SUBMITTED DISSERTATION (for linked in etc)

0

2015

The relationship between principal

components of the human–coastal system at beach nourishment locations along the

US Eastern Seaboard

C. Thorpe

CARDIFF UNIVERSITY 23/01/15

Supervisor: Dr. Eli Lazarus

1

Cover page photo: Ocean City, Maryland (avi8tor4fn, 2007)

Abstract

Beach nourishment is an erosion-control strategy on dynamic shorelines that

attempts to stabilise a naturally eroding shoreline by adding sand to a beach in order

to widen it and provide protection to human infrastructure located behind the beach.

Beach nourishment can be viewed as the link between physical coastal factors and

economic factors in a developed shoreline. A novel comparative dataset was created

for all known beach nourishment episodes along the U.S East Coast, and analysed

using various statistical techniques to determine the nature of any correlation.

The study compared the number of beach nourishment episodes, historic population

data, median house values as local economic indicators, and physical coastal

processes of long term/short term erosions rates and relative sea level rise. This

was in order to identify relationships among principal components of the human–

coastal system at beach nourishment locations along the U.S. Eastern seaboard.

Sea level rise was found to correlate with the number of beach nourishment

episodes, but due to feedback and multiple interfering variables, correlation was not

observed among other dataset pairs. Recommendations are suggested for suitable

techniques to gain further insight into the human–coastal system.

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Contents Abstract ...................................................................................................................... 1

List of Figures ............................................................................................................. 3

Abbreviations & Acronyms ......................................................................................... 5

INTRODUCTION ........................................................................................................ 6

Aims & Objectives ................................................................................................... 8

Geographical setting ............................................................................................... 9

METHODOLOGY ..................................................................................................... 10

Gather information ................................................................................................ 10

Compile dataset .................................................................................................... 13

County boundaries ............................................................................................ 18

Compiling dataset summary .............................................................................. 18

Analysis of database ............................................................................................. 20

Amendments to comparative dataset ................................................................ 20

Histogram .......................................................................................................... 21

Population data ................................................................................................. 21

Descriptive Statistics ......................................................................................... 22

Test for normality .............................................................................................. 22

Regression Analysis .......................................................................................... 23

RESULTS ................................................................................................................. 25

Data Analysis Results ........................................................................................... 25

Comparative Results ............................................................................................ 28

Independent Variable: Number of beach nourishment episodes ....................... 29

Independent Variable: Total Beach Nourishment Volume ................................. 30

Independent Variable: Relative Sea Level Rise ................................................ 32

Independent Variable: Long Term and Short Term Erosion Rate ..................... 35

County Population and Number of Beach Nourishment Episodes .................... 40

3

DISCUSSION ........................................................................................................... 46

Beach nourishment episodes (BN) vs. median house values (MHV) ................... 46

Sea level rise (SLR) vs. beach nourishment episodes (BN) ................................. 47

Erosion rates (ER) vs. median house values (MHV)............................................. 48

Erosion rate (ER) v beach nourishment episodes (BN) ........................................ 49

Sea level rise (SLR) vs. long term erosion rate (LTER) ........................................ 49

Long term erosion rate (LTER) vs. short term erosion rate (STER) ...................... 51

Caveats ................................................................................................................ 52

CONCLUSION ......................................................................................................... 54

Further work ......................................................................................................... 55

ACKNOWLEDGEMENTS ........................................................................................ 57

REFERENCES ......................................................................................................... 57

APPENDICES .......................................................................................................... 63

Appendix 1 - Email from A. Coburn: Associate Director, Program for the Study of

Developed Shorelines ........................................................................................... 63

Appendix 2 - Email from R. Theiler: US Geological Survey .................................. 64

Appendix 3 – Generated comparative dataset ...................................................... 65

List of Figures

Figure 1 - Location of beach nourishment episodes across all states along the East

Coast of USA. Different coloured points indicate beach nourishment episodes in

different states. (Program for the Study of Developed Shorelines, 2014)................. 10

Figure 2 - Long term erosion/accretion rate transects shown intersecting zip code

boundary areas in ArcGIS, Florida shoreline ............................................................ 15

Figure 3 - USGS Coastal Vulnerability Index shape files and zip code shape files in

ArcGIS, showing that the CVI lines did not line up with the zip code shorelines;

numbers indicate SLR at that location. SC = South Carolina state .......................... 16

4

Figure 4 - USGS Coastal Vulnerability Index shape files with a buffer of 0.4 degrees

in ArcGIS; numbers indicate SLR at that location. SC = South Carolina state ......... 17

Figure 5 - Flow diagram of spatial joins carried out in ArcGIS .................................. 19

Figure 6 - Histogram of all BN episodes along the U.S. East Coast 1923 – 2014,

used to determine baseline year of BN records ....................................................... 28

Figure 7 - The dependence of Median House Value on Number of Beach

Nourishment episodes .............................................................................................. 29

Figure 8 - The dependence of Median House Value on Total Beach Nourishment

Volume 1923-2012, per zipcode. A linear regression line is shown ......................... 30

Figure 9 - The dependence of the number of beach nourishment episodes on

average sea level rise, per zip code. A linear regression line is shown. ................... 32

Figure 10 - The dependence of long term erosion rate on relative sea level rise rate

for the entire U.S. East Coast, including BN locations and natural locations............ 33

Figure 11 - The dependence of long term erosion rate on sea level rise, at beach

nourishment locations .............................................................................................. 34

Figure 12 - The dependence of beach nourishment episodes on long term

erosion/accretion rate, at zip codes. A linear regression line is shown. .................... 35

Figure 13 - The dependence of beach nourishment episodes on short term

erosion/accretion rate, at zip codes. A linear regression line is shown. .................... 36

Figure 14 - The dependence of median house value on long term erosion/accretion

Rate, at zip codes. A linear regression line is shown. .............................................. 37

Figure 15 - The dependence of median house value on short term erosion/accretion

rate, at zip codes. A linear regression line is shown. ................................................ 38

Figure 16 - The relationship between long term and short term erosion/accretion rate

at zip codes containing BN. A linear regression line is shown. ................................. 39

Figure 17 - The relationship between long term and short term erosion rate for BN

zipcodes with only LT and ST negative values (erosive areas). A linear regression

line is shown. ............................................................................................................ 40

Figure 18 - Cumulative number of beach nourishment episodes and population for

Duval County, Florida, between 1970 – 2010. ......................................................... 41


Miami-Dade County, Florida, between 1970 – 2010. ............................................... 42

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Monmouth County, New Jersey, between 1970 – 2010. .......................................... 42

Abbreviations & Acronyms

Avg Average

BN Beach nourishment

LT Long term

LTER Long term erosion rate

MHV Median house values

NOAA National Oceanic and Atmospheric Administration

PSDS Program for the Study of Developed Shorelines

SLR Sea level rise

ST Short term

STER Short term erosion rate

U.S United States of America

USGS United States Geological Society

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INTRODUCTION

Eustatic sea level is rising (IPCC 2013), and the rate at which it is doing so is

predicted to increase in the future. Sea level rise (SLR) is claimed to put areas of

coastline at risk from shoreline erosion (Bruun 1962, Leatherman et al. 2000, Zhang

et al. 2004, Barth and Titus 1984, FitzGerald et al. 2008 and Romine et al. 2013),

amplified storm surges (Tebaldi et al. 2012 and McInnes et al. 2001), inundation and

saltwater intrusion (Werner and Simmons 2009). Low lying sandy shorelines, which

are present in most of the United States (U.S.) East Coast, are amongst those

coastal environments that are most vulnerable to rising sea levels (Hapke et al.

2010). The average annual erosion rate on the U.S. Atlantic coast is roughly 2 to 3

feet/year (Heinz, 2000).

Coastal Population along the U.S. East Coast is also rising, with the population in

U.S. coastal counties increasing by 33 million between 1980 and 2003 (Crossett et

al. 2004). This is predicted to greatly affect, and to already have affected,

components of the human–coastal system. In developed shorelines like the U.S.

East Coast, the shoreline has been considerably modified through coastal

engineering strategies, which have artificially stabilised naturally migrating shorelines

in some cases, and increased erosion in others (Nordstrom et al. 2007). It is only

through the presence of humans in these dynamic shorelines that these physical

factors such as erosion and SLR become a problem (Pilkey and Coburn 2007).

Before predictions can be made regarding future coastal change, the dynamics of

the human coastal system need to be understood fully.

Beach nourishment is a commonly used erosion-control strategy on dynamic sandy

shorelines, such as the east coast of the U.S., which attempts to stabilise a naturally

changing shoreline. It involves dredging sand, usually from the seabed, and

depositing it onto an eroded beach. Natural processes then rework this sand and

increase the width of the beach, providing protection to houses and infrastructure

behind the beach. Beach nourishment (BN) is not a permanent solution however, as

the nourished beach is likely to erode again, so the same beach is often nourished

periodically to maintain a chosen width of protection (Lazarus et al. 2011)

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The number of BN episodes per decade along the U.S. East Coast has increased

through time since nourishment began, with a rapid increase since 1960 (Valverde et

al. 2009). A beach is of value as it provides protection to property along the shoreline

against erosion, flooding and storm surges, and is also a recreational feature on

which tourism depends, which is reflected by local economic indicators. A wide

beach provides amenities to an area that are incorporated into the coastal property

values (Gopalakrishnan et al., 2011, Edwards and Gable, 1991, Kreisel et al., 2005,

Landry et al., 2003, Parsons and Powell, 2001 and Pompe and Rinehart, 1995). In

fact, it has been said that beach nourishment is not accomplished in order to protect

a beach, but to protect what is behind the beach (Pilkey and Coburn 2007). Beach

nourishment can therefore be viewed as the link between physical coastal factors

and economic factors in a developed shoreline (Lazarus et al. 2011).

The decision to nourish beaches depends on whether the cost of the beach

nourishment is outweighed by its benefit to the area, i.e the potential cost in damage

to tourism, property and infrastructure without beach the nourishment. A cost-benefit

analysis is useful for determining this, such as that developed by Smith et al. (2009).

Beach nourishment is major process that greatly affects shoreline dynamics. The

total cost of beach nourishment, along the east coast of the U.S. since nourishment

began in the 1920’s is over 6.5 billion dollars (PSDS 2014).

There is therefore much criticism regarding beach nourishment. Coastal erosion is

seen by some as inevitable and spending millions of dollars of taxpayers money in

preventing it is viewed as wasteful, whilst being potentially harmful to ecosystems

(Manning et al. 2013) and affecting the dynamics of neighbouring shorelines

(Lazarus et al. 2011). Controlled dynamism is a compromise solution (Nordstrom et

al. 2007) used to control natural dynamism but retain some natural functions, rather

than attempting to prevent shoreline change completely.

Leatherman et al. (2000) investigated the relationship between long-term shoreline

change and SLR along the U.S. East Coast, finding a correlation. Zhang et al. (2004)

also found a correlation between these variables along the U.S. East Coast, with

rates of coastal erosion around two orders of magnitude greater than the rate of

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SLR. Studies have also been carried out in determining the relationships among

coastal variables to understand coastal dynamics in developed shorelines (Lazarus

et al. 2011; Mcnamara and Werner 2008; Smith et al. 2009). However, research has

not been carried out in comparing principal components of the human–coastal

system at beach nourishment locations across the entire U.S. Eastern seaboard

using primary data and extensive beach nourishment information, which is the basis

for this study.

Aims & Objectives

The aim of the study was to generate a novel comparative dataset of local economic

indicators, physical coastal change, and beach nourishment projects along the U.S.

Eastern seaboard in order to quantify how these principal components of human–

coastal systems are related.

To achieve this aim, the following objectives were identified:

1. Gather information regarding local economic indicators, physical coastal

change, and beach nourishment projects for the U.S. East Coast from the

following publicly available data sources:

a. U.S. beach nourishment projects, maintained by the Program for the

Study of Developed Shorelines

b. Long-term erosion rates mapped by the US Geological Survey

c. Municipal-level US Census data

2. Compile a dataset so that these variables could be compared

3. Analyse dataset to quantify how these principal components of human–

coastal systems are related using:

a. Statistical regression analysis

b. Correlation analysis

c. Timeline series construction

4. Present results based on ‘real’ primary data for the entire U.S. East Coast and

discuss the findings, including strategies for future work.

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The hypothesis was that there would be strong relationships identified among the

principal components of human–coastal systems analysed and that they could be

used to gain a better understating into the special dynamics of developed coastlines.

A literature study was carried out to determine which variables were expected to

show some correlation. This influenced the variables that were selected to represent

physical coastal factors and which variables were compared and tested for

correlation. For example, wave height was not used in the study as Zhang et al.

(2000) showed that there is no relation between wave height and long-term erosion

rate (a physical coastal factor influencing BN decisions).

Geographical setting

The project extends over the entire Eastern Seaboard of the United States of

America, encompassing the following 14 states from North to South:

• Maine, ME

• New Hampshire, NH

• Massachusetts, MA

• Rhode Island, RI

• Connecticut, CT

• New York, NY

• New Jersey, NJ

• Delaware, DE

• Maryland, MD

• Virginia, VA

• North Carolina, NC

• South Carolina, SC

• Georgia, GA

• Florida, FL (east coast only)

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Figure 1 shows the location of the beach nourishment episodes used in the project.

The U.S Atlantic coast is bordered by a chain of roughly 300 barrier islands,

composed primarily of loose sand, which are the most dynamic land masses along

the open-ocean coast. These barrier island coastlines have been retreating landward

for thousands of years in response to slow SLR (Heinz, 2000). The study of

developed shorelines and this study is therefore complex.

METHODOLOGY

Gather information

Information was gathered from publically available data sources regarding BN

episodes, physical coastal change rates and economic indicators in the 14 United

States (U.S.) East Coast states. These datasets and their sources are described in

the following section.

Beach nourishment episodes (1923 – 2014)

A dataset of U.S. East Coast BN projects was obtained from the Program for the

Study of Developed Shorelines (PSDS, 2014). This dataset contains a temporal

Figure 1 - Location of beach nourishment episodes across all states along the East Coast of USA. Different coloured points indicate beach nourishment episodes in different states. (Program for the Study of Developed Shorelines, 2014)

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record of all beach nourishment episodes along the U.S. East Coast since the first

nourishment episode in the period 1923 to 2014. It includes the attributes of beach

location, state, year the beach nourishment episode was completed, volume of

beach nourishment episode, longitude, latitude and some other attributes not used in

the study (Table 1). This database was checked and amended to correct errors in

location data, such as incorrect states and BN episodes with the same beach

locations, by renaming different longitudes and latitudes accordingly.

Table 1 - Sample of beach nourishment database, adapted from PSDS, (2014)

Beach Location State Year

Completed

Latitude Longitude Funding

Source

Volume

(cubic

yards)

Coney Island NY 1923 40.57284 -73.978114 Local/Private 1,700,000

Coney Island NY 1926 40.57284 -73.978114 Local/Private 850,000

Cabrillo Beach,

Los Angeles

County

CA 1927 33.71117 -

118.283443

Unknown 496,660

Cabrillo Beach,

Los Angeles

County

CA 1927 33.71117 -

118.283443

Unknown 500,000

Rockaway Beach NY 1930 40.58522 -73.806914 Unknown 5,200,000

East Beach,

Santa Barbara

CA 1935 34.40981 -

119.690552

Unknown 202,000

Newport Beach,

Orange County

CA 1935 33.61562 -

117.935572

Federal 3,700,000

In the dataset, some of the same beach nourishment locations had different

longitude and latitude coordinates. This suggested that the locations relating to BN

instances were not totally accurate. Andy Coburn, the Associate Director of the

Program for the Study of Developed Shorelines, provided the information that the

coordinates are only used for illustrative purposes (Appendix 1) and do not represent

the exact location of beach nourishment episodes. Therefore, to compare BN

episode points with other variables they were grouped into areas.

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Long and short term erosion/accretion rates

Long and short term erosion/accretion rates (metres per year) for the U.S. East

Coast states were obtained from the U.S. Geological Survey National Assessment of

Shoreline Change Project (USGS 2015) in the form of polylines with a 50 metre

transect spacing for each state. Negative values indicate the presence of erosion,

and positive numbers denote accretion. These long term erosion/accretion rates

were derived from several historical shoreline positions (USGS 2015), ranging from

the 1800’s to the most recent lidar shoreline, using linear regression. Long term

erosion rates for each location were covered with around 150 years of data, with a

minimum of 78 years across all locations (Appendix 2) (Marine USGS 2015). The

data was only included for open ocean shorelines along the U.S. Atlantic Coast, and

therefore does not include Connecticut State, Florida Keys, and bays or inlets.

Short term erosion rates were calculated using the most recent two shoreline

positions; for the 1970s and the latest lidar shoreline (USGS 2015). Short term

erosion rates do not include the northern states of Maine and New Hampshire, and

cover around 30 years of data (Marine USGS 2015); clearly exhibiting coverage of

considerably less than for long term erosion rates.

Relative sea level rise

Relative sea level rise rates (millimetres per year) for the U.S. East Coast were

obtained from the USGS Coastal Vulnerability to Sea-Level Rise project (USGS,

2001) in the form of line shape files along the shore. This data was gathered with

long term U.S. tide gauges and typically covers a 100 year timescale, with a

minimum of 50 years across the locations (Appendix 1). There is extensive coverage

along the U.S. East Coast, incorporating the entire shoreline, including both open

ocean shorelines and bays, inlets and shorelines behind barrier islands.

Median house values

Median house values were selected as an economic indicator as they were easily

obtained and give a reasonable estimate for the wealth of an area. Median house

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values for owner occupied housing units were obtained from the U.S. Census

Bureau for all zipcodes in the East Coast US States (U.S. Census Bureau, 2014).

Five year estimate values for the year 2012 (2008-2012) were used as opposed to

single year values as they are more representative of any fluctuations in house

values over time . In addition, median house values of zip codes only date back to

the year 2000, with gaps in the data present in most years. This made them less

comprehensive than the 2012 5-year estimates. Historic median house values may

have been useful to show changes in house values over time and determine if those

changes correlated with beach nourishment episodes, but the 2012 5-year estimates

were chosen as they represented the most recent data, the largest range and the

most comprehensive median house value datasets available.

Median house values were capped at $1,000,000, and zip codes that had a median

house value of over $1,000,000 were designated the value $1,000,000+. The plus

sign was removed from these values in the dataset, to makes these median house

values useful and usable in analysis. There were gaps in the dataset where there

were no sample observations or too few sample observations available to compute

an estimate (U.S. Census Bureau, 2014).

Population

Historic populations of U.S. counties were gathered from the National Oceanic and

Atmospheric Administration; State of the Coast (NOAA 2013). These values were

derived by the NOAA from U.S. Census data. Populations for all U.S. East Coast

counties were gathered for the years 1970, 1980, 1990, 2000 and 2010. Population

data for zip code areas or within town boundaries was not available prior to the year

2000, so the smallest areas that had historic population data associated with them

were selected for study, which were counties.

Compile dataset

In order to compare the variables described above a combination of Microsoft Excel

and ArcGIS (ESRI, 2014) were used to create a dataset in which these variables

could be represented within the same spatial boundaries to allow them to be

analysed and compared. Andy Coburn (Appendix 1) noted that the latitude/longitude

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of the beach nourishment episodes were mapped using the geographical coordinate

system (GCS) WGS1984, so this was used in ArcGIS. Other layers, such as the

coastal vulnerability index, were in the North American Datum of 1983 GCS. These

were transformed to the WGS1984 when imported into ArcGIS.

The spatial representation of the data was:

beach nourishment episode data was represented as points of

longitude/latitude

erosion/accretion rates and relative sea level rise were represented as lines

median house values and population were represented as polygons (areas)

Therefore, 5-digit zipcode area shape-files were obtained from ESRI (ArcGIS 2013)

and used as boundaries in which to compare the variables. Zip codes were used as

they were found to capture the majority of beach nourishment episode locations,

whilst not losing detail. This was because each zip code covers a relatively small

area, but there is extensive zip code coverage across the U.S. East Coast. Other

area boundaries were tested, including place boundaries (town areas) defined by

Maptechnica (2014), but they captured fewer beach nourishment episode locations,

as the data covered only populated towns and not remote barrier islands and

national parks where beach nourishment had taken place.

The zip code boundaries were imported as a layer into ArcGIS. Variables were

adapted to fit zip code boundaries using the following methods:

Median house values

Median house values of zip codes were imported into ArcGIS as a table. The median

house value attributes were then joined to the zip code shape-file layer based upon

the zip code number attribute (present in both datasets).

Long and short term erosion rates

The long term (LT) and short term (ST) erosion/accretion rate (ER/AR) shape-files

(USGS 2015) of each U.S. East Coast state were imported into ArcGIS. Each

erosion/accretion transect was perpendicular to the shoreline, and therefore each

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transect fell inside a zip code area. This allowed the transect lines to be spatially

joined to the zip code shape files (based upon the transects that intersect the zip

codes) (Figure 2)

Figure 2 - Long term erosion/accretion rate transects shown intersecting zip code boundary areas in ArcGIS, Florida shoreline

Each zip code was updated with the average value of all the long term

erosion/accretion rate transects that intersected it. The same method was used to

provide each zip code with an average short term erosion/accretion rate. An extract

of the table produced from this process is shown in table 2.

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Table 2 - Extract of calculated average LT and ST erosion/accretion rates

ZIP PO_NAME

Average long

term erosion

rate (m/yr)

Average short

term erosion

rate (m/yr)

7760 Rumson -0.427967 3.112667

7762

Spring

Lake 0.124857 0.641642

8006

Barnegat

Light 0.036981 7.341887

8008

Beach

Haven -1.745098 -0.753507

8202 Avalon 0.864407 -0.133898

Average sea level rise (SLR)

USGS Coastal Vulnerability Index (CVI) shape files, containing the relative sea level

rise rate attribute, were imported into ArcGIS as a layer. These shape files, in the

form of line vectors along the shore, did not line up with the shoreline of the zip

codes areas (Figure 3) despite being plotted in the same geographical coordinate

system.

Figure 3 - USGS Coastal Vulnerability Index shape files and zip code shape files in ArcGIS, showing that the CVI lines did not line up with the zip code shorelines; numbers indicate SLR at that location. SC = South Carolina state

17

Therefore, a buffer of 0.4 decimal degrees was applied to the CVI shape file lines so

that the buffered lines fell insides their closest zip code boundary. A spatial join was

then performed so that an average relative sea level rise value was calculated for

each zip code, from the CVI lines that intersected it. The buffer value of 0.4 decimal

degrees was chosen through a trial and error process, in which it was found to be

the maximum distance (to capture as many CVI lines in their closest zip code) before

falling into neighbouring zip codes. This buffer is shown in figure 4

Beach nourishment episodes

The beach nourishment episodes dataset (PSDS, 2014) was imported into ArcGIS

and plotted as points based on longitude/latitudes provided for each nourishment

episode. The beach nourishment episodes dataset was used as the basis for

creating the comparative dataset.

The updated zip code boundaries containing the new attributes of:

median house value,

average long term erosion/accretion rate,

average short term erosion/accretion rate and

Figure 4 USGS Coastal Vulnerability Index shape files with a buffer of 0.4 degrees in ArcGIS; numbers indicate SLR at that location. SC = South Carolina state

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average sea level rise rate

were spatially joined to the beach nourishment episode points layer based on

location, with each point given the attributes of the zip code boundary that it was

closet to.

This updated beach nourishment table, containing a zip code for each point and a

distance to that zip code, was exported to excel. Then the beach nourishment

episodes that had a distance of greater than 91.4 m (100 yards as the dataset

originated in America) to their nearest zip code were identified and their zip code

attributes were deleted, as the nearest zip code was deemed to be too far from the

beach nourishment episode to represent it. A distance of 100 yards was chosen as

the longitude/latitudes of the beach nourishment episodes were only an illustration

and not an exact position.

County boundaries

County boundary shape files, obtained from the U.S. Census Bureau (2013), were

also imported into ArcGIS. These were joined to the associated beach nourishment

episode points’ layer using the same method described above for joining the zip

code boundaries to the points. This added a column for the county of each beach

nourishment episode. The county of each beach nourishment episode was required

to compare variables, such as population, at a county level.

Compiling dataset summary

After performing the spatial joins described in the sections above, the values of

attributes generated were checked to ensure they were realistic and matched values

in the online USGS Coastal Change Hazards Portal (Marine USGS, 2015) and

Beach Nourishment Viewer (PSDS at Western Carolina University, 2015). Figure 5

is a flow diagram showing the order of spatial joins carried out in ArcGIS to create

the comparative dataset.

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Figure 5 Flow diagram of spatial joins carried out in ArcGIS

The result was a comparative dataset (Appendix 3: Master dataset) where each

beach nourishment episode was represented as a row in the dataset and all

information regarding that episode’s zip code (median house value, average sea

level rise rate, average long and short term erosion rate) were given as attributes in

columns (extract shown in Table 3)

Table 3 Extract of comparative dataset created showing beach nourishment episodes as rows, where MHV = median house value ($), Avg SLR = average sea level rise rate (mm/yr), Avg LT ER = average long term erosion rate (m/yr).

After compiling this comparative dataset, it was exported from ArcGIS (ESRI 2013)

to Excel for analysis.

Beach_Location State Zip_Name County_Name Year MHV ($) Avg SLR Avg LT ER

Barnegat Light NJ Barnegat Light Ocean 1991 737300 3.2 0.036981

Bay Head NJ Point Pleasant Ocean 1963 387900 2.85 -0.27041

Beach Plum Island DE Lewes Sussex 1994 325000 2.6 -1.57716

Beesleys Point NJ Marmora Cape May 1966 317200 3.9 0.053077



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Analysis of database

A literature study was carried out to determine which variables were expected to

correlate. This influenced which variables were tested for correlation.

Amendments to comparative dataset

Once compiled, the comparative dataset (Appendix: master) was amended so that

attributes were suitable for comparison.

The following amendments were made:

BN episodes with no ‘year completed’ value were removed as the year of BN

episodes is required to categorise the data into >1970, 1970-2012 datasets.

BN episodes containing ‘pre 1961’ in the year completed field (rather than a

specific year) were left in the >1923 datasets, but removed from the >1970

datasets

The average SLR attribute had some gaps in data for 54% of zip codes. To

amend this, a spatial join was completed in ArcGIS to join the closest SLR line

to each BN episode point. The average of these SLR values for all BN

episode locations in each zip code was calculated. This value was used to

replace gaps in the average SLR attribute, creating a new attribute column

‘updated average SLR’ in which all zip codes had an average SLR (Table 4).

Table 64 shows that the SLR values calculated using both methods were

similar.

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Table 4 Extract of zip code dataset showing SLR columns, where Avg SLR = average relative sea level rise (mm/yr).

Zip code name Original

Avg SLR of

zipcode

Avg SLR calculated

using closest SLR

lines

Updated Avg SLR

Brooklyn 2.73 2.65 2.73

Virginia Beach 0.00 3.9 3.90

Ocean City NJ 3.84 3.9 3.84

Mashpee 1.98 2 1.98

Newburyport 1.15 1.15 1.15

Histogram

The beach nourishment comparative dataset was analysed to determine a baseline

year. A histogram was created to determine when most BN episodes in the dataset

were completed (Figure 6). From this, the year 1970 was chosen as the baseline as

it was identified that ~75% of the BN episodes were completed before 1970.

Therefore, the comparative dataset was split into two main comparative datasets,

one with a baseline of 1970 and another with a baseline of 1923. The >1970 dataset

was used when comparing BN episodes with ST erosion rates, and the >1923

dataset with LT erosion rates.

Population data

Historic county population data was plotted on a time series graph, along with the

cumulative number of BN episodes per county.

The population data ranged from 1970-2010. A new BN dataset was created for BN

episodes from 1970 – 2010, in order that the variables could be plotted on the same

timeline. Counties to analyse were chosen by the following method:

The total number of BN episodes of each county was calculated from this

dataset, along with the median and mean (median = 8, mean = 16).

Counties which had a total number of BN episodes above the mean =16, were

selected. A total of 18 of the 55 counties

These 18 counties were then sorted by population change during 1970-2010

and the 10 with the highest population change were selected for analysis.

22

The cumulative number of BN episodes per county (for these ten counties) was

calculated by sorting the 1970-2010 BN dataset (Appendix 3: 1970-2010) by

‘county’, then by ‘year completed’ and then calculating the cumulative number of BN

episodes per county per year.

For each county a timeline series graph was produced displaying the population and

cumulative number of BN episode data.

Descriptive Statistics

The median, mean and standard deviations of mean house values for each number

of BNs were calculated for periods of time from 1923 to 2012 and from 1970 to

2012. This was required to plot both mean house value datasets against the total

number of BN episodes for each zip code on the same graphs to show trends and

correlation between them. For example, there were 61 zip codes with only a single

beach nourishment event, so if all these points were displayed on the same graph, it

would be unclear. The mean +/- one standard deviation were calculated to show the

spread of the data for the 1923-2012 dataset.

Test for normality

The following variables were tested for normal distribution using the Anderson-

Darling test for normality (Townend, 2002), to determine whether a parametric or

non-parametric statistical test could be used to measure correlation between

variables, as data must be normally distributed to use a parametric statistical test

(Gaten 2000).

Updated average SLR of zipcode

Average LT erosion rate of zipcode

Average ST erosion rate of zipcode

Median house value of zipcode

Total number of BN episodes in zipcode

Total volume of BN episodes in zipcode

SLR for all U.S East Coast

Average LT erosion rate at SLR vectors along all U.S East Coast

23

The results of this test are shown in Table 6. The null hypothesis was that data is

normally distributed. All p-values were less than 0.05 (Table 6) therefore the null

hypothesis was rejected for each variable (Townend, 2002), indicating that data of all

variables did not have a normal distribution, with 95% confidence (Townend, 2002).

Therefore, the non-parametric Spearman’s rank correlation coefficient test

(Townend, 2002) was used to measure the strength of correlation between variables

tested using regression analysis. The results of this test are shown in table 7. The

Spearman’s rho value calculated (table7) for each test was interpreted (ref stats

book) to determine whether the two variables had a significant correlation.

Regression Analysis

Attributes (variables) in the comparative dataset were analysed using regression

analysis. These attributes were compared to one another by creating scatter plots

using Excel. When performing regression analysis, the variables used were modified

so that they could be compared fairly. For example, BN episodes completed in 2013

and 2014 were not included when comparing the independent variable; number of

BN episodes, with the dependent variable; 2008-2012 median house values, as BN

episodes taking place in 2013 and 2014 would not influence 2008-2012 house

values.

By removing certain BN episodes for comparison with other variables, the ‘total

number of BN episodes per zip code’ variable was altered. Therefore multiple

datasets were created for BN episodes between the years 1923-2014, 1970-2014,

1923-2012 (Appendix 3) with ‘number of BN episodes per zip code’ and ‘total volume

of BN episodes per zip code’ variables recalculated in each case.

The following variables were used to compare BN episodes to physical coastal and

economic variables:

number of BN episodes per zip code

total volume of BN episodes per zip code

24

The values for these variables were calculated by sorting the comparative dataset

based on zip code, then manually counting the number of BN episodes per zip code

and adding the volumes of BN episodes in each zip code. A new dataset was

created to display these totals as columns, where each row represents a different zip

code (Appendix 3: Total BN) (extract shown in table 5)

Table 5 Extract of zip code comparative dataset

Zip code name Total

Volume

(cubic

yards)

Number of

BN

episodes

State Median

House Value

($)

Spring Lake 4430600 8 NJ 660400

Barnegat Light 603503 5 NJ 737300

Beach Haven 17135465 51 NJ 744800

Avalon 5146200 12 NJ 1000000

Once plotted, the scatter graphs were studied to identify any outliers that may have

been affecting correlation, but none were found.

25

RESULTS

Where zip code is mentioned in the following results, it is referring to zip codes

containing BN episodes only, as they were solely used in the study.

Data Analysis Results

Table 6 Results from Anderson-Darling test for normality

Variable Anderson-

Darling p-value

Normal

distribution (95%

confidence)

Median house values 1.26E-16 no

Average long term erosion rate 1.65E-11 no

Average short term erosion rate 2.31E-05 no

Updated average relative sea level rise rate 0.002477 no

Total number of beach nourishment episodes

per area

1.31E-52 no

Total volume of beach nourishment episodes

per area

2.94E-57 no

The above table shows results from the Anderson-Darling test for normality for the

data of variables listed. All p-values are less than 0.05 indicating that all data does

not fit the normal distribution, with 95% confidence (Townend, 2002)

26

Table 7 Results from Spearman’s rank correlation coefficient test. DF = degrees of freedom.

Variables tested

Spearman

rho DF

P-

value

Significant

correlation?

Total number of BN

episodes (1923-2012)

of zip code, -0.06 198 0.41 no

Median house value of

zip code (2008-2012)

Total number of BN

episodes (1970-2012)

of zip code, -0.15 145 0.08 no


zip code (2008-2012)

Average SLR of zip

code, -0.03 198 0.66 no

Average LT erosion

rate of zip code

Average LT erosion

rates for SLR vectors, -0.16 1980 0.00 no

SLR for all U.S. East

Coast

Average SLR of zip

code, 0.43 198 0.00 yes

Total number of BN

episodes in zip code

Total volume of BN


(1923 -2012), -0.01 166 0.89 no


zip code (2008-2012)

Total number of BN

episodes in zip code, -0.10 133 0.24 no

Average LT erosion

27

rate of zip code

Average LT erosion

rate of zip code, 0.20 133 0.02 no

Average ST erosion

rate of zip code

(>1970)

Negative average LT

erosion rate of zip

code, 0.54 30 0.00 yes

Negative average ST

erosion rate of zip code

(>1970)

Average LT erosion

rate of zip code (1923-

2012), -0.11 126 0.24 no

Median house values

Total volume of BN


(>1970), -0.03 112 0.75 no

Average ST erosion

rate of zip code

(>1970)

Average ST erosion

rate of zip code (1970-

2012), 0.00 117 0.97 no


zip code (2008-2012)

Table 7 shows the Spearman rank correlation coefficient test results for the variables

tested. The two variables in each test are listed along with the figure number of their

corresponding scatter graph. Only the tests between the following variables showed

significant correlation (Townsend 2002):

‘Average SLR of zip code’ and ‘number of BN episodes in zip code’

28

‘Average LT erosion rate of zip code’ and ‘Average ST erosion rate of zip

code’

Unless stated, the variable dataset range used is all years; 1923 – 2014.

Figure 6 Histogram of all BN episodes along the U.S. East Coast 1923 – 2014, used to determine baseline year of BN records

Figure 6 shows the number of BN episodes per decade. It shows a positive skew,

with an overall increase in the total number of beach nourishment episodes per

decade. The low value seen in the 2010 decade is because the present time is only

mid-way through this decade.

Comparative Results

All results presented below are for the U.S. East Coast. There were a total of 200 zip

codes, containing beach nourishment episodes, used in the following figures

0

50

100

150

200

250

300

1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010s

Tota

l BN

pe

r d

eca

de

Decade (1920's = 1920 to 1929)

29

Independent Variable: Number of beach nourishment episodes

Figure 7 The dependence of Median House Value on Number of Beach Nourishment episodes

30

Figure 7 shows the median values of the 5-year estimate median house values

(2008-2012) for different numbers of beach nourishment episodes per zip code.

There were several zip codes that had the same number of beach nourishment

episodes, so the house values are displayed as median values.

The median values for beach nourishments from 1923 – 2012 are given (in green),

as well as median values for beach nourishments from 1970 – 2012 (in yellow), to

identify any differences in datasets between the long term and short term. The area

in orange represents the mean +/- 1 standard deviation (of the 1923- 2012 dataset),

showing the spread of the median house values.

Figure 7 shows no obvious trend between median house values in zip codes and the

number of BN episodes in that zip code for both data sets; 1923-2012 and 1970-

2012. The results from the Spearman’s correlation test (Table 7) also suggest no

correlation between these two variables.

Independent Variable: Total Beach Nourishment Volume

Figure 8 The dependence of Median House Value on Total Beach Nourishment Volume 1923-2012, per zipcode. A linear regression line is shown

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

$900,000

$1,000,000

0 10 20 30 40 50 60

Med

ian

Ho

use

Val

ue

(20

08

- 2

01

2)

of

Zip

cod

e

Total Beach Nourishment Volume (cubic yards) per Zipcode (1923 - 2012)

Millions

31

Figure 8 shows no obvious trend between median house values in zip codes and the

total BN volume of that zip code. This is supported by the results from the

Spearman’s rank test (Table 7). It is also clear that a lower BN volume is much more

predominant than a higher one with most results clustered near the left hand axis.

The results are fairly evenly distributed around the breadth of the median house

values, with a higher density towards the lower left quadrant providing the very slight

trend observed. The majority of zip codes have a total BN volume < 10 million

yards3 with a median house value between $150,000 and $500,000. The furthest

right point corresponding to an unusually high total BN volume of 54 million yards3 is

for the zip code of Babylon, NY.

32

Independent Variable: Relative Sea Level Rise

Figure 9 The dependence of the number of beach nourishment episodes on average sea level rise, per zip code. A linear regression line is shown.

The average sea level rise per zip code (Figure 9) shows a moderate/weak

correlation with the number of BN episodes per zip code. The results from the

Spearman’s correlation test of 0.434 (Table 7) indicate a moderate correlation. The

majority of the data points are located in the region of the graph related to less than

10 BN episodes.

0

10

20

30

40

50

60

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

Nu

mb

er o

f b

each

no

uri

shm

ent

epis

od

es p

er z

ipco

de

Average Sea Level Rise (mm/yr) per zipcode

33

Figure 10 The dependence of long term erosion rate on relative sea level rise rate for the entire U.S. East Coast, including BN locations and natural locations.

Figure 10 shows data points for relative sea level rise rates, at CVI vectors along the

entire US East Coast (USGS, 2001) (not only areas containing beach nourishment),

plotted against the average long term erosion rate at that location, where available.

The graph shows no obvious trend (Table 7). Above a relative SLR rate of

3.25mm/yr, the majority of data points show erosion, rather than an equal

erosion/accretion split.

-20

-15

-10

-5

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Ave

rage

Lo

ng

Term

Er

osi

on

/Acc

reti

on

Rat

e (m

/yr)

Relative Sea Level Rise (mm/yr)

34

Figure 11 The dependence of long term erosion rate on sea level rise, at beach nourishment locations

Figure 11 shows the average long term erosion/accretion rates (in meters per year)

plotted against the average relative sea level rise (mm/yr), for zip codes containing

beach nourishment episodes along the US East Coast. The graph shows no obvious

trend between the two variables, with a similar erosion/accretion ratio either side of

the horizontal axis for all average LSR values. Results from the Spearman’s

correlation test (Table 7) shows no significant correlation.

-6

-5

-4

-3

-2

-1

0

1

2

3

4

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Ave

rage

Lo

ng

Term

Er

osi

on

/Acc

reti

on

Rat

e (m

/yr)

of

zip

cod

es c

on

tain

ing

bea

ch n

ou

rish

men

t

Average Relative Sea Level Rise (mm/yr) of zipcodes containing beach nourishment

35

Independent Variable: Long Term and Short Term Erosion Rate

0

10

20

30

40

50

60

-6 -4 -2 0 2 4

Nu

mb

er o

f b

each

no

uri

shm

ent

epis

od

es p

er z

ipco

de

(sin

ce 1

92

3)

Average Long Term Erosion/Accretion Rate (m/yr) per zipcode

Figure 12 The dependence of beach nourishment episodes on long term erosion/accretion rate, at zip codes. A linear regression line is shown.

36

Figure 13 The dependence of beach nourishment episodes on short term erosion/accretion rate, at zip codes. A linear regression line is shown.

Figures 12 and 13 show linear regression lines that suggest a weak trend between

BN number and ER variables. However, results from the Spearman’s rank

correlation coefficient indicate there is no significant relationship (Table 7)

0

10

20

30

40

50

60

-4 -2 0 2 4 6 8

Nu

mb

er o

f b

each

no

uri

shm

ent

epis

od

es p

er z

ipco

de

(sin

ce 1

97

0)

Average Short Term Erosion/Accretion Rate (m/yr) per zipcode

37

Figure 14 The dependence of median house value on long term erosion/accretion Rate, at zip codes. A linear regression line is shown.

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

$900,000

$1,000,000

-6 -5 -4 -3 -2 -1 0 1 2 3 4

Med

ian

Ho

use

Val

ue

(20

08

- 2

01

2)

of

Zip

cod

e

Average Long Term Erosion/Accretion Rate (m/yr) per zipcode (containing 1923 - 2012 beach noursihment episodes)

38

Figure 15 The dependence of median house value on short term erosion/accretion rate, at zip codes. A linear regression line is shown.

Figures 14 and 15 show linear regression lines that suggest a weak trend between

MHV and ER variables at zip codes containing BN episodes. However, results from

the Spearman’s rank correlation coefficient indicate there is no significant

relationship (table 7)

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

$900,000

$1,000,000

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Med

ian

Ho

use

Val

ue

(20

08

- 2

01

2)

of

Zip

cod

e

Average Short Term Erosion/Accretion Rate (m/yr) per zipcode (containing 1970 - 2012 beach noursihment episodes)

39

Figure 16 The relationship between long term and short term erosion/accretion rate at zip codes containing BN. A linear regression line is shown.

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

-6 -5 -4 -3 -2 -1 0 1 2 3 4

Ave

rage

sh

ort

ter

m e

rosi

on

/acc

reti

on

rat

e (m

/yr)

per

zip

cod

e

Average long term erosion/accretion rate (m/yr) per zipcode

40

Figure 17 The relationship between long term and short term erosion rate for BN zipcodes with only LT and ST negative values (erosive areas). A linear regression line is shown.

Figure 16 shows no obvious trend between LTER and STER at zipcode locations.

However, the regression line suggests there is a weak trend between short and long

term erosion rates. The spearman’s rank correlation coefficient (table 7) indicates

there is no significant relationship.

Figure 17 is a plot of the bottom left quadrant of figure 16, showing only zipcode

locations with both LT and ST erosion, and not accretion. This plot has a significant

moderate correlation between variables with a Spearman’s rank correlation

coefficient of 0.538 (table 7), showing that in erosive environments the LT and ST

ER are positively correlated.

County Population and Number of Beach Nourishment Episodes

There were a total of 55 counties containing beach nourishment episodes that were

used in the study, from which the following 10 counties were selected. The following

10 counties have:

an above average number of total beach nourishment episodes

the highest 10 county population changes from 1970 – 2010

-2.5

-2

-1.5

-1

-0.5

0

-3 -2.5 -2 -1.5 -1 -0.5 0

Ave

rage

sh

ort

ter

m e

rosi

on

rat

e (m

/yr)

p

er z

ipco

de

Average long term erosion rate (m/yr) per zipcode

41

The following graphs have been shown on their respective axes to compare trends

of population and number of beach nourishments episodes, so that the detail in the

data is clearly visible.

The following figure show a similar positive trend for the cumulative number of BN

episodes and population between 1970 -2010. Suffolk County, NY shows a

deviation in this trend between 1970 and 1994. During this time, the population

increases but this increase is not mirrored by the cumulative number of BN episodes.

Queens County, NY, does not show a correlation between the cumulative number of

BN episodes and population during the entire timeline series. The population in

Queens County, NY, decreases between the years 1970 and 1980, whereas the

cumulative number of BN episodes increases in that period.

Figure 18 Cumulative number of beach nourishment episodes and population for Duval County, Florida, between 1970 – 2010.

528865

578865

628865

678865

728865

778865

828865

0

2

4

6

8

10

12

14

16

18

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Duval, FL

Cumulative BN POPULATION

42

Figure 19 Cumulative number of beach nourishment episodes and population for Miami-Dade County, Florida, between 1970 – 2010.

Figure 20 Cumulative number of beach nourishment episodes and population for Monmouth County, New Jersey, between 1970 – 2010.

1267792

1467792

1667792

1867792

2067792

2267792

2467792

0

5

10

15

20

25

30

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Miami-Dade, FL


459379

479379

499379

519379

539379

559379

579379

599379

619379

2

7

12

17

22

27

32

37

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Monmouth, NJ


43

Figure 21

Figure 22

82996

102996

122996

142996

162996

182996

2

12

22

32

42

52

62

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch

No

uri

shm

ent

Epis

od

es

New Hanover, NC


348753

548753

748753

948753

1148753

1

11

21

31

41

51

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch

No

uri

shm

ent

Epis

od

es

Palm Beach, FL


44

Figure 23

Figure 24

1891325

1941325

1991325

2041325

2091325

2141325

2191325

0

2

4

6

8

10

12

14

16

18

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Queens, NY


50836

100836

150836

200836

250836

0

2

4

6

8

10

12

14

16

18

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch

No

uri

shm

ent

Epis

od

es

St. Lucie, FL


45

Figure 25

Figure 26

1124950

1174950

1224950

1274950

1324950

1374950

1424950

1474950

1

6

11

16

21

26

31

36

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Suffolk, NY


172106

222106

272106

322106

372106

422106

1

6

11

16

21

26

31

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Po

pu

lati

on

Cu

mu

lati

ve N

um

ber

of

Bea

ch N

ou

rish

men

t Ep

iso

des

Virginia Beach, VA


46

DISCUSSION

It is clear that the histogram (Figure 6) shows that the number of beach nourishment

episodes per decade exhibits an overall increasing trend since 1923, which is

supported by Trembanis and Pilke (1999), and Valverde et al. (1999). The

relationship between the variables will now be discussed in turn.

Beach nourishment episodes (BN) vs. median house values (MHV)

Shorelines are usually only nourished to protect investment properties and the local

tourist industry (Pilkey and Coburn 2007), as there is no need to nourish a natural,

undeveloped shoreline that presents no risk to humans. Coastal property values are

a product of the characteristics of the house and property itself, the characteristics of

the neighbourhood and community, and environmental characteristics (King et al.

2000).

This study uses the MHV within zip codes as an economic indicator. These MHVs

range from poor neighbourhoods, such as Georgetown, South Carolina which has a

median house value of $112,300, to wealthy neighbourhoods where the MHV

exceeds $1,000,000. These differences in MHV are not due to environmental factors

alone, as for house value to reflect environmental characteristics only it would be

assumed that all other characteristics or variables associated with the location are

the same. However this is not the case in this study and is probably the main reason

that there is no correlation between ER and MHV in figures 14 and 15.

Not only should property values reflect beach characteristics such as erosion rate

(Gopalakrishnan et al., 2011) but those communities with higher property values

(wealthy towns) are shown to nourish their beaches more often than poorer towns

(Smith et al. 2009), because their benefits from a wide beach are higher; attracting

potential buyers and tourists. However, Figure 7 does not show a correlation

between number of beach nourishment episodes and median house value and

therefore does not support the theory proposed by Smith et al. (2009). This lack of

correlation may be due to the following limitations of the MHV variable used:

MHV includes inland property away from the shoreline

47

MHV is dependent upon variables other than environmental (beach)

characteristics

The median house values used in this study are for zip code areas, and do not solely

represent the MHV of property on the shoreline (and therefore the wealth of the

coastal area alone), as some of the houses from which the MHV was calculated are

over a mile inland from the shoreline. Physical coastal factors and beach

nourishment may have no effect on the value of these houses. In some zip codes all

the properties are situated along the shoreline; in others they are clustered much

further away, leading to a disparity in the data sets. Therefore a density measure of

property would be better to account for this variation and improve the relevance of

the MHV in this study.

The link between the number of BN and the MHV is further limited by a lack of house

density data. (Smith et al. 2009), suggests that higher property values indicate more

frequent BN; however locations with many house with low property values where a

high population would be affected would be more likely to carry out BN than a

location with very few expensive properties. Locations with a large tourist industry

may have low MHV, but their beach will be of great economic value to the

community and local administration, and is likely to be nourished. Politics, which vary

drastically among states in the U.S., may also play a part in determining the number

of beach nourishment episodes in a location, as local governments may have

different beliefs or attitudes towards BN and funding it.

Sea level rise (SLR) vs. beach nourishment episodes (BN)

Figure 9 shows a moderate correlation between relative SLR and the number of BN

episodes in a zip code. This is to be expected, as generally a higher SLR rate

indicates more erosion in sandy shorelines (Leatherman et al., 2000; Romine et al.,

2013), which in turn increases the need for BN.

Eustatic SLR is increasing with time (IPCC, 2013), and the number of BN episodes

along the U.S. East Coast is also increasing with time, with a rapid increase since

1960 (Figure 6) (Valverde et al. 2009). It would therefore be expected to see a

correlation between SLR and number of BN episodes in Figure 9, which is the case.

48

However, the relative SLR rate variable used in this dissertation project is not due to

the eustatic SLR alone, but also subsidence and uplift of the land. With no eustatic

SLR, an area undergoing rapid subsidence, such as Chesapeake Bay (Boon et al.,

2010), would still experience a relative SLR. Therefore it cannot be inferred, like

Zhang et al (2004), that SLR caused by climate change is responsible for increasing

BN.

The link between SLR and number of BN episodes shows a correlation in this study,

whereas the relationship between SLR and ER does not (table7). This may be

because the SLR is not affected by feedback from BN, whereas erosion rates are.

ER also depends on several other factors as well as SLR, making it complex to

detect the direct link between them that is asserted in the theory without accounting

for other variables. It can however be deduced from this study that BN is dependent

upon SLR.

The decision of individual towns to nourish their beaches should be based upon a

number of factors, in which a cost-benefit analysis is undertaken, such as that

created by Smith et al. (2009). However Heinz (2000) showed that for the U.S.

Atlantic and Gulf coast counties they sampled, the expected annual erosion damage

increases the nourishment costs in only 1 out of 10 counties, and BN only passes

benefit-cost tests for federal funding in limited, high density areas.

Erosion rates (ER) vs. median house values (MHV)

Figures 14 and 15 show no correlation between long term (LT) or short term (ST) ER

and MHV. This may be due to the limitations in the MHV as an economic indicator as

previously discussed.

Heinz (2009) shows there is a strong inverse relationship between house price and

the nearest shoreline erosion rate, suggesting that the increased risk of damage to

property is reflected in the house price. Along the Atlantic coast, a house that is 50

years from the shoreline is estimated to be worth about 90 percent of an identical

house located 200 years from the shore Heinz (2009). This again brings into

question the relevance of using zip code MHV, which comprises houses at different

distances from the shoreline, as an economic indicator of coastal characteristics.

49

In addition, this dissertation project only examines locations with a history of BN;

where BN has been carried out to prevent the decrease in property values due to

erosion. Therefore it may be expected that no link would be observed between

erosion rate and median house values in the results as the study only included

locations that had previously undergone BN, therefore correcting for the expected

observed effect. Even though these locations may have a high long term erosion

rate, their median house values may not be decreased because BN has taken

place. The lack of correlation seen in Figures 14 and 15 could be due to this

feedback.

Erosion rate (ER) v beach nourishment episodes (BN)

This study finds that long term erosion rates show no correlation with relative SLR

(Figure 11) or the number of BN episodes in an area (Figures 13, 14), suggesting

that BN is being carried out in areas of high SLR regardless of whether the beach is

actually eroding. However, BN may be masking the link between BN and ER through

feedback processes and other factors such as coastal geomorphology, which differ

from location to location. Figure 13 shows that in the short term there are more BN

locations with accretion than erosion than in the long term (Figure 12), suggesting

that increased BN in more recent times is impacting the ER calculated by the USGS

(2015).

Figures 13 and 14 imply that all States and local authorities make the same

decisions to nourish their beaches based on erosion rate. However, there are

differences in attitudes towards beach nourishment and alternative engineering

strategies in different regions, which mean that some administrative bodies may be

more likely to embrace BN implementation than others. Again, this is another layer of

complexity when trying to compare the two variables alone.

Sea level rise (SLR) vs. long term erosion rate (LTER)

In areas where no BN has been carried out it is expected that there would be a

positive correlation between SLR and LTER. Leatherman et al. (2000) state that SLR

is the main driving factor of ER in sandy shorelines; however their study only

examined locations where no BN had taken place, meaning that those results may

50

not be as applicable to the findings in this dissertation, in which only BN locations

were examined. Figure 11 shows no correlation between ER and SLR; possibly due

to this reason, as well as the effect of BN on ER and the possibility that it is masking

the relationship proposed by Leatherman et al. (2000). Lazarus et al. (2011) show

that feedbacks between these human and natural coastal processes can lead to the

emergence of chaotic shoreline evolution.

Figure 10 also shows no correlation between SLR and LTER, but unlike Figure 11 it

includes locations along the entire U.S. East Coast, including locations where no BN

had taken place. This suggests that the difference in correlation observed between

Figure 11 and the relationship proposed by Leatherman et al. (2000) cannot be

explained alone by the presence of BN.

Leatherman et al. (2000) found long term erosion rate, on average, to be about 150

times (2 orders of magnitude larger) that of sea level rise in areas along the U.S.

East Coast. This has been criticised for being too high and incorrectly calculated

(Sallenger Jr and Morton, 2000; Galvin, 2000). However, Romine et al. (2013) also

found historical rates of shoreline change to be around two orders of magnitude

greater than SLR on the islands of Oahu and Maui, Hawaii. Figure 10 shows that the

LT erosion rate of many locations in this study exceeds this amount. The calculated

average magnitude difference for the LT erosion rate data (removing accretion rates)

used in figure 10 was 522.81 (2 orders of magnitude larger than SLR), supporting

the findings of Leatherman et al. (2000) and Romine et al. (2013). However, the

average LT accretion rate (removing erosion rates) was 450.82 times that of SLR;

also 2 orders of magnitude larger. Sallenger Jr and Morton (2000) criticised the

methods used by Leatherman et al. (2000) to prove the relationship between ER and

SLR, and they found that by removing all accretional and stable changes, the

average shoreline change will over-estimate the sea-level rise erosion signal. If

locations that had LT accretion were removed from Figure 10, a correlation between

SLR and ER might be observed, similar to that of Leatherman et al. (2000). This

would however be biased.

Although the USGS erosion rate transect coverage along the U.S. East Coast was

substantial, it only covered the open ocean shoreline, not including areas of coast

51

that were behind barrier islands or in bays, the Florida Keys chain islands, areas of

Massachusetts and the entire Connecticut state (as it is behind Long Island, NY).

There were therefore gaps in the comparative dataset which could have affected

correlation.

The majority of locations for the entire U.S East Coast with a relative SLR above

3.25mm/yr are undergoing long term erosion (Figure 10), suggesting possibly that

only a SLR rate above 3.25mm/yr will result in long term erosion in this area. It has

been questioned (Poppick, 2013) if relatively low rates of SLR are enough to have a

significant effect on shoreline processes, including ER. Future research into the

relationship between SLR and ER may bring this issue to light.

Figure 11 shows a cluster of locations with high relative SLR (~4mm/yr) that are

accreting. If SLR is driving erosion (Leatherman et al. 2000), then the fact that the

highest SLR locations, seen in figure 11, are accreting, must be examined. This

study, and the work of Leatherman et al. (2000), assumes that SLR drives beach

erosion and the process is a two dimensional onshore-offshore process. However,

beach erosion is a three dimensional problem, caused by interruption of sand

transport in the longshore direction (Galvin, 2000). Therefore, there are more factors

affecting beach ER than SLR, and this is why Figures 22 and 953 show no

correlation.

There are factors driving shoreline change and ER other than SLR. These include:

sediment availability

anthropogenic changes

littoral processes

wave conditions

coastal and near shore geomorphology

These multiple factors make it difficult to establish a direct causative link between

historical shoreline change and SLR (Romine et al. 2013)

Long term erosion rate (LTER) vs. short term erosion rate (STER)

The fact that Figure 16 does not show a significant correlation between LT and ST

erosion/accretion rates is surprising. It would be expected to find that, for example,

locations with high LT accretion would also have high ST accretion. The lack of

52

correlation shows how much variation there is in shoreline change when considered

in short term timescales compared to long term trends.

Figure 16 shows that erosion/accretion rate is more variable in the short term, with

more of a spread above and below the horizontal axis, whereas LTER shows less

variation, suggesting that erosion and accretion becomes more balanced over a

longer timescale. Quartel et al. (2008) state that periods of accretion and erosion

alternate over time and are generally coupled to low and high energy wave

conditions. Recent storm events can affect ST erosion rates; however, barrier

beaches along the U.S. East Coast recover to their long-term trend positions after

storms regardless of storm severity (Zhang et al. 2002).

The top left quadrant in Figure 16 shows zip codes that have LT erosion but ST

accretion. LT erosion with ST accretion could be due to BN causing ST accretion, in

a naturally erosive location. The outlier in this quadrant (long term erosion rate = -

5.27 m/yr, short term accretion rate 2.88 m/yr) represents the Hateras zip code,

North Carolina. This location has had 7 beach nourishment episodes with a total

volume of 6.7877e+5m³ (or 887,801 cubic yards). This is below the mean total BN

volume in the zip codes, which is 3.0411e+6m³ (or 3,977,586 cubic yards). However,

all the beach nourishment episodes at this location took place between 1974 and

2003 (within the short term erosion/accretion range timescale). This could suggest

that BN is causing the short term accretion rates seen at this location.

Zip code locations that have both LT and ST erosion values (with accreting locations

removed) are shown in Figure 17. These variables have a moderate correlation

(Table 7), indicating that in erosive environments the LT and ST ER are positively

correlated, as is to be expected.

Caveats

Points were used to illustrate the location of beach nourishment areas by the

PSDS (2014). A polygon would be a better representation as a beach

nourishment episode extends over an area.

53

Limited historic median house value data was available at town, zip code and

county level, dating only to the year 2000. The study therefore assumes that

only inflation has influenced house value through the years at a national level

that incorporates all locations used in the study

Median house values were capped at $1,000,000. Zip codes that had a

median house value over $1,000,000 contained the value $1,000,000+. The

plus sign was removed from these values in the dataset, to makes these

median house values useful. There were gaps in the dataset where there

were no sample observations or too few sample observations available to

compute an estimate (U.S. Census Bureau, 2014).

Short term erosion/accretion rates are calculated from the last two shoreline

positions from which long term erosion/accretion rates were calculated.

Therefore comparisons between the two are questionable.

33% of the zip codes that included beach nourishment did not include long

term erosion/accretion rates and were therefore not used in the linear

regression (Figure 12)

22% of the zip codes that included beach nourishment did not have short term

erosion/accretion rates data and were therefore not used in Figure 13

The larger the area used for comparison of variables, the greater the loss of

detail. Median house value for zip codes cover an area that includes houses

that are inland from the immediate shoreline and therefore don’t give a true

representation of the house values on the shoreline. These inland properties

are less affected by physical coastal processes and beach nourishment. In

addition, house values in an area depend on the characteristics of the houses,

the characteristics of the community and neighbourhoods and environmental

factors (King et al. 2000). Therefore MHV used in this project represents not

only physical coastal factors and beach nourishment but other characteristics

that maybe skewing correlation results.

County level population data includes even more inland area than zip code

data, and is therefore even less representative of the coastal factors than zip

code areas. Population values may have been influenced by inland areas of

the county, bearing no resemblance to coastal factors.

54

129 out of 1299 beach nourishment episodes did not contain a value for the

volume of sediment used in the episode.

CONCLUSION

Beach nourishment episodes, median house values, long term/short term erosions

rates and relative sea level rise of zipcodes were compared using regression

analysis and tested for correlation. A moderate correlation was found between the

number of beach nourishment episodes and the rate of relative sea level rise,

indicating BN is carried out in regions of high SLR rate. A moderate correlation was

also found between long term and short term erosion rates in erosive locations.

Other pairs of variables compared did not show significant correlation, suggesting

SLR to be the driving factor for the nourishment of beaches. Based on the theory

reviewed it would have been expected to see correlation between the majority of

variables tested. The implications of these results suggest that:

the theory may be incorrect,

there is a lack of detail in the data,

the feedbacks between human and natural coastal processes are affecting

the predicted linkages or

relationships depend upon numerous additional factors that haven’t been

included in the study.

Due to the limited spatial information regarding each beach nourishment episode

and median house values, episodes were grouped together into zip codes, resulting

in a loss of detail and incorporating areas of land not representative of the human-

coastal system. Direct causative links were difficult to identify by comparing two

variables at once, meaning that for further work, multivariate anaylsis would be a

better option.

Although the evidence for SLR causing ER is minimal (Leatherman et al. 2000;

Sallenger Jr and Morton, 2000; Galvin, 2000), the link appears to be observed in this

55

study. It is thought that the main reason correlation between variables is not

observed (except in the case of SLR with BN) is because of feedback processes

between human BN processes and these variables. Lazarus et al (2011) have

shown that decisions regarding BN made by towns do not take into account the BN

of neighbouring towns. ER is not simply caused by SLR but by numerous other

factors which should be taken into account, including accretion caused by BN. The

relationship shown between SLR and BN is stronger and more apparent because

SLR is not affected by feedback from BN, whereas ER is. Therefore it can be

concluded that BN is dependent upon SLR. The theory suggests that relationships

between the variables tested exist, but further work is required for them to be

identified across such a broad scale of the U.S. East coast, taking full account of

other variables such as local politics, house price density (as opposed to absolute

median house values) and the local tourism industry.

The hypothesis was shown to be false in the context of the methods used in this

study, but it is suggested that further work accounting for the factors that led to this

discrepancy would yield the expected correlations. This study has therefore proved

valuable in informing future research techniques in this area.

Further work

Median house values should be refined so that they only represent house

values along the immediate shoreline. Median house value should be

corrected using the hedonic pricing method to reflect only environmental

characteristics with focus on beach characteristics, removing other

characteristics contributing to median house value.

Other indicators could be used including looking at the density of property and

population along the shoreline to examine the human factors influencing the

need for nourishment. Tourism information could be used as an economic

indicator, incorporating economic factors contributing to beach nourishment

that aren’t covered by median house values.

USGS historical shoreline positions could be used to calculate erosion rates

for different timescales (put into decades erosions rates) and then compared

with beach nourishment episodes in that decade.

56

Look at other factors driving shoreline change such as sediment availability,

littoral processes, wave conditions, coastal and near shore geomorphology to

account for differences between geographical locations when comparing

variables.

Use multivariate analysis to compare variables to gain a better understanding

of their relationships

57

ACKNOWLEDGEMENTS

I would like to thank my supervisor Dr. Eli Lazarus, without his guidance and help

this work would not have been possible. I would also like to thank the Cardiff

Undergraduate Research Opportunities Programme (CUROP) for providing the

opportunity and funding to carry out this research project.

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63

APPENDICES

Appendix 1 - Email from A. Coburn: Associate Director, Program for the Study of

Developed Shorelines

RE: Master Nourishment Table Andy Coburn <[email protected]> Wed 06/08/2014 16:18 CUROP To:Curtis Thorpe <[email protected]>; Hi Curtis, Thanks for the message. We used Google Earth and Google Maps, both of which use WGS84, for the lat/long of each nourishment episode. Please note that using a point to illustrate the location of a linear feature (or, more specifically, a polygon) is not ideal (as I’m sure you know). We use it only for illustrative purposes. There may come a time, we hope, when we are able to locate/obtain the geographic coordinates of some/many/recent beach nourishment episodes and more accurately map them. Until then…a point it is. Cheers, Andy Research & Graduate Faculty Associate Director, Program for the Study of Developed Shorelines Western Carolina University 294 Belk, Cullowhee, NC 28723 Tel: 828-227-3027 | Fax: 828-227-7163 | http://psds.wcu.edu From: Curtis Thorpe [mailto:[email protected]] Sent: Wednesday, August 06, 2014 9:11 AM To: Andy Coburn Subject: Master Nourishment Table Dear Mr Coburn, I am an undergraduate student working with Dr Eli Lazarus at Cardiff University, UK. I am using the beach nourishment spreadsheet file titled 'Master Nourishment Table', obtained by Dr Lazarus from your team. I am inquiring which datum was used (probably NAD83 or WGS84) for the latitude and longitude values in the spreadsheet. Regards, Curtis Thorpe

http://psds.wcu.edu/

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Appendix 2 - Email from R. Theiler: US Geological Survey

Re: National Assessment of Shoreline Change Project: GIS Compilation of Vector Shorelines - LT & ST Erosion Rates Rob Thieler <[email protected]> Mon 05/01/2015 14:49 CUROP To:Curtis Thorpe <[email protected]>; Curtis, http://pubs.usgs.gov/of/2005/1326/metadata/Florida/fl_transects_lt.htm process description says long-term data are from 1800s to most recent (lidar) shoreline. That's typically 150 yr for the U.S. http://pubs.usgs.gov/of/2005/1326/metadata/Florida/fl_transects_st.htm process description says short-term data are from 1970s to most recent (lidar) shoreline. That's typically 30-40 yr for the U.S. In the next month or so, we are releasing updates to the Gulf of Mexico and Southeastern U.S. shoreline change reports. These will include a lot more temporal density across both timescales. Regarding the ancient but still useful dds68, the timescale is (O)century. The shoreline change rates are typically 100-150 yr for the U.S. And the relative rates of SLR are from the long-term U.S. tide gauges so they too are typically ~100 yr (or at least 50-60). The wave stats are short term (e.g., period of WIS hindcasts) and of course assuming stationarity is very likely invalid, but it was the best we could do at the time. Hope this helps. From: Curtis Thorpe <[email protected]> Date: Saturday, January 3, 2015 at 9:41 AM To: Rob Thieler <[email protected]> Subject: Re: National Assessment of Shoreline Change Project: GIS Compilation of Vector Shorelines - LT & ST Erosion Rates Also, Regarding the Coastal Vulnerability Index data: http://pubs.usgs.gov/dds/dds68/ What is the timescale for 'relative sea-level rise' and the 'shoreline erosion and accretion rates' used in this study? This information is not given in the metadata Thanks, Curtis Thorpe

From: Curtis Thorpe Sent: 02 January 2015 14:16 To: Rob Thieler Subject: Re: National Assessment of Shoreline Change Project: GIS Compilation of Vector Shorelines - LT & ST Erosion Rates Dear Rob, Regarding the National Assessment of Shoreline Change Project http://coastal.er.usgs.gov/shoreline-change/ What are the timescales for long term and short term erosion rate? for example, does long term cover the past 100 years and short term covers 50 years?

http://pubs.usgs.gov/of/2005/1326/metadata/Florida/fl_transects_lt.htm

http://pubs.usgs.gov/of/2005/1326/metadata/Florida/fl_transects_st.htm

mailto:[email protected]

mailto:[email protected]

http://pubs.usgs.gov/dds/dds68/

http://coastal.er.usgs.gov/shoreline-change/

http://coastal.er.usgs.gov/shoreline-change/

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Thanks, Curtis Thorpe Cardiff University, UK

Appendix 3 – Generated comparative dataset

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