by their tools and their imaginations and disciplinary conventions. Consequently, the informa-tion they provide to the policy makers is limited and socially determined to a degree and therefore there is a complicated feedback relation between the discoveries of science and the setting of policy. While attempting to be objective and focus on understanding river restoration phenomena, scientists and other researchers should be aware of the policy uses of their work and of their social responsibility to carryout science that protects the envi-ronment and human health (Kriebel et al., 2001). In trying to fulfi ll this responsibility, scientifi c and other uncertainty needs to be taken into greater account.

The discussion of some of the value-laden decisions and judgments scientists and other researchers make is not a criticism. Rather, the issue is discussed because a failure to recognize the existence of the value-laden dimensions of science casts serious doubt about even the best and most thorough of scientifi c and technical studies used to inform decisions about river restoration. In other words, unless the value-laden dimensions of scientifi c and technical studies used to derive information are disclosed, the positions of policy makers and decision makers will appear to be justifi ed on objective or value–neutral scientifi c reasoning when, in fact, they will be based in part on often controver-sial or confl icting values of scientists themselves.

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2

Sources of Uncertainty in River Restoration Research

William L. Graf

Department of Geography, University of South Carolina, USA

change is sometimes gradual, as with the development and application of fundamental hydraulics to explain river behavior that evolved over a period of several decades (Chang, 1998; Simons and Sentürk, 1992). Sometimes the change is abrupt, as was the case with the introduction of the concepts surrounding hydraulic geometry that burst upon the fl uvial geomorphology scene, became widely accepted in less than a decade and continued in common use for several decades (Leopold, 1994). The result of this constantly changing theory is that the geomorphologist working in 2007 may perceive a very different system than one working just a few years before or later, even though the physical system in all cases would be the same. Uncer-tainty, therefore, is included in the application of science in its broadest sense.

Another source of ambiguity in theory for river restora-tion is the regional specifi city that is built into much of fl uvial theory. Much of what we theorize about single-thread meandering rivers comes from research experience in northwest Europe and eastern North America (Knighton, 1998), yet the global applicability of this work is largely untested. Most of the streams of northwest Europe and eastern North America that have been inten-sively investigated are relatively small on a world-wide basis, and though some generalities certainly must apply in many locales, the details may differ. Until the late 1990s, much of the theory for dryland rivers came from experiences in the American Southwest (Graf, 1988), but more recent investigations in Australia by Gerald Nanson, Steven Tooth and others, for example, have shown that the American experience is not applicable in all drylands (Nanson and Knighton, 1996).

2.1 INTRODUCTION: GENERAL SOURCES OF UNCERTAINTY

The practice of science in support of river restoration is subject to four primary sources of uncertainty (see Chap-ters 1 and 3 for additional/alternative views) so signifi cant that they may prevent the restoration from achieving its goals. Firstly, the underlying theory applied by investiga-tors to particular problem cases is imperfect and contains substantial gaps in explanatory and predictive capability. Secondly, the research process itself is subject to a variety of operational problems that introduce uncertainty to the use of science. Thirdly, the communication of scientifi c results to decision makers is often fraught with ambiguity derived from the scientifi c sender as well as the policy receiver. Fourthly, the scientists themselves are subject to bias that generates doubt in the outcome of generating scientifi c products and applying them. In the following sections the issues for each of these sources of uncertainty is outlined.

2.2 UNCERTAINTY IN THEORY

All science in support of river restoration begins with theory, because it is theory that allows the investigator to identify what to measure and how to construct a concep-tual model that connects the measurements together. Investigators perceive only those aspects of the river and its operations that theory allows them to see. Practitioners of fl uvial geomorphology tend to revere existing theory as a sacrosanct starting point but, like all sciences, geomor-phology is in a state of constant change and revision. The


If it is true that we must theorize based on what we know best, it must also be true that we are still limited in the range of our collective experience. As a result, when we apply existing theory in new geographic settings, there is reasonable doubt about the applicability of that theory, at least in its totality. Geomorphologists commonly rec-ognize that it is unwise to extend statistical models beyond the numerical ranges of the data. It is equally risky to extend geomorphological models beyond the geographic ranges of their origin. The extension of theory also forces us to consider how much of the geomorphology and hydrology of a particular river is unique, regardless of its geographic location. Each reach (a few kilometers long) of a stream is likely to be unique but the overall operation and form of a river (hundreds of kilometers long) is likely to have many similarities with other systems of similar magnitude. At the more extensive end of this range of magnitudes, generalizations are possible, while at the local end of the range of magnitudes, uniqueness becomes more apparent.

The incompleteness of most fl uvial geomorphologic theory is also a source of uncertainty. This incompleteness is in part purely a function of the natural river system, for which investigators have nine fundamental operating vari-ables, but for which there are only a very few connective mathematical functions (Leopold, Wolman, and Miller, 1964). But an equally important limitation of existing theory is its lack of recognition of human effects. Through-out much of the twentieth century (with a few exceptions), geomorphology as a science pursued explanation for ‘natural’ rivers and many investigators made a conscious effort to avoid the confounding infl uence of technological infl uences. It has only been in the last twenty years that those human infl uences, pervasive and signifi cant in many rivers of the world, have themselves become the objects of study (Costa et al., 1995; Graf, 2001). By defi nition, rivers subject to restoration have undergone changes resulting from human management and technology, but existing theory is remarkably weak with respect to these issues.

The Colorado River in the Grand Canyon in the USA provides an example of the issues related to uncertainty in theory. Glen Canyon Dam, several kilometers upstream from the Grand Canyon controls the fl ow of the river, and especially reduces annual fl ood peaks to less than half of their former magnitude. The dam also reduces the sedi-ment supply to the downstream canyon by more than 80%. As a result, the river has eroded sandy beaches and bars that once were common in the canyon (National Research Council, 1996). River restoration for the canyon included reintroduction of moderate fl oods to move the available sediment from the channel fl oor to elevated positions,

restoring these ecological niches. Despite considerable research, there were no established theories to predict the response of the river to the artifi cial fl oods and although there have been several fl ood-simulating releases from the dam, the restoration results are not yet apparent.

2.3 UNCERTAINTY IN RESEARCH

Research using admittedly limited theory in support of restoration is subject to uncertainty in the specifi cation of variables, assumptions, sampling, measurements and testing of hypotheses. The specifi cation or defi nition of variables, for example, is much entangled in the vagaries of science, law and personal perception of the researcher. Channel width provides an example. Most geomorpholo-gists would agree that channel width is the distance across the active channel from one bank to the other, but the application of this seemingly simple proposition is devil-ishly diffi cult in many rivers. How should semi-permanent islands be taken into account? What about ephemeral bars? How should width be determined in the common circumstance where multiple sets of banks have resulted from episodic incision or simply variable fl ows, which is often the case in arid, semi-arid, arctic or alpine regions. Many legal systems also defi ne the channel as being ‘between the banks,’ but do not specify which banks to use for the description (Graf, 1988).

All geomorphological research includes assumptions which form another source of uncertainty. The geographic and ecological complexity of rivers and their environ-ments imply that when conducting investigations it is essential to focus on a few components and assume away the importance of variability in other factors that go unmeasured. In geomorphology, hydrology and engineer-ing studies that support restoration, investigators often assume stationarity of the hydro–climatic processes ruling the river. Stationarity means investigators assume that the underlying statistical distributions describing climatic variables important to river processes are unchanging. Standard magnitude/frequency analysis includes this assumption so that the researcher can address other vari-ables of interest to planners, including the return intervals for various magnitudes of discharge. However, climate is anything but stationary, and its variation is highly likely to infl uence the statistical distributions upon which return interval concepts depend. This variation is also likely to be signifi cant to the fl uvial system over time scales as short as decades, scales that encompass the likely project life of most restoration efforts. Predictions for the near-term future of a few decades are therefore uncertain because the effects of expected climatic changes are not part of the analysis (see Chapter 13).

Sources of Uncertainty in River Restoration Research 17

Sampling processes in fl uvial geomorphology also cast doubt on the confi dence users may have in the reliability of the resulting data. Rivers present the researcher with long lines or corridors, and if the investigator uses cross sections or point samples, the selection of the locations of these sample points may strongly infl uence the resulting data. The spacing of meanders, riffl es and pools introduces some regularity to the spacing of important geomorphic and hydraulic features of rivers, so that if investigators use regular spacing for sample cross sections or sites, that spacing may coincide with the spacing of particular characteristics of the channel. For example, sample sites might occur only on riffl es, or only in pools, giving a false picture of the system. Truly random spacing for samples may be statistically desirable, but a realistic view of geo-morphic and hydrologic conditions demands that all the various environments of the channel be included in the sample, something that is not possible without some understanding of the basic spatial framework of the system.

Uncertainty may be lessened if scale is part of the plan-ning process for selecting sample sites. Sample schemes couched in the concept of river reaches permit bracketing of relevant parts of the stream. A river reach is from one to a few kilometers in length and contains similar geomor-phic conditions throughout its extent, sometimes with repetitive and alternating channel confi gurations such as pools and riffl es, pools and rapids, or meanders. Several reaches may make up a river segment. Geological bound-aries, confl uences with major tributaries or human struc-tures such as dams and diversion works create the upstream and downstream boundaries of each segment. Sampling schemes constructed with the reaches and segments as geographic frameworks are more likely to be informative for restoration work than truly regular or truly random samples. Project design for restoration requires a clear assessment of the appropriate dimensions and spacing of repetitions to insure long term stability. If the dimensions and spacing are not refl ective of the restored hydraulic conditions, the restored system will be unstable and may disintegrate.

The example of restoration of the Platte River in Nebraska in the USA illustrates the role of uncertainty in research. Upstream fl ood control dams have brought about great changes in the hydrologic regime and geomor-phology of the Platte, a serious issue because the river hosts several endangered bird species. The river originally included valuable habitats for birds, particularly the whooping crane, but the hydrologic and geomorphic changes have reduced their habitat. River restoration includes returning the river to conditions closer to those that prevailed before the dams were in place. Features such

as multiple channels, high and low islands, and complex bars are essential to the restoration. Research to support the restoration has been under way for more than a decade but has produced results that are diffi cult to interpret (National Research Council, 2004). Cross-sectional surveys, for example, are numerous but not often con-ducted at the same places through time so that sampling is an issue. The fl ow parameters that are most important to the species may not be the same parameters that are important to the geomorphology. Stationarity is a particu-lar problem in dealing with the hydrologic records of the Platte because the river is located on the boundary between sub-humid and sub-arid regions and is subject to climatic fl uctuations on decadal and century-long periods which casts doubts on shorter term records.

2.4 UNCERTAINTY IN COMMUNICATION

The successful use of science in formulating public policy for the restoration of rivers relies on accurate communica-tion between researchers and decision makers, but this connection sometimes suffers from failings by both par-ticipants. Scientists are usually accustomed to communi-cating with each other using a specifi c shared language of technical terms. Even terms shared with the general lan-guage may take on specifi c shadings of meaning when used by specialists communicating with each other. A steep river gradient, for example, calls to mind a general picture for most geomorphologists, perhaps of a step-pool sequence for mountain streams or a braided channel in other settings. For the decision maker, a steep river may be one with water falls. More importantly, specialized terms and terms with nuances do not work well when the listener or reader is not trained or experienced in the sci-entifi c specialty. As a result, scientists have an obligation to use language without jargon and without assumptions when communicating with decision makers and the informed public, a task that seemingly challenges many specialists.

Decision makers, on the other hand, have poorly informed expectations regarding their scientifi c advisors. They expect clear, unambiguous answers to their questions and specifi c, robust predictions of future processes that might result from a variety of potential decisions. Science, however, rarely offers truly unambiguous conclusions, and caveats are many in applied geomorphology. Often, it is possible to predict the direction of change, but there is greater trouble predicting the magnitude of that change. To a public servant attempting to protect property values, such predictions may lack the required level of comfort.

A common error in communication that results in uncertainty occurs when the decision maker requests


specifi c numbers, such as ‘what will be the stable, long term width of the restored channel’, a reasonable question in the public policy arena. However, most scientifi c and engineering models predict some most likely value, en-veloped in error bars. The most effective (and honest) report includes the most probable value but also includes some discussion of potential deviation from that expected value. Reporting of potential errors and ranges of possible values rather than single numbers protects the interest of everyone involved and produces more reasonable expecta-tions on the part of the general public.

Restoration of the Elwha River in the state of Washing-ton exemplifi es the issues surrounding uncertainty in com-munication. National legislators directed that the river be restored to its ‘natural’ condition for the benefi t of endan-gered salmon, which use the river for spawning during annual migrations (US House of Representatives, 1992). The method of restoration centers on the removal of two large dams. However, from a scientifi c standpoint, it is impossible to meet the requirements of the law, because even with the removal of the dams, the river will not even approach truly natural conditions. Upstream land use, including logging, have altered the basic hydrologic and sediment regimes of the river, and in downstream areas levees and other structures prevent natural river processes. In fact, ‘natural’ conditions are a model to which restora-tion might aspire, but the principle as a true objective is unworkable.

2.5 BIAS

A fi nal source of uncertainty in science for river restora-tion lies within the intellect of the researchers themselves. All geomorphologists are products of their cultural back-grounds, academic training, experiences and personal characteristics (see Chapters 1 and 4). Researchers raised in intellectually liberal surroundings may be more ques-tioning of established theories than those raised in more rule-bound settings, and each of these types is likely to approach problems, evidence and methods with different biases and preferences. Gender may play a subtle, but as yet relatively unexplored role in our approaches to science. Academic training for fl uvial geomorphology is highly variable from one group of teachers to another, with some groups emphasizing a stochastic rather than a determinis-tic approach. Other contrasting styles include greater emphasis on geographic approaches as opposed to those more strongly oriented toward engineering, research designs that emphasize small-scale (particle-sized) as opposed to ecosystem (or landscape) scale perspectives, or empirical rather than model-based approaches. Once trained, the researcher is further formed by personal fi eld

experiences, with extensive backgrounds in dryland set-tings producing a different perspective than experiences dominated by humid subtropical environments, tropical settings or polar landscapes. Finally, scientists are just like everyone else: some are stubborn, some are open minded; some are methodical while their colleagues make success-ful leaps of logic; and some are quick to reach judge-ments while others seem never to reach closure on their conclusions.

There is nothing inherently wrong with biases, for to be biased is to be human. In river restoration, however, and especially when dealing with decision makers, the wise scientists fi nd ways to communicate their biases to the consumers of the scientifi c products. If the consumer (policy maker or informed citizen) recognizes the biases and takes them into account, the interpretation and appli-cation of the scientifi c products is confi dent by all parties involved. Admission of biases by the researcher, usually in the form of a brief disclosure that clearly defi nes the schools of thought and experience of the researcher, enhances the professionalism of the investigator and fairly discloses to the consumer the background of the knowl-edge that forms the basis of decisions pertaining to public resources. In unusual cases, critics may contest decisions in administrative hearings or in court proceedings, two venues where biases are likely to be revealed and explored in a combative environment. If biases have previously been revealed, they lack sinister overtones in a strategy that benefi ts both researcher and consumer.

2.6 CONCLUSION

Uncertainty is a fact of life as well as an inescapable feature of scientifi c research and decision making for river restoration. Therefore, the researcher has two essential options: either ignore the uncertainty and hope that it is not debilitating for the project at hand, or accept the uncer-tainty and use it as a feature of the research. The researcher can investigate the uncertainty, quantify it in some cases, and reveal it in explicit terms when reporting results. In this latter approach, uncertainty becomes an integral part of research for river restoration, a feature of the work that is a welcome challenge to be embraced and used to achieve a more effective end product. Project design may include a variety of channel dimensions and characteristics, for example, and avoid relying on a single rigidly defi ned morphology, so that if original understandings of the system are not exactly correct the fi nal project will have some fl exibility. In other cases, it may be wise to simply allot more space for channel changes in the designed project to accommodate unforeseen adjustments. By dealing directly with uncertainty, researcher and decision

Sources of Uncertainty in River Restoration Research 19

maker increase the probability in successfully restoring a river with enhanced environmental and social benefi ts.

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National Research Council. 2004. Endangered and Threatened Species of the Platte River. National Academy Press: Washing-ton, D.C.

Simons DB, Sentürk F. 1992. Sediment Transport Technology. Water Resources Publications: Littleton, Colorado.

US House of Representatives. 1992. Joint Hearings on HR 4844, Elwha River Ecosystem and Fisheries Restoration Act, 102nd Congress, 2nd Session. Government Printing Offi ce: Washing-ton, DC.


3

The Scope of Uncertainties in River Restoration

Joseph M. Wheaton1, Stephen E. Darby2 and David A. Sear2

1Institute of Geography and Earth Sciences, The University of Wales, UK2School of Geography, University of Southampton, UK

hope that it is not debilitating for the project at hand, or accept the uncertainty and use it as a feature of the research.’ In this chapter the rich topic of uncertainty is presented in a broader, more generic context. This founda-tion is intended to help separate the sources and types of uncertainties that the various authors in this book present, and meanwhile unravel some of the ambiguities surround-ing uncertainty in river restoration.

As cautioned earlier, potentially signifi cant uncertain-ties are rarely recognised, much less explicitly dealt with in river restoration. Hence, a lexicon and typology for uncertainty is outlined fi rstly in this chapter. This is done to dispel the notion of a certain world with certain outcomes within the broader scope of types and sources of uncertainty. A return specifi cally to river restoration then follows to identify types of uncertainties using the above-mentioned typology. The tremendous diversity of river restoration in the context of uncertainties arising from restoration motives, notions and approaches are con-sidered. A case will be made that a basic strategy for dealing with uncertainty is needed by the river restoration community to allow both the community and individual investigators or practitioners to:

• explore the potential signifi cance (both in terms of unforeseen consequences and welcome surprises) or insignifi cance of uncertainties;

• effectively communicate uncertainties;• eventually make adaptive, but transparent, decisions in

the face of uncertainty.

3.1 INTRODUCTION

The science and practice of river restoration are both still very much in their adolescence (Palmer et al., 1997). Yet, both have been graced with funding and support from a diverse range of interest groups (Malakoff, 2004). One of the premises of this book is that if funding is to continue to be allocated to river restoration, it will have to be shown that river restoration is ‘working’ (see Preface; Wissmar and Bisson, 2003c). Defi nitions of ‘working’ (often equated with success) are understandably subjective and vulnerable to uncertainties in the river restoration process, societal values, the fl uvial system and ecosystem response to restoration management activities. Davis and Slobodkin (2004) argued that defi ning restoration goals and objectives is rightfully a value-based activity, as opposed to scientifi c activity. Each activity is inherently uncertain. Paradoxically, the uncertainties infl uencing river restoration projects are rarely recognised or quanti-fi ed, much less reported to stakeholders or the public (Walters, 1997).

The topic of uncertainty in river restoration is riddled with complexity and confusion. Indeed, uncertainty mani-fests itself in many ways, as established in Chapters 1 and 2. Lemons and Victor (see Chapter 1) have already illus-trated how deep the value-laden dimensions of uncertainty lies, not just in decision making, but in scientifi c research as well. Graf (see Chapter 2) expanded on this theme, citing uncertainties from theories, the research itself, com-munication and biases among investigators. He concluded that the research can either ‘ignore the uncertainty and


Finally, it will be argued that amongst the various strategies for dealing with uncertainty, the only strategy that might provide these aims is one of embracing uncertainty.

3.1.1 The Status Quo in River Restoration

The rapid rise and international popularity of river restora-tion is both encouraging and worrisome (Kondolf, 1996). Although sparse examples dating back to the 1930s exist1, river restoration has primarily developed on the coat tails of the environmental awareness movement of the late 1970s (Graf, 1996; Sear, 1994). It is encouraging that so much enthusiasm exists to restore rivers. Yet, it is interest-ing to note the societal choices between some mix of reactive restoration efforts in response to damage already done, as opposed to pro-active conservation actions to prevent further damage (Boon, 1998). The international popularity of river restoration is evident in the restoration literature (e.g. restoration in 21 different countries reported in Nijland and Cals, 2000), restoration databases (e.g. the United Kingdom River Restoration Centre (RRC), United States Environmental Protection Agency (EPA))2 and an International River Restoration Survey3 launched by Wheaton et al. (2004c) with respondents from 34 different countries. In Denmark alone, 1068 restoration projects had been completed by Danish regional authorities by 1998 (Hansen and Iversen, 1998); whereas in the United States, Malakoff (2004) reported that by 2004 more than $US10 billion had been spent on a total of more than 30 000 projects. The popularity of river restoration is apparent in international, national, regional and local public policy that actively promotes, requires and, in some cases, funds river restoration efforts (Jungwirth et al., 2002). However, their effectiveness is constrained by limited funds and scope to deal with closely related land use issues and other socio-political goals (Tockner and Stanford, 2002).

Despite the popularity of river restoration in the devel-oped nations of the world, the global decline of the physi-cal and ecological integrity of rivers is diffi cult to overstate (Jungwirth et al., 2002; Vitousek et al., 1997). Indeed,

most restoration efforts still pale into signifi cance relative to expanding anthropogenic impacts on riverine land-scapes (Tockner and Stanford, 2002). Even in parts of the world where numerous river restoration efforts are already underway (i.e. Europe, North America and Australia), wet-lands are actively being drained and fi lled, rivers are still diverted and regulated, urban growth is encroaching into fl oodplains and headwaters, while we continue to perma-nently alter basin hydrology and fragment habitats (Collins et al., 2000; Moss, 2004; Mount, 1995). These problems pose even larger threats in the developing nations of the world (Marmulla, 2001). Over 250 new major dams become operational worldwide annually and 75 are planned for the Amazon Basin alone (Robinson et al., 2002). It seems logical that preservation should be easier to achieve than restoration (Frissell et al., 1993), but there seems to be excessive confi dence in the ability to restore (Stewardson and Rutherfurd, see Chapter 5), sometimes reducing restoration to a mitigation measure justifying planned impacts or maintaining the status quo. Both con-servation and restoration are based on the transformation of uncertain science and uncertain notions of what is natural, ecosystem integrity and physical integrity into societal goals (Graf, 2001; Lemons and Victor, see Chapter 1). Additionally, the good intentions of restoration projects may lead to unintended but often foreseeable conse-quences. Even if society is willing to make diffi cult socio-political decisions to support preservation and restoration of rivers, there is no guarantee of desired outcomes following.

Given the dynamism of rivers, it seems obvious that the outcomes of restoration projects are uncertain. However, the restoration community seems hesitant to admit that the goals and science that restoration are founded upon are uncertain too (Stewardson and Rutherfurd, see Chapter 5). Aside from indirect references to uncertainty in adaptive management programs, the river management commun-ity has largely brushed uncertainties aside (Clark, 2002; Wissmar and Bisson, 2003c). It is unclear whether this is a conscious or passive decision, though individual deci-sions to ignore uncertainty can be plausibly attributed to one or more of the following:

• ignorance of uncertainty and/or its signifi cance;• the hope that uncertainty is insignifi cant;• an acknowledgement of uncertainty, but not knowing

how to deal with it;• being misinformed about uncertainty, leading to the

assumption that it is insignifi cant;• being knowledgeable about uncertainty, but having

established its insignifi cance.

1 The United States Department of Agriculture Forest Service started undertaking ‘stream improvement’ in the 1930s with the intent of increasing salmonid production (Everest and Sedell, 1984).2 RRC Database includes over 750 projects within the United Kingdom: http://www.therrc.co.uk; the USEPA River Corridor and Wetland Restoration Database includes over 600 projects throughout the United States: http://yosemite.epa.gov/water/restorat.nsf/rpd-2a.htm.3 Complete real time results, background information and forth-coming interpretations are available on the web: http://www.geog.soton.ac.uk/users/WheatonJ/RestorationSurvey_Cover.asp.

The Scope of Uncertainties in River Restoration 23

Newson and Clark (see Chapter 14) attribute the river manager’s current treatment of uncertainty to a ‘risk-averse’ management culture that prefers to entrench itself in ‘rituals of verifi cation’ aimed at minimising liability (Power, 1999). Uncertainty is also frequently misunder-stood by the general public (Pollack, 2003; Riebeek, 2002) as something negative and undesirable (Newson and Clark, see Chapter 14). A widespread misconception that science embodies certain knowledge persists in the reports of the mainstream media and views of the general public (Clark, 2002; Riebeek, 2002). Such misconceptions fuel expectations that science-based approaches to river resto-ration will yield positive outcomes. Ironically, people con-front uncertainties everyday without hostility and choose to routinely make decisions about the future (Pollack, 2003).

Restoration science and the restoration literature are not much further along than practitioners and decision makers. Wissmar and Bisson (2003b) asserted that ‘a better under-standing of variability and uncertainty is critical to the successful implementation of restoration programs for aquatic and riparian systems.’ Yet, buried within a rich literature on restoration are only occasional passing mentions of uncertainty (Brookes and Shields, 1996) and a handful of explicit treatments (Johnson and Brown, 2001; Johnson et al., 2002; Johnson and Rinaldi, 1997; Johnson and Rinaldi, 1998; Wissmar and Bisson, 2003c). These studies understandably tend to focus on a specifi c type of uncertainty that might be reasonably articulated within a specifi ed page limit, so a more holistic treatment of uncertainty is necessary (Newson and Clark, see Chapter 14; Van Asselt, 2000). Restoration is established as one important component of environmental manage-ment. It would be a shame to lose what public support already exists for restoration if political scrutiny recasts unrealistic expectations of river restoration as a ‘failure’, as opposed to the inadequate consideration of uncertainty they truly stem from.

3.2 WHAT DO WE MEAN BY UNCERTAINTY?

3.2.1 A Lexicon of Uncertainty

In the simplest sense, uncertainty is a lack of sureness about something or someone (Merriam-Webster, 1994). However, uncertainty can be more than simply a lack of knowledge. It persists even in areas where knowledge is extensive; and knowledge does not necessarily equate to truth or certainty (Van Asselt and Rotmans, 2002). There are at least 24 potential synonyms for the noun uncertainty and 27 synonyms for the adjective uncertain

(Table 3.1). There are a number of concepts related to and infl uenced by uncertainty, but which differ from uncer-tainty itself. A selection of these concepts is considered briefl y below.

Accuracy: Accuracy refers to correctness or freedom from error. In measurement, accuracy refers to how close an individual measurement is to the ‘true’ or ‘correct’ value (Brown et al., 1994). The classic accuracy analogy is the location of darts on a dart board – the closer the darts are to the intended position (bull’s-eye) the more accurate. If one can be certain about both the ‘true’ value (e.g. the position of the bull’s-eye) and the value of the individual measurement (e.g. the position of the dart), then the accu-racy is actually a certainty. In practice, accuracy state-ments are uncertain because ‘true’ values are often assumed and measurements have limited precision.

Table 3.1 Potential synonyms of the noun ‘Uncertainty’ and the adjective ‘Uncertain’

Synonyms of Uncertainty Synonyms of Uncertain

Ambiguity AmbiguousIndeterminacy CauselessCapriciousness CapriciousChance Probabilistic– DeferredDanger DangerousDisbelief DisbelievingEquivocation EquivocalDoubt Doubtful– ErraticExpectation –Future condition –Hesitation HesitantIgnorance IgnorantImprobability ImprobableIndecision IndecisiveIndeterminacy IndeterminantInsecurity InsecureIrresolution –Obscurity ObscureSurprise Surprising– UnauthenticUnintelligibility Unintelligible– Unexplained– QuestionableVacillation VacillatingVagueness Vague– UndecidedUnsureness UnsureUnpredictability Unpredictable


Confi dence: Confi dence (e.g. in a statement, hypothesis, measurement, feeling or notion) relates to the degree of belief or level of certainty. Confi dence levels, for example, describe the probability that a given population parameter estimate falls within a designated continuous statistical confi dence interval.

Divergence: Divergence describes a situation when similar causes produce dissimilar effects (Schumm, 1991). Diver-gence relates to uncertainty in situations where problems of cause and process are under consideration.

Error: Error is the difference between a measured or cal-culated value and a ‘true’ value. In every day conversation, an error is a mistake. In science, error is the metric by which accuracy is reported and is not a synonym for uncer-tainty (Ellison et al., 2000). A ‘true’ value is certain by defi nition. If the error between the ‘true’ value and a measured or calculated value is known there is no uncer-tainty in principle. However, in practice ‘true’ values are often not known and instead are assumed to be so, while the measured or calculated value may be uncertain. Hence error becomes representative of uncertainty. Once errors are calculated, it can be helpful to consider whether the error is systematic or random. Systematic errors stem from consistent mistakes and are often constant or predictable, affecting the mean of a sample (i.e. bias, Trochim, 2000). Systematic errors can potentially be constrained as their source is identifi able. By contrast, random errors infl uence the variability of a sample (not the mean) and are generally unpredictable or unconstrainable (Trochim, 2000).

Exactness: Exactness is really a synonym for accuracy. However, it is worth pointing out that exactness has quite a different meaning to exact. Exact statements or exact numbers, in principle, have no uncertainty about them. They are statements of truth. By contrast, exactness is a relative measurement assigned to inexact statements or values (i.e. those with some uncertainty).

Expectation: Expectation has to do with anticipation of probable or certain events. Uncertainty fundamentally relates to expectations. When uncertainties are unknown, not fully considered or ignored, the degree that expecta-tions may be unrealistic will generally increase.

Equifi nality: Equifi nality (also referred to as conver-gence), arises when different processes and causes produce similar effects (Schumm, 1991). In a modelling context, Beven (1996a; 1996b) suggests that ‘the consequences of equifi nality are uncertainty in inference and prediction.’ In a social context, a potentially limitless range of possibili-ties may lead to a single event, such as the election or defeat of a politician.

Precision: Precision is a measure of how closely individ-ual measurements or calculations match one another (Brown et al., 1994). Recalling the dart board analogy from accuracy, a precisely thrown set of darts will cluster around one another, but may be nowhere near the bull’s-eye. In measurement, the precision of an instrument refers to the fi nest scalar unit the instrument can resolve. Preci-sion is related to uncertainty in that it defi nes a detection threshold, below which differences can not be discerned.

Repeatability: Repeatability can be viewed as either the ability to reproduce the same measurement, result or calculation or the variability in repeated measurements, results or calculations. Uncertainty can simply limit repeatability or increase variability.

Risk: Risk is a measure of likelihood that an undesirable event or hazard will occur (Merriam-Webster, 1994). Ward (1998) credited Knight (1921) for making the important clarifi cation between risk and the type of uncertainty for which there exists ‘no valid basis of any kind for classify-ing instances’:

‘He used the term “risk” for situations in which an individual may not know the outcome of an event, but can form realistic expectations of the probabili-ties of the various possible outcomes based either on mathematical calculations or the history of previous occurrences.’

Newson and Clark (see Chapter 14) contrast risk (with ‘known’ impacts and probabilities) with uncertainty (with ‘known’ impacts but ‘unknown’ probabilities) and igno-rance (with ‘unknown’ impacts and probabilities).

It is worth noting that uncertainty itself and all the related concepts outlined above are described in terms of their ‘degree’. That is, none of these concepts are simple Aristotelian two-valued logic concepts (e.g. true–false). Each concept is measured along a continuum of values with end-members of total uncertainty (complete irreduc-ible ignorance) and absolute certainty. Probabilistic uncer-tainty is an example of a quantifi cation of uncertainty, yet not all uncertainty is quantifi able. To quantify uncertainty it is necessary to estimate the degree of our limited knowl-edge. Yet, if a condition of irreducible ignorance is con-sidered as one extreme of uncertainty, it is diffi cult at best to estimate the degree of something we do not even know exists. Within this broad view of uncertainty, uncertainty might also be considered along a continuum that refl ects our ability to quantify it (Figure 3.1).

In summary, when someone mentions uncertainty casu-ally, it is diffi cult to discern whether they are referring to


limited knowledge, a lack of knowledge altogether, or one of the above-mentioned concepts that are infl uenced by uncertainty. Moreover, the lexicon provided here contains concepts that are highly inter-related and easily confused. Similar to vague, pseudo-scientifi c buzzwords and catch-all phrases like holistic and integrated, the term ‘uncer-tainty’ alone evidently has little meaning until its details are unravelled.

3.2.2 A Typology for Uncertainty

Since uncertainty is so hard to defi ne, a classifi cation of uncertainty is often used (Van Asselt and Rotmans, 2002). The utility of any typology or classifi cation is ultimately dependent on its application (Kondolf, 1995b; Lewin, 2001). Rotmans and van Asselt (2001) astutely pointed out ‘there is not one overall typology that satisfactorily covers all sorts of uncertainties, but that there are many possible typologies’. In the context of this review, a typology was sought which considered sources of uncer-tainty and did not unnecessarily ignore any type of uncertainty. The existing van Asselt (2000) typology was chosen over others because of its generic and inclusive consideration of uncertainty. The typology was fi rst intro-duced in detail in van Asselt (2000) and concisely reviewed in Rotmans and van Asselt (2001) and van Asselt and Rotmans (2002).

At the highest level, two sources of uncertainty exist: uncertainty due to variability and uncertainty due to limited knowledge (Figure 3.2). Van Asselt and Rotmans (2002) presented uncertainty due to variability fi rst as these uncertainties ultimately combine to contribute to uncertainty due to limited knowledge. Environmental management is concerned with the management of inher-ently variable natural and managed systems. Knowledge

about natural change and variability in ecosystems, fl uvial systems and hydrologic systems is incomplete and hence contributes to uncertainty due to limited knowledge (Wissmar and Bisson, 2003a). Five distinct subclasses of uncertainty due to variability are proposed: inherent natural randomness, value diversity (socio-political), behavioural diversity, societal randomness and technologi-cal surprise. Inherent natural randomness is attributed to ‘the nonlinear, chaotic and unpredictable nature of natural processes’. The natural variability of river systems should be a fundamental consideration in integrated river basin management and restoration; it is reviewed thoroughly in Wissmar and Bisson (2003c). Value diversity, behavioural diversity and societal randomness each contribute to uncertainties in environmental management, particularly through stakeholder negotiations, public support, project funding, policy making and individual perspectives. Tech-nological surprises result from new breakthroughs, which may provide unforeseen benefi ts and/or bring unforeseen consequences.

Van Asselt and Rotmans (2002) separated seven types of uncertainty due to limited knowledge. Unlike uncer-tainties due to variability, these are thought to map out along a continuum that refl ects the relative degree of uncertainty. At the highest degree of uncertainty are four ‘structural uncertainties’ (van Asselt and Rotmans, 2002):

• Irreducible ignorance: ‘We cannot know.’• Indeterminacy: ‘We will never know.’• Reducible ignorance: ‘We do not know what we do not

know.’• Confl icting evidence: Knowledge is not fact but inter-

pretation, and interpretations frequently contradict and challenge each other. ‘We don’t know what we know.’

Figure 3.1 The quantifi able continuum of uncertainty (Once uncertainties are acknowledge as unquantifi ed uncertainties, increased knowledge about the uncertainties will determine their position on the continuum.)


Van Asselt and Rotmans (2002) then proposed a transition into ‘unreliability’ uncertainties of a relatively lesser degree:

Practically immeasurable: A lack of data or informa-tion is always a reality in studying natural systems. Not only are many natural phenomena incredibly dif-fi cult or impossible to measure, all are fundamentally limited by problems of temporal and spatial resolu-tion, up-scaling and averaging (Kavvas, 1999). ‘We know what we don’t know’ (Van Asselt and Rotmans, 2002).Lack of Observations and Measurements: Although in principle this is easy to identify and augment, in practice this is always a factor. Borrowing from van Asselt and Rotmans (2002): ‘could have, should have, would have, but didn’t.’Inexactness: Related to lack of precision, lack of accuracy, measurement and calculation errors. Under Klir and Yuan’s (1995) typology, these are considered ‘fuzziness’ or vagueness.

The van Asselt (2000) typology is both more general and detailed than other typologies such as Klir and Yuan (1995). However, all provide a reasonable means to deal with the fi rst step to understanding uncertainty. Namely,

they allow a systematic identifi cation of sources and types of uncertainties that could work in either individual river restoration projects or international policy making on water and environmental management (see also Chapters 1 and 2). In practice, it is recognised that the semantics of uncertainty will always be interpreted differently in differ-ent professional contexts (Newson and Clark, see Chapter 14). However, within the context of this chapter, the van Asselt (2000) typology and associated meanings will be used consistently.

3.2.3 How do Knowledge and Uncertainty Relate?

The positivist view (Van Asselt and Rotmans, 2002) con-tends that as knowledge increases, uncertainty decreases. Brookes et al. (1998) made the more restrictive but contradictory generalisation that ‘as knowledge relating to rivers and their fl oodplains increases, uncertainty is increased rather than decreased.’ So, which is it? In reality, there is no unique relationship between uncertainty and knowledge (Van Asselt and Rotmans, 2002), nor is uncer-tainty a fi xed quantity that will always be reduced by sci-entifi c research (Jamieson, 1996). It is a highly contextual relationship dependent on the type of uncertainty (i.e. uncertainty due to lack of knowledge versus variability) and the specifi c circumstances under consideration. A few

Figure 3.2 Typology for sources and degree of uncertainty (Adapted from Van Asselt’s (2000) proposed typology for uncertainties in integrated assessment.)


examples of potential relationships between knowledge and uncertainty using the nomenclature of the van Asslet typology are illustrated in Figure 3.3. Having established the basic terminology of uncertainty, it is possible to discuss the sources of uncertainty within river restoration.

3.3 REVISITING RIVER RESTORATION AND UNCERTAINTY

It is diffi cult to generalise about the importance of uncer-tainty simply because restoration activities and the restora-tion community itself are so diverse. The stakeholders who

Figure 3.3 Some potential relationships between knowledge and uncertainty through time (Contrary to the argument of the positivist, no unique inverse relationship between uncertainty and knowledge exists.)


initiate river restoration projects include private individu-als, non-governmental organisations (NGOs), governmen-tal organisations and various collaborative combinations of the above. The restoration community is also comprised of practitioners, decision makers and scientists. No attempt is made here to list ‘all’ the uncertainties encountered throughout the restoration process as the daunting list would never be comprehensive, and is entirely perspective and project specifi c. For example, there is little consensus over the meaning of the term ‘river restoration’ with at least 30 different authors proposing different defi nitions (Lemons and Victor, see Chapter 1; NAP, 2002; Newson, 2002; Sear, 1994; Stockwell, 2000). Similar to Shields et al. (2003), ‘river restoration’ in this book is used as a catch-all term for a variety of management responses and activities used to address perceived problems with rivers (Kondolf, 1996). As a starting point, a generic decision process, which most restoration projects loosely follow, highlighting some of the common sources of uncertainty is mapped out in Table 3.2.

3.3.1 Motives for Restoration

Once river restoration projects gain momentum, it is easy to lose sight of why they were originally envisioned (Stewardson and Rutherfurd, see Chapter 5). Here, the motives for restoration are considered to represent more generalised aims than formalised and specifi c restoration objectives and activities (i.e. the ‘why’ instead of the

‘what’). Eight common types of motives for river restora-tion (still others exist) are listed below:

1. Ecosystem Restoration2. Habitat Restoration3. Flood Control/Defence4. Floodplain Reconnection5. Property and Infrastructure Protection (bank

stability)6. Sediment Management7. Water Quality8. Aesthetic and Recreational.

Considerable overlap exists between many of the above. For example, fl oodplain reconnection can be a type of fl ood control. Habitat restoration and water quality resto-ration are sometimes considered forms of ecosystem restoration. In another example, water quality restoration could be viewed by some as sediment management or by others as aesthetic or recreational restoration. Thus, a hierarchical organisation of restoration motives would be highly subjective and dependent on individual values and perspectives. This in itself is not necessarily problematic. However, it represents a form of communication uncer-tainty arising out of value diversity which is often taken for granted. Once the motives (why to do it) for restoration are established, restoration aims fall into place, but more specifi c objectives (what and how to do it) require careful consideration.

Table 3.2 Sources of uncertainty in an environmental management decision process structure (Adapted from Chapman & Ward (2002))

Stage in Decision Process Uncertainty About

Monitor the environment and current operations within the organisation

Completeness, veracity and accuracy of information received, meaning of information, interpretation of implications

Recognise an issue Signifi cance of issue, urgency, need for actionScope the Decision Appropriate frame of reference, scope of relevant organisation activities,

who is involved, who should be involved, extent of separation from other decision issues

Determine the performance criteria Relevant performance criteria, whose criteria, appropriate metrics, appropriate priorities and trade offs between different criteria

Identify alternative courses of action† Nature of alternatives available (scope, timing, logistics involved), what is possible, level of detail required, time available to identify alternatives

Predict the outcomes of courses of action† Consequences, nature of infl uencing factors, size of infl uencing factors, effects and interactions between infl uencing factors (variability and timing), nature and signifi cance of assumptions made

Choose a course of action How to weigh and compare predicted outcomesImplement the chosenalternative* How alternatives will work in practiceMonitor and reviewperformance‡ What to monitor, how often to monitor, when to take further action

† = Most decision support systems only provide input at these levels; * = The precautionary principle is implemented here; ‡ = Adaptive management starts here and feeds back through the process as necessary.


Many have argued that uncertainty in assessing restora-tion success arises from inadequate, vague and unclear restoration objectives (Jungwirth et al., 2002; Kondolf, 1995a; Skinner et al., see Chapter 10). Motives may serve well as aims (not necessarily to be achieved by an indi-vidual project) but they are insuffi cient to act as detailed project objectives, which in principle should be achiev-able. Using restoration motives carelessly as objectives produces unrealistic expectations. For example, in a recent request for proposals to fund community-based river res-toration projects by American Rivers and the National Oceanic and Atmospheric Association, applicants were asked to demonstrate that their project: will successfully restore anadromous fi sh habitat, access to existing anadromous fi sh habitat, or natural riverine functions; is the correct approach, based on ecological, social, eco-nomic, and engineering considerations; will minimise any identifi able short or long term negative impacts to the river system as a result of the project . . .’

The problem with requiring an applicant to make such bold statements about individual projects is that it asserts a level of confi dence in restoration simply not warranted by current science or practice and creates unrealistic expectations4 (Stewardson and Rutherfurd, see Chapter 5). Subtly rewording such requirements to account for uncer-tainty could help recast river restoration in a tone com-mensurate with our abilities and uncertainties. Interestingly, these objectives are consistent with Clark’s (2002) critical synopsis of Predictive Management as opposed to adaptive management as the current model in river management.

The restoration community has burdened itself with the idea that restoration objectives should be scientifi cally based (Davis and Slobodkin, 2004). While science surely has an important role in restoration, Davis and Slobodkin (2004) argued that determining restoration objectives is fundamentally a value-based and subjective process. Nothing is seen as inherently wrong with this reality, so long as it is transparently recognised. From an uncertainty perspective, this means that restoration objectives are therefore sources of uncertainty due to variability; namely value diversity, behavioural diversity and societal random-ness. For example, the fate of 81 000 hectares of forest land allocated for ecosystem restoration around the city of Chicago, Illinois has pitted two ‘environmental’ groups against each other based on their contrasting notions of ‘what is natural’. The divergent environmental views are essentially split between preservationists, who wish to pre-serve the forest land planted in the 1800s, and restoration-

ists, who want to restore the pre-settlement (1830s) prairie and savannah (Alario and Brün, 2001). Both evoke emo-tional arguments, which can be supported on scientifi c grounds. ‘Which is right?’ is the wrong question to ask of science. Alario and Brün (2001) concluded that the appro-priate arena to decide such an issue is a political decision making process.

3.3.2 Notions that Drive Restoration

Underlying motives for river restoration and the eventual specifi c techniques tried to achieve them are some very basic, yet highly uncertain notions. Since these basic notions are rarely questioned, it is important to highlight how they introduce uncertainty. Notions are also known as ‘Lietbilds’ – or target visions – and have gained wide-spread acceptance in the restoration literature (Hughes, 1995; Jungwirth et al., 2002; Kern, 1992). Notions, such as those in Table 3.3, that drive restoration strategies are frequently based on societal values and beliefs, or on popular, but by no means certain, scientifi c paradigms (Davis and Slobodkin, 2004; McDonald et al., 2004; Rhoads et al., 1999).

Falkenmark and Folke (2002) argued that sustainable catchment management must be based on ethical princi-ples. They suggest that management based on scientifi c principles alone is primarily concerned with ‘doing the thing right’, whereas notions that drive restoration strate-gies are actually driven by ‘doing the right thing.’ It is a presumption that good ethical practice generally translates into good biological practice (Pister, 2001). Hence notions are vague ideas, perhaps based on scientifi c knowledge, but primarily supported by ethical beliefs and societal values. The restoration literature is rarely explicit in dis-tinguishing the notions it advocates from the science used to support it. Phillip Williams (personal communication) asserts that ‘rigour’ in restoration planning should start with the development of an explicit conceptual model transparently describing our notions of how the river system functions5. Such a conceptual model should iden-tify both the historical context and the present day limita-tions (i.e. uncertainties). Wheaton et al. (2004a) argued that numerous conceptual models in the scientifi c litera-ture already exist and can be borrowed or modifi ed to formulate a site or basin specifi c conceptual model as the basis for restoration. Yet, Stewardson and Rutherfurd (see Chapter 5) describe three levels in restoration from which

4 This is fundamentally a communication uncertainty resulting from socio-political value diversity (see Figure 3.1).

5 In principle, the process of ‘rigour’ in restoration planning still follows the generic environmental management decision process of Table 3.2. In essence what Phillip Williams, a seasoned prac-titioner, describes is an informal Decision Support System (DSS).


epistemological uncertainties emerge: the validity of the conceptual model; whether the proposed intervention results in the planned geomorphic change; and whether the change is sustainable. They then caution that the validity of the conceptual model is the source of the ‘most uncertainty.’ Returning to Phillip William’s concept of rigour in planning, he argues restoration objectives should be based on an understanding of how the conceptual model interacts and responds to various societal motives

(NRC, 1992). Based on specifi c objectives, a measurable set of indicators and target levels can be selected (Doyle et al., 2000; Levy et al., 2000; Merkle and Kaupenjohann, 2000; Smeets and Weterings, 1999). Finally, a comparison of predicted indicator responses to restoration intervention versus inaction should be used to decide whether restora-tion is appropriate. Although available science may be used to inform the steps leading up to this decision (Lemons and Victor, see Chapter 1), the decision whether

Table 3.3 Common motives that guide notions and drive river restoration efforts

Notion Example(s)

What is Natural?Nature is in Equilibrium ‘the equilibrium between sediment supply and available transport capacity.’ (Soar &

Thorne 2001); ‘landforms can be considered as either a stage in a cycle of erosion or as a system in dynamic equilibrium.’ (Schumm & Lichty 1965).

Nature is in fl ux ‘Restored ecosystems are those in which the rates and types of disturbance do not exceed the capacity of the system to respond to them.’ (Hruby 2003).

Nature Constant ‘confi dence on global stability; there are no limitations to development’ (Levy et al. 2000).Nature Balanced ‘the environment is forgiving of most shocks, but large perturbations can knock ecological

variables into new regions of the landscape.’ (Levy et al. 2000).Nature Ephemeral ‘the environment can not safely tolerate human modifi cations’ (Levy et al. 2000).Nature Resilient ‘ecosystems are adaptive, evolutionary, and self organising . . . ecological systems often

thrive under conditions of high variability’ (Levy et al. 2000).

Physical IntegrityPhysical Integrity ‘Physical Integrity for rivers refers to a set of active fl uvial processes and landforms

wherein channel, fl oodplains, sediments, and overall spatial confi guration maintain a dynamic equilibrium, with adjustments not exceeding limits of change defi ned by societal values. Rivers possess physical integrity when their processes and forms maintain active connections with each other in the present hydrologic regime.’ (Graf 2001).

Alluvial River Attributes Several commonly known concepts that govern how alluvial channels work have been compiled into a set of ‘attributes’ for alluvial river integrity (Trush et al. 2000).

Ecological Integrity Ecological Integrity ‘maintenance of all internal and external processes and attributes interacting with the environment in such a way that the biotic community corresponds to the natural state of the type-specifi c aquatic habitat, according to the principles of self-regulation, resilience and resistance.’ (Angermeier & Karr 1994).

High Biodiversity = Ecological Integrity

Natural systems foster biodiversity and artifi cial systems are homogenized and dominated by invasive species (Ward et al. 2002, Lister 1998).

Morphological Diversity = Biological Diversity

Newson (2002) did not dispute the abundance of evidence supporting the linkages between channel dynamics and biodiversity, but criticises the lack of direct collaboration between geomorphologists and ecologists to substantiate the links in river management: ‘the mantra “morphological diversity = biodiversity” currently remains an act of faith.’

What is Sustainable?Sustainability According to Cairns (2003), the notion of sustainability is based on ‘the assumption that

humankind has the right to alter the planet so that human life can inhabit Earth indefi nitely.’

Geomorphic Sustainability ‘sustainability encompasses the notion of self-regulation of spontaneous functions (e.g. sediment deposition, colonisation and succession of vegetation) with minimal intervention and no adverse impact on the future aquatic environment whilst maintaining the functions of the channel demanded by society (fl ood control, navigation etc.).’ (Sear 1996).


or not to proceed is ultimately a political one (Alario and Brun, 2001).

3.3.3 Approaches to Restoration

Generally, river restoration projects consist of three components: planning, implementation and evaluation. The diversity of approaches available to implement these components rather appropriately refl ects the varied types (motives) of restoration projects and physiographic set-tings they are applied in. Thus, historical and spatial con-tingencies are contributing to uncertainties due to natural variability (Phillips, 2001). Indeed, a plethora of restora-tion approaches and strategies has been formalised in both the peer-reviewed and grey literature (Wheaton et al., 2004a). Examples range from generalised approaches for stream restoration (e.g. FISRWG, 1998; Jungwirth et al., 2002; Koehn et al., 2001; NRC, 1992; RRC, 2002) to more specifi c strategies incorporating: fl uvial geomor-phology (e.g. Brookes and Sear, 1996; Gilvear, 1999; Kondolf, 2000; Sear, 1994), ecosystem theory (e.g. Richards et al., 2002; Stanford et al., 1996), hydraulic engineering (e.g. Shields, 1996) and detailed design pro-cedures (Miller et al., 2001; Shields et al., 2003; Wheaton et al., 2004b). Most of the approaches have parallels in structure and ideology (Wheaton et al., 2004a).

Popular labels used to describe restoration approaches include holistic, science-based, integrated and multi-disciplinary (Hildén, 2000; Jungwirth et al., 2002; Wissmar and Bisson, 2003a). Since most approaches purport or aim to be all of these (Wheaton et al., 2004a), and the converse of each is perceived as negative, there is little value in discriminating approaches on these grounds. However, their components (i.e. planning, implementation and monitoring) can be differentiated using three descrip-tive metrics: the scale of restoration; form based versus process based; and active versus passive. These metrics can provide insight into the types of uncertainties encoun-tered and expectations placed on restoration projects during planning, implementation and monitoring.

Since the late 1990s, approaches almost unanimously call for catchment scale planning in restoration6. However, confusion arises over whether this means: restore the entire catchment; use watershed assessments to nest reach scale restoration in a catchment context (e.g. Bohn and Kershner, 2002; Brookes and Shields, 1996; Walker et al., 2002) or undertake a range of management and restoration activities across various spatial scales but nested within a catchment context (e.g. Frissell et al., 1993; Roni et al., 2002). Ecosystem degradation has often taken place over

many decades or centuries and extends across landscape, catchment and regional scales (Palmer et al., 1997). However, restoring an entire catchment is rarely viable (Brookes and Shields, 1996). Even those who call for ecological restoration of the entire catchment (e.g. Frissell et al., 1993) actually advocate achieving this through a range of targeted activities at various spatial and temporal scales.

Most of the restoration literature also points towards a consensus that a ‘process-based’ approach is superior to a ‘form-based’ one (Wheaton et al., 2004a). Much of the form versus process debate simplifi es down to the diffi -culty and/or appropriateness in selecting an analogue or reference condition. The frequently referenced ‘Lietbilds’ or target visions (Kern, 1992) and the popular Rosgen approach to restoration (Malakoff, 2004; Rosgen, 1996) both rely heavily on analogues. Jungwirth et al. (2002) suggest that at least three methods for selecting analogue or reference conditions exist:

• Select an existing reference site with ‘desirable’ condi-tions (location substitution).

• Select a historical reference condition for the site of interest on the basis of historical analysis (time for space substitution).

• Create a reference condition on the basis of theoretical models (either conceptual or mathematical).

In referring to these analogue conditions, is the desired form or the desired process then mimicked? This seems to be the point of departure for opinions within the restora-tion literature. Some argue that any mimicking of reference conditions is a form-based approach (McDonald et al., 2004). Others suggest that as long as ample consideration of sustaining processes and desired functions is made, the use of analogue conditions can be process based (Palmer et al., 1997; Wheaton et al., 2004a). Although exact inter-pretations are themselves uncertain and will continue to spur debate over semantics (confl icting evidence uncer-tainties), most concur that consideration of sustaining processes is fundamental (Wheaton et al., 2004c).

Fundamental methodological disagreements arise in the restoration literature with respect to passive versus active approaches to river restoration (Edmonds et al., 2003; Wissmar and Beschta, 1998). Here, active approaches are referred to as those which involve direct structural modi-fi cation to the river, its fl oodplain or infrastructure therein (e.g. channel realignment, levee removal, instream habitat structures). By contrast, passive approaches are those that ‘rely on the river to do the work’ (e.g. fl ow augmentation,

6 See also Table 3.1.


change in landuse, managing nonpoint sources of pollu-tion, buffer strips) (Wissmar and Beschta, 1998). Using a ‘process-based’ approach can make intuitive sense for passive approaches to restoration. For example, providing fl ow releases from a reservoir to mimic a natural hydro-graph and encourage mobilisation and reorganisation of sediments, may restore the processes that ‘allow the river to do the work’ (Stanford et al., 1996; Trush et al., 2000). However, active approaches are considered favourable when natural or passive recovery may take an unaccept-ably long time (Montgomery and Bolton, 2003). The choice of a passive versus active approach will depend very much on the specifi c social, political, economic and environmental contingencies of individual river basins (Wissmar et al., 2003), as well as the extent to which initial conditions matter (Phillips, 2002). Wheaton et al. (2004b) suggested that in some spawning habitat rehabili-tation contexts, it may be appropriate to employ passive approaches like gravel augmentation in conjunction with active approaches like spawning bed enhancement to kick-start recovery. Ultimately, all these choices are fuelled by an uncertain conceptual understanding of the system and logical ideas about how best to proceed with restoration. Given these inherent uncertainties, adaptive management is well suited to allow practitioners and decision makers to make a decision in the face of uncertainty, and to adjust that decision as time and new challenges unfold (Clark, 2002; Lister, 1998).

3.4 PHILOSOPHIES OF UNCERTAINTY

So, is all this uncertainty bad? By this point, it should be clear that uncertainty in river restoration is ubiquitous. However, different segments of society view uncertainty in very different ways, depending on the context (Lemons and Victor, see Chapter 1). As already mentioned, ordinary people are quite comfortable with the uncertainties of life in an intuitive and nonexplicit sense (Anderson et al., 2003; Pollack, 2003). However, uncertainty in policy and science, especially as reported in the media (Riebeek, 2002), are very different contexts. The choice of what to do about uncertainty is a philosophical question. Five poten-tial philosophical treatments of uncertainty are proposed in Figure 3.4. Each of these philosophies is reviewed in the remaining sections and linked to current attitudes within different segments of the river restoration community.

3.4.1 Ignore Uncertainty

It has already been argued here that the restoration com-munity has tended to passively ignore uncertainty and possible explanations as to why this may be the case pro-posed. For example, managers, policy and decision makers are fearful of admitting uncertainties, as this may be seen as a sign of weakness (Clark, 2002; Levy et al., 2000). Now that public support exists for river restoration, so too does the expectation7 that the problems restoration addresses are well understood. Indeed, these problems are reasonably well understood, but numerous uncertainties remain. Aside from basic, and potentially reducible,

Figure 3.4 Five philosophical attitudes towards uncertainty (The Venn diagram is meant to illustrate the overlap between contempo-rary attitudes towards uncertainty. Note that ignoring uncertainty shares no overlap with contemporary attitudes towards uncertainty.)

7 See Section 3.3.1 for the relationship between expectation and uncertainty.


communication uncertainties the signifi cance of the vast majority of uncertainties associated with restoration are simply not known. Admittedly, specifi c examples of uncertainties in restoration may indeed be insignifi cant. However, to assume insignifi cance on both ethical and technical grounds without fi rst establishing it might ulti-mately backfi re on the restoration community.

3.4.2 Eliminate Uncertainty

The positivist view of the world has fuelled much scien-tifi c progress on the notion that uncertainty is bad, abso-lute knowledge is good, and it is necessary to strive to eliminate uncertainty (Klir and Yuan, 1995; Priddy, 1999; Van Asselt and Rotmans, 2002). This fosters an unneces-sarily narrow view of uncertainty as subsumed entirely within the realm of science. van Asselt and Rotmans (2002) argued this view grew out of the ‘Enlightenment Period’ or ‘Age of Reason’ of the 17th and 18th centuries where science was to be ‘the provider of certainty.’ Further to this endeavour, many scientists assumed that unique causal laws exist for all natural phenomena and ignored the possibilities of indeterminacy and equifi nality (Wilson, 2001). Many physical scientists still subscribe to a ‘posi-tivist’ view (Harman, 1998), implicitly associating uncer-tainty with an inability to quantify the environment, rather than acknowledging a limited understanding about the environment itself (Klir and Yuan, 1995).

Whether specifi c types of uncertainty can be eliminated depends on an individual’s interpretation of semantics. Under the holistic view of uncertainty advocated in this chapter uncertainty cannot be completely eliminated. Pollack (2003) suggests that ‘uncertainty is always with us and can never be fully eliminated’. Other authors (e.g. Knight, 1921) suggest that some types of uncertainty can be transformed into related concepts (e.g. error, expecta-tion, reliability, risk) with the help of mathematical con-structs and knowledge gained from historical inference. Through this transformation, uncertainty of a specifi c type (i.e. uncertainty for which a valid basis for classifi cation exists) in a sense might be ‘eliminated.’ Such a transfor-mation represents an improved understanding of uncer-tainty but does not truly ‘eliminate’ it.

With technological progress has come the expectation of greater predictive power. Priddy (1999) suggested, ‘the strictest standard of truth in science is that of predictabil-ity.’ Although intuitively no one expects prediction to be completely free of uncertainty, the notion that uncertainty can be eliminated is latent in the mainstream media (Riebeek, 2002). Pollack (2003) argues that scientists are accustomed to dealing with uncertainty explicitly, but the general public’s familiarity with uncertainty is implicit

and often confused. Jamieson (1996) suggests that, par-ticularly with respect to decisions about increased envi-ronmental protections, the ‘rhetorical role of uncertainty claims’ are used to suggest no action should be taken until uncertainty is eliminated. Hence, it is concluded that attempts to eliminate uncertainty are misleading and founded on ignorance of the principles of uncertainty.

3.4.3 Reduce Uncertainty

A more pragmatic view of uncertainty seeks to reduce, rather than eliminate, those specifi c elements that are per-ceived as problematic (Klir and Yuan, 1995). This approach to uncertainty is represented diagrammatically in Figure 3.4. Notice that with regards to reducing uncertainty, the key questions are, in order: can it be quantifi ed, is it sig-nifi cant and can it be constrained? So long as the answer is ‘yes’ to all these questions, uncertainty might be reduced. However, if the opposite is true, uncertainty is simply ignored. To move beyond uncertainty as an ambiguous buzzword that will forever plague scientists and decision makers, a broader view of uncertainty as information is appropriate (Newson and Clark, see Chapter 14).

3.4.4 Cope with Uncertainty

Coping or living with uncertainty represents a more proac-tive view of dealing with uncertainty than elimination or reduction. This approach recognises that, regardless of the signifi cance of uncertainty and our ability/inability to quantify or constrain it, we are always forced to cope with it. Especially within the hydrologic and atmospheric modelling literature, uncertainty is actively recognised and specifi c methods to cope with it are continually being proposed (e.g. Beven, 1996a; Beven, 1996b; Osidele et al., 2003; Werritty, 2002).

3.4.5 Embrace Uncertainty

Despite the advantages of efforts to cope with or reduce uncertainty over eliminating it, all the preceding still fun-damentally view uncertainty as negative. Several authors have departed from this view towards a more progressive view of embracing uncertainty (Johnson and Brown, 2001; Newson and Clark, see Chapter 14). One of the earlier proponents of this view appears to be Holling (1978), who argued:

‘while efforts to reduce uncertainty are admirable . . . if not accompanied by an equal effort to design for uncertainty and obtain benefi ts from the unexpected, the best of predictive models will only lead to larger


problems arising more quickly and more often’ (in: Levy et al., 2000).

Klir and Yuan (1995) considered uncertainty in model-ling as ‘an important commodity . . . , which can be traded for gains in the other essential characteristics of models.’ Others have suggested that recognising that not all uncer-tainty is bad will be increasingly important to decision makers who are forced to make decisions in the face of uncertainty (Clark and Richards, 2002; Pollack, 2003). Especially in long term policy analysis (the next 20–100 years) decision makers are faced with what Lempert et al. (2003) referred to as ‘deep uncertainty’. Johnson and Brown (2001) argued that incorporating uncertainty into restoration design allows practitioners to consider multiple causes and hypothesised fi xes; thereby reducing the poten-tial for project failure. It has been argued here that uncer-tainty is not necessarily bad, but ignorance of it can foster unrealistic expectations. Chapman and Ward (2002) argued that uncertainty is not just as a risk, but also an opportunity. Uncertainty due to natural variability, in say fl ow regime, can be a particularly good thing, for example by promoting habitat heterogeneity and biodiversity (Clifford et al., see Chapter 7; Montgomery and Bolton, 2003).

In Figure 3.5, the notions of embracing uncertainty are synthesised in the context of the van Asselt (2000) typol-ogy. This approach embraces uncertainty as information and its potential for helping avoid risks, or embracing unforeseen opportunities. Notice that the uncertainties are not treated uniformly, but instead are segregated by source (i.e. due to limited knowledge or due to variability) and type. Anderson et al. (2003) note that environmental man-agement problems are so diverse that a single approach is unlikely to be appropriate for all. Thus, Chamberlin’s (1890) idea of multiple working hypotheses is emerging in environmental management through advocating plural-istic approaches (e.g. Lempert et al., 2003; Van Asselt and Rotmans, 2002).

The embracing uncertainty framework proposed here emphasises this point by structuring a range of questions and possible management decisions based on the specifi c uncertainties at hand. In the spirit of ‘sustainable uncer-tainty’ as proposed by Newson and Clark (see Chapter 14), this is not at all a rigid framework but instead a loose and adaptive guide built around an uncertainty typology. Unlike the four other philosophical treatments of uncer-tainty, this allows the restoration scientist, practitioner or decision maker to:

• explore the potential signifi cance (both in terms of unforeseen consequences and welcome surprises) or insignifi cance of uncertainties;

• effectively communicate uncertainties;• eventually make adaptive, but transparent, decisions in

the face of uncertainty.

3.5 CONCLUSION

In this chapter a very broad picture of uncertainty in river restoration and environmental management has been painted. This was done to unravel the ambiguities around the notion of ‘certainty’ in restoration and recast uncer-tainty as useful information. In fact, the arguments and evidence presented challenge the view of scientifi c deter-ministic ‘certainty’ and societal beliefs that certainty is necessary in restoration. A typology for discriminating uncertainty was reviewed that can be used to separate uncertainties that can lead to unforeseen and undesirable consequences from uncertainties that lead to potentially welcome surprises. Many of the uncertainties surrounding restoration motives, notions and approaches are most seri-ously manifested as communication uncertainties. That is, instead of being expressed simply as uncertainties due to limited knowledge, they are ignored and miscommuni-cated through the restoration process in a manner that prevents transparent decision making. The signifi cance of the plethora of other uncertainties alluded to is largely situation-specifi c and, to date, unexplored.

Five philosophical strategies for dealing with uncer-tainty ranging from the status quo of ignoring uncertainty to the advocated embracing uncertainty were reviewed. Traditional scientifi c research has focused on a narrow class of uncertainties and adopted ‘eliminate’ and ‘reduce’ uncertainty philosophies. It is argued that it is unethical to assume that uncertainty is insignifi cant. There is an increasing recognition in environmental management that ethical and social dimensions are the primary drivers, with scientifi c and technical dimensions playing a secondary role (Falkenmark and Folke, 2002; Lister, 1998)8. Thus, an emerging challenge which the restoration community is faced with is combining these dimensions to ‘do the right thing right.’ Out of the decision making arena has emerged the pragmatic view of coping with uncertainty. However, from the suggestions and examples in the more general environmental management literature, it is concluded that embracing uncertainty could also help transcend the sci-entifi c research and decision making boundaries in river restoration.

8 Recall the development of notions in Section 3.3.2, and the dis-tinction of Falkenmark and Folke (2002) between technical con-cerns (e.g. ‘doing the thing right’) and ethical concerns (e.g. ‘doing the right thing’).


Figure 3.5 Framework for embracing uncertainty in the decision making process (This framework relies on the Van Asselt (2000) typology of uncertainty.)


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SECTION II

Planning and Designing Restoration Projects


4

Planning River Restoration Projects: Social and Cultural Dimensions

G. Mathias Kondolf1 and Chia-Ning Yang2

1Department of Landscape Architecture and Environmental Planning, University of California, USA2Department of Landscape Architecture, California State Polytechnic University, USA

4.1 INTRODUCTION

Nationwide, since 1990, at least US$17 billion has been spent on restoration projects, a fi gure that is an under-estimate because most reported costs do not include staff time and many projects did not report their costs at all (Bernhardt et al., 2005). In many areas, river restoration has become an industry, with nonprofi t groups, govern-ment agencies and consulting fi rms now depending upon river restoration funds to support large components of their budgets. For example, over four fi scal years from July 2000 to June 2004, over US$100 million was disbursed by the California Department of Fish and Game to recipi-ent groups and agencies in the Fishery Restoration Grants Program for restoration projects in coastal river basins (F. Sime, personal communication, December 2003), mostly to construct habitat enhancement structures in salmon-bearing rivers and streams. Elsewhere in Califor-nia, stream restoration projects employ many in rural com-munities, including former timber cutters (Hamilton, 1993). In the city of Bozeman, Montana, there is suffi cient business to support six fi rms specializing in restoring trout streams.

River and stream restoration can be viewed as a con-temporary phase of the environmental movement. Unlike early phases of the movement, which tended to document and draw attention to the nature and extent of environmen-tal degradation (Carson, 1962; Ehrlich, 1968) and which therefore tended to be negative or pessimistic in tone, restoring rivers and streams has a positive, pro-active con-notation. This is especially true of streams in urban neigh-

borhoods, where restoration projects can provide posi-tive reinforcement and a sense of empowerment to local groups. In many respects, the greatest benefi ts to restoring local urban creeks are probably the social benefi ts that accrue through the process of community building and the public environmental education achieved.

Technical specialists often assume implicitly that resto-ration is a technical problem, and a glance at articles published in restoration-related journals shows a prepon-derance of papers addressing the scientifi c aspects of project design and planning. However, restoration can be viewed as fundamentally a social phenomenon, as it results from a societal decision to restore some functions to a river (Eden et al., 2000). Its goals and implementation approaches can be informed by science, but they are essentially social in nature. The very fact that restoration has become such a widespread activity refl ects a change in public attitudes towards watercourses. The current atti-tudes are possible only thanks to past social investments in waste water treatment following passage of the Clean Water Act in the United States (Wolman, 1971) and comparable legislation in other developed countries. The resulting improvements in water quality now make human contact with urban waters desirable and ecological restora-tion feasible, which was not the case in the past when these channels were open sewers. As society and culture evolve, goals change and the realm of what is ‘feasible’ in river restoration can change dramatically, introducing uncer-tainties for restoration planning and design – uncertainties that do not lend themselves to technical engineering analysis.


This chapter considers social and cultural dimensions of river restoration, and related uncertainties for restora-tion planning and design. While recognized as important, they are probably less well understood by the agencies involved in designing and implementing restoration proj-ects (FISCRWG, 1998). Systematic studies of pub-lic attitudes and expectations regarding river restoration (Tunstall et al., 2000) have been rare in light of the enor-mous societal investment in restoration projects. There is no way this short chapter can do justice to this broad topic, but herein we attempt to raise some issues relevant to the enterprise of river restoration, which we hope will be useful to scientists and practitioners in the fi eld. We fi rst briefl y review some important social aspects of river res-toration: the land-use context of restoration projects, underlying cultural preferences in river restoration design and the increasing importance of public participation in river management and restoration programs. We draw upon recent research to consider various human activities in urban streams and the confl icts among resto-ration goals of various professionals and stakeholder groups. Finally, we present two brief case studies from northern California, which illustrate social issues and attendant uncertainties in river restoration.

4.2 OVERVIEW OF SOCIAL ASPECTS OF RIVER RESTORATION

4.2.1 An Urban–Rural–Wilderness Continuum

Appropriate goals and the solutions possible vary widely with context, from near wilderness to dense urban settings (Figure 4.1). Where catchment processes are relatively unaltered and runoff and sediment load are virtually

unchanged, a restoration project can logically seek to restore pre-disturbance channel conditions, either by giving fl oods and sediment transport the opportunity to recreate natural channel conditions (an approach often termed ‘passive restoration’) or by proactively re-constructing pre-disturbance channel form (the ‘carbon-copy’ approach of Brookes and Shields, 1996). An example would be a channel whose catchment land use has remained constant, but whose form was altered by channel straight-ening or by removal of bank vegetation and consequent instability. At this wilderness end of the continuum, it makes sense to either let natural processes accomplish the restoration or to use the pre-disturbance channel as a template, because the processes that supported the pre-disturbance channel will tend to support the same channel form again. In either case, to maintain ecological values, the channel should be permitted to migrate freely and to fl ood overbank areas (Ward and Stanford, 1995).

Moving towards the urbanized end of the continuum, land use change in the catchment has altered runoff and sediment load, so there is no reason to expect pre-disturbance channel dimensions to be maintained by current processes. At the extreme, urban development in the catchment increases peak fl ows such that the channel tends to incise, which, if uncontrolled, may lead to bank collapse and channel widening. However, encroachment of urban development to the channel margins means that incision and channel widening are socially unacceptable. At this urban extreme, restoration projects must be built to convey urban runoff without fl ooding adjacent lands and to withstand increased shear stresses of urban runoff without erosion. Here, restoration can be viewed as a form of gardening, in which the elements are deliberately chosen and maintained by human input, albeit one that

WILDERNESS HIGHLY URBAN

Unaltered watershed.Channel may be altered.

Transitional cases. Highly altered watershed and channel.Encroached banks.

Attri

bute

s Attributes

Can restore pre-disturbance, historical channel, by either:1) letting river restore itself2) ‘carbon-copy’ approach

Gen

eral

app

roac

h

Can partly restore processes. Must decide what changes to accept as

constraints, what to try to change/restore.

Cannot restore historical conditions. ’:gninedrag‘ sa noitarotseR

choose elements to include but must account for erosive forces, altered hydrology.

Social issues are important: potential to improve ecology/water quality are limited, so emphasis on community-building and environmental education.

General approach

Exam

ples

Flow regime unchanged.Sediment load unchanged.No urban encroachment.

Increase releases from dam?Reduce peak urban flow by detention?

Add gravel below dams?Reduce erosion in watershed?

Remove houses along bank/floodplain?

Flow regime altered.Sediment load altered.

Urban encroachment to banks.

Examples

Figure 4.1 An urban–wilderness continuum in river restoration

Planning River Restoration Projects: Social and Cultural Dimensions 45

requires hard structures to resist erosive forces of urban-runoff-augmented fl oods. In such cases, ‘naturalness’ in restoration may be viewed as more an aesthetic choice than a real design approach. Such channels can be highly successful in providing recreational and aesthetic ameni-ties, linking communities through walking and biking trails, even providing for kayaking and canoeing. However, they may be viewed as ‘water features’ (to borrow a term from the fi eld of landscape architecture) capable of con-veying fl oodwaters within the channel and without eroding banks, rather than large scale, dynamic ecosystems. They can still provide ecological benefi ts at a local scale, but without rebuilding of the urban infrastructure these water-ways are unlikely to support sensitive target species at a large scale. In these highly urban settings, the ecological potential from a restoration project can rarely be compa-rable to that achieved in a less urban setting, and thus urban restoration projects may be best justifi ed by their potential social benefi ts as they respond to human needs and uses.

Many restoration projects can be seen to fall on a con-tinuum between these two extremes, with constraints, but with the potential to restore some natural processes and functions. It is in these intermediate cases that the greatest uncertainties arise, as one person’s ‘constraint’ may be another’s opportunity to restore process. For example, if an upstream reservoir has eliminated the natural magni-tude and frequency of fl oods, should we accept this as a constraint that effectively limits the degree to which natural ecosystem processes can be restored, or do we seek to alter the reservoir operation rules to more closely mimic natural fl ow patterns? Reservoir release patterns have been altered and aquatic ecological conditions improved on rivers such as the Green River, Kentucky (Postel and Ritcher, 2004), the St Mary River, Alberta (Rood and Mahoney, 2000) and Putah Creek, California (Marchetti and Moyle, 2001). Similarly, does the exis-tence of human infrastructure or housing on a fl oodplain mean we cannot inundate this fl oodplain? Or should the restoration project include compensation for moving the inappropriately-sited land use to higher ground, so over-bank fl ooding processes can be restored? These are social/political decisions, which can be informed by science, but which cannot be predicted technically – adding substantial uncertainty to restoration planning.

4.2.2 Cultural Preferences in River Restoration Design

Unstated and often unacknowledged cultural preferences probably underlie many restoration design decisions. For example, grassy banks are preferred over shrubby or

wooded banks along many restored streams in northern Europe, refl ecting the long history of pastoral land use. Similarly, open park-like landscape seems to be broadly preferred in western culture (Appleton, 1975), and resi-dents near urban stream restoration projects in northern California have complained when restored streams become too ‘bushy’ and woody riparian vegetation blocks visual access to the stream bed (Purcell et al., 2002). Similarly, large woody debris in channels imparts a messy look, to which most people have a negative reaction (Piégay et al., 2005).

In North America, restoration projects seeking to create stable, symmetrically-meandering channels have prolifer-ated. In some cases, previously single-thread channels have been reconstructed in attempts to create a more ideal, symmetrical meandering form in the belief that these would be more stable (Smith and Prestegaard, 2005). In other cases, the channels have been reconstructed with the goal of converting braided rivers to single-thread, mean-dering rivers. In many cases, the streams so ‘restored’ were never single-thread meandering channels under natural conditions, and the projects can be viewed as essentially attempts to impose an idealized meandering form onto the river, as illustrated on Uvas Creek, California (Kondolf et al., 2001). Many of these channel reconstructions have washed out within months or years (Figure 4.2). Despite its mixed record of performance, the design approach underlying most of these projects – application of the classifi cation scheme of Rosgen (1994) (NRC, 1992) – continues to be popular among government agencies responsible for funding restoration projects. This is probably due to the ease with which the classifi cation scheme can be used and applied by those without aca-demic training in fl uvial geomorphology, the availability of commercial short courses teaching users how to apply the scheme and – though largely unrealized and unac-knowledged – the likelihood that the channel designs that result from applying the scheme satisfy a deep-seated cultural preference for stable, single-thread meandering channels.

Research on human responses to landscape form suggest that subjects (at least in western culture) tend to prefer the ‘defl ected vistas’ in curved paths, rivers and valleys over straight lines (Appleton, 1975), in part because they elicited curiosity in subjects (Ulrich, 1983). Kaplan and Kaplan (1984) designated this landscape property as ‘mystery’, conveying the opportunity to explore and a promise to learn more with a changing vantage point as one moves more deeply into the scene. What is probably a (near-) universal attraction to the form of meandering channels was recognized in the 18th century by Hogarth (1753), who proposed that the ‘serpentine’ line provided


the greatest aesthetic pleasure, and more so when actively moving. A river moving through a meandering channel thus has the elements needed for the experience of beauty under Hogarth’s theory. This preference also found expres-sion in the work of late 18th century English landscape designers such as Capability Brown, who built serpentine channels on the estates of their wealthy clients.

Despite the evidence for a landscape preference for the meandering channel form, the justifi cations for meander-ing channels specifi ed in river restoration projects in North America are almost always stated in terms of bankfull discharge, width–depth ratios, meander wavelengths etc. The fundamental question as to whether a meandering channel is appropriate at all is rarely addressed. Similarly, the notion that channels should be stable can be viewed as largely anthropocentric. Dynamic channels with vari-able fl ow regimes tend to support the greatest variety of habitats and best ecosystem function (Ward and Stanford, 1995; Poff et al., 1997). Yet the meandering channels constructed using the Rosgen approach have the outside of meander bends armored by root wads and boulders,

with rock weirs at the crossovers to keep the main current away from the banks (e.g. Uvas Creek in Figure 4.2(a)). Indeed, the Rosgen scheme is used to select the ‘proper’ geometry for a site, ‘proper’ meaning it will be stable. We do not argue with the need to armor channels in dense urban areas or elsewhere when infrastructure is threatened by channel migration, but these restoration projects typi-cally include armored banks even at sites where channel migration would not threaten human works. The armoring seems to be accepted in part because it consists of ‘natural’ materials (i.e. it is not concrete) and because those involved in funding and designing these projects hold a belief that a stable channel is preferable to an eroding channel, even if in a rural or park setting.

Finally, stable, meandering channels, fl anked by grassy banks, probably appeal to our aesthetic senses in large part because they are ‘tidy’ landscapes. Natural riparian corri-dors are frequently inaccessible thickets, which, while great habitat for wildlife, are unappealing to our western aesthetic sensibility. Nassauer (1995) demonstrated that for such ‘messy’ ecosystems to be widely accepted, we must set them off within a frame that conveys to the viewer that the messiness is deliberate and not a sign of neglect. She demonstrated how ‘cues to care’ such as a neatly maintained fence around a yard of native prairie could make the otherwise messy bit of landscape accept-able within the context of a suburban street.

To the extent that public support for restoration is based on culturally-driven landscape preferences that are not recognized or articulated, this creates enormous uncer-tainty in river restoration projects, as public support cannot be predicted based on ‘logical’ analysis of how best to improve aquatic ecology or to manage fl oods. There is another factor, which cannot be predicted by technical experts. The topic of human preference in landscape is an area of active research. Many of the fi ndings probably have relevance for river restoration, besides the few touched upon here.

4.2.3 Public Participation and Active Stakeholders

Today, public participation has become an institutional-ized element in stream restoration. Public acceptance and support in many cases determines the ultimate success and sustainability of a project. For example, providing public access to a restoration plan can substantially increase public support for the plan (Bauer et al., 2002). Support for restoration is important not only in advocating for the proposed project, but also in its stewardship after con-struction. Stewardship can be developed by encourag-ing people to experience the restored natural areas (Ryan et al., 2002).

b

a

Figure 4.2 (See also colour plate section) Uvas Creek viewed downstream from Santa Teresa Road bridge: (a) January 1996, two months after completion of the channel reconstruction project; (b) July 1997, after the constructed channel washed out in February 1996 during an approximately six-year fl ow (Photo (a) courtesy of the City of Gilroy, (b) by Kondolf.)


Increasingly the role of stakeholders is not limited to providing review comments on draft documents, but to active participation in setting objectives and selecting implementation strategies. The success of such a collab-orative planning process is often evaluated by whether or not agreement is reached among interest groups. This approach implicitly assumes that there is an optimal solu-tion that satisfi es all interests and is technically feasible. However, there is no a priori reason to assume that this is the case, and in fact there are good reasons to expect it frequently will not be. Accordingly, confl icts can arise among different actors, such as between stakeholder groups, between professionals in different fi elds and between design professionals and residents, all creating uncertainties for restoration planning. These are discussed in more detail in the following pages. Although water policy making and planning remains a much contended arena in California, collaborative planning or policy making has been documented as benefi cial not simply based on whether or not consent is reached among various stakeholder groups, but through the long term, invisible outcomes in terms of collective learning and accumulation of social, political and economic capitals (Connick and Innes, 2003).

An important feature of river restoration today is the proliferation of local creek groups, known in the United States as ‘Friends of’ the local creek, in the United Kingdom as river ‘Trusts’ (e.g. the Eden Rivers Trust). In the San Francisco Bay Area, these groups have formed a signifi cant force in shaping the fate of restoration projects. Friends groups not only voice their desires during restora-tion planning, but in many cases they have become the task force of implementing plans and management regimes. To the restoration project designer, the potential role of local creek groups is a source of uncertainty. If a local group is active, it is important to work closely with it, both to improve the project design with respect to its social functioning and to improve the chances of success-ful implementation and sustainability by virtue of the public support a local group can often provide.

There are also fundamental issues with representative-ness in the stakeholder and public participation process. These processes can be drawn-out, and the long term active participants tend to be agency staff or industry representatives for whom participation is part of their job, or staff of NGOs who are often stretched thinly amongst many such processes. To actively participate, members of the public at large must have the time and energy to devote to meetings over a long period (often exceeding a year) at their own expense. Unless they are strongly motivated – often by an imminent threat such as stopping a develop-ment in their neighborhood – few can fi nd the time to be

active in the public participation process. This is refl ected by the survey results of the ‘befriended’ watersheds in the San Francisco Bay Area. Neighborhoods with active ‘Friends’ groups have a much higher average income than areas that do not form creek groups (Mozingo, 2005), showing urban stream stewardship in the United States still serves a clientele biased toward the upper and middle classes.

4.3 HUMAN USES OF URBAN WATERWAYS

While the habitat requirements of fi sh have been exten-sively studied (Reiser and Bjornn, 1979) and are used as a basis for design of restoration projects oriented towards salmon and trout (Flosi et al., 1998), the habitat require-ments of humans in the stream environment, broadly construed, are less well understood. Recent research into why certain activities occur spontaneously at certain parts of the stream suggests that there are fundamental charac-teristics of streams that encourage, and can be designed for, recreational use. Here we review a range of human uses of stream corridors, emphasizing urban and suburban settings.

4.3.1 Camping by Homeless

Riparian corridors have long been preferred sites for camping by homeless people. River corridors were sites of large camps of migratory workers and tramps in North America during the depression of the 1930s, and homeless encampments are a common element along urban streams in California today, offering a relatively secluded refuge for the ‘down and out’. Homeless camps are often found under bridges, exploiting the shelter from rain, although these sites are more accessible and thus more likely to be visited by others and less private (Figure 4.3). Along Ledgewood Creek near Fairfi eld, California, a camp of fi fty residents had tents with carpeted fl oors, furniture, and battery-powered television; the residents reportedly left during periodic police sweeps, only to return (Fagan, 2005).

Migrants from the provinces of Cuba have settled along the banks of the Almendares River in Havana, forming a squatter community known as ‘El Fangito’ (Figure 4.4). While the streets are mud, many of these dwellings feature cement or tiled fl oors, furniture and television sets. Although the settlement was illegal, utilities have hooked up electrical power and water; sewage fl ows mostly through buried pipes directly to the river. The fl oodplain occupied by El Fangito is fl ooded every few years. Resi-dents take their television sets and leave for higher ground when the river begins to rise. The management plan of the Metropolitan Park of Havana (Fornes, 1994) calls for


The authors are aware of no studies focusing on home-less use of riverine spaces, but our fi eld studies in California suggest that this user group has little tolerance for other user groups and vice versa. We have observed that no other users were present near homeless camps (usually under bridges and behind thickets on fl oodplains). In Sonoma, California, children were often warned off by parents or scared away when they accidentally invaded homeless territory (Yang, 2004). In Japan, while homeless people also frequently reside under bridges of urban streams, the tension was not as high, as homelessness in Japan is regarded mainly as a product of unjust industrial structure (Dohi, 1999), with little association with drug abuse and crime. In America, the fl ood of homelessness during the past four decades is largely attributed to the failure of ‘deinstitutionalization,’ a major initiative under the Community Mental Health program that started in 1963 (CCHR, 2004).

Occupation of river corridors by homeless can be an important source of uncertainty to the outcome of river restoration efforts. Use of stream corridors by homeless people has not (to the authors’ knowledge) been encour-aged by designers. However, it is clearly one of the biggest uses along many urban rivers and streams. Because the presence of homeless people could discourage use by other groups, the actual use of a restored stream corridor may be very different from that anticipated by project designers, introducing uncertainties.

4.3.2 Fishing

Fishing is a traditional use of rivers and streams, ranging from subsistence fi shing with traps and nets to purely sport fi shing in which the fi sh is released back to the stream. Fisheries in urban channels range widely from wild, anad-romous salmonids in urban channels in the Pacifi c North-west of North America to warm water species pulled from the polluted waters of Asian cities. Fishing is usually well regulated by licensing and many streams are artifi cially stocked. Fishing is a well documented and well studied activity in rivers, a large subject well treated elsewhere and beyond the scope of this chapter. However, we point out that fi shing has long been an important activity drawing people to rivers, similar to other activities we discuss below. Improving fi sh habitat is cited as a goal for many restoration projects and many funding sources are avail-able to improve fi sheries.

4.3.3 Water Sports

Urban rivers (if not so polluted as to be unpleasant) have long been used for canoeing and fl oating. On summer

Figure 4.3 (See also colour plate section) Homeless campsite, San Pablo Creek, California (Photo by Kondolf, February 2005.)

Figure 4.4 (See also colour plate section) The squatter neigh-borhood El Fangito, Havana (Photo by Kondolf, March 2005.)

moving these residents from El Fangito to better, perma-nent housing and reforestation of the fl oodplain, but an international aid agency recently granted funds to build a levee around the settlement to protect it from fl oodwaters.


weekends, the Chattahoochee River near Atlanta, Georgia, is packed with young people fl oating downstream on tire inner tubes or rafts. Increasingly, more active forms of kayaking and canoeing are being designed for in urban river restoration projects. For example, the steeper, upper reaches of the restored Boulder Creek in Boulder, Colo-rado, have been designed as a kayak course, and kayakers and canoers commonly continue downstream through the town.

4.4 SPONTANEOUS USES OF URBAN WATERWAYS

Although recreation is commonly cited as a goal of stream restoration projects, it is often treated perfunctorily com-pared to other goals such as fl ood control and habitat, except where its value can be expressed in monetary terms. In the context of cost and benefi t analysis, emphasis on recreation necessarily narrows down to the licensed, quantifi able activities such as fi shing and boating (NRC, 1992). In contrast to such vacation-orientated uses, there is a suite of more intuitive and unplanned activities, hereby named ‘spontaneous uses,’ that involve direct and active interaction with the landscape, such as skipping rocks, catching frogs, collecting nuts and swimming. When human uses are considered at all in urban stream restora-tion projects, the focus is typically on passive uses, such as trail walking and social gathering. The orientation is also often towards adults uses only, whereas children may have very different (and strongly felt) attitudes towards stream environments (Tunstall et al., 2004; Yang, 2004). However, a growing literature suggests that the more interactive activities are crucial to the forming of environ-mental awareness (Chawla, 1988; Harvey, 1989; Orr, 1992) and place attachment (Owens, 1988; Hester et al., 1988; Cooper-Marcus, 1992), and are benefi cial for healthy human development (Nicholson, 1971; Kaplan, 1977; Cobb, 1977; Hart, 1979; Moore, 1986). Whether a restored channel encourages spontaneous use or not is a source of uncertainty to the ‘social’ success of a restoration project.

To understand the specifi c habitat characteristics that permit and encourage spontaneous uses, Yang (2004) reviewed the literature to identify probable habitat charac-teristics encouraging such uses and then undertook sys-tematic fi eld observations, especially of children, and interviews of children and adults in fi eld areas in Califor-nia and Japan. Although many of these interactions were engaged mainly by children, they were not enjoyed by children exclusively. Adults accompanying children, or even among a group of adults, appeared to fully enjoy such uses. From this research, we summarized the most

common types of spontaneous interaction and their habitat requirements.

4.4.1 Quiet and Secluded Use

Users who appreciate the stream environment in a tran-scendent way, go to the stream for a temporary escape, enjoy intimate relationships with signifi cant others and those who pursue quiet reading, thinking etc., are com-monly attached to a specifi c base-point. Their territory may seem small, but the quality demands are high and specifi c. Since such users can stay for hours, a certain comfort level (dry seating, foothold and shade) is nor-mally required. A rock, tree root, log, or a soft grassy spot by water are particularly appealing. Yet more than any-thing else they need privacy, or visual/auditory seclusion from supervision or other users. Lewis (1995) highlighted the value of San Leandro Creek, California, as a secret hiding place and unsupervised play area, with many ‘fi rst-time’ events of local youth. All his interviewees who played there appreciated this quality of nonsupervision. For this reason, they preferred detoured or inconspicuous access and a back screen.

The view toward dense foliage, open fi eld or expression of water surface, the sound of trickling water, the appear-ance of wildlife and easy access to water all tremendously enhance the value of quiet and secluded base-points, as users cite these elements as bestowing the healing power of nature. Both adults and children have been found to seek out space for quiet and secluded use (Yang, 2004). Quiet and secluded base-points are easily lost, not only because privacy is often lost with increased urbanization, but also because planners and designers usually don’t design for such spots, operating instead on a design model of a cheerful park for adult socializing and playgrounds where all children play together.

4.4.2 Adventures

Adventure connects known to unknown parts in the land-scape, expanding cognitive and physical territory. Adven-turers walk, bike, swim, leap, climb, creep and cross to ‘conquer’ a new piece of landscape. A system of base-points connected by diverse, usually three-dimensional paths, plays an important role in the process of expanding territory. Dirt paths apparently possess special values to adventurers. On Marsh Creek, California, some adults favor dirt paths for aesthetic reasons, but children pre-ferred dirt paths for the practical reasons that they are usually avoided by adult bikers and runners, who are often impatient with children in their way, and the dirt path provides more interactive features (Yang, 2004). On dirt


paths children can jump on mounds, leap into muddy puddles, bend under low branches, or crouch to stare into a gopher hole. The mounds, puddles, low branches or gopher holes would all be considered undesirable and eliminated on a paved trail, but on a dirt path they provide tempting invitations for sensorial experiences.

Adventurers are particularly keen to fi nd good stream crossing points. In smaller streams users search shallow and narrow spots with stepping rocks to set foot on or from which to build a bridge. In large rivers swimming across is a common game. If a rope and tree are available and the stream is narrow enough, they swing across the channel. Similarly, children in Turkey Brook, London, asked for ropes to swing on and logs to slide down (Tunstall et al., 2004). When the slope is right and the channel not too wide, bikers or skateboarders fl y across on wheels.

Metal culverts are especially attractive to adventurers: it’s easy to make loud, eerie echoes in them, they are secret hide-outs, they offer the allure of a connection to some-where else and they are perceived somehow off-limits.

4.4.3 Wildlife Contact

Wildlife contact can be by simple observation or active catching, two distinct modes of interaction. Observers are usually interested in all life forms they see, from little bugs to big animals such as otters and raccoons. They interact with the stream with a highly intensive but unintrusive way. Wildlife sightings often occur in unexpected, uncal-culated moments, producing a ‘wow’ experience.

Catchers are more physically active and focus on certain target species, which are small enough to catch. Catching wildlife along creeks has traditionally provided subsis-tence, but in urban areas in developed nations today, catch-ing is usually based upon affi nity toward the target and a sense of achievement. Fish, frogs, tadpoles, shrimps, craw-dads, crabs and insects are fascinating creatures for users to match wits with. The habitats of catchers are as diverse as those of their target species and their spots correspond directly to those of their quarry. Children who actively catch wildlife tend to be agile and willing to access diffi -cult sites, get wet or scratched and in general are highly adaptive to their environments (Figure 4.5). In Marsh Creek, crawdad hunters were often observed thriving at the least ‘user-friendly’ spots, such as among rugged riprap under road bridges or by grassy, muddy shores. Methods of catching are numerous, even for the same species. They range from the bare hand to highly elabo-rated means and tools. Catchers in various regions in Japan and California often stored captured fi sh or craw-dads temporarily in a container or a little pond enclosed with sand or rocks. Most of the trapped creatures were set

free after a short time, but some captures would become pets to be enjoyed at home until they expired. Many catch-ers have learned from experiences which animals ‘work better’ as pets (Yang, 2004).

Profi cient catchers and observers are often knowledge-able; they can usually identify many species and know when and where to fi nd them. Observers and catchers have similar habitat requirements: the environment needs to support a suffi ciently high density of wildlife and a mean-ingful human/wildlife interface. Though the former is a widely claimed goal in restoration and greenway projects, the latter is usually discouraged. For spontaneous users, a meaningful wildlife/human interface provides plenty of chances for close-up observation and hands-on catching, without the need of specialized equipment beyond that which can be made at home or obtained from a grocery store. Examples of such interfaces are water edges framed by vegetation or porous structures where different species hide, or shallow water reaches adjacent to gravel bars where fry of amphibians and fi sh hatch. It is important that water edges designed to sustain a dense wildlife popula-

Figure 4.5 Crawdad from Marsh Creek caught by a child and drawing of Marsh Creek wildlife by 4th grade child (both from Yang, 2004.)


tion also remain accessible to users, except in cases (rare in urban areas) where protected species need to be isolated from human harassment. When physical access is not fea-sible, visual access can be provided from the bank, bridges etc.

4.4.4 Manipulating the Environment

The value of creeks and rivers for spontaneous use depends largely on their provision of loose parts – elements that can be easily manipulated in the environment (Nicholson, 1971). At least three categories of common uses rely on contact with rocks, plants, junk and other kinds of loose parts in stream environments: collecting, building and clever craft:

• Collecting allows one to discern treasures from the basi-cally chaotic stream environment. Once purposely rum-maged or fortuitously encountered, stones and other elements from the bed, plant parts and junk recovered from banks may be used in drama play, building, or in displays in the collector’s yard or room. Gravel bars are prized sources of stones for collecting and clay banks provide material for handcraft, mud ball fi ghts and gray make-up.

• Building projects, whether big (e.g. tree houses, huts, bases, dams, bridges, ponds) or small (arranging rocks and sticks) are rooted in an innate attempt to create an impact on the landscape (Figure 4.6). Through building, users claim their ownership and adapt the stream to themselves. The result of spontaneous building usually is not durable enough to survive fl oods and other natural processes. Building may have practical purposes, but the process is all-important: many children build, destroy and rebuild.

• Clever crafts, the skillful manipulation of materials found in stream environments (Yang, 2004), is usually quite precise in terms of materials and surroundings. For example, to skip a rock (a trans-culturally popular trick), one needs a gravel bar containing platy stones with intermediate axes usually between about 30 and 70 mm, and a fl at pool allowing satisfactory skips. Along Sonoma Creek in California, adults applied red algae to skin rash and children made fl utes with deer grass. Along Kure River in Japan, smashed mugwort was used to heal scratches and defog goggles, while foxtail stalks were made into knots to trap frogs and shrimp.

4.4.5 Wading and Paddling

Small children and other users who don’t want to get very wet will wade in waters shallower than 0.5 m, with cur-rents 20 cm/s or less, such as shallow margins or backwa-ters. The range of paddling by children is usually only a few meters from the water edge and the dry spot. In large streams, some hints of boundary around a smaller space (e.g. a cover or re-entrant in the bank or offshore bar) are needed to overcome the uneasiness induced by an unlim-ited expanse of water. Paddlers prefer gently sloping access to water (rather than grassy or upright banks) and sandy or clay bottoms (which provide comfortable foot-holds) (Yang, 2004).

4.4.6 Swimming, Flushing and Diving

Swimming occurs mostly in pool reaches more than 0.5 m deep, with velocities under 0.5 m/s and with gentle and gradual water edges at bars or ‘ledge’ banks protected by tree roots as entry points. In large or swift rivers, swimmers also require ‘stopover bases’ (island bars, bridge piers etc.) at which to rest. Warm surfaces such as big rocks, pebble beach, concrete blocks, asphalt roads etc. are valuable dry spots. Flushing makes clever use of locally concentrated fl ow (>0.5 m/s) and variations in bed form. Most commonly, fl ushing is done in riffl es: the fl usher would start at the end of the pool where the speed starts to pick up, allowing his body to be carried by the accelerating current downstream to be caught at the crest of riffl e (if shallow) or carried through the riffl e (if deeper) to the next pool. Hard structures in the streams such as bridge piers can also form concentrated currents for fl ushing.

Diving in larger streams and rivers with deep pools is popular on hot days, offering the thrill of a free fall and the sudden impingement of cool water on the body. The

Figure 4.6 12-year-old child’s stove in drama house by Marsh Creek (from Yang, 2004.)


diving height is limited by pool depth and the diver’s skill and nerve. We observed children diving from a 15-m high treetop in the Kagami River, Japan (Figure 4.7). In stream environments, a pool deeper than two meters is rare and considered enough for moderate-height diving. A good diving spot also has a landing spot with a gentler water edge, a path to connect landing and launch spots into a loop and, ideally, choices for different skill levels. Rock out-crops (adjacent to deep pools because they induce scour at high fl ow) provide steady footholds for the launch point and outcrops with complex form and multiple take-off points for different dive heights allow divers to practice and build their courage and skills gradually. Diving from the trees, one experiences the thrill of shaking footholds; diving from a rope swing, one challenges the arm strength, body balance and the timing to let go; diving at concrete levees, one needs to leap forward to avoid the concrete foundation jetting out beneath the mean water level (Yang, 2004). For ‘thrilling’ water contacts such as fl ushing and diving, routes that connect back to the set-in points are indispensable to support their repetitive characteristics.

4.5 CONFLICTS AMONG MULTIPLE GOALS AND OBJECTIVES

Everybody wants more nature but there has been persistent confusion about the meaning of ‘restoration’. The contro-versy over the term refl ects disagreements over goals, even when considered only within the physical science realm. As causative agents, humans constantly change and control nature to ‘help’ it, leading to fundamental questions about goals. Given that nature is in constant fl ux and there is no single correct condition (Hull and Robertson, 2000), the choice of desired end state for restoration will perforce involve societal priorities. Because river restoration proj-ects are now commonly undertaken with the involvement of multiple professionals and stakeholders, and because all the goals and objectives are essentially value-driven, three types of confl icts often arise in project implementa-tion, creating uncertainties for the course of river restora-tion planning and implementation.

4.5.1 Confl icts among Professionals

Engineers, fl uvial geomorphologists, ecologists and land-scape architects are trained to see the stream differently (Figure 4.8). In the past, they have all shaped or reshaped streams with their particular value systems and disciplin-ary tools. Engineering has been the single most powerful profession in past stream transformation, altering rivers for fl ood control, water supply and navigation. Hydraulic engineers model fl ows under conditions where the vari-ables are controlled, usually approximating channel shapes as simpler geometric entities. Deviations from clear water and Euclidean channel shapes are treated with adjustments in formulas. Fluvial geomorphologists tend to approach problems at larger scales and over longer periods. The engineer or manager may pose a question such as, ‘What kind of bank protection should we use along this reach of stream?’ The fl uvial geomorphologist will tend to ask why the bank is eroding in the fi rst place, whether it is simply part of the natural channel migration process or a result of changes in the catchment upstream. Especially in the latter case, it is likely that placing bank protection will not ‘solve’ the problem but will induce problems elsewhere.

Ecologists tend to view streams as organic compounds of habitats. They perceive fi ne details of leaf litter and its decompostion, moss on boulders, food chains, and cycles of nitrogen, carbon etc. Traditional biologists may see unspoiled natural process as the reference condition against which to measure degradation and a return to that condition as a restoration goal. Human activities are viewed as ‘impact.’ Adding a spatial structural perspective,

Figure 4.7 Children diving into the Kagami River, Japan (from Yang, 2004.)


they view streams as ‘corridors,’ a crucial element in land-scape to allow movement of species and therefore to main-tain biodiversity and long term genetic diversity. These professionals have long realized that to maintain a healthy ecosystem, a river needs fl oods (Poff et al., 1997; Junk et al., 1989). Developers and fl ood control agencies often resist losing developable lands to fl ood inundation and, through their infl uence on the political process, typically succeed in implementing fl ood control measures such as dams or levees that permit them to build on fl oodplains. Similarly, natural channel migration is an important process to create diverse habitats, but riverside develop-ments are threatened by bank erosion, resulting in pressure to stabilize the river bank with hard structures. As a result, although ecologists and environmental scientists have

been institutionalized into the planning process since the 1960s, they often remain ‘second-class citizens’ in affect-ing the design of urban stream channels (Riley, 1998).

A traditional tenet of landscape architects is to view landscape in abstract, formal, aesthetic terms: forms, lines, colors, textures and their inter-relationships (Daniel and Vining, 1983). Although visual aesthetics are usually the paramount ‘public’ goal, designers also emphasize the cultural and historical signifi cance of urban streams, as well as the user’s experiences. Some landscape architects are well trained ecologically and effectively integrate eco-logical considerations in their designs; some are involved in successful efforts to redevelop urban waterfronts to revitalize downtown economies, attract tourists and provide recreation opportunities for urban residents (Otto

Figure 4.8 Different perceptions and attitudes towards rivers by engineers, biologists etc. (Source: Hough (1990), adapted from drawings originally prepared by Newbury (unpublished data)).


et al., 2004). These projects provide chances to enhance physical and visual connection with streams by placing walkways along them, or by promoting vistas and facing commercial fronts to the streams (Jones and Battaglia, 1989). Landscape architects also transform fl oodplains to open spaces to accommodate civic activities such as exhibits, concerts, fairs or sports. The design of these open spaces, however, often takes its model from pastoral parks or architectural plazas and while they may provide effec-tive urban spaces, the purported uses and the design schemes are often in confl ict with the chaotic character of fl oods and organic quality of riparian habitats.

4.5.2 Confl icts Among Stakeholder Groups

Humans use rivers for many different purposes, so it should come as no surprise that human expectations and the demands of rivers often confl ict. The same confl icts that manifest themselves in management of existing river channels tend to emerge when river restoration projects are conceived and scoped.

Some of the best documented such confl icts are in the Colorado River below Glen Canyon Dam, where cold water releases have allowed a rainbow trout (Oncorhyn-chus mykiss) fi shery, highly valued by anglers, to become established. The rainbow trout are exotic to the river and would not have survived the high temperatures and high suspended sediment loads characteristic of the pre-dam river. The post-dam river is now unfavorable to native fi sh, such as humpback chub (Cila cypha) and razorback sucker (Xyrauchen texanus). The native fi sh are ugly and undesir-able as sport fi sh, but they are native to the river and their numbers have dwindled such that several species are now listed as threatened or endangered (Schmidt et al., 1998). Where the exotic trout and native fi sh coexist, the trout may prey on the natives. Proposed actions to improve conditions for the native species have encountered resis-tance from trout fi shing groups. It is well established that the reduction in high fl ows effected by Glen Canyon Dam has had numerous ecological effects on the reach down-stream and thus deliberate high fl ow releases are planned in efforts to restore the reach. The fi rst such release, a much-publicized fl ow of 1300 m3 s−1 in 1996, was only about one-third of the average annual pre-dam high fl ow. The fl ow was limited to avoid inundating a rare snail that had extended its range down the canyon walls during the post-dam period (Marzolf et al., 1998). Thus, restoration of a dynamic fl ow regime (with attendant benefi ts for the river ecosystem) was perceived to confl ict with protection of the rare snail. A similar confl ict among user groups is on the North Fork Feather River, California, where high fl ows released periodically to provide fl ows for rafters

have scoured benthic macroinvertebrates, washing these and other organisms downstream (Garcia and Associates, 2005).

4.5.3 Confl icts Between Professionals and Local Groups

Perhaps the best documented example of a professional–local group confl ict involves terrestrial habitat restoration, the Chicago prairie restoration controversy. Efforts to restore 7000 acres of the DuPage County forest reserves in the Chicago metropolitan area back to the historical oak savanna and tallgrass prairie condition were attacked by local groups and residents who opposed removing trees and brush. Ryan (2000) concluded that the discrepancy between restoration planners and neighborhood users stemmed from differences in attachment. While both groups were attached to nature, their attachment can be diverse and contradictory. Scientists and volunteers are attached to a particular type of original landscape, which is established through environmental criteria such as bio-diversity and system integrity. Such attachment is not bound to a place – the same habitat image can be repro-duced elsewhere and still be satisfactory. On the other hand, the attachment of local residents is intertwined in locale and context. Individual trees, albeit non-native, bear an identity in terms of furnishing the spot for children to play tag or a seat for quietness or framing a magnifi cent view toward sundown. In other words, the attachment of local residents is composed of life memories.

A different confl ict between professionals and a local group occurred in the northern Sierra Nevada of Califor-nia in the early 1990s. In planning a restoration project on Jamison Creek in Plumas-Eureka State Park (the Park), a local nonprofi t group active in implementing stream res-toration projects (but without expertise in fl uvial geomor-phology) challenged the effective discharge analysis conducted by a university team, contending that the bank-full discharge was only about one-third that computed by the university team. Despite a thorough and well docu-mented scientifi c report supporting the university team’s analysis, the Park rejected the analysis and sided with the local nonprofi t group, stating that it preferred the smaller design discharge because ‘a smaller channel is better for fi sh habitat’. The Park also cited that fact that the local Coordinated Resource Management Program group (com-posed of local agency staff, landowners and staff of the nonprofi t group (none of whom possessed expertise in fl uvial geomorphology) had voted in favor of the lower design discharge.

The notion that one can arbitrarily choose a design dis-charge and build a stream channel to smaller dimensions


is a fascinating one, but not one supported by geomorphic science. Likewise, the notion that scientifi c questions should be put to a vote by a group without expertise in the fi eld raises questions about the role of science in such a restoration design process. Ultimately, the Park had the authority and responsibility to design and construct the channel as it saw fi t. The university team withdrew from the project and the local nonprofi t group proceeded to design and build a channel reconstruction in 1995. The project was damaged by the high fl ows of 1996, repaired, and then completely washed out by high fl ows in 1997. In 2000, the Park sent out a call for proposals to reconstruct the channel once again.

4.6 CASE STUDIES

4.6.1 Baxter Creek, El Cerrito

Baxter Creek drains an 11-km2 urban area of El Cerrito, California, debouching into San Francisco Bay at Rich-mond (Figure 4.9). In 1997, the City of El Cerrito replaced a 70-m reach of failing culvert (in a small neighborhood park) with an open channel (Figure 4.10). The open channel was stabilized with a series of boulder weirs

(which dissipated energy from the 10% gradient) and the banks were planted with willow (salix spp). Post-project appraisals in 1999 and 2004 (Purcell et al., 2002; Purcell, 2004) showed that the biotic condition of the restored reach was measurably better than an unrestored control section upstream and that the biotic condition did not improve further between 1999 (two years post-project) and 2004 (seven years post-project), indicating the stream may have reached its biotic potential within two years.

Purcell et al. (2002) conducted an attitudinal survey of the residents within one block of the daylighted section of Baxter Creek. Of the 45 responses received, most were positive overall about the restoration, but many expressed concerns that the willow trees, some of which had grown to over 6 m in height, blocked the view across the park and potentially provided hiding places for burglars. In a repeat survey of the neighborhood in 2004 (n = 45), Purcell found that about half of those who had moved to the neighborhood after the completion of the restoration did not realize the creek had formerly been in an under-ground culvert. Nearly all respondents reported they enjoyed living near the creek, many citing the sounds of the water, aesthetics, or accessibility for children or dogs. 69% perceived an improvement since the restoration was completed; 31% said conditions had worsened. Overall, the project was successful in creating a vibrant stream corridor where formerly there had been only a relatively sterile strip of lawn. The success of the project led to the formation of the ‘Friends of Baxter Creek’, a group which subsequently supported two other restoration projects in downstream reaches of Baxter Creek (Lisa Owens-Viani, personal communication, 2006).

San Pablo Bay

San Francisco

Bay

Suisun Bay

BerkeleyBrentwood

Richmond

Pacific Ocean

San Francisco

San Jose

a b

Figure 4.9 Location map, Baxter (a) and Marsh Creeks (b), Contra Costa County.

Figure 4.10 Baxter Creek in Poinsett Park, El Cerrito, Califor-nia. Photo by Alison Purcell, April 2007, about 10 years after construction. Note height of willows, some exceeding 6 m.


There was some negative reaction to ‘overgrown’ vegetation (Purcell, 2004), which is not unusual in urban stream restorations in northern California. Blackberry Creek in the Thousand Oaks School in Berkeley was removed from an underground culvert and replaced with an open channel in 1995. Some residents had negative reactions to the density of willow growth, which pre-cluded access or seeing into the stream channel. In plan-ning the 1990 restoration of Cortland Creek in Oakland, the plans by creek activists to extensively plant willows (for habitat) met with resistance from local residents, who did not want to create a place where criminals could hide (Walter Hood, personal communication, 1995). Though not as well documented as the Baxter Creek case, many other urban stream restoration projects have been marked by similar confl icts between planting willows to enhance habitat and the desire by residents and police to see into the channel to discourage criminals. This has been true especially in low-income neighborhoods, where concern about crime may be greater.

4.6.2 Marsh Creek, Brentwood, California

Marsh Creek drains 332 km2, with its upper basin mostly woodland, rangeland and farmland, and its lower 15 km traversing a broad alluvial fan, which now supports the urban areas of Brentwood and Oakley, about 60 km north-east of San Francisco (Figure 4.9). As typical of Mediter-ranean-climate streams, runoff from the catchment was naturally intermittent in all but wet years. There is little record of the historical channel conditions in Marsh Creek in Brentwood, but historical maps from the late 1800s and early 1900s show multiple, sinuous channels and active channel migration (Robins and Cain, 2002) (Figure 4.11). As agriculture expanded onto the fertile soils in the early–mid 20th century, and in response to fl ooding of downtown Brentwood in the 1950s, the channel of Marsh Creek was straightened, the riparian corridor largely cleared and a fl ood-control reservoir constructed about 3 km upstream of Brentwood (Figure 4.12).

Brentwood has grown rapidly, increasing in population from 7500 in 1990, to 23 000 in 2000, to 33 000 in 2003 (Cain et al., 2003). Many of these residents commute (one-way travel times of over an hour) to jobs in the San Fran-cisco Bay Region. As Brentwood has grown, interest has grown in enhancing the creek corridor for human uses, removing barriers to salmonid migration and improving stormwater detention. A watershed study (Cain et al., 2003) documented historical changes in physical and biological conditions, identifying signifi cant effects of straightening on channel form and instream habitat, effects of the altered fl ow regime on habitat, effects of former mercury mining

upstream and urban/agricultural runoff on water quality and the loss of native plant and animal species.

To better understand the perceptions and preferences of local residents, Yang (2004) surveyed 1800 residents living within 400 m of Marsh Creek in Brentwood to assess their perceptions (and ideal images) of the creek. The residents consistently presented an ideal image of the creek, identifying luxuriant woods, year-round running water, bountiful wildlife and easy access as features of the ‘natural’ or ‘original’ Marsh Creek. However, this idyllic image of the creek is largely inconsistent with the charac-ter of the creek as documented by historical evidence, with its Mediterranean-climate runoff regime. Similarly, many residents delighted in contact with wildlife, but did not realize that the most contacted species, i.e. crayfi sh, bull-frogs, bluegill and largemouth bass, are not native, but exotic generalists. Likewise, with vegetation, most sub-jects did not distinguish native from introduced plant species and even those who could tended to prefer vegeta-tion that ‘looks natural without being overgrown’ regard-less of origin (Yang, 2004).

The problems identifi ed by the residents contrasted sharply with those presented by the professionals in the watershed report. By far the leading concern of surveyed residents was garbage and dumping in the creek. Many residents considered the summer water levels too low, evidently without understanding the highly seasonal nature of fl ow in Mediterranean-climate streams. Residents also regarded ‘mosquitoes/pests’ as more serious than ‘mono-tonous channel form’ and ‘poor habitat value,’ both major issues identifi ed in the watershed report (Cain et al., 2003). Only ‘not enough shade’ was identifi ed as a concern both by the surveyed residents and in the watershed report (Yang, 2004).

The substantial differences in perception and landscape preference between restoration scientists and local resi-dents will be a source of uncertainty in setting restoration priorities and garnering public support for restoration projects. As funding becomes available to plan restoration projects in Brentwood, these gaps will need to be addressed in a participatory context so that confl icts in restoration goals can be reduced.

4.7 CONCLUSIONS

Uncertainties on the social and cultural fronts of stream restoration can be viewed as signifying forward progress in the fi eld, rather than simply further impediments in implementing projects. Twenty years ago, when the notions of creek restoration fi rst became widespread in the United States, many engineers regarded ecological con-cerns as obstacles to be overcome in the single-minded


a b01 0.5 1 km

Figure 4.11 Topographic map details of marsh Creek in Brentwood: (a) 1914 and (b) 1978. (Source: US Geological Survey topo-graphic maps.)

Figure 4.12 (See also colour plate section) View of Marsh Creek channel in Brentwood (Photo by Kondolf, September 1991.)

pursuit of diking, channelizing, straightening and culvert-ing streams. It was only when engineers started to con-front other viewpoints that ‘uncertainties’ were introduced in their modus operandi. While confl icts between engi-neers and ecologists persist in restoration projects, by and large the engineering profession has embraced the need to work effectively with geomorphologists and biologists to achieve effective ecosystem restoration. Now we see increasingly that the ecological engineering approach is perturbed by the uncertainties introduced by social and cultural concerns. In other words, the current phenomenon of restoration professionals experiencing uncertainties on all fronts may be simply an indicator of a rapidly broaden-ing viewpoint and recognition of problems without com-mensurate solutions.


Although the possible range of goals, values, percep-tion, aesthetic taste, use and meanings of a population for a stream can be overwhelming, and confl icts sometimes unavoidable, the goal of sustainable stream restoration increasingly requires integration of diverse points of view. Institutionally, citizen groups have now become an inte-gral part of many restoration efforts. Professionals have learnt that collaborative planning processes may not bring about a fast solution, but it may provide substantial long term benefi ts in terms of political and social capital, and may yield more sustainable restoration projects.

To be successful on a sustainable basis, stream restora-tions must be both technically sound and enjoy strong public support. Although decisions in stream restoration are essentially value driven, sound science is fundamental to constrain the range of possible solutions and evaluate possible alternatives. Without it, a Jamison Creek situa-tion can result, in which the responsible agency selects a scientifi cally unsound option and the project fails. On the other hand, a technically sound restoration plan is unlikely to be funded and implemented without strong public support, and unlikely to be sustainable if built without local buy-in.

Where there are signifi cant uncertainties on social and cultural aspects, these should probably be settled before proceeding to settle technical uncertainties. For example, until the values of large woody debris for fi sh or boaters are established, there may be little point in quantifying its catchment production and morphological qualities. In cities, we fi nd that the recreational potential of spontane-ous uses is often conspicuous in its absence from the agenda of stream restoration. Once their importance is recognized and spontaneous uses and their implied soci-etal values are added to the restoration agenda, more precise research may be needed to assess them.

Cultural preferences (commonly unacknowledged) largely shape restoration goals. Building a culturally pre-ferred form (such as a stable, meandering channel) is perfectly reasonable as a restoration goal, but we suspect that the fi eld would benefi t from an explicit recognition of this as motivation, rather than cloaking such projects in seemingly scientifi c details of channel morphology and (commonly vague) references to improved fi sh habitat. To the degree that cultural preferences remain unacknowl-edged, they introduce greater uncertainty in the trajectory of restoration projects. Cultural preference for tidy land-scapes over messy landscapes (Piégay et al., 2005) should be acknowledged, so that ‘overgrown’ riparian zones can either be ‘framed’ (Nassauer, 1995) or simply avoided in urban areas. For ecological design to be truly successful and widely accepted, designers will need to fi nd ways to make stream restoration compelling as designs (Mozingo,

1997). The concept of ‘eco-revelatory design’ suggests that by accepting humans into the restored ecosystem and designing the project to reveal ecological processes, we may achieve ecosystem restoration (to the extent possible in urban areas) while still gaining public acceptance (Galatowitsch, 1998).

4.8 ACKNOWLEDGEMENTS

The research on which this chapter is based was partially supported by a grant from the University of California, Berkeley, Department of Landscape Architecture Beatrix Farrand Fund. Shannah Anderson contributed substan-tially with supporting research, fi gure and manuscript preparation, and review comments. Louise Mozingo con-tributed valuable ideas and references. The chapter was improved through comments by anonymous reviewers and the volume’s editors, Dave Sear and Steve Darby.

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5

Conceptual and Mathematical Modelling in River Restoration: Do We Have

Unreasonable Confi dence?

Michael Stewardson1 and Ian Rutherfurd2

1Department of Civil and Environmental Engineering and eWater CRC, The University of Melbourne, Australia2Geography Program, School of Resource Management, The University of Melbourne, Australia

5.1 INTRODUCTION

5.1.1 Geomorphic Modelling in Restoration Planning

From the 1960s to the 1980s, applied fl uvial geomorphol-ogy described the degradation of streams in response to human disturbance. This endeavor was part of a larger quest to explain the controls on stream form. From the 1990s, the discipline has found new vigor as skills have been turned to the restoration of those degraded streams. However, this change does not simply represent a new job opportunity; it represents a fundamental test of our knowl-edge of processes controlling stream form.

It is relatively easy to describe the degradation of fl uvial systems, and much of this work has identifi ed associations with human disturbances rather than strict causations. Recall the protracted debates about whether arroyo inci-sion was caused by clearing, channelisation or climate change (Cooke and Reeves, 1976). It is much more chal-lenging, fi rstly to recommend priorities for rehabilitating streams and, secondly, to implement these changes. Geo-morphologists have moved from being observers of human impact, to advisors and managers, actively intervening in stream channels for environmental outcomes. Most of this intervention has been in the areas of: channelised streams (Larson and Goldsmith, 1997), sediment slugs (Ruther-furd, 2001) and mitigating the effects of dams and fl ow regulation. Numerous articles describe the contribution that geomorphologists can make to stream restoration

endeavors and there is no shortage of admonitions to ‘include a geomorphologist on every restoration project’ (Sear, 1994; Brookes, 1995; Brierley et al., 1996; Brookes and Sear, 1996) and on every stream engineering project (Gilvear, 1999).

Fluvial geomorphologists produce conceptual and mathematical models that are central to many stream res-toration projects. In this chapter it is argued that managers, ecologists, and even geomorphologists themselves, can have a false sense of confi dence in these models. As com-munities around the world invest in stream restoration projects, false confi dence can have a huge direct and opportunity cost. We believe it is better to be frank about model uncertainties from the outset to promote realistic expectations of project success and a balanced and well-targeted investment in investigations to reduce these uncertainties. Fluvial geomorphologists contribute to stream restoration projects in three main ways:

1. The most basic work of a fl uvial geomorphologist in a restoration project is to describe how a stream has changed its form over time, and to identify the factors that are responsible for these changes. Such reconstruc-tions have now passed from being a curiosity-driven activity for scientists, to basic consulting practice. The result of these investigations is usually a conceptual model [or a perceptual model in the parlance of Beven (2001)] that may be based on space-for-time substitu-tion or historical coincidence between geomorphic and


other catchment changes. Perhaps the best established geomorphic conceptual models are the incised stream models (Schumm et al., 1984; Simon, 1989) where there may be some empirical guidance for the magni-tude of change (e.g. a threshold width–depth ratio for incised streams Schumm et al., 1984). The conceptual model serves to describe the ‘original’ condition of a system, which often becomes the ‘target’ for the resto-ration project. Further, the conceptual model is used to predict the trajectory of channel change if there is no intervention.

2. Geomorphologists recommend specifi c actions (or interventions) that will alter process and form, to move the stream toward the ‘target’ state, identifi ed from the conceptual model. These actions usually involve chang-ing the fl ow or sediment regime, or modifying the boundary of the channel to cause a change in some geomorphic variable (e.g. width, scour frequency, erosion rate). Design of project specifi cs is often based on a mathematical model of geomorphic response related to the specifi c action proposed.

3. They also predict the ‘secondary’ (perhaps unintended) consequences of stream restoration projects. This could also be described as the ‘sustainability’ of the intervention.

Thus, uncertainty emerges at three levels: the validity of the conceptual model; whether the proposed interven-tion results in the planned geomorphic change; and, fi nally, whether the change is sustainable. Typical examples of in-stream geomorphic actions in restoration projects are:

• Incised, channelised streams are the classical example of stream restoration. A geomorphologist reconstructs the original dimensions of a channelised stream in Denmark by superimposing old maps and photos, and by making painstaking fi eld observations (Neilsen, 1996). Working with an engineer, a ‘stable’ re-meandered stream path that links the palaeochannels for a rehabili-tated stream is then designed. The dimensions of the channel and the variations in depth (the pool-riffl e sequence) are designed to be scaled to catchment area (Newbury and Gaboury, 1993). Finally, the restoration that will lead to increased erosion downstream of the reach is predicted.

• Gullying has dumped a large pulse of sand into a stream. A geomorphologist applies the classical ‘wave’ model of sand slug migration (Gilbert, 1917; Neilsen, 1996) and, on the basis of historical movement of the sand front, concludes that it will take over 50 years for the sand to move through the reach. Extracting the sand at a defi ned rate will protect the downstream reaches

and accelerate recovery (Rutherfurd, 2001). Building artifi cial spur dikes will also create habitat pools (Kuhnle et al., 2002).

5.1.2 Why do we Care about Uncertainty?

Regan et al. (2002) defi ne epistemic uncertainty as uncer-tainty associated with knowledge of the state of a system; it can be classifi ed into six main types including random measurement error, natural variation and model uncer-tainty (compare to the classifi cation presented in Chapter 3). This chapter is specifi cally concerned with these three sources of uncertainty in the context of conceptual and mathematical geomorphic models used in restoration pro-jects. Measurements of various sorts are used to establish the state of a system. Measurements may be used directly (e.g. the diameter of an individual grain of sediment), but can also be transformed using some kind of calibration (e.g. stream discharge, which is often estimated from an observation of stage and transformed to discharge using a rating curve). Measurement error is the result of errors in the direct measurement and subsequent transformations. Natural variation presents a challenge for observing the true state of a system. To address uncertainty, assumptions are often made about the statistical properties of environ-mental variations. Model uncertainty can arise both from the choice of variables and processes to be represented in the model and from the method used to represent the rela-tions between variables.

Uncertainty is defi ned here as the range of possible values for a model variable (input or response). One part of uncertainty is accuracy, which is the difference between a measurement and the ‘true value’. Uncertainties in response variables are the consequence of the need to choose a conceptual and mathematical representation of river processes, and uncertainties in input parameters for the model estimated from fi eld measurements, previous studies or by calibration. Uncertainties can also exist as a consequence of unknown future environmental conditions in particular climatic conditions. It is possible that restora-tion works are destroyed by an extreme fl ood event not considered in the planning process. Design models often consider a range of expected conditions based on condi-tions experienced in the past, but uncertainties in the actual conditions over the life the project create further uncertainty in geomorphic design.

Discussion of uncertainty, in the realm of management, can quickly degenerate into an unfocused quest for greater accuracy, greater precision, more samples and more effort. It is usually scientists who write about certainty and they may have a completely different perspective on the issue

Conceptual and Mathematical Modelling in River Restoration: Do We Have Unreasonable Confi dence? 63

to managers. The notion of ‘certainty’ is not simply a statistical/scientifi c issue; it is at the heart of the social process of decision making. The implications of estimat-ing geomorphic uncertainty can be unexpected to those focused primarily on improved confi dence in model pre-diction. Whilst more certain predictions should improve the chance of project success, consideration of uncertain-ties does not in itself improve model performance and can reduce public support for a project.

It is necessary to be clear about why we care about geomorphic uncertainty in stream restoration. In reality, most scientifi c geomorphologists revel in the uncertainties of river process and form. Most of our endeavour is directed at situations where accepted models do not work, where we are uncertain. If it was all certain, we would do something else. But in the realm of management, uncer-tainty is seldom welcome. Whilst the enthusiasm to reha-bilitate streams will not disappear, the initial fl ush of community and government support for these endeavors could be lost if geomorphologists and other scientists foster unrealistic expectations of these projects. As geo-morphologists engage with engineers and managers, they must decide how to deal with uncertainty (Volkman, 1999), when admitting to too much uncertainty can weaken support for a project and possibly stall it. However, under-playing uncertainties will undermine confi dence in geo-morphic advice when some projects inevitably ‘fail’.

Another reason to care about uncertainty is that it pro-vides the justifi cation for improved geomorphic investiga-tions prior to completion of a restoration plan. As will be demonstrated, in some cases it may be relatively cheap and easy to reduce uncertainty, but it can also be very expen-sive to do so. A strong conclusion of this chapter is that, given the large cost of stream restoration projects, sound geomorphic advice has often tended to be undervalued. Large projects are sometimes launched on the basis of fl imsy conceptual models, possibly because their uncer-tainty has not been properly considered.

Finally, adaptive management is often proposed as the only reasonable way forward in the face of uncertain res-toration outcomes (see Chapter 14). The adaptive approach is to treat the restoration project as an experiment designed to inform our knowledge of how rivers respond to restora-tion. This knowledge is used to improve river restoration decisions for the particular river and presumably else-where. Despite the frequent calls for adaptive management of river restoration, actual examples of success are rare for a number of reasons (Walters, 1997; Ladson and Argent, 2002). We argue that systematic consideration of uncer-tainties in geomorphic modelling is essential to the application of adaptive approaches in river restoration. Management experiments and associated monitoring need

be targeted to reducing these uncertainties if they are to feed back effectively into future restoration practice.

5.1.3 Introduction to Case Studies

Geomorphologists normally acknowledge the uncertain-ties in their models but it is rare for these uncertainties to be quantifi ed or systematically examined. There could be a perception that these uncertainties are relatively small and have limited signifi cance in restoration decisions. There may be limited experience amongst geomorpholo-gists in the quantitative aspects of uncertainty analysis which could discourage attempts to handle these explic-itly. There may also be a concern that quantifying uncer-tainties will undermine support for a project. In any case, there is currently very little published information on the scale of uncertainties in geomorphic studies for river restoration. Uncertainty analyses in case study projects are needed to inform discussion of how best to handle these uncertainties in the future.

This chapter examines uncertainties in geomorphic modelling for two river restoration projects. The two case studies involve, respectively, conceptual and design models for restoration planning. The scale of uncertainties and also the potential benefi ts of a systematic analysis of uncertainties in these projects are examined. Based on these case studies, it is suggested how uncertainties might best be handled in the future by geomorphologists devel-oping conceptual and design models for river restoration planning.

The fi rst case study concerns the development of a conceptual model for planning Australia’s largest stream restoration project, on the Snowy River. The infl uence of the conceptual model on proposed plans is examined and, in particular, the implication of subsequent changes in the geomorphic model. The second case study concerns the design of a fl ushing fl ow in the Goulburn River, Victoria, using a one-dimensional hydraulic modelling approach. In this design problem, multiple sources of uncertainties are quantifi ed and the key uncertainties in designing the fl ush-ing fl ow identifi ed. A two-dimensional numerical model may have had some advantages over a one-dimensional model approach in this problem. However, a one-dimensional model (similar to HEC RAS) is used because it is currently the standard approach used throughout the industry. Thus, we are concerned with the uncertainty asso-ciated with current practice in stream restoration projects rather than with the level of certainty that is theoreti-cally possible with detailed scientifi c study. It is often assumed that uncertainties associated with modelling the physical response of channels are small (c.f. biological responses, see Chapter 8). The case studies demonstrate


that uncertainties associated with the ‘accepted’ approaches to geomorphic modelling can be substantial.

5.2 CASE STUDY OF A GEOMORPHIC CONCEPTUAL MODEL

5.2.1 Introduction

The following section describes the chronological devel-opment of the geomorphic conceptual model for the restoration of the lower 32 km of the Snowy River in south-eastern Australia. The Snowy River drains a catch-ment of 15 800 km2 situated in New South Wales and Vic-toria in south-east Australia, before discharging into Bass Strait (Brizga and Finlayson, 1994). For most of its course, the river fl ows in a narrow valley, that only widens around Bete Belong (Figure 5.1). The river below Bete Belong is a perched, sand-bed, channel between 70 m and 170 m wide. The downstream end of the Snowy River is estua-rine, with tidal infl uences persisting upstream to Orbost.

When the fi rst European settlers arrived in the late 1840s, the Snowy River fl oodplain was swampy wetlands away from the channel, with the higher levees along the channel covered in warm temperate rainforest (described as ‘jungle’) (Owen, 1997). By the 1880s, the stream banks had been cleared and the clearing and draining of the wetlands had begun. By the 1930s, artifi cial fl ood levees had been built along much of the river and much of the swampy wetlands had been drained for grazing. Over the same period, large woody debris was removed from the stream. This began in the 1880s to aid navigation (Seddon, 1994) and reached a peak in the 1950s to combat perceived aggradation of the bed and to reduce fl ood peaks (Finlayson and Bird, 1989).

A second phase of disturbance was initiated by construc-tion in the river’s headwaters under the Snowy Mountain Scheme. Completed in 1967, the Snowy Mountain Scheme diverts water out of the Snowy River catchment for hydro-electric power generation and for the supply of water for irrigation in the neighboring Murray and Murrumbidgee

Figure 5.1 The lower Snowy River in Victoria (from Finlayson and Bird, 1989)


catchments. Erskine et al. (1999) concluded that the Snowy Mountain Scheme had reduced median and low fl ows by 60–70%, as far downstream as Jarrahmond.

5.2.2 The Geomorphic Model

The following is a chronological summary of the history of stream channel change recorded by various workers on the lower Snowy River and explanations for these changes.

1. There are anecdotal suggestions that the lower Snowy River increased its width at the end of the nineteenth century. For example, the Snowy River Mail (July 1, 1884) wrote:

‘The river which, when settlement fi rst appeared here, was not half its present width, is increasing at every fl ood, widening and shallowing the channel and precipitating huge trees into its bed . . .’ (cited in Seddon, 1994).

In reviewing these claims, Finlayson and Bird (1989) concluded that there was not suffi cient evidence to sub-stantiate this catastrophic increase in width.

2. Since the 1930s, people living along the Snowy River have been convinced that the river was becoming shallower and fi lling with sand (Strom, 1936). Flushing out this sand was probably the earliest ‘restoration’ target for the river, leading to desnagging and other works in the stream in the 1950s. Despite the local conviction that the bed has aggraded, comparisons of 16 repeat cross-sections, dating from the 1920s, along the Jarrahmond reach, do not support this view (Gippel, 2002). Instead the bed level fl uctuates over a range of ±2 m. Brizga and Finlayson (1994) suggested that the reduced fl ows of the river since regulation mean that more of the bed is now visible, leading to the illusion of aggradation. This is a controversial suggestion amongst the local people, who still see ameliorating the effects of sedimentation as the major restoration target for the river. This perception may come, in part, from the fact that some deep pools in the river have certainly fi lled-in since the 1970s, particularly around Bete Bolong (Gippel, 2002).

3. In the 1990s, attention turned to the effect of the Snowy Mountain Scheme on the geomorphology of the river. Brizga and Finlayson (1992) mentioned the loss of lateral bars, and their associated pools in the lower Snowy River, and speculated that the cause could be fl ow regulation (Figure 5.2). Erskine and Tilleard (1997) described the loss of the bars and related it more

strongly to the loss of some formative discharges after regulation in 1967. ‘. . . rhythmically spaced, bank-attached, alternate side bars with well defi ned pool-riffl e sequence were present above the estuary at Bete Bolong before 1967 and they have never reformed since then . . .’ (Erskine et al., 1999). Stewardson (1998) concluded that, not only has the frequency of fl ows that are thought to form bars and pools fallen dramatically, but the incidence of low fl ows that can potentially in-fi ll the pools with sediment has increased. Replacing deep pools with a ‘plane bed’ of sand is thought to provide poor habitat, particularly for migrat-ing fi sh (Raadick and O’Connor, 1997). Twelve of the seventeen fi sh species found in the river are migratory. Returning the pools to the lower Snowy River pre-sented an elegant and achievable goal for stream resto-ration and was the main recommendation of the fi rst restoration plan (ID&A, 1998). The recommendation

Figure 5.2 1940 aerial photograph of the Lower Snowy River upstream of Lynn’s Gulch, showing well developed lateral bars and associated pools


phologists that the proposed log structures would not cause any change in fl ood stage or duration. Ironically, the project has been stopped, not because of scientifi c uncer-tainty about the goals of the project, but because the community remains uncertain about something that the engineers and scientists are certain of: the minor hydraulic effect of the works on fl oods.

5.2.3 Analysis of Uncertainties

The foundation for stream restoration projects are concep-tual models of physical and biological change. This case study demonstrates that the problem with much of the proposed restoration comes from a false sense of certainty in these models. Several geomorphologists have contrib-uted to a conceptual model of channel change. The resto-ration plan has proceeded on the basis of a conceptual model that dismissed aggradation by sand as a geomor-phic change, but did identify a clear coincidence between fl ow regulation and the loss of lateral bars. This neat con-ceptual model then formed the basis for a major restora-tion project involving engineering works to artifi cially recreate lateral bars and pools. Over the last fi ve years, the engineering aspects of the project have taken hold. The key question for managers now is not whether pools are an appropriate goal but which engineering design will develop pools most effi ciently.

It is reasonable to imagine that managers begin by being uncertain about how a geomorphic system functions. They then commission investigations that lead to progressively greater certainty, until restoration decisions can be made with confi dence. In the turbulent boundary between science and management (Cullen, 1989), however, cer-tainty is a fi ckle commodity. Consider the chronology of certainty in the Snowy River project:

1. The local community and the river managers were completely certain that the river was aggrading and that the appropriate restoration strategy was to somehow remove the sand. Theories of aggradation were subse-quently dismissed following the geomorphic investiga-tion of Brizga and Finlayson (1994).

2. Early geomorphic assessments concluded that regula-tion had led to the loss of alternate bars and restoration plans were developed to restore them. Gippel’s (2002) subsequent review revealed strong evidence of channel widening in the late 1800s. Since alternate bars have only been found in wider reaches of the lower Snowy River, and only during ideal hydrological periods, alternate bars were probably not a natural feature of the channel prior to European settlement.

was also supported by the Snowy Water Inquiry (1998a,b). Following these reports, the effort in the project swung to designing timber pile fi elds (retards) at the historical locations of the bank attached side bars. It was hoped that these retards would encourage sand deposition and eventually scour pools (ID&A, 1998; Gippel et al., 2002).

4. Gippel (2002) has comprehensively reviewed the evi-dence for channel change in the lower Snowy River, adding the evidence from two student theses. Gippel concludes that it is likely that the Snowy River did dramatically increase in width after the 1870 fl ood, in common with other rivers in Gippsland (Brooks and Brierley, 1997; Brooks et al., 2003). This suggests that the pre-1967 un-regulated river (that has up till now formed the ‘reference’ for the restoration strategy) was in far from ‘natural condition’. Gippel concludes that the evidence that regulation removed the naturally occurring lateral bars, is weak. An alternative possibil-ity is that bars did not form until the river widened after disturbance, during the early period of European settle-ment and, even then, the bars occurred only when hydrological conditions were ideal. Regulation caused these ideal hydrological conditions to be less likely. In addition, Gippel concluded that: After 1940, the alternate bar and pool morphology

only ever existed in the straight Jarrahmond/Bete Bolong reach (10.2 km of the 32 km lowland section of the river).

It has been incorrectly assumed that the alternate bars always occurred in the same location, with the same wavelength, when in fact they moved and changed form.

Even more fundamentally, the link between pools and fi sh diversity has never been well established. Even in the original report by Raadik and O’Connor (1997), the abundance and diversity of fi sh in the reaches without pools were found to be higher than in the reference reaches with pools.

There is some question about whether the pools can be sustained. The persistent low fl ows that character-ise the regulated regime could quickly in-fi ll any pools that are scoured by favourable fl ow events.

At present, lateral bars and pools remain the major focus of the restoration plan on the Snowy River. The Victorian Government is developing a major trial (US$ 1.5 million) of retard structures (in the fi eld and in fl umes), as a pilot project before building the major pile-fi elds. The trial has been held up because the local community would not accept the assurances of the engineers and geomor-


3. Finally, the restoration trial is being delayed because scientists are not able to convince local stakeholders about the minimal hydraulic effect of the proposed works.

There are many examples where geomorphic concep-tual models have been found wanting when used as a basis for management. Much of the criticism in the literature involves situations where ‘real geomorphology’ has been replaced by ‘cookbook’ approaches (Sear, 1994). Much of this criticism has been directed at restoration projects based on classifi cation systems, such as that of Rosgen (1996). In one example, Kondolf et al. (2001) described a meandering channel inappropriately constructed in a naturally braided stream. Miller and Ritter (1996) provide a more general review of the Rosgen method. The Snowy River Project, by contrast, is not superfi cial geomorphol-ogy, but uses the earnest principles recommended by geomorphologists (Kondolf, 2000). Even ‘real geomor-phology’ can be uncertain. Kondolf and Micheli (1995) were correct when they argued that every stream restora-tion project must be considered an experiment. This sits well with the sometimes stark uncertainties that surround diagnosis of geomorphic ‘problems’. However, it does not sit well with multi-million dollar intervention projects. The lower Snowy River restoration project, for example, is planned to cost US$20 million, and this is before the environmental fl ow component is included.

5.2.4 Discussion

Process based conceptual models of stream channel change (with or without biological conceptual models) are one of the key contributions of geomorphology to stream restoration. However, they are also a major source of uncertainty. A restoration project will proceed on the basis of a geomorphic conceptual model about which every-body is initially confi dent. However, the Snowy River case study shows that this confi dence can be misleading. Further investigation can reduce confi dence in a model, but by the time geomorphologists have settled on a con-ceptual model that deserves confi dence, the restoration process has moved on. Changing the model becomes increasingly diffi cult in the political and management process. Multi-million dollar restoration projects can be launched on the basis of uncertain conceptual models. Not surprisingly, managers and engineers are impatient to proceed to the ‘real’ business of restoration, which is building things and changing things. At least here the uncertainty can be quantifi ed.

Geomorphologists have argued strongly the central importance of understanding the geomorphic context of

restoration projects to ensure their sustainability. If this is the case, a restoration plan will be undermined if the conceptual model from which it was developed is wrong. In the case of the Snowy River, the initial model of aggra-dation led to calls for artifi cial sand extraction to restore the river. However, such efforts would have been unsus-tainable since the sediment in the Snowy River bed was not a discrete slug of sand. Any pool formed by sand extraction would have been infi lled during subsequent storms.

The accuracy of a conceptual model can be critical to the success of a project. However, there is often only a poor basis for judging the levels of uncertainty in those models. Some conceptual models are reasonably simple and have been tested in numerous situations. The chan-nelised stream and sand-slug models are examples of well-tested models in general terms, although applying them in specifi c cases is challenging because they provide a direc-tion of change rather than either rates or magnitudes.

So, given that each stream requires a variant of a con-ceptual model, how is the certainty of that model judged? At present the model will be presented, with more or less confi dence, after a geomorphic study. That confi dence is based on either the weight of reconstructed evidence (‘all 12 of the cross-sections changed in the same way’), on precedents provided by analogous cases (‘a very similar model has been described on three nearby rivers’), or on the reputation and forcefulness of the investigator. Given that most of these restoration projects cannot wait for models to be published in the peer-reviewed literature, how can managers judge the uncertainty of these concep-tual models? Here are fi ve proposals:

1. The most obvious way to improve confi dence is to subject a conceptual model to anonymous, external review, by disinterested ‘experts’. Whilst this sounds easy, the pool of appropriate reviewers can be small and, from a manager’s perspective, plans can get bogged down in what appear to be petty, academic debates.

2. Simple guidelines could be established for evaluating the uncertainty of a conceptual model based on the type and strength of evidence provided. Evidence in the form of a mathematical expression of the conceptual model tested on long term geomorphic data should provide more confi dence in the model than a model based on anecdotal accounts. The guidelines could be applied by the geomorphologists developing the model to advise managers on model uncertainty. It would also be possible to use the guidelines to recommend additional investigations that could reduce model uncertainty. Alternate conceptual models could also be compared using these guidelines.


3. A rule-of-thumb in engineering projects is that about 10% of the total cost of a project should be spent on design. Given that stream restoration projects tend to cost AUS$105–$106, perhaps a good guide is to argue that all of the design would be costing around $US 10 000 to $US 100 000, with the conceptual model absorbing from 20–40% of that amount. This might avoid the situation described in the Snowy River Project, where a simple/elegant conceptual model based on a modest study sets in train a management response that it is hard to modify. A problem with this approach is that the geomorphic investigation that establishes the conceptual model may be commis-sioned before the full costs of the restoration project are known. In this case, additional geomorphic inves-tigations may be commissioned later in the planning of the project after preliminary estimates of project costs. The challenge is to avoid advancing too far with the design before the conceptual model is fi nalised.

4. Modellers often develop the model with one set of data and test it on another set. Thus, some data are held back for verifi cation. A similar approach could be used with conceptual models. For example, in the Snowy River project, a proportion of aerial photographs could have been held back and used to verify the model later (given that there are eight sets of photos, this may be possible).

5. All of the data and information could be collected and collated and then passed onto an appropriate third party (or more than one), without interpretation, so that an independent interpretation of the data could be devel-oped. The diffi culty with this is that the conceptual model developed by the third party may require a dif-ferent type of data for testing, which would require further data collation, adding substantially to project costs.

5.3 CASE STUDY OF A GEOMORPHIC DESIGN MODEL

5.3.1 Introduction

So far, the role of geomorphologists in using conceptual models to identify ‘reference’ states and actions that can move a stream toward the ‘reference’ state have been dis-cussed. The conceptual model will help to identify a res-toration action, but a mathematical model can be required to design specifi c aspects of the intervention. The most common restoration interventions recommended in the northern hemisphere relate to structural and fl ow changes that will improve the success of fi sh populations. Having

been involved in many such projects in Australia, we have noted the tendency for managers (and ecologists) to assume that the ‘physical stuff’ – the hydrology, hydraulics and geomorphology – is well understood, with low errors and low uncertainty. They assume that most uncertainty lies in the biological responses to intervention. The fol-lowing section suggests that they may be giving the geo-morphological studies we examine too much credit!

In this section we examine the uncertainties in a math-ematical model used to design a restoration action. The case study concerns the design of fl ushing-fl ows for a gravel-bed stream. Numerous authors have identifi ed the problem of fi ne sediment infi ltration into a stream bed known as colmation (Sear, 1993; O’Neill and Kuhns, 1994; Milhous, 1995; Kondolf and Wilcock, 1996). The loss of competent fl ows below dams means that coarse beds are infi ltrated by fi ne sediment, damaging habitat for macroinvertebrates and fi sh. The only way to clean out the bed is to initiate movement of the coarse fraction. A common geomorphic goal of fl ow management is to ‘turn-over the bed’. The uncertainty in this question is whether the predicted fl ow will turn-over the bed. The cost of the uncertainty is the chance that the bed does not move when the target amount of water is released. If the bed is not turned over, then that volume of water has been wasted. Similarly, if the bed moves at a discharge below the target discharge, then the extra volume of water released has been wasted. Thus, in this example, the uncertainty can be expressed in terms of the extra water that has to be released to be confi dent that the bed will fl ush. This also allows the relative saving in water to be estimated if man-agers do various things to reduce the uncertainty.

The case study considered is that of fl ushing fl ows for the mid-Goulburn River, downstream of the Eildon Dam, in northern Victoria, Australia. The Goulburn River is the largest stream in Victoria (catchment of 20 000 km2) and the largest Victorian tributary to the Murray River. It is a meandering, anabranching river, that is regulated for irri-gation by the Eildon Dam. The bankfull discharge is not clearly defi ned but fl ow along anabranch channels begins at about 120 m3/s in the reach below the Eildon Dam. Abundant data is available for the stream, compiled for a recent environmental fl ow study (DNRE, 2002).

5.3.2 The Geomorphic Model

The volume of water required to deliver the fl ushing fl ow is estimated in the following three stages. It must be emphasised that this is an approach used for designing a fl ushing fl ow. This approach is not advocated for use in other studies. Alternate methods for calculating fl ushing fl ows might be considered, particulary given the magni-


tude of uncertainties in this analysis described later. The three stages are:

1. Estimating the critical bed shear stress for incipient motion of the bed sediments using Shields’ entrainment function; the critical shear stress is given by:

τcrit = (γs − γw)d50τ* (5.1)

where γs and γw are the specifi c weights of the bed sedi-ment and water respectively, d50 is the median grain size of the bed sediments and τ* is Shields’ dimensionless shear stress, often estimated as 0.045 for gravel bed rivers (Buffi ngton and Montgomery, 1997).

2. Estimating the discharge at which the mean shear stress for the reach is equal to the critical shear stress for incipient motion, using a one-dimensional hydraulic model (applied over a 2 km reach of the Goulburn River); and

3. Estimating the volume of water required to mimic the natural duration and frequency of fl ow spells during which bed sediments are mobilised based on an analy-sis of a modelled natural fl ow series.

Importantly, a one-dimensional hydraulic model is used rather than the more sophisticated two- or three-dimensional models now available because it is the approach widely used in practice for restoration projects. The uncertainties associated with a two-dimensional model will be different, but it is not possible to suggest whether they would be smaller or larger than those using a one-dimensional analysis without a proper investigation. The modelling procedure also includes the preparation of input data which includes fi eld surveys and selection of an appropriate value for Shields’ dimensionless shear stress (Table 5.1). 0.045 is used as the value for Shields’ dimensionless shear stress in the initial model, but the

implications of uncertainty in this parameter are also con-sidered. In this case study the concern is not so much with the correct value of this parameter as the effect of param-eter uncertainty.

Using a standard Wolman count (sample size = 100), the median bed material sediment size is estimated as 30 mm. Using Shields’ entrainment function, incipient motion for the grain size occurs at 22 N/m2. Seventeen cross-sections are surveyed along a 2 km reach of the mid-Goulburn river. A one-dimensional hydraulic model is calibrated for these cross-sections using water levels sur-veyed at a fl ow close to the mean. Calibration was achieved by adjusting the Manning n roughness parameter. Assum-ing Manning n is invariant with discharge, the hydraulic model estimates that the mean shear stress for the reach is equal to 22 N/m2 at a fl ow of 360 m3/s.

To estimate the natural frequency of bed mobilisation events the natural fl ow regime is needed, but there is no record of the natural fl ow regime in the mid-Goulburn River because it has been regulated since fl ow gauging commenced. Instead a natural fl ow series is modelled using available streamfl ow data upstream of Eildon Dam. This requires estimation of fl ows from ungauged portions of the catchment by scaling fl ows in the gauged catch-ments, combining fl ows from the various sub-catchments and routing fl ows to the study reach. Using this modelled natural fl ow series, it is estimated that 157 GL/year is required to mimic the natural frequency and duration of fl ows exceeding 360 m3/s.

5.3.3 Analysis of Uncertainties

There are a number of sources of uncertainty in the esti-mated channel fl ushing discharge and volume of water required to mimic the natural frequency and duration of channel fl ushing events (Table 5.1). In this study, uncer-tainty in the estimated threshold discharge for sediment fl ushing is assessed based on a consideration of:

Table 5.1 Summary of errors and uncertainties considered in this analysis (the text in italics shows the values used in the analysis)

Source of Error Channel Hydraulics Critical Shear Flow Regime

Sample uncertainty Cross-section sampling (used n = 17)

Number of particles in sample (used n = 100)

Number of years in the record (used n = 25)

Measurement error (Neglected here) Measurement of grain diameter (± 5 mm)

Error in rating curve (r2 = 0.95)

Model error Manning n (Based on data presented in Hicks and Mason, 1998)

Shields entrainment function* Estimating fl ow from ungauged catchments (20% of catchment area) and fl ow routing*

* see text for explanation.


• errors in measurement of grain sizes;• sampling bed particles to estimate median grain size;• estimating Shields’ dimensionless shear stress (τ*).• assuming the channel roughness parameter (i.e. Manning

n) is constant with discharge;• sampling of cross-sections from the 2 km reach.

Uncertainty in the volume of water to mimic the duration and frequency of sediment fl ushing fl ows is assessed based on a consideration of these same factors in addition to:

• errors in measuring discharge using a rating curve at each gauge;

• using a sample of the fl ow record to represent the long term fl ow regime;

• errors in modelling natural fl ows at the survey site.

In this section the effect of these uncertainties is quanti-fi ed using a Monte Carlo Analysis, which involves running the fl ushing fl ow model many times (1000 in this case) with different, but equally plausible sets of input parame-ters to generate a range of plausible model outputs (Manly, 1997). For each of the 1000 replicates values were ran-domly chosen for each of the input parameters from the range of possible values. These were selected from a prob-ability distribution centred on the best estimate used in the original analysis (described in the previous section). The combined uncertainty is estimated by combining random selections of values for each input variable.

It is necessary to evaluate the uncertainty for each input parameter so that it is known how it should be varied in the replicate model runs. A highly uncertain parameter should be varied over a bigger range than a more accurately known parameter. The methods used to evalu-ate parameter uncertainties are described below. In some cases, these methods are obvious. For the case of the median particle size, it is relatively straight forward to express the estimate of the median grain size as a distribu-tion of possible values, based on the size of the sample of bed grains. It is not so straightforward to evaluate uncer-tainties in recorded discharge series or calibrated values of the Manning channel roughness parameter (n). A best effort is made to quantify these uncertainties, but these are best described as models of uncertainty, albeit models which cannot be truly tested. The result is a stochastic model where some components are deterministic and some are random.

Median Particle Size

The median grain size is estimated from a sample of 100 bed particles (a standard Wolman count). To estimate the

uncertainty associated with sampling grain sizes, 1000 replicate samples (each with 100 grain sizes) are syntheti-cally generated by assuming that the natural log of grain sizes (in millimetres) are distributed normally with a mean of 3.4 and standard deviation of 0.51. This gives a true median particle size of 30 mm, although sample medians for each replicate will vary about this value. Errors in measurement of grain size diameter are modelled as normally distributed with a mean of zero and standard deviation of 3 mm. This gives 90% confi dence intervals on grain size measurements of ±5 mm. To represent measurement errors, each grain size in each of the 1000 replicate samples is perturbed by adding this random component.

Entrainment Threshold

Buffi ngton and Montgomery (1997) collated values pro-vided in the literature for the Shields’ dimensionless shear stress value for incipient motion. A value of 0.045 is often used as the best estimate of τ* for gravel-bed rivers. The uncertainty in τ* is estimated from the range of values compiled by Buffi ngton and Montgomery that are likely to occur in gravel-bed streams with Reynolds roughness number in the range 25 to 1000 (Figure 5.3). The distribu-tion of residuals for the regression in Figure 5.3 was rep-licated by assuming that the distribution was log-normally distributed with a standard deviation in the log-error of 0.03, and randomly generating 1000 replicate values using this error model.

y = 0.017x0.145

R2 = 0.150n = 98

0.01

0.1

10 100 1000

Reynolds roughness number

Dim

ensi

on

less

sh

ear

stre

ss

Figure 5.3 Values of Reynolds roughness number (for the range 25 to 1000) and dimensionless shear stress obtained from pub-lished incipient motion studies and collated by Buffi ngton and Montgomery (1997)


Survey Errors

Uncertainty associated with cross-section survey errors were neglected in this study and likely to be small given the high accuracy of survey equipment relative to other sources of uncertainty. The survey of the Goulburn River reach included a sample of 17 evenly-spaced cross-sections over a reach length of 2 km. There is uncertainty in the estimate of reach-averaged bed shear stress depend-ing on how well the sampled cross-sections represent the variability of the reach. To estimate this uncertainty, rep-licate samples of 17 cross-sections were generated using a bootstrap procedure (Manly, 1997) in which 17 cross-sections were randomly selected from the 17 available, ‘with replacement’. This bootstrap procedure was used to generate 1000 replicate samples.

Errors in the Hydraulic Model

The critical shear stress was converted into a discharge using a one-dimensional hydraulic model. The major uncertainty in this method is the roughness coeffi cient. In the model, Manning n was calculated from a single stage discharge measurement in the reach. The hydraulic model-ing required the assumption that Manning n was then constant over the range of discharges considered (up to bankfull). It is well known that Manning n varies with discharge, sometimes increasing and sometimes decreas-ing with increasing fl ow. The error associated with this assumption was estimated using the large range of rough-ness estimates for gravel-bed New Zealand rivers (Hicks and Mason, 1998). These data reveal that the inverse of Manning roughness parameter ( 1–n) is approximately pro-portional to the log of discharge in most cases (Stewardson and Anderson, 2002). A log regression was fi tted to data for each of 72 sites [provided by Hicks and Mason, 1998] to provide a constant (c) and coeffi cient (k) in this regres-sion equation for each site:

11

nc k

Q

Q= +

−ln (5.2)

where Q– is the mean daily fl ow at the site. About one

quarter of the coeffi cients (k) were negative, indicating increasing Manning n with discharge at these sites. The parameter k defi nes variation in Manning n with discharge. For our site, c was determined by calibration to the observed water surface profi le (using k = 0). The change in shear stress with discharge was then modelled using the one-dimensional model, and run 1000 times, each time using a different value of k selected at random from the 72 values for the New Zealand rivers.

Uncertainty in the Hydrology

Flows at the Goulburn River site are regulated by opera-tion of Lake Eildon, a large water supply reservoir, upstream of the site. Flow data from gauging stations located on nine unregulated tributaries upstream of the Goulburn River site were used to model natural daily fl ows at this site (i.e. fl ows that would occur in the absence of Lake Eildon, neglecting routing effects). A 25 year series of daily fl ows was generated for the survey site from the 25 year fl ow records at nine streamfl ow gauges. These 25 year series are a sample of the long term fl ow regime. A bootstrap procedure was used to generate 1000 replicate 25-year series by sampling random years from the 25 year sequences. Variability in the replicates represents uncer-tainty in the long term fl ow regime. This approach assumes fl ow independence between years.

Flow modelling for the study was based on records of discharge at nine streamfl ow gauges on unregulated tribu-taries of the Goulburn River. Discharge is estimated at the gauges from rating curves. Rating curves are fi tted to periodic measurements of discharge and stage. There are errors in discharges provided by the rating curve as a consequence of errors in these periodic measurements of stage and discharge. Rating curves are often fi tted by a log–log regression and an r2 of 0.95 is generally regarded as a good fi t (Clarke, 1999). Error in discharges derived from the rating curves are represented by a random com-ponent added to the log-discharge where the error is dis-tributed normally with standard deviation of 0.05 times the mean of log-discharges recorded at the gauge. This can be represented by:

Q eQ N

nQ

′ =+ ( ) ∑ln ln 0 0 05

1, .

(5.3)

where Q is the recorded daily discharge, N(0,0.05) denotes a normally distributed random variable with mean of zero and standard deviation of 0.05, and n is the number of days of recorded fl ow. This error would result in an r2 of 0.95 for a rating curve fi tted by log–log regression.

The 25 year natural daily fl ow series at the study site was modeled in two parts: (i) estimating fl ows from the ungauged portion of the catchment by scaling gauged fl ows in unregulated tributaries; and (ii) summing fl ows recorded at the streamfl ow gauges and estimated for the ungauged portions of the catchment. Routing effects, which generally result in attenuation and delay of fl ood pulses further downstream, were ignored because no data were available to calibrate a routing model. Uncertainty in the scaling parameter for the ungauged catchment was evaluated using a comparison of fl ows recorded at seven different gauges (see Appendix 5.1). There is some


uncertainty in the modelled natural fl ows as a consequence of ignoring routing effects. This uncertainty was repre-sented using a routing model (described by Stewardson and Cottingham, 2002), where the routing parameters were randomly perturbed according to an assumed distri-bution of possible values (see Appendix 5.1).

Results of Monte Carlo Analysis

The critical shear stress for incipient motion for this reach has wide confi dence limits varying from 18 N/m2, up to 26 N/m2 (Figure 5.4). A larger uncertainty applies to the

possible range of shear stresses that occur in the reach for a given discharge. The fl ushing fl ow is estimated to be 360 m3/s, but this has 90% confi dence limits of between 250 m3/s and well over 500 m3/s.

Uncertainty in the critical discharge for bed fl ushing is reported using the magnitude of the 90% confi dence inter-val (Table 5.2). Combining all sources of uncertainty, the magnitude of this confi dence interval is 560 m3/s. The major source of uncertainty in estimating the critical dis-charge comes from error in the estimate of Manning n in the hydraulic model. This is because n is calibrated to a single discharge (effectively the mean discharge of 60 m3/s) and becomes increasingly uncertain as discharge increases. This provides a massive 430 m3/s range of dis-charge (Table 5.2). By comparison, if the only source of uncertainty in the estimate was error in measuring the particle size, then the range in the estimate would only be 50 m3/s. The implication of Table 5.2 is that ever more detailed estimates of the bed-particle size distribution, or even increasingly sophisticated bed-load transport thresh-old models, will not remove the major uncertainty associ-ated with the channel hydraulics.

In a good environmental fl ow project, a manager would not simply specify a fl ow magnitude, the frequency and duration of that critical fl ow would also be specifi ed. A popular approach used to defi ne the frequency and dura-tion is the ‘natural fl ow paradigm’ (Poff et al., 1997; Richter et al., 1997), which suggests that an environmental fl ow should mimic the natural regime. To estimate this the average duration and frequency of critical transport events in the natural regime is calculated. The volume of water required to mimic these events is the average duration multiplied by the frequency, multiplied by the threshold discharge. The curve in Figure 5.4(b) shows that, as the fl ow threshold increases, the total fl ow required decreases substantially. If, for example, the threshold fl ushing fl ow

0

10

20

30

40

50

60

70

0 200 400 600 800Flow (m3/s)

She

ar s

tres

s (N

/m2)

reach shear stress

critical shear stress

(a)

0

200

400

600

800

0 200 400 600 800

Flow threshold (m3/s)

Flo

w v

olum

e (G

L/ye

ar)

(b)

Figure 5.4 (a) reach-averaged shear stress estimated using a one-dimensional hydraulic model and the critical shear stress estimated using Shields, entrainment function; and (b) the volume of artifi cial fl ow spells required to mimic the frequency and dura-tion of natural fl ow spells for varying fl ow threshold. (Dashed lines indicate 90% confi dence intervals based on consideration of all errors described in Table 5.1.)

Table 5.2 Size of 90% confi dence interval (expressed in m3/s) for threshold discharge for incipient motion for different sources of error (best estimate of threshold discharge is 360 m3/s)

Source of error Channel Hydraulics Critical Shear

Measurement error Neglected 50Model error 430 180Sample error 200 120Combined 540 220All sources combined 560

Note: The numbers are not additive. The combined uncertainty comes from MonteCarlo simulations using the full distributions. This table should be compared with Table 5.1.


is 260 m3/s, then to generate the natural duration and fre-quency of these fl ow pulses would require about 160 GL/yr, but it could range between 10 GL/year and 360 GL/yr (i.e. the 90th percentile confi dence interval).

The numbers in Table 5.3 are the confi dence intervals on this volume of the fl ushing fl ow. For example, considering all sources of error, the 90% confi dence limits are 10 GL/year and 360 GL/year so the magnitude of the interval is 350 GL/year which is close to 10% of the mean annual fl ow! Expressed another way, 360 GL have to be allocated for the fl ow component in order to be 95% confi dent that there is suffi cient water that the bed will be turned over at a natural frequency and duration, given all of the sources of uncertainty in the method. How much is this water worth? One way to estimate this is to consider the cost of infra-structure projects that would be required to achieve compa-rable water savings (pipelines etc.). The Snowy Water Inquiry (1998a,b) estimated that environmental fl ows, costed in this way, were worth $US 0.45 million per GL/year. Clearly this uncertainty has economic signifi cance.

5.3.4 Discussion

In this simple analysis three other sources of uncertainty have not been considered. The fi rst is the simplifi cation of the fl ushing fl ow problem to a single threshold shear stress. The range of τ* used accounts for many of the well-known problems of hiding, packing and other particle interactions that affect bed material transport, but there is still the issue of hysteresis. The same discharge on the rising and falling limb of the hydrograph has very different transporting capacity (Gomez, 1991). It is also possible that a fl ow exceeding the threshold for incipient motion will be required to mobilize bulk quantities of the bed sediments.

The second uncertainty not considered is the longitudi-nal variation in the effect of the fl ushing fl ow. In this case a threshold fl ow in a single reach has been estimated. The target fl ow may well turn the bed over at the sample site, but what will the fl ow do up and downstream of that site? Given storage effects, a larger fl ow might have to be

released to turn the bed over further downstream. The result might be that the upstream site is not just ‘turned-over’ but it is progressively scoured away. This introduces the third uncertainty which is the sustainability of the process target.

The fl ushing fl ow may work the fi rst time, but the bed will then presumably progressively armour, as there may be no source of coarse sediment below the dam. Either the fl ushing fl ow will have to be progressively increased over time, or the idea of a fl ushing fl ow is simply not sustain-able in this situation. Such analyses are completed as steady-state models when in reality the release of environ-mental fl ows will lead to changes in the input variables. An example of this problem is the major fl ushing-fl ow experiment carried out on the Colorado River (Collier et al., 1997). The fl ood was successful in scouring and rejuvenating the point-bars of the river below the Glen Canyon Dam but this does not mean that it can be suc-cessful indefi nitely.

Major sources of uncertainty have been considered in a reasonably simple geomorphic problem: a fl ushing fl ow. Most discussion of uncertainty in this type of analysis is associated with the entrainment function, or in the mea-surement of the particle size (Kondolf and Wilcock, 1996). It is interesting to note the many sources of uncertainty (and error) in such analyses. In fact, for this example, the main uncertainty comes from the hydraulics, particularly the estimation of roughness.

It is pertinent to ask what options are available to reduce the uncertainty? For a geomorphologist the response may be to argue for the use of a more sophisticated bed-load threshold function, but this will not reduce the uncertainty associated with the hydraulics and hydrology. The geo-morphologist could also argue that more research would reduce uncertainty. This may be true, but it is also fair to say that 150 years of research into bed-load transport reinforces the view that there is unlikely to be a simple principle that will dramatically change our capacity to predict this process. Instead the best option is to make extra measurements at the site. The geomorphologists could either measure variables that reduce the range of

Table 5.3 Size of 90% confi dence interval (in GL/year) for volume of discharge required to mimic natural frequency and duration of bed scouring events for different sources of error (best estimate of volume is 157 GL/year)

Source of Error Channel Hydraulics Critical Shear Flow Regime

Measurement error neglected 37 20Model error 234 167 13Sample error 164 104 107Combined 267 209 125All sources combined 350


uncertainty in each of the modelled variables discussed above, or they could directly measure bed-load movement to observe the discharge at which the bed turns over.

Reducing Model Uncertainty

Extra fi eld measurements can reduce the uncertainty and so the amount of water required for the fl ushing fl ow. The options include:

• extra cross-section surveys;• recording stages along the reach over the range of fl ows

being considered to avoid the need to extrapolate Manning n from calibration at a single discharge; or

• larger bed material samples and more careful measure-ment of particle diameter.

Most of the error in this example lies in estimates of the channel hydraulics (Table 5.4). The most effective way to reduce uncertainty in these modelled estimates is to survey water levels over a range of discharges to allow calibration of Manning n for a range of fl ows. Some uncertainty would remain for extrapolations outside this range of discharges. Similarly, surveying more cross-sec-tions will produce a greater decrease in error than will a more accurate estimate of the entrainment function. Overall, the estimates are insensitive to uncertainties in the fl ow regime, such as an increase in the length of fl ow record, or improvements in the accuracy of the rating curves.

Another way to reduce uncertainty (and the waste of water) is not to mimic a ‘natural’ fl ow regime. The uncer-tainty (and cost) that this can introduce must be appreci-ated. A reductionist approach is to understand the target processes, and release fl ows when required, to achieve this goal. In the case of a fl ushing fl ow, it would be better to monitor the bed processes so that managers knew when the bed needed to be fl ushed (say after a particular series of lower fl ows). Thus, the cost of lost fl ow would be replaced by the cost of monitoring the bed.

A clear conclusion from this analysis is that a few extra measurements can dramatically reduce uncertainty. In this

particular case, it may be cheaper and more accurate to simply observe the processes directly, rather than rely on modelling. The most effi cient way to achieve this is by trial releases of water from dams (in the fl ushing-fl ow case). Dam managers may argue that the water is too valuable to ‘waste’ on such exercises. However, at least in our example, far more water (and hence money) could be wasted as a consequence of the uncertainty.

However, river restoration projects are often carried out under tight time constraints for political or economic reasons. There is rarely time to monitor the processes that underpin the modelling or to check the predictions. In a typical stream rehabilitation project, a consultant is engaged and given perhaps a month or two to report. Any fi eld investigations are expected to be brief inspections to develop a conceptual model.

5.4 CONCLUDING DISCUSSION

A central contribution of geomorphology to the new prac-tice of stream restoration is in developing conceptual models that describe the change of stream form and process over time. This exercise also provides a ‘refer-ence’ condition that can be the target for restoration actions. If the conceptual model is wrong, or is too sim-plistic, then it does not matter how well executed the management actions are, the project outcomes remain highly uncertain.

In this chapter the development of the conceptual geo-morphic model for the restoration of the lower Snowy River has been described. This is an interesting example because it illustrates the tension between the typically iterative process of geomorphic discovery and the need for action by managers. It also shows the dangers of too much confi dence in early interpretations, when these can trigger expensive management actions that are diffi cult to reverse even with adaptive management (Stankey et al., 2003).

Restoration can be a very expensive exercise but there is often little basis for managers to assess how much con-fi dence they should have in conceptual models that are presented to them. Five methods have been proposed that

Table 5.4 Size of 90% confi dence interval (expressed in GL/year) for volume of discharge required to mimic natural frequency and duration of bed scouring events with one source of uncertainty removed (e.g. if completely certain about the hydraulic roughness of the channel, then the confi dence interval would be reduced from 350 GL/yr down to 280 GL/year, as shown in the bottom left cell of the table)

Source of Error Omitted from Analysis Channel Hydraulics Critical Shear Flow Regime

Sample error Cross-sections 280 No. of particles 330 Years of record 340Measurement error – Grain size 350 Rating curve 350Model error Manning n 270 Shields entrainment 320 Extrapolating from gauged

catchments 350


can be used to test the validity of conceptual models that often fall outside the area of peer review: independent review of the model; establishing simple guidelines for evaluating the uncertainty of a conceptual model based on the type of strength of evidence provided; ensuring that an appropriate percentage of the total cost of the project is committed to the development of the conceptual model; holding back some of the information and data that under-pin the model, and using these data to verify the hypotheti-cal model; and passing all of the data and information to a third party, without interpretation, so that a competing model can be developed.

The feasibility has been demonstrated of quantifying uncertainty in a geomorphic design model, using the case study of a fl ow to fl ush the fi ne sediments from the bed of the Goulburn River. In this case, the uncertainty was very large. Surprisingly, the main source of uncertainty came from the hydraulic modelling and relatively little from either the bed-load entrainment function or the hydrologi-cal modelling of the fl ow series. It is also striking how adding extra fi eld measurements can reduce the uncer-tainty in the model estimates, particularly in estimating Manning n. In fact, the key to uncertainty often comes down to modelling versus monitoring.

It is necessary to include some form of data gathering with any geomorphic modelling exercises, including modelling geomorphic response to river restoration. It has been shown how an uncertainty analysis might be used to direct this data gathering effort to most effectively reduce uncertainty in model predictions. It seems logical that modelling and data gathering should be integrated to mini-mise uncertainties in restoration. In many cases, some form of monitoring is carried out as part of the restoration implementation to check that the project achieves what was intended. However, these monitoring programs are rarely designed to verify or improve the models used in planning the restoration. Uncertainty analysis appears to be useful for optimising such monitoring programs to provide improved models for subsequent restoration decisions.

In some cases the most effective way to reduce uncer-tainties is to run trial programs in the actual systems being restored. Even in these cases, the model and uncertainty analysis can provide the basis for designing the trial to ensure that it tackles the key sources of uncertainty in model predictions. In the case of the lower Snowy River, the state government has recognised the value of addi-tional geomorphic measurements and analysis, and invested in a restoration trial which includes fi eld mea-surements and physical modelling to improve the geomor-phic design. It is possible to design monitoring activities without regard to uncertainties during the planning phase of a restoration project. However, it is proposed that

stronger integration of monitoring and modelling activi-ties will lead to greater improvements in the knowledge underpinning river restoration.

Geomorphic models, like models developed in any science, are subject to some uncertainty. Although this uncertainty is widely acknowledged it is rarely evaluated. We conclude that there is unreasonable confi dence in geomorphic models used for river restoration. Certainly we were surprised by the magnitude of uncertainty in the fl ushing fl ow example and we have been involved in many of these modelling studies in Australia. There is rarely any thought given to the best approach to modelling, including the calculation of input parameters to minimise uncertain-ties in restoration decisions. There is a need to rethink our approach to geomorphic modelling in the context of river restoration, in particular the benefi ts of evaluating uncer-tainties in both our conceptual and mathematical models. This requires more expertise, information and funding but the result will be more realistic expectations by those involved in the restoration project and a more careful approach to optimising data gathering for calculating model parameters and verifying model structure.

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APPENDIX 5.1

Flows at the Goulburn River site are regulated by operation of Lake Eildon, a large water supply reservoir, upstream of the study site. Flow data for gauging stations located on nine unregulated tributaries upstream of the Goulburn River site were used to model natural daily fl ows at this site (i.e. fl ows that would occur in the absence of Lake Eildon). Seven of these tributaries are upstream of Lake Eildon and two other tributaries have confl uences with the Goulburn River between Lake Eildon and the survey site (Table 5.5). In total 80% of the 4225 km2 catchment at the survey site is upstream of these fl ow gauges (510 km2 upstream Eildon and 314 km2 between Eildon and the study site). Flows in the ungauged portion of the catchments upstream and downstream of Lake Eildon are estimated by scaling fl ow in the gauged portion of the catchments upstream and downstream of Lake Eildon respectively. The scaling factor was estimated using a linear function of the ratio of gauged and ungauged catchment areas, derived from the available streamfl ow data (Figure 5.5). Daily fl ows at the survey site are estimated as the sum of daily fl ows (for the same day) at the upstream gauges and estimated in the ungauged catchments. Routing effects (i.e. travel times and attenuation of fl ood peaks) are neglected because there

Table 5.5 Gauged tributaries of the Goulburn River upstream of the Goulburn River survey site

Tributary Catchment area (km2)

Upstream of Lake EildonDelatite River 368Howqua River 368Jamison River 368Goulburn River at Dohertys Infl ow 694Big River 619Ford Creek 115Brankeet Creek 121

Between Lake Eildon and the surveys siteRubicon River 129Acheron River 619


Table 5.6 Characteristics of the ratio of daily fl ows at each of seven streamfl ow gauges to the sum of daily fl ows at the other six gauges (Gauges are all located upstream of Lake Eildon)

Gauge Catchment Area (km2)

Mean Flow (ML/day)

Catchment Area Ratio

Mean of Daily Flow Ratio

Standard Deviation of Daily Flow Ratio

Lag-1 Serial Correlation of Daily Flow Ratio

Characteristics for the streamfl ow gauges upstream of Lake Eildon

Delatite River 368 297 0.16 0.10 0.037 0.66Howqua River 368 477 0.16 0.18 0.051 0.94Jamison River 368 560 0.16 0.21 0.056 0.91Goulburn River 694 882 0.35 0.35 0.097 0.93Big River 619 843 0.30 0.46 0.14 0.93Ford Creek 115 34 0.045 0.0049 0.011 0.44Brankeet Creek 121 48 0.048 0.022 0.018 0.56

Characteristics estimated for the ungauged catchments using regression equation in Figure 5.5

Upstream of Eildon 510 – 0.19 0.21 0.063 0.9*Eildon to study site 314 – 0.42 0.51 0.14 0.9*

* selected based on serial correlations derived from streamfl ow gauge data

y = 1.33x - 0.05R2 = 0.87

y = 0.35x - 0.0037R2 = 0.830

0.050.1

0.150.2

0.250.3

0.350.4

0.450.5

0 0.1 0.2 0.3 0.4

Ratio catchment areas

Sta

tist

ic o

f d

aily

flo

w r

atio

mean of daily flow ratio

standard dev. of daily flow ratio

Figure 5.5 Mean and standard deviation of daily fl ows ratio and catchment area ratios for seven streamfl ow gauges upstream of Lake Eildon. Ratios are calculated at each gauge in turn by divid-ing daily fl ows and catchment area at the gauge by the sum of daily fl ows and catchment areas at the other six gauges

at one of the gauges to sum of fl ow at all other gauges. In reality this ratio varies from day to day. The mean, stan-dard deviation and lag-1 serial correlation for this ratio were calculated for each of the seven sites upstream of Lake Eildon (Table 5.6). These characteristics were esti-mated for the ungauged catchments using the regression equation in Figure 5.5. A stochastic model is used to rep-resent uncertainty in the fl ow estimated in the ungauged catchment. This model gives fl ow from the ungauged catchment on the ith day of the 25 year record as:

Q m r m r Q r Nu i i i j i ij

, ,. ,= + −( )[ ] = ( )− ∑1 1 and m s (5.4)

where Qj,i is the fl ow at the jth gauge site on the ith day. Values of m were calibrated as 0.1 to provide a lag-1 serial correlation of 0.9 for the fl ow ratios in ungauged catch-ments upstream and downstream of Lake Eildon. The daily parameter ri is a normally distributed random vari-able. The mean (µ) of ri was set equal to the mean of daily fl ow ratios estimated from the regression in Figure 5.5 The standard deviation (σ) was adjusted to replicate the stan-dard deviations calculated from the regressions in Figure 5.5. Standard deviations chosen for upstream and down-stream of Lake Eildon were 0.27 and 0.61 respectively. Note that these are higher than the values estimated directly from the regression equations to account for the effect of lag correlation in the stochastic model. This sto-chastic model is used to generate replicate 25-year time-series of fl ow from the ungauged catchments.

is no information with which to establish a routing model.

The regression equation in Figure 5.5 is used to estimate a scaling factor for estimating fl ows in the ungauged catch-ments. Data points in this plot show the mean ration of fl ow

6

Uncertainty in Riparian and Floodplain Restoration

Francine M.R. Hughes1, Timothy Moss2 and Keith S. Richards3

1Department of Life Sciences, Anglia Ruskin University, UK2Institute for Regional Development and Structural Planning (IRS), Germany

3Department of Geography, University of Cambridge, UK

Riparian and fl oodplain ecosystems not only have intrinsic and intangible values associated with these high levels of biodiversity, and with their diverse landscape character, but they can also be valued because they con-tribute to timber production and carbon sequestration, fl oodwater storage, groundwater recharge, pollution control and even recreation. The importance of riparian and fl oodplain zones can thus lie in this wide range of natural functions and services that they provide, although there is uncertainty about how these can be valued over time, given their dynamic and varying nature. Such valu-ation is today increasingly necessary, since hydrological pathways in river basins have often been altered indirectly through land-use change and directly through manage-ment of the fl ow regime. In downstream fl oodplain zones in particular there have been many engineered changes to river channels, leading to isolation of fl oodplains from their channels and to severe damage or eradication of fl oodplain ecosystems. All these changes have severely limited or even destroyed the capacity of fl oodplains to deliver their natural functions and services.

The disappearance of these ecosystems from the land-scape is poignantly illustrated by the case of fl oodplain forests in Europe. 90% of these forested ecosystems have disappeared and remaining patches are often in critical condition. They are listed in Annexe I of the European Habitats Directive (92/43/EEC, 1992) as a priority habitat type and are included in the Natura 2000 network of nature reserves. In western Europe they are more reduced in extent than in eastern and central Europe where some

6.1 INTRODUCTION: THE CASE FOR RESTORATION

The riparian and fl oodplain zones of rivers are physically dynamic places, subject to the delivery and removal of water and sediments during fl ood events. Ecosystems that occupy these places have evolved a tolerance to these natural disturbance processes and many of their compo-nent species have become dependent on them for comple-tion of their life cycles. In many river valleys, riparian and fl oodplain zones would once have been occupied by for-ested ecosystems, composed of a dynamic mosaic of forest types in different successional stages, interspersed with more open wetland communities with emergent veg-etation. Such fl oodplain forests have high levels of biodi-versity because they are at the interface between terrestrial and lotic ecosystems (Petts, 1990). In addition, they expe-rience frequent disturbance from fl oods which create the conditions for a heterogeneous mosaic of habitats across the fl oodplain, each supporting a varied mix of species (Nilsson et al., 1991a; Hughes et al., 2005). A high plant species diversity has been recorded on fl oodplains from rivers in many different bioclimatic zones. For example, fl oodplains in the Amazon basin account for 20% of tree species diversity (Junk et al., 1989). In the Tana River fl oodplain forests in Kenya, which stretch for only 200 km of river length and average only 1 km in width, 175 woody plant species, over 250 species of birds and at least 57 species of mammals have been found, including two endemic primates (Medley and Hughes, 1996).



impressive patches remain (Figure 6.1). Even here, their extent is not easy to gauge as many of the areas marked as fl oodplain forest have in fact been converted to areas of forestry on fl oodplains, without any of the characteristic dynamic features of a naturally functioning fl oodplain forest and frequently dominated by non-native species. For example, in Hungary, where fl ood control works have reduced the fl oodplain area across all river systems from 2.3 billion km2 to only 1500 km2, 40% of the remaining areas of fl oodplain forests have been converted to forestry plantations (Haraszthy, 2001).

6.2 POLICY-RELATED WINDOWS OF OPPORTUNITY

However, since the mid-1990s, shifts in policy content and style in the fi elds of fl ood protection, nature conservation and agriculture at European Union (EU) and national levels are creating ‘windows of opportunity’ for fl oodplain restoration (Table 6.1). In the fi eld of fl ood protection, recent major fl ood events in France, Germany and the United Kingdom, for instance, have accelerated the will-ingness of authorities to entertain catchment-oriented approaches and soft-engineering techniques of fl ood pro-

tection, creating new opportunities for fl oodplain restora-tion. The sheer cost of improving and maintaining physical fl ood defences, in particular in rural areas, is raising inter-est in alternative strategies. These alternative, integrated fl ood management strategies developed at the catchment scale are now considered viable to provide appropriate ‘standards of service’ in areas of particular risk, while also ensuring no net loss of ecosystem status, and even allow-ing enhancement of aquatic, riparian and fl oodplain envi-ronments. The evaluation of such strategies refl ects a policy shift from fl ood defence to fl ood risk management, and includes the possibility of increasing the frequency of fl ooding and reducing the standard of service in some fl oodplain locations where land is of relatively low value. This can have the effect of storing fl oodwater and attenuat-ing hydrograph peaks, reducing fl ood potential in down-stream high-value urban fl oodplains that are otherwise at risk.

Water protection agencies, concerned at water short-ages and motivated by the EU Water Framework Directive, are also showing increased interest in water fl ow regimes across whole catchments and in the potential of fl ood-plains to improve water quality as part of a policy shift from downstream protection to upstream river basin man-

Figure 6.1 Map of remaining European fl oodplain forests (based on data from UNEP – World Conservation Monitoring Centre in UNEP–WCMC, 2000 and Girel et al., 2003)

Uncertainty in Riparian and Floodplain Restoration 81

agement. For nature conservationists restored fl oodplains represent important habitats that can contribute to meeting biodiversity targets in accordance with the EU Habitats and Birds Directives; here the policy shift is in part from species protection to habitat enhancement. Political pres-sure is growing for more environmentally-sensitive forms of agriculture and forestry, creating new funding opportu-nities for extensive practices more suited to fl oodplain restoration, in a policy shift from agricultural support to integrated rural development. Agricultural policy focused on agri-environmental management means that fl ood-plains originally expensively drained and protected can now be considered sites for reinstating other functions whose relative values are perceived to have increased. Finally, land-use planning regulations are being modifi ed to offer more effective protection of existing fl oodplains and, in some instances, earmarking land for the future restoration of fl oodplains. Spanning these sectoral policy shifts is a trend towards greater policy integration and stakeholder participation over schemes of this kind (Pahl-Wostl, 2002, 2004), informed in part by debates on sus-tainable development and new forms of governance (Bressers and Kuks, 2003).

Managing these changes is a challenge for the respon-sible agencies, which must grapple with questions of multi-functionality, multiple and nested scales, cross-sectoral activity, policy interplay, actor collaboration and issues of considerable complexity. One institutional frame-work for managing the changes in values, and the associ-ated restoration of natural functions in fl oodplains, is provided by the Water Framework Directive in Europe, and by the tools developed for its implementation – River Basin Management Plans. In the United Kingdom, exist-ing tools for strategic fl ood management (such as Catch-ment Flood Management Plans) now need adaptation to allow for ecosystem enhancement, combining the Envi-

ronment Agency’s roles in relation to fl ood management and conservation (and implementation of the EU Habitats Directive). These tools will thus necessarily focus on multi-functional management across a catchment-reach-habitat scale hierarchy, which will imply a variation in the level of detailed planning at different scales. At the catch-ment scale, the planning is essentially a strategic assess-ment for managing priorities in relation to budgetary provision, while at the reach scale planning is focused on the implementation of specifi c policies. This has implica-tions for the meaning of uncertainty, since qualitative general goals set at the catchment strategic scale may allow greater fl exibility, while more restrictive quantitative goals may be set at the reach scale. However, underlying this is the governance requirement for stakeholder involve-ment, exposing the question of the accountability, in dem-ocratic societies, of those institutions responsible for goal-setting in relation to restoration initiatives. This intro-duces another level of uncertainty.

Decision makers must thus manage complexity and uncertainty, and cope in an adaptive manner with unin-tended negative effects which emerge alongside the advan-tages derived from the policies they enact and implement. In the context of fl oodplain restoration, they may seek to identify ‘reference’ conditions towards which the Water Framework Directive encourages a shift; however, there is considerable uncertainty in the defi nition of such refer-ence, or target, conditions. This ‘goal’ of a restoration initiative may suggest identifi cation of a set of perfor-mance indicators, but here again there is uncertainty. If the strategic goal of restoration is to recover the dynamics of natural process and function (by letting the river do the work again), the performance criteria are very different, and necessarily less rigid given the time-variability of fl ows, than if the goal is set as a restoration of a specifi c (static) form.

Table 6.1 Recent policy shifts conducive to fl oodplain restoration

Policy fi eld Forces for change Policy issues

Flood protection Flooding events; climate change; infrastructure costs; environmental quality

Risk management, soft engineering techniques, natural fl ood storage

Water protection EU Water Framework Directive; water quality/quantity problems

Catchment-oriented approaches, fl ow regimes, wetlands, geomorphology

Nature conservation EU Habitats Directive; concerns for biodiversity

Functional fl oodplain ecosystems

Land-use planning Linkage of fl ooding events to land use Planning mechanisms for protecting and creating areas for fl ood retention

Rural development EU Rural Development Regulation; spatial disparities

Integrated approaches to rural economic development

Agriculture Agenda 2000; public health concerns; environmental degradation

Improved agri-environmental schemes


There has thus been a growing interest in restoration of riparian and fl oodplain ecosystems, driven by increasing knowledge of the biophysical linkages between parts of river basins and, in Europe, by shifting policy directions and an increased frequency and severity of fl ood events. Awareness of the potential impacts of global climate change and of the impacts of river management activities such as dam building on hydrological patterns in river basins (Montgomery and Boulton, 2003; World Commis-sion on Dams, 2000) have led to a broadening of approaches to fl ood management. Coupled with a wider acceptance of multi-functional fl oodplains and more integrated policy contexts for these, this has led to the proliferation of river restoration projects in many countries (Palmer et al., 2005). However, river restoration that involves the planned return of fl ooding and its associated geomorphological processes also reduces the predictability of river behaviour for river managers. The uncertainty associated with this develop-ment is the subject of this chapter, which continues with a consideration of the spatial and temporal dynamics that underpin the natural function of rivers and their riparian and fl oodplain zones.

6.3 THE NATURAL FUNCTIONAL DYNAMICS OF RIPARIAN ENVIRONMENTS

To achieve the goal of integrated management of fl ood-plains, including restoration of their physical and ecologi-cal functions, it is necessary to understand some of the key relationships that determine the health of the aquatic, riparian and fl oodplain ecosystems. This section accord-ingly briefl y reviews this understanding, focusing particu-larly on scale and spatial relationships within the drainage basin, and on the dynamics of inter-related processes that defi ne the functional status of rivers and their fl oodplains (Malanson, 1993).

6.3.1 Scale and Spatial Relationships: Longitudinal and Lateral

It is fi rst necessary to recognise that a river responds to conditions within the upstream catchment draining to it, and that the river corridor is therefore the terrestrial low point which receives water fl ows, sediments, nutrients and plant propagules from its contributing area (Brierly and Fryirs, 2000). Accordingly, the status of a river reach is dependent on its catchment environment. In a natural river reach, there will be a diversity of habitats – pools, riffl es, gravel bars, point bars, steep banks, levees, side channels and chutes, abandoned channels, ox-bow lakes, back-

swamps and fl oodplains. These habitats will be preferen-tially occupied by particular species of fauna and fl ora (see, for example, Marston et al., 1995; Richards et al., 2002). It is often the case that conservation focuses on particular species, but a key to general ecological health is the maintenance of habitats (Ward and Tockner, 2001). However, the habitats refl ect the behaviour of the river at the reach scale. For example, if a river meanders without constraint, and inundates its fl oodplain roughly once every 1–5 years, it is likely that it will create and maintain a natural diversity of habitat. Whether this behaviour occurs will depend on the way in which the catchment is occu-pied and managed, and delivers water and sediment to the reach in question. Thus the crucial connections to under-stand are those between the hydrology, sediment supply and ecology of the contributing catchment area, and the character of the river reach to which it drains. This results in a structure of longitudinal relationships and upstream–downstream connectivity, while lateral connections are also critical in linking the aquatic (river) and terrestrial (fl oodplain) environments across the riparian zone.

The ecological status of a river reach is strongly depen-dent on longitudinal (downstream) connectivity, and this is refl ected in the river continuum concept (Vannote et al., 1980; Petts et al., 2000). This is based on the idea that rivers transport water and sediment downstream through a systematic continuum of conditions from steep, head-water reaches with coarse bed material, and shallow, tumbling fl ow, in narrow valleys, to more gently-sloping lowland reaches with fi ne silty, sandy beds, deeper, slow fl ow and wide fl oodplains. Related to this, the ecology changes systematically from upstream to downstream reaches, and a continuum here refl ects the changing nature of biological processes. Upstream, woody debris is introduced from the riparian vegetation and is ‘processed’ by ‘shredders’ (invertebrates that consume leaves and woody material) in the stream, while also supplying nutri-ents and dissolved organic carbon. Further downstream, these products of upstream processes are used by other species, as when fi ne particulate organic matter is either collected or fi ltered by species which are ‘gatherers’. This implies that management which prevents upstream bio-logical functions will have downstream effects because of the continuous relationships existing along the river course. Similarly, managing the river by introducing dams and weirs will interrupt the continuity of migration of species by inhibiting upstream fi sh migration to spawning sites, and the downstream fl ow of both seeds and plant material from which vegetative reproduction may take place.

There may be some debate about the details of the ‘river continuum’; an alternative is the process domains


concept (Montgomery, 1999), which argues that local-scale geomorphological and biological processes deter-mine the stream habitat, the disturbance regimes and the species interactions that infl uence stream communities and biodiversity. However, for practical purposes, whether there are longitudinal zones, or a longitudinal continuum, is less important than that there are strong upstream–downstream relationships. Aquatic organisms have evolved to be adjusted to the most probable set of physical conditions arising from the fl uvial geomorphology and hydrology. Downstream reaches rely on carbon inputs from upstream, while middle reaches are sites of primary production (with clear water and less shading by vegetation).

Longitudinal relationsips are supplemented by lateral connections (Ward and Stanford, 1995). The high biodi-versity of river corridors is, in part, a refl ection of the diversity of habitat at the margin between two distinct ecosystems, those of the river and the fl oodplain. The riparian zone is an aquatic–terrestrial ecotone, and is a zone of transition and exchange which benefi ts from, and regulates, the processes and functions of the ecosystems it connects. Interference with one inevitably affects the other, and the ecotone is itself a high priority for conserva-tion. When the channel is deepened and dredged, or when embankments are created, the lateral connectivity between river and fl oodplain is disrupted. In some cases this restricts access to lateral channels, side-arms and ox-bow lakes, which convert to terrestrial status. The bi-direc-tional connection between the river and groundwater is inhibited and fl oodplain recharge is restricted. The poten-tial for fi sh species to use fl oodplain woodland as a fl ood-period refuge is prevented and the fi sh population suffers. Bank-side vegetation provides shade, which regulates water temperature, and tree roots a diversity of micro-habitats, which benefi t the aquatic fauna; it also supplies organic material to the stream. Removal of the vegetation removes these functions and causes a loss of productivity, habitat and biodiversity.

6.3.2 Dynamics: Variable Flows, Sediment Delivery and Channel Migration

Aquatic and riparian species are adapted to and require variable fl ows (including overbank fl ows), and are sensi-tive to the timing of that variation. They also require dif-ferent fl ows at various stages of their life cycles. For example, salmonidae require a stable gravel substrate when spawning, but at other times depend on fl ows which dilate the gravel and fl ush out fi ne sediment that has accumulated in the pore spaces. The preferred water

depth, velocity and substrate for a given species will also change between the fry, juvenile and adult life stages, and this requires that there is access to spatial variation of habitat, which provides suitable refuge locations during high fl ows for individuals which cannot survive extreme conditions.

Some riparian tree species, such as black poplar (Populus nigra) and willows, require occasional large fl oods to create new sedimentary surfaces for colonisation and regeneration, but also need a gradual recession during the period of seedling establishment for their sur-vival. The timing of high fl ow needs to match the release of seeds in late spring and early summer; this is an issue in river basins with reservoirs or water transfers that reduce fl ows and change the natural timing of high fl ows. The role of high fl ows is represented in the fl ood pulse concept (Junk et al., 1989; Middleton, 1999), which emphasises that fl oods cause channel migration and create new habitat, and supply nutrients and genetic material (seeds and plant material) to the riparian environment. However, these processes can occur across a range of fl ows, so a general idea of a range of fl ow pulses (Tockner et al., 2000) is also found in the literature on riparian ecology.

Of course, fl ows that are too high may both cause damage and create anaerobic conditions which prevent germination and cause mortality in early seed-lings. This illustrates that the process of managing fl ows for ecological benefi ts requires a subtle understand-ing of the impacts of fl ows on the life histories of a range of species. This has given rise to the concept of ‘environmental fl ows’, which include a wide range of fl ow levels at different times, and with different recurrences, each of which contributes to regeneration and to different life-cycle stages of different aspects of the aquatic and riparian ecology (Hill and Platts, 1991; Mahoney and Rood, 1998). The management of such variable fl ows, seasonally and inter-annually, is a complex and uncertain process demanding an adaptive response to the unfolding of the unpredictable climatically-driven fl ow regime. However, it is a process which is now occurring in many climatic regions and is considered further in the following section.

An important characteristic of natural, unmanaged rivers is that they migrate across their fl oodplains at rates that depend on the energy of their fl ows and the resistance of their perimeter sediments to entrainment, erosion and transport. This means that they gradually turn over their fl oodplain sediments, and that the fl oodplain consists of a mosaic of surfaces of varying age and sedimentology giving rise to a mosaic of diverse habitats (Salo et al., 1986; Nilsson et al., 1991b).


The most well-known form of such migration is that of river meandering, which arises because of bank erosion on the outside of bends and the deposition of point bars on the insides. This process is what creates the diversity of habitat and is therefore very important for biodiversity. It is commonly understood that disturbance of ecosystems is critical for the structuring and maintenance of biodiver-sity, as it stops a plant succession from progressing to a uniform mature state and continually resets the succession locally and re-introduces pioneer species. In some ecosys-tems, disturbance may be caused by wind, fi re or human infl uences (in shifting cultivation, for example). In river corridors, it is caused by erosion, deposition and river migration. The intermediate disturbance hypothesis (Connell, 1978; Ward et al., 1999) argues that maximum biodiversity occurs under conditions of intermediate dis-turbance. Too much disturbance (as in very dynamic braided rivers) results in a preponderance of pioneer species, while too little disturbance allows a succession to progress towards a mature and relatively uniform plant community. Both have lower biodiversity than occurs with rates of disturbance that are intermediate. If in river corridors it is high fl ows, erosion, deposition and channel migration (the river dynamics) that promote biodiversity, it follows that practices of river management designed to inhibit movement of the river (such as embankment and bank protection) are likely to reduce biodiversity. Ecologi-cal status will improve therefore when rivers are given the freedom to move. This is a concept which is now enshrined in policy for the management of the tributaries of the Rhine and the Meuse in The Netherlands, and involves relocation of embankments, increased off-channel fl ood storage and restoration of the functions of abandoned channels and side-arms.

From this outline it is evident that the characteristics of the natural biophysical system of the river-fl oodplain environment are connectivity, spatial variability and tem-poral instability (dynamics). It is generally acknowledged that restoration of these characteristics is a necessary com-ponent of any restoration initiative although the scale at which they can be restored is very variable. The uncer-tainty posed by restoring the very characteristics that were removed in order to make rivers more manageable and by extension less uncertain in their behaviour also needs to be accommodated (see also Gregory and Downs, Chapter 13). This is as much a question of changing attitudes (personal, public and institutional) through education as it is a question of understanding more about the biophysical processes, although it remains the case that such under-standing is highly uncertain when it requires prediction of future behaviour at a specifi c location within a river system (e.g. Chapter 5).

6.4 THE SCALE AND PRACTICE OF RESTORATION

The practice of river restoration is informed by the con-tinuing scientifi c investigation of the vital links, at differ-ent spatial and temporal scales, between biotic and abiotic components across a catchment. However, it is also limited by competing needs for resources within a catchment. The net result in many river basins is that river restoration takes place at relatively small, confi ned sites where re-instatement of the links between the river and its fl ood-plain is limited in degree and extent and primarily focussed on re-establishing lateral connectivity. It is important to distinguish between river restoration that takes place at this spatially limited scale of the reach or section of a reach and river restoration that takes into consideration the longitudinal linkages within a river basin and manages the primary inputs of water and sediment across the whole catchment. There are many thousands of river restoration projects worldwide that have been implemented at the fi rst scale but far fewer examples of resource management taking place at the scale of the catchment (see Chapter 13). At both scales there is a considerable challenge for scien-tists to defi ne ecosystem needs in a way that can guide policy formulation and management action (Poff et al., 2003). However, the challenge is considerably greater at the scale of the catchment because levels of uncertainty about the ecological and other outcomes and the number and range of stakeholders that need to engage with the process increase rapidly as spatial scale increases. Fur-thermore, until quite recently, the water needs of humans and those of ecosystems have been seen in competition (Richter et al., 2003) and the quantum leap in human perception from this to viewing ecosystems as legitimate users of water (King and Louwe, 1998; Naiman et al., 2002) has largely still to be made.

To illustrate the issues and practice of working at different scales, the example of the needs of fl oodplain forest ecosystems and the different scales at which those needs can be provided is considered here. There are many inter-related variables operating in a fully functional fl ood-plain forest ecosystem but, nevertheless, their diverse, mobile vegetation mosaics can be said to have four essen-tial requirements to be self-regenerating (Table 6.2). To get all of these it is necessary to manage the disturbance processes that arrive at a fl oodplain and this can be done in a number of ways at both the reach and catchment scales.

6.4.1 Catchment-Scale Management

At the scale of the catchment, restoration initiatives are likely to involve management of physical processes in one


or more places in the catchment upstream of the fl ood-plain, so that they eventually have an effect on the distur-bances arriving in the fl oodplain zone. This type of disturbance management is ‘indirect’ but it is also the most desirable for long term successful and self-sustaining restoration or management of fl oodplain forests. It allows the river to fl ood and the channel to move and create its own sites for the regeneration of trees. It is, however, quite diffi cult to achieve for a variety of reasons:

• It is not easy to predict where disturbances will have an infl uence in the fl oodplain zone and it is therefore an uncertain form of management.

• Unpredictability and reduced control have not been desirable features for river managers and as such they tend to be avoided.

• In highly managed and fragmented river systems there may be too many human interventions for it to work.

• It requires consensus among a huge number of stakeholders.

The ways in which this catchment-scale management of water and sediment resources takes place is varied but there are now a number of both established and emerging methodologies which have been put into prac-tice, many of which are reviewed more fully elsewhere (Arthington, 1998; Hughes and Rood, 2003; Postel and Richter, 2003):

• Managed releases downstream from impoundments. This methodology is relatively simple in concept and involves planning fl ow releases from structures such as dams so that they provide maximum benefi t to down-stream aquatic and riparian ecosystems as well as to other users. In practice this can only be contemplated where the engineering design of the dam structure gives

Table 6.2 The four essential requirements for a self-regenerating fl oodplain forest (from Hughes and Muller, 2003; Hughes et al., 2005)

Requirement Rationale

Flows needed by fl oodplain forests • Regular fl ows which replenish and maintain fl oodplain water tables. These fl ows allow established trees to grow.

• Periodic high fl ows which cause channel movement and sediment deposition. These provide potential regeneration sites and should be variable between years.

• Well-timed fl ows through the fi rst growing season which allow delivery of seeds to the fl oodplain and establishment of seedlings. Unseasonal high fl ows can cause high mortality to seedlings in their fi rst growing season.

Regeneration sites needed by fl oodplain forests • Open sites as many pioneer tree species typical of fl oodplain forests cannot tolerate competition.

• Sites that are moist through the fi rst growing season to facilitate regeneration.

• Sites near the water’s edge because these tend to be moister and catch organic debris. However, sites right on the water’s edge tend to suffer from fl ow disturbance and waterlogging.

• A variety of sediment types to provide regeneration niches for a variety of species.

Water table conditions needed by fl oodplain forests • Water tables accessible to the roots of seedlings through their fi rst growing season.

• Gradual recession of water tables following a fl ood.• Limited waterlogging.

Propagation materials needed by fl oodplain forests • Seeds which are carried by the river and deposited during fl oods. The phenology of seed release and the timing of fl ood peaks are critical in any year for successful establishment of seedlings.

• Vegetative material which arrives by fl ood or is deposited locally.• Seeds that are carried in the wind. Whereas seeds carried in the river

always move from upstream areas to downstream areas, seeds carried in the wind tend to move in the direction of prevailing winds.


suffi cient control to release fl ows. The approach has been well-tried in North America and particularly good examples can be found in the St Mary River in Alberta, Canada (Rood and Mahoney, 2000) (Case Study 6.1) and in the Truckee River of Nevada, USA (Rood et al., 2003). Both examples use the Recruitment Box Model of Mahoney and Rood (1998) which allows quantitative description of the combined requirement for appropri-ately timed high fl ows to create and saturate suitable fl oodplain sites downstream and subsequent gradual fl ow recession (ramping rates) to permit seedling sur-vival. Detailed knowledge of the requirements of the germination and seedling establishment phases of the life-cycles of target tree species have to be known and ecologically relevant fl ows have to be characterised. There is considerable debate on how best to characterise the most ecologically critical aspects of fl ow regimes (Olden and Poff, 2003) such that an optimal balance is struck between a minimum of hydrological indices and maximum explanation of ecosystem function by these indices. The process becomes more diffi cult as projects go beyond prescribing fl ows for a single ecosystem, like

Case Study 6.1

The ‘Recruitment Box’ model developed by Mahoney and Rood (1998) delineates a zone on a fl oodplain, defi ned by elevation and time, in which riparian cot-tonwood seedlings are likely to become successfully established if streamfl ow patterns are favourable (Figure 6.2). Along the St Mary River in Alberta, Canada, fl ow regulation from a headwater dam built in 1953 led to high mortality of established cottonwood (Populus deltoides) trees and no recruitment of new trees in the fl oodplain downstream due to insuffi cient fl ows at critical times in the growing season (Rood et al., 1995) (Figure 6.3). During the 1990s, after iden-tifi cation of the cause of the problem, regional water resource managers implemented changes in the opera-tion of the St Mary Dam (Rood and Mahoney, 2000) (Figure 6.4). In particular, fl ows were designed to provide a gradual reduction in the falling limb of the hydrograph after the spring snow-melt fl ood rather than the abrupt fall that occurred during the post-dam period. This occurred because the dam operators shut the spillway gates at the dam to divert water into irriga-tion channels. The gradual recession of fl oods was considered vital for replenishment of water tables in the fl oodplain zone and for the establishment of cot-tonwood seedlings whose roots are unable to maintain contact with the water table if it falls too rapidly.

Figure 6.2 The recruitment Box model applied to the lower St Mary River (modifi ed from Mahoney and Rood, 1998)

(a)

(b)

Figure 6.3 (a) A photograph of the St Mary River fl oodplain taken in 1991 shows dead cottonwoodtrees before successful fl ood releases were implemented (Photograph by Francine Hughes). (b) (See also colour plate section) By 2002 there is signifi cant growth of young cottonwood trees on the fl oodplain following planned releases (Photograph by Stewart Rood)


not cause affected ecosystems to degrade or simplify’. There is necessarily a limit to how much water can be taken out of a river, and at what times of the year, if an ecosystem like a fl oodplain forest is to remain self-sustainable. The question of how to allocate water to achieve sustainable water use for a range of ecosystems and human purposes is addressed by a series of holistic fl ow allocation methodologies, many of which have been developed in semi-arid areas in Australia, South Africa and North America. Exampes of these models include the Bench Marking Methodology (Arthington, 1998; Brizga, 2000) and the Flow Restoration Method-ology (Arthington et al., 2000) in Australia; The Build-ing Block Methodology (King and Louwe, 1998) and the DRIFT methodology (King et al., 2003; Brown and Joubert, 2003) in South Africa. Closely allied to these models is the Adaptive Water Management Framework for initiating ecologically sustainable water manage-ment programmes in the United States and elsewhere in the world by Richter et al. (2003). The general approach of these methodologies is to determine the ‘environmental fl ows’ necessary to sustain aquatic and riparian ecosystems and then to integrate the identifi ed fl ow needs with other fl ow requirements within the river basin on seasonal, inter-annual and even decaded time-scales. They are multi-stage, consensual or round-table approaches requiring the input of many experts and data on a range of aquatic and riparian ecosystems, on hydro-logical and geomorphological aspects, on modelling of relationships between hydrological and biological attri-butes and eventually on all the other water uses in the river basin. It is at the stage when environmental fl ows are determined that fl oodplain forest requirements are included in the process. The applicability of these meth-odologies to the restoration of fl oodplain forests in Europe is discussed by Hughes and Rood (2003).

Another holistic approach to the management of water resources is the ‘alternative futures’ approach. This approach includes consideration of land uses across a catchment as well as fl ow allocation and among other places has been applied in the Willamette River Basin in Oregon, USA (Case Study 6.2).

Figure 6.4 The two pairs of graphs depict a stage hydrograph with superimposed recruitment box at the ‘ideal’ time and eleva-tion and with ideal drawdown rates. (a) In 1964, a post-dam fl ood was managed for maximal cut-back rather than naturalised reces-sion. Regeneration of trees did not occur in that year. (b) In 1995, a managed fl ow, using the recruitment box as a guide, success-fully promoted regeneration with a well-timed fl ood peak and suitable fl ood recession rates through the fi rst growing season (from Rood and Mahoney, 2000)

a fl oodplain forest, into satisfying multiple ecosystem needs.

• Flow allocation methodologies. River fl ows are altered as soon as water is used for human purposes. Richter et al. (2003) state that ‘the ultimate challenge of ecologi-cally sustainable water management is to design and implement a water management program that stores and diverts water for human purposes in a manner that does

Case Study 6.2

In the Willamette Basin, in western Oregon, USA, an alternative futures analysis has been carried out to inform com-munity decision making regarding land and water use in the river basin. This is a participatory approach to river basin planning that presents stakeholders with a range of alternative scenarios involving higher or lower levels of land and water use in the basin and their resultant environmental impacts. In the Willamette Basin, the current and historical landscapes were analysed and then three future scenarios were generated, refl ecting varying assumptions about land


Figure 6.5 Percentage change in the ‘forested riparian’ indicator of natural resource condition in the Willamette River Basin, in the historical and three future scenarios. The indicator is the percentage of a 120-metre wide riparian buffer strip with forest vegetation along all streams in the valley ecoregion (Hulse et al., 2002). The future scenario labels are described as: Conservative – a low level of development involving a high degree of natural resource protection; Plan Trend – a level of development consistent with current policies and trends; Development – refl ects a loosening of current policies to allow a freer rein to market forces across all landscape components but still within the range of what stakeholders considered plausible (From Ecological Application (2004) EA14-2, pp. 313–324, fi gure 4. Reprinted with permission from The Ecological Society of America.)

and water use. Historical data on land and water use and population levels dating from 1850 were used and scenarios were projected to 2050 (Baker et al., 2004). Scenarios were evaluated on four areas of resource endpoints that were considered to be of value to stakeholders: water availability; river attributes such as channel structure, riparian and instream ecosystem richness; ecological condition of streams (using fi sh and benthic invertebrates as indicators); and terrestrial wildlife in the basin (Dole and Niemi, 2004) (Figure 6.5).

The fl oodplain of the Willamette River has been studied specifi cally with regard to prioritising parts of the his-torical fl oodplain that might be particularly suitable for restoration using a landscape modelling approach (Hulse and Gregory, 2004). This categorises the fl oodplain into areas of high and low potential for a range of both biophysi-cal properties and socio-economic constraints considered at three spatial scales: the river network, the reach and the focal area. Scale was considered important because interactions between bio-physical and socio-economic factors and with them priorities, change at different spatial scales. Biophysical factors included characteristics like channel complexity and hydrology and fl oodplain vegetation type. Socio-economic constraints included factors such as private or state ownership of fl oodplain land and population density. The units used for applying these categories are ‘slices’ of fl oodplain at right angles to fl ow, each one kilometre long. Sections of the river with low constraints and high opportunity were identifi ed using this process and enabled prioritisation of candidate river and fl oodplain restoration sites. The results are presented as maps with marked areas that integrate representation of processes with patterns (Hulse and Gregory, 2004) (Figure 6.6). This methodology was used in the generation of scenarios for the alternative futures analysis in the Willamette Basin and particularly in assessment of the sensitivity of endpoints in the river valley (Baker et al., 2004).


Figure 6.6 Graphical example of river reaches (1, 2 and 3) with coincident low constraint and high opportunity to restore channel complexity and native fl oodplain forest. The units of (a) Population density, are people per square kilometre circa 1990 within each one kilometre slice of the fl oodplain. The units of (b), number of structures, are rural buildings per square kilometre circa 1990 within each one kilometre slice of the fl oodplain. The units of (c), loss of channel complexity, are net increase or decrease in channel length within each one kilometre slice of the fl oodplain between 1850 and 1995. The units of (d), loss of fl oodplain forest, are net decrease in area of fl oodplain forest within each one kilometre slice of the fl oodplain between 1850 and 1990. (From Hulse & Gregory (2004) Urban Ecosystems 7 (3): 295–314. Reprinted with kind permission from Springer Science and Business Media.)


Although the number of river basins to which these emerging holistic methodologies have been applied is increasing, they remain the exception and not the rule. Richter and Postel (2004) give 350 as the number of river basins worldwide that are listed on a data base held by the United States’ Nature Conservancy and are optimistic that obstacles to implementing these approaches are increas-ingly being overcome. Very positive steps have been taken in Europe to apply more holistic approaches to river basin management through the European Union Water Frame-work Directive and its required River Basin Management Plans. However, management of total water volume and its seasonal distribution (currently carried out by most national river management agencies in Europe) tend to be limited to the management of water quantities in rivers to satisfy requirements for pollution dilution and the main-tenance of specifi ed minimum fl ows. This management is largely effected through control of water abstraction licences (Figure 6.7). It is usual for management of low fl ows to be separated from management of fl oods and this is an area that needs to be addressed to achieve a more holistic approach.

• Sediment management. While a huge amount of litera-ture has appeared on the management of fl ows, far less research has been carried out on the management of sediments and there are also few examples of projects that involve restoration of sediment loads in rivers. In the European context, sediment loads have drastically changed over the last 200 years in response to changes in mass movements in upper catchments, to sand and

gravel extraction in fl oodplain zones, to the installation of upstream impoundments and to the armouring of river banks with artifi cial dykes. In the Drôme River in France measures are proposed to restore sediment loads to the river. These include a moratorium on gravel extraction and clearance of river bank vegetation to re-mobilise sediment through bank erosion (Michelot, 1995). The aim is to improve the delivery of sediment to the Ramieres du Val de Drôme Nature Reserve, which is designated for its high quality fl oodplain forest in two active, braided river reaches.

6.4.2 Reach-Scale Management

At the scale of the reach, there are two main approaches to carrying out river and riparian restoration. The fi rst involves managing physical processes locally in the fl ood-plain zone, for example by introducing sluices into side channels to control water levels in selected parts of a fl oodplain. The second involves managing the landforms in the fl oodplain so that the physical disturbances act dif-ferently on each part of the fl oodplain. Both types of dis-turbance management are more directed to specifi ed reaches of a river than managing fl ows of water and sedi-ment at a catchment scale. However, they have the disad-vantage that they can only have a relatively local effect. In most parts of Europe it is the type of management that can most readily be promoted and in many river basins it has already taken place through a number of river restoration projects. Restoration at this scale is easier to achieve than catchment-scale management for a variety of reasons:

Figure 6.7 A typical environmental allocation for a UK catchment is shown in this graph (from Environment Agency, 2002). It varies considerably through the seasons and is determined through a technical approach called the Resource Assessment and Manage-ment (RAM) framework. First each river reach in the catchment is assessed on the basis of its physical characteristics, fi sh populations, macrophytes and macro-invertebrates to produce an environmental weighting. Five environmental weighting bands are used to class-sify the sensitivity of each reach to the effects of water abstraction. The environmental weighting is used with long term fl ow duration data to derive an Ecological Flow Objective and the percentage left for abstraction. The Ecological Flow Objective seeks to protect low fl ows and fl ow variability by allowing percentages of fl ow bands to be abstracted. The impacts of groundwater abstraction (both seasonally and spatially) on river fl ows is also incorporated into the process. To preserve water levels in fl oodplain sites adjacent to rivers, a site-scale approach has been used in parts of the UK using Water Level Management Plans. These are usually applied to sites designated as wetland nature reserves whose water tables are related to those of adjacent river courses in either a direct or indirect way


• It is more predictable than catchment-scale management of disturbances and as such is relatively easy to control and poses less uncertainties.

• River managers are already used to managing rivers at a local scale through the engineering of fl ood defences.

• It can neatly be fi tted into chosen river sections between reaches where other human interventions dominate.

• It requires consensus among a much smaller group of stakeholders.

Exampes of restoration at this scale are now common and can be categorised into a series of activities which may be carried out singly or in combination at any individual site (Hughes and Muller, 2003):

• Restoration of the channel wetted perimeter. These proj-ects have as a main aim the improvement of instream habitats, often for fi sh populations. They emphasise increasing the heterogeneity of physical habitat. For example, the Ecological recovery of the Vindel and Pitte Rivers (EVP) project, in northern Sweden involves removal of instream structures installed during the nine-teenth century for driving logs for the timber industry. In addition, large boulders that were removed for smooth fl oating of log rafts are being put back to diversify instream habitats (Nilsson et al., 2005).

• Re-connection of side arms in rivers. Most projects in this category aim to remove one or more sections of artifi cial embankments to allow fl ows to penetrate fl ood-plain zones that have been cut off. There are a number of examples of this approach that are well documented in the literature, such as the Regelsbrunner Au project on the River Danube in Austria (Scheimer et al., 1999) or the L’Ile de la Platière and Rosillon Channel sites on the River Rhône in France (Michelot, 1995; Downs et al., 2002). These projects had as a major objective the restoration of both the quality of fl oodplain forests and improved opportunites for forest regeneration by increasing lateral connectivities between the channel and the fl oodplain.

• Increase in fl oodplain storage capacity through setting back defences, lowering fl ood defences or lowering the fl oodplain. There are major and well-documented plans for a range of these activities in the distributaries of the River Rhine in The Netherlands and also in the River Meuse. They form part of a master plan to increase fl ood storage capacity in these rivers in anticipation of sea-level rise and increasing frequency of fl oods from upstream (Middelkoop and van Haselen, 1999). These activities are currently restricted to a series of discrete sites, such as at the Millingerwaard Nature Reserve in

The Netherlands where fl ood defences have been lowered. The general approach in The Netherlands is shown in Figure 6.8. In the United Kingdom fl ood storage washlands are proposed along some rivers but in all these projects the design of drainage dykes and control structures will be very important in determing the amount of control on the length of time and depth that water stays in the washland. Creative confi guration of the fl oodplain surface can mimic natural fl oodplain habitat heterogeneity to give conservation gains as well as fl ood storage gains if water control is fl exible enough to operate for the good of wetland ecosystems as well as for fl ood control.

• Management of the river’s sediment load. Sedimentation is an essential process along the margins of river chan-nels as newly created alluvial bars are prime regenera-tion sites for many species of fl oodplain vegetation. Groynes can be used to create artifi cial ‘beaches’ although their primary purpose is usually to maintain a channel for navigation. Re-activation of erosion in sites where embankments have been removed will alter the sediment loads downstream.

• Management of riparian vegetation. This can take the form of planting fl oodplain forests or waiting for natural regeneration to take place. In either case, management of the vegetation that grows may be considered neces-sary, though this is not in the spirit of self-sustainability. In many river basins, grazing by domestic animals in riparian zones prevents natural regeneration and fencing

Figure 6.8 In The Netherlands, large parts of the Rhine fl ood-plain will be lowered as part of the Flood Action Plans drawn up by the International Rhine Committee. The aim is to increase fl ood storage capacity of the Rhine fl oodplains. The proposed works are shown schematically in this diagram (from Middelkoop and Van Haselen, 1999)


off the riparian zone becomes an important form of management. Management of the vegetation also has implications for the volume of woody debris that arrives in a river. MacNally et al. (2002) suggest that in the Murray-Darling River system in Australia only 15% of the former woody debris load is now present. There are projects where woody debris has been put back into rivers and some where artifi cial log jams have been built to test their restoration effect (Dewberry et al., 1998).

6.4.3 The Use of Reference Systems in River Restoration Projects

It is common to set objectives for river restoration projects by using reference systems. In the EU Water Framework Directive (2000/60/EC) it is a major and perhaps unreal-istic aim to use a network of near-natural reference systems so that the ecological status of rivers can be measured against them. In essence this involves fi nding a river system that has the attributes considered desirable in the restored system and using it as a template on which to base the restoration activities. Criteria specifi cally for establish-ing riparian reference conditions are proposed by Harris (1999) using multivariate analyses of aspects of vegetation community composition and structure. Typically a refer-ence system will be part of or the whole of a less-damaged river system, preferably located in a similar bioclimatic zone and in a river basin exhibiting similar physiographic characteristics. Alternatively it can be an historic system, whose attributes are known from maps, old photographs or written accounts. A critique of the use of reference systems to defi ne objectives in river restoration is given in Hughes et al. (2005), where the following categories of problems encountered with the use of reference systems are discussed:

• There are often no appropriate reference systems to use.

• Many catchment parameters have changed since the times of chosen historic reference systems.

• Climate change has been continuous through the Holocene.

• Projected climate change is of uncertain magnitude.• Alien species have become common in the landscape

and cannot be avoided.• Landscape context changes through time.

Using reference systems can give river managers a mis-placed confi dence in the predictability of ecological out-comes in river restoration projects (Hughes et al., 2005), although the degree to which the project outcomes and the reference system coincide can largely be decided at the

outset when objectives are set (Simons and Boeters, 1998). Evaluation of restoration projects is also usually carried out against the reference system, although with highly variable levels of rigour (Anderson and Dugger, 1998; Stream Corridor Working Group, 1998).

Nevertheless, reference systems can provide useful, broad guiding images for restoration. In the United Kingdom, a number of sources of information are used. At the catchment scale, the National Vegetation Classifi ca-tion (Rodwell, 1991a, 1991b, 1992, 1995, 2000), The National Biodiversity Network (NBN), the Multi-Agency Geographic Information for the Countryside (MAGIC) databases on Habitats and Sites of Special Scientifi c inter-est (SSSI’s), provide information on the distribution of habitats, communities and species across the United Kingdom. For river reaches, there may be River Morphol-ogy, River Habitat and River Corridor Surveys, and Hydro-Morphology Quality Assessments that have already been undertaken in some relatively undisturbed reaches (RSPB, NRA, RSNC, 1994). Using data on the catchment area, slope and river corridor width of the reaches, it may be possible to extrapolate to other river reaches with similar characteristics. Overall, the aim should be to develop a sense of how the spatial structure of the catch-ment hydrology and river morphology and ecology are related, and how the catchment and river function under ‘natural’ conditions, bearing in mind that any system is in a transient state over time.

6.5 MONITORING AND EVALUATING RESTORATION

6.5.1 What is Ecological Success and How Do We Evaluate It?

A widely applicable scheme for evaluating the ecological success of river restoration projects does not currently exist, though many authors have emphasised the need for such a scheme (NRC, 1992; Downs et al., 2002). Part of the problem is in defi ning exactly what is meant by eco-logical success and recently an attempt has been made to identify fi ve criteria that can be used for its measurement (Palmer et al., 2005):

1. The design of an ecological river restoration project should be based on a specifi ed guiding image of a more dynamic, healthy river that could exist at the site.

2. The river’s ecological condition must be measurably improved.

3. The river system must be more self-sustaining and resilient to external perturbations so that only minimal follow-up maintenance is needed.


4. During the construction phase, no lasting harm is infl icted on the ecosystem.

5. Both pre- and post-construction ecological assessment is carried out and the information made available.

Refi nements to this scheme have been proposed by Jansson et al. (2005) and Gillilan et al. (2005), including the need to include stated ecological mechanisms by which any intended restoration will reach its goal. A major diffi culty with this scheme is giving practical meaning to the concepts of resiliency and self-sustainability, princi-pally because these are entirely relative terms that are also closely linked to the concept of dynamic equilibrium.

The term ‘resilience’ was fi rst coined in the 1970s in the fi eld of population ecology and used to characterise the magnitude of population perturbations a system could tolerate before changing into some qualitatively different dynamic state (Holling, 1973; May, 1976a). More recently it has been used to describe the ability of an ecosystem to regain a functional state following disturbance and the rapidity of this process is a measure of its resilience (Waring, 1989). The disturbance is usually temporary and if it is suffi ciently regular, component species of the eco-system often evolve a dependence on it to complete their life cycles. Both ecological and evolutionary time scales must be considered in assessing the signifi cance of distur-bances of different magnitudes and frequencies in river and riparian ecosystems (Poff, 1992; Hughes, 1994; Dodds et al., 2004). As described earlier in this chapter, in the case of riparian and fl oodplain ecosystems, the distur-bance is provided by fl oods and many riparian species have indeed evolved a dependence on these fl oods for the regeneration phase of their life cycles. ‘Dynamic equilib-rium’ encompasses the notion of changing parameters within a stable framework and has often been used in descriptions of whole ecosystems. If the stable framework changes then a qualitatively different dynamic state again prevails. The defi nition of a stable framework is usually determined by the temporal and spatial scales of an inves-tigation and often limited by an investigator’s consider-ation of change through time and over space. However, as stated by May (1976b), in the real world there are no fi xed parameter values; environmental parameters and interac-tions between organisms and between organisms and their environment are constantly fl uctuating. It follows that stable frameworks do not exist in the real world unless they are artifi cially delimited spatially, temporally or both.

Measuring resilience to evaluate how successful we have been in returning it to a riparian or fl oodplain eco-system during a river restoration project is very diffi cult to do. If a framework has been identifi ed to describe a

dynamic equilibrium, then within the defi ned framework it is possible to predict and measure recovery of a dis-turbed ecosystem. However, it is a huge challenge to predict the functions of ecosystems when they begin to fl uctuate along new trajectories caused by environmental change or include new species arrivals. Since all ecosys-tems, including restored ecosystems, are moving along fl uctuating trajectories, it follows that, conceptually, it is impossible to measure resilience and therefore to evaluate ecological success using this criterion (see Chapters 8 and 11 for further discussion). In practical terms it is possible to delineate a framework for measuring resilience though different types of ecologists might defi ne very different frameworks for the same restoration project. A fl oodplain forest ecologist might defi ne a framework where self-sustainability is measured in terms of turnover rates for fl oodplain habitats (102–103 years) related to fl ood return periods (Mahoney and Rood, 1998; Hughes and Rood, 2001). A fi sheries ecologist might assess self-sustainability in terms of provision of instream habitats that permit completion of fi sh life cycles over annual or 101 year time frames (e.g. Frissel and Nawa, 1992).

The evaluation of success using such measurements also changes with scale and is discussed with respect to fl oodplain forests by Hughes et al. (2005). When viewed at a small spatial scale (102 metres), habitat patches in the forest will change from year to year as a response to channel movement and species arrival, death or migration. At a broader spatial scale of a reach or whole fl oodplain, variability becomes less pronounced because the balance of different habitat patches remains more constant (Figures 6.9(a) and 6.9(b)), particularly over short time frames of 101 to 102 years (see Ward et al., 2002). However, over longer periods (103 years), this balance at the reach scale might shift in response to climate change, sea level change, isostatic uplift, change in availability of propagules or changes in the biophysical attributes of the catchment (Figure 6.9(c)). In this scenario, shifting geomorphologi-cal processes (for example from aggradation to down-cutting of the river valley) and changed channel patterns cause major changes to ecological vectors and patterns in the fl oodplain. Evaluation of success can only be relative to these changing frameworks or else within the context of a framework that has been held stable.

6.5.2 What is Ecological Quality and How Does it Relate to Ecological Success?

Closely related to the consideration of how to measure ecological success is the need to give meaning to the concept of ecological quality. Both concepts are dependent on the monitoring or surveillance of a series of physical


and biological parameters that are representative of eco-system function. Whereas the fi rst is tied to the evaluation of a restoration project, the second is descriptive of the state of an ecosystem. However, they are interrelated in functional terms and decisions on what to monitor to measure ecological quality will have a signifi cant impact on our ability to evaluate ecological success in river res-toration, since this will depend on pre- and post-project measurements of ecosystem-relevant parameters. In Europe, the Water Framework Directive (WFD) (2000/60/EC) provides a legislative framework for water policy and among other things aims to maintain and improve the aquatic environment through attention to both water quality and quantity. It requires that member states ensure good ‘chemical status’ and ‘ecological status’ of surface waters. Groundwaters must meet ‘good groundwater status’. There is no objective for riparian and fl oodplain ecosystems; however, the health of groundwater-dependent wetlands is an indicator of ‘good groundwater status’. ‘Ecological status’ is described as ‘an expression

of the quality of the structure and functioning of aquatic ecosystems associated with surface waters’ and member states are required to monitor this status. To do this there has to be clear understanding of what ‘ecological status’ means, and much debate about this has followed publica-tion of the Water Framework Directive (WFD) both at European Union and national levels.

The main descriptors of ecological quality for rivers are grouped into biological, hydrogeomorphological and physico-chemical elements (Table 6.3). Soon after adop-tion of the WFD (22nd December, 2000), the fi rst volume of Common Implementation Strategy (CIS) guidance was produced. Its main aim is to help member states share expertise on implementation of the technical aspects of the WFD, including achievement of good ecological status for surface waters. Annex V of the WFD includes normative defi nitions of the three ecological status categories (High, Good and Moderate). The levels at which these three cat-egories will be set is the subject of a major EU inter-calibration process that should have been completed in

Figure 6.9 At the scale of a whole fl oodplain, progressive or rapid changes can take place in the distribution of fl oodplain vegeta-tion communities. Thus in 6.9(a), t1 is biodiversity at an initial time period and consists of vegetation communities a and b, present in the proportion of 4a to 3b. In 6.9(b), t2 is biodiversity at a later time. Vegetation communities a and b are still present in the same balance but they are all in different locations following shifts in channel location. Over much longer time frames (or over rapid time frames following extreme events or human intervention), there may be changes in catchment parameters that alter the geomorphologi-cal patterns and hydrological activity of the river. In 6.9(c), the meandering river has become braided and a new vegetation community c has arrived. The biodiversity (t3) of fl oodplain vegetation communities has now changed (from Hughes et al., 2005)


2006, but which remains contentious and partial. This process is being carried out by the ‘European Centre for Ecological Water Quality and Intercalibration’ (EEWAI), which aims to compare the different national classifi cation systems for ecological status assessment. Two key parts of the process are identifi cation of reference conditions and establishment of monitoring protocols. It is already known that river hydromorphology will have to be restored (unless the river stretch is designated ‘heavily modifi ed’, i.e. restoration would adversely affect its function for navigation, fl ood protection or power generation) if it is preventing biological elements from achieving ‘good eco-logical status’ (Withrington, Natural England, personal communication).

Individual member states have to integrate implementa-tion of the WFD with their already established protocols for monitoring and evaluating the status of their environ-ment, species and habitats. In the United Kingdom, the statutory conservation agencies have recently introduced a system of ‘Common Standards Monitoring’ (CSM) for sites designated under national legislation and European directives. Site-based conservation is a signifi cant part of biodiversity and earth science conservation in the United Kingdom and evaluation of the effectiveness of measures put into place to achieve biodiversity conservation is the

main aim of the CSM process. The CSM guidance for monitoring rivers designated as important by national leg-islation (Sites of Special Scientifi c Interest (SSSIs) under the Wildlife and Countryside Act, 1981) recommends the use of fl uvial geomorphological audit to defi ne modifi ca-tions to rivers and identify options for river restoration (JNCC, 2004). Riparian zones are included in the CSM guidance for rivers, both in their own right as woodland, grassland or swamp communities and for the contribution they make to in-channel river communities. Flow levels and patterns are mostly assessed in terms of their impor-tance to instream species and habitats rather than to adja-cent terrestrial ecosystems. The functional attributes of riparian zones is considered through measurement of factors such as production of woody debris and leaf litter as is their intrinsic habitat value, for example bird, mammal or invertebrate habitat. Such monitoring schemes for des-ignated sites will be integrated within broader-scale, river basin wide monitoring of ecological quality, under the WFD, of both aquatic and groundwater-dependent terres-trial ecosystems.

The design and density of monitoring networks even-tually established on European rivers will determine how useful they are for also evaluating river restoration success. Numerous research projects funded by the

Table 6.3 Quality elements for the classifi cation of Ecological Status-Rivers (EU Water Framework Directive, 2000/60/EC, Annex V)

Quality Element Characteristics

Biological elements • Composition and abundance of aquatic fl ora• Composition and abundance of benthic invertebrate fauna• Composition, abundance and age structure of fi sh fauna

Hydrogeomorphological elements supporting the biological elements

• Hydrological regime – quantity and dynamics of water fl ow – connection to groundwater bodies• River continuity• Morphological conditions – river depth and width variation – structure and substrate of the river bed – structure of the riparian zone

Chemical and physico-chemical elements supporting the biological elements

• General – thermal conditions – oxygenation conditions – salinity – acidifi cation status – nutrient conditions• Specifi c pollutants – pollution by all priority substances identifi ed as being

discharged into the body of water – pollution by other substances identifi ed as being discharged in

signifi cant quantities into the body of water


European Commission are in the process of exploring monitoring protocols for particular groups of species, such as macroinvertebrates and fi sh, with a particular emphasis on their transferability, comparability and ability to indi-cate ecological quality. For example, the LIFE in UK Rivers Project (1998–2003) produced monitoring proto-cols for rivers with Callitricho-Batrachion vegetation and for 10 river-dependent species such as the otter (Lutra lutra). Others are concerned with defi nition of reference conditions, taking into account climate change (e.g. EUROLIMPACTS).

6.5.3 How Do We Value Biodiversity?

Most commonly, use of the term biodiversity indicates a measure of species numbers, although habitat diversity and genetic diversity are also vitally important (Heywood, 1995; Gopal and Junk, 2001). It is relatively straightfor-ward to measure species diversity compared with ecologi-cal and genetic diversity, although the signifi cance of changing biodiversity remains elusive in all cases. In riparian and fl oodplain habitats, levels of species diversity are related to both the ecotonal situation of these habitats and to their physical heterogeneity. Lateral connectivity between riparian habitats and channels and longitudinal connectivities between upper and lower parts of river basins all contribute to high levels of biodiversity (Tabac-chi et al., 1996). Less well understood are the vertical connectivities between groundwater zones and riparian zones but they contribute in complex ways to habitat quality in interstices of fl oodplain sediments (Lambs, 2004) and to diversity of organisms in the hyporheic zone (Gibert et al., 1997; Hancock et al., 2005).

Species composition on fl oodplains has changed over long and short time scales as shown by changing pollen profi les from peat deposits in fl oodplain zones (Godwin, 1941), responding to climate change and the progression of species through the landscape. In the last few centuries, many new or alien species have arrived on fl oodplains as a result of introductions and garden escapes. Because riparian and fl oodplain zones are physically highly dynamic, and are populated by species able to cope with habitat mobility, they provide excellent habitats for eco-logical pioneers, which many invasive species can be clas-sifi ed as (Planty-Tabacchi et al., 1996). These species have often out-competed native species because hydrological and geomorphological factors have changed and the species that have evolved their life cycles to fi t in with natural hydrological cycles are no longer favoured. In river restoration projects today, the presence of alien species can be viewed in several ways. They can either be eradi-cated by active processes, such as clearance, or they can

have conditions for their regeneration made diffi cult by altering physical inputs, such as fl ood timing to reduce their competitive edge. On the other hand, the number of alien species present in many river systems makes their complete eradication impossible, and acceptance of some of them as components of the ecosystems found in riparian zones is another approach. It is easier to justify this approach in view of predicted climate change, which will signifi cantly alter the range that many species can occupy (Jensen, 2004) and the hydrological patterns in river basins (Montgomery and Boulton, 2003) but in ways that are currently uncertain. It is certain that species assemblages in the landscape will change and perhaps have no present-day analogues and that an open-minded approach to acceptable community composition will have to be taken (Hughes et al., 2005).

6.6 INTERCONNECTIVITY AND BOUNDARY CROSSING: INSTITUTIONAL COMPLEXITIES OF RESTORING FLOODPLAINS

The task of restoring fl oodplains poses multi-dimensional challenges to policy makers and project managers alike. Involving essentially a reconfi guration of the interaction between a river and adjacent low-lying land, fl oodplain restoration has far-reaching implications for existing forms of water and land use. Floodplains provide multiple functions and services for humans as well as the natural environment. These can range from valuable artefacts for socio-economic reproduction, such as crops, timber, water or prime land for development, to less tangible but equally valuable functions, such as protection from fl ooding, attractive landscapes or opportunities for recreational pur-suits. The restoration of functional fl oodplains requires changes to existing activities on the site of the fl oodplain itself, but also – particularly in the case of larger schemes – along whole reaches of a river and even a whole catch-ment. On this wider scale it can signifi cantly infl uence, for instance, levels of fl ood protection, the navigability of a river reach or the viability of current farming practices. In this way, fl oodplain restoration affects a wide range of stakeholders and interests (Adams and Perrow, 1999; Adams et al., 2004; Turner et al., 2000; Adger and Luttrell, 2000), making it potentially highly controversial.

Behind these stakeholders and interests lie institu-tions – understood here as rule systems – designed to protect and provide a variety of private and public goods, ranging from commercial products to rights of access. For each of the policy fi elds affected by fl oodplain restoration – primarily water protection, fl ood defence, nature conser-vation, recreation, navigation, urban and rural develop-ment – complex institutional arrangements have been


designed and adapted over the years. Each institutional arrangement comprises a set of codifi ed norms (such as laws, regulations and contractual obligations), planning instruments and funding mechanisms, as well as stan-dardised procedures of operation, values and accepted practices of the relevant organised and individual actors. A scheme to restore a fl oodplain requires the successful enrolment of these institutions and organisations in such a way as to create a result acceptable to the principal stakeholders. This is a highly complex process, involving multiple uncertainties (on institutional constraints, see WWF, 2000).

Managing the interdependence of multiple functions, actors and institutions is, however, not the only major socio-political challenge of fl oodplain restoration. Consid-erable uncertainty is generated by the diverse spatial scales and time frames involved. As described above, restoring a fl oodplain requires consideration of the longitudinal con-nectivity of a fl oodplain to river uses both up- and down-stream as well as of the lateral connectivity to ways in which adjacent land is used (Adams and Perrow, 1999). Even on site, interventions generally cut across several functional and administrative boundaries. These can relate to the spatial remit of local landowners and farmers, plan-ning authorities, government agencies, protected areas or infrastructure networks (e.g. rail, roads, canals). Tempo-rally, functional fl oodplains are characterised by their dependence on fl ooding events which are, by their nature, periodic and unpredictable, and by signifi cant time lags between changes in biotic and abiotic systems (see above; Adams and Perrow, 1999). Socio-economically, too, the process of restoring a fl oodplain is framed by diverse time scales, ranging from the payback periods for investments in altered practices of agriculture and forestry to the elec-toral periods of key public authorities. Floodplain restora-tion is, therefore, not only highly complex but also highly unpredictable. The institutional challenge is further com-plicated by the fact that many institutional arrangements – in particular for nature conservation – exhibit a strong tendency to protect existing conditions rather than encour-age change, and those which do pursue change are gener-ally geared towards achieving specifi c targets rather than creating suitable frameworks for open-ended processes, as is required for functional fl oodplains.

6.6.1 Effective Institutions: The Search for Optimal Fit, Interplay and Scale

Our knowledge of institutions which can support – or obstruct – the protection of public goods such as water, fl ood defence and biodiversity has been developing rapidly over the past decade (cf. Breit et al., 2003). However, rela-

tively little is known about the institutional dimensions of fl oodplain restoration itself. Exceptions include studies of institutional constraints and complexities (Adams and Perrow, 1999), competing discourses of fl oodplain restora-tion (Adams et al., 2004), relevant European Union policies (WWF, 2000, 2004) and European case studies (e.g. Zöckler, 2000) and economic valuations of the functions and ser-vices provided by fl oodplains or wetlands (Gren et al., 1995; Turner et al., 2000; Adger and Luttrell, 2000).

The Science Plan of the Institutional Dimensions of Global Environmental Change Project (IDGEC) of the International Human Dimensions Programme (IHDP) offers useful analytical frameworks for conceptualising some of the essential institutional challenges of resource management in general and fl oodplain restoration in par-ticular (Young, 1999, 2002). It identifi es three generic factors infl uencing the effectiveness of environmental institutions: problems of fi t, problems of interplay and problems of scale.

The issue of fi t addresses the need to develop institu-tional arrangements that match the properties of the bio-geophysical systems they are designed to regulate. Fit can relate to a variety of ecosystem properties. The following are identifi ed in the IDGEC Science Plan: closed vs open systems; heterogeneity/homogeneity; interdependencies among subsystems; simplicity/complexity; productivity/metabolism; cyclicity/periodicity; resilience; equilibria; dynamics (Young, 1999, p. 47). Problems of spatial misfi t are a particularly common cause of institutional ineffec-tiveness. The territories covered by institutions rarely match those of biogeophysical systems, resulting in an inability of the institutions to internalise external effects (both positive and negative) effectively. The management of fl oodplains is fraught with boundary problems of this kind. Floodplain restoration not only works across a variety of physical spaces along the river and across the catchment, but also involves institutions and organisations from multiple policy fi elds – from nature conservation and fl ood defence to agriculture – each with their own spatial remits.

Interplay relates, by contrast, to interdependencies between different institutions. The assumption here is that the effectiveness of an institution depends not only on its inherent qualities but also on how well it builds on, and is connected to, the broader institutional context. Institu-tional interplay can be horizontal, between different policy fi elds, and vertical, between different levels of social organisation. A further distinction is made between func-tional linkages emanating from the properties of the insti-tutions involved and political linkages as expressions of deliberation (Young, 2002). Problems of interplay are very familiar to efforts to restore fl oodplains, which are often


confounded by the inability to bridge differences in the objectives, power structures and modes of action of the various key organisations. Horizontal interplay is compli-cated by the number of policy fi elds affected and vertical interplay by the increasing role of the European Union and catchment-scale approaches to fl oodplain management.

Problems of scale can be of a spatial and a temporal nature. Spatially the effectiveness of an institution depends on fi nding the appropriate level of social organisation for specifi c instruments and measures, taking consideration of the connectivity between scales and the needs this creates for multi-level and multi-directional forms of governance. Temporally the issue of scale is about managing the diverse time frames within which actors operate, institu-tions work, projects are implemented, ideas are generated etc. Here, too, the relevance to fl oodplain restoration is self-evident. Identifying the appropriate spatial scale for policy development, strategic guidance, operational man-agement, public participation and so on is of paramount importance. Similarly, actors operate according to very different time scales, some of which are rigid and predict-able, others much less so.

Research on problems of fi t, interplay and scale sug-gests that solutions are rarely straightforward. For instance, efforts to overcome problems of spatial fi t by institution-alising river basin management can create new misfi ts and disturb existing modes of interplay (Moss, 2003). Success would appear to be dependent less on attempting to reduce the given complexities and more on fi nding ways of accommodating complexity. It is argued here that a similar approach is required when dealing with uncertainty. Given that substantial uncertainties will continue to surround fl oodplain restoration despite advances in our knowledge of the physical, biological and socio-political systems, it makes sense to consider possible coping strategies. The remainder of this section addresses ways of coping with institutional uncertainty in fl oodplain restoration with an empirically based study of three different approaches.

6.6.2 Coping with Uncertainty I: Keeping Restoration Simple

The fi rst option for coping with uncertainty is to limit the scope and scale of restoration. This was particularly common of the earlier schemes to restore fl oodplains in Europe. Up until the late 1990s most fl oodplain restora-tion schemes were small-scale and site-based. They were typically single-issue projects, targeting environmental improvements as a rule. They involved only a small number of actors and policy instruments, often relying on a single source of funding for the physical interventions. It is generally true to say that these early generation res-

toration schemes were conducted largely in isolation from national or regional policy initiatives, whether for fl ood protection, biodiversity enhancement or rural develop-ment. Examples include the Rheinvorland-Süd project in Germany, a scheme to improve hydrological and ecologi-cal conditions by widening ducts and removing structures in a section of the Rhine fl oodplain near Rastatt, the Long Eau project in England, in which fl ood banks were set back for primarily environmental benefi ts, and the Bourret project in France, a scheme to reconnect an old arm to the River Garonne and restore an alluvial forest – again pri-marily for environmental benefi ts.

Being relatively unambitious and straightforward, schemes of this kind tend to avoid the most pressing prob-lems associated with high levels of uncertainty and com-plexity. They have succeeded in restoring fl oodplain habitats with limited resources and, in some cases, within a short period. With the benefi t of relatively straightfor-ward administrative procedures, organisational structures and funding mechanisms it has been demonstrated how fl oodplain ecosystems can be restored on a small scale. These schemes do, however, have several critical limita-tions. They rarely incorporate a catchment perspective on restoration, but concentrate on the site itself. Being pre-dominantly single-issue schemes, they regularly overlook potential benefi ts for other policy areas, such as fl ood protection, recreation or rural development. Little atten-tion is generally paid to cultivating support for the project in the wider policy making domain, scientifi c communi-ties or even in the local community. The performance of many such projects is rarely monitored or evaluated systematically.

In terms of resolving problems of fi t, interplay and scale it is possible to observe how schemes of this kind are ill-equipped to meet the principal institutional challenges to fl oodplain restoration set out above. Spatially, the small scale and site focus of the projects offer little opportunity to consider the catchment dimensions of fl ow regimes and biophysical connectivity beyond the immediate reach. Institutional interplay may be less diffi cult but only because the number of policy fi elds and organisations involved is kept small. Integrating the schemes into the development of the locality or region may well prove dif-fi cult at a later date for this reason. Problems of scale are reduced to site-based perspectives, missing valuable opportunities to explore multi-scalar solutions.

6.6.3 Coping with Uncertainty II: Embracing the Challenge of Open Outcomes

Following recent shifts in policies towards fl ood risk man-agement, integrated water resources management and


rural development, more favourable framework conditions are creating windows of opportunity for ambitious forms of fl oodplain restoration (see previously). In response to these institutional drivers, and also to a growing recogni-tion of the inadequacies of current ways of managing rivers, a new generation of fl oodplain restoration schemes is emerging which are of a quite different scope to those of the early to mid-1990s. These schemes deliberately set out to address some of the complex challenges to large-scale, integrated fl oodplain restoration. Distinctive fea-tures of the new generation schemes are their multiple objectives (covering, for instance, fl ood defence, biodiver-sity, rural development and water quality management), their wide actor engagement (including the relevant poli-cy fi elds, local authorities, non-government organisations and the general public), their use of various instruments from different policy fi elds (e.g. joint funding from fl ood defence and agri-environment budgets) and their interac-tion with policy making processes, serving for instance as pilot projects for national policy development. In addition, they often have a long term vision for the measures envis-aged and take a catchment – or at least large-scale – per-spective on the fl oodplain. Restoration sites are selected according to their suitability for the catchment and not primarily because they are available.

Examples include: the Lenzen project in Germany, a major scheme to set back fl ood banks along a length of the Elbe river allowing fl ooding primarily for nature con-servation, but also fl ood defence and regional develop-ment, benefi ts; the Parrett Catchment Project in England, an ongoing project to promote more sustainable tech-niques of fl ood management in the whole catchment serving multiple purposes (fl ood protection, water level regulation, biodiversity targets, rural development); and the La Bassée project in France, a planned, large-scale fl ood retention scheme on the Seine upstream of Paris with multiple benefi ts (fl ood protection, biodiversity, regional development). Since these new generation schemes were only launched from the late 1990s onwards and are all at very early stages of implementation it is at present impos-sible to judge their effectiveness. They would at least appear to have the potential to overcome some of the principal institutional constraints to fl oodplain restoration which have thwarted or curtailed efforts in the past.

Our research fi ndings, however, caution against over-optimistic expectations from the new generation of pro-jects. Early signs suggest that the sheer complexity of the tasks they are tackling and the uncertainties they are exposing are posing a major problem for project manage-ment. Building and maintaining the large partnerships takes time and care. Striking an acceptable balance and negotiating trade-offs between diverse policy objectives

is very demanding. Accessing multiple funding sources requires a high degree of fl exibility to satisfy different funding agencies. Attempts to enroll instruments from different policy fi elds can reveal serious incompatibili-ties and inconsistencies. As a result, project design and implementation has become more complex, more time-consuming and more expensive, endangering effective implementation.

Floodplain restoration schemes of this kind are making substantial steps towards addressing problems of fi t, inter-play and scale. Their catchment orientation and long term perspective create a better fi t between ecosystem proper-ties of the fl oodplain and the institutional arrangements for its restoration, both in spatial and temporal terms. Building on better interplay between institutions is central to the new generation projects, as is exploiting different scales of action – from national pilots to local manage-ment teams – for different purposes. What the schemes are revealing, though, are serious secondary problems associated with this more ambitious and integrated approach to fl oodplain restoration. Efforts to take on prob-lems of fi t, interplay and scale are proving, in many cases, too demanding for project management. This does not query the desirability of addressing these core institutional problems but, rather, raises questions about how project managers can be assisted in doing so.

6.6.4 Coping with Uncertainty III: Tightening Controls to Secure Better Policy Delivery

A third way of coping with uncertainty can be identifi ed not at the level of individual schemes of fl oodplain restora-tion but in the development and pursuit of policy. Here the uncertainty addressed relates to the outcomes of new poli-cies and the strategy is to attempt to minimise uncertain-ties of policy delivery by tightening controls over those entrusted with implementation. As described earlier, in several policy fi elds of direct relevance to fl oodplain restoration a more integrated and holistic approach to problem solving can be detected. This applies particularly to fl ood protection, following recent fl ooding events, water resources management, in response to the Water Frame-work Directive, and nature conservation. Changes in policy content potentially conducive to fl oodplain restora-tion can also be observed – if to a lesser degree – across Europe in spatial planning, agriculture, forestry and rural development. Many of these supranational and national policy initiatives are characterised by a more comprehensive problem analysis, longer term visions for improvements, better cross-sectoral policy integration, more strategic guidance and stronger and broader local partnerships. The guiding principles underpinning


Integrated Water Resources Management (IWRM) on a river basin scale are a case in point (WWF, 2004, p. 24).

Ironically, whilst policy content is, generally speaking, taking a broader perspective, modes of policy implemen-tation are to some extent becoming more restrictive. In recent years a growing array of instruments have been introduced which set out in detail not only what policy is to be pursued but also how this is to be done at the opera-tional level. Targets are set to measure progress, strict consultation procedures must be followed to gain planning approval, match-funding is required to demonstrate multi-functionality and audits are conducted to assess the per-formance of projects and programmes alike. These controls are widely justifi ed by governments in terms of their value in improving accountability, policy integration and, above all, cost effectiveness.

Our research suggests, however, that measures of this kind are having important (unintended) negative effects on the ability of project managers to implement fl oodplain restoration schemes. The fi rst problem relates to the cumu-lative effect of the new policy initiatives. Each of the new requirements – whether on policy content or style – may individually make a lot of sense. Experiences of policy implementation show that the combined effect of multiple new requirements can be to create a degree of manage-ment complexity that can severely delay the progress of some restoration projects and cause others to be shelved. Ironically, therefore, effective policy delivery is being jeopardised by the sheer extent of policy reform.

The second problem is more fundamental, having to do with an emergent culture of control in policy making circles. The measures to increase accountability, policy integration and cost effectiveness refl ect not only very justifi able concerns about effective policy implementation and effi cient use of public funds but also the concerns of senior management in many government agencies that the policy rethinking described above is not fi ltering down effectively to the operational level. This argument is used by senior offi cers to justify tighter control – or ‘guidance’ – to assure more effective implementation. Whilst the need for greater strategic guidance over such complex issues as a catchment-scale approach to fl ood protection is undis-puted by all those involved, one of the effects has been to restrict the freedom of action of project and programme managers. In the past their judgement – for example on whether to fund a restoration project – was based on their individual expertise, local knowledge and professional experience; today it is framed much more by targets devised at regional, national or even supranational levels. Consequently, the nature of their work is adapting in order to meet what Michael Power has termed the ‘rituals of verifi cation’ required by auditing processes (Power, 1997).

For the task of restoring fl oodplains this poses a particular dilemma: whilst recent policy shifts and new generation schemes are encouraging fl oodplain restoration to enter-tain greater risks and uncertainties, administrative proce-dures to assure policy implementation are becoming increasingly risk-averse.

In terms of fi t, interplay and scale the picture here is more differentiated. Recent policy initiatives relating to fl oodplain restoration are certainly addressing very clearly problems of spatial and temporal fi t, taking a more catch-ment-oriented and long term perspective on the river and land management. Inter-sectoral interplay is also strong. Vertical interplay and issues of scale appear more prob-lematic, however. The rhetoric of policy documents tend to be very supportive of multi-level and multi-direction governance. The reality – whether intentional or not – is often very different, with control mechanisms of central government agencies reaching down into the operational level of project management to an unprecedented extent. This, it appears, is undermining both project implementa-tion and – ultimately – policy delivery.

6.6.5 Making Policy More Sensitive to the Challenges of Project Management

It has been observed here how policy makers, in their efforts to encourage integrated, cross-sectoral and multi-agency action, often overlook the implications for imple-mentation at the operational level. In future, more consideration needs to be given to how individual policy incentives work in conjunction with others; that is, how they alter the existing institutional setting. In addition, policy makers need to be more sensitive to the contexts of action in which their instruments operate. What makes a policy instrument effective is not the assumed preferences of individuals acting according to a rational choice logic but the real scope and willingness of stakeholders to alter their practices. More feedback into policy making pro-cesses is needed about the real-life experiences of project management and stakeholder involvement at the opera-tional level. This will require more monitoring of how and why policy instruments do or do not work in practice. In addition, if uncertainty and complexity are unavoidable features of ambitious schemes of fl oodplain restoration, as it appears, then project managers and other stakeholders need assistance in assessing the situation and their own ability to meet the challenge. Policy needs to provide not only targets for orientation but also frameworks for devel-oping the necessary economic, social and institutional capital at local and regional level, and instruments capable of adapting to the dynamics of a fl oodplain restoration process. On this basis policy makers and project managers


alike should be better equipped to identify and exploit windows of opportunity for the restoration of fl oodplains in the future.

6.7 CONCLUSION

A case has been made for the re-introduction of diversity and variability in the riparian zone. It is advocated that this is done through the management of physical processes and within a policy context that is sensitive to the com-plexities of successfully implementing river restoration initiatives. Inevitably the re-introduction of diversity and variability will also introduce a signifi cant level of uncer-tainty for river managers. The challenge is to decide what are acceptable levels of uncertainty for different stake-holders, for different scales of involvement (such as catch-ment strategies or planning for a particular reach) and for different purposes (such as biodiversity targets or fl ood management). In the light of projected climate changes it is suggested that the key is to fi nd ways of adapting to uncertainty (see Chapter 14), rather than aiming to reduce uncertainty in the scientifi c sense of the word.

6.8 ACKNOWLEDGEMENTS

Much of the thinking that has gone into this chapter has arisen from the work of the EC-funded project FLOBAR2 (EVK1-CT-1999-00031). We thank all our colleagues on that project for stimulating and enjoyable discussions over many years of working together. We would also like to thank Stewart Rood, John Mahoney, David Hulse and Joan Baker for permission to use their work in the two case study boxes; David Withrington of Natural England for reading and commenting on this manuscript; and Ian Agnew of the Department of Geography at the University of Cambridge for drawing the fi gures.

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7

Hydrological and Hydraulic Aspects of River Restoration Uncertainty for

Ecological Purposes

N.J. Clifford1, M.C. Acreman2 and D.J. Booker2,3

1School of Geography, University of Nottingham, UK2Centre for Ecology and Hydrology, UK

3(now at) National Institute of Water and Atmospheric Research, New Zealand

sophisticated simulation approaches to river restoration design and appraisal in the future.

7.2 THE RIVER AND CATCHMENT AS AN UNCERTAIN SYSTEM

Restoration of rivers may be viewed as the joint product of the physical structure of the channel and its fl oodplain, their hydrological integration and their ecological value and function. Channels and fl oodplains are characterised by the timing and quantity of fl ows and sediments which the channel conveys, and which the fl oodplain stores. As a result, channel restoration in its widest sense encom-passes structural modifi cations of channel form, the re-establishment of natural fl ow regime and the reconnection (or even recreation) of channel and fl oodplain areas (see Chapter 6). Both the problems of, and solution to, hydro-logical and hydraulic uncertainties in river restoration thus arise from considering the channel as an embedded part of the wider fl uvial hydrosystem (Petts and Amoros, 1996). Incorporating hydrological connections or distur-bances requires an holistic approach to streamfl ow man-agement (Hill et al., 1991) in recognition of the many scales of connection or ‘dimensions’ of the fl uvial system (Boon, 1998; and for general review see Clifford 2001).

Approaches to cost effective and multifunction river rehabilitation works have increasingly emphasised the need to include an ecological perspective. Attention has focused on restoring sustainable hydrological and

7.1 INTRODUCTION

This chapter presents river restoration as a hybrid activity, involving hydrological, geomorphological and ecological expertise. It introduces a range of techniques and methods that are appropriate to each of these areas, gives examples of their application and reviews some of the fundamental opportunities and limitations of current restoration prac-tice. River restoration is not only a hybrid activity but also an emerging one (both scientifi cally and practically). ‘Uncertainties’ are present at every stage of restoration intervention. These span such basic issues as: the ability to support ideas of catchment hydrology or fl ow regime within which to frame restoration designs; providing fi eld evidence for conceptual and numerical simulation models; capturing the natural range of variability inherent in complex and dynamic physical–ecological systems; and incorporating all of these uncertainties themselves into restoration design and appraisal practice. The chapter con-cludes that fi ve basic sources of uncertainty (also see Chapters 1 to 3) underpin contemporary river restoration theory and practice: data (type, quality and quantity); characterisation (of those physical and ecological phe-nomena involved in the restoration attempt); coupling (of physical environmental and ecological dynamics); aware-ness (of opportunities and limitations) and fl exibility (in the approach to design and evaluation). Evaluating each of these sources at every stage of the restoration is, perhaps, the best way of managing such uncertainties and, too, of improving prospects for the incorporation of more


hydraulic habitats using principles of fl uvial behaviour (Newbury and Gaboury, 1993) as shown in Figure 7.1. This fi gure illustrates the hierarchy of feedbacks (or inter-connections) between physical structure, hydrological function and ecological responses in an alluvial river–fl oodplain system which are relevant when designing and assessing river restoration or rehabilitation schemes. Flow and sediment transport depend upon catchment inputs of material and energy. These in turn maintain and confi gure the channel shape, determine the sediment and bedform environments and create diversity of fl ow patterns and structures with varying degrees of coherence and spatial coverage. Ecological function of the channel is primarily a response to local (imposed) conditions of velocity, depth and substrate (the hydraulic variables), whose spatial (cross-section, reach-scale) and temporal (event-specifi c

and seasonal) characteristics refl ect wider reach-scale, inter-reach and catchment controls (which are primarily hydrologically determined).

While the nature of the linkages in Figure 7.1 is well understood, giving precise values to quantities and timings of material and energy transfers, and accounting for the feedbacks between them, gives rise to uncertainties at all scales. These uncertainties are compounded by the recog-nition that climate and land use, which determine catch-ment rainfall–run-off response, are nonstationary (that is, they are changing in their mean level and variance) through time. In the United Kingdom, for example, Prudhomme et al. (2003) examine the implications of no less than 25 000 climate scenarios for four typical fl ood events as applied to fi ve catchments. Most scenarios show an increase in both the magnitude and frequency of fl ood

Watershed Inputs

Fluvial Geomorphic Processes

Geomorphic Attributes

Habitat Structure, Complexity, and Connectivity

Biotic Responses(Aquatic, Riparian, and Terrestrial Plants and Animals)

• water• sediment• nutrients

• sediment transport/deposition/scour• channel migration and bank erosion• floodplain construction and inundation• surface and groundwater interactions

• instream aquatic habitat• shaded riparian aquatic habitat• riparian woodlands

• abundance and distribution of native and exotic species• community composition and structure• food web structure

• seasonally inundated floodplain wetlands

• channel morphology (size, slope, shape, bed and bank composition)• floodplain morphology• water turbidity and temperature

• energy• large woody debris• chemical pollutants

NaturalDisturbance

Human LandUse and Flow

Regulation

Figure 7.1 The nested hierarchy of the channel system and its associated habitat potential and biotic response (Tuolumne River restoration program summary report, summary of studies, conceptual models, restoration projects, and ongoing monitoring. Prepared for the CALFED/AFRP Adaptive Management Forum, with assistance from the Tuolunine River Technical Advisory Committee, (2001). Reproduced with permission from Stillwater Sciences.)

Hydrological and Hydraulic Aspects of River Restoration Uncertainty for Ecological Purposes 107

events, but the largest uncertainty arises from the type of Global Climate Model used: the magnitude of modelled change varies by a factor of nine in Northern England and Scotland! At scales below the level of climatic input, major uncertainties remain in the modelling and prediction of rainfall–run-off relationships, upon which channel fl ow ultimately depends. Catchment run-off mod-elling is increasingly able to incorporate greater physical parameterisation, and to distribute parameter combina-tions around the catchment and between surface and sub-surface fl ows. Similar predictions may, however, be obtained using models that incorporate differing levels of sophistication and differing parameter values, giving rise to model ‘equifi nality’. This raises concerns about the degree to which fundamental understanding of process can, in fact, lead to better information on future (or designed) outcomes (Beven, 2001). Another major source of concern is the degree to which model predictions may be appropriately up- or down-scaled (for a comprehensive review of these issues, see Beven, 2000 and Bierkens et al., 2000).

Gilvear et al. (2002) and Hendry et al. (2003) point out that the hydrological basis for determining future fi sheries stocks is complicated by issues of water and sediment quality, as well as quantity. Water quality necessitates consideration of longer- and shorter-term land use histo-ries, run-off behaviour and the monitoring or modelling of diffuse as well as point-source pollutants. ‘Hydrology’ itself, therefore, has many uncertain components, and Clarke SL et al. (2003) call for the development of ‘hydrogeomorphological knowledge’ of catchments supporting ‘tools’ for water resource management. The need to service ever-more complex models and to comply with increasingly complex (but frequently competing) leg-islative requirements will also place growing pressures for the provision of data (both quantity and quality). This is likely to demand changes to long-standing methods of data acquisition and processing (Marsh, 2002). All of these essentially hydrological issues are encountered before the channel-scale is addressed from a hydraulic standpoint!

Within the channel at reach and subreach scales, it is hydraulics which underpin connections between the phys-ical and ecological environments. Determining an appro-priate channel morphology and the characteristics of fl ow behaviour in response to changing discharge and sediment transport are key factors in designing restoration schemes. Such schemes must be both sustainable in a physical sense, and functional in an ecological sense. Both consid-erations require some allowance for natural dynamics and post-design evolution. Over the last two decades, a new subject of ‘eco-hydraulics’ has developed (Leclerc, 2002), in which a range of monitoring and modelling strategies

link the expertise of engineers, geomorphologies and ecologists to design and assess restoration or rehabilitation schemes. Yet, from very basic stages of fl ow ‘characteri-sation’ through to fl ow modelling, habitat simulation and post-project appraisal, numerous uncertainties exist. This chapter reviews the sources and implications of these uncertainties, and provides case study examples of current and prospective restoration practice motivated by eco-hydraulic considerations.

7.3 HYDROLOGICAL ASPECTS AND UNCERTAINTY IN CHANNEL RESTORATION

7.3.1 Connectivity, Disturbance and the Ecological Flow Regime in Channels

A river ecosystem and its associated benefi ts to human-kind are strongly conditioned by the pattern of fl ows between days, seasons and years (including fl oods and droughts) that occur within the drainage basin. For example, fl oods maintain river structure and sediment distribution (Hill et al., 1991), medium fl ows trigger fi sh migration (Junk et al., 1989) and low fl ows maintain species diversity (Everard, 1996). The pattern of fl ows required to support a river ecosystem is called the envi-ronmental fl ow requirements (Dyson et al., 2003). If the river fl ow pattern is altered from its natural regime, then the river ecosystem will change from its natural state. Too much fl ow at the wrong time can be as damaging as too little fl ow. The fl ow regime of a river describes the tem-poral variability of run-off within a single hydrological year (inter-annual, such as between winter fl oods, spring snowmelt run-off, summer basefl ow), and from year-to-year (intra-annual, such as dry years, wet years or alternat-ing periods or cycles of drought and higher fl ows). Recognition of the importance of dynamism and adjust-ment in fl ow regime for the physical and biotic system is something of a recent paradigm shift in river management and restoration (Bergen et al., 2001; Newson, 2002), but poses areas of additional design and management uncer-tainty. Traditional engineering intervention was guided by principles of control and stability, which led to ‘certain’ or fi xed design and performance criteria. Designing-in variability, or allowing for a degree of post-modifi cation ‘naturalisation’ to channel works is not only a novel concept but is also largely untried or tested. It requires the setting of generous tolerances on design functional require-ments, to refl ect the degree of ignorance and paucity of research (Bergen et al., 2001). In this respect, ‘uncer-tainty’ is itself something of an essential design criterion (also see Chapter 14)!

The most extreme changes to a fl ow regime are often associated with construction of a dam, where the entire


pattern of fl ows may be replaced by a constant low fl ow. This can have serious negative impacts on the river eco-system downstream (Petts, 1984). A key part of river res-toration is often the reduction of abstractions (Barker and Kirmond, 1998) or release of water from dams (Acreman, 2002) to restore river fl ows to a more natural pattern. While geomorphologists and hydrologists have tended to emphasise connectivity and transfer of water and materi-als through the fl uvial system, the ‘real’ state of catch-ments in the developed world is of highly fragmented, and largely modifi ed transfers. Graf (2001) estimates that only 2% of the 3.5 million miles of streams in the United States are unaffected by dams, and that 18% of this is actually under the waters of reservoirs. The picture of fragmenta-tion, threat and change is similar in the United Kingdom, where less than 10% of rivers are free from structural modifi cation of channel and banks and 53% of rivers have fl ow regimes altered by more than 20% (Acreman, 2000). Indeed, such is the scale of interference with natural con-ditions of catchments in developed countries, that the most convincing strategy for environmental enhancement in the short to medium term may be to use the capacity for regulation to remediate fl ows. The alternative, to remove expensive, fi xed infrastructure at the larger scale (i.e. restoration of fl ows), may require unrealistic cultural, political and economic shifts (Graf, 2001).

Remediation of fl ows might be accomplished by changes to the operating rules of dams, by redesign and by physical renovation of structures. A high profi le case of relaxing impoundment occurred on the Colorado River in the United States in 1998 (Schmidt et al., 1998) and illustrates

the importance of uniting otherwise confl icting policies to achieve environmental benefi ts. Acreman (2002) addresses the implications of modifi ed natural systems for the practice of scientifi c hydrology, arguing that the realities of modifi ed systems must be incorporated into traditional hydrological training and practice (for review see Clifford, 2002).

The natural fl ow regime of a river is a function of the magnitude, duration, frequency and timing of precipita-tion; the form of the precipitation (rain or snow) and the characteristics of the drainage basin (which determines how precipitation translates into streamfl ow via surface and subsurface run-off). Each stream has a unique fl ow regime, characterised by the stream fl ow hydrograph. Four principles which might be used to guide restoration or enhancement of fl ow for ecologically motivated restora-tion schemes based upon common hydrograph character-istics are described in Figure 7.2 (Bunn and Arthrington, 2002).

From this it can be seen that almost all aspects of the fl ow hydrograph have some importance either to individ-ual species at various stages of their life cycle, or to the determination of species assemblages and hence biotic diversity and abundance more generally. These hydro-graph components may become more or less important in the context of both inter- and intra-annual streamfl ow variation. Hypothesised relationships between water years, hydrograph components and ecological processes developed for the restoration of gravel-bed stream environments downstream of the Sierra Nevada foothills, California, USA, are illustrated in Table 7.1.

dis

char

ge

time

Principle 4

natural regime discourages invasions

Principle 1variation in flow regime

Habitat complexity biotic diversity

Principle 3

access to floodplains (lateral connectivity; longitudinal connectivity) essential

Principle 2regular variation and stable

baseflows determine life history patterns (spawning/recruitment)

Figure 7.2 Basic principles governing ecological response to the stream hydrograph (Source: modifi ed after Bunn and Arthrington, 2002)


Table 7.1 General hypothesised relationships between hydrograph components and ecosystem processes for gravel-bed streams downstream of the Sierra Nevada foothills (Modifi ed from San Joaquin River Restoration Study Background Report, prepared for Friant Water Users Authority, Lindsay, CA, and Natural Resources Defense Council, San Francisco, CA, (2002). Reproduced with permission from McBain & Trush, Inc.)

Hydrograph component

Geomorphic-hydrologic processes

Riparian processes Salmonid life-history processes

Snowmelt peak Wetter years: bed mobility, long duration fl oodplain inundation, moderate channel migration, groundwater recharge

Normal years: bed mobility, short duration fl oodplain inundation

Wetter years: riparian seedling scour within bankfull channel, riparian seedling initiation on fl oodplains, discourages riparian seedling initiation within bankfull channel

Normal years: periodic riparian seedling initiation on fl oodplains

Wetter years: Increase juvenile growth rates by long-term fl oodplain inundation, increase stranding by inundating fl oodplains, stimulate outmigration, reduce predation mortality by reducing smolt density and increasing turbidity

Normal years: Increase juvenile growth rates by short-term fl oodplain inundation, increase stranding by short-term fl oodplain inundation, stimulate outmigration, reduce predation mortality by reducing smolt density and increasing turbidity

Drier years: Increase outmigration predation mortality by increasing density and reducing turbidity

Seasonal recession limb

Gradual decrease in water stage, maintain fl oodplain soil moisture

Wetter years: Allow riparian seedling establishment on fl oodplains

Normal and drier years: Discourages riparian seedling establishment on fl oodplains by desiccating them, encourage seedling establishment within bankfull channel

Wetter years: Increase outmigration success by reducing water temperatures and extending outmigration period

Normal years: Increase outmigration success by reducing water temperatures and extending outmigration period

Drier years: Increase outmigration mortality by increasing water temperatures and shortening outmigration period

Summer–fall basefl ow

Encourages late seeding riparian vegetation initiation and establishment within bankfull channel

Water temperature for over-summering juveniles and spring-run adults, immigration for fall-run adults

Winter–spring rainfall run-off

Wetter years: channel avulsion, signifi cant channel migration, bed scour and deposition, bed mobility, fl oodplain scour, fl oodplain inundation, fi ne sediment deposition on fl oodplains, large woody debris recruitment

Normal years: Some channel migration, minor bed scour, bed mobility, fl oodplain inundation, some fi ne sediment deposition on fl oodplains

Wetter years: mature riparian removal within bankfull channel and portions of fl oodplain, scour of seedlings within bankfull channel, seedbed creation on fl oodplains for new cohort initiation, microtopography from fl oodplain scour and fi ne sediment deposition

Normal years: scour of seedlings within bankfull channel, some fi ne sediment deposition on fl oodplains

Wetter years: partial loss of cohort due to redd scour or entombment from deposition, improve spawning gravel quality by scouring/redepositing bed and transporting fi ne sediment, mortality by fl ushing fry and juveniles, mortality by stranding fry and juveniles on fl oodplains, reduce growth during periods of high turbidity, reduce predation during periods of high turbidity, creation and maintenance of high quality aquatic habitat

Normal years: improve spawning gravel quality by mobilising bed and transporting fi ne sediment, low mortality by fl ushing fry and juveniles, low mortality by stranding fry and juveniles on fl oodplains, reduce growth during periods of high turbidity, reduce predation during periods of high turbidity, maintenance of high quality aquatic habitat

Winter basefl ows Fine sediment transport Increase habitat area in natural channel morphology


The ‘Hydrograph Component Analysis’ illustrated in Table 7.1 enables the user to connect the geomorphic and ecological consequences (or responses) to the results of a hydrological analysis. For example, snowmelt peaks and winter run-off occurring in wetter years may have signifi cantly different consequences in terms of enhanced channel change than those occurring in drier years. Channel changes have associated ecological detriments in the shorter term, but provide ecological benefi ts in the longer term. Reducing hydrological uncertainties before restoration (for design purposes) and after restoration (for monitoring and appraisal purposes) thus requires the deployment of techniques that span the determination of low fl ows, which characterise the occurrence and magni-tudes of high fl ows, and which identify any temporal changes or trends with respect to these. Subsequently, linkage of these hydrological parameters to physical (sedi-ment transport, channel morphological) and ecological responses may be undertaken. Whereas ‘classical’ hydrol-ogy has focused on determining catchment ‘signatures’ with respect to the unit hydrograph (the average shape of storm hydrographs with equal distributions of rainfall), minimising uncertainty in restoration applications entails a more sophisticated paradigm for application. This requires consideration of hydrograph variability, the potential links between this and other properties of the catchment network, as well as knowledge of land use management history. Collectively, it is these which defi ne a ‘natural fl ow’ as the alternative paradigm for hydrologi-cal assessment and river restoration schemes.

While that natural fl ow is now accepted as fundamental to improved management and study of rivers (Richter et al., 1996; Poff et al., 1997), it is also becoming clear that the response of organisms to fl ood and drought may be evidenced over different time scales, and may be funda-mentally different in character. Human modifi cations to the fl ow regime may also alter the course and viability of otherwise natural adaptive strategies (Lytle and Poff, 2004). Thus, while general principles or paradigms can be identifi ed, application to specifi c regions, case studies or species, may require considerable fi eld calibration, and/or engender considerable uncertainty in assessing the output of models and scenarios. The following sections describe some of the techniques used, and identify areas of uncer-tainty associated with their application and interpretation in channel restoration designs.

7.3.2 Determining the Environmental Flow Regime for River Restoration: Some Alternative Frameworks

There is no simple formula or fi gure that can be given for the environmental fl ow requirements or instream fl ow

requirements of rivers. Much depends on the desired future character of the river ecosystem under study, which may be set by legislation or negotiated as a trade-off between water users. The fl ow allocated to a river may thus be primarily a matter of social choice, with science provid-ing technical support to help determine the river ecosys-tem response under various fl ow regimes. Most scientifi c efforts have been directed to the determination of minimum fl ows in rivers, particularly during dry periods of the year (or in drier years). These have been thought to be the most important limiting factor in the ecological status or health of the river environment. During the past 20 years, a range of methods has been developed to help set environmental fl ows, each with advantages and disadvantages in particu-lar circumstances of application.

Where fl ow data exist, methods for the determination of minimum fl ows have been primarily statistical. Criteria for method selection include: the type of issue (abstrac-tion, dam, run-of-river scheme); expertise, time and money available; and the legislative framework within which the fl ows must be set. Ungauged catchments give rise to par-ticular uncertainties, which may be reduced by construct-ing regional regression curves from rivers where data do exist, supplemented by statistical or geomorphologcal models of response (see Smakhtin, 2001 for a comprehen-sive review of most methods and applications). Most recently, means of connecting fl ow to modelled or observed ecological response have been developed to inform hydro-logical analyses. There are thus four broad categories of methods (Acreman and King, 2003) which may be used to reduce uncertainty in assessing the hydrological basis for channel restoration designs: look-up tables; desk top analysis; functional analyses; and habitat modelling. Each of these methods may involve different degrees of ‘expert’ involvement and may address all or just parts of the river system. Consequently, the use of experts and the degree to which methods are holistic are considered as cross-cutting issues. The basic principles, strengths and weak-nesses of each of these methods are outlined below.

Look-up Tables

Worldwide, the most commonly applied methods to defi ne target river fl ows are rules-of-thumb based on simple indices given in look-up tables. The most widely employed indices are purely hydrological but those employing eco-logical data were also developed in the 1970s. Many early applications of environmental fl ow setting focused on single species or single issues. For example, much of the demand for environmental fl ows in North America and northern Europe was from sport fi shermen concerned about the decline in numbers of trout and salmon follow-


ing abstractions and dam operations. Environmental fl ows were then set to maintain critical levels of habitat (includ-ing sediment, fl ow velocity, depth) for these species. Part of the justifi cation was that these species are very sensitive to fl ow and if the fl ow is appropriate for them, it will be for other parts of the ecosystem.

Engineers have traditionally used hydrologically-defi ned indices for water management rules to set com-pensation fl ows below reservoirs and weirs. Examples are percentages of the mean fl ow or exceedence percentiles from a fl ow duration curve. (The fl ow duration curve is a water resources tool that defi nes the proportion of time that a given fl ow is equalled or exceeded). This approach has been adopted for environmental fl ow setting to deter-mine simple operating rules for dams or off-take struc-tures where few or no local ecological data are available. A hydrological index is used in France, where the Fresh-water Fishing Law of 1984 required that residual fl ows in bypassed sections of river must be a minimum of 1–40 of the mean fl ow for existing schemes and 1–10 of the mean fl ow for new schemes (Souchon and Keith, 2001). In Brazil, fl ows below dams must be at least 80% of minimum monthly average fl ow (Benetti et al., 2002). In regulating abstractions in the United Kingdom, an index of natural low fl ow has been employed to defi ne the environmental fl ow. Q95 (i.e. that fl ow which is equalled or exceeded for 95% of the time) is often used. The fi gure of Q95 was chosen purely on hydrological grounds. However, the implementation of this approach often includes ecological information (Barker and Kirmond, 1998).

The Tennant Method (Tennant, 1976) was developed to specify minimum fl ows to protect a healthy river environ-ment. This employed calibration data from hundreds of rivers in the mid-Western states of the United States. Per-centages of the mean annual fl ow are specifi ed that provide different quality habitat for fi sh, e.g. 10% for poor quality (survival), 30% for moderate habitat (satisfactory) and 60% for excellent habitat. This approach can be used else-where but the exact indices need to be re-calculated for each new region. The indices are modifi ed where run-off in the spring is important and are widely used in planning at the river basin level.

Matthews and Bao (1991) concluded that methods based on proportions of mean fl ow were not suitable for the fl ow regimes of Texan rivers as they often resulted in an unrealistically high fl ow. Instead, they devised a method using variable percentages of the monthly median fl ow, based on fi sh inventories and known life history require-ments, fl ow frequency distributions and conditions for special periods and processes (e.g. migration).

The advantage of all look-up approaches is that once developed, application requires relatively few resources.

However, simple hydrological indices are not readily transferable between regions without re-calibration. Even then, they do not take account of site-specifi c conditions. In particular, adjusting hydrological indices does not ensure ecological validity without a corresponding ecological re-assessment, but ecological data may be much more costly and time consuming to collect. In general, look-up tables are thus particularly appropriate for low controversy situations. They also tend to be precautionary.

Desk Top Analysis

Desk top analyses tend to focus on analysis of existing routine data (such as river fl ows from gauging stations and/or fi sh data from regular surveys) although data may be collected as part of a specifi c project at a particular site or sites on a river. Some desk top methods are purely hydrological. For example, Richter et al., (1996, 1997) developed a hydrological method intended for setting benchmark fl ows on rivers, where a natural ecosystem is the primary objective. Development of the method relies upon identifi cation of the components of a natural fl ow regime, indexed by magnitude (of both high and low fl ows), timing (indexed by monthly statistics), frequency (number of events) and duration (indexed by moving average minima and maxima). The method uses gauged or modelled daily fl ows and a set of 32 indices. A range of variation of the indices may then be set, based upon ±1 standard deviation from the mean or between the 25th and 75th percentiles. Variability in stream fl ow is essential in sustaining ecosystem integrity (long term maintenance of biodiversity and productivity) and resiliency (the capacity to endure natural and human disturbances – Stanford et al., 1996). This method is intended to defi ne interim standards, which can be monitored and revised. However, so far, there has not been enough research to relate the fl ow statistics to specifi c elements of the ecosystem.

A particular aspect of desk top analysis relates to the characterisation of high fl ows in rivers, especially the identifi cation of groupings of higher fl ow events, either within or between years. The emphasis on higher fl ows is something of a counter to the previous concentration of hydrological analyses on low fl ow requirements. It refl ects the importance of higher magnitude fl ows in connecting the various parts of the catchment–fl oodplain–channel system, in sustaining sediment transport through the fl uvial system to maintain channel and fl oodplain structure (including larger-scale riparian woodlands – Gregory et al., 2003) and in servicing particular stages of species’ life cycles. In this context, fl ow variability or hydrological disturbance is thus both a potential indicator of land use


change and an important control on river ecology. Knowl-edge of the changing attributes of such disturbance is important in setting the appropriate historical and contem-porary context for stream fl ow restoration (through con-sideration of land use and climatic histories), and for designing-in crucial aspects of fl ow variability.

By way of illustration, Archer and Newson (2002) focus on the number and frequency of rises and falls above selected threshold levels (pulses) expressed as multiples of the median fl ow, together with the duration of the events obtained when analysing the discharge history of the Coal-burn catchment, UK (Figure 7.3). The pulse duration is the time from rising above the threshold to falling below the same threshold, and the number of pulses refl ects the magnitude of the chosen threshold fl ow. Provided that the data series are extensive enough, of suffi cient resolution (for example, daily mean fl ows are unlikely to be an ade-quate basis for analysis when catchment lag is much less than one day) and are accompanied by other appropriate documentary/archival information, the analysis may be extended to consider periods of land use or climatic change, as shown in Figure 7.4.

The Coalburn is a small upland catchment with an area of 1.5 km2 and an altitudinal range from 270–330 m. The natural surface material comprises a cover of blanket peat (0.5–3 m thick) overlying glacial till up to 5 m in thickness. This was ploughed in 1972, resulting in a drainage density 60 times greater than the original stream network. In the spring of 1973, 90% of the catchment was planted with Sitka spruce and, since then, growth rates have been vari-able, reaching 1 m height in 1978 and 7–12 m in 1996, by which time some 60% of the catchment had reached canopy closure. With this knowledge of land use manage-ment, the hydrological data can be analysed as shown in

Figure 7.4 to reveal phases of hydrological response to the land use change. Thus, immediately following ploughing and planting, pulse numbers and total pulse duration increase, and subsequently decline with vegetation matu-rity, whereas the average duration of individual events increases as vegetation matures. These patterns in hydro-logical response are most clearly shown when the analysis is applied to events defi ned by thresholds of 2–6 times the median fl ow. Importantly, the method further demonstrates that, even where peak and time-to-peak of the unit hydro-graph are similar in pre- and post-disturbance situations, there are differences in pulse numbers and pulse magni-tudes, which are thought to help determine habitat poten-tial and ecological response. Not surprisingly given its emphasis on higher magnitude threshold events, the method is least satisfactory with respect to disclosing behaviour of low fl ows, and this limitation is important in relation to use of the technique to ‘set’ regulated low fl ows as described above. In such cases, it may, however, be supplemented by other procedures and the method should be thought of as complementing traditional approaches so as to include hydrological variability as a key ecological determinant.

Another area of research that may be considered under the heading of desk top analysis (but which underpins other methodologies described below) is the prediction of hydrograph characteristics from catchment channel network properties. Here, the focus of attention has been on the effects of network scale and the relative timing of the hillslope hydrograph and channel water routing. Because the density of channels in a river network refl ects climate and land use, there should be a network response (transformation) to any changes in these driving variables (Kirkby, 1993). The goal has been to incorporate network

Dis

char

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3 × Median

2 × Median

Median1

1

1 2

2

2 3

3

4 5

Figure 7.3 Defi nition diagram of pulses above selected thresholds and pulse duration (between arrows) in hydrological time series (Reprinted from D. Archer et al. (2002), Journal of Hydrology 268, 244–258, with permission from Elsevier.)


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1974-82

1983-90

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1967-711974-821983-90

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Figure 7.4 Pulse number (a), total pulse duration (b) and average pulse duration (c) for the Coalburn catchment over the full range of fl ows in pre- and post-drainage and planting periods (Reprinted from D. Archer et al. (2002), Journal of Hydrology 268, 244–258, with permission from Elsevier.)


geometry into a catchment hydrograph forecasting model and this has given rise to the Geomorphological Unit Hydrograph (GUH). This defi nes the average shape of storm hydrographs with equal distributions of rainfall as a function of bifurcation ratio, basin area ratio, stream length ratio, watershed order and a mean waiting time for each order (Rodriguez-Iturbe and Valdes, 1979):

peak discharge = 1.31/ LΩRL 0.43

and

time to peak = 0.44 LΩ RB 0.55 RA−0.55 RL

−0.38

where RB = bifurcation ratio; RA = basin area ratio; RL = stream length ratio; and basin order = Ω.

The GUH may be criticised because of its dependence on network properties that are apparently insensitive to changes and its neglect of some aspects describing network shape, topology and topography. More recent develop-ments of this approach are directed to incorporating measures of channel network width and network link con-centration functions (for detailed review, see Mousouridis, 2001).

Other research has also related river fl ow directly to ecological data, such as population numbers or indices of community structure calculated from species lists. However, it is diffi cult to derive biotic indices that are only sensitive to fl ow and not to other factors (e.g. habitat structure or water quality). At the minimum, biotic indices designed for water quality monitoring purposes should be used with extreme caution (Armitage and Petts, 1992). Generally, such data are scarce, and interpretation is com-plicated where methods of collection encompass point-specifi c measurements obtained at different times, and spatially-distributed methods picking-out more persistent patterns refl ecting longer term aspects of land use and water quality (Dakova et al., 2000). In addition, it is not always clear which fl ow variables to choose to represent different aspects of the fl ow regime which are of most ecological relevance. Studies which directly relate ‘responses’ such as macroinvertebrate abundance to hydro-climatological and sediment variables confi rm the importance of intermediate levels of disturbance as under-pinning greatest habitat diversity, and thus imply that modelling must incorporate more than one aspect of hydrological diversity (Reice et al., 1990).

The lack of complementary hydrological and biological data is often a limiting factor, and sometimes routinely collected data gathered for other purposes turn out to be unsuitable. In addition, time series may not be independent (which can violate assumptions of classical statistical

techniques) while the representation and characterisation of disturbances (‘extremes’) necessitates longer, higher-quality time series. For example, Clausen and Biggs (2000) examined 35 fl ow variables using daily mean fl ows for a seven-year record common to 62 perennial rivers in New Zealand. Based upon a covariance analysis among the sites through a principal components analysis, the 35 variables could be collapsed into four variable groupings relating to: size of river; overall variability of the fl ow; volume of high fl ow; and frequency of high fl ow. Signifi cantly, the statistical properties (particularly intra-annual variability) varied between groups, necessitating the use of a suite of different variables from each group to adequately represent the facets of fl ow of most ecological relevance. This illus-trates the requirements for high quality data and, too, the uncertainties of interpretation and application.

A method recently developed in the United Kingdom involving ecological data is the Lotic Invertebrate Index for Flow Evaluation (LIFE; Extence et al., 1999). The LIFE score is based on the abundance and sensitivity to water velocity of different taxa, collected in routine macro-invertebrate monitoring data. Moving averages of preced-ing fl ow have shown good relationships with LIFE scores over a range of sites (Figure 7.5), but it is as yet uncertain as to how the approach can be used to manage river fl ows. Nevertheless, the principle is believed to be sound and LIFE has the major advantage of utilising the data col-lected by existing bio-monitoring programmes.

Functional Analysis Methods – the Building Block Methodology

The third group of methods to determine fl ow require-ments for restoration and ecological purposes includes those that build an understanding of the functional links between hydrology and ecology in the entire river system. These methods take a broad view and combine hydrologi-cal analysis, hydraulic rating information (to estimate sediment transport and channel capacity) and many aspects of the river ecosystem. Perhaps the best known is the Building Block Methodology (BBM) developed in South Africa (Rowntree and Wadeson, 1998; King et al., 2000). The basic premise of the BBM is that riverine species are reliant on three basic elements (building blocks) of the fl ow regime, each of which has a particular ecological and geomorphological signifi cance: low fl ows, freshets and fl oods. An acceptable fl ow regime for ecosystem mainte-nance can thus be constructed by combining these build-ing blocks. In a typical exercise to determine instream fl ow requirements through the building block method, fi ve stages may be involved (Rowntree and Wadeson, 1998): a general assessment of catchment condition to determine


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Life Score

Figure 7.5 Example River Flow (logarithmic scale) and LIFE Score time series

potential for geomorphological change in a river; an evalu-ation of river network characteristics to help identify rep-resentative river reaches to determine the instream fl ow requirements; an assessment of the hydraulic diversity at various stages for each of the morphological units present in these reaches (see below for discussion of biotopes); an assessment of the magnitude and importance of freshet and fl oods; and an assessment of any inter-basin water transfers.

The BBM thus revolves around a team of physical sci-entists (e.g. hydrologists and geomorphologists) and bio-logical scientists (e.g. botanists and fi sh biologists) who follow a series of structured analyses to come to a consen-sus on the building blocks of the fl ow regime. The BBM has a detailed manual for implementation (King et al., 2000); is routinely used in South Africa to comply with the 1998 Water Act; has been applied in Australia (Arthing-ton and Long 1997, Arthington and Lloyd, 1998); and is being trialled in the United States.

To overcome the diffi culties in relating changes in the fl ow regime directly to the response of multiple species and communities, approaches have been developed that use habitat for target species as an intermediate step. Of those environmental conditions required by an individual animal living in a river, it is the physical aspects that are most heavily impacted by changes to the fl ow regime. Habitat models seek to link data on the physical conditions (such as water depths and velocities) in rivers at different fl ows (either measured data or derived from computer models) with data on those physical conditions which key animal or plant species (or their individual developmental stages) require. Once functional relationships between physical habitat and ecology have been defi ned, they are linked to fl ow scenarios in river restoration and design (see Section 7.3 for further details on habitat modelling).

The Holistic Approach

Where intervention seeks fl ow remediation or restoration, there is an implicitly holistic approach insofar as all ele-ments of the river ecosystem are likely to be supported. However, more and more methods now adopt an overtly holistic assessment of the whole ecosystem, such as associated wetlands, groundwater and estuaries; all species that are sensitive to fl ow (invertebrates, plants and animals); the human context; and all aspects of the hydro-logical regime, including fl oods, droughts, and water quality. A fundamental principle is to maintain natural variability of fl ows and to allocate fl ow based upon likely habitat impact. This ecosystem approach is especially important in the management and restoration of dryland environments, where inherent variability of hydrological ‘extremes’ is a limiting factor ecologically (Thoms and Sheldon, 2002). Ecosystem approaches inherently allow for complexity and diversity, and may also capitalise on features such as persistence and evolution – ecological systems may have self-organising or self-designing attributes (Bergen et al., 2001).

Generally, holistic approaches make use of teams of experts and may also involve the participation of stake-holders, thus extending holism beyond scientifi c issues. Where methods have the characteristic of being holistic they clearly have the advantage of covering the whole hydrological–ecological–stakeholder system. The disad-vantage is that it is diffi cult to identify and to collect the relevant data which capture such diverse perspectives.

7.3.3 Choice of Method

Selection of the most appropriate method for linking hydrological criteria to river restoration designs, where a


range (or set) of methods is available, is considered briefl y here. Some of the advantages and disadvantages which underlie the uncertainties in using different approaches are summarised in Table 7.2.

Broadly, Table 7.2 illustrates two trade-offs: that between local sensitivity or regional coverage, and that between data availability and complexity or simplicity of data inter-pretation. Moving through the table, the more integrated and hence more complex methods are characterised by a greater degree of connection between physical and biologi-cal parameters. The key element of this integration is to describe or model the effects of fl ows on habitat structure and function. In turn, this necessitates the addition of hydraulic and hydrodynamic considerations to supplement the hydrological foundations described above.

7.4 HYDRAULIC ASPECTS AND UNCERTAINTY IN CHANNEL RESTORATION

7.4.1 Connecting Flows, Sediments and Ecological Response: the Physical Habitat and Eco-Hydraulics

Over the past decade, there has been a trend towards approaching river habitat assessment and rehabilitation design using combinations of fi eld survey and predictive hydraulic models (Chapter 5). ‘Eco-hydraulics’ (Leclerc, 2002) is now a commonplace term in both the academic and practitioner literature, and the linkage between physi-cal, chemical and biotic components of the river environ-ment is central in efforts to restore and maintain ecological habitat and function (Kemp et al., 2000). To realise these benefi ts, however, cost-effective (but scientifi cally sound) means of combining traditional fi eld monitoring and

survey with emerging modelling and design-support approaches are required. There remains much work to be done both to encourage multi-disciplinary co-operation and to ‘invent’ new trans-disciplinary areas of research and practical expertise (Janauer, 2000).

Physical habitat can be defi ned as a set of physical conditions that can be measured and compared to the conditions that may be suitable for specifi c species or individuals at a particular stage of their life cycle. The fundamental aspects of the eco-hydraulic approach to river characterisation and restoration are summarised in Figure 7.6. This is based upon the idea that species assemblages and/or abundances are organised to refl ect physical condi-tions at a range of scales: catchment conditions (such as slope, geology and rainfall regime) determine at the most fundamental level the kind of species which may be present; at the reach scale, assemblages or abundances from within this broader range are most likely to be observed depending on fl ow regime; at the sub-reach scale, particular species or individuals at particular stages of their life cycle, are found in niches with particular fl ow and sedimentological conditions.

While the catchment scale conditions the range of habitat potential, at the smaller scales physical habitat is commonly defi ned by combinations of depth, velocity, substrate and distance to cover. In this respect, physical habitat quality is normally considered independently of water quality issues. It may be approached (at its simplest) from visual survey, through statistical models based upon measured associations, and via complex deterministic modelling, in which the physical habitat is fi rst modelled and then linked to ecological response. Although the potential of eco-hydraulic modelling has been generally

Table 7.2 Some advantages and disadvantages of different methods and characteristics of setting environmental fl ows

Method type Sub-type Advantages Disadvantages and Uncertainties

Look-up table Hydrological Cheap, rapid to use once calculated Not site-specifi c. Hydrological indices not valid ecologically

Ecological Ecological indices need region-specifi c data to be calculated

Desk top Hydrological Site specifi cLimited new data collection

Long time series required

Hydraulic No explicit use of ecological dataEcological Ecological data time consuming to collect

Functional analysis Flexible, robust, more focused on whole ecosystem

Expensive to collect all relevant data and to employ wide range of experts. Consensus of experts may not be achieved.

Habitat modelling Replicable, predictive Expensive to collect hydraulic and ecological data


DRAINAGEBASIN

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MICROHABITAT100-10–1 years

Sand siltover cobbles

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Leaf and stickdetritus inmargin

103-102 m

10–1 m

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Figure 7.6 Spatial scale and distribution of stream habitats based upon fl ow–sediment interactions (Source: Naiman et al., 1992 after Frissell et al., 1986)

acknowledged positively, some signifi cant areas of uncer-tainty remain. A fundamental requirement is the ability to demonstrate (rather than assume) a linkage between measured parameters delimiting physical habitat and the species occurrence and abundances (‘responses’ or ‘tolerances’) which these are supposed to condition.

7.4.2 Survey and Visual Methods: Biotopes and Functional Habitats

At present, hydraulic performance and habitat are essen-tially approached separately. Two alternatives are avail-able: ‘bottom up’, in which in-stream habitat units are

inferred from a knowledge of hydraulic conditions that defi ne physical biotopes (channel features characterised through hydraulic measurement or visual survey of surface fl ow type; Padmore, 1997); and ‘top down’, in which functional or meso- habitats are inferred from analysis of biological communities associated with substrate and veg-etation characteristics (Kemp et al., 1999). A common strategy is to visually classify velocity–depth combina-tions (i.e. the biotopes) which might then be associated with functional habitats, or discrete species assemblages as indicated in Figure 7.7 (Newson and Newson, 2000).

Biotopes are easily recognised from fi eld survey without costly fi eld measurement and, when linked with

PHYSICAL BIOTOPES FUNCTIONAL (MESO-) HABITATS

Tree roots/overhangingvegetationPool

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Figure 7.7 Representation of physical biotopes and functional habitats in a stream sub-reach (Reproduced from M. D. Newson and C. L. Newson (2000) ‘Geomorphology, ecology and river channel habitat: mesoscale approaches to basin-scale challenges’ Progress in Physical Geography 24, 195–217, with the permission of Edward Arnold (Publishers) Ltd.)


knowledge of habitat suitability, allow some insight into likely biological function. Despite early optimism with respect to this approach, relating biotopes to meaningful biotic function appears more complicated than fi rst assumed (Kemp et al., 2000; Leclerc, 2002). Demonstra-ble relationships between biotopes and functional habitats remain scarce. While the survey or biotope method is meant to circumvent issues relating to single-species pref-erence curves or single measures of channel environment (since the biotope is an ‘integrated’ habitat), major uncer-tainties in improving links between biotopes (from simple survey or modelling) and functional habitats remain (Newson and Newson, 2000). In part, the scarcity of data refl ects the cost and practical limitations associated with fi ne-scale fi eld survey (Newson et al., 1998) and diffi cul-ties in monitoring highly dynamic, stage-dependent fl ow characteristics. There is also the question of the appropri-ate range of supposed habitat determinants to associate with particular species or species clusters, at differing stages of their life cycles, and which may vary greatly between species found even in close proximity. Key ques-tions which belie the uncertainty in an otherwise appeal-ing approach are: how consistent are our observations? How stable are biotopes with stage? How do biotopes relate to channel morphology and biology?

Biotope mapping has been supported by ecological observations that species patchiness (superimposed on a general downstream continuum) does occur, and that this may further be related to discharge exceedence values (Newson and Newson, 2000). This suggests that there should be a link with similar patchiness of functional habitat, if this, too, can be defi ned. Functional habits may be discriminated on the basis of combined fl ow indices such as the Froude number. (The Froude number is the ratio of depth to the square root of depth multiplied by gravitational acceleration). From a survey of 32 sites in eastern England, Kemp et al. (2000) suggest that func-tional habits fall into two groups: lowest Froude number classes, and those with Froude number above 0.5. Insofar as the Froude number itself is assumed to refl ect biotopes derived from measurement or observation, then a connec-tion between biotopes and functional habitats should exist, but the ecological signifi cance of this link is uncertain in the absence of simultaneous, point-by-point measures of physical and ecological indicators, and because of feed-backs or interdependence between the apparently indepen-dent classifi cation variables. For example, vegetation growth affects velocities and sedimentation, and hence helps determine the local Froude number, which is sup-posedly independent. A recent investigation by Clifford et al. (2006) identifi es the need for more consistent use of bitope and habitat defi nitions, and improved experimental

design when assessing the linkage between fl ows, habitats and ecology. Use of the Froude number may also be ques-tioned because it may obscure differences between very different fl ow–depth combinations.

Figure 7.8 illustrates another basic area of uncertainty in the biotope and physical habitat approach, but also perhaps one way in which this uncertainty might be managed or, in future, reduced. The fi gures show reaches of the River Cole, near Birmingham, UK, and the River Tern, Shropshire, UK, which have been ‘classifi ed’ into zones of statistically similar velocity and/or depth behav-iour at various fl ow stages (Emery et al., 2003). Both rivers have a well-marked riffl e-pool bedform sequence (a common biotope), but the Cole is a less sinuous channel, with more regular bank morphology and less bankside tree growth. The approach uses cluster analysis (a hierarchical statistical method of association) as a reproducible means of assessing both the degree, location and persistence of clustering or patchiness of physical habitat, which might support more objective identifi cation of biotopes.

Cluster analysis is an agglomerative process to identify homogeneous groupings (patches) based upon selected characteristics. In this case, standardised fi eld velocity (measured velocity scaled according to the mean and vari-ance of the total measured distribution) was used. Initially, all velocity observations were considered as separate, but were then successively combined according to ‘distance’ (i.e. differences) from neighbours and from emerging clusters. The next stage was to assess cluster number in relation to morphology, spatial coverage and stability. ANOVA (analysis of variance testing) was used to test differing cluster numbers for statistically signifi cant dif-ferences at each fl ow stage and to assess their coverage and coherence as fl ow stage varied. The cluster analysis is therefore a means of defi ning biotopes on the basis of fi eld measurements, rather than on the basis of visual survey. Figures 7.8(a) and (b) show the spatial coverage and loca-tion of the six distinct velocity clusters at higher fl ow stage resulting from the analysis, while Figures 7.8(c) and (d) detail the velocity characteristics associated with each cluster over a range of fl ow changes from low fl ow to a high in-bank fl ow.

Several points emerge from this analysis which indicate that the method might help both understand and, in time, reduce uncertainties in habitat recognition and appraisal. The method is clearly sensitive to bedform amplitude and planform complexity. Even where the basic biotope struc-ture of rivers is similar, the differing patterns of coverage are suffi cient to indicate a degree of ‘biotope subtlety’ – contrast Figures 7.8(a), where fl ow organisation at high stage is marked by linearity, and 7.8(b), where patchiness is dominant. This is supported by the stage-dependent


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Figure 7.8 Results of cluster analysis to determine coverage and behaviour of velocity ‘patches’ in the Rivers Cole, Birmingham (a and c) and Tern, Shropshire (b and d) (Source: Modifi ed from Emery, 2003)

velocity behaviour seen in Figures 7.8(c) and (d). In the River Cole, the straighter, simpler channel, patch coher-ence increases and cluster number decreases (coverage increases) as fl ow stage rises. Thus, the river ‘simplifi es’ with increasing fl ow stage into three basic groupings: channel margins, channel centreline, and areas above bedform crests. However, in the River Tern, the lower stage complexity is largely maintained as fl ow stage rises, possibly because the fl ow now interacts with large bank-side tree roots creating new or maintaining old, biotopes (for further discussion, see Emery et al., 2003).

The other key points emerging from this analysis are equally important and draw attention to some pitfalls in identifi cation and representation, which might improve survey methods in the future. First, in assessing biotopes and functional habitats, the entire fl ow regime should be considered – what exists at low fl ow may or may not change in character as stage rises. Second, there is an obvious role for hydraulic and hydrodynamic fl ow model-ling. This might support fi eld observations (which may be limited to single fl ows) and assist in restoration design and appraisal, where the ability to predict and to visualise


changing aspects of fl ow and habitat performance as fl ow stage varies is crucial.

7.4.3 Hydraulic Habitat Simulation and Modelling

Assessment of river fl ow management options, including river restoration and changes in fl ow regime, often involves assessing scenarios that fall outside the range of observed conditions. This negates investigation using direct analysis of fi eld observations and necessitates the use of predictive models. Models employ a variety of hydrological, mor-phological and hydraulic parameters to predict variations, which might then be related to the abundance and distribu-tion of aquatic organisms. The parameters vary according to the spatial scale of the simulation. Habitat modelling frequently involves two steps: the physical analysis and evaluation of the hydraulic and morphological aspects of the problem, and the subsequent linkage to ecological response.

Uncertainties in the Hydraulic and Hydrodynamic Modelling Process: the Fundamentals

All hydraulic and hydrodynamic fl ow simulation models begin with equations for the conservation of mass and momentum, which are modifi ed for the bed and surface boundary conditions found in shallow, open channel fl ow. As a result of limitations imposed by the scale of the smaller turbulent motions, the governing equations are not tractable in their original form and require additional modifi cations or ‘closure models’ to provide computa-tional outputs. These assumptions in turn give rise to new terms in the governing equations, which are themselves subsequently manipulated to satisfy the requirements of the solution, which may be represented as a one-, two- or three-dimensional numerical scheme (for full review, see Lane, 1998). Generally, in eco-hydraulic and river restora-tion applications, the uncertainties in model derivation are secondary to those of model application to the particular case study. In effect, modelling is really a recursive process, involving uncertainties at all stages from pre-model data collection, through model verifi cation during the numerical calculation, to post-modelling validation, where results are compared with fi eld measurements and other sources of evidence/expertise to assess, or appraise the model performance.

Traditionally, hydraulic modelling has been focused on one-dimensional representation of open channel fl ow (Chow, 1973), in which the cross-sectionally averaged depth and velocity are obtained. These models may be used to simulate the passage of fl oods through reaches and channel systems, or for the assessment of channel stability

given particular boundary characteristics (see Chapter 5 for more details). In ecological applications, they may be of use to determine depth and timing of inundation associ-ated with particular events. Hydrographs are routed through cross-sections either by running a series of steady-state solutions for different discharge ‘bins’ in the hydro-graph or using unsteady methods to model the hydrograph. Common one-dimensional implementations of this sort are iSIS, HEC–RAS and MIKE11, while the most popular hydraulically-coupled habitat suitability model – PHABSIM (Spence and Hickley, 2000, and below) – uses one-dimenstional approaches to predict velocity and depth in channel cells or slices, defi ned between adjacent mea-sured cross-sections. In their simplest forms, these models rely on the ability to specify the relationship between depth, water surface slope and velocity via an empirical ‘constant’ known as Manning’s n, which is normally assumed to represent the fl ow resistance arising from boundary friction of the channel. The value of n is usually determined from look-up tables (e.g. Chow, 1973). Coef-fi cients of channel expansion and contraction may be used in addition to channel geometry to account for additional components of energy loss arising from fl ow acceleration and deceleration. Away from channels of very simple geometry and boundary characteristics, the appropriate determination of n is more subtle: to maintain predictive success, for example, n is varied inversely with depth and, in many situations, n is really a compound calibration factor expressing (with more-or-less success) the various contributions to fl ow resistance, and hence energy loss in channels: grain, bedform, and spill (channel curvature). Some of the issues in more complex modelling are out-lined below.

Two- and three-dimensional Numerical Schemes in Ecological Restoration Applications

Ideally, two- and three-dimensional approaches are required for realistic simulation of fl ow behaviour and to better rep-resent the diversity and variability of physical habitats (Crowder and Diplas, 2000), thus overcoming the limita-tions of popular habitat simulation models. In particular, accounting for small scale stage-dependent variations in fl ow is important in the maintenance and survival of the biotic community. In this way, more complex numerical fl ow simulation offers potential as a ‘supporting’ function for river rehabilitation schemes. Two- and three-dimensional codes are now widely accessible and can be run over a wide range of discharge conditions, including over-bank fl ows, but there are numerous sources of uncer-tainty at all stages of the modelling process associated with these simulations. Models involve many underlying


assumptions at their input stage, they are sensitive to the numerical structures involved in obtaining the fl ow solu-tion and outputs may be diffi cult to observe or measure in the fi eld. Some of the more common models, model appli-cations and model uncertainties are listed in Table 7.3.

Perhaps the most basic but much neglected area of uncertainty in the application of more complex models rests with the fi eld data requirements for model calibration and validation. This is most onerous where modelling is used at a spatial resolution commensurate with sub-reach detail in habitat in conditions of complex topography (Crowder and Diplas, 2000). These have led some to argue that the ecological and hydraulic/hydrodynamic approaches should proceed independently, because river environments are ‘too complex’ for a coupled approach (Kondolf et al., 2000). Field observations indicate that physical habitat may be structured at an extremely small scale, such as around boulders, or extremely close to channel banks (Railsback et al., 1999), which necessitate huge increases in model effort and computational time, and whose char-acteristics may not, in any case, be picked up by conven-tional fi eld survey. Figure 7.9 for example, illustrates how our knowledge of the character of the river changes as the number and density of fi eld measurements at various fl ow stages is increased or decreased.

Here, the results of an original fi eld survey of more than 350 data points obtained by survey through a 150 m river reach of the River Cole (representing both longitudinal and cross section variation with an average point-to-point spacing of 1–2 m) have been plotted as a frequency histo-gram of velocities, and then subsequently re-plotted in the same way but with the original dataset systematically degraded by a factor of one half or one third. The resultant changes thus give an idea of the effects of varying the fi eld data collection efforts or schemes in characterising ‘true’ fi eld velocities, both prior to modelling simulation and in post-modelling assessment. Several points are worthy of note from this analysis. Firstly, channel velocity charac-teristics differ depending upon fl ow stage: at lower fl ow stage, results are more skewed than at higher stage, but at the higher stage there is the suggestion of multiple modes in an otherwise more even velocity distribution. This change in velocity distribution should be interpreted in conjunction with the results of the biotope analysis in Section 7.4.2, since the fi eld data are common to both. In this case, it seems that, as biotopes ‘clarify’, the distribu-tion of velocities also becomes organised about dominant peaks, refl ecting the infl uence of channel margins, channel centrelines and shallows over bedform crests. The second point to note is that sub-sampling from the original data

Table 7.3 Common numerical fl ow models, their principal applications in river restoration and eco-hydraulics, and some issues relating to parameterisation and model interpretation

Class of model and common implementation

Principal application in channel restoration

Required parameters Principal aspects of uncertainty

One-dimensionnal HEC-RAS ISIS backwater

fl ood routing and channel conveyance

initial water surface elevation (in higher order models); velocity and depth between cross sections in PHABSIM

downstream discharge; upstream water level; Manning’s n; channel expansion and contraction coeffi cients; basic channel cross section morphology

correct estimation of n (particularly with depth and vegetation changes) and channel expansion and contraction coeffi cients; loss of information on cross-sectional fl ow distribution; no account of channel curvature

Two-dimensionnal Telemac RMA

dynamic simulation of channel fl ow and fl oodplain inundation; cross-sectional patterns of habitat suitability

boundary roughness (ks); fl ow information as above; bed topography; turbulence closure model; choice of numerical solver and relaxation coeffi cients;

loss of information on depth-related fl ow properties; time-stepping in unsteady solutions; poor representation of secondary fl ow from channel discontinuities; meshing issues; representation of wetting and drying

Three-dimensionnal SSIIM Fluent CFX

detailed sub-reach modelling of habitat suitability

as above most of the above, plus: mesh-dependence in the vertical as well as cross-section; determination of the free water surface


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does not have an even effect in all velocity classes. This may or may not be of signifi cance but is likely to impart uncertainty into the assessment of model performance if the affected velocity classes are those judged to be of most relevance for habitat suitability. This arises because chang-ing the frequency of particular velocity class observations is equivalent to under- or over-representing the spatial coverage associated with that velocity. For further discus-sion, see Clifford et al. (2002a, 2002b).

Once acceptable fi eld velocity data have been obtained, these must be coupled with representations of boundary topography and channel geometry. This should be suffi -ciently detailed to incorporate both small-scale irregulari-ties in bed and banks, the major breaks of slope associated with channel margins and more subtle fl oodplain topogra-phy. Breaklines are notoriously diffi cult to handle, and if fi ne-scale survey is unavailable then topography has to be interpolated, introducing a further source of uncertainty, which also occurs in areas of low relief, too. Simple linear interpolation, for example, may be preferable to avoid spurious high- and low-points (French and Clifford, 2000). Topography forms the template of the modelling grid or mesh which is draped over the boundary surface and which forms the basis for the numerical solution to the governing fl ow equations. The form of the mesh depends upon the kind of model scheme adopted. Meshes might exploit tri-angular (in fi nite element schemes) or rectangular ele-ments (in fi nite difference and fi nite volume schemes), of either constant or variable size. Triangular meshes are more versatile, but most ecological applications use rect-angular grid elements and structured grid schemes. While allowing elements to vary in size, these always require the same number of elements in each cross plane of solution. In practice, the constraints of gridding normally require some degradation in the quality of the topographic repre-sentation, since multiplying the number of very small ele-ments to represent more detailed topographic variation imparts a disproportionately large increment in computer time required to solve the problem and may result in instabilities in the solution. Many of these issues receive a thorough exploration in Anderson et al. (1996). As com-puter power has increased, more detailed modelling is possible, but approaching closer representations of reality does not simply depend upon enhanced computer speed or memory. A key issue is that, with smaller elements, bed grains and micro-topography may approach or exceed the size of model elements, rendering solution impossible. This is especially pertinent in two-dimensional modelling, where the number and size of elements in the vertical may also be varied, and the issue is related to the wider question of the appropriate parameterisation of multiple scales of fl ow resistance (Nicholas, 2001).

With respect to fl ow resistance, most two-dimensional models often remain reliant on a boundary resistance value specifi ed by n or by Chezy’s conveyance coeffi cient, C. Three-dimensional models employ a boundary rough-ness specifi ed in terms of an equivalent roughness length, ks, which propagates through the boundary cells. While ks can be determined in relation to the boundary grain size, the numerical value required to obtain plausible solutions is generally much larger than grain size alone would imply, giving rise to uncertainty in its interpretation. Flow resis-tance effects beyond skin friction may be incorporated wittingly or unwittingly into the meshing strategy – for example, by varying the size of cells around the boundary as compared to the main body of the fl ow (Clifford et al., 2002a). Other areas of uncertainty in both model applica-tion and result interpretation include: whether the model uses a fi xed water surface (lid) or allows the water surface to freely adjust; the scheme used to allow the simulation of turbulence effects (the closure assumptions); and the complexity of the solver (which determines how many and which neighbouring points are considered in the propagation of the numerical solution through the mesh). Many of these issues are common to both two- and three-dimensional schemes, although similar strategies and choices will not necessarily have the same effects in the differing model implementations. Nicholas (2001) pro-vides a comprehensive starting point for assessing and managing these uncertainties. The particular limitation of two-dimentional models is the parameterisation of sec-ondary circulation effects on momentum transport, which has concentrated on the effects of channel curvature, but not on topographic discontinuities and river confl uences (Lane, 1998).

Rarely do the uncertainties arising with respect to each aspect of the modelling occur independently: Figure 7.10 for example, illustrates the changing fl ow representations arising from coarser- and fi ner-scale element sizes using the SSIMM model of the River Cole, near Birmingham, UK. Apart from the element size, all other model param-eters are held constant. As the results demonstrate, mod-elled velocities vary in both planform and in cross-section and vertical distributions as grid element size is varied from approximately 2–0.5 m. The coarse grid distorts the channel shape from generally rectangular to a trapezoi-dal cross section and this contributes to biasing the faster fl ow towards the channel centre, raising velocities above those measured in the fi eld. This effect is also enhanced by the interaction of grid element size and boundary roughness, which in the model extends to 20% of the near boundary grid element. When elements are large, a larger proportion of the channel is thus affected by the boundary roughness, even though the numerical value of ks is the


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Figure 7.10 (See also colour plate section) Changing SSIIM model output and model ‘fi t’ with as a function of mesh element size in a reach of the River Cole, near Birmingham, UK


same. This effective increase in friction combined with the reduced area of the channel in the coarser grid scheme ‘push’ the fl ow to the centreline. In the streamwise direc-tion, the coarser grid resolution is also insuffi cient to rep-resent the ‘patchiness’ of velocity seen in the fi ner grid case and, hence, both misses and distorts the spatial dis-tribution of potential physical habitat. By contrast, use of the fi ner mesh over-emphasises velocity patchiness and gives rise to problems of near-bank velocity representa-tion. Here, apparently high velocity values cannot be cor-roborated with fi eld data which were collected at coarser resolution.

Some models allow adaptive grids which vary in com-plexity as the problem evolves. The most fundamental illustration of this is the ability (or otherwise) to cope with wetting and drying, which will occur as fl ow stage rises and inundates fl oodplain or channel margins, before reced-ing. Full simulation of this requires an unsteady solution coupled to one of several schemes to either include or exclude fully or partially dry elements. Dynamic solutions are generally confi ned to two-dimensional schemes. A simpler solution is to produce a number of grids, each of which is fully wet for the particular fl ow stage under con-sideration. In analogy with one-dimensional models, the hydrograph may then be simulated from a series of steady state solutions.

Much of the uncertainty in the modelling process may be summarised by the terms verifi cation and validation. These are fundamental considerations in numerical fl ow modelling but have recently been re-examined as the number of models and range of model application has burgeoned. Verifi cation relates to the ability to correctly solve the appropriate equations of the model problem at hand (i.e. whether the solution is an accurate solution of the particular choice of model); whereas validation relates to the plausibility of the model as a whole and the testing of parameters predicted by the model (thus involving the degree of fi t between prediction and measurement). As model applications have increased, the distinctions between the two have become more blurred, and the term model assessment or appraisal has been used (Lane and Richards, 2001). Hardy et al. (2003) follow conventional engineering practice, arguing that verifi cation is the essen-tial element in the assessment process, which must precede attempts at validation. However, relatively little attention has been given to developing means of model assessment that are tailored to the application, particularly with respect to eco-hydraulics. In environmental systems, for example, the degree of experimental closure is much less than in laboratory conditions or engineering problems, and the application of models is as much (if not more) to produce data to support or inform new fi eld strategies or explana-

tory interpretations/insights (Oreskes et al., 1994) as is it is to judge the correspondence between measured and modelled values. Thus, in ecological applications, where the uncertainties of measurements (both physical and bio-logical) are coupled with an intrinsic dynamism and vari-ability, Clifford et al. (2005) have argued that assessment of models necessarily requires a more ‘relaxed’ approach. This is illustrated in Figure 7.11, where planform repre-sentations of model:fi eld correspondence of velocities for the River Cole are shown.

In Figure 7.11(a), the fi t of the model is shown for a higher fl ow stage, purely as the absolute value of the point-by-point modelled-fi eld velocity. On this basis, approxi-mately 80% of the channel area is modelled to within ±0.2 ms−1, and approximately 40% to within ±0.1 ms−1 (Figure 7.11(c)). Figure 7.11(b), however, shows the improvement in fi t when a degree of ‘relaxation’ is applied. In this case, velocity ‘errors’ were recoded to zero provided that two criteria were met: fi rst, the modelled velocity must lie within 0.1 ms−1 of the fi eld velocity and, second, this 0.1 ms−1 difference must also lie within a 1 m radius of the actual fi eld measurement. By allowing this relaxation, the fi t of the model is dramatically improved: reference to Figure 7.11(c) shows that only approximately 5% of the channel fails to fulfi l these joint criteria. The justifi cation for the relaxation lies in the fact that: (a) fi eld measurements are rarely precise enough to warrant abso-lute comparison to modelled results, particularly where both fi eld and modelled velocities have been interpolated to the same grid for comparison; (b) in ecological applica-tions, the uncertainties in relating physical parameters to ecological response are large; and (c) in any case plant and animal communities are mobile, dynamic communities, rather than static and adapt to exploit the most favourable environments. Ecological simulation contains, then, an essential uncertainty, which might usefully be incorpo-rated into numerical model assessment so as to improve interpretation!

Ecological Uncertainties in Eco-hydraulic Modelling

Ecological uncertainties relate mainly to the simplifi ca-tion, observation and testing of habitat suitability criteria. Most frequently, these have been characterised in terms of species’ preferences or abundances, resulting in Habitat Suitability Indices (HSIs), although more recent approaches seek linkages to particular aspects of life cycle and growth of organisms or communities (see the later section on bioenergetics in this chapter). Preference criteria may be simple (univariate) or complex (multivariate) and may be related to the physically-determined environment through correspondence (association) or through the application of


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‘rules’ which allow a degree of uncertainty as an essential element of the procedure. Once physical habitat and eco-logical response are obtained, results of the simulation are expressed in graphical or tabular form as values of the output between limits or above thresholds, often with a spatial component, expressed as Weighted Useable Area (WUA). WUA is an aggregate measure of physical habitat quality and quantity and will be specifi c to a particular discharge and species/life stage. However, it is also clear that patterns or responses in ecology frequently result from persistent variability and renewal of opportunities, rather than the ‘static’ presence/absence of clear habitat delimiters. There have, therefore, been calls to explore the capacity of eco-hydraulic modelling to identify capacity, opportunity and variability rather than the prediction of exclusion via individual tolerance boundaries (Bergen et al., 2001, and Reynolds, 2002).

In Europe, the Water Framework Directive will require river management at the catchment scale (Logan & Furse, 2002) and this is being mirrored in national legislation and subsequent water management activity by organisa-tions, such as the Environment Agency of England and Wales. Therefore, a major research question for habitat modelling is whether existing reached-based methods can be scaled up, or whether an entirely new approach needs to be developed. Physical habitat modelling inves-tigations are typically confi ned to short lengths of river, approximately 50–200 m. A representative reach approach may be taken in which a reach is subjectively chosen to represent a longer length of river, including the direct proportions of habitats within that reach. A habitat mapping approach entails classifying and recording habitat types (pools, riffl es, glides etc) over long lengths of river and then choosing cross-sections to represent the identifi ed habitat types. The results from each cross-section are then weighted according to the proportions of the identifi ed habitat types (Morhardt et al., 1983; Maddock, 1999).

Progress towards catchment-scale modelling has been made in associated fi elds such as fl ood modelling, which provides broad-scale hydraulic predictions. This provides a potential basis for catchment-scale physical habitat assessment using inputs of discharge, water levels and channel geometry supplied from one-dimensional hydrau-lic models. Velocity variations across each cross-section can then be predicted using disaggregation algorithms such as that used in the HEC–RAS model (US Army Corps of Engineers, 2002). Velocity and depth are used to assess habitat quality for target species. A major issue when applying this method is the loss of local detail when scaling up. Booker et al. (2004a) describe the application of a method to the River Itchen, UK. This was able to

predict physical habitat for an entire catchment under dif-ferent water use strategy scenarios compared with present and naturalised situations.

Some of the many (and complex) uncertainties in eco-hydraulic modelling are explored below in the context of the most commonly applied numerical scheme, the PHABSIM model.

7.4.4 The PHABSIM Model

The fi rst step in formulating a habitat modelling approach for rivers was published by Waters (1976). This led to the more formal description of a computer model called PHABSIM (Physical Habitat Simulation) by the US Fish and Wildlife Service (Bovee, 1982; Bovee et al., 1998; Milhous, 1999). The PHABSIM system (Bovee, 1982; Bovee et al., 1998) is a suite of numerical models that allows quantifi cation of physical habitat for a given site, defi ned in terms of the combination of depth, velocity and substrate/cover at a particular discharge (e.g. Johnson et al., 1993; Elliott et al., 1996). This system is most commonly used to assess the availability of suitable habitat for fi sh, although macroinvertebrates (Gore et al., 1998) and macrophytes (Hearne et al., 1994), which have mea-surable physical habitat requirements, have also been the focus of PHABSIM studies. In the United Kingdom the method has been used to assess changes in physical habitat associated with alterations in fl ow regime; for example, application to the Rivers Allen (Johnson et al., 1995), Piddle (Strevens, 1999) and Kennet (McPherson, 1997) to aid management decisions based on the effects of abstraction by groundwater pumping on habitat avail-ability. The model has also been used to assess the impact of channel restoration (Acreman and Elliott, 1996) and difference in habitat caused by different levels of channel modifi cation (Booker & Dunbar, 2004; Booker et al., 2003). Over the years, the methodology used has been adapted by various researchers and institutions (Table 7.4). This has lead to the development of other models that follow basically the same approach (Parasiewicz and Dunbar, 2001).

The approach adopted in many PHABSIM studies has been outlined by Elliott et al. (1999) and Johnson et al. (1995). This approach includes identifi cation of river sectors and species of interest, identifi cation of habitats that exist within the river length of interest, selection of cross-sections which represent replicates of each habitat type and collection of model calibration data (water surface eleva-tion, depth and velocity). The distribution of depths and velocities are then predicted for the range of discharge required. Predicted depths and velocities and measured substrate classes are then compared with HSIs. This allows


Table 7.4 Examples of adaptations of PHABSIM in different countries

Country River Software name Adaptation Reference

France Saint Sauveur brook EVHA French version of PHABSIM Rousel et al. (1999)Taiwan Chou-Shui Creek PHABSIM Incorporates affects of simulated

substrate changesWu & Wang (2002)

Italy Adda PHABSIM Utilises bivariate HSIs Vismara et al. (2001)USA North Fork Middle

Fork Tule RiverPHABSIM Incorporates an individual based

modelVan Winkle et al. (1998)

Canada Waterton PHABSIM Uses hydraulic output from a 2D model

Ghanem et al. (1996)

Finland River Oulujoki FISU Uses hydraulic output from a 2D model

Yrjänä et al. (1999)

France Rhone STATHAB Uses a statistical hydraulics model. Lamouroux et al. (1999)Norway Mandal SSIIM Uses hydraulic output from a 3D

modelFjeldstad (2001)

Switzerland Brenno CASIMIR Incorporates fuzzy rules for fi sh and invertebrate HSIs

http://www.greenhydro.ch

USA Quinnebaug Meso-HABSIM Broader scale, uses more habitat variables

Parasiewicz (2001)

prediction of usable physical habitat for the species/life stage of interest, as WUA in m2 per 1000 m of river channel.

The form of HSIs used in PHABSIM applications will affect the results (Booker and Dunbar, 2004) and is there-fore one source of uncertainty affecting results. HSIs have been categorised into several groups. These are:

• Type I: HSIs that are derived from expert opinion or through information published in literature.

• Type II: HSIs that have been derived through frequency analysis of physical habitat conditions used by different species or life stages as identifi ed in fi eld observations.

• Type III: Type II HSIs that have been corrected for habitat availability. These HSIs are referred to as prefer-ence curves.

• Type IV: Multi-variant HSIs that weight habitat suit-ability based on depth and velocity together.

Site-specifi c HSIs can be developed for particular rivers. However, this is costly and time consuming. HSIs that used pooled results from several rivers in an attempt to create generalised curves that can be transferred between rivers are shown in Figure 7.12. Belaud et al. (1989) reported similarities between four site-specifi c HSIs and generalised HSIs, suggesting that generalised HSIs may be more useful. Roussel et al. (1999) added to the debate

Figure 7.12 Habitat suitability indices (HSIs) calculated for juvenile salmon preference using pooled results from several rivers (Source: Data from Dunbar et al., 2001)


on HSIs by suggested that different HSIs should be used for resting and feeding fi sh. Vismara et al. (2001) com-pared univariate and bivariate HSIs, concluding that depth is much more important than velocity in defi ning habitat suitability requirements when using bivariate models.

Evaluation of PHABSIM

The PHABSIM modelling methodology may be applied before and after a restoration scheme to infer changes in habitat using a methodology such as Elliott et al. (1996). Alternatively, predicted results may be compared with changes in abundance of fi sh derived from surveys before and after the scheme. In both instances, it is important to remember the simplicity of the PHABSIM approaches, both hydraulic and habitat modelling. Successful applica-tion still requires considerable calibration and refi nement. Calibration may be based on empirical (regression) fi ts in an effort to capture stage-dependent as well as spatial variations between vertical cross-section slices (Milhous et al., 1989). Alternatively, local and stage-dependent variations may be estimated from sparse velocity measure-ments at a single stage by back-calculating of Manning’s n on a slice-by-slice basis, having further adjusted to match the modelled fl ow. In this case, however, the physi-cal integrity of the model is then lost and cells are not truly associated by sound hydraulic principles (Ghanem et al., 1996). In neither case, therefore, is the model a real sub-stitute for further fi eld measurement. Frequently, there is an interaction between the uncertainties inherent in a one-dimensional model and those arising in higher-order model applications, since the output of the one-dimensional model is sometimes required to determine the initial water surface profi le for the more complex model.

While PHABSIM remains something of an ‘industry standard’ tool, the biological representation is also often highly simplifi ed and empirical (Pusey, 1998). Wood et al. (2001) found that the strength of ecological relationships increased and model errors are reduced with increasing spatial resolution, indicating that most potential is still at the site-scale but that all modelling efforts are limited by the lack of longer-term data sets, too. However, one advan-tage of this habitat modelling approach is that there are clear manuals that defi ne step-by-step procedures, which allow replication of results by different researchers. The disadvantage of this is that it has led to poor applications by practitioners with little experience. Best results are obtained where teams including hydraulic engineers, hydrologists and ecologists work together, using habitat modelling as a basis for their river-specifi c studies. Major strengths and weaknesses of the PHABSIM methodology are summarised in Table 7.5.

7.4.5 More Complex Hydrodynamic Modelling and Habitat Simulation

Numerous specifi c modelling applications have been described that demonstrate some kind of improvement on simpler schemes. However, these have not given rise to any single package that is the logical replacement to PHABSIM. Greater hydraulic process representation may be achieved using two- and three-dimensional computa-tional fl uid dynamics models (Alfredsen et al., 1997; Booker, 2003). New approaches to quantifying hydraulic habitat have been published (Peters et al., 1995; Nestler and Sutton, 2000). New habitat models have included additional variables and have been expanded to the com-munity level (Bain et al., 1988; Bain, 1995; Lamoroux et al., 1998). All of these improvements currently come at a cost of increased complexity, although in the future it is hoped that they can be used to derive general rules with which to develop improved look-up methods and to defi ne the impacts of river fl ow regulation on populations rather than habitats (Hardy, 1998).

Two-dimensional PHABSIM

The PHABSIM method may also be applied using spa-tially continuous patterns of depth and velocity as pre-dicted by hydrodynamic models (e.g. Ghanem et al., 1996). Spatially continuous information negates any prob-lems caused by sampling frequencies applied in fi eld based studies. This approach also allows visualisation of areas of suitable habitat. A comparison of habitat suit-ability predicted using two different HSIs for a 50 m reach of the Bere stream, Dorset, UK, is shown in Figure 7.13. These maps show how truncation of the depth HSI can lead to changes in patterns of habitat suitability. An accompanying sensitivity analysis during the construction of Figure 7.13 also demonstrates how uncertainty in habitat modelling will depend on the interactions between the sampling density and the HSI used. In analogy with the analysis of velocity data in Figure 7.9, spatially con-tinuous simulations can be sub-sampled to simulate how the number of cross-sections and point measurements might affect results had the information been collected in the fi eld. In general, predicted habitat decreases as more points are added across each section. This occurs because poor habitat at the margins of the channel is more likely to be sampled when more points are measured along each section. When the truncated HSIs were used, the positions of the cross-sections in relation to a deep pool in the middle of the reach had a strong infl uence on predicted habitat. When small numbers of cross-sections were used, the likelihood of the pool being over- or under-sampled


Table 7.5 Major strengths and weaknesses of the application of the PHABSIM methodology

Strengths Weaknesses

The method used is replicable and therefore does not rely on expert opinion

Results may be sensitive to the location of cross-sections and the position of measurements along these cross-sections

The method can be applied using hydraulic predictions calculated using a range of techniques from crude stage-discharge relationships to spatially continuous computational fl uid dynamics modelling.

Inaccuracies in hydraulic modelling, especially in steep boulder rivers (Azzellino & Vismara, 2001) and where vegetation growth causes changes in stage-discharge relationships throughout the year (Hearne et al. 1994) will affect habitat predictions.

Channel change can be incorporated through repeated survey

Requires re-calibration to assess changes in channel morphology over time. Incorporation of channel change would require expensive repeated surveys or sediment transport predictions, which have their own sources of uncertainty.

Multi-variate habitat suitability indices have been developed (Vismara et al., 2001).

Rules for weighting depth, velocity and substrate are not readily available.

Application of habitat mapping has allowed results to be up-scaled to represent the length of river under investigation (Maddock and Bird, 1996; Maddock, 1999).

Typically restricted to depth, velocity and substrate, although variables such as water quality, vegetation cover can be incorporated.

Recent studies have confi rmed that physical habitat is an important factor controlling fi sh populations in the long term (Sabaton et al., 2003; Souchon and Capra, 2003).

Reached-based results (covering approximately 100 m of river length) must be scaled up.

No readily applicable alternative methods have been developed which enable prediction of population changes as the result of water resource impacts.

Calculates habitat suitability and not population numbers or presence/absence.

In most studies physical habitat availability at low fl ow has been the main area of interest, with PHABSIM used primarily to compare the implications of alternative fl ow regulation scenarios on habitat. This has led to an emphasis on the relationship between discharge and usable habitat given a distribution of relatively shallow depths and slow velocities.

was higher. Results also suggest that, as the number of cross-section increases, there is less variation in predicted habitat above six cross-sections when the non-truncated HSIs were used and eight cross-sections when the trun-cated HSIs were used.

Swimming Speeds

A more sophisticated example of habitat modelling using hydrodynamic calculations is to compare patterns of velocities predicted by three-dimensional models with the swimming speeds of fi sh to assess the effects of channel design on fi sh habitat. The swimming performance of fi sh can also be analysed to assess the health of the fi sh when exposed to sub-lethal toxic chemicals (e.g. Alsop et al., 1999). One frequently used measurement is the Maximum

Sustained Swimming Speed (MSSS). This is defi ned as the maximum velocity at which a fi sh can swim for a period of more than 200 minutes (Turnpenny et al., 2001).

Booker (2003) compared simulated velocity patterns to MSSS of roach, dace and chub to assess habitat suitability at high fl ows in two reaches of the River Tame, an urban river in Birmingham, UK. The percentage of in-stream area that was less than the MSSS was used as an indicator of habitat quality. This was the area of river, at a specifi ed distance from the bed, in which fi sh of a certain size and species could theoretically sustain a position for at least 200 minutes. Due to uncertainty as to the exact distance between the fi sh and the river bed during high fl ows, and to simplify the three-dimensional nature of the analysis, different heights above the bed were considered separately.


Figure 7.13 Maps of habitat suitability derived using the same hydraulic patterns but different suitability curves: (a) HSIs given in Dunbar et al. (2001), (b) the same HSIs truncated for depth


The percentage of survivable area for chub, dace and roach at bankfull discharge was calculated at two sites (Figure 7.14). The ‘Highly Modifi ed’ reach was a 91 m straightened reach with an average width of 11.9 m and artifi cially strengthened banks contained within a larger two-stage channel. This reach had no distinct geomorpho-logical features and relatively uniform bed topography. In contrast, the ‘Less Modifi ed’ reach was 139 m in length with an average width of 10.0 m. This reach was also contained within a two-stage channel but had one bank consisting of natural material, a slightly sinuous path and an undulating bed profi le. Figure 7.14(b) shows that as the body length increases there is an increase in the area of habitat in which a fi sh is likely to be able to sustain a sta-tionary position. This is because bigger fi sh can sustain faster swimming speeds. Similarly, as distance from the bed increases the percentage of survivable habitat decreases due to faster velocities. Results showed that at the Highly Modifi ed site the uniform channel structure supported no discrete areas of slower velocity which could be used as refugia. At the Less Modifi ed site a deep pool approximately 40 m from the upstream boundary provided a fl ow refuge that could act as a niche of suitable habitat. There was also a separate discrete area of slower velocity created by the sheltering effect of a change in direction of the river planform.

Bioenergetics

Bioenergetic models attempt to quantify the trade-off between energy gained through feeding and that lost

through swimming ‘costs’. Costs arise from holding posi-tion in a moving fl ow, in the capture of food, as well as through digestion, faeces and urine (Hayes et al., 2000). Fish activity may be directly observed and related to fl ow conditions or estimated on the basis of models that use results of physiological experiments undertaken on fi sh which are forced to swim against fl ows of constant velocity. For example, Booker et al. (2004b) used a three-dimensional Computational Fluid Dynamics (3D-CFD) model to simulate hydraulic patterns in a 50 m reach of the Bere stream, Dorset, UK. This information was then combined with a bioenergetic model that used behavioural and physiological relationships to quantify the spatial pattern of energy gain when feeding on invertebrates drift-ing in the river. The model was tested by comparing pat-terns of predicted energy intake with observed habitat use by juvenile salmonids at different times of day. A map of energy intake predicted by the model compared with observed locations of feeding and resting fi sh at the site is shown in Figure 7.15.

There are many uncertainties that must be considered when using this bioenergetic modelling approach as a tool to assist river restoration. The approach relies on the accu-racy of both the hydrodynamic predictions and the accu-racy of the physiological algorithms and parameters used. Enders et al. (2003) demonstrate that models of fi sh activ-ity cost based upon constant velocity experiments may underestimate actual costs by up to a factor of 4.2, because turbulent fl ow fl uctuations are neglected. Direct observa-tions of feeding behaviour illustrate close relationships to fi sh activity and characteristics of turbulent fl ow (Enders

Figure 7.14 (a) Mean ‘maximum sustainable swimming speed’ and ‘burst swimming speed’ for roach, dace and chub at 8 °C (based on data from Clough & Turnpenny, 2001), (b) Percentage volume of habitat less than the mean ‘maximum sustainable swimming speed’ at different distances from the bed in each reach at 18 m3s−1 (Source: Figure 11 of Booker, 2003)


et al., 2005). Models may also be hard to test against fi eld observations of habitat uses because factors other than energy intake can infl uence microhabitat selection. For example, the proximity to other fi sh (Valdimarsson and Metcalfe, 2001) and predation risk or distance to cover (Mesick, 1988). Fish do not spend 100% of their time drift-feeding. They may feed very effi ciently for short periods and then retreat to more sheltered locations (Gries and Juanes, 1998). Furthermore, the decision to select a certain position may not result from conditions (e.g. drift density) at that time but conditions at a previous time or conditions over a longer period. Also, a fi sh may not have perfect knowledge of its habitat. This means that fi sh feeding in an energetically poor area may not be aware that there are more favourable alternative positions elsewhere.

7.4.6 Alternatives to Physical Habitat Models

Several alternatives to complex eco-hydraulic simulation have emerged. A fi eld approach known as ‘Expert Habitat Mapping’ (EHM) is used in streams that are hydraulically complex, particularly where the irregularity and variabil-ity of boundary conditions makes hydraulic modelling inappropriate (for example, in steep bedrock rivers with large woody debris). EHM uses habitat suitability criteria but then relies on fi sh biologists mapping habitat based on those criteria at a range of fl ows to develop fl ow-vs-habitat curves. The method works well provided that the HSI cri-

teria are used and verifi ed with spot measurements, and the biologists are experienced.

Alternatively, physically-parameterised models may be replaced by stochastic or hybrid approaches such as cel-lular autometa (Chen et al., 2002) or neural network models (Werner and Oback, 2001; Reyjol et al., 2001; Gevrey et al., 2003) as witnessed in hydrology and fl ood-plain inundation studies. As yet, however, these approaches have focused on simulating ecological characteristics: appropriate scales for applications, for the development of rules and training data sets remain to be explored.

7.5 CONCLUSIONS

Restoring and rehabilitating rivers for ecological purposes is an essentially multi-disciplinary and multi-stage activ-ity. There is growing awareness that to be successful (that is, to produce schemes which are sustainable in the medium- to longer-term) truly functional environments are required, where catchment hydrology provides the background, or context, for reach and inter-reach fl ows and sediment transport. In turn, these hydraulic variables structure physical habitats, which themselves support a diverse ecological response. Monitoring of fl ows and aquatic ecology have been long-standing areas of research and continue to be vitally important but, increasingly, emphasis has been upon the modelling or simulation of fl ows and the habitats which these determine. Modelling

Figure 7.15 Predicted net energy intake for a 0.1 m fi sh and observed fi sh locations


is useful in extending the range of fi eld observation, in scenario-type exercises to evaluate restoration schemes before their implementation and also in providing outputs against which schemes might be appraised after imple-mentation. Ideally, modelling efforts should proceed with the closest possible coupling between their physical (hydraulic and hydrodynamic) and biological elements, and in close association with improved strategies for fi eld monitoring, too. Running through this chapter are some very basic themes. These refl ect the principal sources of uncertainty in restoration schemes for ecological purposes and must be addressed to improve both fi eld and model-ling aspects of restoration design:

data requirements – the appropriate amount and kind of fi eld data required to adequately specify pre- and post design aspects of the river;

characterisation – of fl ow coherence and species assem-blages or behaviour, which partly follows from data considerations as above, but which also includes design criteria relating to channel form, as well as parameteri-sation of models;

coupling – of key physical and ecological aspects of river system function in models;

awareness – of model application, limitations and sensi-tivity, and of other limitations;

development – of newer, more fl exible means of model evaluation and of restoration appraisal more generally.

While each of the above remain sources of uncertainty, they represent, too, areas of opportunity for the rapid development of research and intervention protocols. For the practitioner, asking questions from each of these areas – and questions which cut across or link between them – is perhaps the best form of guidance for recognising, reduc-ing and even incorporating uncertainty into river restora-tion activity from an ecological standpoint.

ACKNOWLEDGEMENTS

This chapter was produced as part of research projects NER/A/S/1998/00009, ‘Identifi cation of physically-based design criteria for riffl e-pool sequences in river rehabilita-tion’, NER/D/S/2000/01422, ‘Formation of a new river channel: fl ow, sediment and vegetation dynamics’, and NER/T/S/2001/01250, ‘Vegetation infl uences on fi ne sedi-ment and propagule dynamics in groundwater-fed rivers: implications for river management, restoration and ripar-ian biodiversity’. These were funded in the United Kingdom by the Natural Environment Research Council. SSIIM is produced and made available by Nils R. B. Olsen, Department of Hydraulic and Environmental

Engineering, The Norwegian University of Science and Technology. Scott McBain kindly provided references to the fi gures and tabular information used in the text, and advised on initial chapter layout and content. Nigel Wright made additional suggestions in respect of hydraulic modelling.

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8

Uncertainty Surrounding the Ecological Targets and Response of River and

Stream Restoration

Martin R. Perrow1, Eleanor R. Skeate1, David Leeming2, Judy England3 and Mark L. Tomlinson1

1ECON, Ecological Consultancy, UK.2Consultant Ecologist, UK3Environment Agency, UK

to the development of improved water quality standards – to the limitations of physical habitat structure. For example in the United Kingdom, initial restoration efforts on the River Thames in 1858 were driven by water problems caused by pollution from human and animal wastes (Gameson and Wheeler, 1977), whereas now the focus is on restoring habitat quality, especially for fi sh, in the upper catchment where tributaries have suffered from river engi-neering schemes (Robinson and Whitton, 2004).

What has become the billion dollar industry of river restoration (Malakoff, 2004; Palmer et al., 2005) generally uses a geomorphological (in combination with engineer-ing) approach (Downs et al., 2002; Malakoff 2004), in the hierarchical gradient of process–form–habitat–biota pro-moted by the highly infl uential National Research Council (of the United States) publication Restoration of Aquatic Ecosystems (1992). This is restore natural water and sedi-ment regime; restore natural channel geometry; restore natural riparian plant communities; and restore native aquatic plants and animals.

The widely perceived ecological focus of river restora-tion (Newson et al., 2002; Downs and Skinner 2002; Ormerod, 2004), is therefore the fi nal step in the chain of works. As with ripples on a pond or the cascade response through trophic levels (Carpenter and Kitchell, 1993), the strength of the interaction and the response is likely to weaken the further from the source action. To take a more specifi c example, the huge amount of river restoration

8.1 INTRODUCTION

In his seminal paper, Bradshaw (1987) described restora-tion ecology as the ‘acid test’ of ecology in that if there is suffi cient understanding of form, structure, process and function then it should be possible to restore any particular habitat to a close approximation of that which is desired. The recent Handbook of Ecological Restoration in two volumes detailing principles (Perrow and Davy, 2002a) and practice (Perrow and Davy, 2002b) suggests that res-toration ecology has come of age as a science, although there is great disparity in the level of understanding and thus success in different biomes. The book also reveals the fundamentally different approach to the restoration of lotic and lentic environments.

In lakes, especially shallow ones, there has been a theo-retical shift from ‘bottom-up’, where the form and func-tion was controlled by physical and chemical properties, to ‘top-down’, where components at the top of the food web, especially fi sh, have great bearing on lower trophic levels and even physical properties (Perrow et al., 2002). The focus of lake restoration is therefore often on fi sh (Jeppesen and Sammalkarpi, 2002) embracing and using fundamentally ecological interactions.

In rivers and streams there has also been a shift in focus away from water quality – which is now not seen as the primary limiting factor for a healthy, natural system as it was immediately after the industrial revolution and prior


focused on anadromous fi shes including sturgeon (Waldman and Wirgin, 1998) and particularly salmonids, upon which millions of dollars are spent annually in the US Pacifi c Northwest alone (Roni et al., 2002), effectively hinges on the relationship between the fi sh and limiting habitat variables. However, such relationships are not always clear-cut and may explain a low proportion of the variation in abundance and biomass (Milner et al., 1985). In very simple terms, a ‘restored’ river may be perfectly capable of supporting a high abundance and biomass of fi sh but may actually contain very few. In recognition of the fact that biological and ecological limitations (i.e. stock limited recruitment, disease, predation, competition etc) may be more important than the generic problem of not having enough habitat, release of large numbers of artifi cially raised juveniles may have to be undertaken, e.g. on the River Mattole in California (Mattole Restoration council http://www.mattole.org/) and in rivers in North-East England (Russell, 1994). In a nutshell, the paradigm of getting the physical structure right and then the ‘ecology’ will then surely follow is likely to be a false premise (Ormerod, 2004).

Geomorphological and ecological processes are inextri-cably intertwined and best considered in an eco-hydromor-phic approach to restoration (Clarke et al., 2003). To illustrate, consider a low order boulder-strewn upland stream. Here, geological structure, gradient and geomor-phological processes expressed in fl ow and sediment regime may initially be viewed as dominant over ecological processes. But consider the impact of the presence or absence of trees and their coarse woody debris (CWD) feeding back into geomorphological processes by con-straining channel width and increasing fl ows and promot-ing erosion in one place and slowing fl ows and causing deposition in another. Allochthonous input in the form of leaf fall provides coarse particulate matter (CPOM) to the channel providing the basis of nutrient cycling and spiral-ing (see Newbold, 1992 for an overview) and energy fl ow (Calow, 1992 for an overview) and thus biological produc-tion, ultimately determines the biomass of the biota. Coarse woody debris from the trees promotes retentiveness and decomposition of litter (Lepori et al., 2005), key eco-logical functions infl uencing the abundance and diversity of shredding invertebrates responsible for producing fi ne particulate matter (FPOM) used by other feeding guilds, consumed in turn by fi sh, birds and mammals (Richardson and Jackson, 2002). In this hypothetical example, under-standing and subsequently manipulating ecological pro-cesses, such as succession, herbivory (predation) and competition, to infl uence the structure and composition of the riparian tree fl ora may be as (if not more) effective as manipulating physical habitat structure.

Moreover, whilst ecological processes may be embodied within a particular component of the fauna and fl ora, this is intuitively unlikely to operate over trophic scales within biotic components as well as interact with abiotic compo-nents. Individuals, species populations and their communi-ties cannot be seen as mere products of the physical (and chemical) framework but inextricably linked with it via particular processes. Thus, an engineering analogue of fi tting habitat components back together (i.e. a building block functional habitat approach; Harper et al., 1992) to make a naturally functioning ecosystem is likely to be too simplistic. The nub of an engineering approach, that a habitat component or unit may be expressed as a certain number of individuals or composition of a community, thus takes no account of process, but rather assumes it.

Our experiences of river restoration in the United Kingdom lead us to believe there is a widespread lack of understanding of what ‘ecology’ is and does in river res-toration. Pertinent to this volume, we aim to explore whether this intuitively leads to uncertainty (in this case reducible ignorance, see Chapter 3) and a lack of confi -dence in ecologists amongst other types of restoration practitioner. For example, Downs and Skinner (2002) sug-gested ‘river restoration ecology suffers from the inability of ecologists to defi ne their system requirements as closely as, for instance, engineers can specify and model (although not necessarily achieve) their fl ood defence requirements’. This neatly encapsulates the perception that not only is ecological restoration uncertain but that the scientifi c basis to predict and subsequently evaluate ecological response is severely lacking, perhaps even absent.

One of the aims of this chapter is to determine whether such bold statements are justifi ed in the context of the basic premise of this volume that much restoration of rivers has and is being conducted with considerable uncer-tainty in one form or another. To do this, the sources of uncertainty surrounding ecology (and ecologists) affecting river restoration schemes are explored in stepwise manner, broadly akin to the planning, design and target setting, implementation and outcome and appraisal phases in any project (Figure 8.1). An attempt is made to determine if there is widespread uncertainty in the ecological response and outcome of restoration schemes over and above that expected as a result of natural variation, and, if so, whether uncertainty and unreliability can be reduced.

8.2 SOURCES OF ECOLOGICAL UNCERTAINTY

8.2.1 Inherent Variability

Natural systems are inherently variable in time and space, and probably none more so than rivers. Variation in space

Uncertainty Surrounding the E

cological Targets and R

esponse of River and Stream

Restoration

141

Figure 8.1 Is Ecological River Restoration ‘Uncertain’? Infl uences of Institutional and Ecological Drivers in Project Planning and Implementation


is particularly well developed both longitudinally, laterally and vertically in rivers, as recognised by attempts to understand them within the river continuum concept (RCC) (Vannote et al., 1980). In larger rivers with exten-sive fl oodplain systems the fl ood pulse concept (Junk et al., 1989) has been used to frame interactions between fl oodplain and channel, which typically vary over time. Temporal variation may also contain other important inter-annual (random, periodic or semi-periodic), annual, sea-sonal and diel components, not simply amongst fauna and fl ora but also in the rates of ecosystem processes such as productivity and nutrient and energy fl ow.

Buijse et al. (2002) outline that whilst considerable effort and emphasis have been directed at attempting to quantify and understand biological, hydrological and geo-morphological interactions that together determine the functioning of a river fl oodplain system, these are inevi-tably variable and complex. Complexity occurs at the physical level (variation in types of riverine system), the restoration level (the combination of features requiring restoration are site-specifi c) and the ecological level (com-munity interactions). Hughes et al. (Chapter 6) argue that ‘complexity’ accounts for a degree of the perceived eco-logical ‘uncertainty’ in river restoration and it must be accepted that much may remain unknowable (Chapter 3).

In a wider discussion of management of nature in the United Kingdom, Adams (1997) suggested a recent shift in ecological thinking from looking to restore a ‘stable equilibrium’, often taken to represent historical condi-tions, to a new view that a riverine system is by its nature dynamic and changing, i.e. one that embraces natural vari-ability. This is the basis of ‘recovery enhancement’ where the scope for natural recovery is exploited, after the shack-ling constraints such as structures (e.g. weirs) and modi-fi ed engineered banks, management actions such as intensive vegetation management regimes or even simply grazing by livestock are removed. The river may also be given the space to move (i.e. during the re-connection of the fl oodplain as on the Rivers Cole and Brede, see Kronvang et al., 1998; Holmes and Nielsen, 1998). Such action to favour a potentially less controllable and predict-able system (Hughes et al., Chapter 6) may appear to the engineer, whose target is to achieve control over the system to meet specifi ed fl ood defence and shipping access targets, as simple acceptance of uncertainty.

8.2.2 Limited Understanding

In spite of the wealth of work carried out on riverine systems, several authors (de Waal et al., 1995; Buijse et al., 2002) have argued that there is actually a lack of scientifi c literature about river restoration, and that a

lack of robust datasets has rendered much of the work that has been carried out unpublishable in peer reviewed journals. Although linked, there are actually two issues here, information exchange and scientifi c substance and credibility.

Information exchange has undoubtedly increased enor-mously as the number of river restoration projects across the globe has increased. Malakoff (2004) reports 30 000 projects in the United States alone with tens of thousands more to come in the next few years. The literature search for this chapter uncovered a huge variety of information on various river restoration projects, especially via web-sites such as the European Centre for River Restoration (http://www.rws.nl/rws/riza/home/ecrr/) and its UK (http://www.therrc.co.uk/) and Danish counterparts (http://www2.dmu.dk/) and the National River Restoration Science Synthesis, which operates in seven states of the United States, with a satellite branch in Victoria, Australia (http://www.nrrss.umd.edu/). However, much information is produced in an abbreviated form and it diffi cult to determine whether many projects outlined in such a manner are of real scientifi c substance.

At the opposite end of the spectrum lies the academic peer-reviewed process as a vehicle for information exchange. The fact that Ormerod (2004) found only 300+ papers including the terms river or stream restoration or rehabilitation in the title, abstract or key words in the ISI® database, indicates that only a fraction of projects reach the academic literature. This could illustrate that much work is not submitted for publication and/or many submis-sions are not accepted as a result of inadequate scientifi c rigour. It may also suggest that few academic river ecolo-gists are involved with restoration. In the United Kingdom at least, ecological input in many schemes is undertaken internally by statutory bodies such as the Environment Agency or by consulting fi rms (like the authors of this chapter!).

Overall, whilst there is a proliferation of information, facilitating the rapid development of project planning, target setting, implementation and post-project appraisal on a global scale, the question-mark over data quality and the lack of involvement of academic ecologists may mean the uncertainty due to limited knowledge is not reducing as rapidly as it might.

8.2.3 Project Selection

In the United Kingdom, until very recently, virtually all restoration was opportunistic despite the need for a stra-tegic approach being recognised over a decade ago (ECON, 1993). One of the reasons for this was simply that restora-tion was most effectively directed at rivers (reaches) of

Uncertainty Surrounding the Ecological Targets and Response of River and Stream Restoration 143

highest conservation value, i.e. those closest to the pre-disturbance state. Investment in the most degraded reaches/rivers was misplaced and wasted resources, as they could not achieve high status as a result of over-whelming and complex limiting factors (Kern, 1992). Moreover, as shown for lakes (Benndorf, 1992), the pathway from highly degraded to high quality is long and littered with the opportunity for unpredictable and indirect effects, which may constrain or even reduce ecological value (i.e. colonisation by alien species). In simple terms, long restoration trajectories (see Section 8.2.6) are more diffi cult to plan and pull off.

8.2.4 Project Structure

It is generally acknowledged that river restoration projects require an integrated and interdisciplinary approach, which requires a number of experts from different fi elds working alongside each other (NRC, 1992). The scale of the project appears to have great infl uence as whilst this may have been achieved in the case of large, high-profi le schemes (e.g. the Kissimme River Restoration Project in the USA – http://www.sfwmd.gov/org/erd/krr/ – and the schemes tackled by the River Restoration Centre in the UK – http://www.therrc.co.uk/) it does not appear to be routine. Taking the schemes evaluated in the UK Rivers and Wildlife Handbook as a sample, out of the 25 schemes where the project team was described, 80% did not employ an interdisciplinary approach (RSPB et al., 1995).

Resources are also clearly an issue, with a common desire to ensure that the bulk of resources available are channeled into ‘doing’ restoration works rather than be absorbed into project infrastructure and institutions (Bruce-Burgess and Skinner, 2002). The involvement of too many organisations and stakeholders with confl icting interests may also ultimately constrain the options for restoration and the ability to undertake it (McDonald et al., 2004).

From an ecological perspective it seems obvious to suggest that the more central the position of ecologists in the project, the greater the chance of uncertainties sur-rounding ecological issues being identifi ed early in the life of the project and subsequently tackled. The perception that much river restoration has been undertaken with the idea of improving at least some elements of the biota, especially fi sh (Bruce-Burgess and Skinner, 2002; Ormerod, 2004) gives the impression that such schemes are ‘front-loaded’ by ecologists. Our experience is more that ecologists enter the restoration fray to ‘count bugs’ in the process–form–habitat–biota sequence (see previously), particularly where an opportunity for restoration such as on the back of a maintenance scheme has been taken.

Clearly, it is too late to set meaningful ecological targets if the ecologists are simply used in project monitoring.

8.2.5 Goals and Target Setting

The nature of the goal underpins the ecological restoration process. Restoration in its strictest sense involves the return of the structure and function of an ecosystem to a condition that existed prior to disturbance (NRC, 1992) and this forms a ready-made goal for the project (White and Walker, 1997). However, reliable historic information is often absent and there is also the issue that climatic conditions, amongst other planetary processes, may have changed suffi ciently to mean that in ecological terms the river may no longer function in the same manner, even if it had remained undisturbed. The use of ecologically similar but undisturbed contemporary reference sites or even professional expert opinion based on empirical and/or computational models, represent an alternative means of establishing a suitable goal (White and Walker, 1997; Anderson and Dugger, 1998), although any method still requires understanding of the current nature of the system, range of natural variation, mechanisms whereby impacts have occurred and the specifi c effects of such impacts. Otherwise, restoration may be doomed to failure.

The identifi cation of causes, in particular, can be diffi -cult if they are subtle and far removed in space and time from ecological damage (NRC, 1992). Some of the most commonly overlooked factors are: the number of stresses on biotic components; the multi-causal nature of degrada-tion; and the diversity of resulting problems. These issues may only be resolved by adequate baseline monitoring. Poor baseline data potentially leads to poor target setting. Setting and achieving targets ultimately provides the bench-measure for the success of the project. Without a clear and appropriate goal and precise and accurate targets, there may be a lack of benefi cial impacts, and even detri-mental ones. Kondolf (1998) describes the project at Rush Creek, California, USA, as an example of this type of mistake. The aim to protect the banks from erosion was inappropriate and inconsistent with geomorphological and ecological pressures at the site, and the aim to create a wet meadow to stop bank erosion was probably unrealistic due to hydrologic changes. By stopping bank erosion, the end result of the project was that the stream’s natural processes of recovery and re-establishment of woody riparian vege-tation was arrested.

These sort of experiences may have lead Downs and Skinner (2002) to suggest that one of the two key aspects limiting restoration was the inability of ecologists to set targets (the other was monitoring and evaluation, see below). Certainly, there does seem to be a general lack of


ecological target setting in projects. In the United Kingdom, an evaluation of the 40 different case studies by Holmes (1998) and a further 40 in the New Rivers & Wildlife Handbook (RSPB et al., 1995) showed that although projects were driven by ecological goals, only around 65% actually set any sort or target, and only on one scheme were quantitative targets (based around the restoration of spawning habitat for salmonid fi shes) set and publicised (Table 8.1). Even in the recent Cole and Skerne projects, perhaps representing the pinnacle of res-toration in the United Kingdom to date, there is little sign of ecological target setting, neither qualitative nor qualtita-tive, the benefi ts brought about by the scheme being assessed purely on the results of monitoring (Holmes and Nielsen, 1998; Kronvang et al., 1998; Hoffmann et al., 1998; Biggs et al., 1998; Vivash et al., 1998).

This is in sharp contrast with large scale schemes such as the Kissimmee River Restoration Project RRP (KRRP) in the USA (Trexler, 1995; Toth, 1996; Toth and Anderson, 1998; Toth et al., 1998) and the Rhine in Continental Europe (Buijse et al., 2002). In the latter, ecological condi-tions in a similarly functioning section of the Danube were used to establish target conditions. In the former, around 60 target aims were selected (performance measures), all of which had explicit goals associated with restoring the full range of structural and functional processes, and which were developed with peer review by an independent scientifi c review panel. Targets covered the range of eco-system components and trophic levels and included habitat characteristics (12 – hydrology, geomorphology, water quality), wetland vegetation (10), food base (13 – phyto-plankton, periphyton, invertebrates, herpetofauna) and fi sh and wildlife (25) (Whalen et al., 2002). The feasibility

of the targets was considered in great detail. To evaluate the restoration of the fi sh community a conceptual model outlining aspects of ecosystem function was developed (Trexler, 1995). This integrated information from several levels of biotic organisation (individuals, populations, communities and systems) and focused on the dynamics of fl oodplain–channel nutrients and the movement of larvae, juvenile and adult fi sh and their macroinvertebrate prey.

The experiences from large schemes, especially the KRRP, suggest there are other reasons rather than a lack of ability as to why targets are not routinely set. In simple terms, it may be diffi cult, time consuming and expensive. Particularly if the ‘ultimate’ approach to target setting, namely setting quantitative targets and formulating expec-tations as hypotheses that can be evaluated statistically to provide objective evaluations, is pursued (Toth and Anderson, 1998). Such an approach requires an extensive information base on the river in question. Where this is absent, gathering such data may not be cost effective, espe-cially in the case of reach-scale projects, since understand-ing a complex system could easily take several years and exceed the lifetime of the project (Kondolf, 1998). As a result, a more pragmatic approach may be to set more qualitative targets, although it should be recognised that even qualitative targets may be evaluated in a rigorous manner. Toth and Anderson (1998) suggest that if qualita-tive expectations are to be used as success criteria they must be expressed in an objective scientifi c manner relative to the restoration goal. Assessments of the presence or absence of species, for example, are highly sensitive to sampling effort (see Section 8.2.6) and an increased frequency of occurrence may be a response that cannot be used to dif-ferentiate restoration from habitat enhancement.

Further pragmatism to target setting may be to simply narrow the targets (Toth and Anderson, 1998), such as focusing on a single species, as adopted in the recovery of endangered species populations. Many such species, from mammals (e.g. Eurasian otter Lutra lutra; Ottino and Giller, 2004) to fi sh (e.g. Colorado pikeminnow Ptycho-cheilus lucius in the River Colorado – van Steeter and Pitlick, 1998; Roanoke logperch Percina rex in the Nottoway & Roanoke rivers in Virginia – Rosenberger and Angermeier, 2003) to invertebrates (e.g. freshwater pearl mussel Margaritifera margaritifera – Cosgrove and Hastie, 2001; White-clawed crayfi sh Austropotamobius pallipes – Smith et al., 1996) have been subject to in-depth assess-ment of habitat requirements in riverine environments. After detailed research, specifi c habitat attributes may be readily defi ned for particular life history stages.

Defi ning habitat relationships between any organism and any particular physical parameter, especially fl ow, has

Table 8.1 Proportion (%) of the 80 projects reviewed by Holmes (1998) and presented in the New Rivers and Wildlife Handbook (1995) in England, Wales and Northern Ireland satisfying selected criteria

Criteria Holmes (1998) NRWH (1995)

Wholly/partially ecologically driven

65 55

Sites strategically selected using ecological restoration criteria

43 30

Use of an interdisciplinary team

50 13

With specifi c ecological targets

65 68

With some sort of appraisal 70 100Achieving some measure of

ecological improvement100 92


been exploited by the Instream Flow Incremental Method-ology (IFIM) (Gore and Judy, 1981; Bovee, 1982) and its underlying model, the Physical Habitat Simulation Model (PHABSIM) (see Appendix 8.1). PHABSIM provides a potentially powerful tool that may enable specifi c targets to be set for particular species where enough information of the habitat relationships of the species concerned exists. Specifi c sets of habitat relationships, which may feed into restoration efforts, are particularly well advanced for char-ismatic and/or commercially valuable species such as sal-monid fi shes, particularly in the United States, where detailed and structured attempts at relatively large scale habitat restoration for salmonids has been going on for 40 years or more (White & Brynildson, 1967; Wesche, 1985) with a wealth of detailed guides and manuals on the web (e.g. http://www.wildfi sh.montana.edu/resources/mammals.asp).

In some cases the use of indicators such as fi sh (or birds or mammals) at the top of the food web may at least indi-cate the direction and strength of the response of lower trophic levels as a result of habitat restoration. This has the basic assumption that targeted improvements in higher trophic levels as a result of improvements in habitat diver-sity are also likely to have led to a response in the diversity of lower trophic levels (i.e. species diversity is related to habitat diversity; Gorman and Karr, 1978). Monitoring keystone species may not always be appropriate, however, as it may not provide enough information about other important processes such as organic matter spiraling and ground–surface water interactions (Tockner and Schiemer, 1997).

Nevertheless, where multiple measures are taken, the multiple expectations generated may perversely lead to an ambiguous evaluation of restoration success if all expecta-tions are not met (Toth and Anderson, 1998). In such a case, either all expectations need to be judged collectively, for example by tracking a cumulative count of expecta-tions that are achieved over time, or perhaps expectations could also be weighted according to set priorities. Targets that can be expressed as constants or thresholds are perhaps easiest to evaluate but probably will be limited to a few attributes, for example in many systems enough information is available to formulate constant or threshold expectations for species richness. This approach is readily employed for invertebrates, with the recent development of numerous assessment criteria particularly in relation to fl ow at the assemblage level (Appendix 8.2).

Due to the natural spatial and temporal variability that is associated with the scale of ecosystem restoration, Toth and Anderson (1998) suggest many targets need to be expressed as ranges, although the breadth of the range that is used for restoration expectations cannot be so great as

to mask or preclude evaluation of restoration success. For example, the expectation of small fi sh densities of 0.7 to 1.3 fi sh per square foot of restored marshes on the Kissimmee fl oodplain refl ected sampling variability whilst still providing a useful range for evaluating restoration success. With hindsight, our own experiences on small rivers in the United Kingdom show that such targets could have been readily set. For example, at several sites on the Misbourne, the fi sh community responded in a rather pre-dictable way to the increase in fl ow, with one assemblage replaced by another as habitat conditions changed (Appen-dix 8.3).

Target setting in large and complex schemes, especially the KRRP, but not in smaller schemes is the inverse of that expected. Perhaps it is simply that setting meaningful targets and goals may be diffi cult, time consuming and expensive. In large schemes in which huge investment of time and resources is made, it is prudent that every effort is made to reduce the uncertainty of the outcome, particu-larly where this relates to reducible ignorance in the form of increasing the knowledge base. Conversely, smaller schemes, where the risks of failure may not be as great and where resources are fewer and tend to be directed at ‘doing restoration’, may embrace all aspects of uncertainty.

In general, contrary to the conclusions of Downs and Skinner (2002), we suggest that ecologists may set targets as readily and as well as engineers given the opportunity, especially since Stewardson and Rutherfurd (Chapter 5) argue that there may be unreasonable confi dence in the target setting ability of geomorphologists, who tend to rely on uncalibrated (with real fi eld data) theoretical models dogged by poor estimation of variance.

8.2.6 Time Scales and Trajectories

In any restoration, consideration should be given to the time scale for recovery or restoration, which is infl uenced by how far removed the system is from the goal and targets set, and the trajectory and pathway(s) that may be under-taken (Gregory and Downs, Chapter 13). Anand and Desrochers (2004) observe that the lack of long term observational studies in virtually any ecological system often compels researchers to rely on theoretical models to speculate upon the trajectory that will lead the damaged system to the desired state. According to complex systems theory, which incorporates concepts such as chaos, the nature and direction of a trajectory is governed by the system’s attractor, which may be thought of as its destina-tion. It is hoped that the destination and desired state are one and the same thing or the restoration attempt may be doomed at the outset. Anand and Desrochers (2004) eloquently illustrate the different types of attractor, with


the simplest being the progression of a system to the same stable state regardless of initial conditions, analogous to a climax state of vegetational succession. Where a system has more than one attractor, it may cycle between two alternative states. However, systems may have any number of attracting states and the system may progress to a par-ticular ‘basin of attraction’ depending on the starting point or the initial conditions at which restoration started. This offers the possibility of multiple alternative stable states, which proved to be highly infl uential in the restoration of shallow lakes (Figure 8.2, Scheffer et al., 1993). Ecologi-cal stability itself is also a diffi cult concept to grasp, as large spatial and temporal variation in populations of even key species tuned in to their life cycles or disturbance events (Figure 8.3) may mask underlying trends. Stability is thus best judged over decades rather than from one year to the next.

To the best of our knowledge such concepts have yet to be explored in rivers, although river ‘types’ are the basis of the Rosgen classifi cation used extensively and not without controversy in river restoration (Malakoff, 2004). Notable gaps (i.e. unquantifi ed uncertainties) in our con-ceptual understanding of ecological dynamics within rivers appear to constrain any ability to predict how long nature will take to return the system to a specifi ed historic condition or even what is to be expected.

8.3 DEVISING A MONITORING PROGRAMME

8.3.1 Basic Design

To minimise ecological uncertainty, monitoring needs to be undertaken prior to the work in the form of collection of a robust set of baseline data (see previously). If the pre-restoration system is not fully understood, the results of the scheme cannot be correctly assessed or evaluated. Use of reference reaches (see previously) may help set targets and, to monitor progress against those targets, a suitable monitoring programme needs to be devised. Mon-itoring of controls (e.g. unaltered reaches) undoubtedly helps reduce uncertainty, as this should help distinguish what degree of change is due to natural inter-annual varia-tion and what is due to the restoration works (Stewardson and Rutherfurd, Chapter 5).

Choosing which measurements of biotic and abiotic patterns and processes to monitor is then critical, as these need to be relevant to the goals of the project and the targets set, and must be able to be linked to progress towards those targets. Where the target of restoration is a particular species, this may lead to very specifi c targets and the desire to monitor a few aspects. However, several workers have highlighted the need for wider surveillance rather than aiming monitoring at one aspect of the ecosys-tem. For example, Boon (1998) suggested that fi sheries-

X

YX

Y

(Left) A point attractor. The same final state is reached in both instances X and Y, although the time taken to achieve this (shown by differing arrow lengths) varies depending on the location of the starting position on the restoration trajectory.(Right) A dual attractor. This functions like a pendulum swinging between two magnets, or alternative stable states (A and B). In this instance the restoration time scales are the same, although the final state reached may depend on other ecological factors, for example composi-tion of the community at the point when restoration is commenced.

Figure 8.2 Types of attractors (Reproduced from M. Anand and R. Desrochers (2004) ‘Quantifi cation of Restoration Success Using Complex Systems Concepts and Models’ Restoration Ecology 12 (1), 117–123, with kind permisison from Blackwell Publishing.)


(A) The potential effects of disturbance events (d) on ecosystem structure over time. Disturbance events could constitute anything from pollution or alien species introduction to the impacts of restoration e.g. dredging, and recovery times differ accordingly. (B) Variation in flow at two temporal scales.

Figure 8.3 The effects of extensive spatial and temporal scale variation on ecological stability (Reproduced from P. S. White and J. L. Walker (1997) ‘Approximating nature’s variation: selecting and using reference information in restoration ecology’ Restoration Ecology 5, 338–349, with kind permission from Blackwell Publishing.)

led restoration projects should take a comprehensive ecosystem approach. Tockner et al. (1998) reached a similar conclusion ‘A key challenge in the evaluation of the effects of restoration is the development and testing of an appropriate monitoring scheme, which has to include a wide range of physical, chemical, geomorphic, and eco-logical parameters.’ Monitoring a range of variables pro-vides more information about the system response and the

potential mechanisms behind the response of the target variable or species.

Consequently, a number of groups across the variety of trophic levels are typically used as indicators of ecological status and response. For example, benthic invertebrates, fi sh and aquatic macrophytes were selected as indicators of the ecological status of rivers in the European Union Water Framework Directive (EU, 2000), and benthic


invertebrates, fi sh, plankton, birds, amphibians and ter-restrial and aquatic plants have been used on the project on the Austrian Danube (Buijse et al., 2002). For evaluat-ing biodiversity of river–fl oodplain complexes, a number of species-specifi c groups that differ in their responses to hydrological connectivity, water quality and habitat het-erogeneity should be considered (Buijse et al., 2002; Tockner et al., 1999). The absence of certain species may also be a good indication of ecological deterioration (EU, 2000). For example, the loss of long-distance migratory fi sh (salmonids, coregonids, shads and sturgeons) indi-cates disruption of longitudinal connectivity or a deterio-ration of spawning/nursery areas.

8.3.2 Sampling Requirements

Brooks et al. (2002) pointed out that many commonly used river restoration techniques may never have been scientifi cally tested and, in a demonstration of the need for rigorous scientifi c testing, carried out a fi eld experi-ment designed to mimic restoration to investigate the importance of habitat heterogeneity for macroinverte-brates. The results showed that although a diversity of habitats was required, macroinvertebrate populations were actually more sensitive to individual site conditions at each riffl e than to the heterogeneity treatments. This led to the conclusion that the extremely high variability between replicate riffl es meant that a monitoring pro-gramme for localised restoration projects would be unlikely to detect gradual shifts in community structure until the differences between the reference and treatment sites were extreme. Brooks et al. then suggested that inno-vative measurement of other parameters, such as ecosys-tem function variables (e.g. production, respiration, decomposition), may be more appropriate indicators of change at local scales.

This illustrates the age-old problem faced by ecologists of huge spatial and temporal variation in their subject matter (Figure 8.3). However, rather than looking to other variables that may be technically diffi cult to measure, parameterise and ultimately interpret, the only option to overcome variability is to devise appropriate sampling regimes of suffi cient intensity and frequency and to use robust methods. In the case of invertebrates, methods that allow changes to be evaluated on a community rather than species-specifi c level are likely to be useful (Appendix 8.2). For fi sh, Bohlin et al. (1990) suggested three preci-sion classes for fi sheries studies depending on the nature of the change that needs to be detected, i.e. a factor as small as 1.2, 1.5 or as large as 2.0, with about 80% prob-ability when using a 5% signifi cance level. Unfortunately, when the number of fi sh is relatively low with even moder-

ate variation around a mean value, even the lowest preci-sion class tends to require a high sampling effort. On the River Lambourn, UK, during an attempt to monitor the status of bullhead Cottus gobio, one of the species for which the river was designated as a site of international conservation interest (candidate Special Area of Conser-vation [cSAC]), it was calculated that twenty-seven 100 m sites would need to be surveyed over the 21 km river to detect change on a population level (Perrow and Tomlinson, 2002). Using the same calculations to estimate the number of points required in PASE (see Appendix 8.3) at each site, as few as 39 points were required where the population of bullheads was dense, but up to 150 points were required where bullheads were at low density. The latter probably exceeds the number of points that can be independently sampled, in that the points are far enough apart to not infl uence each other, at any particular site. Where such factors cannot be calculated in advance, rules of thumb are often generated and in the case of PASE this is often regarded as 50 points (Garner, 1997). Below this, the confi dence in the estimates may be reduced, making the response of the fi sh community to restoration diffi cult, if not impossible, to evaluate and interpret. Too few samples may have been another factor contributing to the lack of a signifi cant impact of the installation of channel enhancements (i.e. artifi cial riffl es and fl ow defl ectors) upon fi sh abundance and species richness in low energy lowland alluvial channels (Pretty et al., 2003).

The use of standardised methods and techniques in a ‘one size fi ts all approach’ may also be a particular problem. Standardised approaches are particularly liked by statutory organisations and whilst the desire for com-parability is understandable, many of the standard approaches have been designed for objectives other than monitoring the response of a restoration scheme. Ulti-mately, such approaches may simply provide qualitative targets with no basis for statistical comparison. In relation to the recovery enhancement of the River Misbourne, UK (Appendix 8.3), standard River Corridor Survey (RCS), River Habitat Survey (RHS) and River Macrophyte Survey (RMS) were used at each site at not inconsiderable effort. However, all these methods simply described and mapped the nature of the vegetation present and did not routinely provide quantitative measures that could be evaluated sta-tistically. With the likely difference in hydrological regime between the different monitoring sites along the course of the river, the response of each site had to be evaluated independently. Thus, the use of the standard fi sheries survey through depletion fi shing resulting in n = 1 on each sampling occasion could provide no more than a subjec-tive comparison over time as fl ow was recovered. For this reason, a more specifi c sampling regime using PASE and


number of replicate samples within each site was also used (Appendix 8.3).

Even where the sampling regime has been carefully and perhaps specifi cally developed, particularly amongst fi sh-eries studies, it is essential to be clear that the effects of the scheme are at a population level with an increase (or decrease) in the population, and not simply a change in distribution of the fi sh present. Whilst the latter may still be viewed as a benefi cial response, as it illustrates that the restoration works have produced the required sort of habitat, this may not have actually achieved the target set.

8.3.3 Evaluation and Deviations from Expected Outcomes

Put simply, the measure of success of any scheme is whether the targets have been met after a specifi ed time. Even where the planning and science were sound, poor implementation may have jeopardized the success of a restoration project. For example, in 1991 an attempt was made to restore vegetation on mud fl ats bordering the Milwaukee River, USA, which had been previously sub-merged due to a dam (Drezner, 2004). Although the area was initially seeded with native plants, and non-native plants were regularly pulled up, an oversight resulting in the failure to remove Reed canary grass Phalaris arundi-nacea resulted in its domination in certain areas. When the area was surveyed 11 years later, on average 89% of plant stems per quadrat were non-native. The reasons behind such problems are not necessarily simple careless-ness. Frequently, budgetary decisions relating to a whole project have to be made at the planning stage, which means that the fi nancial fl exibility required to deal with additional and unpredictable implementation issues may simply not be available.

Streams and rivers have an inherent ability for natural recovery, which has been widely exploited in restoration (Brooks and Shields, 1996; Downs and Skinner, 2002). Consequently, there seems to be little concern of whether a river will be restored once the limiting factors are removed but rather when. Thus, once habitat restoration has taken place, ecological communities are left to recolo-nise. The rate of recovery following a disturbance is dependent upon the resilience of the community to the perturbation in the fi rst place as well as the rate of colo-nisation. Many invertebrate species are highly resilient to disturbance through physical adaptations and behavioural change. Magoulik and Kobza (2003) concluded that many seek refuge from disturbance and/or have adaptations that provide refuge, whilst Townsend (1989) highlighted the critical role played by refugia as sources of recolonisation

after spates, and therefore as buffers against disturbance. Important refugia include low fl ow areas (Winterbottom et al., 1997).

The extent and intensity of channel modifi cation (i.e. disturbance) is thought to have an impact on the time scale of recovery. Tikkanen et al. (1994) found that recolonisa-tion of invertebrates occurred rapidly, within ten days, after a small-scale rehabilitation scheme. In contrast, on the River Rib where major habitat reconstruction was undertaken, although there was rapid colonisation by some macroinvertebrates, the community took almost two years before showing indications of stabilisation. Further-more, Niemi et al. (1990) found that many systems took more than fi ve years to recover to the desired ‘endpoints’, and they concluded that the longest recovery times are associated with disturbance that leads to long term altera-tions in physical habitat. For example, the effects of an episodic pollution event are relatively short lived whilst recovery following dredging takes much longer since geo-morphic channel readjustment is often slow. Indeed work carried out by Laasonon et al. (1998) on the recovery of macroinvertebrates following stream habitat restoration works – which took the form of boulder dams, fl ow defl ec-tors, excavations and channel enlargements – indicates that even after 16 years abundances of invertebrates were still lower than in natural streams. Abundances of shred-ders were particularly low in recently restored streams in comparison to streams restored a number of years previ-ously, although even some streams that had been restored 8 or 16 years ago still contained relatively sparse shredder populations. Further work (Muotka et al., 2002) high-lighted the importance of aquatic mosses, which are fre-quently uprooted during restoration works, and which perform important ecosystem functions, e.g. the retention of fi ne particles and the provision of fl ow refugia for invertebrates, which ultimately infl uence the rate of recov-ery of the macroinvertebrate community. At present the recovery rates of aquatic mosses are unknown, making it diffi cult to predict the responses of macroinvertebrate communities in these types of systems.

The time scales implicated in these studies indicate that all too often schemes are probably assessed too quickly, at a point during the colonisation process rather that at the end point target, even if this was defi ned accurately in the fi rst place. Short term fl uctuations in species populations are to be expected before a longer-term stable equilibrium is reached.

There is a wide range of factors infl uencing the rate of recolonsiation that may simply over-ride the level of dis-turbance undertaken, including the source of colonists within the river itself, the proximity of watercourses nearby for aerial colonsiation by winged adults and the


life cycle of the invertebrates concerned and their habitat preferences. On the Rib, there were marked differences in the rates of recovery of different species and four groups were readily recognised: early colonists, medium term colonists, seasonal colonists and long term colonists (Figure 8.4). The Olive dun mayfl y Baetis rhodani was a typical early colonist being an active swimmer as well as a typical component of the drift. Whilst the Riffl e beetle

Elmis aenea also used the drift, as a resident of fast fl owing areas it is adapted to being washed away and thus tends to colonise more slowly. Blackfl y larvae Simulium spp. are typically abundant in the spring and only tend to colonise newly available areas at this time. A typical long term colonist in this system was the predatory caseless caddis Rhyacophila dorsalis, which relies on the adult winged stage to colonise, as the larvae are adapted to fast fl ows

Figure 8.4 The response of the mayfl y Baetis rhodani and the riffl e beetle Elmis aenea at two re-wetted sites on the River Rib, UK.


and are unable to tolerate slower fl ows between suitable habitat patches.

Like invertebrates, fi sh are often assumed to be capable of rapid colonisation and, again like invertebrates, the speed and recovery of colonisation may be primarily dependent on the geographical isolation of the communi-ties and the proximity of potential colonisers. Both down-stream and upstream movement may be readily undertaken provided there are no barriers to the latter. What consti-tutes a barrier varies enormously. Bullhead, a small benthic fi sh is effectively limited by barriers of just 10 cm (Utzinger et al., 1998), whereas Barbel Barbus barbus a large (to 1 m) powerful cyprinid in European rivers has been recorded making movements of up to nearly 20 km, crossing large weirs (to 2 m) in the process (Lucas and Bately, 1996). The ability of many anadromous salmonids (salmon and trout) to leap large obstacles is well known. Even though the restored area may have been recolonised, recruitment success can still fl uctuate owing to a wide range of factors not related to the scheme (e.g. tempera-ture, predation of eggs and larvae etc). Such factors can affect both the abundance and distribution of individuals, and may signifi cantly slow the colonisation process. Moreover, the major structuring forces of predation and competition may rage for some time before a stable con-fi guration is reached. In the Misbourne for example, stick-lebacks may have out-competed other species for some time, even as physical conditions changed, simply as they arrived fi rst and achieved high density as a result of the abundance of potential nest sites and fl ow refugia in the form of emergent and then submerged vegetation. Only when these were removed, did the community shift rapidly, with bullheads dominating through rapid recruitment (Appendix 8.3).

In general, vagaries in the abilities of different organ-isms to colonise intuitively mean that this stage of the project is highly vulnerable to the infl uence of ecological uncertainty. Predicting the likely pattern of colonisation may be problematic, due to both a limited understanding of other populations in the local area, and a general lack of scientifi c knowledge. For example, the factors infl uenc-ing the colonisation of submerged macrophytes, which are of enormous ecological importance, are poorly under-stood. Clearly, better knowledge of the colonisation mech-anisms and movement patterns is vital to understand the processes behind restoration of riverine systems (Fenoglio et al., 2002) and allow a better understanding of the time scale required for recovery to a stable end point. A lack of scientifi c knowledge is not necessarily easy to overcome, since the situation at one site may differ substantially from another and at present there is a lack of longer term studies that may feed into the knowledge base (Figure 8.1).

8.3.4 Appraisal and Feedback

The importance of appraisal has been highlighted by Anderson and Dugger (1998) who concluded that ‘Failure to evaluate projects not only precludes learning anything about a particular restoration, but it also limits the oppor-tunity for improving plans for future projects’. In our expe-rience in the United Kingdom the number of projects that incorporate post-project appraisal (PPA) is increasing fol-lowing early efforts to raise the importance of this aspect (ECON, 1993). Reviewing projects from 1991–1996, Holmes (1998) recorded that 53% of projects had under-gone PPA, 23% of projects did not incorporate any form of PPA and for 25% of projects this information was not known. Today, partly as a result of the efforts of the RRP demonstration projects and subsequently the River Resto-ration Centre (RRC) within the European Centre for River Restoration (see previously), we would suggest that perhaps only the smaller scale schemes do not routinely undertake PPA. As well as the importance of shared information, drivers have included the need for organisations such as the Environment Agency to demonstrate cost effi ciency for their ‘business’ needs, and that the publicity machines of the organisations involved like/need to have something to show to the public and other interested parties.

However, there is potentially little control over the quality of the information produced, with accumulating ecological uncertainties in the earlier stages of the project potentially ultimately resulting in misleading information that could be used to inform future projects. This is a variant of the ‘Almost as bad as no evaluation are poorly planned efforts that waste limited resources while provid-ing meaningless or even misleading information’ warned by Anderson and Dugger (1998). It is diffi cult if not impossible to assess how much this type of situation occurs. Although ecological uncertainty may frequently occur, the concern is that it goes unrecognised, which could lead to inaccurate results and misinformation. If ecological uncertainty is recognised, and if the scheme results in a clear improvement, even if it is not exactly as predicted, this is perhaps less of a concern. However, there is the possibility that the project may be held in less esteem or considered not as successful if targets are not met, and project funding and the prospect of continued restoration may be jeopardised, even though the work may have ultimately had a positive impact on the ecology of the site. Schemes are beginning to allow for this eventual-ity and are constructed to allow adaptive management if the scheme needs to be adjusted to obtain more desirable results.

Even if appraisal is conducted, it is often not easy to evaluate success and for this reason there is a major move


from river ecologists to establish a set of criteria for mea-suring the ecological success of schemes (Palmer et al., 2005). It is hoped that this will fi nally be endorsed by the United Nations Environmental Programme. The criteria specifi ed are that:

• the design should be based on a specifi ed guiding image that a more dynamic, healthy river could exist at the site;

• the river’s ecological condition must be measurably improved;

• the river system must be more self-sustaining and resil-ient to external perturbations so that only minimal follow-up maintenance is needed.

• during the construction phase, no lasting harm should be infl icted on the ecosystem.

• both pre- and post-assessment must be completed and data made publicly available.

The emphasis behind these criteria is the desire to have projects that are funded and implemented in the name of ecological restoration evaluated according to ecological indicators of success rather than according to other social, economic and institutional factors, such as cost effective-ness, stakeholder satisfaction and aesthetic/recreational value (Palmer et al., 2005). These criteria are fl exible enough to apply to both large scale major projects and smaller projects where complex and expensive design is not appropriate. They also encourage the view of restora-tion success as an adaptive process rather than a single defi ned end point.

8.4 CONCLUSIONS AND RECOMMENDATIONS

Throughout this chapter we have sought to outline sources of unwanted uncertainty that ultimately reduce confi dence in restoration and hinder the development of a cost effec-tive, repeatable approach. The latter can only be achieved by contributing to a shared knowledge base, the potential for which is now enormous with a number of dedicated restoration journals and even more specifi c websites. The approach adopted accepts that much uncertainty resulting from natural variation and sheer system complexity, espe-cially in large systems, will remain unknowable. Fortu-nately, this has not dampened the desire for restoration of large systems, which make greatest contribution to biodi-versity, as recent initiatives within the European Union to restore rivers such as the Danube (Tockner and Schiemer, 1997), the Rhine (Buijse et al., 2002) and the Drava and Lech rivers in Austria (Mohl, 2004) testify.

Indeed, investment in large systems such as the KRRP has illustrated that many uncertainties may be quantifi ed

and overcome, with the accumulation of an extensive knowledge and baseline data, gathered over a number of years, which in turn required considerable resources. With such effort, predictions and targets can be detailed, quan-titative and set to within a realistic time scale. This quells the notion of the inability of ecologists to set targets which has been raised as a particular source of uncertainty. Our experiences, especially in the United Kingdom, suggest that the lack of target setting cannot be attributed to a particular inability to do so but rather appears to be more due to a lack of resources in small projects especially. With an increase in post-project appraisal of whether the project succeeded or failed, it is more diffi cult to justify not using targets at all. Project ‘culture’ thus appears to be changing for the better.

There is probably a better knowledge base of the fauna–habitat information than is immediately apparent and this may be developed specifi cally where this is lacking, although this may be expensive of time and resources. Habitat relationships have, after all, been a fundamental component of many species-centred restoration projects, including in rivers. Powerful tools such as PHABSIM and a wide range of classifi cation systems are available to predict and quantify change even in species-rich groups such as invertebrates, although of course the limitations of such tools do need to be recognised.

There seems to be historical sense of ecologists being advocates of a ‘black art’, perhaps particularly to hardened river engineers dealing in mathematical formulae. This may have originated from ecologists being employed to count ‘bugs’ or fi sh or record plant abundance, originating from engineering works. Ecology is essentially a numerate science that can be quantifi ed by relationships and patterns and is no more ‘uncertain’ than any other science, although it does deal with extremely complex systems and the rela-tionships and interactions within them. Ecological under-standing also often takes time and resources. Bringing ecologists into a more central role, with involvement from the early stages of the project, may help reduce uncer-tainty. Engaging a greater range of academic river ecolo-gists with wide understanding of catchment processes, theoreticians and ecological modellers, as well as the ‘samplers and sorters’, who are frequently consultants, is also thought likely to be particularly benefi cial.

Even a lack of historical reference data may be partly alleviated by monitoring reference (control) reaches before and after the impact of the restoration scheme in a before–after–control–impact (BACI) design (e.g. as on the Rivers Cole and Brede; biggs et al., 1998). Particularly where this incorporates any historical information, it lends itself well to target setting with the subsequent means of assessing whether the target in the controls is reached. As more


information is gathered, a fl exible adaptive management approach allows targets to be adjusted (or perhaps even hardened from qualitative to quantitative, if necessary), enables further studies to be planned when needed and can recognise and deal with emerging problems.

The rigorous selection or even design of appropriate monitoring techniques is essential if the major source of uncertainty in the monitoring phase is to be reduced. The use of standard techniques simply because they are routinely used by the organisation involved, is to be guarded against, as such methods have often been devel-oped for a different purpose. It is far better to treat each scheme as a unique individual experiment for which the appropriate method, intensity and frequency of sampling is calculated. In other words, monitoring should be seen as detailed research rather than a mere estimation of general changes.

Although rivers have enormous resilience and an inherent ability for natural recovery the time scale and trajectory of recovery/restoration represents a major source of uncertainty, particularly where a stable endpoint has been poorly defi ned or there is little understanding of what that may be. The ‘how long is a piece of string’ analogy does not sit well with project budgets, which are typically fi nite and allocated to a particular time frame. We suggest that the trajectory of restoration may be more direct and shortened by considered project selection in the fi rst place, where restoration is part of a generic theme adopted by a statutory body or interested organisation. Tackling less damaged sites with good prospects for recovery, particularly where this can be undertaken through recovery enhancement and not major habitat reconstruc-tion, is also likely to lead to a reduced time scale for recovery.

There is clearly a need for more work on the factors surrounding colonisation to enable better predictions to be ultimately made. This may need to be conducted specifi -cally for the river concerned within the project in an adap-tive manner and may also require action to speed up the colonisation process. For example, with problem species such as invertebrates with particularly poor dispersal abili-ties, it may be prudent to introduce these, perhaps through ‘seeding’ of substrate, to reach a stable confi guration as soon as possible. For fi sh, it is important to defi ne any barriers to natural movement that again, may be readily overcome by translocation.

Obviously, the strength of post-project appraisal ulti-mately depends on the uncertainties accumulated earlier in the project. Where these are great this could deliver a fl awed evaluation of little or no value to future projects. Following the approach outlined it should be possible to avoid this scenario in the future.

ACKNOWLEDGEMENTS

We are grateful to the Environment Agency for funding several of the studies documented and to its staff for assis-tance with sampling. ECON staff undertook much of the work on the River Misbourne.

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APPENDIX 8.1 THE USE AND POTENTIAL LIMITATIONS OF IFIM AND PHABSIM

The ‘Instream fl ow incremental methodology’ (IFIM) and its underlying model, the ‘Physical habitat simulation model’ (PHABSIM) represent the most popular approach for simulating habitat preferences (Downs and Skinner, 2002). The relationship between streamfl ow and available physical habitat (defi ned by depth, velocity, substrate and cover) is used to compute the ‘weighted usable area’ (WUA) in a reach as a function of the river discharge, for different life stages and species of fi sh. For each life stage of the target species, the model requires expressions of the relative suitability for that species of the full range of values taken by these variables. These univariate curves or habitat suitability indices may be derived from existing literature, expert opinion or by sampling tech-niques such as electro-fi shing or snorkelling. PHABSIM also contains a number of hydraulic models that predict values of depth and velocity at different simulation dis-charges. These models require calibration using fi eld data collected at two or more calibration discharges. Observa-tions of substrate and cover are recorded using a coding system and are assumed to be independent of discharge. Once calibrated the model can simulate values of micro-habitat variables over the full range of discharge within a river reach. Combining the results with habitat suitability data produces the WUA versus Discharge relationship (CEH website: http://www.nwl.ac.uk/ih). PHABSIM, by relating habitat to discharge, provides a quantitative entity, allowing river ecologists to negotiate prescribed fl ows in equivalent terms to other water resource demands, and offers a practical means of integrating ecological requirements of aquatic species with other water resource demands.

However, numerous procedural, biological and physical limiting assumptions prevented IFIM and PHABSIM from being reliable and, in response to these criticisms, a wide range of more complex approaches based on advanced understanding of the bioenergetics of fi sh biology and on more realistic representation and model-ling of fl ow patterns is now at the research stage (3rd International Symposium on Ecohydraulics, 1999).


At the Instream Flow Incremental Methodology Work-shop held in New Zealand (February 2004), jointly hosted by Fish and Game New Zealand and the Department of Conservation, Cawthron Institute NIWA (Conference website: http://www.doc.govt.nz/Explore/Hunting-and-Fishing/Taupo-Fishery/), several speakers (e.g. Maclean and Death) expressed concern about some of the underly-ing assumptions behind PHABSIM, including that: fi sh are limited by habitat; that habitat preferences are adequately described by depth, velocity and substrate type; that habitat preferences do not change in accordance with diurnal/sea-sonal patterns; and that a large area of suboptimal habitat is preferable to a small area of optimal habitat.

It was also argued that PHABSIM was not appropriate for use in connection with New Zealand (NZ) inverte-brates on the basis that depth, velocity and substrate type were of minor importance in comparison with other factors such as riparian catchment vegetation, disturbance and food supply. Habitat requirements also appeared to be fl exible for many NZ species. The use of habitat suitability curves was also brought into question and it was noted that it needs to be made explicitly clear how they were developed, for example which sites were used, the number of samples taken and whether diurnal patterns were accounted for in the sampling process. The lack of vari-ance estimation (i.e. error bars) was also raised as a limita-tion. Reduced (or increased) fl ows might have effects on other variables that are not taken into account, such as nutrient content, sediment build up, and temperature.

PHABSIM and variants such as the Riverine Commu-nity Habitat Assessment and Restoration Concept (RCHARC) (Nestler et al., 1996) in the United Kingdom on lowland chalk streams suffering from low fl ow have also proved to be of limited use for invertebrates. There is the risk that habitat area/habitat response predictions will be meaningless if the sampling transects are not widely representative. These problems may be more marked for smaller rivers and streams in the UK context. It is known that for chalk streams and other nutrient-rich lowland rivers the seasonal changes in the growth and biomass of macrophytes require, at the very least, a careful calibration of PHABSIM to prevent distortion of results (Hearne et al., 1994).

Overall, whilst PHABSIM is a potentially powerful tool it may be the most effective when evaluating and compar-ing different management options, in instances where physical habitat limits populations. The United Kingdom method only assesses physical habitat, so factors such as water quality and temperature and sediment transport require complementary studies. Indeed, where such factors are the prime constraint on populations the use of PHABSIM is inappropriate.

Much criticism centres on this latter point and PHABSIM like any model is only as good as the data that is entered and makes the assumption that habitat variables are the best descriptor of the abundance of a particular group, which of course need not be the case. Also, the quantity and quality of water cannot limit the distribution or abundance of the specifi ed organism in the fi rst place. Whilst there is an inherent desire for scientists to adopt a quantitative approach in order to reduce uncertainty, this is only successful if a high level of confi dence can be placed in the predictions. In the case of PHABSIM, many users have found that predictions for even ‘straightfor-ward’ schemes do not come close to the quantitative targets set. However, PHABSIM can also be used as a qualitative tool, and may be useful when it comes to selecting the best likely restoration option from a number of possible schemes.

APPENDIX 8.2 CLASSIFICATION TECHNIQUES FOR MONITORING AND POTENTIALLY PREDICTING THE RESPONSE OF INVERTEBRATES TO FLOW RESTORATION

In the United Kongdom low fl ows are seen to be a problem, especially in chalk streams, which are characterised by high invertebrate biomass and abundance and a relatively high diversity of species. The main physical effects of low fl ows in chalk streams are: long term drying of ephemeral or winterbourne reaches; long term drying of spring seep-ages or fl ushes; downstream migration of the perennial head of a chalk stream or river; and reduced water levels and/or velocity in downstream reaches. Much restoration in the United Kingdom has been concerned with ‘Allevia-tion of Low Flow’ (ALF) schemes, which were driven by the concern of the environmental impacts of over-abstraction. The advantages and disadvantages of method-ologies developed to monitor invertebrates in low fl ow systems are outlined below.

Scott Wilson Kirkpatrick (SWK) Methodology

The fi rst national methodology for assessing low fl ow conditions caused by abstraction was not written until 1992 (NRA R&D Note 45, 1992 – a procedural manual by the consultants Scott Wilson Kirkpatrick). This set out a point-scoring prioritising procedure based on the assess-ment of four indicators: Hydrological; Ecological; Land-scape and Amenity; and Public Perception.

For each indicator, individual assessments of a variety of parameters were conducted. The scores for each indica-tor were combined, with separate weightings. The ecologi-cal indicator parameters and their individual weightings


were: invertebrate community parameter (0.4), fi shery parameter (0.2), fi sh stocks parameter (0.3), plant param-eter (0.1) and an optional conservation parameter (0.3). The major disadvantage was that the methodology was based solely upon Average Scores Per Taxon (ASPT) scores, a measure of the balance between pollution-sensitive and pollution-tolerant invertebrates. This pro-vides what is essentially an index of organic pollution without any accommodation of the overall faunal richness of samples, the fl ow velocity associations of species present, or their preference for either perennial or ephem-eral fl ow. The subjective manner of deducing severity index scores, based on targets that were produced on the basis of adjustments to a starting fi gure suited to sites considered to offer highest potential for ASPT, was also of major concern, since it effectively skewed most lowland rivers towards low impact scores, despite the known sever-ity of impacts within chalk catchments at the time.

Instream Flow Incremental Methodology (IFIM) and PHABSIM

Instream Flow Incremental Methodology (IFIM) and PHABSIM have also been used to establish fl ow objec-tives from direct measurement (or modelling) of resident biotic communities and their specifi c habitat requirements, although these models have a tendency to be inaccurate and inadequate, and at best require careful calibration to prevent distortion of results.

The Surface Water Abstraction Licensing Policy (SWALP) Methodology

The SWALP methodology developed for the NRA by the consultant engineers Sir William Halcrow & Partners Ltd in 1995 incorporates an environmental weighting (EW) system, for which scores for sensitivity to abstraction are assigned to physical character, ecology and fi sheries char-acteristics (NRA, 1995). The catchment is fi rst assigned an EW score and thresholds are produced for a set of defi ned critical assessment points (APs).

The principle behind the SWALP ecological scoring system was that the highest scores were allocated to species perceived to require coarse bed materials and rapid/fast current velocities, and lower scores were given to species which are common in still water habitats or are capable of withstanding dessication. In contrast to the SWK method, SWALP targeted environmental weighting towards physical and biotic (ecology and fi sheries) attri-butes, excluding use-related amenity and recreational cri-teria, which were somewhat subjective. It also represented the fi rst attempt to bring together the perceived fl ow pref-

erences of individual macrophyte and invertebrate species in a biotic index concerned solely with fl ow dependency. However, the environmental weightings produced by the SWALP methodology are rather crude and offer only a subjective basis for decision making and a limited capac-ity to monitor improvement or determine the relative fl ow requirements within or between river basins.

The Lotic–Invertebrate Index for Flow Evaluation Index (LIFE)

Following the invertebrate sensitivities to fl ow suggested by the SWALP methodology, Extence et al. (1999) devel-oped LIFE. The method sought to link changes in riverine benthic macroinvertebrate assemblages to prevailing fl ow regimes. The index is based upon the ‘primary fl ow asso-ciations’ of different macoinvertebrate taxa at either BMWP1 family or mixed-species level – deduced, for the most part, from taxonomic keys. Calculation of the LIFE index score for a sample involves individual fl ow scores (fs) for each scoring taxon present in a sample obtained from a matrix. This matrix is based on the infrastructure of the biotic score system proposed by Chandler (1970) for assessing water quality (Extence et al., 1999). Increas-ing abundance of standing water or drought resistant species produces a lower fs score, whilst increased numbers of individuals from running water fl ow groups produces higher scores.

Whilst changes in LIFE score may faithfully refl ect the observed shifts in composition at sites undergoing gross changes in habitat provision, the depletion of fauna caused by an earlier channel-drying event can produce spurious LIFE scores. This may occur, for example, by the temporal exclusion of molluscs normally associated with slack or slow-fl owing water or by rapid colonisation of blackfl ies (Simuliidae) and mayfl ies (e.g. Baetis spp) at a re-wetted site (Figure 8.4). These aspects of recolonisation usually produce a faunal composition with a higher proportion of velocity-dependent species and an artifi cially elevated LIFE score, even though taxon richness and diversity may remain severely depleted. In the case of the River Mis-bourne (Appendix 8.3), LIFE scores were also found to vary signifi cantly between sites in close proximity (100 m apart) linked to spatial differences in habitat representa-

1 The BMWP (Biological Monitoring Working Party) scores refere to a system for assessing water quality based on the mac-roinvertebrate assemblage. Each taxa is allocated a value from 1 to 10 depending on its known tolerance to organic pollution. High scoring taxa have lower pollution tolerance, and are thus an indi-cator of good water quality.


tion in a relatively unmodifi ed chalk stream. Accordingly, the LIFE score response largely depended upon the mag-nitude of temporal habitat change observed at an individ-ual site. This highlights the need to select a representative variety of sites to assess the linear extent of environmental damage attributable to low fl ow.

The MISINDEX

An alternative fl ow dependency index, MISINDEX, was developed for use on the River Misbourne (Appendix 8.3) to accommodate cases where assemblages are transitional and depleted by previous drying. The MISINDEX works in a similar manner to the LIFE index, with the added advantage that it featured extended taxonomic coverage of aquatic species and the inclusion of water dependent (but not fully aquatic) species of Coleoptera. As with the LIFE index, the MISINDEX used fi ve fl ow-dependency groups and used abundance data for taxa in a similar manner to Extence et al. and Chandler’s matrices, but introduced a category of water-related or marsh species that were not necessarily aquatic but depend upon wet places within a river corridor, lake basin or marsh. Moreover, rather than attempting to allocate individual species to a primary fl ow type association that was evidently subjective, the inten-tion was to defi ne them by the limitations of their tolerance to fl ow cessation and to tell it like it is if a taxon was of low or unknown fi delity.

Neither the LIFE or MISINDEX indices explicitly take into account the dispersal ability of species, or their ability to recolonise fl ow derogated sites after fl ow restoration, an important factor that requires further investigation and could improve prediction of the likely time scale of impacts resulting from fl ow derogation or periodic channel drying.

Detrended Correspondence Analysis (DECORANA)

As a multivariate statistical technique DECANORA can be used to quantify similarities between samples or sites either spatially or temporally. From such an analysis, it may be possible to produce meaningful fl ow objectives determined by observed empirical evidence of the links between faunal composition or population sizes of indi-vidual species and river fl ows or other recorded environ-mental characteristics linked to hydrological variables in a particular river reach. For this, longer term time-series of information are required than usually exist at present, as experience suggests that there is considerable fl ux in the composition of assemblages, the abundance of con-stituent species and instream habitat characteristics linked to the fl ow history of sites and many other factors.

APPENDIX 8.3 MONITORING THE ECOLOGICAL RESPONSE OF THE RECOVERY ENHANCEMENT OF THE RIVER MISBOURNE

Introduction

The River Misbourne, a 28 km long, ecologically valuable chalk stream situated in the River Thames catchment in Berkshire, was believed to be amongst the rivers most affected by abstraction in the United Kingdom. Com-pounded by the effects of drought by 1996/1997, migra-tion of the perennial head saw the river dry from its source to the middle reaches. A scheme to restore fl ow was imple-mented by the Environment Agency and Three Valleys Water, which comprised abstraction reduction and reloca-tion of abstraction points. A series of boreholes commis-sioned by the Environment Agency (EA) monitored the hydrological response of these measures, with water quality routinely monitored at four sites.

The ecological response to the attempted fl ow recovery was monitored at six sites along the course of the river, including sites that had dried down and those that had retained fl ow. Sites with differing characteristics were chosen to assess response more thoroughly, although the variation between sites meant that differences between sites needed to be compared. For example, Sites 1 and 2 were dry in 1998; Site 3 remained dry until 2001, retained fl ow until spring 2003, but had dried down again by autumn 2003; Site 4 was dry only in autumn 1997; and Sites 5 and 6 retained fl ow throughout.

An attempt was made to monitor ecological response using a series of standard survey methodologies typically applied twice annually (often spring and autumn) over a three-year period (1996 to 1999). Changes in vegetation within the corridor was monitored with Phase 1 and 2 Habitat Survey coupled with River Corridor Survey (RCS) and in the channel by Macrophyte Survey (MS) according to changes in habitat conditions shown by River Habitat Survey (RHS). Birds were monitored using Common Bird Census (CBC) and Winter Atlas (WA) surveys. For mammals, bat transect surveys were conducted and the presence of otter Lutra lutra and Water voles Arvicola terrestris was to be recorded during RCS.

Macroinvertebrates and fi sh were monitored with more specifi cally designed sampling regimes. In the case of fi sh this provided an opportunity to compare two monitoring techniques: standard depletion electric fi shing within stop-nets, and point-abundance sampling by electric fi shing (PASE) (Copp and Peñáz, 1988; Perrow et al., 1996). Fol-lowing this, the longer term response over a further fi ve years was undertaken using PASE.

The project had a general aim to restore a native fi sh community of Brown trout Salmo trutta and bullhead


Cottus gobio, but no specifi c targets were set; not only was information on the fi sh population scarce, there was also no move to gather more. However, the connection of the Misbourne to the River Colne was thought likely to provide a source of colonists at least to the lower reaches. Shardloes Lake, with a direct connection to the river, was also thought likely to operate as a refuge for fi sh in the upper reaches, although this was also thought to have dried out in 1997/98.

Comparison Between Fish Survey Methods

Each site was sampled twice annually (spring and autumn) between July 1996 and October 1999 using both depletion fi shing and PASE. The same electrofi shing equipment was used for both methods. Depletion electric fi shing was undertaken within a 100 m section created by the use of stop-nets at either end to prevent the escape of fi sh. Two operators, each with a single anode attached to a separate 100 m cable and a hand net fi shed the section, exploring the entire area with sweeping movements. Two runs were conducted at all sites, apart from at Site 6 in spring and autumn in 1997, where emergent vegetation seriously hampered fi shing and only one run was attempted. All fi sh captured were retained in one or two bins carried by addi-tional personnel and processed (identifi ed and measured, with some fi sh also being weighed to calculate length–weight regressions) at the end of each run, before release downstream of the stop-netted area. Using the numbers of each species captured in each run, an estimate of the total population size of each species was generated using the maximum weighted likelihood model of Carle and Strub

(1978). Using the area of the section sampled (100 m × width of the channel), quantitative estimates of numerical (ind. m−2) and biomass (g m−2) density were calculated.

Prior to PASE, a volt meter was used to calibrate the gear to provide a voltage gradient of 0.12 V at 45 cm away from the 40 cm anode. Such a voltage corresponds to the minimum effective voltage at which inhibited swimming occurs (Copp and Peñáz, 1988). The effective sampling area was thus calculated to be 1.3 m2 from which quantita-tive estimates of abundance and biomass were calculated. A total of 50 points were sampled within a 200 m section of river directly upstream of the stop-netted section at each site. As in the depletion fi shing, the anode was attached to a 100 m extension cable from the control box, which was sited mid-way along the section. Points were taken in a stratifi ed random manner at 4 m intervals, with the electric fi shing operator moving diagonally upstream from bank to bank, and sampling a pre-determined number of points in the littoral margin, which was determined from the rela-tive width of the margin to that of the channel.

Overall, both techniques tended to sample a similar number of species (Wilcoxon signed ranks test, n = 17, Z = −2.84, p = ns) and produce similar estimates of total biomass (n = 17, Z = −1.87, p = ns), but signifi cantly dif-ferent estimates of total abundance (n = 17, Z = −3.10, p < 0.01) (Figure 8.5). PASE produced the higher esti-mates of total abundance on account of its tendency to produce signifi cantly higher estimates for bullhead (n = 13, Z = −2.69, p < 0.01) and Ten-spined stickleback Pun-gitius pungitius (n = 9, Z = −2.67, p < 0.01). There was little consequence of the production of signifi cantly higher biomass estimates for both these species (p = 0.01 and

0

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DEPLETION

PASE

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Spring Autumn Spring Autumn Spring Autumn Spring Autumn Spring Autumn Spring Autumn

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ind.

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1997 19971998 19981999 1999

Figure 8.5 Comparison of numerical (ind. m−2) and biomass (g m−2) density estimates obtained by PASE (mean ± 1SE shown) and depletion fi shing (density derived from total population estimate shown) over time at Site 5, dominated by small species such as sticklebacks, stone loach and bullheads

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