climate change impact on cliff instability and erosion

FCRM>10, International Centre, Telford 28th

June – 1st July 2010

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CLIMATE CHANGE IMPACT ON CLIFF INSTABILITY AND EROSION Dr Roger Moore, Dr Jonathan Rogers, and Amy Woodget, Halcrow Group Ltd

Alison Baptiste, Environment Agency ABSTRACT The Floods and Water Management Act requires that FCRM properly reflects recently updated climate change scenarios. The potential impacts of climate change and sea level rise on future cliff instability and erosion present a significant challenge to future coastal management in the UK. The UK Climate Projections 2009 (UKCP09) point to significant increases in winter rainfall frequency, intensity and amount, drier summers and rising sea level which are likely to result in higher rates of cliff recession due to toe erosion and more frequent coastal landslides. These impacts will result in increased levels of risk to many coastal communities and assets at risk of cliff instability and erosion which will require central and local government to develop and implement policies that address the increasing risks, whilst also meeting the inevitable financial constraints. This paper presents an overview of a high-level assessment of the likely climate change impacts on cliff instability and erosion. The work was commissioned by the Environment Agency to support the National Coastal Erosion Risk Mapping Project (NCERM). The results of the assessment have informed where, and how, the NCERM erosion predictions should be modified to take into account the climate change scenarios and predictions derived from UKCP09. This important piece of research helps deliver the requirements of the Floods and Water Management Act through application of climate change scenarios, so supporting the Environment Agency’s overarching role. The research will ultimately ensure that NCERM, a high-profile, national scale undertaking, makes best use of the most up-to-date climate change projections. 1. INTRODUCTION The National Coastal Erosion Risk Mapping Project (NCERM) is currently being conducted as part of the Government’s Making Space for Water programme, on behalf of the Environment Agency. The final output of NCERM is intended to provide an essential improvement to information on coastal instability and erosion both for professionals and for the public; these are being created through an innovative modelling procedure and web-based display, and have been reviewed and amended where necessary by Local Authorities throughout England and Wales. It is clear that predicted changes to UK climate and sea level over the next 100 years are likely to have an adverse effect on coastal instability and erosion, through a number of mechanisms; namely increased sea level leading to greater wave attack on coastal cliffs, and changes in the frequency, intensity and duration of rainfall impacting on pre-existing coastal landslide complexes. The expected implications of climate change and sea level rise present a significant challenge to future coastal management. It is expected that there will be increased levels of risk to many coastal assets; the challenge facing central and local government will be to develop and implement policies which address the increasing risks, whilst also meeting the inevitable financial constraints. This study looks to build contingency for future climate change into the coastal erosion projections currently being developed by the NCERM project. The NCERM outputs provide risk bands of possible coastal erosion for three epochs into the future (20, 50 and 100 years). The aim of this project is to identify the relative vulnerability of cliffs to erosion and landsliding as a result of climate change, to provide qualitative information on the probable impacts of climate change on cliff recession, and to produce recommendations as to where, and how, this increased vulnerability should be reflected in the NCERM output. Reliable projection of cliff recession is fundamental to coastal planning and shoreline management. Indication of the likely position of the cliff line at various epochs over the next 100 years is needed to inform land use policies and to avoid locating new development in areas at risk of cliff recession; this is highlighted in the Department of Communities and Local Government recently published (March 2010) supplement to PPS25 on Development and Coastal Change. Such projections might also be used by coastal authorities to adopt a more proactive approach to evaluating the risks to existing development, to provide warnings of the risks and to mitigate the potential impacts of cliff instability and recession events through various adaptation measures.



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Projections of cliff recession are also needed to decide on the preferred strategic coastal defence option for specific management units identified in shoreline management plans. Where coast protection works or improvements may be required, accurate projections of cliff recession rates are needed to evaluate scheme options and to test their economic viability and cost-effectiveness. Such decision-making depends on the reliability of cliff behaviour assessments and the prediction of future cliff instability and erosion that account for the potential impacts of climate change and sea level rise. Over the past two decades some considerable effort has been invested into identifying the methods available for investigating and predicting cliff instability and erosion, most notably the recognition of different types of cliff behaviour (e.g. regular retreat, sudden or meta-stable change, and episodic landslide events) and the use of probabilistic approaches to address the uncertainty inherent in the cliff recession process (Table 1). Whilst these landmark commissions provide a synthesis of available assessment methods, sensitivity studies and datasets for cliffs, limited published data are available that link historical cliff recession rates with climate and sea level change. This presents a key uncertainty when evaluating the potential impacts of future climate change and sea level rise predictions on cliff instability and erosion processes and associated cliff recession rates. DATE STUDY DESCRIPTION SOURCE

1994-2001

The Investigation and Management of Soft Rock Cliffs (SRC)

Prediction of recession rates and erosion control techniques; the objectives of this research included the development of analytical methods for predicting cliff recession rates for the wide variety of differing situations around the coast

MAFF

2000-2001

Preparing for the Impacts of Climate Change

This project developed a broad-scale approach to evaluating the potential impacts of climate change and relative sea level rise along the south coast of England. The approach involved identifying and characterising shoreline behaviour models which provide the basis for assessing changes in hazard and risk

SCOPAC

2000-2002

Futurecoast This was a regional-scale study of the coast of England and Wales to inform the 2

nd round Shoreline Management

Plans. Futurecoast provides a robust geomorphological framework for conceptualising coastal evolution. It includes a database of coastal cliff behaviour units for England and Wales including various parameters of interest such as sensitivity to climate change and cliff recession potential.

Defra

2005-2006

Risk Assessment of Coastal Erosion (RACE)

The aim of this project was to develop, test and disseminate a robust and consistent probabilistic method for assessing the hazard and risk of coastal erosion. The approach is supported by data and information from the Futurecoast cliff database, strategic coastal monitoring programmes and risk-based inspections. Five techniques are developed of increasing degrees of sophistication and data input, ranging from expert judgement to probabilistic cliff recession prediction.

Defra

2006-present

National Coastal Erosion Risk Mapping Project

This project aims to map lengths of coastline susceptible to cliff instability and erosion from natural processes whilst taking account of the possible impacts of climate change and coastal defence policy and management. The project has developed innovative web-based software utilising the methods developed by RACE.

EA

Table 1 – Previous work on cliffs in England and Wales 2. CLIFF INSTABILITY AND EROSION - DEFINITIONS Coastal erosion is a process driven by wave and tidal energy at the coast which is dissipated by frictional drag associated with the scour, mobilisation and transport of sediments by tides and waves. Where coastal hinterlands and backshores are elevated relative to sea level, the action of waves and tides will erode and undercut the base of cliffs which will develop a characteristic morphology and profile reflecting their geological composition, structural form and evolution.



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Cliff instability involves slope failure and mass movement of a coastal slope or cliff and may result in the heave and deposition of debris on the beach and foreshore, noting that some landslides are large-scale and extend a considerable distance inland and offshore, and deep below beach level. Therefore, cliff instability and erosion in this context is a four stage process involving detachment of particles or blocks of material, the transport of this material through the cliff system, its deposition on the foreshore and its removal by wave and tidal action. Cliff recession is the landward retreat of the cliff profile (from cliff toe to cliff top) in response to cliff instability and erosion processes. It is not a simple or uniform process in space or time and depends on a variety of factors that control the rates of detachment and transport. 3. CLIFF BEHAVIOUR UNITS The concept of a cliff behaviour unit (CBU) provides an important framework for the investigation and management of cliffs (Lee 1997, Moore et al. 1998). Cliff behaviour units include the foreshore and the cliff top as these component landforms, and the processes that act upon them, can have a significant influence on cliff instability and recession behaviour. They are cascading systems or open sediment transport systems characterised by inputs, throughputs and outputs. Defra’s ‘Investigation and Management of Soft Rock Cliffs’ (Lee and Clark 2002) provides further background to the concept and classifies diagnostic CBUs according to their relative complexity, as follows:

Figure 1 – CBU types in England and Wales: top left, Simple Cliff, Birling Gap East Sussex; top right, Composite Cliff, Purbeck Dorset; lower left, Simple Landslide, Scarborough North Yorkshire;

lower right, Complex Cliff, Black Ven Dorset Simple cliff face systems: a single sequence of inputs from falls or slides leading almost directly to foreshore deposition. There is usually a steep cliff face, narrow degradation zone and rapid response to toe erosion. This type of cliff will fail as a result of toe erosion and undercutting, so relative sea level rise (RSLR) is the main forcing parameter. Examples include the Chalk cliffs of Sussex (Figure 1) and the hard rock cliffs of Cornwall.

Simple landslide systems: a single sequence of inputs and outputs with variable amounts of storage. A marked degradation and storage zone is usually apparent affording limited buffering against toe erosion. This system will be affected by erosion of the landslide toe and excess groundwater in the slide zone; therefore both sea level and rainfall are key forcing parameters of equal importance. Examples include the cliffs formed in glacial tills in North Yorkshire (Figure 1) and the London Clay in Essex and Kent.



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Composite cliff systems: partly coupled sequences of contrasting simple sub-systems, typically comprising interbedded hard and soft rocks. Around the coast of England and Wales composite cliffs are formed where hard cap rocks are underlain by clayey strata giving a distinct steep upper cliff face and a tendency for high magnitude, low frequency failures. Composite cliffs are sensitive to changes in toe erosion and groundwater where soft rock occurs above hard rock. Examples include the till-capped cliffs of North Yorkshire and the hard rock capped cliffs of Purbeck and Portland (Figure 1).

Figure 2 – Futurecoast CBU parameters and statistics (values in km) Complex cliff systems: strongly coupled sequences of scarp and bench sub-systems, each with their own inputs, storage and outputs of sediment. The output from one system forms a cascading input to the next resulting in close adjustment of process and form with complex feedbacks. The sub-system storage results in significant buffering against the immediate effects of toe erosion, although elevated groundwater levels can trigger major events that can transform the behaviour of the whole system (e.g. major mudsliding episodes), hence groundwater is the main forcing parameter in the short-term.



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The impact of toe erosion occurs over much longer timescales i.e. 100s or 1000s of years. Examples include the Ventnor Undercliff, Isle of Wight, and Black Ven, Dorset (Figure 1). Relict cliff systems: sequences of pre-existing landslides susceptible to reactivation and exhumation by coastal erosion operating over long timescales. They include abandoned cliffs and slopes formed by ancient landslides that have the potential to become reactivated by toe erosion and or excess groundwater levels. Examples include the Undercliffs at St Catherine’s Point, Isle of Wight, and Bindon, South Devon. The Futurecoast Cliffs Database (Halcrow 2002) provides a systematic assessment of the coastal cliffs of England and Wales in accordance with this CBU classification. Out of a total of 6,251km of coastline in England and Wales (NCERM database), 3,327km (53%) are cliffs subject to cliff instability and erosion. The database includes information on cliff materials, failure mechanisms, presence of cliff engineering and coastal defences, activity status, recession potential magnitude and frequency, sediment supply potential, sensitivity to climate change and uncertainty assessment (Figure 2). These data provide a valuable resource for cliff behaviour assessments and recession prediction studies. 4. CLIFF RECESSION PREDICTION Cliff behaviour assessment involves the measurement of historical and contemporary rates of cliff recession, consideration of the geological, geomorphological, geotechnical and hydrogeological controls, and the influence of coastal processes. At some sites, specific monitoring of cliff behaviour is carried out and there is now an increased awareness and commitment to carrying out strategic monitoring of the coastline and cliffs for scientific and management purposes (Bradbury 2001). In the past, most cliff recession studies were necessarily qualitative due to the lack of accurate spatial and temporal data, with heavy reliance given to expert judgement and the use of historical records where the errors in data and interpretation were neither quantified nor made explicit. Technology now exists to make better use of historical mapping and aerial photographs where the errors of data and interpretation of features can be quantified to a high degree of accuracy. In the context of a wider cliff behaviour assessment, use of a quantitative approach can significantly improve confidence with future projections of cliff recession (Moore et al. 2003).

Figure 3 – Cliff Behaviour and Prediction Model Framework To gain an understanding of coastal cliff behaviour, it is necessary to have some factual information on historical and current rates of change in the landform and of the geology and geomorphology. This information can then be used to develop a conceptual cliff behaviour model, encapsulating interacting geomorphic components, which can then be used to derive a predictive model (Figure 3). Future cliff behaviour is determined by the impact of changing system controls on past behaviour. Changing system controls include external factors, such as sea level rise and climate change, and

BEHAVIOUR MODEL

PREDICTIVE MODEL: 100 YR RECESSION

PROFILES

GEOLOGY AND GEOMORPHOLOGY

• Futurecoast

• Academic papers

• Geological Survey memoirs

• Consultants’ reports

• Ground investigation data

HISTORICAL AND CURRENT CHANGES

• Futurecoast

• SMP II

• EA records

• Published papers and diaries

• Maps, DTMs & photos



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internal factors such as variations in materials or hinterland geomorphology. The impact of changing system controls on the behaviour of coastal landforms can be determined by a range of approaches, including the following:

• Extrapolation of historical data

• Expert judgement, e.g. from cliff behaviour models

• Probabilistic simulation modelling

• Process-response simulation modelling

• Empirical modelling These approaches involve an increasing degree of analysis, but do not necessarily provide an increasing level of accuracy. Furthermore, all approaches rely on the provision of historical recession rates, either as the basis for prediction or for model calibration. The ultimate precision of outputs is constrained by uncertainty over future climate and weather conditions, the physical properties and behaviour of the landform, the precise timing and magnitude of individual recession events and the future cliff recession rate. Walkden and Hall (2005) have developed a model, known as Soft Cliff and Platform Erosion (SCAPE) to represent the main processes that influence an eroding shoreline under RSLR. Lee (2005) provides a summary of the issues and a comparison of various deterministic and probabilistic approaches for cliff recession prediction, and presents a simple judgement-based model for evaluating cliff recession risk. These and other approaches are generally limited in their application by the absence of quality empirical datasets to quantify the relationships between cliff recession and the various controlling factors. An example is the relationship between historical cliff recession rates and sea level rise; Lee (2005) presents some data for the Suffolk coast but little is known about this relationship elsewhere along the coast of England and Wales. Similarly, only a few studies (e.g. Moore et al., 2007) have assembled datasets to quantify the effects of winter rainfall on coastal instability and landslides which are a major contributor to coastal recession at a number of key sites. Therefore, it is not possible to reliably model the effects of climate change on coastal cliffs using a quantitative modelling approach, particularly where this is based on extrapolation of past events alone. Such approaches do not take account of the potential uncertainty and variability of the cliff recession process. Consequently a judgment based assessment which brings together data concerning historical cliff behaviour with latest projections of UK climate change provides a pragmatic approach where the uncertainties can be explicitly expressed and accounted for, utilising numerical models as appropriate. A key objective of cliff behaviour assessment is not only to understand the materials, forms and processes of cliff systems but also their sensitivity to change and evolution through time. The timescales over which these factors are considered has a significant bearing on how the cliff instability and erosion process appears to have operated in historical times. The significance of past events and cliff recession rates need to be evaluated in this context. Lee and Clark (2002) identify three important timescales for cliff management purposes: 1. short-term, where recession can be a highly variable process 2. medium-term, where fluctuations are smoothed out and there is an apparent balance between

form and process 3. long-term, where the nature and rates of cliff behaviour may change in response to internal and

external influences, such as variations in lithology and sea level In the context of this study, it is important to note that certain CBUs will be characterised by frequent erosion (typically represented as an annual rate), whereas others will be characterised by less frequent (episodic) landslides, or indeed through a combination of both. These mechanisms and processes control the timing (frequency) and amount (magnitude) of cliff recession. The RACE and NCERM cliff recession prediction models provide a hierarchy of approaches in line with the above review (Table 2). These have increasing levels of complexity, providing incremental improvements in the quality of output, but also necessitating higher levels of data input, knowledge and time. Proportionate effort is therefore a consideration in deciding upon the technique to adopt. The description of each technique includes guidance on how it can be carried out, advice on suitability of use and limitations of the method.



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Technique General Description Main Points

1 Technical Judgement

Experience based assessment for use with minimal data

Quick and easy method. Crude examination.

2 Futurecoast Assessment

Uses data from the Futurecoast Cliffs Database

Consistency of available data lends itself to national application.

3 Site Specific Assessment

Combines data from Futurecoast with real data (e.g. more up to date aerial photographs)

More accurate than Technique 2, although some aspects remain imprecise. Some Local Authorities may already have such studies available.

4 Single Recession Rate Method

Uses purely real data and methods recommended by the SRC manual to calculate single recession rates.

Very robust method that will deliver reliable results. Data requirements exceed Techniques 1 to 3. Methods require extensive data and expert input.

5 Probabilistic Method

Uses purely real data and methods recommended by the SRC manual to calculate single recession rates.

Can provide most accurate output when informed by good quality input data and expert input.

Table 2 – RACE/NCERM cliff recession prediction approaches 5. IMPACTS OF CLIMATE CHANGE (UKCP09) SCOPAC (Halcrow 2001) commissioned a landmark project to review the potential impacts of climate change along the south coast between Lyme Regis and Shoreham-by-Sea where it is anticipated that coastal flooding and erosion will present a significant challenge to future coastal management. The review highlighted that climate change is likely to result in changes to three key forcing agents including sea level rise, changes in wave direction and increased winter rainfall, with a tendency for more extreme events (Hosking and Moore 2002). The present study has reviewed these forcing parameters against the latest UKCP09 climate change projections (Table 3). To capture the full range of possibilities, climate change projections of RSLR and winter effective rainfall (WER) under high and low emissions scenarios (HES and LES) are considered. Neither of these parameters are directly output by UKCP09 and require interrogation and processing of projected datasets. Analysis of these projections revealed that projected changes in wave direction impact on cliffs in England and Wales was a relatively insignificant parameter compared to RSLR and WER. Furthermore, regional variations in projections were also found to be relatively insignificant and that the rate of change was broadly linear over the next 100 years across the full range of scenarios. The impact of RSLR and increased WER are likely to result in higher coastal erosion rates and more frequent coastal landslides. The rate of change of these impacts cannot be reliably quantified; however an estimate may be made by expert judgment. Whilst such judgements are based on limited empirical data, they represent the best available estimates and are informed by current data and knowledge of specific sites and the potential impacts of climate change (e.g. Lee 2005; Moore et al., 2007) based on this approach. Table 3 presents a matrix of estimated impacts of UKCP09 HES and LES projections on cliff erosion rates and cliff instability (landslide) frequency for cliffs of various sensitivity to climate change and sea level rise (from Futurecoast) over the next 100 years. Source Parameters UKCP09 LES UKCP09 HES

Predicted rise in RSLR 26-37cm 40-51cm UKCP09

Predicted rise in WER 5-24% 9-40%

Increase in cliff erosion rate 50-75% 50-100% Impact on ‘high’ sensitivity cliffs Increase in cliff instability and

landslide frequency 100-200% 100-400%

Increase in cliff erosion rate 10-25% 10-50% Impact on ‘medium’ sensitivity cliffs Increase in cliff instability and


Increase in cliff erosion rate 0-5% 0-10% Impact on ‘low’ sensitivity cliffs Increase in cliff instability and


Note: values in bold indicate the extremes of the ranges as used for validation of NCERM

Table 3 – Estimated impact of UKCP09 scenarios on cliff instability and erosion



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6. NCERM VALIDATION AND CLIMATE CHANGE IMPACT ASSESSMENT This section presents the findings of the review and validation of historical cliff behaviour and cliff recession projection of 172 CBUs which form 22 priority sites (Figure 4). These sites form a small sub-set of a total of 116 sites identified by Futurecoast as ‘high’ sensitivity to climate change. They were selected due to the availability of site-specific data on cliff instability and erosion from Halcrow’s project archives which provide independent validation of the Futurecoast and NCERM datasets.

Figure 4 – NCERM validation sites



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The main objectives of the site-specific validation and review of cliff recession projections accounting for climate change were as follows:

1. Review of the defined NCERM CBU extents, and the relative significance of annual toe erosion and the magnitude and frequency of cliff instability and landslide events that govern future cliff recession.

2. Development of rule sets for validation and cliff recession prediction for Complex and Non-Complex Cliffs accounting for the impacts of climate change (principally RSLR and WER).

3. Critical review of long-term historical cliff recession rates and landslide event magnitude and frequency, and determination of validated empirical values for use by NCERM.

4. Independent projection of future cliff recession accounting for UKCP09 HES and LES climate change impacts and comparison with non-validated NCERM projections.

Non-Complex Cliffs For simple cliffs, simple landslides and composite cliffs considered in this assessment, a simplified assumption is made that future cliff recession will be driven by toe erosion. Therefore, these CBU types are treated together as ‘non-complex cliffs’. By definition, these cliff types are characterised by various rates of cliff toe erosion which is generally in balance and equal to the rate of cliff top recession (Lee & Clark 2002). The feedback mechanism from the point of toe erosion to cliff top recession is in many cases subject to delay or time-lag. However, the frequency of erosion and landslide events is sufficiently high or short-term (i.e. <1:100 years) to be accounted for by assessments of long term historical erosion dating back to the 19th Century. Thus, for non-complex cliffs it is appropriate to use the historical annual cliff recession rate as a basis for future prediction. The first step in calculating projected recession distances for non-complex cliffs is to validate the baseline historical recession rate. This baseline is taken to be the long-term historical value derived from accurate measurement of historical maps and aerial photographs of the site (Table 4). For example, the validated long-term historical recession rate for Site 19: Happisburgh is 3.25m/yr. This baseline rate is projected to increase over the next 100 years due to the impact of climate change on high sensitivity cliffs subject to erosion (Table 3). Thus, the recession rate under the UKCP09 HES is incremented by 1% per year, so that by year 100 the rate has increased by a total of 100% i.e. 6.5m/yr; note that this value is the upper bound increase projected under the full range of UKCP09 scenarios. The cumulative recession distance year on year is then calculated up to year 100. This allows projected recession distances for 20, 50 and 100 years under UKCP09 low and high emissions scenarios to be determined (Table 4).

Projected cliff recession rate under UKCP09 HES (m/yr)

Site No.

Site Location Validated historical cliff recession rate

(m/yr) 2029 2059 2109

Barn Scar & Carl Crag, Cumbria 0.25 0.3 0.38 0.5 1

Whitriggs Scar, Cumbria 0.18 0.21 0.27 0.36

2 Barton to Naish Farm, Hampshire

1.03 1.23 1.55 2.06

3 Mundesley to Bacton Green, Norfolk

0.9 - 1.51 1.08 - 1.81 1.35 - 2.27 1.80 - 3.02

4 Swanage Bay, Dorset 0.09 0.11 0.14 0.18

5 Church Cliff, Dorset 0.2 0.24 0.3 0.4

6 Higher Sea Lane, Dorset 1.5 1.8 2.25 3.0

11 St.Bees/Pow Beck, Cumbria 0.59 0.71 0.89 1.18

12 Nethertown, Cumbria 0.07 0.08 0.11 0.14

14 Swanage, Dorset 0.3 0.36 0.45 0.6

16 South coast, Isle of Wight 0.11 - 0.41 0.13 - 0.49 0.17 - 0.62 0.22 - 0.82

17 Southeast coast, Isle of Wight 0.26 0.31 0.39 0.52

18 Fairlight, East Sussex 0.64 0.77 0.96 1.28

19 Happisburgh, Norfolk 3.25 3.90 4.88 6.50

20 Cayton Bay, North Yorkshire 1.0 1.20 1.50 2.0

21 & 22 Filey South 0.2 - 1.0 0.24 - 1.2 0.30 - 1.5 0.40 - 2.0 Note: for multiple CBUs where variations in geology are significant the range of cliff recession rates is quoted

Table 4 – Validated cliff recession rates and HES projections for non-complex cliffs



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The percentage increase in annual recession due to climate change is applied linearly to 2080 yet NCERM projections are provided to 2109. For the purposes of this assessment, the percentage increases in recession due to climate change are applied for the next 100 years, to 2109, in line with NCERM; however, it is noted that this involves projection of the UKCP09 climate change projections beyond their intended time limit and as a consequence may under-estimate the full impact of climate change on cliff recession beyond 2080, which is uncertain.

Projected landslide frequency under UKCP09 HES (years)

Site No.

Site Location Validated landslide

magnitude (m)

Validated landslide frequency

(years) 2029 2059 2109

2 Barton, Hampshire 2 - 5 5 - 13 5 - 13 3.3 - 8.7 2.5 – 6.5

5 & 6 East Cliff, Spittles &

Black Ven, Dorset 30 - 50 25 - 30 25 - 30 8.3 - 10 5 - 6

7 Warden Point, Isle of

Sheppey, Kent 30 35 - 50 35 - 50 12 - 17 7 - 10

8 Covehurst Bay,

Fairlight, East Sussex 60 50 50 17 10

9 Ventnor Undercliff,

Isle of Wight* 50 500* 500 500 500

10 Cayton Cliff, North

Yorkshire 10 40 40 13 8

11 St Bees/Pow Beck,

Cumbria 50 500 500 167 100

13 Lyme Bay, Dorset 30 - 92 30 - 500

30 - 500

10 - 167

6 - 100

15 Gurnard Ledge, Isle

of Wight 15 50 - 500* 50 - 500 16.7 - 500 10 - 500

16 South coast, Isle of

Wight 20 - 50 50 - 500* 50 - 500 16.7 - 500 10 - 500

17 Southeast coast, Isle

of Wight 10 - 50 10 - 500* 10 - 500 3.3 - 500 2 - 500

18 Fairlight, East Sussex 10 50 50 16.7 10

22 Filey South, North

Yorkshire 10 10 10 3.3 2

*denotes CBUs where landsliding frequency is historically very low and where the impact of climate change is not expected to be significant over the next 100 years

Table 5 – Validated magnitude and frequency and HES projections for complex cliffs Complex Cliffs For complex cliffs (Figure 4), the frequency and magnitude of episodic landslide events, caused by long-term coastal erosion and site-specific ground conditions, are dominant controls of cliff top recession. Many of the UK’s complex and relict coastal landslide systems are many thousands of years old and have remained dormant over the historical period. However, they are subject to ground movement, reactivation and occasional landslide events which pose a risk to development and infrastructure located upon them. An important feature of these systems is the presence of a rear scarp some distance inland of the sea-cliff or landslide toe; erosion of the landslide toe and adverse ground conditions potentially influences recession of the rear scarp, therefore, an important consideration of such assessments is to accurately map the full extent of complex cliffs. This is often a shortcoming of SMPs and other regional cliff studies.



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For complex systems where annual erosion rate is used alone for future projection, it is likely that this may lead to significant under-prediction of future cliff recession where change is driven by episodic large-scale landslide events. Conversely, applying both annual erosion and episodic landslide events recession may lead to over-prediction. Thus for complex cliffs, landslide magnitude and frequency only are used as a basis for prediction of future cliff recession. The first step in calculating projected future recession distances for complex cliffs is to validate the historical landslide frequency (Table 5). The validated landslide frequency is then converted to an annual probability. The annual probability is then modelled to increase with climate change (Table 3), with the total increase in landslide frequency achieved by year 100. The increase in landslide frequency is applied linearly to 2109; however, this involves projection of UKCP09 data beyond their intended timeframe and as a consequence may under-estimate the full impact of climate change on landslide frequency beyond 2080, which is uncertain. Cumulative probabilities are then calculated; when the cumulative probability is equal to 1 a landslide event and associated cliff recession will have occurred. It is not possible to precisely predict the next landslide occurrence so it is assumed the first landslide event in the series occurs in year 1. By multiplying the number of landslide occurrences within each time frame (20, 50, 100 years) with landslide magnitude, the amount of cliff recession is determined for LES and HES climate projections. This is a generic approach applied to all complex cliffs where recession of the headscarp is expected as a result of episodic coastal instability and erosion in the next 100 years. The exceptions to the rule are those CBUs where headscarp recession is not expected in the next 100 years and for these cases the projected recession distance is set to 0m. Despite the lack of headscarp recession of some complex cliffs they remain at risk of coastal instability and erosion at any time involving toe erosion, ongoing ground movement and periodic landslides within the mapped extent of the cliff system. 7. Results and Discussion The results of the cliff behaviour assessment and validation of 22 priority sites has revealed the following. There is close agreement between NCERM and independently validated cliff recession projections for high sensitivity non-complex cliffs where there is a relatively direct link between toe erosion and cliff top recession rates. The NCERM projections (defined by best and worst case) have sufficient range or contingency to account for UKCP09 climate change scenarios up to 50 years from now and in many cases up to 100 years from now. This is a significant outcome of the study as it confirms that NCERM, a high-profile, national scale undertaking, accounts for the most up-to-date climate change projections for about 90% of the cliffs database for England and Wales. Complex cliffs comprise less than 10% of the cliffs database and nearly half of these are rated as high-sensitivity to climate change (Figure 2). They are characterised by an indirect and delayed response between toe erosion and cliff top recession controlled by site-specific ground conditions and excess groundwater levels triggering episodic large landslide events; individual CBUs have unique historical cliff behaviour and sensitivity to future climate change and sea level rise. The results of independent validation and recession projection for high-sensitivity complex cliffs have revealed significant differences with NCERM projections. The differences are randomly distributed and highlight issues regarding mapping of complex cliff extents, and validation of appropriate landslide magnitude and frequency values. The majority of NCERM projections show significant under-prediction of potential cliff recession for complex cliffs and generally do not have sufficient range or contingency to account for UKCP09 LES and HES under any timeframe; further it is not considered realistic to modify NCERM to account for climate change without introducing orders of magnitude of uncertainty in future cliff recession projections. Therefore, complex cliffs should be considered a special case and assessed on their individual merits as toe erosion is not necessarily the main driver of cliff top recession in the short- and medium-term, a recommendation which is being taken forward by NCERM. ACKNOWLEDGEMENTS The authors are grateful to the Environment Agency for permission to publish this work. The contributions of colleagues at Halcrow Group Ltd are also acknowledged. REFERENCES Bradbury A (2001). Strategic monitoring of the coastal zone - towards a regional approach. Proc. 36th

DEFRA Conf. of River and Coastal Engineers, 5.2.1-13.



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development of soft rock shores. Coastal Engineering 2005, 52(6), 535-563.

climate change impact on cliff instability and erosion

Documents

coastal change

coastal instability

coastal erosion projections

future climate change

future coastal management

coastal assets

coastal cliffs

coastal communities