ciria land use management effects on flood flows and sediments

��

� � ��

��

��

��

��

��

��

��

��

��

�� !

"�� #�� $�� %�� "#&�#'(')**&*+

,�� "�� -��&"�� -�� . ��

�� #� ��

�� /�� 0��

1�� #�� / �� ,��

2 �� 0�� -��

�

�

�

�

�

C719

C719Land

usem

anagementeffects

onflood

flows

andsedim

ents-guidance

onprediction

CIRIA

� ��

Land use management effectson flood flows and sediments

- guidance on prediction

Who we areEstablished in 1960, CIRIA is a highly regarded, industry-responsive, not for profit research and information association, which encompasses the construction and built environment industries.

CIRIA operates across a range of market sectors and disciplines, providing a platform for collaborative projects and dissemination by enhancing industry performance, and sharing knowledge and innovation across the built environment.

As an authoritative provider of good practice guidance, solutions and information, CIRIA operates as a knowledge-base for disseminating and delivering a comprehensive range of business improvement services and research products for public and private sector organisations, as well as academia.

How to get involvedCIRIA manage or actively participate in several topic-specific learning and business networks and clubs:

Where we areDiscover how your organisation can benefit from CIRIA’s authoritative and practical guidance – contact us by:

Post Classic House, 174–180 Old Street, London EC1V 9BP, UKTelephone +44 (0)20 7549 3300Fax +44 (0)20 7253 0523Email [email protected] www.ciria.org

(for details of membership, networks, events, collaborative projects and to access CIRIA publications through the bookshop)

zz Core membershipAllows your employees to assist with the development of and access to good practice guidance, formal networks, facilitation, conferences, workshops and training.

zz Associate membershipAllows your employees to access CIRIA’s services. Members are able to access exclusive content via the CIRIA website.

zz CIRIA Books ClubMembers can buy most CIRIA publications at half price and can attend a range of CIRIA conferences at reduced rates.

zz The CIRIA NetworkA member-based community where clients and professionals meet, develop and share knowledge about specific topics relevant to construction and the built environment.

zz Project fundingProject funders influence the direction of the research and gain early access to the results.

zz CEEQUALCIRIA co-manages this environmental award scheme, which promotes environmental quality in civil engineering and infrastructure projects.

zz Local Authority Contaminated Land NetworkLACL helps local authorities address responsibilities under Part IIA of the Environmental Protection Act 1990.

zz European Marine Sand and Gravel GroupCIRIA provides secretariat support to EMSAGG, including management of the Group’s conferences, workshops and website and producing its newsletter.

zz SAFEGROUNDS Learning NetworkA forum for disseminating good practice guidance on the management of radioactively and chemically contaminated land on UK nuclear and defence sites.

zz SD:SPURThe initiative was developed to establish safe, socially, economically and environmentally sustainable practices arising from the decommissioning of nuclear sites.

zz LANDFoRM (Local Authority Network on Drainage and Flood Risk Management)A platform for sharing knowledge and expertise in flood risk management and sustainable drainage.

zz BRMF (Brownfield Risk Management Forum)Promoting sustainable and good practice in brownfield projects in the UK.

CIRIA C719 London 2013

Land use management effects on flood flows and sediments –

guidance on prediction

Edited by

Neil McIntyre

Imperial College London

Colin Thorne

University of Nottingham

Classic House, 174–180 Old Street, London EC1V 9BPTel: 020 7549 3300 Fax: 020 7253 0523Email: [email protected] Website: www.ciria.org

Land use management effects on flood flows and sediments – guidance on predictionii

Land use management effects on flood flows and sediments – guidance on prediction

McIntyre, N, Thorne, C (editors)

CIRIA

C719 ©CIRIA 2013 Con186 ISBN: 978-0-86017-722-7

British Library Cataloguing in Publication Data

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

Keywords

Flooding, flood risk, land use, land management, predictions

Reader interest

Land use policy, land use planning, flood risk management catchment management

Classification

Availability Unrestricted

Content Rural land management guidance

Status Committee-guided

Use National government, local government, drainage authorities, environmental consultants, environment agencies, rural land managers, other rural land management stakeholders

Published by CIRIA, Classic House, 174–180 Old Street, London, EC1V 9BP, UK

This publication is designed to provide accurate and authoritative information on the subject matter covered. It is sold and/or distributed with the understanding that neither the authors nor the publisher is thereby engaged in rendering a specific legal or any other professional service. While every effort has been made to ensure the accuracy and completeness of the publication, no warranty or fitness is provided or implied, and the authors and publisher shall have neither liability nor responsibility to any person or entity with respect to any loss or damage arising from its use.

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, without the written permission of the copyright holder, application for which should be addressed to the publisher. Such written permission must also be obtained before any part of this publication is stored in a retrieval system of any nature.

If you would like to reproduce any of the figures, text or technical information from this or any other CIRIA publication for use in other documents or publications, please contact the Publishing Department for more details on copyright terms and charges at: [email protected] Tel: 020 7549 3300.

CIRIA C719 iii

Executive summary

Why is land use management important?

Management of rural land use offers a potentially attractive approach to reducing the need for structural flood defences and channel maintenance to control sediment. The Flood Risk Management Research Consortium (FRMRC) (2004 to 2012) was tasked with increasing knowledge concerning the efficacy of rural land use management for these purposes, and with developing suitable tools for predicting the effects of rural land use on downstream flood risks and sediment yields.

This guide presents the scientific basis for natural flood and sediment management through rural land use management in the UK, drawing primarily on evidence obtained from FRMRC research, but also including some findings generated by other projects. It provides guidelines for evaluating and predicting land use change effects on flood flows and catchment scale sediment dynamics, by reviewing the applicability and utility of analysis tools and approaches that are available in the UK.

What are the main findings?

The results of FRMRC and allied studies demonstrate that appropriate rural land use management measures can significantly reduce peak flood flows, flow volumes and times to peak at plot to field scales. For example, at the plot scale, experiments at the Pontbren catchment showed that tree planting can in some circumstances reduce surface runoff by orders of magnitude. At the small catchment scale, which may be considered as catchment areas less than 10 km2, land use effects may also be significant. For example, model results show that low footprint, strategic tree-planting in a 6 km2 sub-catchment at Pontbren would be expected to reduce the peak flow by nearly 30 per cent for a short return period rainstorm, decreasing to five per cent for a very extreme, long return period rainfall event.

However, the effects of land use management on flooding are expected to diminish as the scale of the catchment increases. For example, in the 260 km2 Hodder catchment the median reduction in the flood peak associated with an extreme rainfall event produced by a realistic suite of land use changes was only two per cent, assuming that channel conveyance did not change. The finding that land use changes had limited effects on peak flows was corroborated by parallel modelling studies in the Parrett catchment in Somerset. The Parrett study further established that diffuse, on-farm storage can be more effective than land use change. However, the wide extent of storage required to deliver significant flood risk benefits would require concerted and co-ordinated actions by a large number of landowners.

Uncertainty in these predictions is high, even in case studies where good quality hydrological data are available. For example, for an expected flood peak reduction of two per cent in the Hodder case study, the 95 per cent uncertainty bounds indicated the result could be anywhere from a one per cent increase in flood peak to a six per cent decrease. Predictions of land use effects can only be taken as indicative, even using best available data and tools. Consequently, all predictions should be accompanied by an expression of confidence and be employed within suitable decision-support frameworks.

When considering catchment sediment yields, the hydrological effects of land use change are magnified by the strong effect of increased runoff on erosion and sediment transport in fluvial systems. For example, the Pontbren experiments showed a ten-fold increase in bedload yield and a five-fold increase in suspended load per unit basin area when comparing a less intensively farmed sub-catchment to a more intensively farmed one. Surveys in the lower River Tone (a tributary of the River Parrett) illustrated that fine sediments generated in headwater basins can travel through the drainage network to accelerate siltation and reduce conveyance in lowland reaches close to the tidal limit, especially around flow control structures. These studies provide evidence that flood risk reduction benefits stemming from changes to

Land use management effects on flood flows and sediments – guidance on predictioniv

land use can only be correctly recognised when soil-runoff-sediment-flooding interactions are properly accounted for at the catchment scale.

What decision support methods and tools are available?

The FRMRC and other recent research programmes have developed a range of methods and tools that can provide quantitative assessments and predictions of catchment hydrology and sediment dynamics. This includes methods for up-scaling the effects of local land use changes, sensitivity analysis tools for identifying where in the catchment a specific management strategy will have most benefit, and a sediment modelling toolbox that brings together a variety of methods and models to suit most projects and applications.

In addition to providing natural flood and sediment management, it should be recognised that catchments supply a range of other ecosystem services, for example agriculture, wildlife habitat and carbon storage. Sustaining these services requires effective engagement with land managers and other stakeholders to identify acceptable trade-offs. Polyscape is a GIS-based visualisation and decision support tool that allows the scientific evidence base to be assimilated in an approximate manner. This is so that farmers and landowners can engage with hydrologists, natural scientists and agri-economists in identifying acceptable land use trade-offs.

These tools have limitations in terms of their aims, underlying assumptions and data requirements, and guidelines concerning the selection and application of the tools, and alternative available tools, are provided in this guide.

Gaps in knowledge

The evidence base relating to effects of rural land use change on floods and sediments is at an early stage of development and is ongoing. Although recent research has substantially added to the evidence and the ability to predict effects, this has covered only a fraction of UK land use and land management regimes, and has sampled only a fraction of possible climatic futures. To reduce uncertainty in extrapolation of the evidence, further monitoring and experimental work is needed, targeting other nationally important land use and land management regimes and, ideally, running over periods of decades. The modelling and decision support frameworks described here will require continued testing and development as the evidence base on land use, flood risk and sediment dynamics expands and evolves.

CIRIA C719 v

Table 1 Guidance on the evidence for regulators, consultants and landowners/managers

Topic Regulators Consultants Landowners/managers

Scal

e

Hydrologic effects of changes in land use decrease with scale and are unlikely to be measurable in catchments larger than around 10 km2. Sediment effects are more marked and may be cumulative.

FRMRC research demonstrates the hydrologic and sediment effects of land use in small catchments, provides up-scaling tools for larger catchments and highlights the need for careful representation of stream geomorphology in flood inundation models.

Inappropriate land use and/or land management may be responsible for significant local effects including muddy floods. At meso- and large-scales, management of channel roughness, morphology and connectivity become increasingly important.

Extr

eme

flood

s

FRMRC modelling shows that land use has little influence on extreme floods but strongly affects channel-forming flows with return periods of two to five years. So, the longer-term history of land use may affect flooding through its effects on channel geomorphology. Further research on this is essential.

Floods, with return periods of 25, 50 or 100-years are usually the “design events” for flood alleviation schemes. So, often consultants focus their attention on these extreme events. FRMRC research stresses the need to include a wider range of events and account for sediment and morphological change.

Flooding that occurs naturally due to intense rainfall cannot be attributed to inappropriate land use. However, poor land use can exacerbate flooding locally and in small catchments. It may also affect extreme flooding indirectly via geomorphic effects.

Clim

ate

chan

ge

and

varia

bilit

y Effects of climate change and increased variability on runoff, sediment yield and flooding cannot be avoided but can be mitigated by agricultural de-intensification and land use management.

Research demonstrates that future effects of climate change are indeterminate, so that scenario-based and/or probabilistic hydrologic modelling is required.

The near-term effects of land use change on hydrology are expected to be small compared to inter-annual variability, but sediment and channel stability effects may be more profound.

Gra

zing Continue promoting reduced

stocking densities and farm de-intensification to avoid over-grazing.

Data, field evidence and analyses generated by FRMRC and partners can be used to evaluate grazing effects.

High stock densities and over-grazing lead to soil degradation, excessive runoff, and muddy flooding downstream.

Fiel

d dr

aina

ge

Continue to promote blocking of artificial surface and under drainage as part of de-intensification and sustainable land use.

FRMRC research has demonstrated the need to accurately represent ditches and under drainage in hydrologic and sediment models.

Recognise that artificial surface and under drainage may increase downstream flood peaks, sediment loads and channel instability.

Pond

s Encourage construction of suitably located, designed and maintained ponds to provide diffuse catchment storage for runoff and flood waters.

Use results of improved analyses and empirical evidence from trial ponds to better account for diffuse storage in hydrological models.

Involve regulators and consultants in evaluating potential for diffuse storage and identifying possible sites and funding sources.

Tree

s

Expand strategic tree planting under agri-environmental schemes to achieve and improve multiple hydrologic, sediment, ecosystem and economic benefits.

FRMRC has provided new information on farm-scale hydrologic, sediment and ecosystem benefits of trees, but catchment scale effects remain uncertain.

Seek advice on selecting suitable species and optimising locations, alignments and dimensions of shelter belts, buffer strips and riparian corridors.

Ecos

yste

m s

ervi

ces Land use decision making cannot

be regulated based on the benefits to flood risk management alone. Regulation should reflect a wide range of issues and goals for land use, including provision of ecosystem services and agricultural sustainability.

The variety of risks and benefits associated with evaluation of land use decision making dictates that consultants adopt a cross-disciplinary approach and apply multi-criteria analysis. This should include considering the provision of ecosystem services.

Landowners and managers are generally well aware of the importance of working with natural processes. The idea of attempting to measure the economic value of the services provided free by nature only “monetarises” benefits that are already widely understood.

Unce

rtai

nty

Relationships between land use, hydrology, sediments and flood risk are inherently uncertain. This is due partly to limitations with data, theory and models, but also due to natural variability. Recognising this, the way catchments are regulated should allow for some uncertainty.

Research on the ways that risk and uncertainty can be assessed, analysed, expressed and communicated was a feature of FRMRC1 that was carried forward in FRMRC2. New methods and techniques are set out in FRMRC and CIRIA publications, which consultants may apply as appropriate.

Research can reduce uncertainty in data, theory and models, but natural variability is an attribute of the land that cannot be avoided. Consultants and regulators can never be 100 per cent confident based on science alone and local experience remains crucial to making good land use decisions.

Land use management effects on flood flows and sediments – guidance on predictionvi

Build

ing

the

evid

ence

bas

e

In the UK monitoring occurs at relatively few hydrometric stations and records seldom extend back over more than 50 or 60 years. Sediment records are shorter, rarer and more sporadic. Catchment regulation will be hampered by lack of data until long-term monitoring is extended to many more sites.

Research in intensively instrumented test catchments by the FRMRC and its partners has shown what can be achieved when key hydrologic and sediment parameters are measured and mapped. Consultants working in gauged and ungauged catchments can extend the evidence base if they pool their data.

Landowners, farmers and managers can help scientists and consultants in extending the evidence base by helping the monitoring of hydrologic, sediment and flood peak responses to changes in land use and management.

Dec

isio

n m

akin

g

Regulators not only make decisions affecting land use and flood risk, they also influence the decisions of multiple stakeholders, including local authorities and individual landowners. In doing so, they should make use of the new science in this guide.

Consultants are used by stakeholders when specialist skills and expertise are required to support decisions concerning land use and land management. The insights and analyses presented in this guide should help them provide the best advice possible.

Land use decisions should be based on the best information available. This requires mixing quantitative data with qualitative observation, experience and judgment. In this context, POLYSCAPE can be used to help maximise net benefits to the farm.

CIRIA C719 vii

Table 2 Guidance on tools for regulators, consultants and landowners/managers

Topic Regulators Consultants Land managersR

ules

of t

hum

b an

d qu

alita

tive

opin

ion

Usually gets the direction of change correct, but significant risk of poor decisions. Difficult or impossible to write into regulations and so depends on the judgment of the individual regulator.

Usually gets the direction of change correct, but significant risk of poor decisions. Not easily transferrable between companies or even individual consultants. Region and even catchment limited.

Relies on the past experience of the individual and inter-generational transfer of knowledge. Cannot account for climate change and unprecedented hydrological events.

Phys

ics-

base

d hy

drol

ogic

al a

nd

sedi

men

t mod

els

The weak relationship between the scientific sophistication of physics-based models and the reliability of their results limits their utility in regulating catchment land use.

The advanced, specialist training and extensive data required to apply these models requires long-term investment at a level that is unsustainable for most consultants.

Constraints due to time and cost prevent application of these models by all but the largest landowners with the resources necessary to support long-term studies.

FEH

/ReF

H

These generalised flood risk tools need further research and development to make them suitable for use in analysing the hydrologic impacts of land use change. They cannot currently account for sediment effects at all.

These models are used by consultants to assess catchment sensitivity to land use change, based on perturbing the HOST classes of catchment soils. However, the suitability of such an approach is uncertain.

The training and experience required to reliably apply these tools rules out their use by most landowners and managers. However, those with sufficient need and the resources necessary could access the tools via their consultants.

Lum

ped

conc

eptu

al m

odel

s

Lumped models use spatially averaged parameters to define key catchment parameters, limiting their resolution. Distributed models are preferred except for screening and sensitivity analyses

Consultants routinely use lumped models due to their ease of application. They can be used for sensitivity analysis but lack the capability for finer, spatial analysis that is crucial to investigate land use effects.

These can be regarded as entry level hydrologic and sediment models. Their simplicity makes them attractive and accessible, but results are indicative at best, especially for sediments yields.

Dis

trib

uted

co

ncep

tual

m

odel

s

These are the generally the preferred option for catchment management and regulation. However, their application requires reliable spatially-distributed data, which is often unavailable.

Recent developments in model estimation have improved the applicability of these models, which are close to being the industry standard in the UK. However, data limitations remain.

Larger organisations and the owners of extensive estates may be able to justify the development of a distributed hydrological model. Adding a sediment component is still rare.

Chan

nel

netw

ork

The channel network is usually represented using a 1D hydraulic model. This is acceptable for catchment-scale applications but of limited utility at the reach-scale.

Use of 1D, channel network models is now routine and most consultants can also offer 2D models for application at the reach-scale. Sediment modelling is rarer.

1D hydraulic models are easy to apply but may give misleading results in inexperienced hands. 2D models and the use of sediment modules are only for specialists.

Sens

itivi

ty

anal

ysis

In situations where funds or access to specialist expertise is limited, sensitivity analysis can supply an important, spatial dimension to land use effect assessment.

New tools, including those reported here, now allow efficient estimation of the spatial variability of lan- use effects on hydrology, sediments and flood risks.

The reduced complexity approaches developed here are not yet amenable to application by landowners and managers, but indicate how this may be achieved.

Unce

rtai

nty

anal

ysis

Given the uncertainties inherent to assessment of land use change effects on hydrology, sediments and flood risk, uncertainty analysis should be required in future.

FRMRC research has produced an uncertainty toolbox and a forthcoming CIRIA report on uncertainty provides consultants with fresh insights and guidance on uncertainty.

Landowners/managers should recognise that uncertainty concerning land use and flood risk should and need not prevent them making sustainable land use decisions.

Neg

otia

tion

tool

s

New GIS tools, including those reported here, are available to assist with communication and negotiation between regulators and landowners/managers

Consultants increasingly recognise the importance of engaging with stakeholders and the new GIS tools now available can assist in this regard.

GIS-based tools like POLYSCAPE have been tested and found beneficial at the farm and small catchment-scale. They show how stakeholders can fully engage with scientists.

Mul

tiple

crit

eria

an

alys

is

The new GIS tools developed by FRMRC and its partners can illustrate trade-offs between different land use functions and identify areas where changes can reduce runoff and sediment yield while improving ecosystem services and boosting productivity.

Consultants adept in the use of multi-criteria analysis and stakeholder engagement should have a competitive edge in terms of the services they can supply to clients ranging from local authorities to individual landowners.

The low entry level for use of multi-criteria evaluation tools based on the reduced-complexity models developed by FRMRC and its partners (eg POLYSCAPE) makes increasingly accessible to landowners, farmers and other decision making stakeholders.

Land use management effects on flood flows and sediments – guidance on predictionviii

Acknowledgements

This guide was written as an output of Super Work Package 5 of the Flood Risk Management Research Consortium, Part 2 (FRMRC2).

Funders of FRMRC2 were the Engineering and Physical Science Research Council (EPSRC grant EP/F020511/1), the Environment Agency of England and Wales, the Department of the Environment and Rural Affairs, the Northern Ireland Rivers Agency, the Scotland and Northern Ireland Forum for Environmental Research, and Ireland’s Office of Public Works.

Other research projects that have contributed to this report are the Flood Risk from Extreme Events programme funded by the Natural Environment Research Council and monitoring funded by the Environment Agency of England and Wales associated with United Utilities/RSPB Sustainable Catchment Management Programme.

Essential support was provided by the Pontbren farmers and landowners, Coed Cymru, and the Super Work Package 5 Steering Group, which consisted of Adam Baylis (Environment Agency), Wendy Brooks (Environment Agency), Amy Parrott (Environment Agency), Edward Evans (Consultant), Tom Nisbet (Forestry Commission), Katherine Pygott (Halcrow), James Skates (Welsh Assembly Government), Angus Tree (Scottish Natural Heritage), Chris Uttley (CCW).

The guide benefitted greatly from the reviews of Malcolm Newson, Adam Baylis, Rob Lamb and Steve Rose.

Editors

Neil McIntyre BEng MSc PhD MICE CEng

Neil McIntyre is a Reader in surface water hydrology at Imperial College London. After obtaining his BEng in civil engineering from Edinburgh University he worked as a civil engineer in the water industry in Scotland on wastewater infrastructure projects, then moved to Imperial to specialise in hydrology. His research and other professional activities have focused on water quality modelling, hydrological modelling, water resources and flood hydrology. From 2004–2012, he co-led the land use management work packages of the FRMRC.

Colin Thorne BSc PhD Affil M ASCE

Colin Thorne is Professor and Chair of physical geography at the University of Nottingham. He has over 25 years of professional experience, including appointments at Colorado State University, Queen Mary London, the US Army Corps of Engineers Waterways Experiment Station, and the USDA. His research concentrates on fluvial hydraulics and sediment transport in rivers, particularly with respect to the implications for flood risk. He was a theme leader in the Flood Foresight Project and deputy chair (dissemination) of the FRMRC.

CIRIA C719 ix

Author team

Caroline Ballard Aqualinc Research Ltd, Christchurch

Nataliya Bulygina Imperial College London

Ian Cluckie Swansea University

Stephen Dangerfield Malford Environmental Consulting

John Ewen Newcastle University

Zoe Frogbrook Environment Agency of England and Wales

Josie Geris Newcastle University

Alex Henshaw Queen Mary University

Bethanna Jackson Victoria University of Wellington

Miles Marshall Centre for Ecology and Hydrology, Bangor

Neil McIntyre Imperial College London

Tim Pagella Bangor University

Jong-Sook Park Swansea University

Enda O’Connell Newcastle University

Greg O’Donnell Newcastle University

Brian Reynolds Centre for Ecology and Hydrology, Bangor

Fergus Sinclair Bangor University

Imogen Solloway Imperial College London

Colin Thorne University of Nottingham

Howard Wheater University of Saskatchewan

Project funders

66 Engineering and Physical Sciences Research Council (Grant EP/FP202511/1)

66 Department of Environment, Food and Rural Affairs/Environment Agency Joint Research Programme

66 United Kingdom Water Industry Research

66 Office of Public Works Dublin

66 Northern Ireland Rivers Agency.

Land use management effects on flood flows and sediments – guidance on predictionx

Contents

Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

Abbreviations and acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Flooding and rural land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Project background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Objectives of this guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.4 Limitations of this guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.5 Structure of the guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.6 Relationship to other guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Flood flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 The UK evidence base. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Evidence pre-FRMRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.2 Evidence delivered during FRMRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.3 “Rules of thumb” from the evidence base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Quantitative methods of predicting flood flows and example results . . . . . . . . . . . . . . . . . . . . . . .21

2.3.1 Classification of approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.3.2 General modelling procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.3.3 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.3.4 Screening methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.3.5 Physics-based distributed models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.6 Conceptual distributed modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.3.7 Uncertainty analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.4 Land use impact mapping tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

2.5 Remaining gaps in knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

3 Sediments and geomorphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .483.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

3.2 The UK evidence base. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

3.2.1 Evidence pre-FRMRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2.2 Evidence delivered during FRMRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.3 Quantitative methods for predicting changes in sediment yield and channel morphology . . . . .57

3.3.1 Modelling sediment dynamics and morphological impacts for flood risk management . 57

3.3.2 Example 1: Predicting land use and climate change effects in upland catchments using CAESAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.3.3 Example 2: investigating the effects of sediment loading on channel morphology and flood risk in a lowland river system using HEC-RAS SIAM . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.4 Remaining gaps in knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81

CIRIA C719 xi

4 Stakeholder negotiation of ecosystem services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .834.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83

4.2 Polyscape – a tool for multi-objective rural land management planning . . . . . . . . . . . . . . . . . . . .85

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94Statutes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Useful websites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112

Figures

Figure 2.1 Schematic diagram demonstrating relationship between outputs from rainfall-runoff models and quantification of hazard and risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Figure 2.2 Potential for integrated runoff control to reduce flood risk, pollution and erosion . . . . . . . . . . 8

Figure 2.3 The River Parrett catchment and its sub-catchments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Figure 2.4 Dried and compacted soil at a potato farm in Somerset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Figure 2.5 Location of Pontbren experimental catchment within the UK . . . . . . . . . . . . . . . . . . . . . . . . . .12

Figure 2.6 Pontbren study site instrumentation location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Figure 2.7 Schematic diagram demonstrating the relative difference in infiltration rates (white arrows) and annual runoff volumes (black arrows) for (a) ungrazed and tree planted (b) ungrazed and (c) grazed plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Figure 2.8 The relationship between catchment properties (x-axis) and the log10 of the response time, T, illustrating that improved grassland coverage and open water (lakes and ponds) coverage are together the principal factors within Pontbren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Figure 2.9 The Hodder catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

Figure 2.10 Monitoring sites and UU SCaMP works in the Brennand catchment . . . . . . . . . . . . . . . . . . . . .17

Figure 2.11 Schematic showing locations of flow gauges in the Hodder catchment . . . . . . . . . . . . . . . . . .18

Figure 2.12 Flow at Hodder Place (260 km2) and Footholme (25 km2, lies just upstream of Dunsop Bridge) for October 2000 flood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Figure 2.13 Peak discharge per unit catchment area (mm/h) plotted against catchment area (km2) for six rainfall events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Figure 2.14 Summary of selected rainfall runoff models and their attributes . . . . . . . . . . . . . . . . . . . . . . .23

Figure 2.15 Structure of the physics-based, distributed models used in the FRMRC study of the Parrett catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Figure 2.16 Daily and hourly measured rainfall gauges in the Parrett catchment used in the FRMRC study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Figure 2.17 Soil type (HOST) (a) and land use type (b) in the Parrett catchment . . . . . . . . . . . . . . . . . . . . .30

Figure 2.18 Conceptual distributed modelling framework developed by the FRMRC . . . . . . . . . . . . . . . . .33

Figure 2.19 Downstream impact hydrograph at location C produced by simple routing of runoff impact hydrographs from sub-catchments A and B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Figure 2.20 Impact map for changes in the flood peak at the outlet of the Hodder catchment (drainage area = 260 km2) based on land use changes associated with the SCaMP (exceedence probability = 50 per cent, units = m3s-1 per square km) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44

Figure 3.1 Suspended sediment transport in the Nant Pen-y-cwm and Nant Melin-y-grug during a sequence of high flow events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54

Figure 3.2 Bedload yields measured in the Nant Pen-y-cwm and the Nant Melin-y-grug during a series of high flow events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

Figure 3.3 Contrasting levels of channel stability and morphological activity in the Nant Pen-y-cwm (a) and Nant Melin-y-grug (b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56

Figure 3.4 Runoff duration curves for the Nant Pen-y-cwm and Nant Melin-y-grug . . . . . . . . . . . . . . . . . .56

Figure 3.5 Relative contributions of interpretational and analytical approaches in different methods and models contained within the FRMRC Sediment Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . .58

Land use management effects on flood flows and sediments – guidance on predictionxii

Figure 3.6 Balancing cost and risk when selecting the appropriate level of model complexity for a sediment study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58

Figure 3.7 Balancing management resources, the science base for sediment methods and models, and stakeholder attitudes for project success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58

Figure 3.8 Conceptual structure of CAESAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

Figure 3.9 Plots of cumulative hourly rainfall in sequences representing different time period/ emissions scenario combinations constructed using Weather Generator 2.0. . . . . . . . . . . . .62

Figure 3.10 Comparison of CAESAR-predicted and meta-modelling procedure-predicted hydrographs for different m value/land use scenario combinations. Black lines represent meta-modelling procedure-predicted streamflow. Red lines represent CAESAR-predicted streamflow . . . . . .64

Figure 3.11 Variability in predicted sediment yields under the baseline combination of continued present day climate and land use for the next 30 years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65

Figure 3.12 Box and whisker plots of total 30-year sediment yields from the Nant Pen-y-cwm sub-catchment predicted under twelve combinations of the scenarios for future changes in land use and climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65

Figure 3.13 Comparison of sediment yield simulations under 1990s (a) and tree strips scenarios for the 2050s (b), high emissions climate change scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66

Figure 3.14 DEM change map for the Nant Pen-y-cwm sub-catchment showing land surface elevation changes driven by the 30-year, hourly rainfall sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67

Figure 3.15 The River Parrett catchment, with the upper and lower limits of the sediment model for the lower River Tone identified by green bars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

Figure 3.16 Predicted rates of change in river bed elevation for Reach T3 under a range of wash load yield scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73

Figure 3.17 Predicted river bank morphological change for Reach T5 under a range of wash load yield scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73

Figure 3.18 Predicted river bank morphological change for Reach T7 for a range of wash load yield scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 3.19 Predicted river bed morphological change for reach T3 for a range of flow regime and wash load yield scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .76

Figure 3.20 Predicted river bank accretion rates in reach T7 for a range of flow regime and wash load yield scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77

Figure 3.21 Predicted rates of bed elevation change for reach T1 under a range of bed material load scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78

Figure 3.22 Predicted percentage change in average hydraulic depth for reach T1 under a range of bed material load scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .79

Figure 4.1 Three contrasting land use configurations reflecting different stakeholders’ objectives in a simplified Welsh farmland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84

Figure 4.2 Key steps in the proposed stakeholder engagement methodology are shown in white nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86

Figure 4.3 Polyscape output for the Pontbren catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .89

Figure 4.4 Polyscape flood impact mapping for the Pontbren catchment . . . . . . . . . . . . . . . . . . . . . . . . .90

Figure 4.5 Farm impact layer for the Pontbren catchment overlaid with 2006 aerial photography. Subset comments are derived from ground truthing with farmers . . . . . . . . . . . . . . . . . . . . . .91

Figure 4.6 Polyscape single layer outputs for the Elwy catchment with the Meirchion (a) and the Gallen (b) sub-catchments highlighted. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92

Figure 4.7 Ground truthing land use data in the Elwy catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92

Tables

Table 1 Guidance on the evidence for regulators, consultants and landowners/managers . . . . . . . . . v

Table 2 Guidance on tools for regulators, consultants and landowners/managers . . . . . . . . . . . . . . . vii

Table 2.1 A summary of the climate, land use and land management of the three principal FRMRC case study catchments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

CIRIA C719 xiii

Table 2.2 Saturated hydraulic conductivity Ksat for Cegin soil series at Pontbren. . . . . . . . . . . . . . . . . .15

Table 2.3 Summary of the effect on flow peaks of commonly adopted land use practices . . . . . . . . . . .20

Table 2.4 Changes to FEH parameters for rural land use sensitivity analysis in CFMPs . . . . . . . . . . . . .24

Table 2.5 Comparative attributes of spatially lumped and distributed models. . . . . . . . . . . . . . . . . . . . .26

Table 2.6 Changes in simulated peak flows under tree planting scenarios for the Tone catchment using the enhanced MIKE SHE/11 model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

Table 2.7 Summary of changes in peak streamflow for three land use change scenarios during a synthetic extreme rain storm event at gauge 6 in the Pontbren catchment. . . . . . . . . . . . . . .35

Table 2.8 Examples of scenario effects for a 25 km2 Hodder sub-catchment . . . . . . . . . . . . . . . . . . . . .36

Table 2.9 Examples of scenario impacts for 3.2 km2 and 4.1 km2 Pontbren sub-catchments . . . . . . . .38

Table 2.10 Examples of routing methods and models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Table 3.1 Physiographic characteristics of the Pontbren sub-catchments . . . . . . . . . . . . . . . . . . . . . . . .54

Table 3.2 RMSE (m3s-1) of the relationship between peak discharge values predicted by CAESAR and the meta-modelling procedure for the 30 largest flood events of the calibration period under different m value/land use scenario combinations. Lowest RMSE values represent best fit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63

Table 3.3 Combinations of land use scenarios, climate scenarios and rainfall sequences used in CAESAR model runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64

Table 3.4 Local sediment balance (tonnes/year) for sediment reaches under different wash load scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72

Table 3.5 Local sediment balance (tonnes/year) for sediment reaches under scenarios for 10 and 20 per cent increases in runoff due to climate change, coupled with estimated wash loads of 20 000, 10 000 and 5000 tonnes/year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Table 3.6 Local sediment balance (tonnes/year) for sediment reaches under scenarios for 10 and 20 per cent decreases in runoff due to land use change coupled with estimated wash loads of 20 000, 10 000 and 5000 tonnes/year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Table 3.7 Local sediment balance (tonnes/year) for sediment reaches under selected bed material load scenarios reflecting stabilisation of the channel upstream (eliminating the supply of bed material load) and the effects of land use and/or climate change in elevating future bed material loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78

Land use management effects on flood flows and sediments – guidance on predictionxiv

Glossary

Agriculturally improved Land that has been managed to improve yields, often involving ploughing, artificial seeding, fertilisation, and field drainage.

Algorithmic differentiation Differentiating an entire numerical algorithm (or an entire model) mathematically by differentiating its source code line by line.

Analogue HOST classes HOST classes speculated to apply under a change in land use.

Bedload Sediment particles transported along the bed of rivers by the flow.

Blanket peat or blanket bog Large areas of peatland commonly found in moist cool environments such as the uplands of north-west England.

Brooks and Corey relationship A function that describes the relationship between soil water content and soil water pressure.

Compaction The deterioration of soil structure by mechanistic pressure, predominantly from agricultural practices (eg by machinery, livestock trampling).

Darcy’s law A linear relationship between the velocity of flow and the pressure gradient.

Data based mechanistic An established method of statistical modelling, the results of which modelling can be interpreted in terms of physical flood response characteristics.

Diffusive wave A simplification of the Saint-Venant equations that assumes that friction, slope and pressure dominate the forces on the water.

Emulation The approximation of one model using another simpler model.

Falling head permeameter A device that approximately measures saturated hydraulic conductivity, as a measure of a soil’s ability to infiltrate water.

Field drainage An artificial drainage system, used to control the in-field water tables by enabling the rapid transmission of excess soil water to streams. This includes open ditches, clay tile pipes, perforated plastic pipes and secondary drainage treatments, such as mole drains.

Flashy Describing a catchment where the river flow rates tend to rise quickly in response to rainfall.

Global sensitivity analysis The analysis of how the output of a model changes when its inputs are perturbed around multiple values of the inputs.

Grips Open artificial drainage ditches installed to drain peatlands, usually straight and with steep banks.

Guelph permeameter A device that approximately measures saturated hydraulic conductivity, as a measure of a soil’s ability to infiltrate water.

Gullies Open drainage channels, can be meandering with eroded sides.

Hydraulic conductivity A measure of how fast water will travel under a unit pressure gradient, usually used in context of soils or other porous media.

Incision A process of channel adjustment by which the stream bed scours into and ultimately establishes a lower bed elevation.

CIRIA C719 xv

Kinematic wave A simplification of the Saint-Venant equations that assumes that friction and slope dominate the forces on the water.

Land use In this guide, used as a general term to refer to the way land is used and the way the land is managed given its use.

Leaf area index An index related to the relative number of leaves present on trees.

Local sensitivity analysis The analysis of how the output of a model changes when its inputs are perturbed around specified values of the inputs.

Macropores Systems of pores, cracks or other conduits that in wet conditions allow water to drain relatively quickly from a soil.

Manipulation plots Small areas of land that are subject to controlled changes as part of an experiment to understand the individual and/or combined effects of the changes.

Meta-model The simpler of the two models used in emulation, also called the emulator.

Muddy floods Floods carrying substantial and visible amounts of fine sediments.

Non-inertia approximation Approximation where inertial terms are neglected (closely related to to Saint-Venant equations diffusive wave modelling).

Over-parameterised A term used to describe a model that has more parameters that can be estimated with reasonable precision using available data.

Percentage runoff The percentage of rainfall that causes a short-term increase in flow at the catchment outlet during an event.

Poaching The compaction and waterlogging of soil, and over grazing of riparian vegetation, caused by grazing animals.

Preferential pathways Pathways by which water will travel more freely (in the context of flow through soil, generally the same thing as macropores).

Probability distributed A simple rainfall-runoff model often used in the UK for simulating moisture (PDM) model continuous time flows.

Regionalised indices Properties of flow response at a site that have been estimated using information from gauged sites in the same region (rather than from the actual site).

Response time A measure of the effective storage of a catchment and the ability of the catchment to attenuate a flood peak.

Runoff generating elements Spatial elements of a catchment such that within each element type the hydrological response is assumed to be similar and the land use is uniform.

Runoff generating model The model used for a particular runoff generation element.

Saint-Venant equations The form of the momentum and mass balance equations commonly used to describe overland and open-channel flow.

Scrape A shallow pond-like feature that contains water for all or much of the year. Can provide good habitat for wading birds.

Sediment source fingerprinting Technique used to identify sources of suspended sediment in river catchments. The basic principle underlying the approach is that different potential sediment sources can be characterised, or fingerprinted, using several diagnostic physical and chemical properties and that comparison of these fingerprints with equivalent information for suspended sediment samples permits the relative importance of the different potential sources to be determined.

Land use management effects on flood flows and sediments – guidance on predictionxvi

Slaking The breakdown of large soil aggregates into smaller soil particles by water action.

Soil matrix The solid component of soil. Consists of particles that vary in chemical and mineralogical composition as well as in size, shape and orientation. Also contains organic material.

Soil water retention curves Curves that describe how easily a soil drains.

Standard matric pressure The amount of work needed to transport an infinitesimal quantity of water from a pool at the elevation and the external gas pressure of the point under consideration, to the soil water at the point under consideration, divided by the volume of water transported.

Standard percentage runoff The percentage of rainfall that causes a short-term increase in flow at the catchment outlet averaged over a long period.

Step-pool morphology Characteristic bedform morphology of steep upland streams. Steps are accumulations of cobbles and boulders that are transverse to the channel, while pools are intervening, deeper areas with finer bed sediments. Steps and pools alternate to produce a characteristic, repetitive sequence of bedforms, with a stepped longitudinal profile resembling a staircase.

Stocking density The number of animals per unit area.

Sub-aerial processes Processes that occur at the Earth’s surface including weathering (freeze-thaw action, thermal expansion/contraction), and erosion by wind and rain etc.

Surface runoff Flow on the land surface, also often called overland flow.

Synchronisation The coming together of flood peaks from different parts of a catchment.

Time to peak The time between a unit of effective rainfall and the corresponding peak streamflow, often approximated as the time between observed peak rainfall and peak streamflow.

Tree shelter belts Strips of trees planted to provide shelter to livestock.

Wash load Sediment that is not found in appreciable quantities in the channel bed or lower banks, which is usually taken as the grain size of which 10 per cent of the bed material is finer (ie D10).

CIRIA C719 xvii

Abbreviations and acronyms

BFI Baseflow index

CFMP Catchment Flood Management Plan

CN USDA curve number

DTM Digital terrain model

DEM Digital elevation model

FEH Flood Estimation Handbook

FRMRC Flood Risk Management Research Consortium

FRMRC2 Flood Risk Management Research Consortium (Phase 2)

HEC-RAS Hydrologic Engineering Centers River Analysis System

HOST Hydrology of Soil Types

ISIS Hydrodynamic model marketed by Halcrow

MIKE11 Hydrodynamic model marketed by Danish Hydraulic Institute

MIKE-SHE A hydrological modelling system by Danish Hydraulic Institute

MORECS The Met Office Rainfall and Evaporation Calculation System

MOSES Met Office Surface Exchange System

NS Nash-Sutcliffe efficiency

PR Percentage runoff

RGE Runoff generating elements

RGM Runoff generating models

SHE Systeme Hydrologique Europeen (hydrological model)

SIAM Sediment Impact Analysis Method

SPR Standard percentage runoff

USDA United States Department of Agriculture

UU SCaMP United Utilities Sustainable Catchment Management Programme

WaSim Water balance simulation model

Land use management effects on flood flows and sediments – guidance on predictionxviii

CIRIA C719 1

1 Introduction

1.1 FLoodING ANd RuRAL LANd usEEuropean flood management policy is currently defined by the Floods Directive 2007, implemented in the UK by the Flood and Water Management Act 2010 and the Flood Risk Management (Scotland) Act 2009. These Acts require that the environmental objectives of the Water Framework Directive 2000 are duly considered. This recognises that sustainable approaches to flood risk alleviation should be used to complement, and where possible replace, traditional, structural flood defences and sediment maintenance activities.

One potentially effective approach to flood risk alleviation is to manage flood water and sediments at or near their source through appropriate management of the rural landscape. To make rational decisions about whether and how to integrate this approach into planning and policy, quantitative evidence is required.

The challenge of quantifying impacts of rural land use on flooding was reviewed comprehensively by O’Connell et al (2004). They concluded that the existing evidence base was insufficient and there was a lack of suitable guidance on how to quantify effects. This led to renewed research effort, which produced the new evidence and tools covered in this guide.

Sediments are an inherent part of the link between land use and flooding. The energy from flood water causes erosion of soils and river channels, with the eroded material being carried and deposited downstream, in some circumstances, reducing channel conveyance and increasing the probability of over-bank flow. When sediment loads are elevated by untimely and/or inappropriate land use and land management practices, this can amplify the damage from floods.

“Land use” is used as a general term throughout this guide to refer to both the way land is used (for example, forests, crops, grassland, moorland) and the way the land is managed given its use (for example, type of drainage, stocking density).

1.2 PRojECT bACKGRouNdThis guide represents the culmination of a seven year long, integrated research project that involved over 20 researchers from six different research institutions, funded primarily by the UK’s Flood Risk Management Research Consortium (FRMRC). The FRMRC <www.floodrisk.org.uk> was a consortium of 20 UK research institutions. Between 2004 and 2012 it was funded to address the wide range of scientific challenges that limit effective flood risk management. Rural land use was one of five main research areas.

1.3 objECTIvEs oF ThIs GuIdEThis publication provides guidance to policy makers, farmers, landowners and land managers, governing bodies and consultants on the evidence and tools now available for supporting decisions concerning land use in the context of flood and sediment management. It aims to:

66 promote interest in the role of sustainable land use in flood risk reduction

66 provide stakeholders with a better informed basis for adopting policy guidance related to land use and flooding

66 provide guidance on quantification of land use effects and identify strengths and weaknesses associated with currently available modelling tools

66 provide advice on the methods available to identify multiple benefits of sustainable land use actions.

2Land use management effects on flood flows

and sediments – guidance on prediction

This guide is intended for use by all those involved in flood risk quantification. Also, it provides information useful to other parties, including at-risk communities, insurers and other rural land management stakeholders.

1.4 LIMITATIoNs oF ThIs GuIdEThe information in this guide is largely based on evidence collected in the UK and models developed and tested specifically for UK rural applications. While the guide is based on the significant progress made under the FRMRC and associated research programmes since 2004 as well as previous information, the evidence base is at an early stage of development and does not yet cover all types of catchment and land use.

The guide does not aim to cover new evidence from relevant monitoring programmes that overlap in time with the FRMRC. These include:

66 Belford project (University of Newcastle, 2012)

66 Demonstration Test Catchment programme (DEFRA-EA, 2012)

66 EMBER project (Brown et al, 2009)

66 Holnicote project (Rose et al, 2011)

66 Plynlimon paired catchment experiment (Center for Ecology and Hydrology, 2012)

66 Tarland catchment initiative (James Hutton Institute, 2012)

66 Environmental Change Network (ECN, 2012).

The guide aims to support, but does not aim to provide an alternative to, expert advice or case-specific data collection and modelling, which continue to be necessary for reliable decision support.

The guide does not aim to recommend or describe in detail a single procedure for evaluating land use effects, rather it summarises some available modelling approaches and provides guidance on the choice between them.

The guide does not cover methods for flood risk evaluation, rather it is limited to the evaluation of flood flows and sediment loads, with the view that the results could feed into a flood risk assessment. Also, it does not give advice about overcoming practical difficulties of land use change such as financing and authorisation.

The guide is based on several research components that did not always feature consistent study periods or rainfall events of similar magnitudes and intensities. This makes it difficult to compare the effects from one case study to another.

1.5 sTRuCTuRE oF ThE GuIdEThis guide is divided into four chapters:

Chapter 1: Introduction

Chapter 2: Flood flows describes current knowledge about the effects of rural land use on flood flows. Experimental evidence across scales is presented, followed by established rules of thumb concerning the direction and relative degree of effects related to common land use actions. The chapter describes and reviews older modelling tools as well as new tools developed under FRMRC to quantify catchment scale effects of rural land use in the UK. Guidance is given on how to select an appropriate model depending on the purpose of the application, data availability and user skill.

Chapter 3: Sediments and geomorphology describes current knowledge concerning the effects of rural land use on erosion and fluvial geomorphology with particular relevance to flood risk management.

CIRIA C719 3

Evidence from the literature is reviewed, and new evidence collected by FRMRC is presented. The chapter describes and reviews older modelling tools as well as new tools developed under FRMRC to quantify effects of land use on sediment dynamics. Guidance is given on how to select an appropriate model depending on the type of application, data availability and user skill.

Chapter 4: Stakeholder negotiation of ecosystem services addresses the role of land management stakeholders in exploring and optimising how the provision of multiple ecosystem services can best be achieved through sustainable rural land use. The ecosystem services that may complement or compete with flood management are identified, and the challenges and opportunities for finding multi-criteria solutions are defined. Finally, a new negotiation and visualisation tool (POLYSCAPE), based on a geographical information system (GIS), is described.

1.6 RELATIoNshIP To oThER GuIdANCEThis guide accompanies several CIRIA publications that provide guidance on flood management:

66 C713 Retrofitting to manage surface water (Digman et al, 2012)

66 C689 Culvert design and operations guide (Balkham et al, 2010)

66 C687 Planning for SuDS making it happen (Dickie et al, 2010)

66 C697 The SUDS Manual (Woods Ballard et al, 2007)

66 C624 Development and flood risk (Lancaster et al, 2004)

66 C625 Model agreements for sustainable water management systems (Shaffer et al, 2004)

66 Uncertainty guidelines (FRMRC draft)

66 Infrastructure (FRMRC draft)

66 Flood inundation (FRMRC draft).



2 Flood flows

2.1 PRobLEM dEFINITIoNRecent floods in the UK have focused attention on the effects of rural land use on flood risk (Wheater, 2006). Since WWII agricultural intensification has been widespread across the UK, with increases in stocking density, ploughing, reseeding and drainage of fields, use of heavy machinery, and the removal of trees and hedgerows from the landscape. Whether or not these changes in land use are increasing the frequency and degree of flooding remains a question.

Rural land use changes have been observed to affect local surface runoff (eg Marshall et al, 2009, and O’Connell et al, 2004). However, in many cases the evidence is limited to a relatively small range of land use actions and a few soil and climate types. Many attempts to identify the causal effects of land use change at the catchment scale have failed. This is largely because the characteristics of the network of drain, stream and river channels exert an important control on the attenuation and synchronisation of the flood peaks from different parts of a catchment, making it difficult to identify the effects of changes in land use on downstream flooding. Several other reasons have been proposed to explain this failure, including climate variability, poorly constrained spatial and limited historical information defining the distribution of land use types (Beven et al, 2008). Due to these limitations, the degree to which the local effects of land use changes propagate downstream to influence flood probability has remained an unanswerable question (O’Connell et al, 2007).

Flood risk is the consequence of a flood combined with its probability, often integrated over a range of possible floods to give an annual expected damage. In rivers, the source of the risk can be described by a flow hydrograph, although for practical reasons is often quantified solely by the maximum flow rate averaged over a suitable time period, for example one hour. This allows the flood probability to be expressed as a flood frequency curve, which relates the probability of non-exceedence of the flood to its peak flow rate. Figure 2.1 demonstrates the relationship between flood flow and flood risk. To quantify the effect of land use on flood risk, it is imperative to understand the effect on the flood hydrograph. Also, prediction of the rate of sediment transport relies on accurate prediction of the flood hydrograph. Chapter 2 focuses on flood flows and their prediction.

Figure 2 .1 Schematic diagram demonstrating relationship between outputs from rainfall-runoff models and quantification of hazard and risk

Key

Q = peak flow p = probability of non-exceedence h = peak water level t = time

CIRIA C719 5

Section 2.2 presents a review of the UK evidence base of the effects of land use change on runoff generation and flooding. Some qualitative rules of thumb are presented in relation to this evidence as well as new information gained from the FRMRC modelling programme.

Section 2.3 reviews several quantitative methods for predicting flood flows and changes in flood flows related to land use change, including analysis of uncertainty. Guidance is provided about how to select appropriate modelling tools based on case specific requirements.

Section 2.4 describes a new method for quantifying the sensitivity of the properties of the catchment scale flood hydrograph to changes in land use in a spatially explicit manner.

In Section 2.5 remaining gaps in knowledge are reviewed, identifying areas for future research.

2.2 ThE uK EvIdENCE bAsEThe following section provides a summary of the effects of rural land use change on hydrology and stream flows. The evidence is divided between information available pre-FRMRC and evidence delivered by FRMRC up to the end of 2011. The evidence is used in Section 2.2.3 where appropriate to make generalisations and rules of thumb about the typical direction of change in stream flows related to a variety of land use changes.

2 .2 .1 Evidence pre-FRMRC

In recent years, some major flooding events have been experienced in the UK, notably in 1996, 1998, 2000, 2004, 2005, 2007 and 2008. When such floods occur, debates arise as to the possible causal mechanisms, and there has been speculation that changes in land use practices may have amplified flood responses. Soil saturation was believed to be more frequent and widespread than expected, particularly during the extensive flooding of 2000, and this was linked to the loss of soil structure through compaction by livestock and farm machinery. Later surveys of soils in several catchments revealed some evidence of compaction (Hollis et al, 2003, and Holman et al, 2001), but it was concluded that a comprehensive review of the literature was needed to establish the current state of knowledge concerning proposed links between changes in land use practices and increased flooding. A national review of the effects of rural land use and management on flood generation was commissioned through Defra/EA R&D Project FD2114 by a consortium of experts in agriculture, soil science, hydrology, hydrogeology and socio-economics (O’Connell et al, 2004). The overall objective of the review was: to review the factors contributing to runoff and flooding in the rural (managed, not natural) environment, and to scope out the research needed to improve the identification of the management policies and interventions to reduce the impact of flooding.

The evidence available for both local and catchment scale effects was summarised, and gaps in knowledge identified. The review, which concentrated only on the peer-reviewed literature in assembling the evidence, was wide-ranging, and covered the following topics and areas:

1 Field experiments, available data, models, and flood analysis and prediction methods.

2 Catchment modelling and the prediction of effects.

3 Current state of managed land in England and Wales such as arable (including cereals, oilseed rape, maize and root crops), annual feed crops, woodland/forestry, grassland, livestock, and field drainage.

4 Effects of current farming practices on soil structure and runoff.

5 Flood mitigation practices, including cover crops, minimum tillage, hillslope runoff control, use of machinery, retention structures and wetlands.

6 Monitoring and modelling studies (plots, fields, hillslopes and catchments).

7 Socio-economic aspects, including the response of land managers to measures and policies, categorised in terms of a Drivers-Pressure-State-Impact-Response (DPSIR) framework.



8 The future: Agri-Environmental Schemes, Common Agricultural Policy reforms, long-term Foresight scenarios (Government Office for Science, 2010), climate change etc.

9 Integrated runoff management at the farm scale, generating wider benefits by, for example, reducing erosion and agricultural pollution.

10 Implications for water resources.

Local scale impacts

Substantial evidence exists that local surface runoff is increased by a number of ‘modern’ farm management practices including increased stocking densities on grassland (Heathwaite et al, 1989, and 1990), the prevalence of autumn sown cereals (Sibbesen et al, 1994), the increase of maize crops, the production of fine seedbeds (Speirs and Frost, 1985) and trafficking on wet soils (Davies et al, 1973, and Young and Voorhees, 1982). There does not appear to be a strong link with soil type, but sandy, silty, and slowly permeable seasonally wet soils are more susceptible than others to degradation and associated increases in surface runoff. There is a growing body of statistical data on the spatial extent of field sites showing evidence of reduced infiltration and increased surface runoff associated with “modern” practices (Hollis et al, 2003, Palmer 2002, 2003a and 2003b, and Souchere et al, 1998).

There is quantifiable evidence that field drainage and associated secondary drainage treatments (such as mole drainage and subsoiling) can increase or decrease peak drain flows and the time to peak flow by as much as two to three times either way. The behaviour appears to depend on the soil type and wetness regime (Armstrong and Harris, 1996, Leeds-Harrison et al, 1982, and Robinson and Rycroft, 1999).

Evidence that improved surface runoff generation because of some of these modern farming practices can generate local-scale flooding has been reported in several studies. There is evidence from long-term studies in small catchments of the South Downs in south-east England that there is a significant relationship between the presence of autumn-sown cereal fields and local “muddy floods” in autumn (Boardman et al, 2003). Also, there is evidence that the frequency of these floods can be reduced using appropriate arable land management practices (Evans and Boardman, 2003). This evidence is supported by studies in France (Papy and Douyer, 1991 and Souchere et al, 1998) and Belgium (Bielders et al, 2003 and Verstraeten and Poesen, 1999). Evans (1996) has provided evidence that muddy floods, and the erosion and later deposition of substantial amounts of eroded soil, generate substantial economic damages each year, most of which occurs off-farm.

In contrast to the substantial evidence of changes to runoff generation processes, there is very little direct evidence on how such changes affect flows in surface water networks. Here such evidence as is available comes from small scale catchments, with drainage areas less than about 10 km2. There is quantifiable evidence for the effects of conifer afforestation in reducing runoff, and this is considered further under Mitigation of impacts. Also, there is some evidence for the effects of field drainage, but it is difficult to interpret. Small scale catchment studies generally do not supply good information on the amount, location and age of artificial drainage works. Most of the monitoring evidence comes from the Ray and Catchwater catchments (Robinson, 1990), for which it was concluded that general statements on whether artificial land drainage “causes” or “reduces” flooding downstream are over-simplifications of the complex processes involved. This study indicated that river channel improvements had much greater effects on peak flows than did field drainage. This issue will be considered further in the following sub-section on catchment scale effects.

Catchment scale impacts

As previously discussed, there is only very limited evidence that surface runoff associated with modern land use practices is transferred into upstream headwater channel networks. Evidence for the propagation of effects downstream to larger catchment scales was limited by this finding. In addition to any evidence that was available from catchment experimentation, evidence was also sought in the following sources(O’Connell et al, 2004):

CIRIA C719 7

66 statistical analysis of peak discharge and rainfall runoff flood event data

66 modelling studies involving catchment scale prediction of impacts.

National analyses of flooding trends (Institute of Hydrology, 1999 and Robson et al, 1998) have not shown significant effects resulting from either climate or land use changes. This is largely because of the over-riding influence of year to year climatic variability, which make trends associated with climate and land use difficult to identify. It is stated (Institute of Hydrology, 1999) that most of the records used in the study were not from catchments experiencing major land use change, but “land use” may be equated here with land cover, and not with land use changes. Guidance by the Institute of Hydrology (1999) is based on two methods of flood estimation, the statistical approach and the rainfall-runoff approach. Regression relationships linking flood statistics (eg the median annual flood) or rainfall runoff method parameters (eg the time to peak of the unit hydrograph) with catchment characteristics incorporate an urban extent variable. However the sample of available hydrometric data used to drive the relationships was dominated by large and medium size catchments, and would be unlikely to reveal the effects of rural land management practices.

As has been the case with the UK landscape, UK river channels have also undergone substantial modifications over the past 70 years because of land drainage schemes or flood alleviation schemes protecting urban and rural floodplains (Newson and Robinson, 1983, and Robinson and Rycroft, 1999). Channels have been subject to many different modifications, including straightening, re-sectioning, embanking, culverting and the construction of weirs and sluices. Recently, there has been a move towards the restoration of channels and floodplains to more natural states and functions, as part of biodiversity and natural flood management schemes. A comprehensive review of evidence for the effects of these modifications and other river management interventions lay outside the scope of the review conducted by O’Connell et al (2004). However, it is clear that these modifications will have changed the flood routing characteristics of watercourses in many UK catchments. These effects should be taken into account when assessing the evidence that changes to runoff generation processes at the local scale might have affected flood generation at larger catchment scales. However, disentangling the different effects on flood propagation at the catchment scale is a formidable challenge. The overall effect on catchment-scale flooding will be a function of the spatial location and extent of the landscape areas and river channel reaches affected, as well as the relative timings of runoff contributions from both the affected and unaffected landscape elements

Following the review by O’Connell et al (2004), the EA/Defra commissioned an R&D project in which historical rainfall runoff data sets were analysed to look for effects of land use change on flood generation (Beven et al, 2008). Six, predominantly rural catchments (75–1134 km2) were selected. These were considered to be catchments where land use effects might have taken place, and had reasonably long rainfall and runoff records available.

Data based mechanistic modelling was used to characterise the rainfall-runoff relationship, and the parameters were analysed for evidence of changes linked to land use. No clear evidence for significant effects was found. This does not necessarily mean that such effects do not exist, but only that they were not detectable for the catchments analysed due to significant hydro-climatic variability, and noise in the data analysed. At much smaller scales, analyses carried out elsewhere have attributed effects detected in the short-term variability of runoff to changes in land use (eg Archer and Newson, 2002 and Archer, 2007).

Mitigation of effects

There is considerable evidence in the published literature to support the argument that changing land use can mitigate or reverse the adverse effects of past land use practices on local flooding. The majority of these interventions are aimed at source control of on-farm runoff through the use of good land use practices. There is quantifiable evidence, mainly from free-draining loamy and sandy soils, that the production of fine seedbeds reduces surface infiltration (Speirs and Frost, 1985), and also reduces surface depression storage (Edward et al, 1994). There is quantifiable evidence, mainly from



free-draining loamy, silty and sandy soils, that for maize cropping, rough ploughing in the autumn and spring can reduce field plot runoff by between 30 and 100 per cent compared to a conventional spring tilled system (Clements and Donaldson, 2002, Kwaad and Mulligen, 1991). The success of other management techniques such as direct drilling, cover crops and soil mulches appears to be much less certain and dependent on soil type. Results vary from an 80 per cent reduction in surface runoff using winter cover crops (Schafer, 1986) to no significant difference using under-sown rye grass or winter cover crops (Clements and Donaldson, 2002). There is substantial evidence from specific sites to show that direct drilling or reduced cultivation can significantly reduce in-field runoff by 17 to 48 per cent for a range of arable crops (Charman, 1985). There is also evidence that the carefully targeted use of vegetated strips in arable systems can reduce edge-of-field runoff by a factor of 10 (Auerswald, 1998, and Melville and Morgan, 2001).

All of these results are site specific. Evidence, quantified in terms of runoff percentages etc is site specific and cannot be extrapolated reliably to other sites, nor can it be used at the catchment scale.

Some of the more desirable management practices for mitigating field-scale runoff generation are depicted in Figure 2.2. Guidance on implementation has been provided through the FARM Tool (O’Connell et al, 2004), taking into account specific on-farm topographic, soil and cropping conditions. Also, such measures can control nutrient pollution and sediment transport, which generates multiple benefits for the water environment.

In the recent Belford study (Wilkinson et al, 2010, and Nicholson et al, 2012, see Useful websites), the use of on-farm storage to mitigate flooding in the village of Belford in north Northumbria, has been explored. Funded by the EA local flood levy, numerous runoff attenuation features (RAFs) have been installed, including: a range of online and offline storage ponds, multiple large woody debris zones created by using locally felled trees, willow barriers to provide storage in farm ditches, and new channel diversion points created to direct peak flow into fields and ponds. In total, 30 RAFs have been constructed in an area of 5.7 km2. The work has been cited in the recent EA response to the Pitt Review (Environment Agency, 2012). These features are being monitored to provide evidence of how runoff propagates and attenuates through these features. Nicholson et al (2012) show that transient storage effects are more important that the absolute physical storage capacity of the RAFs.

Figure 2 .2

Potential for integrated runoff control to reduce flood risk, pollution and erosion

CIRIA C719 9

The evidence for the mitigation effects of forests is difficult to interpret, as this involves the effect of the forest land cover, and the effects of the management practices associated with the crop cycle, on runoff properties. Most catchment monitoring studies in the UK have focused on upland catchments dominated by conifer plantations or rough grassland. These have all shown that the water yield is less from forest than from pasture. There is some evidence that afforestation reduces peak flows and increases times to peak. However, this evidence shows that the effects of forests on flood hydrographs cannot be predicted simply by using the above-mentioned water yield data. In their general review of the history of forest hydrology, McCulloch and Robinson (1993) concluded that forests should reduce flood peaks, except for the effects of drainage and forest roads. However, in the Coalburn experiment, peak flows actually increased by 20 per cent in the first five years after forest planting (decreasing to five per cent after 20 years) and times to peak decreased (Robinson, 1986 and 1998). This is thought to be the result of plough drainage and ditching. A review of results from 28 monitoring sites throughout Europe (Robinson et al, 2003) concluded that the potential for forests to reduce peak flows is much smaller than often has been claimed, and that forestry appears to “... probably have a relatively small role to play in managing regional or large-scale flood risk”.

As is the case with the propagation of changes in runoff generation from the local to the larger, catchment scale, there is little or no evidence that local scale flood mitigation measures are effective at the catchment scale. In this context, the potential effectiveness of mitigation measures should be viewed in terms of the proportion of area covered, their spatial locations within the catchment, and their effects on runoff generation and delivery to the channel network. Local-scale mitigation measures can be viewed as “prevention at source”, but since their effect will be to delay or attenuate the delivery of runoff, the overall effect on the catchment flood hydrograph will depend on how they interact with other changes (eg to river and floodplain management).

Case study: River Parrett

The Parrett catchment (1700 km2, see Figure 2.3) in south-west England has a long history of use for agriculture and settlement and its physical characteristics have lead to many river basin management issues. The Parrett catchment includes the River Isle, the River Yeo, the Rive Tone, the Coastal Zone (Stogursey Brook catchment), West Sedgemoor, and the River Cary and King’s Sedgemoor Drainage area.

Figure 2 .3 The River Parrett catchment and its sub-catchments

The Parrett catchment is predominantly rural. Cattle and sheep farming dominate the lowlands, while wheat, barley and maize prevail on the uplands, and there are significant pockets of woodland throughout the catchment. Much of the low-lying area is designated as a wetland of outstanding ecological importance. A tidal range of up to 15 m and storm surges up to 1 m cause coastal flooding, with runoff from the upland areas adding to the flood frequency and extent (Environment Agency, 2003). These features of the Parrett drive the interest in land use to retain stormwater in the upper catchment.



The Environment Agency surveyed the degradation of topsoil and upper subsoil affected by the intense rainfall of autumn 1999 and 2000 and winter 2001–2002, at several selected sites. Extensive degradation was found in 46 per cent of the sites examined in the Parrett catchment. It was estimated that this could cause an increase in runoff up to 25 per cent (Environment Agency, 2003). This may be associated with use of heavy machinery or working the soils during wet conditions (Palmer, 2002, 2003 and 2004). Cultivated land, particularly winter cereal crops on steeper slopes, was much more likely to be degraded than permanent grass (Palmer, 2002 and 2003, Godwin and Dresser, 2003).

The worst cases of soil erosion provide clear evidence of the potential for pollutant and sediment delivery to water courses. However, many of the insidious features identified, for example slight surface slaking or loss of subsurface structure, were not considered to be linked to environmental deterioration (Environment Agency, 2003, Palmer, 2002 and 2003). Figure 2.4 shows the loss of surface soil on a potato farm during a wet winter through development of gullies and soil compaction.

It was recommended that sustainable management in the upper Parrett catchment should aim to improve soil drainage properties. This is expected to reduce antecedent wetness, have the capacity to accept greater rates of infiltration by breaking surface caps and subsurface pans, and provide sufficient surface depression storage to allow time for infiltration (Environment Agency, 2003).

2 .2 .2 Evidence delivered during FRMRC

Evidence on flood flows delivered during the FRMRC project primarily consists of the data sets collected during the Pontbren and Hodder experimental catchment programmes, the latter being the hydrological monitoring output from the United Utilities Sustainable Catchment Management Programme (UU ScaMP). The evidence presented in this section is in the form of summaries of key data and their empirical analysis. Further evidence, including that from the Parrett and Tone catchment case studies, is presented in sections 2.3 and 2.4 as examples of simulation model outputs. While these case studies were chosen to represent as far as practicable UK climate and rural land use and land management, they are a small sample and extrapolation to other areas carries high uncertainty. In particular, impacts may depend upon local features that affect the connectivity of areas affected by the land use change to the stream, and upon cycling of land use (Newson, 2010), which is not well represented by the case studies. Table 2.1 provides a summary description of the climate, land use and land management of the three case studies.

Figure 2 .4

Dried and compacted soil at a potato farm in Somerset (Park, 2006)

CIRIA C719 11

Table 2 .1 A summary of the climate, land use and land management of the three principal FRMRC case study catchments

Pontbren hodder Parrett and Tone

Region Powys, mid-Wales Lancashire, north-west England Somerset, south-west England

Major catchment Severn Ribble Parrett

Area 12 km2 260 km2 1700 km2 and 414 km2

Elevation (AOD) range 170 m to 438 m 40 m to 544 m 3 m to 200 m

Annual average rainfall 1449 mm 1602 mm 1200 mm (upper) to 675 mm (lower catchment)

Soils Slowly permeable silty clay loams with peat in upper areas

Peat and other slowly permeable soils

Slowly permeable loamy soils, with siltstone and sandstone, with floodplain alluvium

Geology Mudstones and sandstone with overlying deposits of glacial till

Millstone Grit and Carboniferous Limestone.

Permian basal sediments and sandstone in upper catchment, mudstone in lower reaches

Hydrology and geomorphology

Little base flow, flashy response, steep slopes, significant drainflow

Flashy response. Surface waters are abstracted for public supply

Flashy response in steep upper catchment. Lower reaches buffered by floodplains. Coastal flooding affects low-lying areas

Land cover and use

Sheep grazing with small areas of arable, cattle grazing, conifer plantations and deciduous woodland. Uplands include three significant lakes/ponds

Predominantly livestock farming on permanent grassland in the lowlands, with rough grazing and game rearing at higher elevations

Dominated by grassland and arable, with urban areas and small pockets of woodland

Land management

Most of catchment is agriculturally improved, with a history of intensive sheep grazing and artificial drainage. The Rural Care Project over last decade has made tree strips a common feature, with some restored wetlands

Grassland has been agriculturally improved.Upland areas undergoing changes under UU ScaMP: reductions in stocking densities, tree planting and the blocking of drains in peatland

Permanent pasture in the upper catchment with ley grasslands, winter cereals, maize, potato crops, and sheep and cattle grazing in middle reaches. Floodplain supports open moorland, pasture, withy beds and maize

Local land management and flooding issues

Perceived recent increases in flood flows and channel erosion. Over last 10 years much of catchment has been included in environmentally sensitive farming initiative by Pontbren cooperative

Flood risk is low as the catchment is sparsely populated. Upland areas are designated as a SSSI, parts of which are under poor conditionDegradation of peatland resulting in discoloration of runoff

Soil degradation and erosion, and excessive sediment delivery to the watercourses. Diffuse pollution, contaminated sediments. Covered by Catchment Sensitive Farming and is a pilot catchment for WFD

References for further information

Wheater et al, 2008Marshall et al, 2009

Ewen et al, 2010Geris, 2012

Environment Agency, 2009b

The Pontbren experiment

Pontbren is a 12 km2 agricultural catchment located in mid-Wales, in the headwaters of the River Severn (see Figure 2.5), that was intensively instrumented under the FRMRC programme. Pontbren is characteristic of much of the Welsh uplands, ie the soils are shallow and clay-rich. They overlie low-permeability subsoil and so are seasonally wet. Most fields have been agriculturally improved including installation of field drainage systems and the area is largely used for sheep farming. Over the last decade, the Pontbren Rural Care Project has included reducing stocking density, planting of tree features and restoration of wetlands. Conclusions from the experimental data included:

66 in agriculturally improved grazed grassland, flow from artificial field drainage is the primary runoff component overall, with saturation-excess surface runoff becoming dominant during annual maxima flood events



66 compared to grazed grassland, there is significantly less surface runoff at local scale when sheep are excluded and less still when trees are planted. This effect is apparent within one year of sheep exclusion and tree planting

66 catchment-scale stream flow data demonstrated that sub-catchments dominated by agriculturally improved land produced higher flow peaks than those with moorland landscapes and those with significant surface water storage volumes

66 climatic variability has a very significant influence on runoff processes due to the direct effect of climate on soil structure, and this may mask land use effects. Longer-term experiments are recommended

66 differences between sub-catchments were amplified when considering sediment responses (see Section 3.2).

The basis for these conclusions is described briefly as follows. Full details of the experimental programme and main results can be found in Wheater et al (2008, 2010), Marshall et al (2009) and McIntyre and Marshall (2010).

Figure 2 .5 Location of Pontbren experimental catchment within the UK

The experiment was run over seven years, with monitoring across scales, from plot scale (~100 m2) to small catchment scale (12 km2). At the plot scale, the effects of land use change on hydrological response were investigated directly at four sets of manipulation plots, as well as within four tree strips. Data from the hillslope scale (~ 0.1 km2) was used to support physics-based and data-based model development and underpin the conceptual understanding of the hydrological response. Catchment-scale (0.2 to 12 km2) monitoring took place at several locations, including different land management regimes. This was to better understand the integrated effects of land use on flow peaks at different scales. Figure 2.6 demonstrates the locations of the main instrument sets.

The four replicated manipulation plots in the Pontbren catchment area were established to investigate plot scale effects of land use change. The sites were all located on agriculturally improved grassland used for sheep grazing.

CIRIA C719 13

Note

LH = Llyn Hir, HT = Tyn y Bryn Hilltop, TF = Tyn y Bryn Top field, HB Instrumented Hillslope – The Bowl, HT = Instrumented Hillslope – Tree planted hillslope, Tn = Tree planted study site, Mn = Manipulation plots, HU = Hirrhos Uchaf, ▲n = stream and drain flow monitoring site, ▲ = rain gauge.

Figure 2 .6 Pontbren study site instrumentation location

The most significant results from the plot scale experiments were the quantification of the effects of land use changes on infiltration rate and surface runoff. In general, the response from the manipulation plots was highly heterogeneous, with large differences between events and between sites. However, there was strong evidence of changes in infiltration and volumes and peaks of surface runoff. Infiltration rates were found to increase following both stock exclusion and stock exclusion plus tree planting, with increases of 1.4 and seven times respectively the values in the control plots. Between four and 16 months following tree planting, total surface runoff volumes were reduced on average by 78 per cent compared to the control grazed plots. Similarly, over the same period, a 45 per cent reduction in total annual surface runoff was observed for the ungrazed plots relative to the control. This result is reflected in the sample of runoff coefficients obtained by measuring each event separately. Significant reductions in the sample medians are seen when going from the grazed to the manipulated land uses (Marshall et al, 2012). Observations indicate the increased surface vegetation in the ungrazed and tree planted plots led to increased times to peak. Figure 2.7 provides a summary of the main changes in hydrological processes and response observed at the manipulation plots (Marshall et al, 2012).



Figure 2 .7 Schematic diagram demonstrating the relative difference in infiltration rates (white arrows) and annual runoff volumes (black arrows) for (a) ungrazed and tree planted (b) ungrazed and (c) grazed plots

Although most of the trends observed were statistically significant, there were large differences between sites and between individual rainfall events. The pre-treatment period was also strongly affected by soil cracking during a particularly hot and dry summer (2006). In some cases, the effect of the dry period was more significant than the effect of the land use changes.

To investigate the hydrological response at the hillslope scale, a representative improved grassland hillslope was monitored during the experiment. The hillslope is about 300 m long and ranges in width between 70 m and 130 m, with an average slope of 12.5 per cent. An established tree shelterbelt was located about halfway down the field.

The dominant runoff pathways from the improved grassland part of the hillslope were flow in the under-drains followed by surface runoff. The low permeability soils remain saturated for prolonged periods throughout the year, resulting in sustained periods of drain flow. Flow in the under drains was highly responsive to rainfall inputs, suggesting that macropores or other preferential pathways were directing water to the drains. In larger rainfall events, surface runoff was also significant.

Measurements showed significantly greater hydraulic conductivity in the soils within the tree planted shelterbelt compared to the improved pasture (see Table 2.2). Examining differences in the soil water retention curves between the improved grassland and shelterbelt showed that the soils under the trees generally had greater saturated water contents and were more freely draining due to a greater proportion of larger soil pores and flow pathways provided by the tree roots (Solloway, 2012).

a b c

CIRIA C719 15

Table 2 .2 Saturated hydraulic conductivity Ksat for Cegin soil series at Pontbren (from Marshall et al, 2009)

horizon Land use Q1 (md-1) Median Ksat (md-1) Q3 (md-1) IQR (md-1) n

A Improved pasture 1.70 3.43 6.55 4.85 13

A Shelterbelt 5.15 8.34 12.21 7.05 6

B Improved pasture 0.0036 0.026 0.075 0.072 13

Note

A-horizon determined using falling head permeameter. B horizon determined using Guelph permeameter. Q1 and Q3 = lower and upper quartile values respectively, IQR = interquartile range (Q1–Q3), n=number of samples.

Differences in stream flow response were observed between the sub-catchments within Pontbren (Wheater et al, 2008, and McIntyre and Marshall, 2010). Significant differences in the response time, T, which represents the flashiness of the flood response, were identified visually and using a data-based mechanistic modelling framework. These differences are explained largely by the proportion of the sub-catchment area under improved grassland and the proportion of the sub-catchment covered by lakes and ponds. Also, the sub-catchment area had a statistically significant effect, while the effect of trees and soil type could not be identified, partly due to their limited variation over the Pontbren sub-catchments. Figure 2.8 demonstrates the relationship between some sub-catchment properties and the log10T, where a small value of log10T represents a flashy sub-catchment with a tendency for higher flood peaks. Non-linearity in flow routing at different scales within Pontbren was higher than explained by idealised surface and subsurface flow processes, indicating the importance of considering thresholds in flow pathways (McIntyre et al, 2011, and McIntyre, 2012).

Figure 2 .8 The relationship between catchment properties (x-axis) and the log10 of the response time, T, illustrating that improved grassland coverage and open water (lakes and ponds) coverage are together the principal factors within Pontbren (McIntyre and Marshall, 2010)



SCaMP Hodder experiment

Extensive upland restoration work has been carried out in the Hodder catchment, NW England, under the United Utilities Sustainable Catchment Management Plan, starting in 2005 (UU SCaMP, 2012, see Useful websites). A programme of field monitoring and numerical modelling has been running since 2008 to investigate the resulting effect on flooding. This programme was initiated in Project SC060092, and funded by the Environment Agency (Ewen et al, 2010).

The River Hodder drains an area of 260 km2. It joins the River Ribble just downstream of the Environment Agency flow gauge at Hodder Place, about 4 km south-west of the town of Clitheroe in NW England (Figure 2.9). In the headwaters, the Dunsop, Langden and upper Hodder (upstream of Stocks Reservoir) catchments have been used for surface water abstraction since the early 20th century. There is some minor groundwater abstraction. Stocks Reservoir has a contributing area of 37 km2 and surface area of 1.4 km2.

The upland areas in the Hodder catchment have rich organic soils supporting grassland and moorland vegetation, and receive high annual rainfall in excess of 1500 mm. At lower elevations, in the main Hodder valley and the River Loud catchment, there are mineral soils supporting improved grassland, making the area more favourable for agriculture. The annual rainfall in these areas is around 1100 mm. Land use in the Hodder catchment is predominantly permanent grassland (45 per cent), which has been improved in lower lying areas by drainage and fertiliser/lime application, and rough grazing (50 per cent) at higher elevations, which is also used for game rearing. There are some small areas of arable farming, largely wheat, maize and horticulture, and mainly in the Loud catchment and in the main Hodder valley downstream of Stocks Reservoir. Commercial coniferous plantations were planted in about 1950 in the Dunsop Valley (2 km2) and at Gisburn Forest (12 km2) upstream of Stocks Reservoir in the Bottoms Beck catchment. There is an extra area of coniferous forest, mixed with some native woodland, along the slopes of Longridge Fell. Pockets of native woodland are scattered throughout the lower Hodder Valley.

Flood risk in the catchment is generally very low as most of the land is rural, but there are local problems at Slaidburn, located at the confluence of Croasedale Brook and the River Hodder, and at Dunsop Bridge, next to the River Dunsop (Environment Agency, 2007). There have been some major inundation events. For example, during a localised event in 1851 it was reported that houses in Chipping, a village in the Loud, were inundated by six feet of water (Weld, 1851).

Figure 2 .9

The Hodder catchment

CIRIA C719 17

The UU SCaMP changes in land use are the latest in a long series of changes in the Hodder catchment. For example, as part of post-WWII agricultural intensification, there have been changes throughout north-east Lancashire, including the gripping of peat, stocking density changes, forestation/logging, and modifications to river channels. Eight types of restoration works were carried out in UU SCaMP, with the aim of helping prevent further deterioration of raw water quality (especially water colour production), and to improve the condition of Sites of Special Scientific Interest (SSSI):

1 Blocking gullies and grips to increase the water levels in blanket peat, to help improve the condition of the peatland.

2 Tree planting.

3 Reducing or relocating sheep grazing.

4 Controlling the extent and frequency of burning.

5 Controlling bracken.

6 Restoring vegetation on eroding bare peat.

7 Further management (eg re-seeding) of the plant communities of heath and blanket bog.

8 Scrape creation to provide habitat for wading birds.

Figure 2.10 shows the monitoring sites and UU SCaMP works in the Brennand catchment.

The monitoring instruments in the Brennand catchment are part of a network installed to complement the pre-existing rainfall and flow gauges managed by the Environment Agency (Ewen et al, 2010). At the

Figure 2 .10

Monitoring sites and UU SCaMP works in the Brennand catchment



peak of monitoring, there were 28 flow gauges in the Hodder catchment (Figure 2.11). These covered a range of scales from small sites where UU SCaMP works were adopted, all the way up to Hodder Place, where the Environment Agency gauge measures the flow close to where the Hodder joins the Ribble. Many of the gauged catchments are nested within other gauged catchments. For example, the gauges at locations 20, 15, 2, 4 and 11 represent a series of nested catchments with areas 0.0014, 1.7, 11, 25, and 260 km2.

The hydrographs for river flows within the Hodder catchment tend to be quite flashy, as a result of steep slopes and shallow soils, even for the largest events (eg Figure 2.12).

Data from the Hodder catchment are being used in a wide variety of studies designed specifically to improve the scientific basis on which predictions are made for the effect of changes in land use. The studies include:

66 developing new methods for modelling complex landforms, to investigate the fundamental scale at which the effect is generated (Geris et al, 2010)

66 analysis of extreme events (O’Donnell et al, 2011)

Figure 2 .11

Schematic showing locations of flow gauges in the Hodder catchment (colour coding shows the types of works undertaken upstream)

Figure 2 .12

Flow at Hodder Place (260 km2) and Footholme (25 km2, lies just upstream of Dunsop Bridge) for October 2000 flood

CIRIA C719 19

66 experimental new methods for studying sensitivity to change (see Section 2.4)

66 new methods for calculating metrics for model performance (Ewen, 2011), which are needed when calibrating models used to estimate the effects caused by changes in land use in flashy catchments.

However, the fundamental work that the nested instrument network was designed for concerns the detection and scaling of hydrograph effects (Ewen et al, 2010).

The underlying principle for the method being used when detecting the effects of the UU SCaMP works is if a model calibrated for the pre-change period also accurately simulates the post-change hydrograph it indicates that change has not been detected. The current conclusion from this analysis is that no effect from UU SCaMP has been detected, except at scales well below 1 km2 (Geris, 2012).

Figure 2.13 shows the peak flows per unit catchment area for six rainfall events measured and modelled over a range of scales (Ewen et al, 2010). This figure is based on simple modelling (simple mass balance) and simple scaling (logarithmic dashed lines in figure). This result suggests that the catchment response is quite stable and resilient to land use changes. This is because the behaviour of the peaks is currently simple despite the fact that numerous changes of land use have occurred in the catchment during the last few centuries.

Figure 2 .13 Peak discharge per unit catchment area (mm/h) plotted against catchment area (km2) for six rainfall events (observed – closed circle, simulated using simple model– open circle, broken lines are for a simple scaling equation)

2 .2 .3 “Rules of thumb” from the evidence base

In this section, a summary of the experimental evidence as well as the lessons learned from recent modelling efforts (see Section ) are combined to give some general (qualitative) guidance about predicting impacts of land use on flood flows.



The exact effects of land use change will vary between catchments due to differences in, for example, soil types and climates. Using site specific information to make generalisations can be misleading. However, some general “rules of thumb” can be synthesised from the evidence.

In most cases, it is observed that the effects of land use change are relatively large only in smaller catchments. This is due in part to the direct effects of scale (eg the fraction of land area affected by change tends to fall as the catchment area increases) and also to various inefficiencies in the way that effects propagate downstream through the stream network. Also, the effects of land use change are generally more significant for smaller events compared to larger ones. This is primarily because the relatively small changes in storage related to most land management interventions become less significant and can be overwhelmed by large events.

Table 2.3 provides a qualitative summary of the potential effects on runoff due to various different land use interventions. Stating the relative degrees of effects would be highly speculative and so only directions of change are listed. The potential effects in Table 2.3 are indicative, based largely on observations and modelling of what happens at small scales. The actual effect at larger scales at flood sites downstream will depend on many factors. For example, it can depend on the fine details of the timings of the various component of runoff and stream flow. These timings can depend on many factors such as the antecedent rainfall and the travel times from the various sites of change to the flood site.

Table 2 .3 Summary of the effect on flow peaks of commonly adopted land use practices

Land use Land use Changed from direction of change

TreesDeciduous plantationsConiferous plantationsDeciduous tree strips

Ungrazed moorlandReductionReductionReduction

GrazingHeavy grazingLight grazing

Ungrazed moorlandIncreaseIncrease

PeatlandDrainedBlocked drains

Intact peatlandIncreaseUncertain

Storage ponds Grazed grassland Reduction

The directions of change listed in Table 2.3 are based on generalisations from the literature and the evidence provided by FRMRC. Significant variability in response for any given land use change can be expected based on the land management practices and the quality of the land use changes (Newson, 2010). For example, while the potential reductions in flow peaks associated with trees are large, the potential counter-effects of plantation drainage and access roads are well known (Robinson, 1986).

Appropriate land use changes primarily have the potential to reduce flooding through one of more of the following mechanisms:

66 increasing infiltration into the soil

66 increasing soil storage

66 increasing surface storage

66 decreasing surface runoff velocities

66 desynchronisation of flood peaks from different parts of the catchment.

It is important to identify the land management practices that can improve the effect of these mechanisms. Two case studies investigated under the FRMRC project are used here to illustrate this.

Case study 1: Selecting tree types and forestry management options

When selecting tree types and forestry management options, important characteristics include:

CIRIA C719 21

66 trees with greater canopy storage and interception can reduce throughfall. The mature trees with larger root systems can also improve infiltration and soil water storage. This will require careful consideration of climatic and soil conditions and compatibility with different tree species

66 distributed planting (ie tree strips or shelterbelts) rather than planting blocks of trees can be used to minimise footprint, with careful attention to siting the trees in areas that have maximum catchment area and can activate maximum subsurface storage

66 allowing and supporting undergrowth on the forest floor to reduce runoff velocities and increase water storage

66 land preparation and management, for example ploughing and drainage before tree planting, can lead to significant increases in peak flows for up to the first decade of the forest’s lifespan. Poor forest harvesting techniques can lead to large increases in stream flow due to soil compaction, reduced evaporation and increased conveyance along roads.

Useful guidelines have recently been produced by the Forestry Commission and the Environment Agency of England and Wales (Forestry Commission, 2011, and Nisbet et al, 2011).

Case study 2: Selecting peatland drainage management options

In terms of peatland drainage management, three alternatives commonly exist:

1 No installed drainage.

2 Drained using shallow open ditches (“grips”).

3 Installed drainage that is subsequently blocked.

In almost all cases where peatlands have been drained, peak flows have increased. Very little benefit is achieved through drainage, as water table drawdown is only effective very close to drains and the practice is generally discouraged.

Drain blocking techniques currently used do not necessarily return peatlands back to their intact state. In most observed cases, the hydrological effects of drain blocking are very difficult to detect in flow records.

Those drains that are most likely to result in reduced runoff if blocked are those that are steeper and poorly vegetated. Effective drain blocking should also aim to spill water in a diffuse way rather than promoting a concentrated down slope flow.

See Armstrong et al (2009), Ballard (2011) and Ballard et al (2012) for further guidance on the effects of drain blocking on flood flows. For a more general account of the environmental effects of peatland drainage and drain blocking see Ramchunder et al (2009).

2.3 QuANTITATIvE METhods oF PREdICTING FLood FLows ANd ExAMPLE REsuLTs

2.3.1 Classification of approaches

Hydrological models are invaluable tools for predicting hydrological responses under a range of possible future scenarios. Numerous hydrological models have been developed, however, each model has inherent advantages and disadvantages for any given application. Different hydrological modelling approaches can be broadly classified based on their theoretical basis and how they represent spatial variation and temporal continuity, as follows:

Theoretical basis

Metric models seek to characterise a system response solely through statistical relationships developed from observations.



Conceptual models use equations defined a priori, based on the processes that are perceived to be important. Typically, this is done through a series of conceptual stores, for which parameters are calibrated based on observations.

Physics-based models use the fundamental equations of physics to determine the system response. Some parameters of these models can be measured in the field, although usually calibration is also required to achieve good performance.

Spatial representation

Lumped models represent a catchment with one averaged set of model parameters. They do not take into account the spatial distribution of rainfall, evapotranspiration, land use, soils or topography.

Distributed models take into account the spatial patterns of hydrological response within a catchment through an explicit spatial representation of the climate inputs and outputs and/or the catchment properties. Alternatively, statistical distribution functions are used to represent spatial variability but not explicit spatial patterns.

Temporal continuity

Event models evaluate the flows for a single event. For example, this may be useful for sites where there is a particular flood event that provides a reference point for change evaluation. However, event models are frequently biased due to poor initial condition selection.

Continuous time models use continuous inputs to simulate the wetness state of the catchment so that initial conditions do not need to be separately estimated, and given a long enough simulation period a flood frequency curve can be derived from the outputs.

2 .3 .2 General modelling procedure

Adopting and documenting a rigorous modelling procedure aims to reduce the error in predictions and make the degree of confidence in them transparent. This is important for land use effects modelling, due to the numerous sources of uncertainty and the risk of generating false evidence or conveying a misleading amount of confidence. A summary of a general good practice procedure is:

66 specify the modelling task

66 identify constraints (eg data, computers, expertise)

66 review available data (for quality and sufficiency)

66 select the appropriate model and state the assumptions implicit to it

66 estimate parameters through calibration and uncertainty analysis

66 test and validate the model

66 apply the model

66 report the results (stating the level of confidence)

66 review results and assess need to improve the model.

While each of these steps is an essential part of good practice, it is beyond the scope of this guide to report them all in detail. However good modelling practices were followed throughout and readers should refer to guidance on uncertainty analysis (Beven et al, 2012) and other specialised sources (eg Wheater et al, 1993, Wagener et al, 2004, Jakeman et al, 2006 and Beven, 2012). Coverage here instead focuses on reviewing the modelling tools available for land use scenario analysis.

2 .3 .3 Model selection

Recognising that there were serious short-comings in most of the rainfall-runoff models available for

CIRIA C719 23

assessing the impacts of land use change on flood hydrographs, O‘Connell et al (2004) recommend that modelling “should be distributed and be capable of running continuous simulations. It should also be partly or wholly physically based so that the physical properties of local landscapes, soils and vegetation can be represented.” However, they also noted that “there are significant methodological issues with extrapolating small scale experimental observations for catchment-scale applications”. The practical outcome is that constraints related to model parameterisation, data availability and run times often dictate the use of simpler, conceptual type models for large and medium catchments.

In the FRMRC research programme, selected modelling approaches (summarised in Figure 2.14) with different capabilities and resource requirements were reviewed through case study applications. The approaches were selected based on their theoretical basis, input requirements, ease of implementation and methods of impact quantification.

Figure 2 .14 Summary of selected rainfall runoff models and their attributes

2 .3 .4 Screening methods

Simple methods exist that should be classed as screening tools, as their basis for representing land use change is anecdotal and the models are not validated. While these tools are simple, they are also easy to use, and may be particularly useful for preliminary impact assessments. A comparison of some of these methods with a more complex model (Section 2.3.6), using Pontbren as a case study, is reported in Bulygina et al (2012).

Land use sensitivity analysis using FEH

The Flood Estimation Handbook (FEH) rainfall-runoff method and its revitalisation (ReFH) are event-based approaches to identifying flood frequency curves for a given location in the UK (Institute of Hydrology, 1999, and Kjeldsen et al, 2005). The methods are based on regionalisation of a rainfall depth/duration frequency relationship, a percentage runoff (PR) equation and a triangular unit hydrograph across the UK. The triangular unit hydrograph requires two parameters, the peak ordinate, Qp, and the time to peak, Tp. These parameters, along with the PR are derived from regressions on catchment characteristics. The method is widely used by practitioners throughout the UK for estimation of T-year return period flows.

The FEH (Institute of Hydrology, 1999) discusses the effects of land drainage and forestry, but does not provide methods to incorporate these land use changes within the FEH method, nor does the ReFH resolve this. During the derivation of Catchment Flood Management Plans (CFMPs), the discussion was developed into quantitative changes to the FEH model parameters to evaluate a catchment’s sensitivity to land use change (see Table 2.4). Where the CFMP indicates that a catchment is sensitive to land use change, screening methods are inappropriate and more detailed approaches should be used.



Table 2 .4 Changes to FEH parameters for rural land use sensitivity analysis in CFMPs

Land use change suggested change to time to peak (Tp)

suggested change to percentage runoff (PR)

Increase in woodland cover Reduce Tp by three hours for immature cover – no change for mature growth Reduce PR by 10%

Improved land drainageReduce Tp by two hours for low PR soilsIncrease Tp by two hours for high PR soils

–

Agricultural intensification – Increase PR by 15%

O’Connell et al (2004) proposed another short-term improvement to the FEH model to make it suitable for land use analysis within CFMPs (Packman et al, 2004). This method also made adjustments to the standard percentage runoff (PR) and time to peak (Tp) but did so using analogue hydrology of soil type (HOST) classes (Boorman et al, 1995) to represent degraded soil conditions. Selection of analogue HOST classes was based on expert opinion. The method can be applied using an Excel spreadsheet (see Packman et al, 2004) but requires a HOST class soil map of the area of interest. The main assumptions and limitations of this adaptation of the FEH method are that:

66 the speculative changes suggested to PR and Tp are a reasonable approximation of reality

66 methods for changing PR and Tp are only proposed for land management practices that lead to soil degradation

66 continuous hydrological predictions are not possible

66 the spatial distribution of land use changes is not accounted for.

The main strengths of the adapted FEH methodology are that:

66 many practitioners will be familiar with its application as the FEH methodology is widely used throughout the UK

66 the method is relatively quick and can be effectively used over large areas making it suitable as a screening tool with which to identify sensitive areas that require more detailed analysis.

Application of the adapted FEH methodology requires either the FEH CD-Rom or alternative data to estimate the design precipitation and model parameter values for the study catchment (see Useful website).

A tool developed under the CFMP programme

A method developed in 2010 under the Environment Agency’s Catchment Flood Management Plan programme allows estimation of changes in daily flood frequency curves depending on climate, soil type, and land management type (Hess et al, 2010, and Environment Agency, 2010b). This serves as a screening tool to identify the flood risk management (FRM) “policy units” (represented by a combination of agro-climatic zone, soil type, land cover and land management type) in a catchment that are the most sensitive to land use change. In developing this tool a continuous time, daily water balance model, WaSim, was used to estimate antecedent conditions in a policy unit for the period 1961–2006 based on its soil properties and land cover/management types (Hess and Counsell, 2000). Surface runoff comprises of two components infiltration excess overland flow (due to intense rainfall) and saturation overland flow (due to water logging of the soil). Infiltration excess overland flow is estimated using the curve number (CN) method (USDA, 1986), while any rain falling on saturated soil is assumed to runoff as saturation excess overland flow. Precipitation that does not generate either type of overland flow is assumed to infiltrate. The frequency distribution of annual maxima runoff is derived for each FRM policy unit, and the estimated change in runoff associated with a change from one unit type to another is provided. The CFMP tool reports this change for the 5-, 10-, 50-, 75- and 100-year return period rainfall events.

CIRIA C719 25

The main assumptions and limitations inherent to the 2010 CFMP tool are:

66 the combination of climate, land management and soil type under investigation can be adequately represented by the 68 agro-climatic areas defined by Smith (1976)

66 WaSim can be parameterised using physical properties representative for each HOST-type soils, which avoids the need for calibration

66 the effects of the spatial distribution of responses within the catchment are negligible

66 the CN system, originating from data from the mid-west USA, can adequately represent conditions in England and Wales (see Holman et al, 2011, and Bulygina et al, 2011)

66 continuous hydrological predictions are unavailable, ruling out estimation of land use effects on specific hydrograph characteristics such as time to peak and peak flow

66 the results given are for T-year daily maxima, rather than sub-daily maxima.

The main strengths of the 2010 CFMP tool are:

66 computational efficiency and ease of use

66 the large number of land use scenarios (improved grassland, cereals, horticulture/non-cereal, semi-natural vegetation, and woodland, which covers a range of return periods) that can be explored for any catchment in England and Wales.

Application of the 2010 CFMP tool does not require any input data because runoff frequency curves for all possible scenarios are stored within the tool’s own database. The tool is held by the Environment Agency (Environment Agency, 2010b) and due to the data embedded in the tool and associated license issues it is not currently available for general use.

The FD2106 approach using regionalised parameters

The regionalisation method developed under the Defra and Environment Agency FD2106 project (Calver et al, 2005) use multiple regression to estimate four parameters in a continuous, spatially lumped, hourly probability distributed moisture (PDM) model (Moore, 2007). The four model parameters represent relevant catchment properties including altitude, slope, drainage density and length, soil type distribution, presence of lakes and reservoirs, and the proportions of urban and grassland (Calver et al, 2005). Another parameter, which defines the division of effective rainfall between fast and slow water releasing stores, is set to the value of the Standard Percentage Runoff (SPR) derived from the HOST classification (Boorman et al, 1995).

The main assumptions and limitations of the method used in the FD2106 project are that:

66 the regression equations (developed from data from 39 catchments) provide sufficient information to adequately represent the variability in runoff responses

66 the spatial distribution of land use within the catchment is not important so that the model parameters are a function only of the land use proportions

66 as only two types of land use (grassland and urban) are directly represented by inputs to the regression equations, speculative changes to BFI and SPR are used to simulate the effects of other land use changes, such as over-grazing (Packman et al, 2004).

The main strengths of the FD2106 method are that:

66 it is computationally efficient

66 the simplicity of the model and accessibility of the published regression equations allow the method to be adopted without the need to obtain the original tool.



The data requirements of the FD2106 method are:

66 the catchment descriptors as inputs to the regression equations (these include descriptors other than those in the FEH)

66 a continuous time series of hourly precipitation and potential evaporation, the latter varying (which varies with each land use scenario).

Enquiries about accessing the tools associated with the FD2106 method should be directed to the Centre for Ecology and Hydrology, although the method can be carried out by referring to the publications cited here. JBA (2007) is a good example of an impacts study based on this approach.

2 .3 .5 Physics-based distributed models

Where a screening assessment indicates that land use changes may significantly impact catchment hydrology, or where it emerges that screening tools of the type described above are too simplistic to produce reliable indications of catchment sensitivity to land use change, consideration must be given to application of more powerful and flexible models. In such cases, use of a physics-based, distributed model may be appropriate, based on the comparative attributes of lumped and distributed models summarised in Table 2.5.

Table 2 .5 Comparative attributes of spatially lumped and distributed models (from Refsgaard and Storm, 1996)

Lumped model distributed model

Output

At one point:Runoff

→ single variable

At many points:RunoffSurface water levelGroundwater headSoil moisture

→ multi variable

Success criteria(excluding problem of selecting which statistical criteria to use)

Measured <–> simulated runoff, one site

→ single criteria

Measured <–> simulated RunoffMulti sitesWater levels, multi sites groundwater heads, multi sitesSoil moisture, multi sites

→ multi criteria

Typical model applicationRainfall runoffStationary conditionCalibration data exist

Rainfall-runoff, unsaturated zone, groundwater, basis for later water quality modelling effects of man’s activityNon-stationary conditions. Calibration data do not always exist

Validation test

Usually “split-sample test” is sufficient

→ well-defined practice exists

More advanced tests required:Differential split sample testProxy basin test

→ need for rigorous methodology

Modelling scale

Model: catchment scaleField data, catchment scale

→ single scale

Model: depends on discretisationField data: many different scales

→ multi scale problems

CIRIA C719 27

Distributed models have particular advantages for modelling the effects of changes in land use related to their capability to accurately represent the spatial location of the change and explicitly upscale from local to catchment scales. Also, increasing computing power and wider availability of spatial data from, for example, digital elevation models and remotely sensed imagery, makes such models more technically viable. However, physics-based distributed models simulate hydrological processes explicitly and account for spatial variability not only in those processes but also inputs, boundary conditions, and catchment characteristics. So to run them users need data to represent the spatial distributions of a large number of parameters and variables including precipitation, temperature, wind speed, catchment topography, soil type, land use, groundwater f low, surface runoff, vegetation and seasonal growing cycle (Refsgaard, 1997).

In practice, the high degrees of complexity and variability characteristic of hydrologic systems, coupled with lack of experimental data often prevent formulation and application of a generalised, physics-based, distributed model. Instead, the user applies a model selection procedure to identify a model suitable for the individual problem at hand. Also, the lack of experimental data and uncertainties concerning how well experimental data directly relate to the model parameters dictate that at least some of the model parameters have to be calibrated (Beven, 1979). Finally, while the most important processes and inputs are represented as being spatially distributed, in most cases some of these are combined to reduce complexity and make the model viable (Abbott et al, 1986a and 1986b, Singh, 1995, and Refsgaard, 1997), which casts further doubt on its validity.

In selecting a physics-based, distributed model to predict the effects of land use change the main question is whether the model is fit-for-purpose. To this end, several models have been investigated using “blind” tests (Bathurst et al, 2004, Ewen and Parkin, 1996, and Parkin et al, 1996) performed using SHETRAN (Ewen et al, 2000). These test were “blind” in that modellers were not allowed to see the observed hydrological variables (eg discharge) that were being simulated until after the model’s results had been passed to an independent referee. These tests revealed the potential pitfalls of attempting to predict the effects of change using physics-based, distributed models, and established that the confidence intervals around predictions usually will be wide.

In summary, while application of physics-based, distributed models is an attractive approach to land use effects prediction, significant practical challenges remain in identifying and validating a suitable model. In this section and Section 2.3.6 the challenges of using these models, and the ways that they can be overcome to more reliably predict land use effects are illustrated and explored, based on experience gained through model applications performed by the FRMRC.

Implementation of physics-based distributed models by the FRMRC

The overall aim of the River Parrett case study (see Section 2.2.1) was to evaluate the changes in catchment hydrology that could be brought about by changes in rural land use. Two physics-based, distributed models with different levels of complexity were built (Figure 2.15) and applied to simulate hydrological response to land use and other anthropogenic activities in the catchment (Park et al, 2009).

An updated version of the MIKE SHE/MIKE11 model was applied to the Parrett catchment. For comparison with that model, a simpler model based around the digital terrain model (DTM) was built and applied to the River Tone (492 km2), which is a tributary of the River Parrett. The Tone catchment was selected because it is particularly well-gauged (Figure 2.16).



Figure 2 .15

Structure of the physics-based, distributed models used in the FRMRC study of the Parrett catchment (Park et al, 2009)

Enhanced MIKE SHE/11 Model DTM based distribution model

Precipitation Precipitation

Simple ET storage method

Simple bucket (Dickinson, 1984)/Kristensen and

Jensen method (1975)

Finite difference 2D computational method

1D Diffuse wave approximation

(overland flow)

1D kinematic wave (river hydrodynamics

Philip Method (Philip, 1957)

Brooks and Corey (1966)

2 Layer Water Balance Model (Yan and Smith, 1994)

Darcy Law

Linear Reservoir6(Zoch, 1934, 1936, 1937)

Evapotranspiration Evaporation

Canopy layer

Root zone

Interflow storages

Baseflow storages

Overland flow

Canopy layer

Overland flow

Groundwater layer

Interflow

Interception

Infiltration

Interflow (h) Percolation (v)

Infiltration

Percolation (v)

Baseflow

Interception

Flow routing

Flow routing

Soil layer

Figure 2 .16

Daily and hourly measured rainfall gauges in the Parrett catchment used in the FRMRC study

CIRIA C719 29

MIKE SHE, a development of the original SHE system (Abbott et al, 1986a and b), is a physics-based model that includes modules of surface runoff, unsaturated and saturated flow, evapotranspiration and their various interactions (Graham and Butts, 2006). The model was updated by the FRMRC to support evaluation of the utility of rural land use and flood retention storage ponds in reducing flood flows in the Parrett catchment (Figure 2.16).

The updated MIKE SHE/11 model uses the finite difference, 2D computational method to simulate overland flow. The MIKE 11 model simulates unsteady flow in the drainage network using a 1D, kinematic wave model. A two layer water balance model (Yan and Smith, 1994) is used to compute actual evapotranspiration and groundwater recharge. This method is useful for areas with a shallow water table, such as wetlands, where the actual evapotranspiration rate is close to the potential rate, although it does not represent flow in the unsaturated zone. A linear reservoir model was used to compute the water movement in the saturated zone owing to the scarcity of observations with which to calibrate a physics-based groundwater model.

The DTM based model (Figure 2.15) (Park et al, 2009), compared with the updated MIKE model is an extension of the model built for the Pearl River in Southern China. It comprises of three layers representing the canopy, soil and groundwater. This model is discretised into cells each having a unique combination of rainfall, vegetation type and soil properties. Canopy interception is modelled using a simple bucket, with the bucket capacity depending upon the leaf area index (Dickinson, 1984). In the soil layer, the potential infiltration rate is calculated using Philip’s equation to generate surface runoff (Philip, 1957), with interflow and percolation calculated using the Brooks and Corey relationship. Groundwater flow is modelled using Darcy’s Law. Hillslope surface flow is governed by the 1D kinematic wave. A 1D diffusive wave is used for routing water through the drainage network.

For both models, the cell size selection balances a desire to incorporate more spatial features with the need to keep model run times reasonable. The River Parrett meanders tortuously in its lower course and the lower catchment features a dense network of artificial drains. So, the model required a high degree of spatial resolution and the cell size was set at 100 m × 100 m. Data defining topography, soil type and depth, vegetation cover, stream slope and impermeable areas were input for each cell. Soil types and land uses in the Parrett catchment are mapped in Figure 2.17 Building the model demonstrated that selecting an optimal cell size and associated scaling of runoff response continues to be a challenge (Section 2.5).

Spatial distributions for the physical properties of the soil and land use were derived from 1 km resolution National Soil Resources Institute (NSRI) data. These data provided initial values of model parameters prior to optimisation (Morgan, 2005). The growing cycles of cultivated crops and vegetation types were included in the model.

Potential evapotranspiration was calculated based on the UK Meteorological Office MORECS and MOSES systems. The surface roughness was computed using the Stickler roughness coefficient and the resistance values were obtained by reference to Chow et al (1988). Further information is available in Park (2006).

Unfortunately, the number of rain gauges recording at 15-minute intervals (which is the preferred time interval for parameterisation and model evaluation in this case) is limited in the Parrett catchment (shown as blue dots in Figure 2.16). However, there is a relatively dense network of daily rainfall gauges (shown as grey dots in Figure 2.16). Although using the daily gauges results in a loss of temporal resolution that limits the capacity of the model to replicate high flow peaks, the importance of providing an adequate spatial coverage to represent the heterogeneity of rainfall was such that this was necessary (Park et al, 2010). Use of spatial information from high resolution radar data has the potential to assist with this problem (Zhu et al, 2008).

Often, parameter optimisation for a physics-based distributed model is the most challenging aspect of the modelling procedure. The large number of parameters and their interactions, together with data limitations, make it complex to identify a set of parameter values that is the best-performing



statistically. In the Parrett and Tone applications, sensitivity analyses were applied to identify the parameters that were most important to optimise (Park et al, 2010, and Ren, 2010). This revealed that soil thickness, saturated conductivity and porosity were in general key parameters, although a reasonably comprehensive analysis required run-times of up to three weeks on a desktop personal computer, illustrating the practical challenge of reliably testing model sensitivities in a large, distributed model. The important parameters were then optimised to observed flow data using the Shuffled Complex Evolution (SCE) method (Duan et al, 1992).

Summary of simulated land use effects in the Parrett case study

66 tree planting scenarios (Table 2.6) indicate that the degree of effect depends on area and season. For example, tree planting in riparian zones has little effect on infiltration of surface runoff

a

b

Figure 2 .17

Soil type (HOST) (a) and land use type (b) in the Parrett catchment

CIRIA C719 31

regardless of season, possibly due to the small area affected and the relatively high degree of pre-event soil wetness. For the other tree planting scenarios in Table 2.6, there are relatively large differences between winter and summer events. Planting trees reduced peak flows most in summer when interception and evapotranspiration were higher, and pre-event wetness was lower

66 areas in the upper and middle catchment, where surface runoff is predominantly controlled by geophysical parameters, such as slope, and where there are already large areas of woodland, were not significantly affected by land use changes. Changes in flatter areas with more arable agriculture and potential for conversion had more impact, although hydrological responses varied depending on crop growing cycles

66 scenarios involving construction of flood retention ponds simulated between 0.5 per cent and three per cent of catchment areas being used for storage, with 50 m x 50 m footprints for each storage pond. 0.5 per cent coverage was found to be too small to have any measurable effect on river flows. One per cent coverage had a modest but significant effect, although achieving such coverage in this catchment would require concerted action by a large number of farmers and landowners. So, modelling suggested that on-farm flood retention and storage would have relatively little effect at scale of the Parrett catchment, although the space-time scaling effects require further numerical study

66 although the hydrological effects of feasible land use changes in these relatively large catchments appear to be small, this does not preclude the potential for water quality and sediment management benefits (see Chapter 3).

Table 2 .6 Changes in simulated peak flows under tree planting scenarios for the Tone catchment using the enhanced MIKE SHE/11 model

Land use change scenario site

Chan

ged

area

(% o

f ca

tchm

ent)

Largest event in january 2002 Largest event in May 2002

Calib

rate

d m

odel

pea

k flo

ws

(m3 s

-1)

Scen

ario

pea

k flo

ws

(m3 s

-1)

Incr

ease

in p

eak

flow

s (m

3 s-1)

Incr

ease

in p

eak

flow

s (%

)

Calib

rate

d m

odel

pea

k flo

ws

(m3 s

-1)

Scen

ario

pea

k flo

ws

(m3 s

-1)

Incr

ease

in p

eak

flow

s (m

3 s-1)

Incr

ease

in p

eak

flow

s (%

)Tree planting on steep slopes

Greenham 30 4.3 4.2 -0.1 -2 2.2 1.7 -0.5 -21

Bishops Hull 37 15.2 15.1 -0.1 -1 9.0 7.9 -1.1 -13

Halsewater 35 4.5 4.5 0.1 1 3.1 3.0 -0.1 -5

Knapp Bridge 19 23.1 23.1 -0.0 -0 15.1 13.6 -1.4 -9

Tree planting on riparian areas

Greenham 5 4.3 4.4 0.1 3 2.2 2.2 -0.0 -1

Bishops Hull 8 15.2 15.5 0.3 2 9.1 8.9 -0.2 -2

Halsewater 7 4.5 4.6 0.1 3 3.1 3.0 -0.1 -2

Knapp Bridge 9 23.1 23.6 0.5 2 15.1 14.8 -0.3 -2

Tree planting on arable lands

Greenham 3 4.3 4.3 -0.0 -0 2.2 2.1 -0.1 -4

Bishops Hull 48 15.2 14.9 -0.3 -2 9. 7.8 -1.2 -14

Halsewater 83 4.5 4.3 -0.1 -3 3.1 2.3 -0.8 -27

Knapp Bridge 65 23.1 22.5 -0.6 -2 15.1 12.2 -2.9 -19



Summary guidance on using physics-based distributed models

The power of physics-based distributed models for evaluating effects of land use change was illustrated in the Parrett case study described previously in this section. This was by the spatially explicit representation of changes to processes, the interaction between coastal and fluvial flooding, and interaction between engineered and more natural management intervention effects. The case study also highlighted the difficulties of parameter estimation due to the inherent uncertainties in over-parameterised models, and associated with the long run-times required for adequate sensitivity analysis and optimisation.

In summary, the use of physics-based distributed models may be recommended where other models are considered too simple. Also, where the potential benefits justify the large investments of time and resources required to support skills development and software training for staff, data collection, model construction and parameter estimation/optimisation. Despite these investments, the potential exists for uncertainties to remain stubbornly high even in physics-based distributed models and so a measure of confidence in their results and predictions should be reported.

2 .3 .6 Conceptual distributed modelling

The computational burden and difficulty of parameter estimation can limit the practical use of physics-based models for catchment scale modelling. Conceptual distributed modelling is an alternative approach that still accounts for the distributed nature of catchment characteristics. This approach is referred to as conceptual (rather than physics-based) because of the simplified way that such models represent the relevant hydrological processes.

A generalised framework for the development and application of conceptual distributed models to investigate the effectiveness of different land use changes in natural flood risk management was developed by the FRMRC (Figure 2.18).

Development of a conceptual distributed model for any given catchment starts with collation of available catchment information. The catchment is divided up into runoff generating elements (RGEs). RGEs may be delineated by hydrological response units or a uniform grid. A stream network, connecting each of the RGEs to the catchment outlet is then derived using a digital elevation model for the catchment. The RGEs are then categorised into various runoff classes, based on their hydrological similarity. Suitable measures of hydrological similarity may include:

66 soil type

66 soil depth

66 slope

66 land use type

66 land management (eg field drainage).

Mathematical models are developed to describe locally generated runoff for each runoff class, and how locally generated runoff is transferred to the catchment outlet.

For each runoff class, an appropriate simple conceptual model structure and associated parameter set(s) are identified. Here, these are referred to as runoff generating models (RGMs). Each RGE within the catchment is then allocated a time series of runoff created by the RGM for the runoff class of that element. The local runoff is then transferred to the catchment outlet using the routing model. Land use change effects can then be explored by altering the spatial distribution of RGEs within the semi-distributed catchment model.

CIRIA C719 33

Figure 2 .18 Conceptual distributed modelling framework developed by the FRMRC



In the following sections, two alternative methods are introduced to estimate values of model parameters for different RGMs:

1 Upscaling of small scale physics-based models.

2 An approach that estimates the model parameters using regionalisation.

Descriptions of these methods are followed by a discussion of potential routing algorithms. A summary of strengths, limitations and assumptions, and case study applications is also included.

Upscaling of physics-based models

One potential method for estimating the parameters needed for the RGMs is to optimise their values so that they emulate the results obtained using smaller scale, physics-based models. This approach aims to capitalise on understanding of how land use affects physical properties and processes in more computationally efficient RGMs and catchment models (Jackson et al, 2008, Wheater et al, 2008, and Ballard, 2011).

For any particular application, physics-based models can be developed to represent a generic RGE for each runoff class within the catchment of interest. The model structure should be capable of explicitly representing those processes and properties that are affected by land use changes, and either new models can be developed, or existing detailed physics-based hillslope models can be employed (providing that they capture the necessary processes related to the relevant land use).

Ideally, these physics-based models should be tested and calibrated using data derived from detailed, field scale observations. However, even without hydrological measurements for a site of interest, physics-based models can be developed and tested using a combination of:

1 Information on small scale hydrological processes and properties gathered from the literature.

2 Information from surrogate sites.

3 Qualitative information about hydrological responses obtained from hydrologists familiar with the field area in question.

Emulation or “meta-modelling” of the physics-based models requires careful handling. A meta-model is, by definition, a model structure with fewer parameters and faster run-time than the original physics-based model (Barton, 1998). Typically, the structure consists of a runoff generation component (eg the probability distributed moisture model) followed by reservoir stores in parallel and/or in series (Ballard, 2011). Having selected an appropriate meta-model structure, meta-model parameters that lead to the greatest transfer of information from the physics-based models need to be identified. This can be assessed through optimising a user-defined objective function that measures flood flow performance and/or differences in flood flows between relevant land uses. Following identification of the appropriate meta-model structures and parameters, the simulated flow then can be incorporated in the distributed modelling framework (see Figure 2.18), to support catchment scale predictions of land use and changes.

The main assumptions and limitations of the RGMs derived from physics-based models are:

66 physics-based models are suitable representations of reality

66 spatial variations within the catchment, including under all future scenarios, can be sufficiently represented by a set of RGEs

66 these type of models require specific modelling expertise to ensure appropriate use

66 significant resources are required to develop and apply appropriate physics-based models, both in terms of human and computer resources

66 information may be diluted through the meta-modelling process.

The main strengths of the physics-based derived RGMs:

CIRIA C719 35

66 small scale interventions can be explicitly incorporated through the development of user defined physics-based model

66 information from a wide range of experimental observations and previous knowledge can be incorporated into the model structure and parameters.

The upscaling technique was applied by the FRMRC to:

1 A catchment with multi-scale monitoring information (Pontbren)

2 A catchment where no small scale monitoring or physical property information was available (Footholme – a sub-catchment of the Hodder).

A large quantity of high quality data was available for the Pontbren catchment. So it was possible to develop, with relatively low uncertainty, a detailed physics-based distributed model capable of representing many of the hydrological processes and land use effects relevant to natural flood management. Parameters for this model were estimated and constrained by locally measured properties, and the model was further conditioned to replicate observed runoff response from the instrumented Pontbren hillslope described in Section 2.2.2. The physics-based model was run for nine alternative runoff classes, to represent current land management within the catchment and the outcomes of alternative land use interventions. The models were run in Monte Carlo framework to account for uncertainty in the model parameters (Jackson et al, 2008, and Wheater et al, 2008).

Following identification of a suitable meta-model structure (Wheater et al, 2008), each individual detailed model simulation was then emulated through automated calibration based on a least squares optimisation. A number of different parameter sets were identified as behavioural according to the Generalised Likelihood Uncertainty Analysis approach (Beven and Freer, 2001) to representing uncertainty in the data and meta-model. The catchment was discretised into a number of irregularly shaped field units (the fields within Pontbren are generally surrounded by drainage ditches and can be considered to be hydrologically independent). The resulting, semi-distributed catchment model combines the predicted locally generated runoff with a simple constant celerity (wave-speed) routing algorithm, adopting the general framework set out in Figure 2.18. Conditioning of both the meta-model and the routing model parameters was performed using gauged streamflow records.

An extreme flood event was simulated using the Pontbren distributed model for the baseline land use conditions plus three different scenarios for land use change. The aim was to explore land use effects during extreme storms with high rainfall volumes and intensities. As no particularly extreme events were observed during the monitoring period at Pontbren, the event that led to severe flooding in Carlisle in January 2005 (140 mm of rain falling over two days at the Aisgill gauge, with an estimated return period of 180 years), was used as an input to the model. To the extent that this event was not actually observed at Pontbren, the predicted changes in peak streamflow listed in Table 2.7 are to a degree speculative.

Table 2 .7 Summary of changes in peak streamflow for three land use change scenarios during a synthetic extreme rain storm event at gauge 6 in the Pontbren catchment. 95 per cent confidence intervals are in parenthesis

Land use change Area affected (%) Mean change in peak flow (%) Normalised change in peak flow[1] (-)

Remove trees 7 +5 (3 to 7) +73 (42 to 100)

Add tree strips 7 -5 (-2 to -11) -71(-29 to -157)

Full afforestation 93 -36 (-10 to -54) -39 (-11 to -58)

Note

1 This is the mean change in peak flow divided by the area affected expressed as a percentage.



The approach was also trialled in a 25 km² sub-catchment of the Hodder where significantly less and measured data were available to support development of physics-based models and soil types and land management questions to the Pontbren catchment. In particular, large areas of the catchment are formed of upland blanket peat, and riparian planting and coniferous plantations are also present within the catchment.

Two physics-based models were developed to represent the range of different runoff classes within the sub-catchment. The first model represented the upland blanket peat areas, being capable of representing intact, drained and drain-blocked peatlands using 200 m × 200 m grid squares. Differences between the different management options were simulated principally by changing the structure of the drainage network in the physics-based model to reflect the characteristics, presence or absence of drains (Ballard et al, 2011 and 2012). The second model was used to represent those parts of the catchment with mineral soils, also at a 200 m × 200 m grid scale, but with land use scenarios varying between grazed grassland, moorland, deciduous trees and coniferous trees. Differences in land use were simulated through changes in the parameters representing infiltration rate, canopy storage and surface roughness (Ballard, 2011).

Information used to develop and condition the local scale physics-based models for the Hodder was limited to that available in the hydrological literature. It emerged that knowledge concerning hydrological processes, properties and peak flow responses associated with peatland drainage management is relatively limited in contrast to the information available concerning forestry (Ballard, 2011). However, data were available from a surrogate drained peatland site to assist in the evaluation and conditioning of the peatland model (Ballard et al, 2011).

The catchment was divided in 200 m × 200 m grid cells and categorised into RGE classes based on HOST data and land cover maps. For each RGE class, a meta-model was identified that provided a good replication of both flood hydrographs simulated by the physics-based models and differences across RGE classes. As at Pontbren, multiple parameter set samples were propagated forward to the catchment scale simulations to account for uncertainty, and runoff time series from the RGEs were integrated to produce catchment flows using a simple streamflow routing algorithm (Ballard, 2011).

One-year simulations were conducted with the effects of land use scenarios being represented by changes in the average of the 10 largest peak flows (Table 2.8).

In these examples it is assumed that changes in hydrological properties (including soil and vegetation properties) resulting from changes in land use jump rather than transition from one state to another and that if the changes were reversed the predicted effects would be equal and opposite. The model could be improved to simulate how these properties would actually evolve in response to a change (for example, gradual changes in vegetation associated with blocking drains), by introducing further assumptions (Ballard, 2011), the nature of which would significantly affect results.

Table 2 .8 Examples of scenario effects for a 25 km2 Hodder sub-catchment. 95 per cent confidence intervals are in parenthesis

scenario (% of catchment) Area affected 10 largest peak flows (%)

Increase in mean of increase (∆flow/area) Normalised

Full coniferous planting of mineral soils 29 -7 (-3 to -13) -24 (-46 to -10)

Full deciduous planting of mineral soils 29 -4 (0 to -9) -15 (-2 to -32)

Deciduous riparian planting 9 -2 (0 to -3) -17 (-1 to -36)

Unblocking drains 8 0 (-2 to 1) -0.1 (-20 to 11)

Comparison of the meta-model predictions with those derived directly from the physics-based model showed that unless emulations are conducted carefully meta-modelling has the potential to increase uncertainty and introduce bias into the predictions. The scenario analyses indicate that the effects of land use change are reduced for more extreme events (Table 2.8). The effects predicted for the Hodder

CIRIA C719 37

sub-catchment are consistent with those obtained in a comparable case study of the Skell catchment at Ripon (JBA, 2007), which was based on the FD2106 method (see Section 2.3.4).

Using regionalised indices

An alternative method to estimating the RGMs in a conceptual distributed model is to use regionalised values of flow indices taken from nationally available data sets. For example, flow indices may be derived from the HOST soil classification system (Boorman et al, 1995) or the CN system (USDA, 1986).

Each RGM, corresponding to specified grid-squares in a catchment, is calibrated using the regionalised values of the flow indices. To allow for uncertainty in the estimation of the indices, many hundreds of parameter sets are sampled for each RGM. The likelihood of a sampled parameter set is proportional to the consistency of the simulated hydrological response with the regionalised values of the response indices. Consistency is measured on a scale defined by the uncertainty in the indices. For example, an index with large variance in its regionalised estimate would produce only a small difference in likelihood between parameter sets and higher parameter uncertainty. A large sample of parameter sets and associated likelihoods are propagated to estimate uncertainty in the predictions.

The main assumptions and limitations of using regionalised indices in this way are:

66 change in flood flow response due to the relevant land management changes can be captured by the available regionalised indices

66 the response can be captured by the chosen conceptual rainfall-runoff model

66 small scale land management changes are difficult to evaluate because regionalised indices such as BFI are typically derived from catchment-scale data and are not applicable to very small RGMs

66 in the UK, widely available indices are limited to those in the HOST database, which does not provide information concerning rural land management effects. Either speculative changes to HOST indices are needed to allow for land management, or mapping to international systems of indices, such as the CN (USDA, 1986), is needed

66 estimates of the joint probability distribution of the regionalised indices are required. As this information is seldom provided in available databases, this may require some judgement by the modeller. Also, it includes assumptions about the dependence between pre-change and post-change parameter probability distributions.

The main strengths of using regionalised indices in this way are:

66 parameter estimation is straightforward and computationally efficient because of the explicitly defined likelihood function and relative simplicity of the model

66 prediction uncertainty is estimated

66 the method is capable of representing a wide range of land management scenarios, including different agricultural uses and management practices, different types of forestry and urbanisation.

To investigate this method in a case study, it was applied to the two sub-catchments of the Pontbren catchment represented by gauges 6 and 9 (see Section 2.2.2). The first has a drainage area of 3.2 km² and is largely improved grassland. The second has an area 4.1 km² and is largely moorland. Details of the case study are given by Bulygina et al (2009).

The sub-catchments were represented by 100 m x 100 m grids connected to the outlet via a stream network. The conceptual RGM was based on a conceptual probability-distributed moisture model (Moore, 2007) with two linear stores in parallel and constant celerity stream routing. The current land management and three scenarios were considered:

1 Heavy grazing over the whole catchment area.

2 Complete afforestation with deciduous trees.

3 Deciduous tree strips on fields under improved grassland, occupying 12 per cent of the field area.



The performance of the model for the current land use was found to be acceptable, although there was considerable uncertainty in parameter values. To evaluate land management impacts, the five and 10-year return period flood peaks were estimated by fitting a log-normal distribution to the annual maxima series from a 20-year simulation period. Simulated changes under the scenarios are listed in Table 2.9.

Table 2 .9 Examples of scenario impacts for 3.2 km2 and 4.1 km2 Pontbren sub-catchments, 95 per cent confidence intervals are in parenthesis

scenario Gauge (Figure 2.6) Increase in T = 5 year peak flow (%)

Increase in T = 10 year peak flow (%)

Heavy grazing6 10 (2 to 21) 11 (2 to 22)

9 13 (2 to 22) 13 (1 to 22)

Afforestation6 -12 (-26 to -3) -12 (-27 to -4)

9 -9 (-22 to -3) -10 (-22 to -4)

Tree strips on improved grassland

6 -1 (-3 to -0) -1 (-3 to 0)

9 0 (-1 to 0) 0 (-1 to 0)

If best estimate values are considered, these predictions are strikingly different from those for the comparable Pontbren scenarios in Table 2.7. This is largely because of the different sources of information used. In this case, the results in Table 2.7 should be considered more reliable because they are derived from local knowledge and data, whereas Table 2.9 is derived from regional to national scale generalisations. In particular, for the final scenario in Table 2.9, the simpler regionalisation method cannot consider the spatial location of the tree strip, whereas the more physics-based methods can. However, for many practical applications, the regionalisation method would be more suitable due to its lower resource requirements. This illustrates why it is important to identify the method that is most suitable to the particular task.

Channel routing models

Runoff from the land surface travels to flood sites via open-channel flow in networks of ditches, streams and rivers. Any one of literally dozens of methods and models can be used to estimate the downstream flood hydrograph driven by runoff from a landscape represented by a set of sub-catchments or computational cells. The list of methods and models in Table 2.10 is far from exhaustive. Open-channel flow models are widely used by flood management practitioners, including in hydraulic design and real-time flood forecasting. The particular challenge considered here is how to estimate the effect that changes in land use have on the rate that water is supplied to a flood site, rather than the resulting effect on the hydraulics within the flood site. In many cases, 1D modelling of the routing paths is adequate for this task, but the discussion here also applies when special account (eg 2D hydraulic modelling or simple lumped modelling) is taken to represent overbank flow and temporary storage at selected points along the routing paths.

There is advice in the literature about what type of routing method to use (eg Tsai, 2003). None of this advice is related to choosing a method suitable for modelling the effect (ie effect) of changes in land use. It is mostly related to choosing a method that is as simple as practical based on the steepness of the catchment.

The effect can be represented in the form of an effect hydrograph. For example, suppose that the hydrograph caused by a rainfall event is q1(t) but would have been q2(t) if there had been a change in land use conditions before the event. The effect hydrograph for this pair of hydrographs is q2(t)-q1(t). Figure 2.19 shows the downstream effect hydrograph at location C, which results from changes in sub-catchments A and B. The flow at C has increased and been made more flashy, so the effect hydrograph has positive, flashy peaks. Conversely, if it was made less flashy, the “peaks” would have been inverted.

CIRIA C719 39

Table 2 .10 Examples of routing methods and models

Type Examples

Models based on Saint Venant Equations for open-channel flow

See Novak et al (2010) Chapter 7Numerous models, including HEC-RAS, Sobek, Mike 11, Mike 21, ISIS, FLO-2D, Noah-1D

Models based on approximations to Saint Venant Equations for open-channel flow

Kinematic wave models (Singh, 1996)Numerical and analytic diffusion wave models (eg SHETRAN: Ewen et al, 2000, and analytic model: Moussa, 1996)Non-inertia modelling (eg Akan and Yen, 1981, and Yen and Tsai, 2001)A non-inertia model called DNRM is described later in this section

Some other routing methods

Muskingum-Cunge and its variants (Ponce and Yevjevich, 1978, and Todini, 2007)PAB: Parabolic and Backwater (Todini and Bossi, 1986)Unit hydrograph (Jakeman et al, 1990)Lag and route (Fread, 1985)Time-area (Clark, 1945)Network width (Naden, 1992)

In practice, the pair of downstream hydrographs needed to support the calculation of a downstream effect hydrograph can be simply found. A catchment rainfall-runoff model is run and then rerun after suitable adjustments are made to the parameters representing land use. However, note that catchment models, which are calibrated to accurately fit observed hydrographs, can have false sensitivities and give inaccurate estimates for effect (Ewen et al, 2006). To avoid such problems arising in the modelling of routing, the routing modelling in the rainfall-runoff model should be accurate when simulating the propagation of perturbations in flow (eg for Figure 2.19, what is important is the propagation of the perturbations in flow defined by the effect hydrographs ∆q at locations A and B. In particular how these perturbations move, interact, and accumulate within the network to result in the effect at location C).

Figure 2 .19 Downstream impact hydrograph at location C produced by simple routing of runoff impact hydrographs from sub-catchments A and B

The main physical process that propagates perturbations in flow in rural upland catchments is kinematic flow, which dictates that waves that move at a speed of roughly 1.5 times the bulk flow velocity. At a first approximation, the impact downstream resulting from changes made at several locations in a catchment is simply the sum of the various runoff impact hydrographs, after taking into account the kinematic wave travel times. However, there are many other physical processes and effects involved:



1 Hydrodynamic dispersion: friction and within-channel storage causes flood waves to attenuate and disperse as they travel downstream (Henderson, 1966).

2 Nonlinear kinematic dispersion: temporal and spatial variations in wave speeds (Saco and Kumar, 2002). This includes effects from accumulation of runoff, and can steepen the rising limb of the hydrograph.

3 Geomorphological dispersion: hydrographs downstream are affected by the relative timing of the various contributions from upstream, so depend on the spatial distribution of travel distances within the catchment (Rodriguez-Iturbe and Valdes, 1979).

4 Nonlinearity: including hydraulic effects associated with variations in the channel stage and cross-section area and shape, and backwater effects at junctions.

5 Spatial effects: for example, the direct and indirect effects that the distribution of rainfall falling on the catchment have on the network hydraulics.

6 Variations in flow regime and friction: including the effect of vegetation on friction and the effect of the underlying flow rate on the propagation of perturbations in flow.

7 Off-line inertial effects and storage: including threshold effects triggered when the flow overtops the banks to inundate the floodplain or generates local flooding at one or multiple sites.

8 Debris effects: due to the recruitment, movement and storage of natural and anthropogenic debris that can increase channel roughness and block hydraulic structures during storms.

9 Structures and water level management: weirs, sluices, culverts.

In general, these effects cannot be safely ignored and the modeller should select a routing model that takes enough of them into account to make it fit-for-purpose, based largely on personal experience and expertise. However, model selection will be limited by the models and data that can be acquired and applied within the resources available.

These resources usually include:

1 Blue lines on geographical maps, indicating the channel network.

2 Digital elevation models (DEMs) that can be used to map contributing areas, define drainage patterns, construct drainage networks, and calculate travel distances.

3 Historical records of rainfall and stream flow, which provide the basis for model calibration and testing.

4 Channel geometry data in the form of cross-sections or hydrographic surveys that define the size, shape and roughness of the channel, together with hydraulic geometry equations that allow transfer of field data from one location to another.

5 Information on channel and floodplain characteristics that can be used to estimate Manning’s “n” values based on libraries of previously established values.

6 The results of field surveys that can be used to test, validate and extend previous knowledge concerning the hydrologic, hydraulic and geomorphic properties of the channel network.

7 Inverse modelling from observed hydrographs, for example to estimate average travel times from times to peak.

8 GIS providing platforms for data storage, spatial analysis, modelling and displaying results.

9 Access to advanced numerical analysis techniques, which is especially useful for solving nonlinear, network, and junction problems.

10 Experience – the most valuable of all modelling resources.

It should be clear from these lists that flood modelling can be a complex undertaking, particularly when the aim is to model the effects of changes in land use. A “cook book” approach to model selection and use is not practical. In general, the importance of the role played by routing increases with the degree of the effect and flashiness of the effect hydrograph.

CIRIA C719 41

When selecting a model, the first question to ask is “what is known about the various effect hydrographs and timings within the network?” The importance of this question can be illustrated in two contrasting examples:

66 land use change at a particular upstream location is thought to have reduced the volume of flood runoff at a downstream location that experiences long-duration floods that rise and recede slowly

66 it is desired to establish the extent to which land use changes across a broad area could reduce the flashiness of flooding.

In the first example, a relatively simple routing model would suffice, while a more sophisticated model would be needed in Case 2. In general, selection of the appropriate routing model depends on the type and extent of the land use change, the nature of the catchment and its drainage network, the characteristics of the flood problem being investigated, and the question being asked.

The FRMRC research in the Hodder catchment aimed to establish the efficacy of a variety of land use changes intended to reduce downstream flood peaks in a catchment with a relatively flashy hydrograph. Most of the resources listed here were available including a detailed flood model built for an earlier study of land use change effects under SCaMP (see Section 2.2.3). The detailed meta-modelling and dataset for runoff generation parameters described in Section 2.3.6 were incorporated in the model. To achieve this, the flood model had to be upgraded to use fine sub-grid land patches that represent variations in land use, land management and land condition. The resulting model includes the Dense Network Routing Model (DNRM) (see Table 2.10), which was designed specifically for use when simulating the propagation of perturbations through networks. It is challenging to estimate the effect of widespread detailed changes in land use in a catchment with a flashy hydrograph, and this degree of model development was essential to making the catchment model fit for the purposes of the FRMRC research. Some results from this modelling are used in Section 2.4.

2 .3 .7 Uncertainty analysis

Conducting simulations within an uncertainty analysis framework can highlight the significance of predicted changes in flood flows relative to the uncertainty and indicate the strength of evidence provided by the prediction. Beven et al (2012) provides detailed guidance that is applicable to the land use problem, on selecting methods for uncertainty estimation. While the brief guidelines provided here should be sufficient for most applications, advanced users are referred to Beven et al (2012).

In a model’s predictions uncertainty arises from:

66 model parameters

66 model structure

66 model inputs (eg rainfall and evaporation).

Different approaches are applied in estimating the uncertainty in main model outputs associated with each of these three sources. However, it is not always possible, or even necessary, to assess all three sources of uncertainty. Beven et al (2012) provide a logical approach to assessing the sources that should and should not be included.

Parameter uncertainty

In assessing parameter uncertainty, it is necessary to:

1 Identify the parameters that should be included in the uncertainty analysis.

2 Establish suitable ranges or probability distributions for these parameters.

3 Draw a sufficient number of samples from these distributions so that a meaningful sample of possible outcomes is modelled.



Parameters to be included in the uncertainty analysis should be identified using a sensitivity analysis (Saltelli et al, 2000), which involves perturbing parameters and recording the resulting changes in key model outputs such as flood peaks, timings and volumes. If the main outputs are insensitive to changes in a parameter, then it may be fixed at its best-estimate value during the remainder of the uncertainty analysis. However, if the model is to be used to predict the effects of alternative land use scenarios, it may be necessary to calculate the sensitivity under all the candidate land use changes, as the sensitivity of principal outputs may be scenario dependent.

The approach adopted for establishing suitable ranges or probability distributions for model parameters depends on how the parameters are estimated. If values are taken from the literature or based on expert judgement then these sources will also be used to define their ranges of potential variability. This usually requires that parameters are treated independently, because the information necessary to establish joint probability distributions is unavailable. If calibration is used to estimate parameters, then Monte Carlo methods such as Generalised Likelihood Uncertainty Estimation (GLUE) (Beven and Freer, 2001) are recommended, and these can account for inter-dependencies between parameters. If regionalisation is used, then statistical analysis of the regionalisation equations or pooling groups can provide uncertainty estimates (Lamb and Kay, 2004, Wagener et al, 2004, McIntyre et al, 2005, and Bulygina et al, 2011). Parameter ranges are likely to be wider than usual when land use scenarios are being investigated because assumptions are required about how the estimated parameter distributions should be extrapolated to represent alternative future conditions (see Estimation methods in Section 2.3.6).

Once a parameter range or distribution is identified, a Monte Carlo simulation can be performed (see Section 2.3.6) to establish the how uncertainty in that parameter affects the main model outputs. In this simulation a number (N) of parameter sets are sampled from the parameter distributions and then run through the model. This leads to N predicted hydrographs, from which confidence limits on main outputs such as the discharge and return period of the flood peak can be derived. N should be sufficiently large that sampling error in the confidence intervals is acceptable. Conversely, N should be kept to a minimum to reduce computation time. Generally, the minimum number of samples can only be found by increasing N until the change in the confidence intervals is no longer significant. Interest in reducing N has led to derivation of variance reduction techniques (eg MacKay et al, 1979). If it is infeasible to use a large enough sample of parameter values to obtain reliable confidence intervals, a relatively small number of samples can be selected. The outputs are then interpreted as scenarios of what might happen under a land use change, rather than aiming to estimate confidence intervals.

Model structure uncertainty

Assessing uncertainty in model structures (ie the set of equations that make up the model) is more challenging than parameter uncertainty. Often, model structures are application-specific, negating the possibility of developing a generic approach to estimating the associated uncertainties. The best approach is to use several different, plausible structures. The outcomes can be propagated forward into the probabilistic forecasts to establish confidence intervals for main outputs, or use within a scenario analysis. In practice, either approach is too resource-intensive and time consuming for many applications. So, though model structure uncertainty is potentially significant, its evaluation would need to be carefully considered using the guidelines in Beven et al (2012).

Input uncertainty

Input uncertainty relates to uncertainty in the climatic drivers of the model. If the model is being used to simulate downstream flood response to alternative land use scenarios under unchanged climatic conditions then uncertainties associated with hydrologic inputs may largely cancel each other out. However, the effects of land use changes on downstream flood risk may be sensitive to input errors. For example, land use effects can be sensitive to errors in the spatial distribution of rainfall (see Section 2.4). Ideally, land use effects should be evaluated under input scenarios that represent the possible range of variability in hydrologic inputs.

CIRIA C719 43

Summary

While quantitative specification of the confidence intervals on the main model outputs is desirable and can be achieved using the methods outlined here, the necessary investment of time and resources may not always be justified. Where the risks associated with flooding at the site being modelled are low, or a screening level approach is being adopted and/or resource restrictions rule out an objective quantitative analysis, a more subjective assessment of uncertainty is acceptable. In such cases, scores or ranks may be used to represent the uncertainties arising from one, two or all three sources.

2.4 LANd usE IMPACT MAPPING TooLThe FRMRC spent considerable time and resources investigating questions about how changes in land use at the scales of single agricultural fields, hillsides or sub-catchments affect flooding further downstream in the drainage network. The research established that in general there are no simple answers to these questions. Fundamentally, this is the case because the downstream effects of land use change depend only partly on the type and extent of the actual change. Also, they are influenced by how the catchment functions at the macro-scale to generate and convey runoff, and how flow is modified at smaller scales by multiple factors including the event-specific spatial and temporal distributions of rainfall and evaporation, the local terrain and its roughness, and variability in the hydrological properties of the soil, the land surface, and the drainage network.

These and other more detailed insights stem in part from the development and application of a new method of investigating flooding called “information tracking” (O’Donnell et al, 2011) that can help unravel the multiple effects of scale and variability. The principle underpinning “information tracking”, in the form used here, is simply that the overall effect of multiple land use changes at different locations in the catchment is the sum of its contributing parts, which assumes that the flood system possesses “spatial linearity”. This method is still in its infancy and its potential has not been fully explored, but FRMRC used it to develop a simple, practical tool with which to map the effects of land use change on downstream flooding.

Specifically, the aim of this element of FRMRC research was to produce a method of mapping the downstream flood effects of programmes of land use change. In particular, the interest was in peak discharge rates downstream, so the effect or “impact” is the change in the peak discharge rate resulting from the changes in land use. For a change made at several, relatively small sites the resulting “impact map” would show the contribution to the overall effect on a site-by-site basis. General-purpose impact maps of this type, showing the potential contributions for all sites in the catchment, would be all that is required to evaluate the total potential reduction in peak discharge rate downstream produced by alternative programmes of land use changes proposed within the catchment. Different storms will have different rainfall patterns and different impact maps. Because the changes in land use are represented as changes in the parameters for the runoff modelling, the impact for a site is given by:

I= p sp∆p

where, for a given rainfall event, sp = sensitivity of the peak discharge rate downstream to changes in runoff parameter p at the site, and ∆p = magnitude of change in parameter p between the pre-change and post-change conditions for land use at the site.

Impact mapping was undertaken for the Hodder catchment, using a distributed model with 2634 patches of land (average area 0.0925 km2) selected based on land use and HOST (Boorman et al, 1995). Runoff from each patch was modelled using an eight-parameter meta-model, based on the probability distributed moisture model described in Section 2.3.6. The channel network modelling is as described at the end of Section 2.3.6. So there were 2634 × 8 = 21 072 parameters that could change because of a change in land use.

For each rainfall event, the 21 072 model sensitivities were found accurately (to better than one part in a billion) and efficiently (hundreds of times faster than any alternative method) using reverse algorithmic



differentiation (Griewank, 2000, and Hascoët and Pascual, 2004). Algorithmic differentiation involves mathematically differentiating the source code for the catchment model, line-by-line. In the new code created, mathematical derivatives are tracked as they propagate through the catchment, forwards and backwards in time. All 21 072 sensitivities are calculated simultaneously, within a single simulation.

The assumption of spatial linearity is crucial to this approach, so was tested in detail as part of the FRMRC research using the Hodder case study. The test method made use of the fact that if there is spatial linearity then there should be close agreement when the overall impact on the peak discharge is calculated from:

1 The difference in peak discharge between pre- and post-change simulations.

2 The impact map.

There were very good results for autumn and winter events, but noticeably poorer results for summer events because of non-linearity associated with wetting up of dry soils.

Figure 2.20 presents the impact map for a programme of land use changes in the Hodder catchment, based on SCaMP (USDA, 1986). The types of changes considered are listed in Section 2.2.3. 100 impact maps were generated, using 100 equally-likely sets of runoff meta-model parameters (see Section 2.3.6), and the figure shows the contributions to impact, patch-by-patch, that are exceeded in 50 of the 100 maps.

Figure 2 .20 Impact map for changes in the flood peak at the outlet of the Hodder catchment (drainage area = 260 km2) based on land use changes associated with the SCaMP (exceedence probability = 50 per cent, units = m3s-1 per square km)

In the impact map, orange indicates land where the change in land use has caused the simulated downstream flood peak to increase, and grey where the change has caused a decrease. The spatial patterns evident in the impact map result from interactions between the underlying distributions of rainfall, HOST class, land condition, travel distance to the catchment outlet, and, particularly, land use change. The orange squares are associated with inbye land (ie improved land near the farm buildings). One of the SCaMP changes is to bring sheep in for lambing, so the land condition of the inbyes is modelled as deteriorating from fair to poor, resulting in slight rises in the simulated flood peak downstream at the catchment outlet.

CIRIA C719 45

Patches where stocking levels are reduced, or stock is excluded for habitat restoration, show up as dark grey squares, indicating that improvements in land condition from fair or poor to good result in reductions in the downstream flood peak. Conversely, grip blocking over an area of ~2.5 km2 near co-ordinates (800, 900) produces only a small impact on downstream flooding according to the impact map.

The impact map shown in Figure 2.20 refers to the flood peak at the outlet of the Hodder catchment (drainage area = 260 km2) generated by a winter rainstorm. It should be noted that the map would be different if it referred to flood peak at a different location, such as:

66 the outlet of the Dunsop catchment (drainage area = 25 km2)

66 a more intense winter rainstorm

66 for the same event in a different season

66 for a flood with a different return period.

An impact mapping tool has been built into a GIS. This uses general-purpose impact maps derived for uniformly-applied land use changes. The tool gives five per cent, 50 per cent and 95 per cent exceedence impact maps for any prescribed spatial pattern of land use change for several historical winter rainstorms.

While FRMRC research has demonstrated that impact mapping is both practical and potentially useful for exploring hydrologic variability and impacts, two major problems with the method remain to be addressed.

1 Creating a distributed catchment model that is sufficiently accurate to support information tracking requires considerable effort. Section 2.3.6 listed some of the complications and difficulties faced in detailed, distributed modelling of a channel network. Distributed modelling of a whole catchment introduces another tier of problems. These are associated with the difficulty of creating runoff models that are appropriately sensitive to changes in land use at the scale of the individual patches of land that supply runoff to the channel network.

2 Interpreting the results of information tracking is also problematic. The aim is to be able to establish that land use change “X” at location “Y” within the catchment produces a change of “Z” cumecs in the peak discharge for a downstream flood, gauged at the catchment outlet. The account presented here has already demonstrated that “Z” is not a single number, because it varies between events. However, the concept of spatial linearity that underpins the method is based on assuming (and testing) that “Z” is a single number, at least for a given event. As noted previously, this has limitations because in reality “Z” depends not only on the land use change made at location “Y”, but also on changes made elsewhere in the catchment. This is important because it is the impact (ie the value of “Z”) that informs decision making with respect to the adoption or rejection of options for land use changes in a catchment intended to reduce downstream flooding. Information tracking can be used to investigate these limitations. Where “Z” is shown to be insensitive to off-site changes, land use decisions can be made on a site-by-site basis using impact maps.

Potential uses for information tracking and impact mapping include:

66 supplying hard numbers to feed into decision support tools such as Polyscape (see Chapter 4)

66 potentially helping such decision support tools handle some of the variability, uncertainty and non-linearity that arises when multiple land use changes are proposed within the same catchment.

2.5 REMAINING GAPs IN KNowLEdGESection 2.1 identified gaps in knowledge and data with respect to land use and downstream flooding that existed before FRMRC. While FRMRC research has generated substantial new knowledge, significant gaps remain unfilled. This section summarises the remaining gaps and makes recommendations for further research.



Multi-scale hydrologic monitoring, ranging from single fields to large catchments, performed by FRMRC, has helped explain how the effects of land use change propagate downstream. While FRMRC research has proven the benefits of multi-scale monitoring, the spatial and temporal resolutions of data collected at Pontbren and in the Hodder and Parrett catchments are limited. Also, while these sites are representative of large areas of upland and lowland Britain, they are limited in their breadth of scale and provide a limited sample of landscapes and land management issues. Gaps in knowledge still exist and further such experiments are needed in other landscape/land use contexts and at the scale of very large catchments to allow evidence-based decision making with respect to “natural flood management”.

A second gap in knowledge that remains relates to long duration field studies. Renewed funding that supported a second phase of the FRMRC meant that field studies continued for up to six years. This not only improved the reliability of the data, but also highlighted how short-term climatic variability obscures the effect of land use changes when field monitoring is not sustained. FRMRC’s experience, together with that gained in other recent research studies (eg Beven et al, 2008) shows that high quality data, collected continuously over decades, is vital for building a strong evidence base with respect to the link between land use and downstream flooding. Where possible, other monitoring programmes, such as those listed in Section 1.4, should be maintained over decades to seek evidence of land use effects. While the spatial signals of land use may be used to infer effects based on short records, the uncertainties involved make it impossible to generalise with the datasets currently available (McIntyre and Marshall, 2010). These uncertainties are particularly associated with:

66 spatial rainfall estimation

66 stream gauging (especially during flood events)

66 changes in vegetation

66 lack of spatial-temporal data on land use change (for example, changes to stocking intensity and drainage management)

66 lack of data on subsurface hydrological properties and processes.

FRMRC research carefully examined the roles of runoff generation and routing through the channel network in producing and modifying the downstream effects of land use change. The findings indicate that while effects at small scales (eg local and muddy floods) are marked, they decrease as the scale of the catchment increases with distance downstream. There are three main reasons for this:

66 scale effects, in that the fraction of land area affected by change tends to decrease as the area draining to the flood site increases

66 the effects of changes made in multiple sub-catchments may not be consistent because each sub-catchment has its own characteristics with respect to runoff generation and response to land use change

66 flood waves tend to be attenuated as they travel through the channel network and the arrival times and types of effects generated by various sub-catchments depend on the lengths and morphologies of the channels linking the sub-catchments to the flood site.

Uncertainties are especially large for the second and third of these effects, due to natural spatial and temporal variability in rainfall, and hydrological and hydraulic response characteristics of the various sub-catchments. Such is the natural variability in these factors, that on rare occasions several of the factors may work in harmony to either magnify or reduce effects at some downstream locations. Limited understanding of the characteristics of natural variability and the controls on the probability of hydrological processes working in harmony or discord, and whether these are related to the size of the flood, constitutes a third remaining gap in knowledge.

Except for simplistic problems, t

There is still no scaling theory that allows prediction of the effects to be transferred across scales, except for simplistic problems. Also, there is not yet any general scientific guidance on suitable grid or

CIRIA C719 47

sub-catchment sizes for distributed hydrological models. The output of a typical hydrological model depends on the spatial and temporal scales over which the inputs and model states are averaged. So, lack of understanding of how best to deal with issues of scale constitutes another gap in knowledge that continues to limit ability to predict the effects, especially in large catchments.

The land use scenarios used in FRMRC, although informed by consultation with landowners/managers and Foresight studies (Government Office for Science, 2010), were largely speculative. Models do not yet exist that can reliably simulate how land use at relevant scales will respond to climate and socio-economic change. So, the challenge of creating realistic scenarios for land use futures in hydrologic modelling, remains to be adequately addressed.

The FRMRC strengthened the links between scientific evidence, the outputs of hydrological and flood routing models and estimates of downstream effects. A new tool for mapping effects was developed and tested. However, along with more extensive and prolonged experimental programmes, further effort is required to assimilate better evidence and nationally applicable tools. Further development of links between science and decision-support tools, as presented in Chapter 4, is strongly recommended.

This chapter has focused on the hydrologic effects of land use change. The next chapter of this guide explores interactions between runoff, stream flows and sediments and demonstrates why the sediment effects of land use change should be accounted for when assessing effects on flooding. Before considering sediment effects in detail, it is worth noting here that the limited understanding of how sediment effects influence and interact with the hydrological effects examined in this chapter remains a significant gap in knowledge and another priority for further research.



3 sediments and geomorphology

3.1 PRobLEM dEFINITIoNErosion and sediment redistribution are integral, naturally-occurring components of any river system. However, dysfunctional sediment dynamics can pose significant threats to people, property, infrastructure and the environment and represent an important catchment management issue (Thorne et al, 2010a). Soil erosion alone is thought to be responsible for estimated economic losses of about £700m per year in England and Wales (Evans, 1996) but it is now widely acknowledged that “off-site” effects associated with the delivery of eroded sediment to watercourses represent a much greater problem (Walling and Collins, 2005). Soil erosion on cultivated land can lead to muddy floods (Boardman, 1990, and Verstraeten et al, 2003a and 2003b), which can cause considerable financial and psychological damage, while sediment also plays a critical role in diffuse pollution, both as a contaminant (ie smothering habitats, reducing light penetration) and as a pollutant vector (nutrients, organic contaminants, and trace/heavy metals) (Collins, 2008). For example, sediment-associated phosphorus has been shown to account for 20 to 90 per cent of the total phosphorus loss from agricultural land in the UK (Collins, 2008, and Morgan, 2006). Nutrient enrichment of water bodies can lead to algal blooms, reduced water clarity, loss of submerged plants, production of algal toxins, deoxygenation, fish kills and increased water treatment costs (Withers et al, 2007, and Withers and Sharpley, 2008). Also, the sediment supply can directly or indirectly affect river habitat and ecological status though its effects on the feeding and health of aquatic organisms, and community structure and function (Wood and Armitage, 1999). Yarnell et al (2006) established that sediment supply was important in defining the amount and quality of in-stream habitat (diversity of sediment textures and geomorphic features), particularly in situations where there is a lack of semi-stable, in-channel sediment features such as riffles or boulder steps. The smothering of fish spawning gravels is often associated with excessive sedimentation, which detrimentally affects the permeability (controlling the rate of oxygen supply and waste removal) and porosity (controlling the intra-gravel movement and emergence of fish fry) of the substrate (Soulsby et al, 2001). Excessive sedimentation can also detrimentally affect aquatic macrophyte (Clarke and Wharton, 2001) and invertebrate communities (Ward et al, 1998 and Wood and Armitage, 1999).

More recently, attention has extended to links between sediment, geomorphology and flood risk. Alluvial rivers are complex, dynamic entities whose morphologies (channel dimensions and geometry) vary over time and space in response to changes in the input regimes of water and sediment (Schumm, 1969). This can have important implications for flood risk management in multiple ways. For example, a reduction in sediment supply can lead to the deterioration or failure of flood defence assets by assisting rising levels of channel scour (HR Wallingford, 2008). Similarly, a rise in sediment supply can also increase flood probability by instigating in-channel sedimentation that extends the frequency, lateral extent and depth of flooding. This is the case because the morphology of a river channel and its surrounding floodplain are important controls of its conveyance capacity and so changes can modify the water surface elevation for a given discharge (Lane and Thorne, 2007). For example, Plate (2002) observed that the construction of embankments along the Yellow River in China accelerated sediment deposition within the channel leading to a decrease in conveyance capacity. Also, Verstraeten et al (2003a and b) linked increased flood probability in Belgian rivers to the deposition of large amounts of fine sediment that reduced channel capacity.

River training has been blamed for raising flood flow elevations (Pinter and Heine, 2005), though it is easy to misrepresent causal links between specific management actions and morphological responses (Watson et al, in press). Also, it is important to distinguish between natural fluctuations in sediment supply (which can lead to a temporary build-up of sediment between occurring competent “flushing” flows) and anthropogenically-triggered, instability that may lead to irreversible change in the fluvial

CIRIA C719 49

system. New and better approaches to addressing these issues are required due to growing recognition of the environmental significance of sediment and geomorphology, and the increasing emphasis on “working with natural processes” in national legislation stemming from the Habitats and Water Framework Directives, which restrict the use of traditional management responses such as hard bank protection and dredging. Similarly, Flood Risk Management Plans developed under the Floods Directive 2007 should not conflict with programmes of measures required to reach good ecological status or potential under the river basin management plans (RBMPs) developed under the Water Framework Directive 2000. Indeed, they should actively support each other.

A river’s flow and sediment supply regimes represent the integrated effects of climate, vegetation, soils, geology and basin physiography, which are often assumed to be relatively constant over engineering timescales. These factors may change in response to a variety of natural influences including climate change (eg Macklin and Lewin, 2003) and valley floor uplift or subsidence (eg Bell et al, 2009), which can have significant effects on catchment hydrology, sediment dynamics and channel morphology.

Evans et al (2004a and b, and 2008) concluded that sediment delivery to UK rivers has been sensitised to climatic variability because of historical land use changes. They also concluded that sediment delivery is likely to increase because the types of rainfall-runoff events that are most effective in mobilising, transferring and delivering sediment to rivers are likely to occur more frequently in the future. Understanding how this is likely to affect catchment runoff, sediment yields and channel morphologies, and determining whether sediment management issues could be treated “at source” through changes to land use are important and urgent research needs.

This chapter examines the potential for land use change to influence catchment sediment supply, affect river sediment regime and instigate morphological changes. Quantitative methods of simulating and predicting the impacts of projected increases in the seasonality and intensity of rainfall (eg Murphy et al, 2009) and the mitigating effects of changes to land use under different flood risk management and environmental policy scenarios (eg Parrott et al, 2009, and Reed et al, 2009) are investigated in both upland and lowland catchments.

3.2 ThE uK EvIdENCE bAsEThe following sections review knowledge (pre- and post-FRMRC) concerning the effects of land use change on sediment dynamics and channel morphologies in the UK. While these effects can be broadly grouped according to the type of land use change and spatial scale, it is important to recognise that, as with hydrological impacts, generalisation is both difficult and often inappropriate. Land use management effects vary in both time and space and are dependent on a multitude of factors such as soil type, gradient, hydrological event characteristics (magnitude, antecedent conditions, closeness to historical events) and both absolute and relative location within the catchment. As a result, land use changes should always be assessed within the context of the catchment and sediment systems that they are implemented in.

3 .2 .1 Evidence pre-FRMRC

Anthropogenically-triggered changes in sediment yields can be conceptualised as point-scale manifestations of a diffuse, catchment-scale delivery process (Lane et al, 2006), in which the influence of land use change varies through time and space. It follows that identifying the sediment “hot-spots” where sensitivity to change is highest (Newson, 2010) and hydrological links between the catchment and the channel are strongest is crucial to understanding and, potentially, managing catchment sediment dynamics (eg Wilkinson et al, 2010).

The majority of research on UK land use and catchment sediment dynamics has been carried out in small, upland catchments conducive to field monitoring and experimentation. Investigations during the 1980s and 1990s explored effects associated with planting of fast-growing, non-native conifers that was encouraged throughout the British uplands in the 20th century to address shortfalls in domestic timber



production. These studies revealed that moorland ditching, road construction, tree cultivation and clear felling led to substantially increased sediment yields and elevated levels of morphological activity (Newson, 1980, Moore and Newson, 1986, Ferguson and Stott, 1987, Leeks, 1992, Stott, 1997, and Stott and Mount, 2004). However, until recently the effects of major concurrent changes in British upland agriculture on erosion, sediment transfer and river morphology received considerably less attention, despite being carried out over a much larger spatial area.

Local-scale impacts: drainage

Post-WWII agricultural intensification in the British uplands had multiple implications for local-scale sediment dynamics within river catchments. Land drainage can significantly reduce surface and near-surface runoff in upland pastures by drawing down the water table and increasing the moisture storage capacity of soils (Newson and Robinson, 1983, and Robinson, 1990). Areas downslope of open ditches aligned parallel to contour lines also experience a reduction in water table elevation because flow from upslope areas is intercepted and diverted away (Holden et al, 2006). In addition to reducing waterlogging and improving growing conditions, land drainage has implications for erosion and sediment yield.

Soils are more susceptible to structural alteration when wet (Climo and Richardson, 1984, and Patto et al, 1978). By promoting drier conditions in which saturation-excess surface runoff occurs less frequently, artificial drainage is likely to reduce both soil erosion and hydrological connectivity with nearby streams (Heathwaite et al, 2005, and Lane et al, 2006). These effects might be expected to restrict erosion and overland transfer of fine sediments (and attached nutrients/contaminants) to watercourses.

However, the lateral influence of both open and pipe drains in lowering the water table in upland soils is actually severely limited (Hudson and Roberts, 1982, Robinson and Newson, 1986, and Stewart and Lance, 1991). Saturation-excess overland flow remains a common occurrence in many drained pastures, particularly during winter. Even where drainage does lower the water table, the effect may be short-lived. For example, in peaty soils, improved aeration and the drying of surface layers can lead to an increase in soil bulk density because of shrinkage, decomposition and macropore collapse (Silins and Rothwell, 1998). These effects can negate any tendency for reductions in soil erosion and sediment loads in surface runoff from drained upland pastures.

Also, any decrease in surface runoff and sediment loads input to watercourses is likely to be heavily outweighed by the concomitant increase in the sediment mobilised within, and/or exported via the artificial drainage systems. The digging of open drains throughout the British uplands exposed abundant, easily-mobilised soil and sediment to concentrated, channelised flow. It also created a network of transport-efficient pathways delivering eroded material to the natural channel system downstream. Field studies have demonstrated that ditching can result in the input to watercourses of unnaturally large amounts of both coarse and fine sediment (Newson, 1980, Burt et al, 1983, Moore and Newson, 1986, and Holden et al, 2007a). Although ditch sediment production tends to decline over time, amplified levels of sediment supply can continue for many years (Robinson, 1980).

The steepness of the terrain is a key control on sediment production, with incision and widening common in ditches draining slopes exceeding 2 to 4 degrees (Newson, 1980, and Holden et al, 2007a). Conversely, blocking open drains can be highly effective in restoring artificially elevated sediment yields from drained peat lands to more natural levels (Holden et al, 2007a and b). However, further research is needed to characterise and predict sediment production and export from networks of agricultural drainage ditches as these differ in several respects from those in plantation forests that most upland drainage studies have been focused on to date.

Widespread under-drainage of British uplands may have enhanced hydrologic and sediment connectivity between eroding areas in pastures and down slope drains and natural watercourses. Cracks and macropores generated during pipe installation and through later soil drying can allow eroded sediment to pass down through the soil matrix and into nearby ditches and streams through the subsurface drainage pipe network (Walling et al, 2002). The sediment effects of under-drainage have been observed at both arable and grassland sites in lowland catchments (Culley et al, 1983,

CIRIA C719 51

Kronvang et al, 1997, and Russell et al, 2001) and shown to have major effects on sediment yields (Chapman et al, 2005, Culley and Bolton, 1983, and Foster et al, 2003). Recognising this, the effects of under-drainage on upland sediment yields was further investigated by the FRMRC and the results are reported later in this chapter.

Local-scale impacts: livestock

Relatively modest peak rainfall intensities and soil protection provided by natural vegetation tend to limit erosion rates in the British uplands compared to other parts of the world (Lewin et al, 1974, and Moore and Newson, 1986). National surveys have estimated that two per cent of uplands in England and Wales (McHugh et al, 2002) and 12 per cent of upland Scotland (Grieve et al, 1995) are significantly affected by soil erosion. Increased intensities of livestock grazing have been identified as a significant causal factor in several regions including mid-Wales (Thomas, 1965), the Peak District (Evans, 1977), the Southern Uplands (Tivy, 1957) and the Shetland Islands (Birnie and Hulme, 1990).

Trampling, overgrazing and rubbing by sheep can damage or even destroy surface vegetation, creating patches of bare soil that are vulnerable to erosion by sub-aerial and hydrologic processes (Evans, 1997 and 1998, and Sansom, 1999). The severity of erosion is affected by stocking type and density, soil wetness, altitude, aspect, season, and the duration of livestock grazing (Johns, 1998). If carefully managed, agriculturally-improved upland pastures are generally more resistant to erosion than areas of undisturbed moorland. However, they are prone to severe soil loss if over-grazing is allowed to damage vegetation or ploughing and reseeding is timed or performed inappropriately (Costin, 1980, and James and Alexander, 1998). Likewise, livestock access to riparian areas and watercourses should also be carefully managed to avoid increased sediment delivery from soil, bed and bank erosion (see Trimble and Mendel, 1995). This is especially important for cattle, while sheep tend to avoid wet ground around streams (Platts, 1981).

In addition to the direct effects of increases in stocking densities, related changes in the physical properties of soils may have indirectly accentuated soil erosion problems in the British uplands, through their influence on soil hydrology (Section 2.2).

Increases in surface runoff and soil erosion triggered by intensive stocking and/or over grazing have been shown to significantly increase the supply of fine sediment to watercourses in intensively managed lowland grassland environments (Bilotta et al, 2008). There have been relatively few equivalent studies in the British uplands, an exception being the study by James and Alexander (1998), which recorded increased surface runoff from heavily grazed improved pastures in the Clwydian Hills, north Wales. However, there is evidence that livestock have caused structural damage to soils throughout upland Britain (Carroll et al, 2004a, and Holman et al, 2003). So, it is highly likely that intensification has elevated the frequency and magnitude of surface runoff across large areas, enhancing the potential for soil erosion and sediment transfer to watercourses via overland flow pathways.

In this context, the decline in livestock numbers in the British uplands since their late 20th century peak may to some extent mitigate these effects. Support for this view may be drawn from unpublished data recently collected by Zhao and Holden et al (2007b). These data suggest that while trampling by sheep has damaged the physical and hydrologic properties of moorland soils in Wharfedale and Teesdale, livestock exclusion allows soil properties to recover relatively quickly. Specifically, soils properties at sites where grazing had been prohibited for only five years were found to be comparable in status to those at sites that had not been grazed for over 40 years. Similarly, long-term monitoring by Evans (2005a) at Hey Clough and Back Tor in the Peak District revealed how seriously eroding sheep scars were recolonised by vegetation following a reduction in stocking densities in the 1980s.

Local-scale impacts: vegetation

These studies indicate that the effects of drainage and agricultural intensification on vegetation in the British uplands during the mid to late 20th century have been central to increases in the potential for soil erosion and sediment transfer from pastures to watercourses via surface flow pathways. However,



natural upland vegetation has been subject to other pressures, for example burning of moorland and intensification of grazing. These could lead to significant increases in the frequency, volume and flashiness of overland flow (see Section 2.2). These are then likely to amplify soil erosion in affected areas and improve their connectivity to neighbouring ditches and natural watercourses, leading to elevated sediment yields.

Recent evidence suggests that rehabilitating native vegetation in the British uplands may, conversely, help to improve the hydrologic condition of soils and so influence sediment dynamics. The plot-scale experiments at Pontbren in mid-Wales (Section 2.2.1, and Carroll et al, 2004b) demonstrate that tree belts can be installed to intercept and infiltrate surface flows. These have the potential to reduce fine sediment transfer from upland pastures to neighbouring watercourses by encouraging deposition in a similar manner to grass buffer strips in lowland agricultural settings (eg Owens et al, 2007). However, further research is required to establish their practical effectiveness in reducing sediment transfer to the natural drainage network.

Effects in upland rivers?

The broader-scale effects of post-WWII agricultural intensification are less understood. Data from lake bed sediment cores taken from sites in the English Lake District have revealed large increases in sediment accumulation rates over the past 50 to 60 years. This period coincides with changes in stocking densities and grassland improvement activities in the catchments upstream (Hatfield et al, 2008). In the case of Bassenthwaite Lake, modern sediment influx rates were found to be unprecedented over the last ~5500 years (Hatfield and Maher, 2009).

Sediment source fingerprinting studies also indicate that a large proportion of the modern, extra sediment is probably derived from erosion of land used primarily for grazing sheep. For example, Collins et al (1997a) found that of the suspended sediment sampled in the Rivers Rhiw and Vyrnwy 90 per cent and 84 per cent respectively originated from grazed, agriculturally-improved pastures. Walling et al (1999) calculated that 60 per cent to 85 per cent of all fine material transported by the Rivers Swale, Ure, Nidd and Wharfe in North Yorkshire was sourced from pasture and moorland rather than arable fields. Also, fine sediment from grazed pastures was identified as an important, often dominant, component of the total suspended load of rivers in the Upper Severn basin, despite the fact that a large percentage of their headwaters had been afforested (Collins et al, 1997b).

These findings may reflect an increase not only in erosion but also sediment connectivity between pasture sources and natural channels in the British uplands brought about by factors associated with post-1945 agricultural intensification, including increased levels of surface runoff and the installation of ditches and/or under-drainage. However, little is known concerning channel response to changes in catchment hydrology and sediment dynamics. This is because, as with analyses of long-term river discharge data (see Section 2.2), it is extremely difficult to isolate the land use signature in a historical record from those associated with synergistic environmental changes (see Beven et al, 2008, Lane, 2004, Orr and Carling, 2006, Sullivan et al, 2004, and Yeloff et al, 2006).

In large catchments, the relative effects of different land use changes on erosion and sediment delivery vary depending on the spatial (catchment) and temporal (hydrological event/history) contexts within which they are assessed (Pattison and Lane, 2012). This makes it impossible to generalise let alone predict the trends and sequences of morphological adjustments triggered in the fluvial system downstream by complex response to upstream changes in runoff and sediment yield.

Impacts in lowland rivers?

There is little information on the effects of land use change on sediment dynamics in lowland catchments. Sediment in lowland rivers is derived from a variety of sources including not only the supply from headwater streams, but also lowland tributaries and local erosion. However, there is strong evidence that most of the transported sediment originates from erosion of the catchment surface and that it is derived by processes influenced by land use (Collins and Walling, 2007).

CIRIA C719 53

Long-term investigations suggest that 40 per cent of arable land is affected by soil erosion (Evans, 1996, Evans, 2005b, Chambers and Garwood, 2000, and Collins, 2008). Erosion rates are known to vary widely (0.01 and 263 tonnes/ha) depending on what causes the erosion such as crop type (eg winter cereals exhibit more erosion, and crops such as maize, potatoes and sugar beet are higher risk). Other contributing factors in lowland environments include:

66 the effect that livestock and farm vehicles have through trampling, poaching and compaction that physically detach and mobilise soil particles making them available for erosion (Brazier et al, 2007)

66 the removal of barriers to overland flow such as hedgerows leading to increased slope length and runoff velocities, reduction of soil organic matter and excessive poaching through livestock intensification (Heathwaite et al, 1990).

Livestock also contribute to sediment availability by excretion of faeces and application of manures. Both can contribute significantly to sediment concentrations in watercourses (Brazier et al, 2007).

Withers et al (2007) report that 60 per cent to 96 per cent of suspended sediment measured in rivers during storm events was derived from surface sources. Walling and Collins (2005) reviewed the findings of 48 sediment sourcing studies across the UK and identified that agricultural soils (supporting pasture, arable, moorland and woodland) typically account for 85 per cent to 95 per cent of the total suspended sediment load in rivers. However, while farming was clearly responsible for most of the suspended sediment load measured by Walling and Collins (2005), this was not universally sourced from arable land. In fact, the contribution from arable land varied widely (one per cent to 78 per cent), with higher contributions from mixed agriculture catchments in southern England. This emphasises the potential for erosion in pasture, moorland and woodland areas to contribute significantly to lowland sediment loads.

Numerous studies have identified un-metalled roads as an important primary source of sediment (Wemple et al, 2001). Others have established the role of roads as an important secondary source of sediment stored temporally between storm events (Gruszowski et al, 2003, and Morschel et al, 2004). Perhaps most importantly, roads are efficient sediment pathways linking catchment sediment sources to the channel network (Collins and Walling, 2004). The potential of roads to contribute and/or transfer sediment should not be underestimated. UK studies assessing sedimentation in lowland, urban catchments suggested that roads may be responsible for 30 per cent to 50 per cent of the total suspended sediment load (Collins, 2008, and Gruszowski et al, 2003).

Similarly, there is growing evidence that the field, mole or tile under-drains often present in both lowland and upland improved grasslands in the UK may be responsible for sourcing and/or transferring a significant proportion of the total suspended load (particularly the colloidal material in the range 0.1-1 µm) of lowland rivers (Brazier et al, 2007). A study by Russell et al (2001) found that field drains accounted for 27 per cent to 55 per cent of suspended sediment in catchments in Herefordshire and Derbyshire, with differences in soil characteristics and drain condition/maintenance regime thought to account for most of the observed spatial variation.

Despite the established influence of land use on sediment delivery and dynamics in lowland rivers, as with their upland counterparts, little is known about how their morphologies respond to anthropogenically-induced change. Indeed, uncertainty relating to how finer “wash load” sediment leads to longer-term morphological adjustments (and later environmental and flood risk responses) in the lower courses of river systems is highlighted in the conclusions of the reports by Evans et al (2004a, 2004b and 2008) and is a topic that requires significant further research. Newson’s statement that geomorphological research in the UK has almost exclusively focused on natural fluvial forms and processes in unmodified reaches of small streams remains as valid today as it was when written (Newson, 2002).

3 .2 .2 Evidence delivered during FRMRC

Experimental work conducted by the FRMRC in the Pontbren experimental catchment has generated significant new knowledge concerning the hydrologic and sediment effects of post-1945 agricultural



intensification in the British uplands. Initially, differences in sediment dynamics between streams draining paired sub-catchments with similar physiographic characteristics (Table 3.1), but differing land use histories were investigated (Henshaw, 2009).

Table 3 .1 Physiographic characteristics of the Pontbren sub-catchments

Nant Melin-y-grug (control)

Nant Pen-y-cwm (modified)

Area (km2) 4.06 3.17

Mean elevation (m) 372 330

Average slope (°) 7.0 5.1

BFIHOST 0.34 0.28

Improved grassland coverage during 1990s (%) 13 77

Improved grassland coverage during 1930s (%) 13 24

Moorland/heath/rough grazing land coverage during 1990s (%) 82 12

Moorland/heath/rough grazing land coverage during 1930s (%) 82 58

The first of the paired sub-catchments was the Nant Pen-y-cwm, which drains an area that largely consisted of rough pasture and moorland in the 1930s but is now dominated by heavily grazed, agriculturally-improved grassland. The second sub-catchment was that of the Nant Melin-y-grug, which drains moorland that has been relatively unaffected by recent changes in land use. Suspended (fine grained) sediment yields measured during flood events in the intensified sub-catchment were shown to be substantially higher than those observed in the neighbouring, unimproved sub-catchment (Figure 3.1). Specifically, the annual suspended sediment yield of the Nant Pen-y-cwm (17.31 t km-2 yr-1) was five times greater than that of the Nant Melin-y-grug (3.21 t km-2 yr-1). This was broadly comparable with published sediment yields from catchments in that region that have been affected by afforestation, an upland land use that is well-known for generating large quantities of fine sediment at various stages of the commercial forestry cycle (Henshaw, 2009, Leeks, 1992, Leeks and Marks, 1997, and Moore and Newson, 1986).

Note

Fine sediment yields are markedly greater in the Nant Pen-y-cwm, which has been subject to agricultural intensification, than in the Nant Melin-y-grug, which is largely unimproved.

Figure 3 .1

Suspended sediment transport in the Nant Pen-y-cwm and Nant Melin-y-grug during a sequence of high flow events

CIRIA C719 55

Both the amount and frequency of bedload (coarse) sediment transport during flood events were also found to be significantly higher in the Nant Pen-y-cwm (Figure 3.2). This resulted in a 12-fold difference in annual bedload yield (0.36 t km-2 yr-1 in the Nant Pen-y-cwm compared to 0.03 t km-2 yr-1 in the Nant Melin-y-grug) (Henshaw, 2009).

The elevated sediment yields in the Nant Pen-y-cwm compared to the Nant Melin-y-grug cannot be explained by differences in basin properties (Table 3.1) or stream hydraulics (estimates of specific stream power at each measurement site indicated sediment transport potential was significantly higher on the Nant Melin-y-grug) but result from elevated catchment sediment supply and channel instability in the Nant Pen-y-cwm (Henshaw 2009, and Henshaw et al, 2013). Specifically, rates of surface erosion and sediment delivery from the agriculturally-improved, intensively grazed pastures (47 kg ha-1 yr-1) were found to be very low (in comparison to rates reported from elsewhere in the UK). This indicated that fine sediment was sourced and transferred predominantly via under-drains installed in the Nant Pen-y-cwm sub-catchment during the late 20th century (Henshaw, 2009). Field measurements showed that individual field drains were capable of producing in excess of 29 kg of fine sediment per year (Henshaw, 2009). The effect of this field drainage has previously been reported in lowland, arable catchments (Section 3.2.1), but the new FRMRC research reported here suggests that it may be significant in the uplands.

Field measurements established that the majority of the excess sediment carried by the Nant Pen-y-cwm was in fact generated from within the stream channel network. Monitoring of bank retreat and bed level change revealed that channel changes supply over 19 tonnes per year of sediment to the Nant Pen-y-cwm, predominantly because of bed scour and fluvial bank erosion (Henshaw et al, 2013). The propensity for channel instability in the Nant Pen-y-cwm contrasts strongly with the situation in the Nant Melin-y-grug (Figure 3.3), which displays very little evidence of lateral or vertical scour and a stable, step-pool morphology that helps to dissipate energy during in-bank flows (Chin, 1998 and 2003).

Morphological adjustments evident in the Nant Pen-y-cwm are probably prompted by hydrological responses to agricultural intensification and over-stocking in the sub-catchment, which make peak runoff discharges in the Nant Pen-y-cwm sub-catchment significantly higher than those in the upper Nant Melin-y-grug (Figure 3.4). Confirmation of this response to intensification would require a long duration monitoring study. However, it would be expected because similar responses have been observed where other “ramped” (see Brunsden and Thornes, 1979) disturbances such as urbanisation have altered the hydrological regime of rivers (eg Wolman and Schick, 1967, and Booth, 1990). Also, the stabilities of other British upland river systems have been shown to be sensitive to relatively small changes in runoff and sediment supply (Lewin et al, 1988).

Note

Coarse sediment yields are much greater in the Nant Pen-y-cwm, which has been subject to agricultural intensification, than in the Nant Melin-y-grug, which is largely unimproved.

Figure 3 .2

Bedload yields measured in the Nant Pen-y-cwm and the Nant Melin-y-grug during a series of high flow events



Figure 3 .3 Contrasting levels of channel stability and morphological activity in the Nant Pen-y-cwm (a) and Nant Melin-y-grug (b)

Figure 3 .4 Runoff duration curves for the Nant Pen-y-cwm and Nant Melin-y-grug (from Henshaw, 2009)

Alongside the field investigation, FRMRC also conducted a series of computer model-based experiments to explore the sensitivity of the sediment system to changes in catchment hydrology and the runoff regime (see Section 3.3.2). The results add further support to the hypothesis that relatively small changes in peak discharges in response to land use change can substantially modify sediment yields and levels of morphological activity in small headwater streams. The experimental simulations indicated that carefully targeted, small-scale tree planting in the Nant Pen-y-cwm sub-catchment could help to reduce future sediment yields, while reversion to more intensive agriculture would accentuate channel instability in the stream network and increase downstream sediment yield. Also, while significant increases in sediment yield and channel instability were predicted under most future scenarios due to changes in rainfall seasonality and intensity, results also indicate that these outcomes could be avoided through strategic land use changes.

These findings are catchment-specific and may not be directly transferable to other locations. Also, the spatial scale of the FRMRC sediment research at Pontbren was small and further research is needed to investigate how the significance of sediment yield changes further downstream in the fluvial system. However, the results illustrate several important points that have significant implications for current and future land use and river management, including:

1 Figures 3.1 and 3.2 demonstrate that fine and coarse sediment fluxes are highly unsteady and non-uniform, ie they vary through time and space. They adjust continuously even in streams that are in dynamic equilibrium.

2 When climate and/or land use changes occur, this changes catchment runoff. Such is the sensitivity of catchment sediment yield and channel stability that relatively subtle hydrological changes may trigger disproportionate responses in the fluvial system. These lead to extensive morphological changes in headwater streams.

CIRIA C719 57

3 In the UK, climate change is likely to increase flood frequency and peak flow rates. So sediment yields and rates of channel change are likely to increase over coming decades.

4 Strategic, targeted land use changes could avoid or at least help to mitigate existing and future sediment and channel problems in upland catchments. This can be done by increasing the resilience of their sediment systems to the destabilising effects of both long-term climate change and individual flood events.

3.3 QuANTITATIvE METhods FoR PREdICTING ChANGEs IN sEdIMENT yIELd ANd ChANNEL MoRPhoLoGy

3.3.1 Modelling sediment dynamics and morphological impacts for flood risk management

Despite attempts to rectify the situation (eg the development of GeoRHS, Branson et al, 2005), a severe lack of national-scale, geomorphologically-relevant data (eg bed material size information, historical planform adjustment) continues to limit the scope of quantitative sediment studies. Currently, the standard method for investigating catchment-scale sediment dynamics in British rivers is the fluvial audit (Thorne et al, 2010b). Using this approach, detailed field and documentary investigations are conducted by an experienced fluvial geomorphologist to divide the river network into “geomorphic reaches”. Each of these is designated as either a sediment source (erosional), a sediment transfer reach (dynamic equilibrium) or a sediment sink (depositional).

The fluvial audit has proved useful in a large number of river conservation and restoration projects. However, it does not yield the quantification of sediment dynamics necessary to interface effectively with the engineering components of strategic flood risk assessments, CFMPs or RBMPs. Crucially, the fluvial audit has no inherent predictive capacity and it cannot be used alone to predict river system response to potential flood risk management actions such as strategic land use changes. Also, the fluvial audit is unable, even qualitatively, to simulate how the performance of flood risk management options may vary in response to projected changes in climate, which cannot be considered independently of future land use changes.

Numerical models offer an alternative method of investigating the effects of changes in land use and climate on sediment dynamics and river morphology (Van de Wiel et al, 2011). A large number of models exist that are capable of simulating sediment fluxes and morphological changes at the scale of an individual bend, confluence or reach with varying degrees of physical process representation (eg Fang and Wang, 2000, Darby et al, 2002, Guo and Jin, 2002, and Rüther and Olsen, 2005). However, these models often have high computational demands as they rely on solving the Navier–Stokes equations or an approximation of them to calculate a flow field, the boundaries of which continually changes as channel morphology evolves (Van de Wiel et al, 2011). Also, geomorphic models of this type tend to require high levels of (usually unavailable) input data. The combined result of these issues is that physics-based, numerical sediment models can only be applied at small spatial and temporal scales.

Available, catchment-scale sediment models usually treat sediment (and the nutrients, pesticides and herbicides attached to it) as a pollutant, the USDA’s Soil and Water Assessment Tool (SWAT) (Neitsch et al, 2005) and AGricultural Non-Point Source Pollution Model (AGNPS) (Young et al, 1989) being good examples. While these and other similar models account only for sediment sourced from sheet and rill erosion by overland flow, AGNPS recently has been revised to consider sediment derived from ephemeral and classical gullies formed where runoff concentrates along preferred pathways (Parker et al, 2007, and Bingner et al, 2007). These and other agricultural models have potential, but their applicability in the UK is limited by their heavy data requirements, complexity and a shortage of scientists and engineers with the skills, training and experience necessary to apply them reliably.

Recognising this, a toolbox of both existing and novel methods and models capable of investigating sediment dynamics at much broader spatio-temporal scales was assembled during the first phase of



the FRMRC (Thorne et al, 2011). The toolbox was designed to provide users with the capability to characterise sediment dynamics in ways that make best use of available data and knowledge, based on combining analytical methods with expert interpretation. This would allow the morphological (and by implication ecological) effects of existing and planned flood alleviation schemes to be assessed, and their sensitivity to future changes in runoff and sediment supply evaluated. The toolbox is intended to build upon, rather than replace, qualitative methodologies such as the fluvial audit, and the methods and models contained within should not be used in isolation.

The FRMRC sediment toolbox includes the following models and methods, which are described further in Thorne et al (2011):

66 Stream Power Screening Tool

66 River Energy Audit Scheme (REAS)

66 Sediment Impact Assessment Method, embedded in HEC-RAS (HEC-RAS SIAM)

66 Hydraulic Engineering Center River Analysis System (HEC-RAS v4.0)

66 ISIS Sediment

66 Cellular Automaton Evolutionary River and Slope model (CAESAR).

The complexity, cost and input data requirements of the individual tools vary. They can be simple methods that can be performed using readily-accessible or easily predictable information on river slope, width and discharge (Stream Power Screening Tool, REAS). They can be more advanced sediment transport models that route sediment and adjust channel morphology over durations extending from a series of flood events (ISIS Sediment) up to several thousands of years (CAESAR), but which are costly, expertise- and data-intensive, and time-consuming. The uncertainty inherent in sediment transport modelling (see Section 3.4) and the often chronic lack of sediment data for UK rivers mean that the results of analyses performed using any of the tools should be viewed as indicative and will require careful interpretation. However, the tools vary in terms of their position along a continuum between purely being interpretative and fully analytical (Figure 3.5).

When selecting a method or model to assist with the characterisation of fluvial sediment dynamics, users should balance the risks associated with misrepresenting the behaviour of a catchment or river using a simple tool with the rising costs associated with application of the more complex tools (Figure 3.6). Decisions should take into account the nature of the investigation (ie the sort of questions asked, how the

Figure 3 .5

Relative contributions of interpretational and analytical approaches in different methods and models contained within the FRMRC Sediment Toolbox (adapted from Thorne et al, 2011)

Analytical

Interpretive

Stream power screening tool

River energy audit scheme

HEC-RAS 4.1 Sediment impact assessment

HEC-RAS 4.1 Sediment transport method

ISIS Sediment transport

CAESAR

CIRIA C719 59

results will be used), the type and scale of environment/system under investigation, and the resources (financial, data, technical expertise) available to the investigator (see also Section 2.3.3).

Also, selection decisions should incorporate the needs and opinions of stakeholders. For example, it may be difficult to convince funders that the extra costs involved in advanced sediment transport and geomorphological modelling are justified. However, the public and politicians can be reticent towards approaches that do not have the required blend of cognisance (stakeholders can understand and accept the principle and methods of an approach) and credibility (the approach is so simple and schematised that stakeholders may refuse to believe results) (Thorne et al, 2010b). Figure 3.7 illustrates these considerations, emphasising the need for users to select a method or model that sits within the central zone of a triangle of stakeholder requirements.

Two examples are presented in the following sub-sections that apply two of the models in the FRMRC Sediment Toolbox in novel ways to predict the effects of changes in land use and climate on river sediment yields and channel morphology in an upland (Section 3.3.2) and in a lowland setting (Section 3.3.3). These models were selected over others in the FRMRC Sediment Toolbox and industry standard models (eg MIKE 21C) based on their:

66 ability to simulate effects over long time periods and at large spatial scales

66 computational speed in relation to the large number of simulations to be performed

66 functionality in terms of modifying hydrological response, sediment delivery etc under different land use scenarios

Stak

ehol

der a

ttitu

des

Cogn

isan

ce

Cred

ibilit

y

Constraints

Managem

ent

resources

Support

Simplicity Science Complexity

Project success

Figure 3 .7

Balancing management resources, the science base for sediment methods and models, and stakeholder attitudes for project success (adapted from Thorne et al, 2011)

Figure 3 .6

Balancing cost and risk when selecting the appropriate level of model complexity for a sediment study (adapted from Thorne et al, 2011)



66 input data requirements

66 cost (both the models selected are free).

Section 3.3.2 describes a study to assess the combined effects of land use and climate change in an upland catchment using the CAESAR landscape evolution model. A new methodological approach is employed in which the meta-modelling procedure described in Section 2.3.6 is used to calibrate the model so that it can simulate the hydrological response of the catchment to different land use scenarios. Long-term (30 year) hourly rainfall sequences that represent a variety of present-day and future climate scenarios are then used as input variables to simulate morphological adjustments and sediment yields under different land use/climate combinations. The rainfall sequences are based on UKCP09 climate projections and produced using a weather generator. This allows forecasting to be undertaken in a stochastic manner and uncertainty to be assessed.

Section 3.3.3 uses the reach-based, sediment accounting model (SIAM) to predict how different land use and climate scenarios could modify sediment dynamics and channel morphology in a relatively large, lowland river. Predicted changes in channel morphology are then incorporated into a 1D flood model to examine their implications for flood risk, based on assessing changes in water surface elevations, frequencies and durations for a range of selected flood events.

3 .3 .2 Example 1: Predicting land use and climate change effects in upland catchments using CAESAR

Overview

The CAESAR (Cellular Automaton Evolutionary Slope and River) landscape evolution model is used to predict how sediment yields from the 3.2 km2 Nant Pen-y-cwm sub-catchment at Pontbren (Section 3.2.2) are likely to change under a range of future land use and climate change scenarios. Specifically, the exercise examines the potential effects of both a reversion to intensive agricultural practices and strategic woodland planting within the sub-catchment, together with changes in the seasonality and intensity of precipitation under different emissions scenarios. The method presented could be used to generate input data for more detailed, physics-based simulations of the effects of changes in sediment yields on downstream channel morphology conducted at the reach-scale.

CAESAR model description

This section provides a brief description of CAESAR and its operation but users are referred to Coulthard et al (2000 and 2002), Van de Wiel et al (2007) and Coulthard (2011) for more detailed information.

CAESAR is a 2D flow and sediment transport model that can simulate morphological changes in river catchments and reaches on a flood-by-flood basis, over timescales ranging from a few hours to thousands of years. Catchments and reaches are represented using a regular mesh of grid cells, each of which possesses information about elevation, water discharge and depth, vegetation cover and sediment size. The model is based on the cellular automaton concept, where the continued iteration of a series of local process rules on these cells governs the behaviour of the entire system. In CAESAR, rules representing fluvial and hillslope processes and interactions between individual cells and their neighbours determine the spatial distribution of erosion and deposition during a given time step. The properties of grid cells (eg elevation) are updated accordingly providing the starting point for the next time step. Water and sediment fluxes at the catchment or reach outlet, along with spatially distributed elevation and sediment size information, are output at specified time intervals. The conceptual structure of CAESAR is summarised in Figure 3.8.

CIRIA C719 61

Figure 3 .8 Conceptual structure of CAESAR (adapted from Van de Wiel et al, 2007)

The model uses an hourly rainfall record (or discharge record when performing a reach-based simulation) as the input for a hydrological model based on TOPMODEL (Beven and Kirkby, 1979). The hydrological effects of different land use/vegetation types can be represented in the model by altering m – a parameter that controls the rise and fall of the soil moisture store. Selection of an appropriate m value can be informed by previous research (eg table of values in Beven, 1997) or calculated directly using hydrological data if available (see section on Model setup and land use representation). The output from the hydrological model is then routed across the catchment or reach in one of two ways according to the model version selected:

66 in CAESAR 6.2, a scanning multiple flow algorithm sweeps across the catchment, routing flow from a cell to its three downslope neighbours (see Murray and Paola, 1994). Where total flow exceeds subsurface flow, the excess is treated as overland flow and a flow depth is calculated using an adaptation of the Manning’s equation

66 in CAESAR-Lisflood, flow is routed according to a 2D hydrodynamic flow model (based on the Lisflood-FP code, Bates et al, 2010) that conserves mass and partial momentum.

For all cells with a flow depth, fluvial erosion and deposition are calculated using either the Wilcock and Crowe or Einstein-Brown mixed-size bedload transport equations. This allows the selective erosion, transport and deposition of nine grain size fractions, leading to spatially variable sediment size distributions in both the planform and vertical dimensions, which helps the development of surface armouring and stratigraphy. Slope processes such as soil creep and mass failure (when a critical stability threshold is exceeded) are also represented, at a resolution dependent on the grid size. The degree to which slopes are coupled to steams is used to represent how much of the material eroded from each hillslope is fed into the fluvial system.

See Useful websites for information on stand-alone executable versions of CAESAR (along with their source code).

Inputs: sediment size distribution, land use

(represented as m value)

Input: initial topography (DEM)

Topography

Fluvial processes Hillslope processes

Erosion/deposition

Topography adjustment

outputs: hourly discharge and sediment yield,

topography, erosion/despotion maps, sediment

size distribution

Input: hourly rainfall record

Nex

t ite

ratio

n



Input data and climate representation

The input data required for the simulations were:

1 Digital elevation model (DEM) of the Nant Pen-y-cwm sub-catchment.

2 Bedrock layer DEM. This represents the maximum depth to which CAESAR can erode during a simulation. It was set at 3 m for all cells, representing the maximum thickness of exposed till observed in the sub-catchment.

3 Particle size distribution, derived using particle size analysis of sediments sampled in the sub-catchment. Henshaw (2009) gives full details.

4 Multiple, 30-year duration, hourly rainfall series representing baseline and future climate scenarios.

The rainfall sequences were compiled using Weather Generator 2.0, a freely available tool developed as part of the UK Climate Impacts Programme that produces statistically plausible future time series for rainfall and other climate variables under various emissions scenarios (Jones et al, 2009). The outputs have a spatial resolution of 5 km and are consistent with probabilistic UKCP09 climate change projections (Murphy et al, 2009).

The tool was used to derive 50 individual (but statistically equivalent) hourly rainfall sequences for each of the following time period/emissions scenario combinations:

66 baseline – rainfall sequences B1-B50

66 2050s low emissions (B1) – rainfall sequences L1-L50

66 2050s medium emissions (A1B) – rainfall sequences M1-M50

66 2050s high emissions (A1F1) – rainfall sequences H1-H50.

Cumulative hourly rainfall plots for the sequences in each of the four time period/emissions scenario combinations are presented in Figure 3.9.

Figure 3 .9 Plots of cumulative hourly rainfall in sequences representing different time period/emissions scenario combinations constructed using Weather Generator 2.0

CIRIA C719 63

In terms of mean annual rainfall, there was no difference between the baseline climate and any of the 2050s climate scenarios, apart from under the 2050s high emissions scenario where a small (2 %) increase was evident. However, a large increase in the number of wet days was evident in all of the 2050s climate scenarios. Compared to the present (baseline) climate, the average number of days per annum on which rainfall exceeds 25 mm increased by 21 per cent under the 2050s low emissions scenario, and by 26 and 30 per cent, respectively, under the medium and high emissions scenarios. The equivalent increases in the average number of days per annum where rainfall exceeds 50 mm were 56, 61 and 65 per cent, respectively.

Model setup and land use representation

The meta-modelling procedure described in Section 2.3.6 and Wheater et al (2008) was used in conjunction with hydrological data collected in the Pontbren catchment between 2006 and 2008 (Section 2.2.2, and Marshall et al, 2009) to simulate the effect of the following land use scenarios on streamflow in the Nant Pen-y-cwm subcatchment:

66 present-day: existing land use configuration including all Pontbren Rural Care Project tree strips and woodland areas planted to date

66 tree strips: tree strips are added to all existing areas of grazed pasture

66 1990s: all Pontbren Rural Care Project tree strips and woodland areas are removed, returning the sub-catchment to an intensive agricultural landscape similar to that of the early-1990s.

The hydrological model within CAESAR was calibrated to replicate the hydrological responses of the sub-catchment expected under the different land use scenarios described above. This was achieved by conducting a series of model runs (using observed rainfall from the 2006 to 2008 experimental period as input data) in which the m parameter in CAESAR’s hydrological model was varied incrementally between 0.04 and 0.13. Appropriate m parameter values for each of the three land use scenarios were then selected through:

1 Assessment of the goodness of fit between the peak discharge values predicted by CAESAR and those generated using the meta-modelling procedure, for the 30 largest flood events of the calibration period (Table 3.2).

2 Visual inspection of flood hydrographs (Figure 3.10).

Table 3 .2 RMSE (m3s-1) of the relationship between peak discharge values predicted by CAESAR and the meta-modelling procedure for the 30 largest flood events of the calibration period under different m value/land use scenario combinations. Lowest RMSE values represent best fit

Land use m value Present day Tree strips 1990s

0.04 0.39 0.33 0.49

0.05 0.31 0.32 0.36

0.06 0.28 0.30 0.39

0.07 0.38 0.29 0.48

0.08 0.40 0.31 0.51

0.09 0.37 0.25 0.48

0.10 0.40 0.31 0.51

0.11 0.44 0.35 0.54

0.12 0.44 0.37 0.55

0.13 0.46 0.35 0.54



Figure 3 .10 Comparison of CAESAR-predicted and meta-modelling procedure-predicted hydrographs for different m value/land use scenario combinations

Simulations were conducted for each of the land use, climate and rainfall sequence combinations listed in Table 3.3 (a total of 600 simulations). Sediment yields at the outlet of the Nant Pen-y-cwm sub-catchment were recorded on a daily basis throughout each simulation.

Table 3 .3 Combinations of land use scenarios, climate scenarios and rainfall sequences used in CAESAR model runs

Land useClimate

Present day Tree strips 1990s

Baseline B1–B50 B1–B50 B1–B50

2050s low emissions L1–L50 L1–L50 L1–L50

2050s medium emissions M1–M50 M1–M50 M1–M50

2050s high emissions H1–H50 H1–H50 H1–H50

Note

B1–B50 = Baseline climate rainfall sequences 1–50L1–L50 = 2050s low emission climate rainfall sequences 1–50M1–M50 = 2050s medium emissions climate rainfall sequences 1–50H1–H50 = 2050s high emissions climate rainfall sequences 1–50

Results and discussion

Cumulative sediment yields produced by individual simulations were found to be highly variable. For example, Figure 3.11 shows the records produced for the baseline condition in which current climate

Note

Black lines represent meta-modelling procedure-predicted streamflow. Red lines represent CAESAR-predicted streamflow

CIRIA C719 65

and land use continue into the future unchanged. The high degree of variability is to be expected as the occurrence of sediment transporting events and sediment yields is controlled by the frequency, size and sequencing of transport events. These differ markedly between the different rainfall time series input to the model from the weather generator. Cumulative plots for the other scenarios have similar levels of variability.

Note

Grey lines represent individual model runs and reflect the impact of the weather generator in producing different sediment futures under an unchanging climate and with no change in land use. The thick black line represents the mean cumulative sediment yield and the shaded grey area represents +/- one standard deviation of variation around the mean.

Figure 3 .11 Variability in predicted sediment yields under the baseline combination of continued present day climate and land use for the next 30 years

Figure 3.12 illustrates why sediment yields should never be predicted deterministically. This is because the sediment future of a catchment is not just uncertain, it is unknowable. It depends strongly on the frequency, size and sequencing of weather events that have not happened yet and to which the sediment system is highly sensitive. In fact, what the simulation studies demonstrate is that the only certainty concerning a deterministic prediction of the future sediment yield from a catchment is that it will be wrong. The probability of accurately predicting the cumulative sediment yield curve that will actually occur using a single model run is negligible.

When multiple model runs are used represent this natural variability, the influences of land use and climate changes can confidently be discerned in the outputs for overall, 30-year sediment yields (Figure 3.12).

Considering first the future effects of land use change, under baseline climatic conditions, the median 30-year sediment yield in Figure 3.12 is 11 per cent lower for continuation of present day land use than it would be under the 1990s (intensive) land use scenario. These simulated results suggest that hydrological impacts of the Pontbren Rural Care Project have successfully reduced catchment erosion, channel instability and sediment yield. Also they indicate that they are likely to go on doing so in future provided that they are continued and sustained.

In the absence of climate change, the model indicates a 77 per cent reduction in the median 30-year sediment yield under the tree strips scenario in comparison to that predicted under the present-day



land use. This model finding indicates that if strategic tree planting were to be continued and expanded it could deliver very marked reductions in catchment sediment yield. This will decrease the volume of sediment supplied to rivers downstream and accumulating in flood alleviation schemes within this part of the Severn catchment.

Figure 3 .12 Box and whisker plots of total 30-year sediment yields from the Nant Pen-y-cwm sub-catchment predicted under twelve combinations of the scenarios for future changes in land use and climate

While the messages that the simulations deliver with respect to land use and sediment yields are clear, interpretation of the projected sediment impacts of future climate change is less straightforward.

Under present-day land use conditions, there is no significant difference between the median 30-year sediment yields produced by the model for the baseline and 2050s low emissions climate scenario, at the 95 per cent level of confidence. However, modelled sediment yields under the 2050s medium and high emissions scenarios are more than 10 per cent greater than under the baseline climatic scenario. This indicates that unless emissions are reduced climate change may be expected to lead to increased sediment yields from Pontbren through its effects on rainstorm frequency, intensity and seasonality. This finding is probably transferrable to other small, upland catchments throughout the UK.

Land use management decisions could significantly amplify or moderate the effects of climate change on catchment erosion and sediment yield. For example, reverting to intensive farming (the 1990s land use scenario) increases the median 30-year sediment yield by 18 per cent and 27 per cent compared to that under the baseline climate together with present-day land use when coupled with medium or high emissions, respectively. In contrast, the effect of strategic planting of tree strips is to maintain median 30-year sediment yields at levels lower than those predicted under baseline climatic and present-day land conditions for all three 2050s emissions scenarios.

The simulations also reveal that land use affects long-term catchment sediment yield primarily through its influence on the sensitivity of the catchment and channel system to erosion and destabilisation by rainstorms. Figure 3.13 shows the response of the sub-catchment to fifty identical rainfall sequences under the 1990s land use (a) and the tree strips scenarios for the 2050s (b), high emissions climate scenario.

CIRIA C719 67

Note

Grey lines represent the 50 individual model runs, the thick black line represents the mean and the shaded grey area represents +/- one standard deviation.

Figure 3 .13 Comparison of sediment yield simulations under 1990s (a) and tree strips scenarios for the 2050s (b), high emissions climate change scenario

Some very high steps in the cumulative sediment yield records in Figure 3.13 indicate the occurrence of a few rainstorms that individually contributed a considerable proportion of the total 30-year yield in a simulation. Careful comparison of the graphs for the 1990s and tree strips scenarios indicates that such events are about equally likely under either scenario. These are truly extreme events that evidently cross one or more of the geomorphic thresholds necessary to trigger severe and extensive catchment erosion and channel instability.

The frequency of small- to medium-sized steps in the cumulative records is much greater under the 1990s land use scenario (Figure 3.13a) and their contributions are mainly responsible for the larger total sediment yield compared to that under the tree strips scenario (Figure 3.13b).

DEM change maps generated using CAESAR confirmed the importance of channel instability in elevating catchment sediment yields (Figure 3.14). In all model runs, the vast majority of sediment exported from the sub-catchment is shown to have originated due to bed scour and bank erosion within the channel network as opposed to washing in from the surrounding catchment. This is significant given that the CAESAR model was carefully set up to represent surface runoff and sediment connectivity in the catchment. It illustrates that buffer strips are effective in reducing sediment yields, not only through trapping sediment, but also through reducing the peak flows responsible for morphological changes involving in-channel scour and bank erosion.

Figure 3 .14

DEM change map for the Nant Pen-y-cwm sub-catchment showing land surface elevation changes driven by the 30-year, hourly rainfall sequence



These findings suggest that strategic land use changes could be used to reduce the sensitivity of sediment transfer systems in small, upland catchments to future climate change. This could potentially buffer river reaches further downstream from excessive sediment inputs and associated channel instability. This type of land use adaptation would also enhance the resilience of catchments adversely impacted by post-WWII land use intensification.

However, for the reasons explained previously, variability in predicted future sediment yields is large, even before other issues of uncertainty in the modelling procedure are considered (Section 3.4). This makes it impossible to predict and difficult to demonstrate the benefits of managing land use to reduce downstream sediment yields and associated flood risks. While decision makers are not always able to justify woodland planting based on sediment control alone, these potential benefits should be viewed as both intrinsic and complementary to enhancement of other ecosystem services (see Chapter 4).

3 .3 .3 Example 2: investigating the effects of sediment loading on channel morphology and flood risk in a lowland river system using HEC-RAS SIAM

Overview

It is hypothesised that excessive amounts of supply-limited, wash-material load sediment derived from erosion of the upper catchment can transition to capacity-limited bed-material load, which is then deposited in the lower reaches of a river system. Such deposition can alter channel morphology and conveyance capacity to produce significant, adverse effects on flood risk and performance of flood defence assets. Management of wash-material sediment at source can reduce this effect and prevent the need for intervention.

In this case study, SIAM (Sediment Impact Assessment Model) (Biedenharn et al, 2006a, and Thorne et al, 2010b) is used to investigate how changes to sediment yields and alterations to the flow regime affect sediment dynamics in the lower River Tone, which is a tributary to the River Parrett in Somerset, England. This case study concentrates on the fate and consequences of sediment, primarily wash-material sediment, after it has entered the river system from the upstream catchment.

Predicted changes in channel morphology are then incorporated into an existing Environment Agency ISIS flood model. This model examines the implications for flood risk, based on assessing changes in water surface elevation, frequency and duration for a range of flood events.

This case study is not intended to provide an exhaustive critique of SIAM functioning and outputs, which is covered elsewhere in existing literature (see references in the next paragraph). Rather it is intended to demonstrate the applicability of the model in the context of a UK lowland river sediment study. This case study shows how using a relatively simple model, sediments can be better integrated into river assessments, generating information that can be used by river managers to underpin assessment and cost-benefit analysis of potential sediment management options. These include land use change, sediment source control, in-channel maintenance, or flood defence asset management.

HEC-RAS SIAM model description

SIAM is a relatively new model developed at the US Army Engineers Research and Development Centre (ERDC) in collaboration with Colorado State University and Nottingham University during Phase 1 of the FRMRC. This section provides an overview of SIAM, but full descriptions of the model and its data requirements are provided by Biedenharn et al (2006a) in Chapter 3, and summarised in Thorne et al (2010b). Further information on the wash load–bed material load concept, which underpins SIAM, is given in Biedenharn et al (2006b) and applications of SIAM in the USA are described in Biedenharn et al (2006c).

SIAM supports rapid assessment and quantification of local sediment imbalances and downstream sediment yields under different catchment and river management scenarios. It is incorporated within the

CIRIA C719 69

“hydraulic design” module of the US Army Engineer, Hydrologic Engineering Center’s River Analysis System (HEC-RAS) (Gibson et al, 2006, and Thorne et al, 2010b).

SIAM divides any stream network into a series of user-defined sediment reaches. These are typically delineated based on observed locations of significant morphological change such as

66 tributary junctions

66 changes in channel gradient

66 planform or geometry

66 changes in bed material composition.

SIAM combines sediment, hydrological and hydraulic information for a channel network to determine an average annual sediment budget for the stream network on a reach-by-reach basis.

The model integrates predicted transport rates with flow duration information to compute an average annual sediment transport capacity in tonnes per year. Computed average annual sediment transport is compared with the average annual sediment load supplied by the reach upstream to evaluate the balance between sediment supply and transport capacity for each sediment reach in the stream network. Sediment continuity is then used to classify reaches as sediment sources, pathways or sinks.

Like CAESAR, SIAM also performs reach averaged sediment transport computations by grain size class. Accounting for different grain size classes allows the model to separately track the movement of wash load and bed material load through the drainage network. This allows SIAM to identify links between the sources and sinks of the relatively fine material that moves quickly through the system and those of relatively coarse sediment moving more slowly. This concept underpins the use of SIAM as a sediment management tool.

Wash load is defined as sediment that is not found in “appreciable quantities” in the bed of the channel. It is usually taken as the grain size of which 10 per cent of the bed substrate is finer (ie D10 of the bed material particle size distribution). In SIAM, sediment is defined as either wash load or bed material based on a user defined wash load threshold grain size diameter (usually the D10 of the bed material) in each sediment reach. Wash load is a relative rather than an absolute term. This allows sediment that is wash load in one reach to transition into bed material in a downstream reach as the bed material size becomes finer with distance downstream in the fluvial system.

The use of a wash load–bed material load threshold also allows SIAM to treat the movement of fine and coarse material differently. An appropriate sediment transport function is used to calculate the transport capacity in the reach for bed material load while the load of finer particles in the wash load can be assumed to be supply, as opposed to transport, capacity limited.

When linking sediment sources to sinks the value of this assumption is demonstrated by considering a river that flows from steeper reaches with coarse bed material, to lower gradient downstream reaches with finer bed materials (eg the River Tone). Sand particles are included in the wash load in the upper reaches (their size is < D10 for the local bed material). In the lower reaches the sand becomes part of the bed material load because its size is > D10 for the local bed material. An increase in the sand supply due to accelerated catchment erosion would have limited morphological effect in the upper reaches, but would be likely to have a much larger effect in the reaches downstream.

SIAM requires the following input parameters for each sediment reach:

66 bed material composition: a representative bed material size gradation. Needed to define the percentage of sediment present in the bed material for each grain size class, set the wash load threshold diameter, and select the most appropriate sediment transport function

66 hydrological records: long-term flow gauge records. These define the flow regime and corresponding flow duration curve that represents an average annual hydrologic cycle



66 hydraulic records: reach-averaged hydraulic parameters are needed to run the HEC-RAS hydraulic model. These define the depth, cross-sectional area, mean velocity, hydraulic radius, wetted perimeter, top width, friction slope and roughness for each modelled discharge

66 local sediment sources: are required to define sediment grain size distributions and loadings (tonnes/year) for significant channel and catchment sediment sources. These may include field and ditch erosion, stream bank erosion, and inputs from tributaries and other point sources.

The primary SIAM output is a local, bed material load sediment balance. It is defined as the difference between the annually-averaged supply of bed material sized sediment and the average annual transport capacity for a sediment reach (negative imbalance = excess transport capacity = erosion potential, positive imbalance = excess supply = deposition potential). Other outputs include average annual transport capacities, bed material and wash load supplies, and local sediment supply totals for each reach. All outputs are listed as a total for each sediment reach as well as by grain size class.

SIAM assesses the average annual sediment balance under the defined hydrologic, hydraulic and sediment supply conditions. However, it is not a morphological model, so the channel geometry is not updated based on the predicted net erosion or deposition. SIAM produces reach-averaged outputs and so it cannot supply information on the local distribution of erosion and deposition and the types of morphological adjustments (eg aggradation/degradation, and narrowing or widening) because of any sediment imbalance. Interpretation of the morphological implications brought about by predicted sediment imbalances requires the input of a qualified fluvial geomorphologist.

Version 4.1 of HEC-RAS, which incorporates SIAM, is now available to download (USACE, 2010).

Input data and boundary conditions

A sediment model was constructed of the lower River Tone. The model encompassed the river from below its confluence with the Halse Water tributary, through Taunton and downstream to the tidal limit at Newbridge, a river distance of just over 14 km (Figure 3.15).

Eight sediment reaches were defined:

66 T1: most upstream reach, with a natural river profile and active morphology

66 T2: upstream of the first of the major in-channel structures (French Weir) and affected by its backwater

66 T3: a constrained, engineered channel within Taunton between French Weir and the second major in-channel structure, Firepool Weir

66 T4: a highly engineered channel assumed to have a non-erodible bed, downstream of the town centre and between Firepool and Bathpool Weirs

66 T5: incised reach with a more active morphology located between Bathpool Weir and the M5 road bridge

66 T6: reach extending downstream of the M5 bridge to the third major in-channel structure (Ham Weir)

66 T7 and T8: low gradient, perched and embanked reaches extending from Ham Weir downstream to tidal limit at New Bridge.

SIAM used the following input data:

66 channel geometry: an existing Environment Agency ISIS model was used to develop a HEC-RAS (version 4.1) hydraulic model of the study reach. Included within the model were 96 cross-sections with an average spacing of ~150 m. The model was simplified by removing interpolated cross-sections and it included significant in-channel structures, such as weirs and road bridges

66 bed material: a representative bed material particle size distribution was defined for each sediment reach based on multiple, integrated bucket and sediment grab samples, pebble counts and sediment coring

66 washload threshold: the wash load threshold value for each sediment reach was based on the D10 of bed material samples taken from the River Tone. Values ranging between 0.004 and 1 mm

CIRIA C719 71

Figure 3 .15 The River Parrett catchment, with the upper and lower limits of the sediment model for the lower River Tone identified by green bars

66 hydrological records: flow gauge data from two Environment Agency gauging stations Bishops Hull GS on the River Tone (Station ref no. 520560) and Halse Water GS (Station ref no. 520580), which are both located upstream of the modelled reach, were collated and combined to provide a flow record for the lower River Tone. A continuous, 15-minute flow record from 1 January 1992 to 31 December 2009 was generated

66 flow duration: the gauge record was used to generate an average annual flow duration curve. For calculating the average annual sediment load, flows were divided into 25 bins encompassing a range of discharges from 1.9 m3s-1 to 94.3 m3s-1

66 local sediment sources: three sediment sources were included within the River Tone model upstream wash load input, upstream bed material load input, and channel bank input. The particle size distributions for upstream wash and bed material loads were obtained from the outputs of a separate SIAM model for the Halse Water, which included sediment source inputs from arable land, pasture, road verges and channel banks.

The percentage contribution of each of the four sediment sources in the Halse Water catchment was estimated using sediment fingerprinting (Collins, 2008). This method is underpinned by linking the geo-chemical properties of a given sediment deposition sample and the properties of its potential sources. A range of sediment deposition sources, collected between 2007 and 2010, were used to determine the mean average (and mean range) for each land use as follows:

66 pasture 22 per cent (12 to 29 per cent)

66 arable 37 per cent (5 to 57 per cent)

66 river bank 25 per cent (2 to 61 per cent)

66 road verge 16 per cent (11 to 22 per cent).

Size distributions for River Tone channel bank inputs were generated by sampling exposed bank materials. Wash load yields were estimated based on Environment Agency records of measured discharges and suspended sediment concentrations. The local input of sediment from bank erosion was estimated based on field observations of rates and spatial distributions of bank erosion in the River Tone.



66 sediment transport equation: Ackers-White sediment transport equation was selected for use in the simulations as it is the industry standard in the UK and has been shown to be applicable to rivers like the Tone.

Model run scenarios

SIAM was run for the following scenarios in the lower River Tone:

1 No wash load: this provided the baseline to assess the predicted effects of varying wash load yields on sediment balance and morphology. Bed material load (1000 tonnes/year) and channel bank inputs (600 tonnes/year) were included, based on field monitoring and observation. This scenario used the actual flow record obtained from the EA gauges.

2 Upstream wash load inputs: a range of wash load yields (2500, 5000, 10 000, 15 000 and 20 000 tonnes/year) were modelled to represent and sensitivity test a range of possible sediment outputs from the upper River Tone and Halse Water sub-catchments. Bed material load and channel bank inputs were included as previously explained under “local sediment sources”. These scenario runs also used the actual flow record.

3 Altered hydrology: three wash load yield scenarios (5000, 10 000 and 20 000 tonnes/year) were modelled with the flow record modified (+10 per cent, +20 per cent, -10 per cent and -20 per cent) to represent the effects of future climate change (increased runoff and flow) and land management for flood control (reduced runoff and flow). Bed material load and channel bank inputs were included as previously explained under “local sediment sources”.

4 Altered upstream bed material load inputs: the wash load yield scenario of 10 000 tonnes/year and actual flow was modelled with altered upstream bed material loads (1000, 2000 and 4000 tonnes/year). Channel bank inputs were included as previously explained under “local sediment sources”.

Results and discussion

Assessing the impacts of different wash loads on sediment dynamics and channel morphology

The results of sediment modelling to assess the effects of different wash load yields on local sediment balance (ie the potential for sediment erosion or deposition) within each sediment reach are listed in Table 3.4. Comparing the model outputs against the baseline “no wash load” scenario, wash load supply can be seen to influence the local sediment balance in three sediment reaches: T3, T5 and T7.

Reach T3 is located in the centre of Taunton and shows a deficit of sediment (~1000 tonnes/year) under the no wash load scenario, but reaches parity with a wash load input of 10 000 tonnes/year. The reach then becomes a sediment sink for greater wash load yields. About 1000 tonnes/year is deposited with a wash load yield of 20 000 tonnes/year. This occurs because the coarse sand fraction of the wash load becomes bed material load in this reach, and is deposited.

Table 3 .4 Local sediment balance (tonnes/year) for sediment reaches under different wash load scenarios

ReachesActual flow

Wash off 2.5k wash 5k wash 10k wash 15k wash 20k wash

T1 548 548 548 548 548 548

T2 443 443 443 443 443 443

T3 -1103 -826 -548 5 559 1112

T4 0 0 0 0 0 0

T5 -1728 -1540 -1355 -984 -613 -242

T6 1809 1809 1809 1809 1809 1809

T7 -2808 -1378 51 2912 5771 8630

T8 1531 1531 1531 1531 1531 1531

CIRIA C719 73

Reach T5 is downstream of Taunton and is the first section that is not highly constrained by historic engineering structures. This reach is always a net supplier of sediment with a net sediment yield ranging from ~1,700 tonnes/year under the no wash load scenario decreasing to 200 tonnes/year under a 20 000 tonnes wash load yield scenario. The reduction in erosion is accounted for by the fine to very coarse sand fractions of the higher wash load inputs being deposited in this reach.

Reach T7 is the very low gradient, classic “Somerset Level” river reach, which is wide, deep, embanked and slow flowing. This reach has a net deficit of sediment (~2800 tonnes/year) under the no wash load scenario, which reaches parity with a wash load of 5000 tonnes/year. The reach becomes a sediment sink for greater wash load yields. About 3000 tonnes/year is deposited with a wash load yield of 10 000 tonnes/year, rising to about 8500 tonnes/year with a wash load yield of 20 000 tonnes/year. This is accounted for by very fine to coarse silts being deposited within this reach.

Under the average annual flow conditions only the clay fraction passes through the fluvial system into the downstream, tidal reaches.

SIAM, being a reached-based model, cannot define specific locations of erosion and deposition with each reach or provide information on the extents or types of morphological change expected to result from sediment imbalances. However, the results of the local sediment balance calculations were used to derive indicative rates of channel change based on the channel dimensions input to the hydraulic model. In Reach T3 it was assumed that sand would be deposited primarily on the channel bed. In Reaches T5 and T7 it was assumed that silt would be deposited primarily on the channel banks.

Figure 3.16 indicates that the elevation of the channel bed in Reach T3 is predicted on average to lower by 3 cm/year under the no wash load scenario. This will maintain parity under the 10 000 tonnes/year wash load scenario, and to rise by 3 cm/year under the 20 000 tonnes/year wash load scenario. Field observations indicate that in reality the majority of any deposition that does occur in this reach is concentrated immediately upstream of Firepool Weir and around the road bridges, where the channel has been further over-widened. These rates are probably within the error band for SIAM, but indicate the sensitivity of bed elevation change in Reach T3 to changes in the supply of wash load that might result from changes in land use in the catchment upstream.

In Figure 3.17, Reach T5 is predicted to erode both of its banks by 20 cm/year under the no wash load scenario, a rate that steadily decreases with the introduction of greater wash loads. A 10 000 tonnes/year wash load scenario reduces bank erosion to ~10 cm/year and a 20 000 tonnes/year wash load scenario reduces the predicted bank erosion rate to just 3 cm/year.



Figure 3 .16 Predicted rates of change in river bed elevation for Reach T3 under a range of wash load yield scenarios

Figure 3 .17 Predicted river bank morphological change for Reach T5 under a range of wash load yield scenarios

According to Figure 3.18, the channel banks in Reach T7 are predicted to erode by 6 cm/year under the no wash load scenario, maintain parity under the 5000 tonnes/year wash load scenario, accrete by 7 cm/year under the 10 000 tonnes/year wash load scenario and accrete by ~20 cm/year under the 20 000 tonnes/year wash load scenario.

CIRIA C719 75

Figure 3 .18 Predicted river bank morphological change for Reach T7 for a range of wash load yield scenarios

These results are for the average annual flow conditions, and the silt fraction of the sediment is likely to be carried out of the fluvial system during moderate-high flow events, when the majority of the finer sediment is moving through the system. When deposition does occur (ie when fluvial flow and water levels are tide locked due to tidal influence or operation of the tidal sluice) silt is likely to accumulate on the channel banks until it either reaches a critical angle/thickness and slumps into the thalweg, or is re-mobilised by high discharge fluvial flows. Either way the sediment is likely to be re-entrained and carried downstream into the tidal reaches, limiting the potential for long-term, progressive sedimentation in this part of the River Tone fluvial system.

In the absence of the large amount of measured sediment load data needed for calibration and verification of conventional sediment routing models (which very rarely exist), SIAM provides a relatively simple tool. It offers sensitivity testing and comparing different sediment yields against the limited field data that are available (ie longitudinal and cross-sectional surveys) to establish a likely average annual sediment yield. For this study it appears that an upstream wash load input to the study reach of about 10 000 tonnes/year best matches the evidence supplied by the model with observations made in the field under current land use conditions.

Assessing the effects of changes to the flow regime on sediment dynamics and morphology

SIAM was used in FRMRC research to investigate the sediment effects in the lower River Tone of alterations to the flow regime and supply of fine sediment from the upper River Tone and Halse water catchments upstream, that could result from climatic or land use changes (Tables 3.5 and 3.6). In these tables, the baseline “actual flow” scenario is compared against future scenarios representing both decreases and increases in runoff for three wash load yields (low = 5000, actual = 10 000, high = 20 000 tonnes/year). While sediment effects were predicted throughout the river, discussion here focuses on the three reaches where wash load dynamics are currently significant. Specifically:

66 reach T3: reduced runoff results in increased deposition or reduced erosion, whereas increases in runoff result in reduced deposition or increased erosion

66 reach T5: reduced runoff results in decreased erosion, whereas increases in runoff result in increased erosion

66 reach T7: reduced runoff results in increased deposition, whereas increases in runoff result in reduced deposition or increased erosion.



Table 3 .5 Local sediment balance (tonnes/year) for sediment reaches under scenarios for 10 and 20 per cent increases in runoff due to climate change, coupled with estimated wash loads of 20 000, 10 000 and 5000 tonnes/year

ReachesActual flow Flow plus 10% Flow plus 20%

20k wash 10k wash 5k wash 20k wash 10k wash 5k wash 20k wash 10k wash 5k wash

T1 548 548 548 437 437 437 317 317 317

T2 443 443 443 547 547 547 660 660 660

T3 1112 5 -548 733 -373 -927 287 -819 -1373

T4 0 0 0 0 0 0 0 0 0

T5 -242 -984 -1355 -615 -1357 -1728 -1004 -1746 -2117

T6 1809 1809 1809 2294 2294 2294 2842 2842 2842

T7 8630 2912 51 8055 2337 -524 7421 1703 -1159

T8 1531 1531 1531 1937 1937 1937 2377 2377 2377

Table 3 .6 Local sediment balance (tonnes/year) for sediment reaches under scenarios for 10 and 20 per cent decreases in runoff due to land use change coupled with estimated wash loads of 20 000, 10 000 and 5000 tonnes/year

ReachesActual flow Flow plus 10% Flow plus 20%

20k wash 10k wash 5k wash 20k wash 10k wash 5k wash 20k wash 10k wash 5k wash

T1 548 548 548 648 648 648 735 735 735

T2 443 443 443 349 349 349 265 265 265

T3 1112 5 -548 1430 323 -230 1686 579 26

T4 0 0 0 0 0 0 0 0 0

T5 -242 -984 -1355 102 -640 -1011 429 -313 -684

T6 1809 1809 1809 1393 1393 1393 1032 1032 1032

T7 8630 2912 51 9137 3419 557 9602 3884 1023

T8 1531 1531 1531 1189 1189 1189 889 889 889

Reaches T3 and T7 have been selected to illustrate likely morphological responses in reaches where the current sediment balance is broadly maintained (T3) and changes from sediment balance to net accretion (T7) as the wash load increases from 5000 to 20 000 tonnes/year. The morphological effects predicted for different changes in runoff for selected wash load inputs are illustrated in Figures 3.19 and 3.20 for reaches T3 and T7, respectively.

For low wash load input conditions (5000 tonnes/year), reach T3 acts as a mild sediment source, but the rate of bed degradation increases somewhat as runoff increases due to climate change. However, if future runoff could be reduced by improved land use management, any tendency for degradation could be avoided and dynamic equilibrium maintained.

With the current wash load input of 10 000 tonnes/year, the reach is in sediment balance and the bed elevation is stable. However, it is somewhat sensitive to changes in runoff, becoming a mild sediment source with increased runoff, but a sediment sink under conditions of reduced runoff. This illustrates the importance of taking care to align future flow and sediment regimes and so avoid destabilising reaches that are currently dynamically stable.

CIRIA C719 77

Figure 3 .19 Predicted river bed morphological change for reach T3 for a range of flow regime and wash load yield scenarios

With an excessively high wash load input of 20 000 tonnes/year, reach T3 would be a sediment sink under the current flow regime, and the rate of aggradation increases due to reduced runoff under both land use scenarios. Conversely, increased runoff due to climate change would increase the capacity to move more sediment through the system and bring this reach closer to balance, lowering the rate of deposition to a negligible value.

So, although this reach is responsive to changes in runoff, the maximum predicted rate of aggradation is only 4 cm/year even under the unlikely scenario that the supply of wash load remains excessively high (20 000 tonnes/year), while improved runoff management produces a 20 per cent reduction in discharge. At the other extreme, the maximum rate of degradation is only 4 cm/year when the wash load input is just 5000 tonnes/year and climate change produces a 20 per cent increase in runoff. These results illustrate that morphological responses to future changes in the flow regime and supply of wash load sediment entering reach T3 are likely to be muted.

Under the current flow regime, sediment input and transport capacity are predicted to be balanced in reach T7 if the wash load supply decreases (5000 tonnes/year). The morphological effects of changes to the flow regime are muted, with increased runoff due to climate change indicating mild degradation and reduced discharge due to improved runoff management leading to a tendency for aggradation.

However, with the current flow regime and wash load supply (10 000 tonnes/year), the reach is prone to siltation, and this would be exacerbated by an excessive wash load input (20 000 tonnes/year). Increased runoff due to climate change could offset this tendency for siltation by increasing the capacity of the reach to transfer sediment downstream into the tidal zone, but under a “worst case” scenario where improved runoff management reduces discharges. However, this is not coupled with improved erosion control to reduce the wash load supply, the sediment imbalance could be exacerbated and the sedimentation rate accelerated.



Figure 3 .20 Predicted river bank accretion rates in reach T7 for a range of flow regime and wash load yield scenarios

In summary, alterations to the flow regime may act to either increase or decrease the rate that wash load sediment is deposited at in reach T7, depending on the supply of wash load from the upper River Tone and Halse water catchments upstream. If effective soil conservation measures reduce the supply of wash load (5000 tonnes/year) and runoff increases by 20 per cent, the reach could become a sediment source, exhibiting mild bank erosion at both banks. An excessive supply of wash load (20 000 tonnes/year) coupled with a 20 per cent reduction in runoff due to changes in climate and/or land use could lead to net accretion on both banks. Accreted silt would likely be re-mobilised by high flows or would periodically slump to the bed, where it would be re-entrained by the flow. However, if the silt was not re-mobilised siltation could lead to significant narrowing of the channel over decadal periods.

Assessing the effects of changes to the bed material load on sediment dynamics and morphology

Sediment modelling using SIAM was used to assess the sensitivity of the sediment balance in reach T1 of the lower River Tone to changes in the supply of bed material load from the upper River Tone and the Halsewater. For the baseline current condition, bed material sediment supply was estimated to be 1000 tonnes/year. The potential effects of possible increases or decreases in bed material supply due to changes in climate and land use were investigated under three scenarios, represented by bed material load inputs of 0, 2000 and 4000 tonnes/year (Table 3.7). Reach T1 is located upstream of the heavily engineered reaches within Taunton. It features a semi-natural channel morphology, with actively eroding banks and mobile sand and gravel bars.

The current sediment balance in this reach is slightly positive, indicating a mild tendency for aggradation at a rate of ~3 cm/year, through the net accumulation of about 500 tonnes/year of sand and gravel. This situation is broadly consistent with field observation of the presence of active bars in the channel where it meets the back water curve created by French Weir. The sensitivity of the local sediment balance to a reduction in the supply of bed material load from upstream is illustrated by the fact that reducing that input to zero results in a change from net aggradation at 3 cm/ year to net degradation at about the same, low rate.

Doubling the baseline bed material load accelerates rate of the net rate accumulation to over 1500 tonnes per year, causing net aggradation at a non-negligible rate of 9 cm/year. Doubling the supply of bed material load again (to 4000 tonnes/year) further exacerbates the rates of accumulation and aggradation, to over 3500 tonnes/year and 22 cm/year, respectively.

CIRIA C719 79

Table 3 .7 Local sediment balance (tonnes/year) for sediment reaches under selected bed material load scenarios reflecting stabilisation of the channel upstream (eliminating the supply of bed material load) and the effects of land use and/or climate change in elevating future bed material loads

ReachesActual flow

0 bed 1k bed 2k bed 4k bed

T1 -455 548 1551 3556

T2 443 443 443 443

T3 5 5 5 5

T4 0 0 0 0

T5 -984 -984 -984 -984

T6 1809 1809 1809 1809

T7 2912 2912 2912 2912

T8 1531 1531 1531 1531

In summary, Reach T1 is primarily a sediment sink due to the effect of French Weir in trapping sediment due to its effect in backwatering flow approaching from upstream. Only in the unlikely event that the supply of bed material load is practically eliminated does the reach switch to a condition of net degradation and even then the rate of bed lowering (~3 cm/year), which is an increase in average hydraulic depth by 1.5 per cent, is predicted to be low (Figures 3.21 and 3.22). In the worst case scenario where climatic and/or land use changes destabilise the drainage network upstream to elevate the supply of relatively coarse, bed material load to 4000 tonnes/year, the rate of net deposition is predicted to exceed 20 cm/year (a reduction in average hydraulic depth by 12.5 per cent). This would pose issues for morphological stability in reach T1 as well as altering the operation of flood storage areas and potentially increasing flood risks upstream of Taunton.

Figure 3 .21 Predicted rates of bed elevation change for reach T1 under a range of bed material load scenarios



Figure 3 .22 Predicted percentage change in average hydraulic depth for reach T1 under a range of bed material load scenarios

These applications indicate that SIAM can provide relatively quick assessments of the sediment status as well as the sensitivity to destabilisation of sediment reaches to changes in the yields of wash and bed material sediment loads received from upstream. Observations made in the field suggest that reaches T1 and T2 are in balance and dynamically stable, and this supports the earlier finding that the average annual bed material load is about 1000 tonnes/year. Similarly, according to SIAM an average annual yield of 10 000 tonnes/year of wash load results in dynamic equilibrium in reaches T1 and T2. The ratio of predicted wash to bed material loads of 10:1 is consistent with the widely held view that total sediment load comprises about 90 per cent wash load and 10 per cent bed material load.

Assessing the implications of sediment for flood risk in the lower River Tone

Changes to channel dimensions, geometry and roughness caused by sediment imbalances have implications for flood elevation, inundation extent and the risks associated with events of specified return periods (Thorne et al, 2010b).

The potential effects of flood risk and morphological changes in the lower River Tone were assessed using an existing, ISIS 1D hydraulic model. The results for reach T7 are presented here as an example of the findings. Three scenarios were modelled:

1 Baseline scenario: current flow regime with 10 000 tonnes/year of wash load supplied from the upper River Tone and Halse Water catchments upstream.

2 Increase in wash load supply and/or reduction in runoff.

3 Worst case scenario representing the accumulation of a blanket of 0.5 m of silt over several years, and with no sediment management.

The flood model was run for a range of flood events with return periods of 2, 5, 10, 25, 50, and 100 years.

The model results indicate that if siltation in Reach T7 were evenly distributed throughout the reach, this would increase flood elevations at the most upstream section of this reach by amounts decreasing from 14 mm for the two-year flood to 11 mm for the 100-year flood. In all other reaches and for all flood events increases in flood elevation are less than 10 mm, which is within the error margin of the model and should be discounted.

Siltation can affect the frequency and duration of flooding as well as the peak water surface elevation. However, the downstream reaches of the River Tone feature tidal influence, control structures and large

CIRIA C719 81

artificial flood storage areas that dissipate the effects of changes in channel roughness and morphology. So evaluation of the main flood storage area at Currymoor showed no measurable changes to flood durations or frequencies under any of the modelled scenarios.

These results are as expected given that the width of channel of the lower River Tone in reach T7 is about 30 m and distributing the deposition resulting from a net annual sediment imbalance evenly along the ~4 km long reach produces less than a two per cent reduction in cross-sectional area even for the worst case scenario of net accumulation approaching 10 000 tonnes/year for several years. The small reduction in cross-sectional area, coupled with the fact that the water surface profile is also partially controlled by tidal influence and downstream control structures in this regulated system, limits the effect of siltation on flood risk, especially within the floodplain storage areas.

It is probable that in practice siltation would not be distributed evenly throughout the sediment reach, instead being concentrated where local hydraulic conditions dictate (ie around or upstream of flow control structures), a phenomenon that is not represented in SIAM. While current siltation rates are low and problems at structures in the lower River Tone are manageable, this does not rule out more troublesome sedimentation at structures under future climate and land use changes scenarios.

Conclusion

When reviewing the applicability of existing sediment models, for example ISIS-sediment, Thorne et al (2011) concluded that given the uncertainties surrounding sediment modelling that the use of simple, fast running sediment models within a stochastic or probabilistic framework may at present be the best way to handle uncertainty when predicting future sediment dynamics. SIAM has proved to be a tool that can usefully investigate sediment dynamics in the lower River Tone, which is a not untypical, lowland river system. It has been shown to be capable of answering ‘what if?’ questions by exploring catchment sediment yields under scenarios of climate and land use changes. Such changes could affect both the flow regime and the supply of sediment from the sub-catchments upstream. In this context, the main strengths of SIAM are the ease with which the modeller can alter the input hydrology and adjust the sediment inputs from a variety of designated sources, and its very short run time. SIAM computes outputs for a given model run in a few seconds, which is compared to two to three days for a single ISIS-Sediment run. The use of SIAM is demonstrated when it is considered that during the overall research about 250 SIAM model runs were generated. This is the equivalent of 500 to 800 computing days if using ISIS-Sediment.

Also, the outputs of SIAM are generated differently. A model such as ISIS-Sediment will generate hundreds of gigabytes of data for a handful of model runs compared to SIAM, which generates a handful of gigabytes of data for hundreds of SIAM model runs. This makes it easy to access, review, manipulate and share SIAM data.

These attributes allow simulation of multiple future scenarios, and support the sensitivity testing necessary to establish and understand the uncertainties inherent to sediment modelling and prediction. This is especially useful in the context of assessing the implications of current and possible future sediment yields, in situations where the availability of sediment data is limited, which is the case for most UK rivers. SIAM’s value stems from its ability to supply catchment managers with the information necessary to link sediment sources to sinks at the catchment scale and so make informed decisions concerning strategic land use and sediment management.

Research on the lower River Tone has demonstrated that a complex and detailed sediment routing model, such as ISIS-Sediment, does not appear to provide clear benefits over a more simple sediment continuity model such as SIAM. Indeed, the perception that a more complex model will equate to a greater confidence of outputs, which paradoxically may be the reverse unless large amounts of calibration and verification data are available, which is rarely the case, may be a weakness of using a complex sediment model. Also, the use of a complex model may lead to a reduced reliance on the input of an experience geomorphologist. This could result in a lack of interpretation and checking of model outputs.



SIAM has been shown to be a useful tool for investigation sediment dynamics at the catchment scale or through a large river network. However, the overall research indicates that for the majority of studies assessing lowland river sediment dynamics a range of sediment assessment tools should still be used. Studies should collate and review all existing and empirical data including establishing sediment budgets and collecting field-based data, for example by undertaking a fluvial audit. Also, SIAM outputs should be translated into a format that is consistent with other river functions/services, for example river morphology, flood risk, habitats etc, which is readily accessible by river managers.

SIAM demonstrated that under current, baseline, conditions, sedimentation in the lower River Tone is localised both spatially and temporally and, in practice, sediment problems are dealt with on an ad hoc basis. However, scenario modelling suggested that changes in the supplies of wash and bed material loads could alter local sediment balances, with some reaches becoming sources and others sinks depending on the calibre and quantity of sediment input to the lower river, and the nature of changes to the flow regime.

Under a worst case scenario (ie an excessive wash load input of 20 000 tonnes/year, ie double the current yield, plus 4000 tonnes/year of bed material load, ie four times the current input, coupled with a 20 per cent decrease in runoff) sediment deposition was predicted to exceed 3500 tonnes/year in the reach upstream of the first, large weir in Taunton (reach T1) rising to ~10 000 tonnes/year in the low gradient reach above the tidal limit (reach T7).

Rates of morphological change caused by these sediment balances are relatively low and appear unlikely to lead to significant increases in flood risk. Also, high rates of soil loss from the catchment would have negative effects on the fertility and productivity of agricultural land, and the associated sediment accumulation in the river network could adversely affect in-stream habitats, natural capital and the ecosystem services that the river now provides. If sedimentation is focused at or around hydraulic structures, this might require more intense or frequent maintenance actions that would increase flood defence costs and risk further damaging the natural environment.

If the outputs of application of SIAM to the lower River Tone are accepted, this suggests that the current effects of sediment imbalances on flood risk are insufficient to justify the costs related to changing land use solely to reduce sediment yields with the aim of managing down flood risks or channel maintenance costs.

However, the research demonstrates the potential for wash load sediment to influence channel morphology in reaches where it transitions into bed material load. So, elevated wash loads may influence flood risk substantially in more sediment-sensitive catchments including those with streams where sedimentation may lead to blockage of critical flood defence infrastructure such as sluices, culverts and flood defence channels. Also, the types of land use changes instigated to reduce runoff and sediment yields (winter crop cover, buffer strips, riparian corridors etc especially when part of a co-ordinated catchment sediment management plan) produce other benefits that may be even more valuable than those related to flood risk reduction. Benefits are associated with the improvement of multiple ecosystem services including protection of the soil resource and improvements to stream water quality, nutrient and carbon cycling, biodiversity, aesthetics and amenity.

In this context, it appears that the EU Habitats Directive, Water Framework Directive, and the proposed EU Soils Framework Directive (Defra, 2011) will be the primary influences on policy for catchment sediment management. The requirement to manage floods through sediment management, which is embedded within the Flood and Water Management Act 2010, will be a valuable, though secondary, influence.

3.4 REMAINING GAPs IN KNowLEdGEWhile numerical models provide the basis for users to predict how sediment yield and channel morphology may respond to changes in land use and climate, modelling uncertainties remain high and

CIRIA C719 83

it is crucial that this is recognised and acknowledged. For example, in Example 1 (Section 3.3.2) annual sediment yields from the Nant Pen-y-cwm sub-catchment predicted under baseline climate and present-day land use conditions are an order of magnitude higher than those actually measured in the field (Section 3.2.2) and such disparity is not uncommon in sediment modelling more generally. Particularly significant gaps in knowledge exist with respect to the ability to model:

66 sediment entrainment by surface and sub-surface runoff

66 sediment connectivity in surface and sub-surface hydrological systems

66 the effectiveness of tree strips in buffering watercourses from elevated fine sediment yields.

Sediment transport equations are either derived from or calibrated using limited field or laboratory data, and this raises questions about the extent to which their empirical parameters are applicable beyond the original data (Hodge et al, 2007). By way of illustration, comparative assessment of 12 bed load transport equations against both flume and field data by Gomez and Church (1989) found that no single equation performed consistently well and this remains true in 2013. Also, there are major issues with numerical models of fluvial systems related to the way that they represent space and time, data availability (particularly bed sediment data), calibration and validation, and uncertainty relating to both scientific understanding and the available data (Van de Wiel et al, 2011).

For these reasons, it is recommended that users avoid relying on absolute values of predicted sediment fluxes or morphological change but instead focus on relative fluxes and trends of morphological change indicated under different climate and/or land use scenarios. Also, it follows that sediment studies should always make clear the uncertainties inherent to sediment modelling and avoid making deterministic predictions of future sediment yields.

Despite these limitations and uncertainties, numerical sediment modelling remains the only way users can predict the effects of future land use and climate changes at the catchment-scale. While none of the tools in the FRMRC sediment toolbox is exempt from the limitations outlined here, and they can be subject to serious errors if misapplied, when used in conjunction with qualitative assessments and limited data collection they can help flood risk managers and modellers acquire useful insights concerning catchment-scale sediment dynamics. Given that increasing levels of protection are being afforded to hydromorphology and ecology under national legislation stemming from the EU Habitats, Water Framework and Floods Directives, failure to account for sediment and morphology in flood risk management is no longer acceptable (Environment Agency, 2010a). Indeed, in its 2008 update for the Pitt Review (Evans et al, 2008), the Foresight Project on Future Flooding found that:

“a clash between FRM and environmental objectives could lead to a 3-fold increase in flood risk in the 2050s, rising to a 4-fold increase in the 2080s.”

and that:

“under Global Sustainability (one of four Flood Risk Futures examined by Foresight), lower climate change and economic growth combined with greater environmental consciousness

result in River Vegetation and Conveyance, Environmental Regulation, and River Morphology and Sediment Supply topping the table of flood risk drivers in the 2050s.”

(Evans et al, 2008)

The imperative to properly align flood risk and environmental management justifies the further research needed to improve understanding of interactions between catchment sediment dynamics, morphological responses in the river network and changes to natural capital and the ecosystem services the river provides, including natural flood management. This need cannot be met without further development of the predictive tools important for assessing future flood and environmental risks. In a future that will be characterised by unprecedented climate changes and increased pressures on land and water resources, sediment-related flood and environmental risks are far too important to be ignored.



4 stakeholder negotiation of ecosystem services

4.1 PRobLEM dEFINITIoNChanging land use in agricultural landscapes affects not only flooding and sediments but also a range of other ecosystem services, including agricultural production. So the benefits of land use management for flood and sediment management are supplemented by benefits gained in terms of these other services and their values to associated stakeholders. Also, land use management challenges, including recognising and accounting for land use interactions with ecosystem services, are inherently spatial. For example, trees are a keystone landscape element for water because a small change in cover, if it is in the right place, can result in a significant affect on not only flows, but also sediment, plant debris, micro-biota and the dissolved minerals, nutrients, pollutants that determine water quality. Similarly, other land use interventions that mitigate flood risk, such as water storage ponds and wetland areas, are also spatially sensitive.

Informed decision making in the rural environment requires tools that enable participatory decision making at scales appropriate to the ecosystem services in question. Natural management of floods and sediments based on strategic land use changes is unlikely to be successful without the enabling tools that assist stakeholders in deciding what land use changes to make and where in the catchment to make them. This part of the guide outlines the issues associated with developing operational approaches aimed at modifying the delivery of ecosystem services and demonstrates a tool (Polyscape) aimed at helping negotiation between stakeholder groups.

What are ecosystem services?

Ecosystem services are the benefits that humans derive from ecosystems (Costanza et al, 1997, Daily, 1997 and MEA, 2005). Since publication of the Millennium Ecosystem Assessment in 2005, there has been increased policy interest in adopting more comprehensive approaches that incorporate ecosystem services within land management in the UK (Defra, 2007).

The conceptual framework used by the Millennium Ecosystem Assessment divided ecosystem services into four categories (Alcamo et al, 2003):

66 provisioning services are the products obtained from ecosystems, including food, fuel and water

66 regulating services are the benefits derived from regulating ecosystem processes such as climate regulation and natural hazard regulation, including reducing flood and sediment-related risks to people, property, infrastructure and the ecosystem

66 cultural services are non-material benefits that people derive from ecosystems, including recreation and aesthetic values

66 supporting services are those necessary for the production of ecosystem services.

The need for spatially explicit approaches

The benefits generated by ecosystem services are delivered across a spectrum of temporal and spatial scales (Fisher and Turner, 2008). These range from in situ benefits (such as the provision of shelter to livestock) to benefits realised at a global scale (such as mitigation of global warming through increased carbon sequestration). The potential for an intervention (such as a planted woodland) to deliver a change to ecosystem service provision will vary spatially across the landscape. For example, if woodland is

CIRIA C719 85

sited lower down a hill slope it is likely to deliver a different effect both in terms of the type of services delivered and the size of the associated effect than if it was planted higher up the slope. Several extra factors, such as species composition, alignment to slope, and post-planting woodland management practices will also affect the delivery of services. Also, different stakeholders (and rural sectors) may have conflicting visions of desired outputs from “their” landscape, although there may be synergies between these visions (Figure 4.1).

Note

Scenario A: represents typical tree configurations implemented by hill farmers prioritising tree strips (shelter belts) to protect livestock against the prevailing wind and limited enhancement of provisioning services such as firewood and timber. Scenario B: shows a hypothetical arrangement of trees focused on natural flood management where intercepting surface runoff and sediment is the main priority in both the uplands and in riparian zones. Scenario C: shows trees positioned for a biodiversity agenda driven landscape where habitat connectivity and conservation are the primary objectives.

Figure 4 .1 Three contrasting land use configurations reflecting different stakeholders’ objectives in a simplified Welsh farmland

Scale is important when considering the flow of ecosystem services and how humans interact with the ecosystems and the services they provide. The sphere of influence of an ecosystem varies between services. While it is relatively straightforward to define boundaries for ecosystem services confined by typography (eg the surface watersheds with respect to natural runoff regulation) often it is more difficult to define a tangible boundary for other services (such as air quality regulation or pollinating services).

Similarly, there is variation in the scales at which decisions concerning ecosystem services are influenced and eventually made. Two scales that have been widely identified are:

1 Strategic scale: this is the scale at which communities recognise and prioritise ecosystem services, based on inputs from a broad range of stakeholders. Interest here focuses primarily on the whole catchment.

2 Local scale: this is the scale at which ecosystem stewards (potentially interacting with intermediaries) make operational changes to the land use intended to alter the flow of ecosystem services. Interest here is focused primarily on the point of service provision.

There are clear differences between the types of data and modelling required to support strategic and operational scale decision making. Operational tools need to represent the land use at the same scale that the ecosystem stewards understand and manage it. This leads to a requirement for local, high resolution datasets linking hydrologic processes, land use, habitats and biota. Strategic tools rely on broader datasets with generally coarser resolutions to support exploration of trends in ecosystem service provision.



However, linking local land use decision making to downstream flood risk requires operational tools that are capable of working across multiple scales. Before the FRMRC, no such tools had been developed and published, limiting the ability to explore the cumulative effects of multiple, small scale (eg sub-field level) land use changes on the provision of ecosystem services (including natural flood management) at the broader, catchment scale.

Data limitations

In many cases, the current level of understanding of processes associated with ecosystem service delivery is rudimentary (Kremen and Ostfeld, 2005), particularly at landscape and catchment scales. Also, the quality of mapped data varies significantly. Given this, most decision making about modifications to ecosystem services take place in data-poor environments (de Groot et al, 2010). Often, local land stewards have substantial, but largely untapped, knowledge related to local landscape processes (Sinclair and Joshi, 2000). Using this knowledge provides a valuable resource with which both to plug gaps in knowledge and improve participation by increasing ownership of the issues. Local knowledge was identified as a largely untapped resource in environmental management in the UK more than a decade ago (Edwards-Jones, 2001) and this situation still pertains.

4.2 PoLysCAPE – A TooL FoR MuLTI-objECTIvE RuRAL LANd MANAGEMENT PLANNING

This section describes and demonstrates the use of Polyscape, using it as an example of a tool suitable for participatory multi-objective rural land use planning. This GIS tool explores trade-offs and synergies among ecosystem services associated with spatially explicit application of land cover interventions, including flood and sediment management measures, creating impact maps and quantifications of the effect of change on a variety of ecosystem services. Polyscape is supported as a tool for ESRI’s ArcGIS versions 10 and above. Most algorithms run in the entry level licensing ArcGIS product ArcView, while the interactive capabilities require ArcEditor or ArcInfo versions of the software. More detailed, technical descriptions of the Polyscape tool can be found in Jackson et al (2012) and Pagella et al (2012). Polyscape acknowledges that:

1 Decisions about which ecosystem services to represent should be informed by stakeholders. In locations where livelihood can be affected, the livelihood objectives of the landowners should be included, as should the implications of any management change on this livelihood.

2 Determining the relative importance of different ecosystem services is difficult, and perceptions of relative value can differ between stakeholders. Potential effects on all relevant services should be shown individually to avoid undue weighting, and the approach offers various means of valuing combined effects of services.

3 Stakeholders need to see opportunities not only at the operational scale where land use changes are made (ie the local scale), but also at the wider, landscape scale relevant to strategic decision making (ie the scale of the Catchment Flood Management Plan).

4 In most applications, data concerning ecosystem service provision will be limited. So the tool was designed to make use of commonly available (generally, national scale) spatial datasets, supplemented where appropriate by incorporating local knowledge and data.

Incorporation of local knowledge ensures stakeholder engagement in application of the tool and also ownership of its outputs. Collective development of the specification for output also helps participation and knowledge exchange between agencies. So the process of developing output for stakeholder engagement tools should be an iterative and participatory process (Figure 4.2).

CIRIA C719 87

Figure 4 .2 Key steps in the proposed stakeholder engagement methodology are shown in white nodes

Step 1 represents the scoping phase where strategic priorities for ecosystem service provision within a specified landscape are identified. Priorities will vary between landscapes with, for example, runoff and sediment yield reductions being higher priorities in catchments with large populations living in flood prone areas downstream. Also, priorities may be influenced by socio-cultural, economic and policy drivers. Where stakeholders inhabit or derive their living from the study area this should be recognised explicitly in the modelling process because candidate interventions could potentially interfere with their livelihood strategies.

The specifications for mapping can then be generated collectively with both local stakeholders and experts (step 2). This can include identifying features within the landscape that are important for the provision of the selected ecosystem services and, where appropriate, potential flow pathways.

These specifications can then be used to identify data requirements to produce maps (step 3). Given that uncertainties associated with ecosystem function are high and bearing in mind the likelihood that data availability will be limited, the rules used to generate mapped output draw on either quantitative or qualitative data, and allow for incorporation of local knowledge where appropriate.

These rules can then be used to choose suitable existing algorithms (as is or with modifications as appropriate) or to develop new algorithms (step 4).

This results in a series of initial mapped output (step 5).

In the final step (6) results are presented back to stakeholders for validation. Where issues are identified in step 6, iteration is started, with new specifications or data being incorporated and algorithms modified, discarded or substituted as appropriate. Iterations continue until issues are resolved.

Once stakeholder “buy in” has been achieved, the final outputs provide a validated, spatially explicit environment for wider negotiation, which identifies areas of opportunity and tension in land use decision making and ecosystem service provision.

In practice, the first iteration may include limited or no stakeholder engagement in steps 2 and/or 3 in the first iteration. Instead, experts and/or or a limited set of stakeholders may bring existing algorithms or specifications to the table to provide a starting point for discussion. However, steps 2

Identification of key ecosystem

servicesValidation

Maps produced

Development of algorithms

Data gathered to

produce maps of each layer

Specification dfeveloped for

each layer

Final output used for

negotiation1

2 5

6

3 4

Note

Stakeholder interaction should be central to all processes and inform all activity (represented by the gray area), with output only finalised once an appropriate level of consensus is reached



and 3 remain critical in further iterations to allow stakeholder specifications and other feedback to be incorporated as necessary.

Polyscape currently includes algorithms to explore the effects of land use change on:

66 runoff and flood risk

66 sediment yields

66 habitat connectivity

66 carbon sequestration

66 agricultural productivity.

Also, novel algorithms to explore synergies and trade-offs between these ecosystem service effect have been developed and carried out.

Proposed changes to land use can be input at sub-field, farm and sub-catchment scales and, once validated, Polyscape can produce impact maps for catchments with areas of up to around 1000 km2 in seconds to minutes, allowing rapid visualisation of the effects that different options would produce on the selected ecosystem services at the landscape scale.

Polyscape’s interactive capabilities support stakeholder engagement and allow local issues and knowledge to be incorporated into decision making. However, in catchments larger than around 1000 km2, run times may be prolonged, making stakeholder interaction and engagement more difficult.

Maps in Polyscape use a five colour “traffic light” system. For the individual service layers areas of the map in red indicate high existing value for the ecosystem service in question. Areas in maroon have some existing value and orange indicates neutral or marginal value. Green indicates high opportunity for change – with lighter green indicating the highest level of opportunity. For trade-off layers, green areas indicate synergies in opportunities to improve services, red areas indicate synergies in current ecosystem provision, orange indicates trade-offs or negligible synergies in either opportunities or current provision. Potentially, there are an almost infinite number of options for numerical evaluation of trade-offs. Four are included in the current version of Polyscape (see Jackson et al, 2012). These have some effect on the parts of the landscape that are assigned as ‘green’ or ‘red/maroon’. The output demonstrated in this guide uses the ‘additive’ option. With this option, light green indicates opportunities to improve all services under consideration, dark green indicates opportunity to improve some services with no degradation of any, red indicates existing provision by the landscape to all services, and maroon indicates existing provision from some services and no opportunity to significantly improve others.

Example applications at Pontbren and the Elwy valley

The types of output that Polyscape can provide are illustrated here with examples from the:

1 Pontbren catchment in mid-Wales.

2 Elwy catchment in North Wales.

The Pontbren catchment is relatively small (~12 km2), upland catchment featuring a mixture of improved pasture and semi-natural moorland (see Section 2.2). The hydrologic and sediment studies reported in Chapters 2 and 3 establish that flood peak discharges and sediment yields at Pontbren have both been amplified by post-WWII intensification and drainage but that woodland planting has the potential to reduce runoff and sediment loads. Application of Polyscape at Pontbren focused initially on three ecosystem services:

66 farm productivity

66 tree habitat connectivity

66 natural flood management.

CIRIA C719 89

With a drainage area of ~270 km2, the Elwy catchment is much larger than that at Pontbren and provided the opportunity to investigate the use of Polyscape at a broader scale. The Elwy is a tributary of the River Clwyd. The main flood generating areas lie in the west of the catchment, an area that also has a history of high sediment delivery. For these reasons the catchment was selected as a test catchment for Tir Cynnal, the current entry level agri-environment scheme in Wales (which will be replaced by Glastir in 2013). The main farming activity is livestock production, mainly sheep or mixed herds, with a small amount of dairy production towards the eastern edge of the catchment.

Pontbren and most of the Elwy lie within less favoured areas (LFA) and, as such, the farmers are eligible for Tir Mynydd, an area-based payment for eligible forage land.

The aim of Polyscape mapping at Pontbren was to identify land that was of low value agriculturally, but could be used for increasing habitat connectivity, reducing flood risk or improving both ecosystem services simultaneously. Farm productivity, tree habitat and productivity/habitat trade-off layers for Pontbren are shown in Figures 4.3a to c respectively.

The farm productivity layer (Figure 4.3a) represents the existing livelihood priorities within the landscape. An initial set of rules for valuing land was developed, taking into consideration information provided by the farmers. The specification was to identify land that was of low value to agriculture, which could then be used for either increasing habitat connectivity or reducing flood risk (or both). At Pontbren the farmers identified two main factors affecting the productivity of their land: soil drainage and slope. Two slope thresholds were identified in consultation with agricultural machine manufacturers with land below 5o generally considered to be of particularly high value, and land above 15o of marginal value for production services (as this is the slope that start to cause problems for tractors). The farmers identified categories for drainage:

66 non-waterlogged land was classed as being of high value

66 semi-waterlogged land was of intermediate value

66 heavily waterlogged land was of marginal value.

Also, they noted soil fertility as a factor, but as this varied little over Pontbren it was not taken into account in the initial set of rules. Farmers also identified several other factors that could affect where trees were planted. These were idiosyncratic and so were excluded from the generic rule set, although they could be incorporated into the models for individual farms using Polyscape’s editing tools.

Feedback from the initial application suggested the designed algorithms worked well in the Pontbren context. However further feedback was provided by farmers within the Elwy catchment where fertility was more variable across the catchment and consideration of aspect appeared to be more critical. Those new specifications were incorporated into Polyscape, so that land value is categorised according to its slope angle, degree of water logging and fertility. An option exists to downweight the extent of waterlogging (where soils are prone to this) where aspect provides benefit, similarly to slightly upweight fertility where aspect favours improved growth.

Figure 4.3b demonstrates tree habitat connectivity at Pontbren. The algorithms follow the calculation procedures and parameterisations of Forest Research’s woodland habitat connectivity tool (Watts et al, 2008). The output was based on nationally available land use data (in this case the Countryside Council for Wales Phase 1 dataset, Blackstock et al, 2005). The output shows that opportunities for increasing habitat connectivity (red areas) are generally higher to the west of the catchment.

These two layers (Figures 4.3a and b) when traded off against each other (Figure 4.3c) identify areas in green where the woodland can be expanded with little effect on farm productivity. However, much of the catchment is either high value for agriculture or low value in terms of its connectivity.



Note

Farm production impact layer (a), tree habitat connectivity layer. Maps use a five colour traffic light system. For the individual service layers, areas of the map in red indicate high existing value for the ecosystem service is question. Areas in maroon have some existing value and orange indicates neutral or marginal value. Green indicates high opportunity for change with lighter green indicating the highest level of opportunity, and the trade-off between the agricultural and habitat layers, where green areas suggest locations where expanding habitat will have a minimal effect on farm productivity (c).

Figure 4 .3 Polyscape output for the Pontbren catchment

The natural f lood management ecosystem service layer used a novel algorithm that identified functional units according to their hydraulic properties and spatially explicit topographical routing. The National Soil Resources Institute (NSRI), Soilscapes and Countryside Council for Wales’ Phase 1 datasets were used to estimate water storage and permeability based on soil type and land use. Topographic routing was then analysed, with the algorithm removing any flow that accumulates on permeable, high water holding capacity areas. These areas were considered to be of low priority for interventions because they already provide a natural f lood mitigating effect. Conversely, areas where a large amount of f lood flow concentrates were treated as opportunity areas for change (see Figure 4.4a). Figure 4.4b provides an example of how the f lood impact map changes as modifications in land management are made, with further tree and pond cover added to the land use input information (based on recent changes to land use in the Pontbren catchment after the land use survey data used for this application was collected). The recent changes have increased existing flood mitigation provision, and reduced opportunities for further mitigation.

Figure 4.4c shows a trade-off map between the farm impact map (Figure 4.3a) and the flood impact map. This identifies where the tree planting will have the greatest effect at lowest cost to the farming system. Note that Figure 4.4c shows that the opportunities lie in the middle of the catchment in comparison with Figure 4.3c. By bringing in several layers it is possible to identify areas where interventions provide multiple benefits.

CIRIA C719 91

Note

Initial output (a), output using modified land use data. Initial output identified grassland contributing area > 500 m2 as high priority (shown in light green). Areas with moderate grassland contributing area (100 – 500 m2) are shown as dark green, areas with negligible grassland contributing area (<100 m2) are shown as orange, and areas already with trees or other flow sinks shown as red. A trade-off map between farm production and flood risk (c). A trade-off for farm production, habitat connectivity and flood risk (d).

Figure 4 .4 Polyscape flood impact mapping for the Pontbren catchment

Figure 4.4c shows a trade-off map between the agricultural productivity map (Figure 4.3a) and the natural flood management map (Figure 4.4a). This identifies where the planting trees would have the greatest beneficial impact on natural flood management at lowest cost to farm productivity. Figure 4.4c indicates that the best opportunities lie in the centre of the catchment, in contrast to those for expanding woodland without reducing farm productivity, which lie in the west of the catchment (see Figure 4.3c).

Identifying areas where land use change can provide multiple benefits requires several ecosystem service layers brought together. So, Polyscape includes algorithms to trade-off individual layers against each other in several ways, including:

66 an additive option, which treats all services equally (see Figure 4.4d for an example of this)

66 a weighted additive option, which allows the addition of user-defined weightings for individual services

66 a conservative option, which identifies only areas where synergies exist. Fewer opportunities are mapped, but they are all synergistic

66 a Boolean option, which enables users to select a combination of additive and conservative options for each service (see Jackson et al, 2012).

Validation exercises were carried out with the Pontbren farmers (examples in Figure 4.5). The results indicate that local stakeholders (farmers, environmental managers and policy makers) had successfully used Polyscape to explore options for land use change and that they understood and could interpret the implications of the service provision and trade-off maps. Incorporation of interactive landowner preferences (through parameter, data and condition editing capabilities) was found to be important to ground-truthing land cover data, capturing local knowledge and involving stakeholders. The capability to add parameters that could be discussed and, where appropriate, changed was vital to fostering co-learning among scientists, policy makers, environmental officers and farmers.



Figure 4 .5 Farm impact layer for the Pontbren catchment overlaid with 2006 aerial photography. Subset comments are derived from ground truthing with farmers

The farmers in the Elwy catchment also reacted positively to the mapped outputs produced using Polyscape. However, in the Elwy application, there were greater issues with the input data, and particularly with the Countryside Council for Wales Phase 1 land use dataset that was used to produce the land use maps, which was generated in the mid-1980s and known by local stakeholders to be considerably out of date. Farmers were quickly able to correct inaccuracies and update the initial land use map (Figure 4.5). The land use dataset also excluded hedgerows and other small scale features. The habitat connectivity maps generated adverse feedback as the methodology used generic rather than real species (see Watts et al, 2008) and farmers were unhappy with this.

Agricultural production and natural flood management layers for the Elwy catchment are shown in Figures 4.6a and b. As mentioned previously, implementation in the Elwy first used the initial rule set developed for Pontbren. Two sub-catchments were selected for initial testing of the outputs:

66 an upland sub-catchment very similar to Pontbren (the Gallen)

66 a more fertile lowland sub-catchment (the Meirchion).

The wide range of fertilities encountered quickly led to changes to the initial rule set to explicitly account for fertility and to respect local and regional variations in this as well as the broad national classifications. Interviews with farmers revealed that the aspect of fields was an important extra factor within the agricultural layer. Fields with south facings aspects were more highly valued then shaded fields with northerly aspects as they received more light and the grass growth was considerably better (and the fields tended to be drier). This led to the options to adjust fertility and drainage categorisations by aspect, where appropriate, as described earlier.

CIRIA C719 93

Note

Farm production layer (a) This layer included new algorithms to place a higher value on land with southerly aspects. Natural flood management layer (b). Green areas identify opportunities for land use change to produce improvements along the steeper river valleys.

Figure 4 .6 Polyscape single layer outputs for the Elwy catchment with the Meirchion (a) and the Gallen (b) sub-catchments highlighted

Exploring the underlying land use in the catchments revealed another significant difference. In areas identified as marginal in terms of productivity within the Meirchion sub-catchment, land use featured extensive areas of tree cover, whereas in the Gallen sub-catchment significantly more of the marginal land was being used for agricultural production and there was less tree cover.

As in Pontbren, validation interviews were conducted with local farmers and other stakeholders working in the Meirchion and Gallen sub-catchments to check the plausibility of the output. While the farmers reacted positively to the mapped output they recognised greater inaccuracies than at Pontbren with the underlying data, particularly associated with the land use datasets. The NSRI Soilscapes data used in UK maps were at a 1 km2 resolution and did not accurately capture all the local variation in the soil at farm scales. The land cover data had better resolution (10 m2) but also contained many inaccuracies at farm and field scales largely due to the age of the data collection (see Cherrill and McClean, 2001). Farmers were able to ground proof these elements quickly, both for their own farms and within the wider catchment, enabling the maps to be updated. As in Pontbren the farmers identified missing features that they recognised as important, particularly the lack of hedgerow data and other small woodland features (see Figure 4.7). The farmers associated these features with having a strong influence on water and habitat. For example the farmers drove their vehicles along hedgerows rather than across the middle of fields as they know the soil is drier there. There was a clear recognition of the drying function of hedgerows.

Given that issues with resolution and quality of datasets arose throughout the process it was important that stakeholders understood that the output was a starting point for negotiation rather than a finished product, which differs from how most people intuitively understand maps. In presentations of this data many local stakeholders were surprised at the poor quality of the underlying datasets.



Note

The two figures shows the effect of incorporating local knowledge within the flow accumulation maps before (a) and after (b) discussions with the farmer. The maps are standard Polyscape output as shown in Figure 4.6 but with orange (neutral) areas removed for clarity. These layers are overlaid over aerial photography (from 2004). The farmer identified the conifer block at point A as being low value for intervention as the land under it was very steep with no understory and evidence of substantial soil erosion. The wetland in area B had been recently drained and converted into improved grassland. The effect of modifying the land use data to reflect this is illustrated in the second figure.

Figure 4 .7 Ground truthing land use data in the Elwy catchment

Conclusions

Decisions concerning land use change for improved flood and sediment management need to be made within the broader perspective provided by consideration of multiple ecosystem services provided by the landscape. Tools are needed to enable prioritisation of land use management at a range of scales appropriate to a broad range of ecosystem services – both to recognise critical conflicts of interest and to identify potential synergies.

Polyscape was developed to address these issues and to assist with spatially explicit negotiation of ecosystem service provision in agricultural landscapes at the scales appropriate to managing the effect of land use change on ecosystem services. It combines the use of readily available data on topography, soils and land cover with spatially explicit algorithms for calculating selected ecosystem service effects. Polyscape helps local engagement and ownership of its outputs through its capabilities for participatory validation and incorporation of local knowledge identifying where farmers are willing (and unwilling) to contemplate land use changes.

Polyscape was initially developed for consideration of land use changes to improve natural flood management and woodland habitat networks without adversely affecting farm productivity in conjunction with a group of farmers at Pontbren in mid-Wales. However, now it has been applied in contrasting landscapes to other parts of the UK, Africa and New Zealand and is being expanded to include sediment transfer, carbon storage and water quality (Jackson et al, 2012, and Pagella et al, 2012).

CIRIA C719 95

References

ABBOTT, M B, BATHURST, J C, CUNGE, J A, O’CONNELL, P E and RASMUSSEN, J (1986a) “An introduction to the European Hydrological System – Systeme Hydrologique Europeen. ‘SHE’, 1: History and philosophy of a physically based, distributed modelling system” Journal of Hydrology, vol 87, 1, Elsevier Science BV, UK, pp 45–59

ABBOTT, M B, BATHURST, J C, CUNGE, J A, O’CONNELL, P E and RASMUSSEN, J (1986b) “An introduction to the European Hydrological System – Systeme Hydrologique Europeen. ‘SHE’, 2: Structure of a physically based, distributed modelling system” Journal of Hydrology, vol 87, 1, Elsevier Science BV, UK, pp 61–77

AKAN, A O and YEN, B C (1981) “Diffusion-wave flood routing in channel networks” Journal of the Hydraulics Division, ASCE, vol 107, HY6, pp 719–732

ALCAMO, J, ASH, N J and BUTLER, C D (2003) Ecosystems and human well-being: a framework for assessment, first edition, Island Press, Washington DC (ISBN: 978-1-55963-403-8)

ARCHER, D and NEWSON, M (2002) “The use of indices of flow variability in assessing the hydrological and instream habitat impacts of upland afforestation and drainage” Journal of Hydrology, vol, 268, 1–4, Elsevier Science BV, UK, pp 244–258

ARCHER, D (2007) “The use of flow variability analysis to assess the impact of land use change on the paired Plynlimon catchments, mid-Wales” Journal of Hydrology, vol 347, 3–4, Elsevier Science BV, UK, pp 487–496

ARMSTRONG, A C and HARRIS, G L (1996) “Movement of water and solutes from agricultural land: the effects of artificial drainage”. In: M G Anderson and S M Brookes (eds) Advances in hillslope processes, Wiley, Chichester, UK, pp 187–211

ARMSTRONG, A, HOLDEN, J, KAY, P, FOULGER, M, GLEDHILL, S, MCDONALD, A T and WALKER, A (2009) “Drain-blocking techniques on blanket peat: A framework for best practice” Journal of Environmental Management, vol 90, 11, Elsevier Science BV, UK, pp 3512–3519

AUERSWALD, K (1998) “Effects of grass ley set-aside on runoff, erosion and organic matter levels in sandy soils in east Shropshire, UK” Soil and Tillage Research, vol 46, Elsevier BV, UK, pp 41–49

BALKHAM, M, FOSBEARY, C, KITCHEN, A and RICKARD, C (2010) Culvert design and operation guide, C689, CIRIA, London (ISBN: 978-0-86017-689-3). Go to: www.ciria.org

BALLARD, C E (2011) The role of physics-based models for simulating flood flow responses to rural land management scenarios, PhD Thesis, Department of Civil and Environmental Engineering, Imperial College London, UK

BALLARD, C E, MCINTYRE, N, WHEATER, H S, HOLDEN, J and WALLAGE, Z E (2011) “Hydrological modelling of drained blanket peatland” Journal of Hydrology, vol 407, Elsevier BV, UK, pp 81–93

BALLARD, C E, MCINTYRE, N and WHEATER, H S (2012) “Modelling the effects of peatland management on peak flows” Hydrology and Earth System Sciences, vol 16, Copernicus Publications on behalf of the European Geosciences Union, pp 2299–2310

BARTON, R R (1998) “Simulation meta-models”. In: Proc 30th conference on winter simulation, WSC ’98, 13–16 December, Washington DC, IEEE Computer Society Press, Los Alamitos, CA (ISBN: 0-7803-5134-7) pp 167–176

BATES, P D, HORRITT, M S and FEWTRELL, T J (2010) “A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling” Journal of Hydrology, vol 387, Elsevier Science BV, UK, pp33–45



BATHURST, J C, EWEN, J, PARKIN, G, O’CONNELL, P E and COOPER, J D (2004) “Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses” Journal Hydrology, vol 287, 1–4, Elsevier BV, UK, pp 74–94

BELL, F G, CULSHAW, M G, FORSTER, A and NATHANAIL, C P (2009) “The engineering geology of the Nottingham area, UK”. In: M G Culshaw, H J Reeves, I Jefferson, T W Spink (eds) Engineering geology for tomorrow’s cities (Geological Society Engineering Geology Special Publication), The Geological Society, UK (ISBN: 978-1-86239-290-8), pp 1–24

BEVEN, K J (1989) “Changing ideas in hydrology – the case of physically-based models” Journal of Hydrology, vol 105, 1–2, Elsevier Science BV, UK, pp 157–172

BEVEN, K J (1997) “TOPMODEL: a critique” Hydrological Processes, vol 11, 9, John Wiley & Sons Ltd, UK, pp 1069–1086

BEVEN, K J (2012) Rainfall-runoff modelling: the primer, second edition, Wiley-Blackwell, Chichester (ISBN: 978-0-47086-671-9)

BEVEN, K J and FREER, J (2001) “Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems” Journal of Hydrology, vol 249, 1–4, Elsevier Science BV, UK pp 11–29

BEVEN, K J and KIRKBY, M J (1979) “A physically based, variable contributing area model of basin hydrology” Hydrological Sciences-Bulletin-des Sciences Hydrologiques, vol 24, 1, Blackwell Scientific Publications, pp 43–69

BEVEN, K J, YOUNG, P C, ROMANOWICZ, R, O’CONNELL, E, EWEN, J, O’DONNELL, G, HOMAN, I, POSTHUMUS, H, MORRIS, J, HOLLIS, J, ROSE, S, LAMB, R and ARCHER, D (2008) Analysis of historical data sets to look for impacts of land use and management change on flood generation, FD2120, Joint Defra/EA Flood and Coastal Erosion Risk Management R&D programme, Department for Food, Environment and Rural Affairs, London

BIEDENHARN, D S, GIBSON, S A, LITTLE JR, C D, MOONEY, D M, THORNE, C R, WALLERSTEIN, N P and WATSON, C C (2006a) “Sediment Impact Assessment Model (SIAM)” In: N P Wallerstein, et al (eds) Accounting for sediment in rivers – a tool box of sediment transport and transfer analysis methods and models to support hydromorphologically-sustainable flood risk management in the UK, FRMRC Research Report UR9, University of Nottingham

BIEDENHARN, D S, HUBBARD, L C, THORNE, C R and WATSON, CC (2006b) Understanding sediment sources, pathways and sinks in regional sediment management: Application of wash load and bed-material load concept, SWWRP Technical Notes Collection, ERDC TN-SWWRP-06-4, US Army Engineer Research and Development Centre, Vicksburg, MS, USA

BIEDENHARN, D S, HUBBARD, L C, THORNE, C R and WATSON, C C (2006c) Understanding sediment sources, pathways and sinks in regional sediment management: Wash load and bed-material load concept, SWWRP Technical Notes Collection, ERDC TN-SWWRP-06-3. US Army Engineer Research and Development Centre, Vicksburg, MS, USA

BIELDERS, C L, RAMELOT, C and PERSOONS, E (2003) “Farm perception of runoff and erosion and extent of flooding in the silt-loam belt of the Belgian Walloon Region” Socio-economic Factors in Soil Erosion and Conservation, vol 6, 1, Elsevier Science BV, UK, pp 85–93

BILOTTA, G S, BRAZIER, R E, HAYGARTH, P M, MACLEOD, C J A, BUTLER, P, GRANGER S, KRUEGER T, FREER J and QUINTON J N (2008) “Rethinking the contribution of drained and undrained grasslands to sediment-related water quality problems” Journal of Environmental Quality, vol 37, 2, ASA, CSSA, and SSSA, USA, pp 906–914

CIRIA C719 97

BINGNER, R L, THEURER, F D, GORDON, L M, BENNETT, S J, PARKER, C, THORNE, C and ALONSO, C V (2007) “AnnAGNPS Ephemeral Gully Erosion Simulation Technology”. In: Proc IV International Symposium on Gully Erosion, 17–19 September 2007, Pamplona, Spain, J Casali and R Gimenez (eds), Public University of Navarre, Spain, pp 20–21

BIRNIE, R V and HULME, P D (1990) “Overgrazing of peatland vegetation in Shetland” Scottish Geographical Magazine, vol 106, 1, Taylor & Francis, UK, pp28–36

BLACKSTOCK, T, HOWE, E, and STEVENS, J (2010) Habitats of Wales: a comprehensive field survey, 1979–1997, University of Wales Press, Wales (ISBN: 978-0-70832-257-4)

BOARDMAN, J (1990) “Soil erosion on the South Downs: A review”. In: J Boardman, I D L Foster and J A Dearing (eds) Soil erosion on agricultural land, John Wiley & Sons Ltd, Chichester (ISBN: 978-047192-602-3)

BOARDMAN, J, POESEN, J and EVANS, R (2003) “Socio-economic factors in soil erosion and conservation” Environmental Science and Policy, vol 6, 1–6, Elsevier Science BV, UK pp 1–6

BOORMAN, D, HOLLIS, J and LILLY, A (1995) Hydrology of soil types: A hydrologically-based classification of the soils of the United Kingdom, IH Report 126, Institute of Hydrology, Wallingford, UK

BOOTH, D B (1990) “Stream-channel incision following drainage-basin urbanization” JAWRA Journal of the American Water Resources Association, vol 26, 3, pp 407–417

BRANSON, J, HILL C, HORNBY, D D, NEWSON, M and SEAR, D A (2005) A refined Geomorphological and floodpodplain component River Habitat Survey (GeoRHS), R&D Technical Report SC020024/TR, Environment Agency, Bristol

BRAZIER, R E, BILOTTA, G S and HAYGARTH, P M (2007) “A perspective on the role of lowland, agricultural grasslands in contributing to erosion and water quality problems in the UK” Earth Surface Processes and Landforms, vol 32, 6, Wiley & Sons, UK pp 964–967

BROWN, L, HOLDEN, J and PALMER, S (2009) EMBER: Effects of moorland burning on the ecohydrology of River systems, University of Leeds, UK. Go to: www.geog.leeds.ac.uk/research/projects/ember.html

BRUNSDEN, D and THORNES, J B (1979) “Landscape sensitivity and change” Transactions of the Institute of British Geographers, NS 4, Blackwell Publishing, UK, pp 463–484

BULYGINA, N, MCINTYRE, N and WHEATER, H (2009) “Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis” Hydrology Earth System Sciences, vol 13, Copernicus Publications on behalf of the European Geosciences Union, pp 893–904

BULYGINA, N, MCINTYRE, N and WHEATER, H (2011) “Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes” Water Resources Research, vol 47, W02503, American Geophysical Union, US

BULYGINA, N, MCINTYRE, N and WHEATER, H (in press) “A comparison of rainfall-runoff modelling approaches for estimating impacts of rural land management on flood flows” Hydrology Research, IWA, UK

BURT, T P, DONOHOE, M A and VANN, A R (1983) “The effect of forestry drainage operations on upland sediment yields – the results of a storm-based study” Earth Surface Processes and Landforms, vol 8, 4, Wiley & Sons, UK, pp 339–346

CALVER, A, CROOKS, S, JONES, D, KAY, A, KJELDSEN, T and REYNARD, N (2005) National river catchment flood frequency method using continuous simulation, FD2106, Joint Defra/EA Flood and Coastal Erosion Risk Management R&D programme, Environment Agency, Bristol

CARROLL, Z L, REYNOLDS, B, EMMETT, B A, SINCLAIR, F L, RUIZ DE ONA, C and WILLIAMS, P (2004a) The effect of stocking density on soils in upland Wales, Countryside Council for Wales, UK



CARROLL, Z L, BIRD, S B, EMMETT, B A, REYNOLDS, B and SINCLAIR, F L (2004b) “Can tree shelterbelts on agricultural land reduce flood risk?” Soil Use and Management, vol 20, 3, Wiley & Sons, UK, pp 357–359

CHAMBERS, B J and GARWOOD, T W D (2000) “Monitoring of water erosion on arable farms in England and Wales, 1989–1990” Soil Use and Management, vol 16, 2, Wiley & Sons, UK, pp 93–99

CHAPMAN, A S, FOSTER, I D L, LEES, J A and HODGKINSON, RA (2005) “Sediment delivery from agricultural land to rivers via subsurface drainage” Hydrological Processes, vol 19, 15, Wiley & Sons, UK, pp 2975–2897

CHARMAN, P E V (ed) (1985) Conservation farming, Soil Conservation Service of New South Wales, NSW (ISBN: 978-0-73051-216-5)

CHERRILL, A and MCCLEAN, C (2001) “Omission and commission errors in the field mapping of linear boundary features: implications for the interpretation of maps and organization of surveys” Journal of Environmental Planning and Management, vol 44, 3, Taylor & Francis, UK pp 331–343

CHIN, A (1998) “On the stability of step-pool mountain streams” The Journal of Geology, vol 106, 1, JSTOR, USA, pp 59–69

CHIN, A (2003) “The geomorphic significance of step-pools in mountain streams” Geomorphology, vol 55, 1–4, Elsevier Science BV, UK, pp 125–137

CHOW, V T, MAIDMENT, D and MAYS, L (1988) Applied hydrology, McGraw-Hill, London (ISBN: 978-0-07010-810-3)

CLARK, C O (1945) “Storage and the unit hydrograph” Transactions of the American Society of Civil Engineering, vol 110, ASCE, Reston, US, pp 1419–1446

CLARKE, S and WHARTON, G (2001) “Sediment nutrient characteristics and aquatic macrophytes in lowland English rivers” Science of the Total Environment, vol 266, 1–3, Elsevier Science BV, UK, pp 103–112

CLEMENTS, R O and DONALDSON, G (2002) Soil erosion control in maize, EA Technical Report, P2-123/TR, Environment Agency, Bristol

CLIMO, W J and RICHARDSON, M (1984) “Factors affecting the susceptibility of three soils in the Manawatu to stock treading” New Zealand Journal of Agricultural Research, vol 27, 2, Sir Publishing, Wellington, New Zealand, pp 247–253

COLLINS, A (2008) Cross sector source apportionment of sediment and associated nutrients: the Somerset Levels ECSFDI priority catchment, final report to the Environment Agency, UK

COLLINS, A L and WALLING, D E (2004) “Documenting catchment suspended sediment sources: problems, approaches and prospects” Progress in Physical Geography, vol 28, Sage Publications, UK, pp 159–196

COLLINS, A L and WALLING, D E (2007) “The storage and provenance of fine sediment on the channel bed of two contrasting lowland permeable catchments, UK”, River Research and Applications, vol 23, 4, Wiley & Sons, UK, pp 429–450

COLLINS, A L, WALLING, D E and LEEKS, G J L (1997a) “Fingerprinting the origin of fluvial suspended sediment in larger river basins: combining assessment of spatial provenance and source type” Geografiska Annaler: Series A, Physical Geography, vol 79, 4, Wiley & Sons, UK, pp 239–254

COLLINS, A L, WALLING, D E and LEEKS, G J L (1997b) “Sediment sources in the Upper Severn catchment: a fingerprinting approach” Hydrology and Earth System Sciences, vol 1, European Geosciences Union, UK, pp 509–521

CIRIA C719 99

COSTANZA, R, D’ARGE, R, DE GROOT, R, FARBER, S, GRASSO, M, HANNON, B, LIMBURG, K, NAEEM, S, O’NEILL, R V, PARUELO, J, RASKIN, R G, SUTTON, P and VAN DEN BELT, M (1997) “The value of the world’s ecosystem services and natural capital”, Nature, vol 387, 6630, Nature Publishing Group, UK, pp 253–260

COSTIN, A B (1980) “Runoff and soil and nutrient losses from an improved pasture at Ginninderra, Southern Tablelands, New South Wales” Australian Journal of Agricultural Research, vol 31, 3, CSIRO Publishing, Australia, pp 533–546

COULTHARD, T (2011) The CEASAR model, University of Hull, UK.Go to: www.coulthard.org.uk/CAESAR.html

COULTHARD, T J, KIRKBY, M J and MACKLIN, M G (2000) “Modelling geomorphic response to environmental change in an upland catchment” Hydrological Processes, Special Issue: Geocomputation in Hydrology and Geomorphology, vol 14, 11–12, Wiley & Sons, UK, pp 2031–2045

COULTHARD, T J, MACKLIN, M G, and KIRKBY, M J (2002) “A cellular model of Holocene upland river basin and alluvial fan evolution” Earth Surface Processes and Landforms, vol 27, 3, Wiley & Sons, UK, pp 269–288

CULLEY, J L B and BOLTON, E F (1983) “Suspended solids and phosphorus loads from a clay soil. II: Watershed study” Journal of Environmental Quality, vol 12, 4, ASA, CSSA, and SSSA, USA, pp 498–503

CULLEY, J L B, BOLTON, E F and BERNYK, V (1983) “Suspended solids and phosphorus loads from a clay soil: I. Plot studies” Journal of Environmental Quality, vol 12, 4, ASA, CSSA, and SSSA, USA, pp 493–498

DAILY, G C (ed) (1997) Nature’s services – societal dependence on natural ecosystems, first edition, Island Press, Washington DC (ISBN: 978-1-55963-476-2)

DARBY, S E, ALABYAN, A M and VAN DE WIEL, M J (2002) “Numerical simulation of bank erosion and channel migration in meandering rivers” Water Resources Research, vol 38, 1163, American Geophysical Union, US

DAVIES, D B, FINNEY, J B and RICHARDSON, S J (1973) “Relative effects of weight and wheel slip in causing soil compaction” Journal of Soil Science, vol 24, 3, Wiley & Sons, UK, pp 339–409

DE GROOT, R S, ALKEMADE, R, BRAAT, L, HEIN, L and WILLEMEN, L (2010) “Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making” Ecological Complexity, vol 7, 3, Elsevier Science BV, UK, pp 260–272

DEFRA-EA (2012) Demonstration test catchments. Department for the Environment, food and Rural Affairs, London and Environment Agency, Bristol. Go to: http://tinyurl.com/auuyx9b

DEFRA (2007) Securing a healthy natural environment: an action plan for embedding an ecosystems approach, Department for Environment, Food and Rural Affairs, London. Go to: http://tinyurl.com/a6su665

DEFRA (2011) EU Soils Framework Directive, Department for Environment, Food and Rural Affairs, London. Go to: www.defra.gov.uk/food-farm/land-manage/soil/soil-framework-directive/

DICKIE, S, IONS, L, MCKAY, G and SHAFFER, P (2010) Planning for SuDS – making it happen, C687, CIRIA, London (ISBN: 978-0-86017-687-9). Go to: www.ciria.org

DICKINSON, R E (1984) “Modeling evapotranspiration for three-dimensional global climate models” In: J E Hansen and T Takahashi (ed) Climate processes and climate sensitivity, geophysical monograph, vol 29, American Geophysical Union, Washington DC, USA, pp 58–72

DIGMAN, C, ASHLEY, R, BALMFORTH, D, BALMFORTH, D, STOVIN V and GLERUM, J (2012) Retrofitting to manage surface water, C713, CIRIA, London (ISBN: 978-0-86017-715-9). Go to: www.ciria.org



DUAN, Q, SOROOSHIAN, S and GUPTA, V (1992) “Effective and efficient global optimization for conceptual rainfall-runoff models” Water Resources Research, vol 28, 1015, American Geophysical Union, US

ECN (2012) UK Environmental Change Network: www.ecn.ac.uk

EDWARDS, G M, TAYLOR, N C and GOODWIN, RJ (1994) “The influence of soil surface configuration on depression storage, runoff and soil loss”. In: R J Rickson (ed) Conserving soil resources: European perspectives, selected papers from the first international congress of the European Society for Soil Conservation, Cabi International, UK, (ISBN: 0-85198-948-9) pp 387–398

EDWARDS-JONES, G (2001) “Should we engage in farmer – participatory research in the UK?” Congrès European Common Agricultural Policy (CAP) Meeting, Lisbon, vol 30, 2, pp 129–136

ENVIRONMENT AGENCY (2003) Review of soil management techniques for water retention and minimising diffuse water pollution in the River Parrett catchment, R&D Technical Summary P2-261/10/TS, Environment Agency, Bristol

ENVIRONMENT AGENCY (2007) Integrated catchment management plan for the Ribble, South Preston, EA NW Region, Environment Agency, Bristol. Go to: http://tinyurl.com/cumck3o

ENVIRONMENT AGENCY (2009) Parrett catchment flood management plan, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk/research/planning/114342.aspx

ENVIRONMENT AGENCY (2010a) Key recommendations for sediment management: a synthesis of river sediments and habitats. Phase 2: Impacts of capital works and maintenance, Environment Agency, Bristol (ISBN: 978-1-84911-203-1)

ENVIRONMENT AGENCY (2010b) Land management CFMP tool, Environment Agency, Bristol.Go to: http://cdn.environment-agency.gov.uk/geho0610bsoc-e-e.pdf

ENVIRONMENT AGENCY (2012) Greater working with natural processes in flood and coastal erosion risk management. A response to Pitt Review, Recommendation 27, Environment Agency, BristolGo to: http://tinyurl.com/a4gt7yo

EVANS, R (1977) “Overgrazing and soil erosion on hill pastures with particular reference to the Peak District” Grass and Forage Science, vol 32, 2, Wiley & Sons, UK, pp 65–76

EVANS, R (1996) Soil erosion and its impacts in England and Wales, Friends of the Earth Trust, London (ISBN: 1-85750-297-3)

EVANS, R (1997) “Soil erosion in the UK initiated by grazing animals: a need for a national survey” Applied Geography, vol 17, 2, Elsevier Science BV, UK, pp 127–141

EVANS, R (1998) “The erosional impact of grazing animals” Progress in Physical Geography, vol 22, Sage Publications, UK, pp 251–268

EVANS, R (2005a) “Curtailing grazing-induced erosion in a small catchment and its environs, the Peak District, Central England” Applied Geography, vol 25, 1, Elsevier Science BV, UK, pp 81–95

EVANS, R (2005b) “Monitoring soil erosion in lowland England and Wales – a personal view of its history and outcomes” Catena, vol 64, pp 142–161

EVANS, R and BOARDMAN, J (2003) “Curtailment of muddy floods in the Sompton, South Downs, West Sussex, southern England” Soil Use Management, vol 19, 3, Wiley & Sons, UK, pp 223–231

EVANS, E, ASHLEY, R, HALL, J, PENNING-ROWSELL, E, SAUL, A, SAYERS, P, THORNE, C and WATKINSON, A (2004a) Foresight Future Flooding. Scientific summary: Volume I Future risks and their drivers, Office of Science and Technology, London. Go to: http://tinyurl.com/c2494za

CIRIA C719 101

EVANS, E, ASHLEY, R, HALL, J, PENNING-ROWSELL, E, SAUL, A, SAYERS, P, THORNE, C and WATKINSON, A (2004b) Foresight Future Flooding. Scientific summary: Volume II Managing future risks, Office of Science and Technology, London. Go to: http://tinyurl.com/7jrzcwp

EVANS, E P, SIMM, J D, THORNE, C R, ARNELL, N W, ASHLEY, R M, HESS, T M, LANE, S N, MORRIS, J, NICHOLLS, R J, PENNING-ROWSELL, E C, REYNARD, N S, SAUL, A J, TAPSELL, S M, WATKINSON, A R and WHEATER, H S (2008) An update of the Foresight Future Flooding 2004 qualitative risk analysis. An independent review by Sir Michael Pitt, Cabinet Office, London. Go to: http://tinyurl.com/73xstgp

EWEN, J (2011) “Hydrograph matching method for measuring model performance” Journal of Hydrology, vol 408, 1–2, Elsevier Science BV, UK, pp 178–187

EWEN, J and PARKIN, G (1996) “Validation of catchment models for predicting land-use and climate change impacts: 1. Method” Journal of Hydrology, vol 175, 1–4, Elsevier Science BV, UK, pp 583–594

EWEN, J, PARKIN, G and O’CONNELL, P E (2000) “SHETRAN: distributed river basin flow and transport modeling system” Journal of Hydrologic Engineering, vol 5, 3, American Society of Civil Engineers, USA, pp 250–258

EWEN, J, O’DONNELL, G, BURTON, A and O’CONNELL, P E (2006) “Errors and uncertainty in physically-based rainfall-runoff modelling of catchment change effects” Journal of Hydrology, vol 330, 3–4, Elsevier BV, UK, pp 641–650

EWEN, J, GERIS, J, O’DONNELL, G, MAYES, W and O’CONNELL, P E (2010) Multiscale experimentation monitoring and analysis of long-term land use changes and flood risk, Final Science Report SC060092, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk

FANG, H-W and WANG, G-Q (2000) “Three-dimensional mathematical model of suspended-sediment transport” Journal of Hydraulic Engineering, vol 126, 8, American Society of Civil Engineers, USA, pp 578–592

FERGUSON, R I and STOTT, T (1987) “Forestry effects on suspended sediment and bed load yields in the Balquidder catchments, Central Scotland” Transactions of the Royal Society of Edinburgh: Earth Sciences, vol 78, 4, The Royal Society of Edinburgh, Scotland, pp 379–384

FISHER, B and TURNER, R K (2008) “Ecosystem services: classification for valuation” Biological Conservation, vol 141, 5, Elsevier Science BV, UK, pp 1167–1169

FORESTRY COMMISSION (2011) The UK Forestry Standard, Forestry Commission, EdinburghGo to: www.forestry.gov.uk/forestry/INFD-6J2JBS

FOSTER, I D L, CHAPMAN, A S, HODGKINSON, R M, JONES, A R, LEES, J A, TURNER, S E and SCOTT, M (2003) “Changing suspended sediment and particulate phosphorus loads and pathways in underdrained lowland agricultural catchments; Herefordshire and Worcestershire, UK” Hydrobiologia, vol 494, 1–3, Springer, UK, pp 119–126

FREAD, D L (1985) “Channel routing”. In: M G Anderson and T P Burt (eds) Hydrological Forecasting, John Wiley & Sons, UK, pp 437–603

GERIS, J (2012) Multiscale experimentation, monitoring and analysis of the impacts of local scale, upstream, land use management changes on downstream flooding, PhD Dissertation, Newcastle University, Newcastle upon Tyne, UK

GERIS, J, EWEN, J, O’DONNELL, G M and O’CONNELL, P E (2010) “Monitoring and modelling the pre- and post-blocking hydrological response of moorland drains”. In: Proc British Hydrological Society third international symposium, Role of hydrology in managing consequences of a changing global environment, 19–23 July 2010, Newcastle, UK



GODWIN, R J and DRESSER, M L (2003) Review of soil management techniques for water retention in the Parrett Catchment, R&D Technical Report P2-261/10/TR, Environment Agency, Bristol (ISBN: 978-184432-146-9)

GOMEZ, B and CHURCH, M (1989) “An assessment of bed-load sediment transport formulas for gravel bed rivers” Water Resources Research, vol 25, 6, American Geophysical Union, US, pp 1161–1186

GOVERNMENT OFFICE FOR SCIENCE (2010) Land use futures: making the most of land in the 21st century. Exective summary, URN 10/631, Foresight Land Use Futures Project, LondonGo to: http://tinyurl.com/cmkxmjs

GRAHAM, D and BUTTS, B (2006) “Flexible integrated watershed modeling with MIKE SHE” In: V P Singh and D K Frevert (eds), Watershed Models, CRC Press, Boca Raton, Florida, USA, pp 245–272

GRIEVE, I C, DAVIDSON, D A and GORDON, J E (1995) “Nature, extent and severity of soil erosion in upland Scotland” Land Degradation and Rehabilitation, vol 6, 1, John Wiley & Sons, UK, pp 41–55

GRIEWANK, A (2000) Evaluating derivatives: principles and techniques of algorithmic differerentiation, Society for Industrial and Applied Mathematics, USA (ISBN: 978-0-89871-451-7)

GRUSZOWSKI, K, FOSTER, I, LEES, J and CHARLESWORTH, S (2003) “Sediment sources and transport pathways in a rural catchment, Herefordshire, UK” Hydrological Processes, vol 17, 13, John Wiley & Sons, UK, pp 2665–2681

GUO, Q C and JIN, Y C (2002) “Modeling nonuniform suspended sediment transport in alluvial rivers” Journal of Hydraulic Engineering, vol 128, 9, American Society of Civil Engineers, USA, pp 839–847

HASCOËT, L and PASCUAL, V (2004) TAPENADE 2.1 User’s Guide, Report 0300, Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia-Antipolis, FranceGo to: www-sop.inria.fr/teams/tropics/papers/Hascoet2004T2u.html

HATFIELD, R G and MAHER, B A (2009) “Holocene sediment dynamics in an upland temperate catchment: climatic and land use impacts” The Holocene, vol 19, 3, Sage Publications, UK, pp 1143–1158

HATFIELD, R G, MAHER, B A, PATES, J M and BARKER, P A (2008) “Sediment dynamics in an upland temperate catchment: changing sediment sources, rates and deposition” Journal of Palaeolimnology, vol 40, Springer, UK, pp 1143–1158

HEATHWAITE, A L, BURT, T P and TRUDGILL, S T (1989) “Runoff, sediment, and solute delivery in agricultural drainage basins: a scale-dependent approach”. In: Regional characterization of water quality. IAHS Press, Wallingford (ISBN: 0-947571-02-7), pp 175–191

HEATHWAITE, A L, BURT, T P and TRUDGILL, S T (1990) “Land-use controls on sediment production in lowland catchment, south-west England”. In: J Boardman, I D L Foster, J A Dearing (eds) Soil erosion on agricultural land, John Wiley and Sons, UK, pp 70–86

HEATHWAITE, A L, QUINN, P F and HEWETT, C J M (2005) “Modelling and managing critical source areas of diffuse pollution from agricultural land using flow connectivity simulation” Journal of Hydrology, vol 304, 1–4, Elsevier BV, UK, pp 446-461

HENDERSON, F M (1966) Open channel flow, Prentice Hall, London (ISBN: 978-0-02353-510-9)

HENSHAW, A J (2009) Impacts of land use changes and land management practices on upland catchment sediment dynamics: Pontbren, mid-Wales, PhD thesis, University of NottinghamGo to: http://etheses.nottingham.ac.uk/889/1/Henshaw_(2009).pdf

HENSHAW, A J, THORNE, C R and CLIFFORD, N J (2013) “Identifying causes and controls of river bank erosion in a British upland catchment” Catena, vol 100, pp 107–119

HESS, T M and COUNSELL, C (2000) “A water balance simulation model for teaching and learning – WaSim”. In: Proc ICID British Section Irrigation and Drainage Research Day, 29 March 2000, HR Wallingford, UK. Go to: http://hdl.handle.net

CIRIA C719 103

HESS, T M, HOLMAN, I P, ROSE, S C, ROSOLOVA, Z and PARROTT, A (2010) “Estimating the impact of rural land management changes on catchment runoff generation in England and Wales” Hydrological Processes, vol 24, 10, John Wiley & Sons, UK, pp 1357–1368

HODGE, R, RICHARDS, K and BRASSINGTON, J (2007) “A physically-based bedload transport model developed for 3-D reach-scale cellular modelling” Geomorphology, vol 90, 3, Elsevier Science BV, UK, pp 244–262

HOLDEN, J, EVANS, M G, BURT, T P and HORTON, M (2006) “Impact of land drainage on peatland hydrology” Journal of Environmental Quality, 35, 5, American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, USA, pp 1764–1778

HOLDEN, J, GASCOIGN, M and BOSANKO, N R (2007a) “Erosion and natural revegetation associated with surface land drains in upland peatlands” Earth Surface Processes and Landforms, vol 32, 10, John Wiley & Sons, UK, pp 1547–1557

HOLDEN, J, SHOTBOLT, L, BONN, A, BURT, T P, CHAPMAN, P J, DOUGILL, A J, FRASER, E D G, HUBACEK, K, IRVINE, B, KIRKBY, M J, REED, M S, PRELL, C, STAGL, S, STRINGER, L C, TURNER, A and WORRALL, F (2007b) “Environmental change in moorland landscapes” Earth-Science Reviews, vol 82, 1–2, Elsevier Science BV, UK, pp 75–100

HOLLIS, J M, DRESSER, M, THOMPSON, T R E and NEWLAND, R (2003) Comparison of agricultural soil conditions in the Uck and Bourne catchments during the winter periods of 2000/2001 and 2002/2003, draft report, Contract No. 12622, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk/

HOLMAN, I P, HOLLIS, J M and THOMPSON, T R E (2001) Impact of agricultural soil conditions on floods – Autumn 2000, R&D Project Record W5B-026/PR, Environment Agency, BristolGo to: http://bfw.ac.at/crue_documents/pjr_156_414.pdf

HOLMAN, I P, HOLLIS, J M, BRAMLEY, M E and THOMPSON, T R E (2003) “The contribution of soil structural degradation to catchment flooding: a preliminary investigation of the 2000 floods in England and Wales” Hydrology and Earth System Sciences, vol 7, European Geosciences Union, UK, pp 754–765

HOLMAN I P, HESS T M and ROSE S C (2011) “A broad-scale assessment of the effect of improved soil management on catchment Baseflow Index” Hydrological Processes, vol 25, 16, John Wiley & Sons, UK, pp 2563–2572

HR WALLINGFORD (2008) River sediments and habitats: review of maintenance and capitol works, Final report. Environment Agency, Bristol

HUDSON, J A and ROBERTS, G A (1982) “The effect of a tile drain on the soil moisture content of peat” Journal of Agricultural Engineering Research, vol 27, 6, Elsevier Science BV, UK, pp 495–500

INSTITUTE OF HYDROLOGY (1999) Flood Estimation Handbook, Centre for Ecology and Hydrology, Wallingford, UK. Go to: www.ceh.ac.uk/feh2/fehintro.html

JACKSON, B M, WHEATER, H S, MCINTYRE, N R, CHELL, J, FRANCIS, O J, FROGBROOK, Z, MARSHALL, M, REYNOLDS, B and SOLLOWAY, I (2008) “The impact of upland land management on flooding: insights from a multiscale experimental and modelling programme” Journal of Flood Risk Management, vol 1, 2, John Wiley & Sons, UK, pp 71–80

JACKSON, B, PAGELLA, T, SINCLAIR, F, ORELLANA, B, HENSHAW, A, REYNOLDS, B, MCINTYRE, N, WHEATER, H and EYCOTT, A (2013) “Polyscape: a GIS mapping framework providing efficient and spatially explicit landscape-scale valuation of multiple ecosystem services” Landscape and Urban Planning, vol 112, Elsevier Science BV, UK, pp 74–88

JAKEMAN, A J, LITTLEWOOD, I G and WHITEHEAD, P G (1990) “Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments” Journal of Hydrology, vol 117, 1–4, Elsevier Science BV, UK, pp 275–300



JAKEMAN, A J, LETCHER, R A and NORTON, J P (2006) “Ten iterative steps in development and evaluation of environmental models” Environmental Modelling and Software, vol 21, 5, Elsevier Science BV, UK, pp 602–614

JAMES, P A and ALEXANDER, R W (1998) “Soil erosion and runoff in improved pastures of the Clwydian Range, North Wales” Journal of Agricultural Science, vol 130, 4, Cambridge Journals, UK, pp 473–488

JAMES HUTTON INSTITUTE (2012) The Tarland catchment initiative, The James Hutton Institute, Scotland. Go to: http://yourcatchment.hutton.ac.uk/2012/04/24/303/

JBA (2007) Ripon land management project, final report, Department for Food Environment and Rural Affairs, London. Go to: http://archive.defra.gov.uk/environment/flooding/documents/manage/jbareport.pdf

JOHNS, M (1998) The impact of grazing and upland management on erosion and runoff, R&D Technical Report, P123, Environment Agency, Swindon

JONES, P D, KILSBY, C G, HARPHAM, C, GLENIS, V and BURTON, A (2009) UK climate projections science report: projections of future daily climate for the UK from the Weather Generator, University of Newcastle, UK (ISBN: 978-1-90636-006-1). Go to: http://tinyurl.com/b5o8y46

KJELDSEN, T R, STEWART, E J, PACKMAN, J C, FOLWELL, S S, and BAYLIS, A C (2005) Revitalisation of the FSR/FEH rainfall runoff method, version 1.3, R&D Technical Report FD1913/TR, Department for Environment, Food and Rural Affairs, London and Environment Agency, BristolGo to: www.ceh.ac.uk/feh2/documents/ReFHSpreadsheet_UG_v1.3.pdf

KREMEN, C and OSTFELD, R S (2005) “A call to ecologists: measuring, analyzing, and managing ecosystem services” Frontiers in Ecology and the Environment, vol 3, 10, The Ecological Society of America, USA, pp 540–548

KRONVANG, B, LAUBEL, A and GRANT, R (1997) “Suspended sediment and particulate phosphorus transport and delivery pathways in an arable catchment. Gelbaek Stream, Denmark” Hydrological Processes, vol 11, 6, John Wiley & Sons, UK, pp 627–642

KWAAD, F and MULLIGEN, E J V (1991) “Cropping system effects of maize on infiltration, runoff and erosion on loess soils in South-Limbourg (The Netherlands): a comparison of two rainfall events” Soil Technology, vol 4, 3, Elsevier Science BV, UK, pp 281–295

LAMB, R, and KAY, A L (2004) “Confidence intervals for a spatially generalized, continuous simulation flood frequency model for Great Britain” Water Resources Research, vol 40, W07501, American Geophysical Union, US

LANCASTER, J W, PREENE, M and MARSHALL, C T (2004) Development and flood risk – guidance for the construction industry, C624, CIRIA, London (ISBN: 978-0-86017-624-4)

LANE, S N (2004) “More floods, less rain? Changing hydrology in a Yorkshire context”. In: M Atherdon (ed) Global warming: a Yorkshire perspective, York St. John University, York, (ISBN: 978-0-95406-642-0) pp 2–21

LANE, S N and THORNE, C R (2007) “River processes” In: C R Thorne, E P Evans and E C Penning-Rowsell (eds) Future flooding and coastal erosion risks, Thomas Telford Publishing, London (ISBN: 978-0-72773-449-5) pp 82–99

LANE, S N, BROOKES, C J, HEATHWAITE, A L and REANEY, S (2006) “Surveillant science: Challenges for the management of rural environments emerging from the new generation diffuse pollution models” Journal of Agricultural Economics, vol 57, 2, John Wiley & Sons, UK, pp 239–257

LEEDS-HARRISON, P, SPOOR, G and GODWIN, R J (1982) “Water flow to mole drains” Journal of Agricultural Engineering Research, vol 27, 2, Elsevier Science BV, UK, pp 81–91

CIRIA C719 105

LEEKS, G J L (1992) “Impact of plantation forestry on sediment transport processes” In: P Billi, R D Hey, C R Thorne, P Tacconi (eds) Dynamics of gravel bed rivers, John Wiley & Sons Ltd, Chichester (ISBN: 978-0-47192-976-5) pp 651–670

LEEKS, G J L and MARKS, S D (1997) “Dynamics of river sediments in forested headwater streams: Plynlimon” Hydrology and Earth System Sciences, vol 1, European Geosciences Union, UK, pp 483–497

LEWIN, J, CRYER, R and HARRISON, D I (1974) “Sources for sediments and solutes in mid-Wales”. In: K J Gregory and D E Walling (eds) Fluvial Processes in instrumental watersheds, Special Publication No 6, Institute of British Geographers, London), pp 73–85

LEWIN, J, MACKLIN, M G and NEWSON, M D (1988) “Regime theory and environmental change – irreconcilable concepts?” In: W R White (ed) International conference on river regime, first edition, John Wiley & Sons, UK (978-0-47191-955-1), pp 431–445

MACKAY, M D, BECKMAN, R J and CONOVER, W J (1979) “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code” Technometrics, vol 42, 1, Special 40th Anniversary Issue, JSTOR, USA, pp 55–61

MACKLIN, M G and LEWIN, J (2003) “River sediments, great floods and centennial-scale Holocene climate change” Journal of Quaternary Science, vol 18, 2, John Wiley & Sons, UK, pp 101–105

MARSHALL, M R, FRANCIS, O J, FROGBROOK, Z L, JACKSON, B M, MCINTYRE, N, REYNOLDS, B, SOLLOWAY, I, WHEATER, H S and CHELL, J (2009) “The impact of upland land management on flooding: results from an improved pasture hillslope” Hydrological Processes, vol 23, 3, John Wiley & Sons, UK, pp 464–475

MARSHALL, M R, BALLARD, C E, FROGBROOK, Z L, SOLLOWAY, I, MCINTYRE, N, REYNOLDS, B and WHEATER, H S (in press) “The impact of rural land management changes on runoff processes: results from experimental plots in upland Wales” Hydrological Processes, John Wiley & Sons, UK

MCCULLOCH, J S G and ROBINSON, M (1993) “History of forest hydrology” Journal of Hydrology, vol 150, 2–4, Elsevier Science BV, UK, pp 189–216

MCHUGH, M, HARROD, T and MORGAN, R (2002) “The extent of soil erosion in upland England and Wales” Earth Surface Processes and Landforms, vol 27, 1, John Wiley & Sons, UK, pp 99–107

MCINTYRE, N (in press) “Apportioning non-linearity in conceptual rainfall-runoff models: examples from upland UK catchments” Hydrology Research, IWA Publishing, UK

MCINTYRE, N and MARSHALL, M (2010) “Identification of rural land management signals in runoff response” Hydrological Processes, vol 24, 24, John Wiley & Sons, UK, pp 3521–3534

MCINTYRE, N, LEE, H, WHEATER, H S, YOUNG, A R and WAGENER, T (2005) “Ensemble predictions of runoff in ungauged catchments” Water Resources Research, vol 41, W12434, American Geophysical Union, US

MCINTYRE, N, YOUNG, P, ORELLANA, B, MARSHALL, M, REYNOLDS, B and WHEATER, H (2011) “Identification of nonlinearity in rainfall-flow response using data-based mechanistic modeling” Water Resources Research, vol 47, W03515, American Geophysical Union, US

MEA (2005) Millennium Ecosystem Assessment Synthesis Reports, first edition, Island Press, Washington DC. Go to: www.millenniumassessment.org/en/Synthesis.html

MELVILLE, N and MORGAN, R P C (2001) “The influence of grass density on effectiveness of contour grass strips for control of soil erosion on low angle slopes” Soil Use and Management, vol 17, 4, Wiley & Sons, UK, pp 278–281

MOORE, R J (2007) “The PDM rainfall-runoff model” Hydrology and Earth System Sciences, vol 11, 1, European Geosciences Union, UK, pp 483–499



MOORE, R J and NEWSON, M D (1986) “Production, storage and output of coarse upland sediments: natural and artificial influences as revealed by research catchment studies” Journal of the Geological Society, vol 143, 6, Geological Society of London, pp 921–926

MORGAN, R P C (2005) Soil erosion and conservation, third edition, John Wiley & Sons, UK (ISBN: 978-1-40514-467-4)

MORGAN, R P C (2006) “Managing sediment in the landscape: current practices and future vision” In: P N Owens and A J Collins (eds) Soil erosion and sediment redistribution in river catchments: measurement, modelling and management, CABI, Wallingford (ISBN: 978-0-85199-050-7)

MORSCHEL, J, FOX, D M and BRUNO, J-F (2004) “Limiting sediment deposition on roadways: topographic controls on vulnerable roads and cost analysis of planting grass buffer strips” Environmental Science and Policy, vol 7, Elsevier Science BV, UK, pp 39–45

MOUSSA, R (1996) “Analytical Hayami solution for the diffusive wave flood routing problem with lateral inflow” Hydrological Processes, vol 10, 9, John Wiley & Sons, UK, pp 1209–1227

MURPHY, J M, SEXTON, D M H, JENKINS, G J, BOOTH, B B B, BROWN, C C, CLARK, R T, COLLINS, M, HARRIS, G R, KENDON, E J, BETTS, R A, BROWN, S J, HUMPHREY, K A, MCCARTHY, M P, MCDONALD, R E, STEPHENS, A, WALLACE, C, WARREN, R, WILBY, R and WOOD, R (2009) UK Climate Projections Science Report: Climate change projections, version 3, Met Office Hadley Centre, Exeter, UK. Go to: http://tinyurl.com/aor48rn

MURRAY, A B and PAOLA, C (1994) “A cellular model of braided rivers” Nature, vol 371, Nature Publishing Group, UK, pp 54–57

NADEN, P S (1992) “Spatial variability in flood estimation for large catchments: the exploitation of channel network structure” Hydrological Sciences – Journal des Sciences Hydrologiques, vol 37, 1, Taylor & Francis Online, pp 53–71

NEITSCH S L, ARNOLD J G, KINIRY J R, SRINIVASAN R and WILLIAMS J R (2005) “Soil and Water Assessment Tool Theoretical Documentation, version 2005” Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Temple, Texas

NEWSON, M D (1980) “The erosion of drainage ditches and its effects on bed-load yields in mid-Wales: Reconnaissance case studies” Earth Surface Processes, vol 5, John Wiley & Sons, UK, pp 275–290

NEWSON, M D (2002) “Geomorphological concepts and tools for sustainable river ecosystem management” Aquatic Conservation: Marine and Freshwater Ecosystems, vol 12, 4, John Wiley & Sons, UK, pp 365–379

NEWSON, M D (2010) “Understanding ‘hot-spot’ problems in catchments: the need for scale-sensitive measures and mechanisms to secure effective solutions for river management and conservation” Aquatic Conservation: Marine and Freshwater Ecosystems, vol 20, issue supplement 1, pp 62–72

NEWSON, M D and ROBINSON, M (1983) “Effects of agricultural drainage on upland streamflow: Case studies in mid-Wales” Journal of Environmental Management, vol 17, Elsevier Science BV, UK, pp 333–348

NICHOLSON, A R, WILKINSON, M E, O’DONNELL, G M and QUINN, P F (2012) “Runoff attenuation features: a sustainable flood mitigation strategy in the Belford catchment, UK” Area, vol 44, 4, John Wiley & Sons, UK, pp 463–469

NISBET, T, SILGRAM, M, SHAH, N, MORROW, K and BROADMEADOW, S (2011) Woodland for water: woodland measures for meeting Water Framework Directive objectives, Forest Research Monograph, 4, Forest Research, Surrey (ISBN: 978-0-85538-830-0). Go to: http://tinyurl.com/bevgw3g

NOVAK, P, GUINOT, V, JEFFREY, A and REEVE, D E (2010) Hydraulic modelling – an introduction, first edition, CRC Press, Abingdon, UK (ISBN: 978-0-41925-020-3)

CIRIA C719 107

O’CONNELL, P E, BEVEN, K, CARNEY, J N, CLEMENTS, R O, EWEN, J, FOWLER, H, HARRIS, G, HOLLIS, J, MORRIS, J, O’DONNELL, G M, PACKMAN, J C, PARKIN, A, QUINN, P F, ROSE, S C, SHEPHER, M and TELLIER, S (2004) Review of impacts of rural land use and management on flood generation. Report A: Impact study report, R&D Technical Report FD2114/TR, Department of Environment, Food and Rural Affairs, London

O’CONNELL, P E, EWEN, J, O’DONNELL, G and QUINN, P (2007) “Is there a link between agricultural land use-management and flooding?” Hydrology and Earth Systems Science , vol 11, 1, Copernicus Publications, Germany, pp 96–107

O’DONNELL, G, EWEN, J and O’CONNELL, P E (2011) “Sensitivity maps for impacts of land management on an extreme flood in the Hodder catchment, UK” Physics and Chemistry of the Earth, Parts A/B/C, vol 36, 13, Elsevier Science BV, UK, pp 630–637

ORR, H and CARLING, P A (2006) “Hydro-climatic and land use changes in the River Lune catchment, north west England, implications for catchment management” River Research and Applications, vol 22, 2, John Wiley & Sons, UK, pp 239–255

OWENS, P N, DUZANT, J H, DEEKS, L K, WOOD, G A, MORGAN, R P C and COLLINS, AJ (2007) “Evaluation of contrasting buffer features within an agricultural landscape for reducing sediment and sediment-associated phosphorus delivery to surface waters” Soil Use and Management, vol 23, s1, John Wiley & Sons, UK, pp 165–175

PACKMAN, J, QUINN, P, HOLLIS, J, and O’CONNELL, P (2004) Review of impacts of rural landuse and management on flood generation. Short-term improvement to the FEH rainfall-runoff model: Technical background, R&D Project Record FD2114/PR3, Joint Defra/EA Flood and Coastal Erosion Risk Management R&D Programme, Department for Environment, Food and Rural Affairs, London

PAGELLA, T F, JACKSON, B M, REYNOLDS, B and SINCLAIR, F L (in press) Requirements for spatially explicit negotiation of ecosystem service synergies and trade-offs, Ecology and Society, UK.Go to: www.ecologyandsociety.org/about/

PALMER, R C (2002) Soil conditions in the Tone and Parrett catchments during February and March 2002, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk/

PALMER, R C (2003a) Soil conditions in the Tone and Parrett catchments during February and March 2003, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk/

PALMER, R C (2003b) Soil conditions in the Upper Avon, Nadder and Wylye catchments during February and March 2003, draft report, Environment Agency, Bristol. Go to: www.environment-agency.gov.uk/

PAPY, F and DOUYER, C (1991) “Influence des états de surface du territoire sur déclenchement des innondations catastrophiques” Agronomie, vol 11, 3, EDP Sciences, France, pp 201–215

PARK, J S (2006) Whole system modelling of the impact of land use management in the Parrett catchment, PhD thesis, Department of Civil Engineering, University of Bristol, UK

PARK J-S, REN Q, CHEN, Y, CLUCKIE, I D, BUTTS, M and GRAHAM, D (2009) “Effectiveness of complex physics and DTM based distributed models for flood risk management of the River Tone, UK”. In: I D Cluckie, Y Chen, V Babovic, L F Konikow, A Mynett, S Demuth, D Savic, (eds) Hydroinformatics in hydrology, hydrogeology and water resources, proc of symposium JS.4 at the Joint IAHS & IAH Convention, Hyderabad, India, September 2009, IAHS Publications, pp 114–121

PARK, J S, GRAHAM, D, BUTTS, M and CLUCKIE, I D (2010) “Sensitivity of catchment flood management strategies for the River Parrett using a physically distributed model”. In: Proc BHS 2010: Role of hydrology in managing consequences of a changing global environment, BHS Third International Symposium, Newcastle, 19–23 July 2010 (ISBN: 1-903741-17-31), pp 246–250



PARKER, C, THORNE, C, BINGNER, R L, WELLS, R R and WILCOX, D L (2007) “Automated mapping of potential for ephemeral gully formation in agricultural watersheds”. In: Proc of the 2010 Federal Interagency Sedimentation Conference, 27 June to 1 July 2010, Las Vegas, Nevada

PARKIN, G, O’DONNELL, G, EWEN, J, BATHURST, J C, O’CONNELL, P E and LAVABRE, J (1996) “Validation of catchment models for predicting land-use and climate change impacts: 2. Case study for a Mediterranean catchment” Journal of Hydrology, vol 175, 1–4, Elsevier Science BV, UK, pp 595–613

PARROTT, A, BROOKS, W, HARMAR, O and PYGOTT, K (2009) “Role of rural land use in flood and coastal risk management” Journal of Flood Risk Management, vol 2, 4, John Wiley & Sons, UK, pp 272–284

PATTISON, I and LANE, S N (2012) “The link between land-use management and fluvial flood risk: A chaotic conception?” Progress in Physical Geography, vol 36, 1, Sage Publications, UK, pp 159–196

PATTO, P M, CLEMENT, C R and FORBES, T J (1978) Grassland poaching in England and Wales. Permanent grassland studies 2, Joint Permanent Pasture Group (Great Britain), Grassland Research Institute, Hurley

PHILIP, J R (1957) “The theory of infiltration: 4. sorptivity and algebraic infiltration equation” Soil Science, vol 84, 3, Lippincott Williams & Wilkins, USA, pp 257–264

PINTER, N and HEINE, R A (2005) “Hydrodynamic and morphodynamic response to river engineering documented by fixed-discharge analysis, Lower Missouri River, USA” Journal of Hydrology, vol 302, 1–4, Elsevier Science BV, UK, pp 70–91

PLATE, E J (2002) “Flood risk and flood management” Journal of Hydrology, vol 267, 1–2, Elsevier Science BV, UK, pp 2–11

PLATTS, W S (1981) “Sheep and streams” Rangelands, vol 3, 4, JSTOR, USA, pp 158–160

PONCE, V M and YEVJEVICH, V (1978) “Muskingum-Cunge method with variable parameters” Journal of the Hydraulics Division, vol 104, 12, ASCE, USA, pp 1663–1667

RAMCHUNDER, S, BROWN, L E and HOLDEN, J (2009) “Environmental effects of artificial drainage, drain-blocking and prescribed vegetation burning in UK upland peatlands” Progress in Physical Geography, vol 33, 1, Sage Publications, UK, pp 49–79

REED, M S, BONN, A, SLEEC, W, BEHARRY-BORGD, N, BIRCH, J, BROWN, I, BURT, T P, CHAPMAN, D, CHAPMAN, P J, CLAY, G D, CORNELL, S J, FRASER, E D G, GLASS, J H, HOLDEN, J and HODGSON, J A (2009 “The future of the uplands” Land Use Policy, vol 26, Elsevier Science BV, UK, pp s204–s216

REFSGAARD, J C (1997) “Parameterisation, calibration and validation of distributed hydrological models” Journal of Hydrology, vol 198, 1–4, Elsevier Science BV, UK, pp 69–97

REFSGAARD, J C and STORM, B (1996) “Construction, calibration and validation of hydrological models” In: M B Abbott and J C Refsgaard (eds) Distributed hydrological modelling, Kluwer, Dordtrecht, The Netherlands (ISBN: 0-7923-4042-6), pp 41–54

ROBINSON, M (1986) “Changes in catchment runoff following drainage and afforestation” Journal of Hydrology, vol 86, 1–2, Elsevier Science BV, UK, pp 71–84

ROBINSON, M (1990) Impact of improved land drainage on river flows, Centre of Ecology and Hydrology, Wallingford, UK (ISBN: 0-94854-024-9)

ROBINSON, M (1998) “30 years of forest hydrology changes at Coalburn: water balance and extreme flows” Hydrology and Earth System Sciences Discussions 2, vol 2/3, European Geosciences Union, UK, pp 233–238

ROBINSON, M and NEWSON, M D (1986) “Comparison of forest and moorland hydrology in an upland area with peaty soils” International Peat Journal, vol 1, University of Nottingham, UK, pp 49–68.Go to: www.peatsociety.org/publications/international-peat-journal

CIRIA C719 109

ROBINSON, M, COGNARD-PLANCQ, A-L, COSANDEY, C, DAVID, J, DURAND, P, FÜHRER, H-W, HALL, R, HENDRIQUES, M O, MARB, V, MCCARTHY, R, MCDONNELL, M, MARTIN, C, NISBET, T, O’DEA, P, RODGERS, M and ZOLLNER, A (2003) “Studies of the impact of forests on peak flows and baseflows: a European perspective” Forest Ecology and Management, vol 186, 1–3, Elsevier Science BV, UK, pp 85–97

ROBINSON, M and RYCROFT, D W (1999) “The impact of drainage on streamflow”. In: W Skaggs, W and J van Schilfgaarde (eds) Agricultural drainage, American Society of Agronomy, Madison, Wisconsin, pp 753–786

ROBSON, A J, JONES, T K, REED, D W and BAYLISS, A C (1998) “A study of national trend and variation in UK floods” International Journal of Climatology, vol 18, 2, John Wiley & Sons, UK, pp 165–182

RODRIGUEZ-ITURBE, I and VALDES, J B (1979) “The geomorphologic structure of hydrologic response” Water Resources Research, vol 15, 6, American Geophysical Union, US pp 1409–1420

ROSE, S, ROSOLOVA, Z, LAMB, R, WORRALL, P, HAMMOND, G and HESTER, N (2011) Defra multi-objective flood management demonstration project, Penny Anderson Associates Ltd, UK.Go to: http://tinyurl.com/aqmgdnd

RUSSELL, M A, WALLING, D E and HODGKINSON, R A (2001) “Suspended sediment sources in two small lowland agricultural catchments in the UK” Journal of Hydrology, vol 252, 1–4, Elsevier Science BV, UK, pp 1–24

RÜTHER, N and OLSEN, N R B (2005) “Three-dimensional modeling of sediment transport in a narrow 90° channel bend” Journal of Hydraulic Engineering, vol 131, 10, American Society of Civil Engineers, USA, pp 917–920

SACO, P M and KUMAR, P (2002) “Kinematic dispersion in stream networks 1. Coupling hydraulic and network geometry” Water Resources Research, vol 38, 1244, American Geophysical Union, US

SALTELLI, A, CHAN, K and SCOTT, M (eds) (2000) Sensitivity analysis, Wiley Series in Probability and Statistics, John Wiley and Sons, New York (ISBN: 978-0-47074-382-9)

SANSOM, A L (1999) “Upland vegetation management: the impacts of overstocking” Water Science and Technology, vol 39, 12, Elsevier Science BV, UK, pp 85–92

SCHAFER, K (1986) “Experiments with catch crops for erosion control in maize production” Wirtschaftseigene Futter, vol 32, pp 60–71

SCHUMM, S A (1969) “River metamorphosis” Journal of the Hydraulics Division American Society of Civil Engineers, vol 95, HY1, ASCE, USA, pp 255–73

SHAFFER, P, ELLIOTT, C, REED, J HOLMES, J and WARD, M (2004) Model agreements for sustainable water management systems. Model agreements for SUDS, C625, CIRIA, London (ISBN: 978-0-86017-625-1)

SIBBESEN, E, HANSEN, A C, NIELSEN, J D and HEIDMAN, T (1994) “Runoff, erosion and phosphorus loss from various cropping systems in Denmark”. In: R J Rickson (ed) Conserving soil resources: European perspectives. Selected papers from the First International Congress of the European Society for Soil Conservation, CAB International Wallingford, UK (ISBN: 0-85198-948-9) pp 87–93

SILINS, U and ROTHWELL, R L (1998) “Forest peatland drainage and subsidence affect soils water retention and transport properties in an Alberta Peatland” Soil Science Society of America Journal, vol 62, 4, Soil Science Society of America, ASA, CSSA, and SSSA, USA, pp 1048–1056

SINCLAIR, F L and JOSHI, L (2000) “Taking local knowledge about trees seriously”. In: A Lawrence (ed) Forestry, forest users and research: new ways of learning, European Tropical Forest Research Network (ETFRN), Wageningen, The Netherlands, pp 45–61



SINGH, V P (1995) “Watershed modelling” In: VP Singh (ed) Computer models of watershed hydrology, Water Resources Publications, Colorado, USA (ISBN: 978-0-91833-491-6), pp 1–22

SINGH, V P (1996) “Kinematic wave modeling in water resources” Hydrological Processes, vol 14, 4, John Wiley & Sons, UK, pp 671–706

SMITH, L P (1976) The agricultural climate of England and Wales: Areal averages 1941–70, The Stationery Office, London (ISBN: 978-0-11240-896-3)

SOLLOWAY, I (2012) Effects of tree shelterbelts on the hydrology of upland areas, PhD Thesis, Department of Civil and Environmental Engineering, Imperial College London, London

SOUCHERE, V, KING, D, DAROUSSIN, J, PAPY, F and CAPILLON, A (1998) “Effects of tillage on runoff directions: consequences on runoff contributing areas within agricultural catchments” Journal of Hydrology, vol 206, 3–4, Elsevier Science BV, UK, pp 256–267

SOULSBY, C, YOUNGSON, A F, MOIR, H J and MALCOLM, I A (2001) “Fine sediment influence on salmonid spawning habitat in a lowland agricultural stream: a preliminary assessment” The Science of the Total Environment, vol 265, 1–3, Elsevier Science BV, UK, pp 295–307

SPEIRS, R B and FROST, C A (1985) “The increasing incidence of accelerated soil water erosion on arable land in the east of Scotland” Journal of Agricultural Research Development, vol 2, pp 161–168

STEWART, A J A and LANCE, A N (1991) “Effect of moor-draining on the hydrology and vegetation of northern Pennine blanket bog” Journal of Applied Ecology, vol 28, JSTOR, USA, pp 1105–1117

STOTT, T A (1997) “Forestry effects on bedload yields in mountain streams” Applied Geography, vol 17, 1, Elsevier Science BV, UK, pp 55–78

STOTT, T A and MOUNT, N (2004) “Plantation forestry impacts on sediment yields and downstream channel dynamics in the UK: a review” Progress in Physical Geography, vol 28, 2, Sage Publications, UK, pp 197–240

SULLIVAN, A L, TERNAN, J L and WILLIAMS, A J (2004) “Land use change and hydrological response in the Camel catchment, Cornwall” Applied Geography, vol 24, 2, Elsevier Science BV, UK, pp 119–137

THOMAS, T M (1965) “Sheet erosion by sheep in the Plynlimon area” Occasional Publication, British Geomorphological Research Group, vol 2, British Society for Geomorphology, UK, pp 11–14

THORNE, C R, HENSHAW, A J, BATES, P, BROWN, D, COULTHARD, T, GURNELL, A, LEWIN, J, LONGFIELD, S, MAAS, G, MACKLIN, M, NEWSON, M, NICHOLAS, A, PARKER, C, REES, J, SEAR, D, SURENDRAN, S, WALKER, J and WARBURTON, J (2010a) “Applied fluvial geomorphology for sustainable flood risk management”. In: Proc Defra annual flood and coastal risk management conference, July 2010, Telford, UK

THORNE, C R, SOAR, P J, SKINNER, K S, SEAR, D A and NEWSON, M G (2010b) “Driving processes II. Investigating, characterising and managing river sediment dynamics” In: D A Sear, M G Newson and C R Thorne (eds) Guidebook of applied fluvial geomorphology, Thomas Telford, London (ISBN: 978-0-72773-484-6) pp 120–195

THORNE, C R, WALLERSTEIN, N P, SOAR, P J, BROOKES, A, WISHART, D, BIEDENHARN, D S, GIBSON, S A, LITTLE, C D, MOONEY, D M, WATSON, C C, GREEN, A P E, COULTHARD, T J and VAN DE WEIL, M J (2011) “Accounting for sediment in flood risk management”. In: G Pender and H Faulkner (eds) Flood risk science and management, Wiley-Blackwell, Oxford, UK (ISBN: 978-1-44432-484-6), pp 87–113

TIVY, J (1957) “Influence des facteurs biologiques sur l’erosion dans les Southern Uplands écossais” Revue de Géomorphologie Dynamique, vol 17, Édition de la Societé d’édition d’enseignement, pp 146–158

CIRIA C719 111

TODINI, E (2007) “A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach” Hydrology and Earth System Sciences, vol 11, 5, European Geosciences Union, UK, pp 1645–1659

TODINI, E and BOSSI, A (1986) “PAB (Parabolic and Backwater) an unconditionally stable flood routing scheme particularly suited for real time forecasting and control” Journal of Hydraulic Research, vol 24, 5, Taylor & Francis Online, UK, pp 405–424

TRIMBLE, S W and MENDEL, A C (1995) “The cow as a geomorphic agent – a critical review” Geomorphology, vol 13, 1–4, Elsevier Science BV, UK, pp 233–253

TSAI, C W (2003) “Applicability of kinematic, noninertia, and quasi-steady dynamic wave models to unsteady flow routing” Journal of Hydraulic Engineering, vol 129, 8, American Society of Civil Engineers, USA, pp 613–627

UNIVERSITY OF NEWCASTLE (2012) Proactive catchment management – Belford, School of Civil Engineering and Geosciences, Newcastle University. Go to: http://research.ncl.ac.uk/proactive/belford/

USACE (2012) HEC-RAS, version 4.0, Hydrologic Engineering Center (HEC), US Army Corps of Engineers, CA, USA. Go to: www.hec.usace.army.mil/software/hec-ras/

USACE (2010) HEC-RAS, version 4.1, CDP-68, Hydrologic Engineering Center (HEC), US Army Corps of Engineers, CA, USA. Go to: www.hec.usace.army.mil/software/hec-ras/

USDA (1986) Urban hydrology for small watersheds, Technical Release 55, Conservation Engineering Division, United States Department of Agriculture Natural Resources Conservation Service, Washington DC, USA. Go to: http://cset.sp.utoledo.edu/~nkissoff/pdf/CIVE-3520/Modified-tr55.pdf

VAN DE WIEL, M J, COULTHARD, T J, MACKLIN, M G and LEWIN, J (2007) “Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model” Geomorphology, vol 90, 3–4, Elsevier Science BV, UK, pp 283–301

VAN DE WIEL, M J, COULTHARD, T J, MACKLIN, M G and LEWIN, J (2011) “Modelling the response of river systems to environmental change: progress, problems and prospects” Earth-Science Reviews, vol 104, 1–3, Elsevier Science BV, UK, pp 167–185

VERSTRAETEN, G and POESEN, J (1999) “The nature of small-scale flooding, muddy floods and retention pond sedimentation in central Belguim” Geomorphology, vol 29, 3–4, Elsevier Science BV, UK, pp 275–292

VERSTRAETEN, G, POESEN, J, GOVERS, G, KATLEEN, G, VAN ROMPAEY, A and VAN OOST, K (2003a) “Integrating science, policy and farmers to reduce soil loss and sediment delivery in Flanders, Belgium” Environmental Science and Policy, vol, 6, 1, Elsevier Science BV, UK, pp 95–103

VERSTRAETEN, G, VAN ROMPAEY, A, POESEN, J, VAN OOST, K and GOVERS, G (2003b) “Evaluating the impact of watershed management scenarios on changes in sediment delivery to rivers” Hydrobiologia, vol 494, 1–3, Springer, UK, pp 153–158

WAGENER, T, WHEATER, H and GUPTA, H (2004) Rainfall – runoff modelling in gauged and ungauged catchments, Imperial College Press, London (978-1-86094-466-6)

WALLING, D E and COLLINS, A L (2005) “Suspended sediment sources in British rivers” In: D E Walling and A J Horowitz (eds) Sediment budgets 1. Proc of symposium S1 held during the seventh IAHS scientific assembly, Foz do Iguaçu, Brazil, April 2005, IAHS Publication 291, Wallingford, UK pp 123–133

WALLING, D E, OWENS, P N and LEEKS, G J L (1999) “Fingerprinting suspended sediment sources in the catchment of the River Ouse, Yorkshire, UK” Hydrological Processes, Special Issue: River Basin Sediment Dynamics, vol 13, 7, John Wiley & Sons, UK, pp 955–975



WALLING, D E, RUSSELL, M A, HODGKINSON, R A and ZHANG, Y (2002) “Establishing sediment budgets in two small lowland agricultural catchments in the UK” Catena, vol 47, 4, Elsevier Science BV, UK, pp 323–353

WARD, J V, BRETSCHKO, G, BRUNKE, M, DANIELPOL, D, GIBERT, J, GONSER, T and HILDREW, A G (1998) “The boundaries of river systems: the metazoan perspective” Freshwater Biology, vol 40, 3, John Wiley & Sons, UK, pp 531–569

WATSON, C C, BIEDENHARN, D S and THORNE, C R (in press) Analysis of the impacts of dikes on flood stages in the middle Mississippi River, Journal of Hydraulic Engineering, American Society of Civil Engineering, New York, USA

WATTS, K, HANDLEY, P, SCHOLEFIELD, P and NORTON, L (2008) Habitat connectivity – developing an indicator for UK and country level reporting. Phase 1 Pilot Study contract report to Defra, Defra Contract CR0388, Forest Research and Centre for Ecology and Hydrology, UK

WELD, A (1851) “An account of a remarkable flood at Chipping in Lancashire” Philosophical Magazine Series 4, vol 2, 10, Taylor & Francis Online, pp 209–215

WEMPLE, B C, SWANSON, F J and JONES, J A (2001) “Forest roads and geomorphic process interactions, Cascade Range, Oregon” Earth surface processes and landforms, special issue: Hydrologic and geomorphic effects of forest roads, vol 26, 2, John Wiley & Sons, UK, pp 191–204

WHEATER, H S (2006) “Flood hazard and management: a UK perspective” Philosophical Transactions of the Royal Society of London, vol 364, 1845, The Royal Society, London, pp 2135–2145

WHEATER, H S, JAKEMAN, A J and BEVEN, K J (1993) “Progress and directions in rainfall-runoff modelling” In: A J Jakeman, M B Beck and M J McAleer (eds), Modelling change in environmental systems, Wiley-Blackwell, Oxford, UK (ISBN: 978-0-47194-263-4), pp 101–132

WHEATER, H S, REYNOLDS, B, MCINTYRE, N, MARSHALL, M, JACKSON, B, FROGBROOK, Z, SOLLOWAY, I, FRANCIS, O J and CHELL, J (2008) Impacts of upland land management on flood risk: multi-scale modelling methodology and results from the Pontbren experiment, FRMRC Research Report UR16, CEH Project Number C02699, Flood Risk Management Research Consortium, Manchester

WHEATER, H S, MCINTYRE, N, JACKSON, B M, MARSHALL, M R, BALLARD, C E, BULYGINA, N S, REYNOLDS, B and FROGBROOK, Z (2010) “Multiscale impacts of land management on flooding” In: G Pender and H Faulkner (eds) Flood risk science and management, Wiley-Blackwell, Oxford, UK, pp 39–59

WILKINSON, M E, QUINN, P F and WELTON, P (2010) “Runoff management during the September 2008 floods in the Belford catchment, Northumberland” Journal of Flood Risk Management, vol 350, 4, John Wiley & Sons, UK, pp 285–295

WITHERS, P J A and SHARPLEY, A N (2008) “Characterization and apportionment of nutrient and sediment sources in catchments” Journal of Hydrology, vol 350, 3–4, Elsevier Science BV, UK, pp 127–130

WITHERS, P J A, HODGKINSON, R A, BATES, A and WITHERS, C L (2007) “Soil cultivation effects on sediment and phosphorus mobilization in surface runoff from three contrasting soil types in England” Soil and Tillage Research, vol 93, 2, Elsevier Science BV, UK, pp 438–451

WOLMAN, M G and SCHICK, A P (1967) “Effects of construction on fluvial sediment, urban and suburban areas of Maryland” Water Resources Research, vol 3, 2, American Geophysical Union, US

WOOD, P J and ARMITAGE, P (1999) “Sediment deposition in a small lowland stream - management implications” Regulated Rivers Research and Management, vol 15, 1–3, John Wiley & Sons, UK, pp 199–210

WOODS BALLARD, B, KELLAGHER, R, MARTIN, P, JEFFERIES, C, BRAY, R and SHAFFER, P (2007) The SUDS manual, C697, CIRIA, London (ISBN: 978-0-86017-697-8). Go to: www.ciria.org

CIRIA C719 113

YAN, J S and SMITH, K R (1994) “Simulation of integrated surface-water and ground-water systems – model formulation” Journal of the American Water Resources Association, vol 30, 5, Wiley Online, UK pp 879–890

YARNELL, S M, MOUNT, J F and LARSEN, E W (2006) “The influence of relative sediment supply on riverine habitat heterogeneity” Geomorphology, vol 80, 3–4, Elsevier Science BV, UK, pp 310–324

YELOFF, D E, LABADZ, J C and HUNT, C O (2006) “Causes of degradation and erosion of a blanket mire in the southern Pennines, UK” Mires and Peat, vol 1, Art. 4. Go to: http://tinyurl.com/aaxy5u3

YEN, B C and TSAI, C W S (2001) “On noninertia wave versus diffusion wave in flood routing” Journal of Hydrology, vol 244, Elsevier Science BV, UK, pp 97–104

YOUNG, R A and VOORHEES, W B (1982) “Soil erosion and runoff from planting to canopy development as influenced by tractor wheel-traffic” Transactions of the ASABE, vol 25, 3, American Society of Agricultural and Biological Engineers, Michigan, pp 708–712

YOUNG, R A, ONSTAD, C A, BOSCH, D D and ANDERSON, W P (1989) “AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds” Journal of Soil and Water Conservation, vol 44, 2, Soil and Water Conservation Society, IOWA, USA, pp 168–173

ZHU, D, PARK, J-S, RICO-RAMIREZ, M A and CLUCKIE, I D (2008) “Sensitivity analysis of a distributed hydrological model for the Upper Medway Catchment using point and radar-based rainfall data”. In: Proc Sustainable hydrology for the 21st century, 10th BHS National Hydrology Symposium, 15–17 September 2008, University of Exeter, UK (ISBN: 1-90374-116-5)

StatutesCouncil Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora (EU Habitats Directive)

Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks (Floods Directive 2007)

Proposal for a Directive of the European Parliament and of the Council establishing a framework for the protection of soil and amending Directive 2004/35/EC (proposed Soils Directive)

Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy (Water Framework Directive)

Flood and Water Management Act 2010 (c.29)

Flood Risk Management (Scotland) Act 2009 (asp 6)

Useful websitesCEASAR Model downloads: www.coulthard.org.uk/caesardownloads.html

UNITED UTILITIES (2012) SCaMP: http://corporate.unitedutilities.com/scamp-index.aspx

CENTER FOR ECOLOGY AND HYDROLOGY Plynlimon Experimental Catchments: www.ceh.ac.uk/sci_programmes/Plynlimon.html

MIKE 21C–2D river hydraulics and morphology: www.dhisoftware.com/Products/WaterResources/MIKE21C.aspx

Runoff Attenuation Features: Links to RAF handbook, maps and other materials: http://research.ncl.ac.uk/proactive/belford

AECOM Ltd

Arcadis UK Ltd

Arup Group Ltd

Atkins Consultants Limited

BAA plc

Balfour Beatty Civil Engineering Ltd

BAM Nuttall Ltd

Black & Veatch Ltd

Bureau Veritas

Buro Happold Engineers Limited

BWB Consulting Ltd

Cardiff University

Davis Langdon LLP

Environment Agency

Galliford Try plc

Gatwick Airport Ltd

Geotechnical Consulting Group

Golder Associates (Europe) Ltd

Halcrow Group Limited

Health & Safety Executive

Highways Agency

Homes and Communities Agency

HR Wallingford Ltd

Institution of Civil Engineers

London Underground Ltd

Loughborough University

Ministry of Justice

Morgan Sindall (Infrastructure) Plc

Mott MacDonald Group Ltd

MWH

National Grid UK Ltd

Network Rail

Northern Ireland Water

Northumbrian Water Limited

Rail Safety and Standards Board

Royal Haskoning

RSK Group Ltd

RWE Npower plc

Sellafield Ltd

Severn Trent Water

Sir Robert McAlpine Ltd

SKM Enviros Consulting Ltd

Temple Group Ltd

Thames Water Utilities Ltd

Tube Lines

United Utilities Plc

University College London

University of Bradford

University of Greenwich

University of Reading

University of Salford

University of Southampton

WYG Group (Nottingham Office)

May 2013

Core and Associate members

��

� � ��

��

��

��

��

��

��

��

��

��

�� !

"�� #�� $�� %�� "#&�#'(')**&*+

,�� "�� -��&"�� -�� . ��

�� #� ��

�� /�� 0��

1�� #�� / �� ,��

2 �� 0�� -��

�

�

�

�

�

C719

C719Land

usem

anagementeffects

onflood

flows

andsedim

ents-guidance

onprediction

CIRIA

� ��

Land use management effectson flood flows and sediments

- guidance on prediction

ciria land use management effects on flood flows and sediments

Documents