The Impact Of Storm Surge Mitigation

Published Date: 02 Nov 2017

Name: George Argyropoulos

Degree course: GIS and Environmental Management

Proposed supervisor: Dr Martin Smith, Principal Lecturer

Title: A GIS based method to evaluate the impact of storm surge mitigation on cliff retreat and how the geology contributes: a case study of the chalk cliffs of Southern England.

Introduction to the Dissertation

Climate change is likely to affect the intensity and the frequency of storms (IPCC, 2007) and therefore will affect the increase of extreme water levels by the increase of sea level and the higher storm surges (Wang et al., 2008). Storm surges are provisional increases of sea level produced by very strong winds combined with low atmospheric pressure. A potential storm surge can create physical and chemical processes that may force sediments to accumulate upon rock debris slope. Those processes will be evaluated in this project in order to avoid any activities that can increase slope instability or cause landslides such as watering lawns and draining septic tanks that can rise ground-water levels (Morton, 2013).

Data acquisition & Study area

Risk of Cliff Collapse (ROCC) is a funded research programme created by geologists and engineers from the University of Brighton and the University of Havre that was introduced in 1998 and provides information including aerial photos, professional reports and papers regarding cliff failures and geo-engineering data such as slope, dip and strike. With the assistance of a GIS software system, it was succeeded the wrapping of aerial photos into maps as well as the production of detailed topographic and geology maps (Mortimore & Duperret, 2004). All of the main data will be acquired by the British Geological Survey (BGS) and Ordnance Survey (OS). Additional data that can improve our results will be provided by Brighton and Hove city council and UK Environmental Agency. The study zone lies between the areas of Newhaven and Eastbourne (Table 1).

Table . Coordinates of the study area

Area

Eastings

Northings

Reference system

Cuckmere Haven

551702

97699

British National Grid

Holywell, Eastbourne

560034

96697

British National Grid

A wide variety of data will be processed and examined in this project as shown in Table 2.

Table . Data which will be inspected.

Data

Source

What will be assessed?

Sea level

BODC

Increase or decrease rate

Wave data

BODC

Height of the waves

Wind speed

BODC

m/km per hour; power

Barometric pressure

BGS

Air pressure (Atmosphere)

Onshore water level (Boreholes)

BGS

Intrusion of the sea water

Height measurement

BGS

Cliff’s height

Geology of the area & cliffs

BGS

Lithology, faults, joints

Aerial photos

Brighton & Hove city council

Identify shoreline’s position

LIDAR photos

BGS

Identify shoreline’s position

Geology

The Chalk Formation of the area of Newhaven has been deposited for the period of the Upper Cretaceous (100-65 million years ago). Normally, this Formation is pure and is composed by calcium carbonate coccolith debris (Mortimore, 2001). A major role in cliff failure events, has the fragmentation of the chalk by normal and reverse faults and conjugate shear joints. The structure of the Formation is mainly controlled by tectonic folds which are the Friars Bay Anticline and the Old Steine Anticline. The impact of those anticlines can influence the type and scale of a cliff failure (Mortimore & Duperret, 2004).

Aim

This study will try to decode geological and meteorological data in order to identify the elements that influence the rates of cliff retreat. On the one side the aim of this project is to determine the relation between the rates of cliff retreat and how can be related with storm surge events and geological factors. On the other side, there will be a description of how climate change can influence storm surges and what is expected in the future from their active relationship.

Objectives

The objectives of the project are:

To understand the involvement of the geology during a cliff retreat by assessing factors that contribute in cliff failures and their resulting types, scales and volumes. (Mortimore et.al, 2004).

To find trends in met-ocean parameters (wave height, bathymetry, storm’s power) that can help us predict a shoreline retreat rate model.

To study and analyse storm surge (mitigation) results upon cliff retreat and coastal erosion.

To identify how climate change affects the volume of a storm surge?

To define any impacts storm surge has on coastal geologic structures.

Literature review

According to Williams et al. (2004), storm surge events may have affected the large scale cliff retreat event happened at Seven Sisters cliffs in 1914. To comprehend and examine the geology of an area Mortimore et.al (2004), suggested that the magnitude of the coastal cliff geohazards depends on the type of chalk (lithology), the presence of folds, faults and joints on the rock and the height of a cliff. Furthermore, marine erosion at the base of a cliff, frost events and rainfall plus the material capping the cliff play a major role on the development of a geohazard. Periods of onshore winds, high levels of water (including storm surge) and rainstorm events can control thresholds for cliff base erosion and its spread up through the cliff profile (Brooks, 2012). Climate change and particularly a sea level rise is alleged to have an impact on cliff retreat rates (IPCC, 2000) with the prospect that shoreline retreat rate will accelerate in the near future bringing cliff instability issues ahead (Bray & Hooke, 1997).The Geographical Information Systems (GIS) can be utilized for natural hazard risk and disaster management due to the wide availability of the spatial databases nowadays and therefore to evaluate the danger of a cliff failure. Additionally, GIS extend beyond mapping, to spatial analytical capabilities and decision making support tools for a better understanding of the complex nature of a disaster (Zerger & Smith, 2002). According to Williams (2004), the height of a cliff is important to be obtained in order to make measurements about the run out and the energy of the falling debris. The bedding, clay and joining content of chalk affects the type of a cliff collapse (Williams, 2004). Another major factor to a large scale fall is the porosity and the low density of the chalk that makes it susceptible (Mortimore & Duperret, 2004). In Sussex area chalk lies below 40% (Hutchinson, 2002).Wave energy distribution along the shoreline can provide guidance for producing hazard maps which combined with available geophysical and hydrogeological data help to recognise the conditions of cliff failures. It is also stated that the mechanisms that relates the wave height with the cliff collapse are highly complex and are greatly related to the geological structures, the geology of the cliff, the degree of saturation and the abrasion degree from the sediments locating at the base of the cliff (Mitchell & Pope, 2004).The prediction of the wave energy distribution to the coastal area is required in order to assess the likelihood of a cliff collapse. The sudden changes in the bathymetry of the near shore region occur focusing of the wave energy and thus high waves are expected in the coast line. The above phenomenon takes place when waves bent to a shallow area producing superposition of waves to the region downstream of the shallow point (Mitchell & Pope, 2004). According to Mitchell & Pope (2004), long-term cliff recession rates can be correlated to wave energy distribution in the coast line and provide an estimate of the implication of the waves in the formation of patterns in the shorelines. This can be connected with storm surge events that have a greater impact in shoreline’s changes and in the rates of a cliff retreat. During a storm, the effect that waves have in the cracks of a cliff and in the coastal structures becoming worst when waves break against the crack entrance and produce high pressure peaks (Walter & Muller, 2004).

Methodology

To recognise the mechanisms of cliff recession and to detect any association with storm surges we will use a GIS software system in addition to a GPS handheld device:

A Differential GPS device to provide better accuracy in the measurement of coordinates and to environmental parameters.

Computer software systems listed in(Table 2), ArcMap, ArcScene and Minitab statistical software will analyse environmental parameters such as slope, elevation, dip, temperature, strike, wave height and will produce maps plus 3D models representing the stratigraphy and/or the bathymetry of the study area. These maps are important because they will help us understand the crash power of a storm surge on a cliff and the factors that create it. Minitab will try to decode any climate patterns related to storm surges linked to cliff retreat rates in order to produce prediction models.

Table . . GIS and Statistical computer software

Computer Software

Company

Version

ArcMap

ESRI

10.1 (possibly 10.2)

ArcScene

ESRI

10.1 (possibly 10.2)

Minitab

Minitab Inc.

16

Table . Maps with data relative to the study. They will be added/created and analysed in ArcGIS.

Map

Source

Additional details

Shoreline position

OS

Changes during time

Bathymetric

BGS

Sea and sub sea bed

Topographic

BGS

Contours

Stratigraphic

BGS

Rock and soil layers

Additionally, we will try to identify the susceptibility of the rock during the occurrence of a storm surge. A variety of met-ocean parameters it will be analysed using the Digital Shoreline Analysis System (DSAS) which was developed by the United States Geological Survey (USGS) and co-operates with Geographic Information System (ArcGIS) software of the Environmental Systems Research Institute (ESRI). The target is to estimate the average rate of the shoreline retreat by comparing historical shoreline positions (Stavrou, 2011) and visualise past and future scenarios of the shoreline evolution (Koukoulas, 2005). GIS techniques will be used to digitise the records of the changes in the coastline position from OS historical maps and aerial photos (Brooks, 2012). More detailed, different historical shorelines during different times will be digitized with large scale aerial photos and data will be inserted in the DSAS tool in ArcGIS for producing statistical outputs regarding the difference in the positions of the coastline (Stavrou, 2011). Variations of chalk’s cliff retreat rates will be calculated for a long period of time for the cliff area between Newhaven and Holywell. According to Dornbusch et al. (2006), the accuracy in the shoreline position of the historical maps found that contains an error of ±3 m whereas the error for the aerial photos is approximately ±0.3 m. In addition, average covered distances, rates of retreat and the standard deviation are going to be assessed in Minitab statistical software. Therefore, any future shoreline prediction is difficult to be calculated due to uncertain variables such as weather condition, sea behavior structures over the time and the level of sea rise (Halcrow Group Limited, in association with Mark Lee and Terry Oakes Associates, 2007). According to Lee and Clark (2002), to create a prediction model is important to be considered the variability of the values during a certain period of time by the standard deviation of the average rates. Thus, the equation that predicts the recession rates can be stated in this way:

Recession by year A = (Average rate + Standard Deviation)

×T years (Lee and Clark, 2002)

T is the factor that symbolizes the period among the most recent digitized coastline.

Structure of the Dissertation

Title page

The title of the dissertation

Acknowledgements

Reference to individuals who helped in the dissertation

Abstract

A concise summary of the research

Table of contents

List of figures and tables

Introduction

Expand the information stated in the abstract

Statement of the problem

Research objective

Organization of the study

Literature review

Awareness on how my study fits into the overall background of research regarding my field; research question

Methodology

Approach of the problem

Research philosophy

Collection of the data

Analysis of the data

Access

Validity and reliability

Ethics (if needed)

Limitations

Results

Findings in a descriptive format

Figures and Tables of my outcomes

Conclusion & Discussion

Review of the research and discussion about limitations, findings and future perspective

References

Appendices

Supplementary material

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Possible research areas

Complete semester 1 modules

Literature review

Identify topic and advisor

Submit Proposal

Data collection

Data analysis and feedback

Write up

Edit & submit

Figure : Time line for the dissertation project