Predicting Morphological Changes in Rivers, Estuaries and Coasts

Integrated Flood Risk Analysis

and Management Methodologies

Predicting morphological changes in rivers,

estuaries and coasts

Date March

2007

Report Number T05-07-02

Revision Number 1_0_3_P31

Deliverable Number: D5.1

Due date for deliverable: February 2007 Actual submission date: March 2007

Task Leader University of Plymouth

FLOODsite is co-funded by the European Community

Sixth Framework Programme for European Research and Technological Development (2002-2006) FLOODsite is an Integrated Project in the Global Change and Eco-systems Sub-Priority

Start date March 2004, duration 5 Years Document Dissemination Level

PU Public PU

PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

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Title Predicting morphological changes in rivers, estuaries and coasts Lead Author Prof D E Reeve

Contributors

Dr Y Li, Prof M Larson, Prof H Hanson, Dr C Donnelly, Dr J A Jiménez, Dr E T Mendoza, Prof Y Zech, Dr S Soares Frazão, Dr R Bettess, Dr S Stripling, Dr A Brampton

Distribution Public Document Reference T05-07-02

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Date Revision Prepared by Organisation Approved by Notes

26/07/06 1_0_1 D. Reeve UOP Initial draft

24/02/07 1_0_2 D. Reeve UoP 1st _{full draft}

05/03/07 1_0_3 D. Reeve UoP Final

04/04/07 J Bushell HRW Final formatting for publication and

change of name from ‘T05-07-03.doc’.

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The work described in this publication was supported by the European Community’s Sixth Framework Programme through the grant to the budget of the Integrated Project FLOODsite, Contract GOCE-CT-2004-505420.

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This document reflects only the authors’ views and not those of the European Community. This work may rely on data from sources external to the FLOODsite project Consortium. Members of the Consortium do not accept liability for loss or damage suffered by any third party as a result of errors or inaccuracies in such data. The information in this document is provided “as is” and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and neither the European Community nor any member of the FLOODsite Consortium is liable for any use that may be made of the information.

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This report is the deliverable from the FLOODsite partners contributing to Task 5 – Predicting Morphological Changes in Rivers, Estuaries and Coasts. In Task 5, research has been undertaken to improve understanding, models and techniques for the analysis of the performance of the whole flood defence system and its diverse components, including natural and man-made defences (e.g. seawalls, embankments, dunes). In particular, two specific aims were to (a) to develop an improved understanding of morphological change of beaches over large time and spatial scales and provide a better predictive tool for the response of dunes to storm loading, and (b), to critically review current knowledge and on-going programmes or river and estuarial morphology, summary existing knowledge and identify a forward programme of detailed and justified research.

This document provides a new source of information upon which risk management tools and analyses may be based. It links with a number of other tasks within FLOODsite. Specifically, Task 2 - Estimation of extremes, Task 4 - Understanding and predicting failure modes, Task 7 - Reliability analysis of flood defence structures and systems, and Task 26 – Pilot Study of the Ebro Delta.

The research on coastal morphology has led to a number of new developments. These include a stochastic model of beach plan shape variability, a regional model for regional scale changes, a rapid coastal evolution model, beach overwash and dune erosion models. These have all been used on actual sites, in order to illustrate their application. The stochastic model has been applied to Christchurch Bay on the south coast of the UK. The regional model methodology for assessing the coastal vulnerability to storm impacts has been applied to the Catalan coast. Storms on the Catalan coast have been classified in terms of their inundation and erosion potential. The methodology has been applied to the longest existing wave record in the Catalan coast for two different coastal types. The rapid coastal evolution model is a fully integrated, dynamically linked coastal management tool, GTI-SEAMaT, which is illustrated through an application to the shoreline of Calabria in Italy. For beach overwash and dune modelling the analytical model proposed by Larson et al. (2004) to simulate dune erosion and dune foot retreat during severe storms was further developed and tested. Four different data sets on dune erosion, originating from the laboratory and the field, were employed to validate the model. An analytical model was formulated to describe the response of a dune to wave impact and overwash. The approach has been applied to sites in the USA and the Ebro delta.

With an increased knowledge of the impact that fluvial and estuarine morphology can have on flood risk, it is possible to implement more appropriate management strategies to deal with morphological change that has an impact on flooding and, therefore, potentially mitigate the likelihood and the consequences of flooding. This rationale has motivated the investigation of existing approaches to fluvial and estuarine morphological modelling and which has led to the development of a method for incorporating the impact of morphological change in an evaluation of flood risk.

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1. Introduction to Task 5 and related activities ... 1

1.1 Introduction ... 1

1.2 Background... 2

1.3 Aims and objectives... 3

1.4 Scope ... 3

1.5 Links to other FLOODsite tasks ... 3

1.6 Outline of this report... 4

2. Coastal Morphology... 5

2.1 Introduction to coastal issues... 5

2.2 Development of Rapid Coastal Evolution Models ... 2

2.2.1 Introduction ... 2

2.2.2 Review of current modelling methods ... 3

2.2.3 Development of mega-scale process-based numerical modelling system11 2.2.4 Guidance for use ... 25

2.2.5 Conclusions and further development... 25

2.3 Regional Evolution Models for Regional changes ... 27

2.3.1 Storm classification... 27

2.3.2 Storm induced response ... 32

2.3.3 Storm induced coastal hazard... 37

2.3.4 Discussion and conclusions... 39

2.3.5 Acknowledgments... 40

2.4 Statistical Techniques for describing change in coastal morphology... 40

2.4.1 Introduction ... 40

2.4.2 Equation development of bay shape... 43

2.4.3 Monte Carlo Simulation ... 46

2.4.4 Case study ... 47

2.4.5 Results and Discussions ... 51

2.4.6 Conclusions ... 53

2.5 Beach overwash... 53

2.5.1 The storm ... 54

2.5.2 Barrier morphological changes ... 56

2.5.3 Overwash sediment transport ... 58

2.5.4 Discussion and conclusions... 62

2.5.5 Acknowledgments... 62

2.6 Dune Models... 63

2.6.1 Introduction ... 63

2.6.2 Mathematical modelling of dune erosion... 65

2.6.3 Mathematical modelling of overwash ... 73

2.6.5 Numerical model of dune erosion and overwash ... 93

2.7 Knowledge gaps and research priorities ... 102

2.7.1 Introduction ... 102

2.7.2 Knowledge gaps ... 102

2.7.3 Uncertainty... 103

2.7.4 Research priorities... 104

3. Fluvial and Estuarine Morphology... 106

3.1.1 Background ... 106

3.1.2 Nature of Morphological Change... 106

3.1.3 Timescales of Change ... 107

3.1.4 Spatial Scales of Change... 107

3.1.5 Chapter Structure ... 107

3.2 Impact of River Works on Morphology ... 108

3.2.1 Introduction ... 108

3.2.2 Short-term and long-term timescales ... 108

3.2.3 Short-term morphodynamic response ... 108

3.2.4 Lane’s balance for long-term morphodynamic response ... 110

3.2.5 Sedriv model ... 117

3.2.6 Examples ... 118

3.3 Impact of Morphological Change on Flood Risk ... 122

3.4 Description of Case Studies... 122

3.4.1 Thames Estuary... 122

3.4.2 Severn Estuary ... 133

3.4.3 The Upper German Rhine ... 140

3.5 Review of Existing Knowledge ... 166

3.5.1 Introduction ... 166

3.5.2 Fluvial Geomorphological Processes ... 166

3.5.3 Estuarine Processes ... 172

3.5.4 Geomorphological techniques... 173

3.5.5 Numerical modelling... 173

3.5.6 Physical Modelling... 176

3.5.7 Data Requirements ... 176

3.5.8 Information for studies on estuary morphology ... 178

3.6 Knowledge gaps and uncertainty... 178

3.6.1 Introduction ... 178

3.6.2 Knowledge gaps ... 178

3.6.3 Uncertainty... 179

3.7 Assessing morphological impact on flood risk... 179

3.7.1 Background ... 179

3.7.2 Framework for assessing impacts of morphological change on flood risk 180 3.7.3 Interpreting the future geometry flood risk assessment ... 182

3.7.4 Implication of morphological change on system analysis... 183

3.8 Proposed programme of research ... 185

3.8.1 Introduction ... 185

3.8.2 Processes ... 186

3.8.3 Data collection methods ... 188

3.8.4 Modelling ... 189

3.8.5 Application and testing ... 191

3.9 Conclusions ... 192

4. Discussion and Conclusions... 193

4.1 Key findings ... 193

4.1.2 Coasts ... 194

4.1.3 Rivers and Estuaries... 194

4.2 Links with other Tasks ... 195

4.3 Concluding remarks... 195

5. References ... 196

Tables

Table 2.3.1 Mean characteristics of storm classes. ([%]: percentage of occurrence; n: number of storms; Hs máx: wave height at the peak of the storm; Tp máx.: peak period associated to Hs máx;

Energy: 2 2 1 t s t H dt

∫

Table 2.3.2 Mean and standard deviations of wave run-up Ru2% in reflective and dissipative beaches

and storm surge for each storm class. 33

Table 2.3.3 Erosion potential measured as mean eroded volumes in reflective and dissipative beaches

for each storm class. 36

Table 2.3.4 Erosion potential (mean beach retreat) in reflective beaches for each storm class. 36

Table 2.6.1 Summary of data used to calibrate and validate the overwash model 96

Table 3.2.1 Summary of quantitative Lane’s balance 116

Table 3.4.1Historical anthropogenic activities in the Thames Estuary 124

Table 3.4.2 Historical changes in intertidal and subtidal area, volume and depth 127

Table 3.4.3 Modelled changes in spring tide water levels arising as a result of morphological changes

over the last century 129

Table 3.4.4 The modelled effect of historical changes in morphology on extreme levels in the Thames

Estuary, assuming no operation of the Thames Barrier 132

Table 3.4.5 Representative stations along the upper Rhine. 149

Figures

Figure 1.1.1 Summarises the structure of activities and actions within Task 5. 1

Figure 1.1.2 Flow chart of the ‘Source-Pathway-Receptor’ risk paradigm used within FLOODsite. 2 Figure 2.1.1Examples of beach lowering near the base of a seawall after a storm (left) and wave overtopping a seawall affecting major transport infrastructure (right). (This event was not particularly severe in terms of the wave heights but was estimated by regional authorities to have a return period of over 100years, due mainly to its unusual surface wind direction, which was strong easterlies.) (Photos

courtesy of Prof D Reeve). 6

Figure 2.1.2 Groyne field illustrating alongshore variation in beach levels. 7

Figure 2.1.3. Timber groyne and steps illustrating difference of beach levels. Each step has a height of

about 20cms. (Photo courtesy of Prof D Reeve). 1

Figure 2.1.4 Dune system subject to erosion at its toe. (Photo courtesy of Prof D Reeve) 2 Figure 2.2.1Ranking of the six variables in the CVI for the US eastern coastline (from Thieler and

Hammer-Close, 1999) 5

Figure 2.2.2 The structure of GTI-SEAMaT 12 Figure 2.2.3Example of extent of WAVEMaT coverage 14 Figure 2.2.4 Identification of 73 sediment cells around Calabria, adapted from TECHNITAL et al.

(2002) 14

Figure 2.2.5 Example of WAVEMaT output; interactive wave rose and nearshore depth contours 16 Figure 2.2.6 Representation of sediment cell as shore-normal transects – wave climates are derived

automatically at the seaward end of each transect 17

Figure 2.2.7 Display of transect data within GTI-SEAMaT 18 Figure 2.2.8 Sediment transport calculations for each transect – here transect 10 of 10 19 Figure 2.2.9 Cross-shore distribution of drift in the vicinity of Roccella, Regione Calabria, Italy,

showing the tendency for drift convergence to occur 20

Figure 2.2.11 Profile derivation within GTI-SEAMaT searching for defence-type from NFCDD. 23 Figure 2.2.12 Broad-scale shoreline evolution and toe level at defence within GTI-SEAMaT 24 Figure 2.2.13 The RASP Framework. A risk-based modelling approach based on the

Source-Pathway-Receptor concept 24

Figure 2.3.1 Area of study and location of measurement points. 28

Figure 2.3.2 Cluster analysis of storms (Top: threshold of 1.5 m, bottom: threshold of 2 m and

double-peak criteria). 29

Figure 2.3.3 Mean values of Hs max vs storm duration for each storm class. 31

Figure 2.3.4 Directional distribution of storm types. 31

Figure 2.3.5 Seasonal distribution of storm types. 32

Figure 2.3.6 Main variables used to characterize storm-induced profile changes: eroded volume ∆V

(m3/m) in the inner part of the beach and, beach retreat, ∆X (m). 34

Figure 2.3.7 Computed eroded volumes in reflective and dissipative beaches vs corresponding JA dt

values. 35

Figure 2.3.8 Hazard areas to inundation induced by storms of each class in a reflective beach

(s’Abanell, Girona). 38

Figure 2.3.9 Hazard areas to erosion induced by storms of each class in a reflective beach (s’Abanell,

Girona). 38

Figure 2.4.1 Sketch of bay in static equilibrium, (adapted from González and Medina, 2001). 43

Figure 2.4.2 Map for the Christchurch Bay in the UK. 47

Figure 2.4.3 Field surveys from SANDS for the Christchurch Bay in the UK. 48

Figure 2.4.4. Zoom of the Christchurch Bay in details (from SANDS), located in the town of Barton as

shown in Figure 2.4.2. 49

Figure 2.4.5 Inshore wave directions located at the Christchurch Bay from October 15, 1986 to

September 6, 2004. 49

Figure 2.4.6 Comparison between the static equilibrium of the Christchurch bay according to the

parabolic shape and one survey in February 2005 (Barton-on-sea to Milford-on-sea). 50

Figure 2.4.7. The comparison of the normal probability distribution (denoted as red dotted line) and

the wave direction data (denoted as blue ‘+’ symbol) in the Christchurch bay. 51

Figure 2.4.8. Comparison of the dimensionless variances of crenulate shaped bay shape, 0

R

R

against

the angles between the wave crests and radius

R

Figure 2.5.1 Qualitative hazard scale of coastal changes during storms as a function of water level (USGS, 2001). From left to right: swash, collision, overwash and inundation regimes. 54 Figure 2.5.2 Vertical aereal view of Assateague Island (Google Earth) and oblique photos taken after

the impact of the storm (USGS). White rectangle indicates the area of study. 55

Figure 2.5.3 Top: Run-up (Ru2%), total mean water above NAVD88 and freeboard over the barrier (Ru-D) during the storm. Bottom: Recorded nearshore (9 m depth) significant wave height at Ocean City

and water level at Ocean City inlet. 56

Figure 2.5.4 Barrier cross-profile types as a function of the response. 57

Figure 2.5.5 Frontal barrier height change due to the storm impact for the different profile types

(post-storm vs pre-(post-storm heights). 58

Figure 2.5.6 Definition sketch of sediment volume deposited in the backbarrier (Qow) and sediment

volume eroded from the barrier seaside (Qer). 59

Figure 2.5.7 Top: Along-barrier distribution (from S to N) of cumulative freeboard cumulative freeboard over the pre-storm barrier configuration (χpre-storm), sediment volume deposited in the backbarrier (Qow), sediment volume eroded from the barrier seaside (Qer). Bottom: Along-barrier

distribution of barrier height before storm impact. The dashed line indicates the height of the frontal

dune if present. 60

Figure 2.5.8 Left: Ratio of sediment deposited in the backbarrier over sediment eroded from the barrier seaside vs cumulative freeboard. Right: Cumulative overwash transport during the storm duration vs

cumulative freeboard over the pre-storm barrier configuration. 60

Figure 2.5.9 Definition sketch of sediment volume deposited in the backbarrier (Qow) and sediment

Figure 2.6.1 Examples of beaches with a wide foreshore backed by coastal dunes. 63 Figure 2.6.2 Morphological processes controlling coastal dune and barrier island evolution: (a)

overwash fans at Assateague Island and (b) breach at Ocean City, Maryland. 64

Figure 2.6.3 Definition sketch for modelling dune erosion due to the impact of runup waves. 67 Figure 2.6.4 Non-dimensional time evolution of the relative dune foot location for different relative

dune heights (zm/R=0.2). 69

Figure 2.6.5 Eroded volume during a number of storms from the Birkemeier et al. (1988) data base as

a function of an impact parameter obtained from an analytical solution. 73

Figure 2.6.6. Definition sketch showing the cross section of a barrier beach subject to overwash by

wave runup. 74

Figure 2.6.7. Unconfined (a) and confined (b) runup overwash during non storm overwash on the Ria

Formosa Barrier Islands, Portugal (Matias et al. 2003). 74

Figure 2.6.8 Cross shore profiles taken prior to and following Hurricane Hugo at Folly’s Beach, North Carolina, showing typical profile change after runup overwash (Eiser and Birkemeier, 1991). 75 Figure 2.6.9. Definition sketch showing the cross section of a barrier beach subject to inundation

overwash. 76

Figure 2.6.10. Cross-shore profiles taken prior to and following two northeaster storms at Assateague Island, Maryland, showing typical profile change after inundation overwash (Larson et al. 2004d).76 Figure 2.6.11. Confined washover fan deposited on Ocracoke Island, North Carolina, during Hurricane

Isabel, September 2003. 76

Figure 2.6.12 Zones of sediment transport during overwash (after Larson et al. 2004d). 82

Figure 2.6.13 Conceptual model for flow on the back slope. 83

Figure 2.6.14 Definition sketch for analytic model of dune erosion due to wave impact and overwash 85 Figure 2.6.15 Non-dimensional dune elevation (s’) as a function of non-dimensional time (t’) for different values on the ratio between initial overwash transport and dune erosion due to wave impact

(qdo/qo) (ξ=(R-zo)/so was set to 1.5). 87

Figure 2.6.16 Non-dimensional evolution of geometric properties describing a rectangular dune

subject to transport from overwash and dune impact. 88

Figure 2.6.17 Definition sketch for analytic barrier island response model 89

Figure 2.6.18. Empirical distribution function describing the probability of non-exceedance for a specific eroded volume during a storm event attacking a high dune at Ocean City, Maryland. 92 Figure 2.6.19 Empirical distribution function describing the probability of non-exceedance for a specific duration of dune erosion for a storm event attacking a high dune at Ocean City, Maryland.92 Figure 2.6.20 Empirical distribution function describing the probability of non-exceedance for a specific eroded volume during a storm event attacking a barrier profile at Assateague Island,

Maryland. 93

Figure 2.6.21 Empirical distribution function describing the probability of non-exceedance for a specific eroded volume during a storm event attacking a barrier profile at Assateague Island,

Maryland. 94

Figure 2.6.22 Empirical distribution function describing the probability of non-exceedance for a specific duration of dune erosion for a storm event attacking a barrier profile at Assateague Island,

Maryland. 94

Figure 2.6.23. Results of model calibration for profile 2883 at Folly Beach, SC. 98

Figure 2.6.24 Results of model calibration for profile 4230 at Garden City Beach, SC. 98 Figure 2.6.25 Results of model calibration for profile GPS4 at Assateague Island, MD. 99 Figure 2.6.26 Results of model calibration for profile GPS3 at Assateague Island, MD. 99

Figure 2.6.27 Results of model calibration for profile 10 Metompkin Island, VA. 100

Figure 2.6.28 Results of model calibration for profile 74 Ocean City, MD. 100

Figure 2.6.29 Results of model validation for profile 2330 Assateague Island, MD (also included is the

pre-storm profile GPS1 from Assateague Island) 101

Figure 3.2.1 Sketch of situation 109

Figure 3.2.2 Sketch of initial changes 110

Figure 3.2.3 Lane’s balance (after Lane, 1955) 111

Figure 3.2.5 Schematic representation of an alluvial channel. Left: long profile; right: cross-section117

Figure 3.2.6 Four-point implicit Preismann finite-differences scheme. 118

Figure 3.2.7 Sketch of local constriction 119

Figure 3.2.8 Local constriction: short-term changes (7 days) 119

Figure 3.2.9 Local constriction: mid-term changes (100 days) 119

Figure 3.2.10 Local constriction: long-term changes (5 years) 120

Figure 3.2.11 Sketch of local enlargement 120

Figure 3.2.12 Local enlargement: short-term changes (7 days) 121

Figure 3.2.13 Local enlargement: mid-term changes (100 days) 121

Figure 3.2.14 Local enlargement: long-term changes (5 years) 121

Figure 3.4.1 Location Plan 125

Figure 3.4.2 Summary of changes to Thames Estuary morphology between the 1910s and 1990s (Light grey indicates depth has reduced, dark grey indicates depth has increased. Changes of less than 0.5

metre are not shown) 128

Figure 3.4.3 Modelled changes to spring tide levels in response to changes in morphology 129 Figure 3.4.4 Modelled changes to spring tide range in response to changes in morphology 130

Figure 3.4.5 Study area along the northern shoreline of the Severn Estuary 134

Figure 3.4.6 (a) the full course of the Rhine river; (b) the considered reaches between Basle and Mainz 141 Figure 3.4.7 Planform evolution of the Upper German Rhine from 1800 till today (from Casper, 1959)

141

Figure 3.4.8 Confinement of the Rhine at Blodelsheim (adapted from Casper, 1959) 142

Figure 3.4.9 Exposed bedrock outcrops at Istein, a few kms downstream of Basle (Google Earth®_,

2006) 143

Figure 3.4.10 Canalisation of the Rhine by (a) full derivation into the Alsace Canal, here at Fessenheim; (b) short derivation and restitution to the Rhine, here at Gerstheim (Google Earth®_{, 2006)}

144 Figure 3.4.11 Meander shortcuts between Maxau and Mannheim. Left: shortcut names and years;

Right: old (red) and new (white) river courses (Google Earth®_{, 2006) 145}

Figure 3.4.12 Meander shortcut at Erfelden in successive stages of development: figures reproduced

from Casper (1959) and overlain on aerial photographs (Google Earth®_{, 2006) 146}

Figure 3.4.13 Meander shortcuts at Gemersheim (in service since 1833). Above: sketch reproduced from Casper (1959); below: aerial photograph of the present situation (Google Earth®_{, 2006) 147} Figure 3.4.14 Longitudinal profile along the Rhine. Horizontal: distance from Kembs; vertical:

elevation in meters above sea level 148

Figure 3.4.15 Example of planform of the Rhine captured from aerial photographs in wgs-84 world

co-ordinates. Present course in colour, old course in black and white 150

Figure 3.4.16 Daily discharges measured at the Maxau station over the period 1989-1999. Data obtained from the Deutsches Gewässerkundliches Jahrbuch (available at http://www.dgj.de) 150

Figure 3.4.17 Long profiles of two characteristic grain sizes (d50 and d90) 151

Figure 3.4.18 Long profile of the Rhine from Basle to Maxau, in the initial situation (c.a. 1840) 152 Figure 3.4.19 Example of planform of the Rhine captured from aerial photographs in wgs-84 world

co-ordinates. Present course in colour, old course in black and white 152

Figure 3.4.20 Morphological evolution over 1 year after constriction; reaches extending from Basle to

Maxau 154

Figure 3.4.21 Morphological evolution over 5 years after constriction; reaches extending from Basle to

Maxau 155

Figure 3.4.22 Evolution of sediment transport rates during the 1st year of morphological evolution;

reaches extending from Basle to Maxau 156

Figure 3.4.23 Pre- and post-shortcut channel planform from Maxau to Mainz. Left: documented satellite mosaic image (from Google Earth®); middle: pre-shortcut channel; right: post-shortcut

channel 157

Figure 3.4.24 Pre-shortcut (red) and post-shortcut (blue) long profiles from Maxau to Mainz 157 Figure 3.4.25 Pre-shortcut (red) and post-shortcut (blue) profiles of channel slope from Maxau to

Figure 3.4.26 Pre-shortcut (red) and post-shortcut (blue) profiles of channel slope from Maxau to

Mainz, plotted versus latitude 158

Figure 3.4.27 Pre-shortcut (red) and post-shortcut (blue) profiles of channel width from Maxau to

Mainz, plotted versus chainage 159

Figure 3.4.28 Evolution of water (blue) and bed (red) profiles for the pre-shortcut simulation from

Maxau to Mainz 160

Figure 3.4.29 Rating curve of sediment discharges versus water discharges at a representative station

located at x=130 km downstream of Maxau. Pre-shortcut simulation 161

Figure 3.4.30 Pre-shortcut bed level changes from Maxau to Mainz, in m. Positive values indicate

deposition, negative values indicate erosio 161

Figure 3.4.31 Evolution of water (blue) and bed (red) profiles for the post-shortcut simulation from

Maxau to Mainz 163

Figure 3.4.32 Post-shortcut bed level changes from Maxau to Mainz, in m. Positive values indicate

deposition, negative values indicate erosion 164

Figure 3.4.33 Pre-shortcut (dashed blue line) and post-shortcut (plain red line) yearly sediment

transport rates, averaged over the full simulation period 164

1. Introduction to Task 5 and related activities

1.1 Introduction

This report is the deliverable from the FLOODsite partners contributing to Task 5 – Predicting Morphological Changes in Rivers, Estuaries and Coasts. Task 5 is part of sub theme 1.2. It is split into two activities:

1. Morphological changes coastal; 2. Morphological changes rivers/estuaries.

Within each activity there are a number of actions which have been the responsibility of individual partners. Figure 1.1.1 summarises the structure of activities and actions within Task 5.

Figure 1.1.1 Summarises the structure of activities and actions within Task 5.

Figure 1.1.1 Summarises the structure of activities and actions within Task 5

Theme 1 of the FLOODsite project is comprised of three subthemes. The principal objectives of each subtheme of Theme 1 are:

1.1 To improve understanding of the primary drivers of flood risk (waves, surges, river flow etc.) through research targeted at key issues and processes that contribute most to current uncertainty in flood risk management decisions.

1.2 To Improve understanding, models and techniques for the analysis of the performance of the whole flood defence system and its diverse components, including natural and man-made defences (e.g. seawalls, embankments, dunes) and the extent of inundation.

1.3 To understand the vulnerability and sensitivity of the receptors of risk and to improve and harmonise the methods to evaluate societal consequences and to estimate flood event damages

Task 5: Morphological changes in rivers, estuaries and coasts

Activity 1

Leader: UPC Morphological changes coast Action 1 New techniques describing statistical behaviour

(UOP)

Action 2 Regional evolution models for regional changes (UPC)

Action 3 Development of rapid coastal evolution models (HRW)

Action 4 Dune models (UniLund)

Activity 2

Leader: HRW

Morphological changes river / estuary Action 1 Investigating suitable modelling approaches

that could be used to assess the potential for morphological change (HRW)

Action 1a Investigating suitable modelling approaches that could be used to assess the potential for morphological change (UCL)

Time: 13-36 PM: 43.4

Task 5 will provide an improved ability to predict morphological changes over both short and longer timescales in (i) coastal regime (morphological change of beaches and predictive tools for the response of dunes to storm loading); and (ii) riverine regime

As stated in the Description of Work, the ‘…FLOODsite framework and tools should be consistent. This requires a sound knowledge of relevant processes and interactions associated with flood hazard and vulnerability, including all constraints and potential change. Even with risk mitigation measures in place, a degree of residual risk will remain which requires “acceptable” flood risk to be determined. To allow the risk analysis to be performed at different decision levels, methodologies and models will be developed for feasibility level (holistic approach) as well as for preliminary and detailed design level. The proposed research in this theme is structured in three sub-themes according to the concept of hazard and vulnerability and in conjunction with the well-established source-pathway-receptor model.’

The source-pathway-receptor model is shown in Figure 1.1.2 below, which also shows how the overall flood risk and residual risk is derived. Subtheme 1.2, (and Task 5), contributes to the box titled ‘Risk Pathways’.

Figure 1.1.2 Flow chart of the ‘Source-Pathway-Receptor’ risk paradigm used within FLOODsite. Figure 1.1.2 Flow chart of the ‘Source-Pathway-Receptor’ risk paradigm used within FLOODsite

This report details the findings of research work undertaken as parts of Activity 1 (Actions 1, 2, 3 & 4), and review work performed under Activity 2 (Actions 1 and 1a).

1.2 Background

The riverbed level or beach level is a critical factor in determining the stability of flood defences. The bed is not usually visible during extreme storms and measurements of bed variations are relatively scarce. The uncertainty associated with this lack of understanding is reflected in conservative design codes and also in the difficulties faced by operating authorities in planning maintenance works, issuing flood warnings to the public and organising emergency actions in response to flooding events.

In rivers, scouring of the bed can lead to undermining of the toe of flood defence structures. In coastal locations the interactions are more complicated because again the level of the beach at the toe of the structure is important in determining its stability. But also, the level and slope of the beach controls the propagation of waves across the foreshore and can affect the rate and direction of sediment transport.

Evaluation of

„Toler-able“ Risk

Residual Flood Risk

t f

R

Risk Sources

Risk Pathways

• Loads & Resistances • Defence failures • Inundation

Risk Receptors

• People & property • ecological impact • Risk perception

Expected damages

E(D)

Predicted Flood Risk

c c

f f

R P E(D)

= ⋅

Some progress in understanding the key processes has already been made in these regards. However, the problem is complicated by the range of time and space scales over which changes occur and over which management decisions have to be made. At one end, large but localised scour occurring over the duration of a single storm may lead to localised flooding or structural damage which can be made good during the course of maintenance programmes. At the other end, long term more gradual changes in the foreshore/riverbed morphology may eventually need complete removal or realignment of flood defence schemes – requiring a much more strategic approach.

1.3

Aims and objectives

The work in Task 5 aims to provide an improved ability to predict morphological change over both short and longer timescales in both coastal and riverine regimes. The two specific aims of this task are as follows:

Specific objective 1) - Morphological change – coasts –aims to develop an improved understanding of

morphological change of beaches over large time and spatial scales and provide a better predictive tool for the response of dunes to storm loading.

Specific objective 2) Morphological change – rivers and estuaries - the specific aim of this task is to

critically review current knowledge and on-going programmes, summary existing knowledge and identify a forward programme of detailed and justified research.

This document provides a new source of information upon which risk management tools and analyses may be based. The document can, of course, be updated and extended in the future as knowledge of coastal, riverine and estuarine morphodynamics increases. Morphodynamics is an active area of academic research and as such, new results are appearing continuously as this document goes to press.

1.4 Scope

The assessment of sea defence assets seldom takes account of the morphological variability of the bed levels and slopes in front of the defence. Changes in bed morphology can have a significant influence on the wave conditions reaching the defence and may also lead to siltation (closing) of river mouths (e.g. under microtidal conditions as is the case in the Mediterranean) and, thus, enhance flooding risks. Additionally there still exists significant uncertainty in determining the pre-storm foreshore morphology and its likely drawdown during a storm (both at the toe of the defence in terms of scour as well as more extensive foreshore lowering). Therefore, the research in this task aims to improve our ability to predict morphological change in the short and longer term through detailed analysis of coastal and fluvial morphological processes and changes.

Work on this subject is somewhat further advanced in the coastal arena than for estuarine and rivers. As such, the focus of the Task partners is that the actions in Activity 1 have a strong technical research thrust, while the actions in Activity 2 provide a review of existing methods.

1.5

Links to other FLOODsite tasks

This work links with a number of other tasks within FLOODsite. Specifically there are links and exchanges of information with:

• Task 2 - Estimation of extremes. There are soft links here as some of the concepts and techniques developed within Task 2 have direct parallels with those used in Task 5;

• Task 3–Information derived from beach/barrier/dune morphodynamics during storm impacts are used in Task 3 to see their influence for Coastal Flood Hazard Mapping;

• Task 7 - Reliability analysis of flood defence structures and systems. Findings from task 5 will feed through to this task, mainly as soft links via Task 4;

• Task 26 – Pilot Study of the Ebro Delta. Models derived in Task 5 for beach/barrier/dune morphodynamics under storm impacts will be used in Task 26 to assess the beach response in selected areas of the Ebro delta coast to storms characteristics of the area.

1.6

Outline of this report

2. Coastal Morphology

2.1

Introduction to coastal issues

As noted in the DoW, flooding from rivers, estuaries and the sea poses a threat to many millions of the citizens of Europe. In the Netherlands more than half of the population lives on former river floodplain or land below mean sea level. In the UK about 10% of the population lives in areas of fluvial, tidal or coastal flood risk; and in Hungary about a quarter of the population live on the floodplain of the Danube and its tributaries. At the fiftieth anniversary of the 1953 North Sea floods, which caused about 2500 deaths across the UK, Netherlands, Belgium and Germany, great progress has been made in the science and practice of flood management both on the coast and rivers. Nevertheless, flooding remains the most widely distributed natural hazard in Europe. For example flooding leading to significant economic and social impacts, including loss of life, occurred in Britain (1998, 2000), the Czech Republic (1997, 1998 and 2002), Slovak Republic (1993 and 1997), Poland and Germany (1997, 2002), Eastern Slovakia and Hungary (1998 and 2001). The potential for flood damage is also increasing on many rivers and coastal plains arising from social and economic development bringing pressures on land use.

In the UK the Government department responsible for coast and flood defence, DEFRA, and the

Office of Science and Technology sponsored a review of the risk posed by climate change. This was

published in 2004 as the Foresight Flood and Coastal Defence Project 1 _{. This shows that the UK’s} assets at risk from flooding by the sea are valued at £132.2 billion and from coastal erosion at £7.8 billion, and some 4 million people and properties in England and Wales alone are under threat. This value is expected to grow significantly in the future as projected changes in climate lead to increasing frequency and magnitudes of severe storms and sea level rise. The protection of coastal communities and the effective Government planning of capital expenditure both rely on our ability to predict the impact of storms on sea defences.

Coastal flood defences are usually designed to withstand events with a return period of between 50 to 200 years, taking account of sea level rise. A design event could simply be a given wave height or high water level. In coastal and estuarine areas the design event will usually be a combination of many variables including wave height, wave period, wave direction, wave and wind set-up, tides, atmospheric surge, river flows and the foreshore level. Two important derived variables are: the water

depth – the difference between the still water level and foreshore level; and the freeboard – the

difference between the crest level of the structure and the still water level. The water depth provides some control on the height of waves that can reach the structure while the freeboard is a major factor in determining the volume of wave overtopping.

Flooding occurs when there is a failure of a defence. This can arise from a functional failure (the conditions exceed those for which the defence was designed) or a structural failure (where some element or components of the defence do not perform as intended under the design conditions). The former arise from society’s need to find a compromise between the cost of the defence and the consequences of a flood. Structural failures are generally more dangerous, as they are unexpected, and have been the source of recent notable flooding events. Two major causes of structural failure are: low freeboard leading to excessive wave overtopping of the defence leading to erosion of the back and crest of the defence, or even damage to the armour layers; and toe failure, where erosion of the foreshore at the base of the defence occurs to such an extent that the structure is undermined and collapses.

1

An example of scour at a seawall in Teignmouth after a severe easterly storm, showing beach lowering near the base of the structure. Beach levels in this photograph are approximately 2m below their ‘typical’ levels.

Wave overtopping of a seawall at Teignmouth on the main London-Penzance mainline. The overtopping resulted in the temporary closure of the mainline and extensive damage to sea defences along a large portion of the south and Southwest coastline.

Figure 2.1.1Examples of beach lowering near the base of a seawall after a storm (left) and wave overtopping a seawall affecting major transport infrastructure (right). (This event was not particularly severe in terms of the wave heights but was estimated by regional authorities to have a return period of over 100years, due mainly to its unusual surface wind direction, which was strong easterlies.) (Photos

courtesy of Prof D Reeve).

Figure 2.1.2 Groyne field illustrating alongshore variation in beach levels.

Figure 2.1.3. Timber groyne and steps illustrating difference of beach levels. Each step has a height of about 20cms. (Photo courtesy of Prof D Reeve).

Beaches can be an efficient absorber of wave energy and are a major component of many new sea defence schemes. However, beaches are not static in form and their mobility introduces a degree of uncertainty into the performance of defence schemes that rely on them to absorb some of the incoming wave energy. It is currently not practicable to predict the response of a beach by following the movement of individual particles of sediment. Rather, a length of shoreline is characterised by, for example, its cross-sectional profile and plan shape. Numerical models that are used to predict the evolution of the shape of the shoreline thus play a key role in assessing the safety of coastal developments. In current practice, numerical models are employed in a deterministic fashion and do not explicitly account for the variability likely to be experienced in real situations.

In practise, the wave climate, as described by spectral parameters such as Hs and Tz, varies over distinct

time scales. Examples of these timescales would be: the typical duration of a storm, the duration between storms, and the seasonal variation in storm occurrence. The importance of treating the temporal correlation may be appreciated when the beach response to a single large event or a succession of storms is considered. Under such conditions the beach is likely to reach extremes of excursion from its mean alignment, raising the risk of flooding and/or damage to beach control structures. The temporal correlation function can describe both the duration and ‘groupiness’ of storm events; while the probability density function describes the likelihood of a storm of a specific intensity.

Figure 2.1.4 Dune system subject to erosion at its toe. (Photo courtesy of Prof D Reeve)

However, dunes are particularly vulnerable to erosion, and if there are fixed assets being defended by a dune system, active management may be required. Figure 4 shows an example of a dune system which has been subject to recent erosion due to storm waves during a high tide and surge event. A reliable means of predicting the rate of dune erosion under wave attack would be a valuable tool for coastal managers, as would a means of quantifying the amount of overwash to expect should the dunes be breached. Both these questions are addressed in the following sections.

2.2

Development of Rapid Coastal Evolution Models

2.2.1 Introduction

It is recognised that there is a need to consider the wider-scale impacts of intervention where coastal engineering is concerned (e.g. Defra, 2003a), although there is little advice relating to exactly what constitutes “wider-scale”, which is understandable given the computational difficulties associated with defining processes over great distances. Decisions such as those which contributed to the loss of the village of Hallsands in Devon during a storm in 1917 (Hails, 1975) have forced engineers and scientists to examine the consequences of intervention in more detail.

Through a gradual increase in process understanding together with the benefit of past experience (documented through gradually improved monitoring programs), it is recognised that intervention requires analysis on a broad-scale, both in time and space. This is mega-scale modelling, which is considered here to be time-scales of the order decades to centuries, and space-scales of 10s to 100s of kilometres.

Since the early 1970’s, engineers and managers charged with defending coastlines have had recourse to advanced numerical methods as aids to understanding the processes governing their particular coastline of interest. These tools are generally subject to continual enhancement and refinement at a pace akin to the advances in understanding of the physical processes taking place. Also of benefit is the rapid development and general availability of phenomenal processing power. Understandably, when considering complex and dynamic systems, the application of such progressive tools has always been accompanied by appropriate caveats, for example; by defining levels of confidence in results, or describing uncertainties, or possible sources of inaccuracy, to name but a few.

response to such pressure has been that measurement and monitoring programmes are also developing apace. For example, during the late 1980’s the Rete Ondametrica Nazionale (RON) wave measurement network around the entire coastline of Italy was established. Some of the oceanographic buoys in this centrally controlled monitoring system have now provided over 15 years of directional wave data. Italy’s version of Teletext (Televideo) provides real-time “latest measured” wave data at discrete locations around the coast. In the UK, more recently, the Channel Coastal Observatory was set up with £8M of Government money to monitor the south-east coastline, and has since been granted with additional funds to increase the coverage to include the south-west of England.

The result of such national-scale monitoring programmes is that they inevitably produce vast quantities of data, the management of which is also subject to specialist research. At the same time the quality of scientific advancement in the field of coastal engineering is such that numerical model reliability is also increasing apace. One challenge is to identify methods through which numerical modelling can exploit the existence of the emerging data that has long been absent.

It is presently impractical to provide blanket coverage of measurements of certain metocean parameters over large areas of the nearshore zone. Time-series of wave conditions, for example, can be measured at discrete “offshore” locations. When considering mega-scale spatial scales, however, it is prohibitively expensive as well as impractical to measure wave conditions on a fine enough spatial resolution to resolve local effects of bathymetric changes. Numerical models of wave propagation, on the other hand, can be tuned to reproduce observed discrete time-series. This process of tuning is known as calibration, and it is not unreasonable to expect a calibrated numerical model to provide reliable predictions throughout much of the model domain. Given the existence of powerful desktop processors, broad-scale monitoring programmes and reliable numerical models, it is now becoming possible, as described in this paper, and cost-effective to “fill in the gaps” inherent with monitoring schemes with calibrated numerical tools able to cover broad regions.

The benefit to coastal managers, who have long recognised the need to consider the broad-scale effect of intervention, is that broad-scale processes can now be examined with robust and reliable numerical and probabilistic tools over the time- and spatial-scales required.

Understanding how a section of shoreline, in the context of the broader-scale, might behave in the future, in terms of a combination of both the seasonal and the long-term morphology, is key to solving many of the problems faced by managers, not least by enabling the dynamic rather than static treatment of beaches in flood and erosion risk assessment methods. By treating beaches as dynamic defence units, rather than static defence units, the flood risk assessment constitutes a more thorough analysis, taking the assessment one step closer to advising on optimal flood management practice.

A clear, efficient, and cost-effective method of exploiting the existence of both numerical tools and measured data sets is presented here. This report introduces the concept and basic functionality of the fully integrated, dynamically linked coastal management tool GTI-SEAMaT (Geographical and Temporal Interface – Shoreline and Environment Analysis and Management Tool), and how this tool offers the potential to enhance the process of flood risk assessment.

2.2.2 Review of current modelling methods

GEOMORPHOLOGICAL ANALYSES

Geomorphology is the study of the features that make up the earth’s surface and their relationship to the underlying geology. A geomorphological study will provides a conceptual picture of coastal processes and the potential behaviour of the coastal system. This includes taking into account changes in the bedrock composition that could affect the potential rate of future coastal evolution. The results tend to be qualitative, rather than quantitative.

techniques, as advocated by, for example, Cooper and Pilkey (2004). This section reviews the derivation of a sediment budget, and précis several prominent recent research programmes which have had a significant geomorphological component. Geomorphological behavioural models are also reviewed.

Sediment Budget

Sediment budgets are often constructed to assist with coastal management. A sediment budget allows an estimate to be made of the rate of accretion or erosion of sediment within a predefined area of the coastal zone (see Rosati, 2005, for a recent review). The main steps involved in constructing a sediment budget are:

• Set appropriate boundaries for the sediment budget and for internal boundaries that separate sub-cells within the overall area to be considered;

• Identify sources, pathways, stores and sinks of sediment within the budget area;

• Calculate the rate of erosion from sources and stores and accretion in stores and sinks. These estimates may come from numerical models but are more likely to be derived from data; • Calculate the sediment transport rates at the boundaries of the sub-cells and estimate the

uncertainty in each transport rate. The calculations of transport rate may come from data but are more likely to be derived from numerical models; and

• Integrate the gains and losses within each section to obtain an overall sediment budget.

USGS Coastal Vulnerability Index

The US Geological Survey (USGS) has devised a physically based coastal vulnerability index

(CVI) to assess the vulnerability of the coastline to climate change (Hammer-Close and Thieler, 2001; Thieler and Hammer-Close, 1999, 2000a, 2000b). The prediction of future coastline position is a difficult task, for which no standard predictive techniques have been developed. The National Research Council (1990) report listed the following approaches and outlined the limitations of each:

• extrapolation of historical data (e.g. coastal erosion rates); • static inundation modelling;

• application of a simple geometric model (e.g. the Bruun Rule); • application of a sediment dynamics/budget model; or

• Monte Carlo (probabilistic) simulation based on parameterized physical forcing variables. In addition to the limitations of the approaches, the data needed to apply the approaches is likely to be of variable quality (if it exists at all). Furthermore human intervention at the coast will affect its development and the priorities of coastal management. The USGS team collected data on the following six physical variables (Hammer-Close and Thieler, 2001; Thieler and Hammer-Close, 1999, 2000a, 2000b):

1. Geomorphology derived from state geology maps.

2. Shoreline erosion and accretion rates (m/yr) from the Coastal Erosion Information System (May et al., 1982);

3. Regional coastal slope (percent), from the sub-aerial coastal plain to the submerged continental shelf. This was calculated using data from up to 50km offshore, as coastal slope affects the risk of flooding and coastal erosion (Pilkey and Davis, 1987);

4. Rate of relative sea-level rise (mm/yr) from tide gauges; 5. Mean tidal range (m) from the National Ocean Service; and

6. Mean wave height (m) from the USACE Wave Information Service.

Large tidal ranges were assigned a low risk as high tidal levels and high storm surges will occur together for relatively short periods of time compared to situations with a low tidal range.

Figure 2.2.1Ranking of the six variables in the CVI for the US eastern coastline (from Thieler and Hammer-Close, 1999)

The CVI for each section of coastline was calculated using Equation 1, where a, b, c, d, e and f are the integer rankings of the six variables in Figure 2.2.1.

CVI = (a ×b×c× d ×e× f ) 6 (2.1.1)

The CVI values were placed in rank order and the 25th, 50th and 75th percentiles were chosen as the boundaries between the ranges for low, moderate, high, and very high risk areas (Thieler and Hammer-Close, 1999). Different variables contributed the most to vulnerability in different sections of the coastlines mapped. Examining the results at a more detailed scale showed that erosion and accretion rates contributed the greatest variability to the CVI at short (~3 km) spatial scales (Thieler and Hammer-Close, 1999). The rates of shoreline change were believed to be the most poorly documented variable used, indicating that improvements to the methods of determining shoreline position and adopting a consistent approach along the whole of a section of coastline to be considered would lead to improvements in the vulnerability assessment.

Boruff et al. (2005) developed a coastal social vulnerability index (CoSVI) to determine the socio-economic vulnerability of coastal counties to sea level rise. They also combined the CVI with the CoSVI to determine an overall place vulnerability index (PVI). Maps of CVI, CoSVI and PVI were produced for US Atlantic coastal counties, Gulf coastal counties and Pacific coastal counties.

Eurosion

Eurosion (European Commission, 2004) was a European study into coastal erosion at a European scale. Its outputs were:

• A map-based assessment of European coasts exposure to coastal erosion; • A review of existing practices and experience of coastal erosion management;

• Guidelines to incorporate coastal erosion into environmental assessment, spatial planning and hazard prevention; and

• Policy recommendations to improve coastal erosion management.

region. The maps also include the location of engineering works (whether harbours, jetties groynes or breakwaters). There is an additional map for regional exposure to coastal erosion.

Eurosion concluded that a more strategic and proactive approach to coastal erosion is needed for the sustained development of vulnerable coastal zones. It developed the concept of coastal resilience: the inherent ability of the coast to accommodate changes induced by sea level rise, extreme events and occasional human impacts, whilst maintaining the functions fulfilled by the coastal system in the longer term. To promote coastal resilience, Eurosion introduced the concept of favourable sediment status: the situation where the availability of coastal sediment supports the objective of promoting coastal resilience in general and of preserving dynamic coastlines in particular. This should be achieved for each coastal sediment cell by designating strategic sediment reservoirs: supplies of sediment of appropriate characteristics that are available for replenishment of the coastal zone, either temporarily (to compensate for losses due to extreme storms) or in the long term (at least 100 years). They can be identified offshore, in the coastal zone (both above and below low water) and in the hinterland. A coastal sediment cell is a coastal compartment that contains a complete cycle of sedimentation including sources, transport paths, and sinks. The cell boundaries delineate the geographical area within which the budget of sediment is determined, providing the framework for the quantitative analysis of coastal erosion and accretion. Eurosion considered that coastal sediment cells constitute the most appropriate units for achieving the objective of favourable sediment status and hence coastal resilience (European Commission, 2004).

Futurecoast

Futurecoast (Halcrow, 2002, Burgess et al., 2002) was commissioned by Defra (2003b), to improve the understanding of coastal evolution for the open coast of England and Wales. Futurecoast is a starting point for any assessment of future coastline behaviour over decadal timescales. It contains:

• Shoreline behaviour statements that give an improved understanding of coastal behaviour and qualitative predictions of future coastal evolution at both large and small scales;

• Assessment of future behaviour for an unconstrained scenario (with no defences or management) and a managed scenario (where present management practices continue indefinitely); and

• A ‘toolbox’ of supporting information and data including cliff behaviour statements, historical shoreline changes, wave modelling, an uncertainty assessment, morphological measurements including beach width, a coastal geomorphology reference manual and a thematic studies on onshore geology, offshore geology, coastal processes, climate change and estuaries.

Honeycutt and Krantz (2003) also illustrated how the local geology affected shoreline change rates along the Delaware coast, using data from high-resolution seismic-reflection profiles, cores and historic shoreline positions. They believe that it may be possible to quantify the effect of large-scale changes in geology on shoreline erosion, but not small-scale ones. Honeycutt and Krantz (2003) provide a different scientific basis for modifying calculations of past shoreline change rates to estimate future shoreline change rates.

Risk Assessment of Coastal Erosion (RACE)

Defra/EA Joint R&D project FD2324, Risk Assessment of Coastal Erosion (Burgess et al., 2006) set out to establish a robust and consistent probabilistic approach to assess coastal erosion risk. It developed methods that could be applied at a range of scales to suit different end user requirements. Erosion potential and defence failure potential were considered using a range of approaches with different levels of complexity. The choice of approach will depend on the nature of the problem, the importance of the assets, the data available and the accuracy required, and the methodology requires a significant level of user time and input.

• Broad numerical analysis, using NFCDD and other data on, for example, beach levels; • Detailed calculation of failure potential

• Probabilistic models, including failure mechanisms and interactions between structural components.

The five methods for assessing erosion potential are: • Technical Judgement based on experience; • Futurecoast assessment;

• Site Specific Assessment, which adds local data to Futurecoast; • Single recession rate method;

• Probabilistic method.

The probability of erosion for a given distance and the probability of erosion for a given time can be output. Erosion maps can be produced, which can be linked to other databases of, for example, property value. The results from FD2324 will become available from the Defra Science Search web site http://sciencesearch.defra.gov.uk/.

PROCESS-BASED MEGA-SCALE NUMERICAL MODELLING

Current process-based predictive numerical modelling capability, allowing coastal evolution over a time period of decades to be examined numerically, has been achieved through several decades of committed research by numerous authors (e.g. de Vriend et al., 1993; Hanson et al., 2003). These models tend to be based upon representations of physical processes and typically include forcing by waves and/or currents, a response by sediments to this energy input, and a morphological updating module.

However, long-term morphological behaviour is still relatively poorly understood (de Vriend, 2003; Hanson et al., 2003). As a result, such models are subject to considerable degrees of uncertainty and their application requires a high level of specialist knowledge. These models include 1-line models (e.g. Hansen and Kraus, 1989; Ozasa and Brampton, 1980), and coastal area models (e.g. Chesher et

al., 1993).

Southgate and Brampton (2001) provide a guide to model usage, which considers engineering and management options and the strategies that can be adopted, while working within the limitations of a shortfall in our scientific knowledge and data. They also include a short description of the major classes of model and some of their descriptions are used in the following sections, which have been augmented by additional references and comments.

1-line models

In these models, the sand beach morphology is represented by a single contour, and such models are therefore often referred to as “one-line” models. Usually the x-axis is established approximately parallel to the coastline, and the y-axis directed offshore. The changes in the position of this contour, together with other parameters such as wave conditions, currents, and sediment transport rates, are functions of only longshore position (x) and time (t) and so the model is referred to as “one-dimensional”.

Predictions of changes in the beach and nearshore seabed plan-shape are produced. The beach profile is usually assumed to be constant, i.e. unchanging with time. A good starting point for those interested in the theory and application of beach plan-shape models is the paper by Bakker et al. (1970). This not only discusses the simplest “one-line” approach to such modelling but also takes the first step in the development of a model that allows some variation in profile along the shoreline.

One-line numerical models originated from analytical solutions to the diffusion equation for the small amplitude departures from a rectilinear coastline (Pelnard-Considere, 1956, Falques,

(Falques, 2003; Murray and Ashton, 2003; Reeve, 2006) but most one-line modelling for coastal erosion management is likely to be performed using numerical models (e.g. Hanson and Kraus, 1989; Ozasa and Brampton, 1980) due to their flexibility in modelling realistic, non-idealised coastlines. Numerical models can include seawalls and groynes.

Sometimes the one-line model is extended to model a number of different contours. These models are known as n-line models, but they are relatively uncommon compared to one-line models.

Whilst extensive portions of coastline can be covered by rectilinear one-line models, the coverage is only extensive relative to, say, that covered by a coastal area model and is usually limited to a few tens of kilometres. This is mainly due to the basic assumption of the rectilinear one-line model that the coastline is infinitely long and straight not holding in general. Even in such cases where a curvilinear coordinate system is used, such as that presented by Jiminez and Sànchez-Arcilla (2004) where the evolution of the strongly curved Trabucador-La Banya spit complex (Spain) is simulated, the lateral extent of the model is often restricted to several tens of kilometres by an alongshore variation in wave climate induced by complex nearshore bathymetry.

Coastal area models

Process-based coastal area models have been used for years to study short term (generally depth-averaged) hydrodynamic and sediment transport problems, and given their ability to simulate fields that are both identifiable and (potentially) verifiable, there is appeal in the potential for applying such models to longer term problems. However, the issues associated with application of process based models are long-established (see for example, de Vriend et al., 1993), and include problems associated with the requirement to model large areas, with relatively fine meshes (in order to resolve the relevant processes) and the need to simulate relatively long timescales. There are also the associated problems of supplying the model with the correct set of input conditions (and sometimes the sequence of these conditions) that will determine the morphology.

In order to drive the model for long-term simulations it is necessary to perform simplifying or filtering techniques. These are of 2 main types:

• Input filtering involves selecting a number of representative cases, rather than running a full time series;

• Process filtering involves reducing the number of computations made by, for example reducing the number of calls to the flow model and using continuity, for example, to adjust flow speeds between full runs of the flow model.

One of the limitations of 2D coastal area models for considering beach evolution in front of coastal structures are that surf-zone processes, such as undertow, are not represented in the model. Wave reflection and diffraction are only rarely included in coastal area models.

Coastal area models, whilst capable of a more detailed representation of physical processes such as wave propagation and tidal behaviour, are computationally relatively expensive. If sediment transport determination and morphodynamic behaviour are added to the list of outputs, computational requirements can be prohibitive unless a reduced length of coastline is to be examined. For this, and other reasons, coastal area models in their present form are generally considered here to be inappropriate tools with which to solve mega-scale problems.

Quasi-3D systems model: SCAPE

climate change and the construction of local defences to be included. The model may be run over the time-scale of decades (here long-term, but “mesoscale” to Walkden and Hall, 2005). SCAPE has been used to model the soft cliff and platform erosion at the Naze, Essex (Walkden and Hall, 2005) and the between Weybourne and Happisburgh, Norfolk (Dickson et al., 2005).

The Regional Coastal Simulator (RCS) under development by the Tyndall Centre for Climate Change Research (Pearson et al., 2005) incorporates the SCAPE model, driven by a finite-element model of hindcast wave transformation (TOMAWAC, EDF). The results of the SCAPE model are entered in to a flood-defence reliability model (Dawson et al., 2005), which then feeds a fast inundation flood propagation model (Bates et al., 2005) which is linked to an economic damage model (Koukoulas et

al., 2005) within GIS. The addition of the GIS helps presentation and interpretation of the results.

For the time-being, the RCS is a fragmented framework which requires extensive specialised knowledge to operate, and due to the nature of the Tyndall Centre (which spreads throughout Universities around the UK) each element of the RCS is operated independently from the other at each University site. Nevertheless, it is entirely feasible that the RCS could become a very useful unified modelling suite in the future.

STOCHASTIC MODELLING

A single run of a process-based numerical model gives a single deterministic prediction of the future shoreline. It is common practice among numerical modellers to also perform a series of sensitivity tests of a model, where input variables are systematically altered by some estimate of their uncertainty to see how much the output changes. This gives an indication of how sensitive the output is to the likely error in the inputs.

Stochastic modelling is related to, but different from sensitivity testing. The emergence of stochastic modelling signals a shift from making a single deterministic prediction to making a statistical forecast by generating a probability distribution of outcomes and thereby acknowledging the uncertainty in any prediction.

A statistical distribution is obtained for each of the major sources of uncertainty in stochastic modelling, which may be forcing variables or variables in the parameterisation of a process. The model is then run many times using a different random selection of variables each time and a statistical forecast is made of the output variables of interest. Examples of stochastic modelling include Dong and Chen (1999), Spivack and Reeve (2002), Reeve (2004, 2006) and Cowell et al. (2006).

Reeve (2004) highlighted the fact that stochastic modelling is relatively less well developed than deterministic modelling. Moreover there are relatively few people trained in the running of such models and the advantages of stochastic models are relatively poorly understood. Measures of central tendency from a stochastic model are analogous to the result from a single deterministic ‘best estimate’ model run (Cowell et al., 2006). Stochastic models also provide an indication of the variability about the central tendency and can be used to establish confidence limits and determine the statistical significance of differences caused by varying effects.

BEHAVIOUR-ORIENTED MEGA-SCALE MODELLING

Behaviour oriented modelling relies to some considerable degree on the existence of extensive datasets. Some behaviour-oriented approaches are entirely reliant upon measurements, others are a hybrid which utilise some degree of process-based deterministic modelling and parameterisation.

Geomorphological behaviour