Barrier-Island and Estuarine-Wetland Physical-Change 

Assessment after Hurricane Sandy

By Nathaniel G. Plant, Kathryn E.L. Smith, Davina L. Passeri, Christopher G. Smith, and Julie C. Bernier

Open-File Report 2017–1157

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director 

 exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2018 

For more information on the USGS—the Federal source for science about the Earth,

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must be secured from the copyright owner.

Suggested citation: 

Plant, N.G., Smith, K.E.L., Passeri, D.L., Smith, C.G., and Bernier, J.C., 2018, Barrier-island and estuarinewetland physical-change assessment after Hurricane Sandy: U.S. Geological Survey Open-File Report 2017–

1157, 36 p., https://doi.org/10.3133/ofr20171157.

ISSN 2331-1258 (online)
iii

Acknowledgments

This report presents interpretations of data collected with the assistance and guidance from a 

number of Federal land managers, including Kevin Holcomb, Virginia Rettig, and Paul Castelli from the 

U.S. Fish & Wildlife Service, and Neil Winn and Bill Hulslander from the National Park Service. We 

thank them for their interest and input to our research efforts. 

This report summarizes and synthesizes work conducted by a wide range of U.S. Geological 

Survey (USGS) staff who collected and analyzed data, developed work plans, and administered complicated contracts to produce a broad range of products. The report authors acknowledge the following USGS staff: John Barras, Elisha Ellis, Catherine Wheaton, Scott Adams, Arnell Forde, Kristy Guy, 

Marci Marot, Bruce Richmond, James Bishop, Brent Lunghino, Haunani Kane, SeanPaul La Selle, 

Bruce Jaffe, Joseph Terrano, Steven Douglass, Danielle Swiderski, Nick Zaremba, Kara Doran, Hilary 

Stockdon, Karen Morgan, Neil Ganju, Zafer Defne, David Nagle, Ellen Raabe, Rudy Troche, Teresa 

Bellino, Laura Bricker, Joe Carroll, Sandy Coffman, Mark Hansen, Cheryl Hapke, Patti Hartsing, Susan 

Helmer, Lisa Morrow, Dick Poore, Susan-Russel Robinson, and Rob Wertz. We thank Neil Ganju and 

Hilary Stockdon for their critical review of the first draft; their suggestions led to revisions that improved the clarity of this report. Betsy Boynton, Heather Schreppel, and Marilyn A. Billone supported 

the review, editing, and publication process. 
iv
v

Contents

Acknowledgments ...................................................................................................................................................... iii

Introduction..................................................................................................................................................................1

Methods.......................................................................................................................................................................2

Remotely Sensed Data............................................................................................................................................3

Sedimentological Data and Analysis........................................................................................................................4

Assessment Models.................................................................................................................................................5

Wetland-Shoreline Ecological-Change Assessment ............................................................................................6

Estuarine-Shoreline-Change Models ...................................................................................................................7

Sandline-Change Models...................................................................................................................................10

Assessment Model Training and Testing Approach ...........................................................................................10

Results.......................................................................................................................................................................11

Long-Term Estuarine-Shoreline-Change Assessment...........................................................................................11

Storm-Driven Estuarine-Shoreline-Change Assessment.......................................................................................12

Long-Term Sandline-Change Assessment ............................................................................................................12

Storm-Induced Sandline-Change Assessment (Storm Driven)..............................................................................15

Wetland-Shoreline Ecological-Change Assessment..............................................................................................18

Discussion .................................................................................................................................................................18

Physical Process Interpretations ...........................................................................................................................18

Alternate Approaches to Extending Remote-Sensing Observations Using Satellite Data.....................................21

Future Applications ................................................................................................................................................21

Conclusions...............................................................................................................................................................24

References Cited.......................................................................................................................................................25

Appendix 1. BN Models..........................................................................................................................................29

Shoreline Change ..................................................................................................................................................29

Sandline, Long-Term..............................................................................................................................................34

Figures

1. Example of barrier-island and wetland changes, Kegotank Bay, Virginia, showing overwash deposits

burying marsh and tidal channels..................................................................................................................2

2. Example of barrier-island and wetland data extracted from aerial imagery...................................................3

3. Landsat 5 imagery classification based on radiometrically corrected image, unsupervised classification

to identify water areas, and seven classes ...................................................................................................5

4. Image showing marsh and overwash core locations at Assateague Island, Maryland and Virginia..............6

5. Example of overwash-deposit core from transect 4 showing a layer of peat intermixed with

sand deposits ................................................................................................................................................7
vi

6. Wetland-shoreline ecological-change assessment showing historical and modern classification of

wetland vegetation or bare sand ...................................................................................................................8

7. Schematic diagram of model variables used to predict back-barrier-shoreline change ................................8

8. Schematic diagram of models used to predict long-term sandline change and Hurricane Sandy-induced

sandline change ..........................................................................................................................................10

9. Maps showing the observed long-term and predicted long-term estuarine-shoreline change, and

prediction certainty at Assateague Island, Maryland and Virginia ...............................................................13

10. Maps showing the observed long-term and predicted long-term estuarine-shoreline change, and

prediction certainty at Sandy Hook, New Jersey. ........................................................................................13

11. Maps showing the observed and predicted storm-driven estuarine-shoreline change, and prediction

certainty at Assateague Island, Maryland and Virginia................................................................................14

12. Maps showing the observed and predicted storm-driven estuarine-shoreline change, and prediction

certainty at Sandy Hook, New Jersey..........................................................................................................14

13. Maps showing the observed and predicted long-term sandline change, and prediction certainty at

Assateague-Island, Maryland and Virginia..................................................................................................16

14. Maps showing the observed and predicted long-term sandline change, and prediction certainty at

Sandy Hook, New Jersey. ...........................................................................................................................16

15. Maps showing the observed and predicted storm-induced sandline change, and prediction certainty at

Assateague Island, Maryland and Virginia ..................................................................................................17

16. Maps showing the observed Hurricane storm-induced sandline change, predicted Hurricane Sandyinduced sandline change, and prediction certainty at Sandy Hook, New Jersey ........................................17

17. Maps showing assessment of back-barrier-shoreline type based on historical data and recent data at

Assateague Island, Maryland and Virginia ..................................................................................................19

18. Maps showing assessment of back-barrier-shoreline type at Long Beach, New Jersey, based on

historical data and recent data ....................................................................................................................19

19. Imagery, shorelines, and sandlines derived from aerial and satellite sources, and comparison

of digitized sandlines and shorelines...........................................................................................................22

20. Elements required to maintain and extend barrier-island estuarine-wetland physical-change analysis

include updated observations of changes in features, associated data to tie these changes to physical

processes, and updated models that address coastal management decisions .........................................23

Tables

1. Combined data sources used to estimate changes attributed to long-term and Hurricane Sandy

shoreline and sandline change.........................................................................................................................4

2. Variables used in the shoreline-change and sandline-change models ............................................................9

3. Long-term and storm-driven back-barrier-shoreline-change-prediction accuracy using weighted and

unweighted skill with independent and hindcast models in the New Jersey and Assateague study areas .....12

4. Skill with independent and hindcast models for Skill metrics Sandline Change Bayesian Network output...15

5. Constant flux and sedimentation model results for the cores ........................................................................20
vii

Conversion Factors

International System of Units to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre

square centimeter (cm2) 0.001076 square foot (ft2)

Volume

cubic meter (m3) 264.2 gallon (gal) 

liter (L) 61.02 cubic inch (in3) 

Flow rate

meter per year (m/yr) 3.281 foot per year ft/yr) 

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Density

kilogram per cubic meter (kg/m3) 0.06242 pound per cubic foot (lb/ft3) 

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

Datum

Vertical coordinate information is referenced to the (insert datum name (and abbreviation) here, for example, North American Vertical 

Datum of 1988 (NAVD 88)).

Horizontal coordinate information is referenced to the (insert datum name (and abbreviation) here, for example, North American Datum of 

1983 (NAD 83)).

Altitude, as used in this report, refers to distance above the vertical datum.
viii

Abbreviations

BN Bayesian network

COAWST coupled-ocean-atmosphere-wave-sediment transport

DOQQ digital orthophoto quarter quadrangle

DSAS Digital Shoreline Analysis System

GPR ground-penetrating radar

HS storm driven

LLR likelihood ratio

LSR linear sedimentation rate

LT long term

M number of predicted outcomes

NOAA National Oceanic and Atmospheric Administration

USGS U.S. Geological Survey
Barrier-Island and Estuarine-Wetland Physical-Change 

Assessment after Hurricane Sandy

By Nathaniel G. Plant, Kathryn E.L. Smith, Davina L. Passeri, Christopher G. Smith, and Julie C. Bernier

Introduction

The Nation’s eastern coast is fringed by beaches, dunes, barrier islands, wetlands, and bluffs. 

These natural coastal barriers provide critical benefits and services, and can mitigate the impact of 

storms, erosion, and sea-level rise on our coastal communities. Waves and storm surge resulting from 

Hurricane Sandy, which made landfall along the New Jersey coast on October 29, 2012, impacted the 

U.S. coastline from North Carolina to Massachusetts, including Assateague Island, Maryland and Virginia, and the Delmarva coastal system. The storm impacts included changes in topography, coastal 

morphology, geology, hydrology, environmental quality, and ecosystems (Buxton and others, 2013). 

In the immediate aftermath of the storm, light detection and ranging (lidar) surveys from North 

Carolina to New York documented storm impacts to coastal barriers, providing a baseline to assess 

vulnerability of the reconfigured coast. The focus of much of the existing coastal change assessment 

is along the ocean-facing coastline (Sopkin and others, 2014); however, much of the coastline affected by Hurricane Sandy includes the estuarine-facing coastlines of barrier-island systems. Specifically, the wetland and back-barrier shorelines experienced substantial change as a result of wave 

action and storm surge that occurred during Hurricane Sandy (fig. 1; see also USGS photograph, 

http://coastal.er.usgs.gov/hurricanes/sandy/photo-comparisons/virginia.php). Assessing physical 

shoreline and wetland change (land loss as well as land gains) can help to determine the resiliency of 

wetland systems that protect adjacent habitat, shorelines, and communities. 

To address storm impacts to wetlands, a vulnerability assessment should describe both longterm (for example, several decades) and short-term (for example, Sandy’s landfall) extent and character of the interior wetlands and the back-barrier-shoreline changes. The objective of this report is 

to describe several new wetland vulnerability assessments based on the detailed physical changes 

estimated from observations. The scope includes understanding changes caused by both short- and 

long-term processes using both remotely sensed and in situ observations to characterize changes to 

the wetland in terms of accretion/expansion and erosion/contraction. Accretion may be due to net vertical and (or) horizontal deposition, including estuarine-shoreline change due to overwash. Wetland 

erosion may be due to elevated waves and water levels in the estuary itself. We included additional 

information based on wave runup and storm-surge elevations based on models (Aretxabaleta and others, 2014; Birchler and others, 2014; Zambon and others, 2014; Ganju and others, 2016; U.S. Geological Survey, 2016a) and elevation data (Stockdon and others, 2013; Overbeck and others, 2017). 

We then developed a predictive assessment for wetland vulnerability that describes the likelihood of 

changes of the estuarine shoreline and the landward extent of sand overwash driven by processes occurring on the ocean-facing shoreline. This assessment is intended to be linked to the beach and dune 

vulnerability assessments that have been developed previously (Doran and others, 2012; Stockdon 

and others, 2012; Birchler and others, 2014). 
2

Methods

Historical and modern maps, aerial photographs, satellite imagery, and lidar elevation data were 

included in this analysis to document physical changes associated with Hurricane Sandy and to provide 

a long-term context for this storm event. Surficial sediments, sediment cores, and geophysical data were 

collected and analyzed to characterize Hurricane Sandy sediment deposits and historical deposits along 

the barrier island as well as the barrier and mainland estuarine shorelines. These ground-truth data serve 

to document the depositional amounts attributed to Hurricane Sandy and can be used to interpret the 

results of the remotely-sensed wetland change analysis; they also provide historical deposition to put 

Hurricane Sandy in a broader context. The detailed analysis of the sediment data provides an accurate 

understanding of site-specific sensitivity of deposition associated with cross-barrier sediment transport 

and transport associated with estuarine processes. 

Figure 1. Example of barrier-island and wetland changes, Kegotank Bay, Virginia, showing overwash deposits burying 

marsh and tidal channels. (Yellow arrow marks a reference feature that is visible in each image.)
3

The approach to developing an assessment of estuarine-shoreline and wetland physical changes 

consisted of the following three components: 

• identifying and measuring estuarine-shoreline changes resulting from Hurricane Sandy along 

Virginia, Maryland, and New Jersey coastlines using remotely sensed data;

• sampling and analyzing sediment cores to understand long-term geologic processes and provide 

ground-truth measurements for the remote-sensing analysis; and

• assessing the likelihood of wetland change resulting from future storm events or long-term processes and linking to the existing beach and dune vulnerability assessment. 

Remotely Sensed Data

The approach used for measuring changes from remotely sensed data (fig. 2) has been published 

previously (Bernier and others, 2015; Guy, 2015b; c). To summarize, several coastal features were extracted from aerial photography spanning the period from the late 1980s to just after Hurricane Sandy’s 

landfall on October 29, 2012. The variables used to quantify coastal change included the ocean-facing 

shoreline position (digitized at the visually identified water line), the estuarine (back-barrier) shoreline, 

Figure 2. Example of barrier-island and wetland data extracted from aerial imagery.
4

and areas of unvegetated sand. The landward limit of these unvegetated sand areas denotes a sandline 

that identifies the landward extent of overwash immediately after storms and it identifies the seaward 

extent of vegetation, as plant recolonization takes place long after overwash occurs. 

The specific remote-sensing data required by this assessment were augmented by additional data 

that had been derived previously (Himmelstoss and others, 2010; National Oceanic and Atmospheric 

Administration, 2016; Swiderski and others, 2016) and these data were combined to estimate barrier and 

wetland changes at two timescales: a long-term linear regression to determine change rates over the past 

decades and a quantification of the net change attributed to Hurricane Sandy. The combined data sources 

utilized for these analyses are described in table 1.

Table 1. Combined data sources used to estimate changes attributed to long-term and Hurricane Sandy shoreline and 

sandline change.

Change rates of the shorelines (ocean facing and estuarine) and sandlines were estimated along 

linear transects perpendicular to the shore, with transects separated by 50-meter spacing in the alongshore direction. The Digital Shoreline Analysis System (DSAS; ver. 4.3.4730 for ArcGIS 10) (Thieler 

and others, 2008) was used to estimate linear regression rates for the long-term analysis and net shoreline movement was used to compute shoreline change from the period immediately before and immediately after Hurricane Sandy’s landfall. These estimates of shoreline change are available as U.S. Geological Survey data releases (Terrano and Smith, 2015). Sandline movement was estimated similarly, 

with net movement between pre-sandline and post-sandline position as the metric of change.Although 

we will rely on the high-resolution aerial imagery for the analysis presented here, we were interested in 

developing an observation and analysis approach that could be applied broadly along the Nation’s coastlines and updated with new data rapidly and automatically. Previous work assessed the utility of Landsat 

imagery (fig. 3) for extracting shorelines and sandlines (Bernier and others, 2015). We will discuss the 

relevance of additional data sources to broader scale implementation (that is, national-scale as opposed 

to the regional demonstrations presented here) of the approaches presented in this report. 

Sedimentological Data and Analysis

Sedimentological data (figs. 4 and 5) have been collected previously at Assateague Island, Maryland and Virginia (hereinafter, we refer to this region simply as Assateague), in order to sample depositional characteristics in the wetlands (Smith and others, 2015) and the deposition associated with overwash from the ocean into the wetlands (Bernier and others, 2016; Zaremba and others, 2016a). These 

Years of data for New Jersey Years of data for Assateague Island Source Publisher

1880, 1881, 1883, 1899, 1926, 1927, 

1932, 1943, 1950, 1970, 1974, and 

1981

1849, 1850, 1915, 1933, 1942, 1959, 

1961 and 1962 T-sheet* NOAA1

1991, 1995, 2002, 2006, 2007, 2008, 

2010, 2012,^ and 2013^

1989, 1994, 2004, 2005, 2006, 2007, 

2008, 2009, 2011,^ 2012,^ and 2013^ DOQQ2 USGS3

*

Data used to obtain oceanfront and back-barrier shorelines only (no sandlines).

^

Data used for pre- and post-Hurricane Sandy change estimates.

1

National Oceanic and Atmospheric Administration; see https://shoreline.noaa.gov/data/datasheets/t-sheets.html. 2

Digital orthophoto quarter quadrangle image.

3

U.S. Geological Survey; see https://doi.org/10.3133/ds960; https://doi.org/10.3133/ds928.
5

ground-truth samples add both a broader historical context and constrain depositional processes that can 

only be inferred, but not directly inspected, from the image-based observations used to assess barrier 

and wetland physical changes. Additional data collection has occurred at Forsythe National Wildlife 

Refuge, New Jersey (Bishop and others, 2016b; Bishop and others, 2016a), and Fire Island, New York 

(La Selle and others, 2017), where sediment cores were sampled and analyzed to interpret overwash 

depositional processes. The sedimentological analysis of the cores includes estimates of grain-size 

distributions and texture, water content, bulk density, porosity, organic content, and radiometric analysis 

of lead-210 (Pb-210). The sedimentological analysis results in a description of the depositional textures 

and rates which are used to infer depositional processes responsible for observed estuarine-shoreline and 

sandline changes. Coincident with the overwash sediment sampling, ground-penetrating radar (GPR) 

surveys were conducted to resolve the stratigraphy that connects the discrete sample locations where 

sediment cores were recovered (Zaremba and others, 2016a; Zaremba and others, 2016b). These data 

can be related to the geologic structure produced by barrier-island evolution, and can be related to actual 

elevation changes, such as those measured using lidar methods (Stockdon and others, 2013; Sopkin, 

2014; Wright and others, 2014).

Assessment Models

The variables that are used to assess long-term changes (that is, occurring over decades to centuries) and short-term changes (that is, due to Hurricane Sandy) to the barrier wetlands are (1) the estuarine shoreline, (2) the sandline (which describes the interaction between ocean processes, via overwash, 

and the interior or estuary side of the island), as well as (3) the actual degree of vegetation at the estuarine-shoreline edge. The regression and event-based analysis of the remote-sensing-derived data characterize our best estimates of actual changes, but they do not capture the sensitivities of those changes 

to geomorphic, vegetation-mediated, or oceanographic drivers of sediment transport and deposition. 

We developed several predictive models to assess our ability to relate estuarine-shoreline and sandline 

changes to a broader set of variables that are intended to capture the processes that actually cause wetland changes. 

Figure 3. Landsat 5 imagery classification based on (A) radiometrically corrected image, (B) unsupervised classification to 

identify water areas, and (C) seven classes (water, wet marsh, marsh, forested, mixed vegetation, vegetated bare earth, and 

bare earth). The enlargement shows (D) sample classifications and (E) line features. 

A B C

D

E

EXPLANATION

Water Wet marsh Marsh Forested Mixed vegetation Vegetated bare earth Bare earth Sea shoreline Sand line Back-barrier shoreline Marsh island
6

The first two assessments are implemented numerically using Bayesian networks (BN). Bayesian 

networks (appendix 1) have been used by others to predict shoreline changes due to sea-level rise (Gutierrez and others, 2011), to predict shoreline and dune erosion due to extreme storms (Plant and Stockdon, 2012), and to relate diverse geomorphic features occurring on barrier islands to each other (Gutierrez and others, 2015). An advantage of the BN approach is that it formalizes the expected correlations 

between input and output variables of a coastal-change process, even if an explicit numerical model for 

these processes does not exist, and it can capture the expected uncertainty due to variability in the accuracy of the input and output data. The third assessment characterizes the observed, long-term changes 

in the conversion of vegetated and unvegetated wetland shoreline in order to resolve the ecological 

changes that are associated with the physical changes that are evaluated by the first two assessments. 

Wetland-Shoreline Ecological-Change Assessment

Smith and others (2015) define the following two different types of evolutionary pathways for 

back-barrier marshes based on seven cores from marshes around Assateague Island: (1) young, emergent marshes adjacent to low-relief barrier islands and (2) established marsh platforms along more protected regions of the barrier island (fig. 6). If existing backshore land cover is not persistently bare sand, 

as is the case for areas that perpetually overwash, then they may be old wetlands that have remained so 

over the long term (100+ years) and, therefore, are not dominated by recent overwash (Bernier and others, 2016; Zaremba and others, 2016a). Other possible explanations are that the wetland can be newly 

Figure 4. Image showing marsh and overwash core locations at Assateague Island, Maryland and Virginia.
7

colonized overwash or flood-tide-shoal deposits, or they can be wetlands that have been converted to 

sandy environments through burial from overwash. These classifications of the ecological transition 

assessments are valuable additional information that can be used to interpret the physical changes in the 

positions of shorelines and sandlines.

Estuarine-Shoreline-Change Models

Storm-driven (HS) and long-term (LT) estuarine-shoreline changes are influenced by oceanic, 

estuarine, and geomorphic variables. To capture these influences statistically, a BN model was constructed that used input variables that reflected the process of barrier-island overwash and barrier-island 

migration (also called barrier-island rollover). These variables (fig. 7; table 2) include barrier-island 

height, width, and ocean-front wave height, which influence the likelihood of overwash occurring and 

modulate the impacts of overwash at the estuarine shoreline. Estuary width, depth, and estuarine waveheight influence estuarine-shoreline erosion and accretion. The long-term ocean-facing shoreline change 

is driven by storms and sea-level rise (Gutierrez and others, 2011) and is a driver of both long-term 

barrier-island migratory processes. The shoreline change on the estuarine side of the island is one of 

the predicted variables in the model (LT backshore change, fig. 7). Likewise, ocean-front storm-driven 

shoreline change should represent short-term, storm-influenced drivers of the short-term backshore 

change (HS backshore change, fig. 7). 

Figure 5. Example of overwash-deposit core from transect 4 (core id 14CTB-427W) showing a layer of peat intermixed with 

sand deposits.
8

Figure 6. Wetland-shoreline ecological-change assessment showing historical (top left) and modern (bottom left) classification of wetland vegetation or bare sand. The assessment (right) describes the state changes of ecological classifications.

Figure 7. Schematic diagram of model variables used to predict back-barrier-shoreline change (m, meter; m/y, meters 

per year).
9

Variable Description Model

Barrier width Distance (m) between the back-barrier and the oceanic shoreline calculated using areal imagery (Guy, 2015b; Guy, 2015c)Shoreline

Sandline (LT&HS)

Barrier height Maximum height (m) of the barrier island, extracted from lidar data that 

were acquired in 2010 and 2012 (National Atmospheric and Oceanic 

Administration [NOAA], 2010; Bonisteel-Cormier and others, 2011; 

U.S. Geological Survey, 2016b)

Shoreline

Sandline (LT&HS)

Estuary width Width (m) of the estuary from the back-barrier shoreline to the closest land 

(mainland or other island) shoreline (Terrano and Smith, 2015)

Shoreline

Estuary depth Maximum depth of the estuary (m), obtained from the Ocean City digital 

elevation model published by NOAA

Shoreline

LT ocean-front change

 or 

 LT ocean shoreline 

change

Long-term ocean-facing shoreline change rates (m/y), calculated from 

historical shorelines spanning 1845–2000 (Terrano and Smith, 2015)

Shoreline

Sandline (LT & HS)

LT backshore change Long-term backshore-shoreline change rates (m/y), calculated from historical shorelines spanning 1849 to 2013 (Terrano and Smith, 2015)Shoreline

Beach HS wave height

 or

 ocean-wave height

Hurricane Sandy ocean wave height (m) obtained from U.S. Geological 

Survey’s reanalysis model results (U.S. Geological Survey, 2016a)

Shoreline

Sandline (LT)

Bay HS wave height Maximum height of Hurricane Sandy estuary wave height (m) for the 

bay side of the island from reanalysis model (U.S. Geological Survey, 

2016a)

Shoreline

HS oceanfront change Net shoreline movement (m) calculated by the Digital Shoreline Analysis 

System (DSAS) for the pre- and post-Hurricane Sandy foreshore rate of 

change analysis (Terrano and Smith, 2015)

Shoreline

HS backshore change Net shoreline movement (m) calculated by DSAS for the pre- and postHurricane Sandy backshore rate of change analysis (Terrano and Smith, 

2015)

Shoreline

Hurricane Sandy surge 

elevations

Hindcast hydrodynamic simulations of water level (m) using a model with 

separate model domains for Chincoteague Bay, Maryland and Virginia 

(U.S. Geological Survey, 2016c), and Barnegat Bay, New Jersey (U.S. 

Geological Survey, 2016d). The maximum surge elevation was interpolated to the 20-m isobath along the coastline and projected onto the 

shoreline at 10-m-spaced transects.

Sandline (HS)

Hurricane Sandy runup 

elevations

Maximum runup (m) expected for Hurricane Sandy, calculated using the 

Stockdon and others, 2007 parameterization, using wave heights from 

the coupled ocean-atmosphere-wave-sediment transport (COAWST) 

model provided (U.S. Geological Survey, 2016b, a, d, c)

Sandline (HS)

Long-term sandline 

change (m/y)

Determined from the methods described in Terrano and Smith, 2015 Sandline (LT&HS)

Hurricane Sandy sandline change (m)Determined from the methods described in Terrano and Smith, 2015 Sandline (LT&HS)

Table 2. Variables used in the shoreline-change and sandline-change models.

[m, meters; m/y, meters per year; HS, storm driven; LT, long term]
10

Sandline-Change Models

Two BN models were constructed to predict long-term (estimated over decadal time periods) and 

Hurricane Sandy-induced sandline change along the Assateague and New Jersey coasts. Geomorphic 

and hydrodynamic variables derived from the remote-sensing analysis and numerical model output were 

extracted at 50-m-spaced transects (Terrano and Smith, 2015). The eight variables used in the BN models are divided into three categories: driving forces (that is, maximum ocean wave height, surge height, 

and runup height during Hurricane Sandy, and long-term ocean shoreline change), geological boundary 

conditions (that is, barrier-island height and barrier-island width), and response variables (that is, Hurricane Sandy-induced sandline change and long-term sandline change). Correlations between variables are 

represented with interconnected nodes that are resolved into discrete bins representing a range of scenarios spanned by the input data (fig. 8). The nodes are connected based on the understanding of how each 

variable relates to one another. The conditional probabilities that relate each variable are determined from 

the existing datasets (Terrano and Smith, 2015). Negative values of sandline change indicate the landward 

extent of overwash deposits (that is, the sandline), whereas positive values indicate the seaward extent of 

the sandline resulting from processes such as storm-surge ebb flows or vegetation growth.

Assessment Model Training and Testing Approach

Each model is tested on its ability to predict changes in long-term and event-driven (that is, Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input 

variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained 

to produce a prediction of an updated conditional probability of backshore or sandline change at each 

location. The BN predictions are produced at the same spatial scale as the input variables. At locations 

where some or all of the data are missing (for example, due to differences in the spatial resolution of 

the datasets), predictions can still be made because missing data are taken into account with prediction 

Figure 8. Schematic diagram of models used to predict A, long-term (LT) sandline change, and B, Hurricane Sandy-induced 

sandline change. In A, the ocean wave height, Hurricane Sandy-induced sandline change, and LT ocean-shoreline change 

are considered to be driving forces; barrier-island width and barrier-island height are considered to be boundary conditions, and the long-term sandline change is the response variable. In B, Hurricane Sandy surge elevation, runup height, and 

LT ocean-shoreline change are considered to be driving forces; barrier-island width and barrier-island height are considered to 

be boundary conditions; and the Hurricane Sandy sandline change is the response variable.
11

uncertainty. For example, with no specific inputs, the BN prediction returns the prior distribution of the 

output variable, which reflects the spatial variability of that variable over the entire study area. 

To evaluate the ability of the BN to reproduce the observations used to train the model, three 

statistical metrics were computed: the skill, log likelihood ratio (LLR), and success rate. The skill score 

calculates the fraction of the observed variance that is explained by the model predictions, averaged 

over all possible outcomes, and can be computed using just the predicted value or using a weight that 

depends on the confidence of the prediction (Plant and others, 2016). The LLR ratio measures the performance of the predictions relative to an alternative model consisting of the prior probabilities. If the 

likelihood of the updated prediction increases over the prior, the log likelihood ratio is positive, meaning 

that the update is either more accurate or more certain (or both). If the updated prediction is worse than 

the prior, the log likelihood ratio is negative; this may occur if the predicted outcome has a high probability but differs from the observed outcome. The definitions of skill and likelihood ratios are given in 

equations 4 and 5 in Plant and others (2016). The success rate was calculated as the percentage of cases 

where the observed backshore or sandline change fell within the discrete bin with the highest predicted 

probability (that is, the most likely change prediction). The percentage of cases where the observation 

fell within an adjacent bin to the most likely prediction was also calculated. The evaluations were performed to test the calibration accuracy, where the tests were performed on the same data that were used 

to train the model. And the tests were performed on validation datasets, where data used for testing were 

different from the calibration data. This was done by splitting data from different geographic locations, 

such that training was conducted on data from New Jersey, and testing was conducted on data from New 

Jersey (calibration testing) and Virginia and Maryland (validation testing). 

Results

Here, we present the results of the BN model predictions and the ecological assessment. The 

trained BN models can be used to make probabilistic predictions of long-term or short-term changes. 

BN models predict the likelihood of a number of different outcomes based on constraints provided by 

the input variables at each location. Then, the most likely outcome (result with highest likelihood) or expected outcome (mean-value based on results) can be used to represent future long-term response of the 

coastline or response to a Hurricane Sandy-like storm. The degree of uncertainty in the assessment can 

be measured by the probability associated with the most likely outcome, where 100 percent indicates 

the highest assessment confidence and 100/M percent (where M is the number of predicted outcomes) 

indicates an extremely uncertain assessment: M=5 for the backshore BN and M=3 for the sandline BN. 

These results can be downloaded from Smith and others (2017).

Long-Term Estuarine-Shoreline-Change Assessment

The BN model for long-term estuarine-shoreline change demonstrates skillful predictions of the 

expected shoreline change rate when hindcast testing was performed. Hindcast testing uses the same 

data that was used to train the model (table 3). More than 3,000 barrier-island profiles were analyzed in 

total, including 1,110 locations for Assateague and 2,348 locations along the New Jersey coast. Hindcast 

predictions show an overall skill of 0.58. Prediction skill was slightly higher for Assateague (skill=0.67) 

when trained using all data (table 3). A model trained with just Assateague data and tested against 

Assateague showed an increased skill of 0.70, indicating that there are input constraints or shorelinechange responses that are specific to the Assateague coastline and not well generalized across the two 

regions. Training on and testing only New Jersey data did not change the prediction skill, indicating 
12

that input constraints and shoreline response are generalizable across the two regions, or indicating 

that the data in New Jersey dominated the training. When the BN was trained on New Jersey data and 

used in the Assateague prediction, the skill was 0.20, indicating that just 20 percent of the New Jersey 

input-response variability was similar to that of Assateague. The alternative test (train New Jersey and 

predict Assateague) resulted in a skill of 0.11. We can conclude that the improved predictions using both 

datasets in training produces the most generalizable model because either the input constraints or the 

responses (or both) are not generalizable across the two regions. 

Predictions also include uncertainty estimates at each point that measure the model’s confidence. 

Using the probability of the most likely prediction as a confidence metric and setting an arbitrary threshold for “high” certainty at a probability of 0.75, we found that 75 percent of profiles had certainties 

equal to or exceeding this value. (Note that the probability threshold for suitably high certainty can be 

set to any tolerance, depending on requirements for scientific testing or decision making. Because the 

output in this case could fall into one of five possible categories, a threshold of 0.2 would correspond to 

total uncertainty because probability would be distributed equally in all five bins.) Maps (figs. 9 and 10) 

show the spatial variability of the observations, predictions, and prediction certainty for the long-term 

estuarine-shoreline change for the Assateague (fig. 9) and part of the New Jersey study areas (fig. 10).

Table 3. Long-term and storm-driven back-barrier-shoreline-change-prediction accuracy using weighted (and unweighted) 

skill with independent and hindcast models in the New Jersey and Assateague study areas.

Storm-Driven Estuarine-Shoreline-Change Assessment

The BN model for storm-driven back-barrier-shoreline change produced prediction skills that 

were similar to long-term prediction skill values. Hindcast prediction using data from all study sites had 

a skill of 0.58. Predication skill was slightly higher for Assateague (0.53) than New Jersey (0.46) when 

model was trained on all the data (table 3). Training with both datasets did not improve the prediction 

skill for Assateague and slightly decreased the prediction skill for New Jersey compared to training 

and testing with the same data. Most predictions had either moderate (0.5≤ Pml<0.75) or high certainty 

(Pml≥0.75), where Pml is the probability of the most likely outcome. Less than 12 percent of the profiles 

had low certainty (Pml<0.5). Mapped results (figs. 11 and 12) show the spatial variability in observations, 

predictions, and certainty. For example, at the north end of Assateague (fig. 11), the storm-driven change 

is negative (erosion), which is the opposite sign from the long-term trend (fig. 9). At this location, observations and predictions are in agreement for both storm-driven and long-term response. 

Long-Term Sandline-Change Assessment

Overall, the skill metrics (table 4) indicate that the BN models (fig. 8A and 8B) can predict both 

the long-term and Hurricane Sandy-induced sandline change across the Assateague and New Jersey 

Testing

Calibration Long term (LT) Storm driven (HS)

New Jersey Assateague

Both LT and 

HS New Jersey Assateague

Both LT and 

HS

New Jersey 0.55 (0.58) 0.20 (0) 0.52 (0.21) 0.09 (0)

Assateague 0.11 (0) 0.70 (0.67) 0.09 (0) 0.53 (0.29)

Both LT and HS 0.54 (0.55) 0.67 (0.63) 0.58 (0.55) 0.46 (0.18) 0.53 (0.29) 0.49 (0.22)
13

Figure 9. Maps showing the observed long-term (left panel) and predicted (middle panel) long-term estuarine-shoreline 

change, and prediction certainty (right panel) at Assateague Island, Maryland and Virginia. Negative values indicate an eroding estuarine shoreline.

Figure 10. Maps showing the observed long-term (left panel) and predicted (middle panel) long-term estuarine-shoreline change, 

and prediction certainty (right panel) at Sandy Hook, New Jersey. Negative values indicate an eroding estuarine shoreline.
14

Figure 11. Maps showing the observed (left panel) and predicted (middle panel) storm-driven estuarine-shoreline change, 

and prediction certainty (right panel) at Assateague Island, Maryland and Virginia. Negative values indicate storm-driven 

estuarine-shoreline erosion.

Figure 12. Maps showing the observed (left panel) and predicted (middle panel) storm-driven estuarine-shoreline change, and 

prediction certainty (right panel) at Sandy Hook, New Jersey. Negative values indicate storm-driven estuarine-shoreline erosion.
15

domains. As with the shoreline-change results, the output from the long-term sandline change BN can 

be mapped at each location to show observed long-term sandline change, predicted long-term sandline 

change (most probable prediction), and certainty (figs. 13 and 14). For example, the results at Sandy 

Hook, New Jersey (fig. 14), show broad agreement between predictions and observations of the spatial 

trends of long-term sandline change due to repeated overwash in the northern and southern regions. 

Positive sandline change, indicating that the sandline is moving seaward, occurs in the central region. 

Seaward movement typically reflects a long-term increase in vegetation, perhaps in response to a large 

storm occurring near the beginning of our analysis period, or in response to coastal management that 

could include dune planting or beach nourishment. Landward sandline movement typically corresponds 

to progressive overwash and open-coast shoreline-erosion events that push the barrier island as a whole 

in the landward direction. The predictions in the New Jersey region have moderate certainty, indicating 

that long-term sandline change is more complex than estuarine-shoreline change. The skill metrics for 

the long-term sandline change (0.65) are about 20 percent higher than the shoreline-change skills, indicating that lower certainty does not result in an overall reduction in accuracy. In the Assateague study 

area, predictions overestimate the magnitudes of change, where there was too much sandline retreat and 

advance. Nonetheless, the regions of advance or retreat were accurately identified by the predictions. 

Prediction uncertainty was mostly moderate to high in this study region. This might indicate that the BN 

model was somewhat overfit to the data, explaining the occurrence of an overconfident prediction of 

excessive sandline advance or retreat. 

Table 4. Skill with independent and hindcast models for Skill metrics Sandline Change Bayesian Network output.

Storm-Induced Sandline-Change Assessment (Storm Driven)

Hurricane Sandy-induced sandline-change predictions (figs. 15 and 16) are predicted with higher 

skill than the long-term sandline changes (table 4). The mapped BN output matches the observed dominance of landward movement (typically exceeding 10 meters) of the storm-induced sandline change in 

the Assateague region, where overwash was prevalent, as was documented by others (Sopkin, 2014), 

and vegetation was buried or eroded or both occurred. In the Sandy Hook, New Jersey region, the BN 

accurately predicts more overwash in the northern region, but the confidence in the predictions is low 

to moderate at nearly all locations (fig. 16). This indicates that while the most likely prediction is consistent with the observations (skill of 0.74, table 4), variability in the actual response across both study 

regions is predicted as well. Inspection of the underlying data and imagery indicated that in some areas, 

the demarcation of the sandlines where the vegetation was sparse was itself a noisy estimate. For instance, the southern portion of Sandy Hook (fig. 16) was observed and predicted to have seaward sandline movement, indicating vegetation growth. This section of coast was unusually wide and was sparsely 

vegetated before and after Hurricane Sandy, contributing to low predictive uncertainties. This noise is 

included in the BN training and reduces the prediction uncertainties. At Assateague, the BN predicts 

the dominance of overwash (sandline retreat), consistent with the observations but with a slightly lower 

skill (0.65) than the predictions for the New Jersey coastline. There is variability in the certainty, which 

is moderate at the two ends of the island and low along the central section of the study area. 

Predict long-term

sandline change

Predict storm-driven

sandline change

Skill 0.65 0.74
16

Figure 13. Maps showing the observed (left panel) and predicted (middle panel) long-term sandline change, and prediction 

certainty (right panel) at Assateague-Island, Maryland and Virginia. Positive sandline change values indicate seaward-directed 

movement.

Figure 14. Maps showing the observed (left panel) and predicted (middle panel) long-term sandline change, and prediction 

certainty (right panel) at Sandy Hook, New Jersey. Positive sandline-change values indicate seaward-directed movement. 
17

Figure 16. Maps showing the observed Hurricane storm-induced sandline change (left panel), predicted Hurricane Sandyinduced sandline change (middle panel), and prediction certainty (right panel) at Sandy Hook, New Jersey. Positive sandline 

change values indicate seaward-directed movement. 

Figure 15. Maps showing the observed (left panel) and predicted (middle panel) storm-induced sandline change, and prediction certainty (right panel) at Assateague Island, Maryland and Virginia. Positive sandline-change values indicate seawarddirected movement. 
18

Wetland-Shoreline Ecological-Change Assessment

The result of the wetland shoreline ecological-change assessment, which is based on the observed 

long-term changes in vegetation, documents both wetland formation and destruction behind barrier 

islands (figs. 17 and 18). At Assateague (fig. 17), imagery and sandline data show the continuum of 

marsh maturity proposed by Smith and others (2015), ranging from wetlands persisting through multiple 

image sets to recent wetlands emerging between image sets reflecting colonization of recent breaches 

and overwash. Only at the narrow northern end of the island is burial of historic wetlands prevalent 

where the long-term estuarine shoreline had accreted (built into the estuary, fig. 9), indicating loss of 

some wetlands. The historic land cover in this region had a mix of bare sand (land) and wetlands, so the 

persistent accretion not only buried old wetlands, but also formed regions where vegetation could grow. 

At Long Beach Island (fig. 18), ecological-change data show a similar continuum between mature and 

young marsh. Here, the southern half of the study area highlights the emergence of a young marsh on a 

pre-1880 barren region. In contrast, the north portion of Long Beach Island had wetlands present pre1880 that either persisted through time or were buried by sand. 

Discussion

Assessments of physical changes in barrier islands and associated wetlands have been conducted 

that describe impacts of Hurricane Sandy and long-term changes. These assessments include analysis 

data derived primarily from remotely sensed imagery that was sufficient to document the evolution of 

open-ocean and estuarine shorelines and the evolution of the sand-vegetation boundary associated with 

the landward extent of overwash deposits. Model predictions of storm-driven and long-term impacts 

to these features were developed to explain spatial variations in evolution and to provide a means to 

extend what was learned through Hurricane Sandy to the future. This extension depends on understanding the connection between the remotely sensed features and their evolution to on-the-ground impacts 

and implications, such as the change in land cover as discussed in the previous section. It is possible that 

extension to future applications and analyses at other locations could benefit with new remote-sensing 

methods. This discussion addresses (1) the relation between our remote-sensing-based assessments and 

corresponding in situ observations of barrier-island evolution, (2) the use of other remote-sensing methods, and (3) a strategy for continued application of our approach. 

Physical Process Interpretations

Model-based assessments in the estuarine shoreline and the sandline are capable of capturing 

much of the detail resolved by the observation-based assessment. For example, the extent of sandline 

change due to Hurricane Sandy shows spatial variability that is controlled by a combination of island 

width and height. The impact of overwash processes on the estuarine shoreline in the long term and 

short term result in accumulation of sediments that are important for both geomorphic and ecosystem 

resilience in the future. For example, the thickness of Hurricane Sandy overwash deposits (table 5) was 

similar at the northern (transect 1, fig. 4) and southern (transect 4, figs. 4 and 5) locations; however, 

the cumulative thicknesses of overwash deposits are greater at transect 1 (Bernier and others, 2016; 

Zaremba and others, 2016a), where overwash deposits reached the estuary and extended the position of 

the shoreline. Geologic analysis shows that some of the overwash deposits included in the Hurricane 

Sandy analysis are actually attributed to multiple storms, and this is corroborated by the imagery at both 

Assateague and New Jersey locations (Bishop and others, 2016b; Zaremba and others, 2016a). Where 

changes in the sandline do not reach the estuarine shoreline, overwash deposits contribute to elevation 
19

Figure 17. Maps showing assessment of back-barrier-shoreline type based on historical data (left panel) and recent data 

(middle panel) at Assateague Island, Maryland and Virginia. Changes are used to show wetland transformations or stability in 

terms of a geologic classification (right panel).

Figure 18. Maps showing assessment of back-barrier-shoreline type at Long Beach, New Jersey, based on historical data 

(left panel) and recent data (middle panel). Changes are used to show wetland transformations or stability in terms of a geologic classification (right panel).
20

gains in the island interior. Geologic GPR assessments across the island from uniquely document this 

elevation gain in areas that were either too low or too vegetated to be measured quantitatively by other 

means, such as from lidar surveys (Zaremba and others, 2016a).

The in situ geologic observations can be used to quantify both the amount and characteristics 

of sediment deposits that correspond to the changes in shorelines and sandlines. The contributions of 

inorganic material may add to or be the only contribution to the elevation budget of the barrier and its 

wetlands. The inorganic contribution distinguishes two marsh types. At type 1 sites, accretion rates are 

high due to the inorganic sediment deposition from overwash and aeolian processes (Bishop and others, 

2016b). One core (14CTB-14M, table 5), obtained from what we consider to be type 1 marshes (fig. 17), 

provided reliable chronology. For this core, the linear sedimentation (vertical accretion) rate inclusive of 

inorganic sediment layers was 0.8 centimeter (cm) y-1(~0.8 g [gram] cm-2 y-1) with inorganic sediment 

layers constituting an estimated 60 to 70 percent of the total accumulation.

Type 2 marshes had LSRs that were lower (0.41±0.11 cm y-1) than the type 1 and transition 

marshes. In contrast, when only the organic-rich sections were considered (that is, excluding episodic 

inorganic deposition), the average vertical accretion was quite uniform for all sites (0.24±0.05 cm y-1). 

This accretion rate is slightly below the 60-year relative sea-level trend observed at Kiptopeke, Virginia (0.36±0.03 cm y-1), and the 30-year relative sea-level trends of 0.56±0.10 and 0.54±0.09 cm y-1 

Core ID Depth (cm) n r2 MAR estimate MAR error LSR estimate LSR error

Type 1 marshes

14CTB-04M 0–5 5 0.817 0.05 0.01 0.09 0.01

14CTB-04M* 0–5, 30–36 11 0.871 0.07 0.01 0.12 0.02

14CTB-11M 0–5 5 0.942 0.03 0.00 0.07 0.00

14CTB-14M 0–38 38 0.553 0.79 0.12 0.79 0.09

14CTB-14M* 0–6, 19–26 13 0.308 0.23 0.10 0.33 0.09

Transition

14CTB-06M 0–34 34 0.671 0.57 0.07 1.00 0.05

14CTB-06M* 0–1, 7–11, 20–34 19 0.883 0.19 0.01 0.18 0.01

Type 2 marshes

14CTB-13M 0–32 32 0.519 0.13 0.02 0.54 0.03

14CTB-13M* 0–3, 10–32 25 0.531 0.10 0.02 0.23 0.01

14CTB-25M(A) 0–23 23 0.492 0.08 0.02 0.44 0.10

14CTB-25M(A)* 0–13, 17–23 19 0.615 0.05 0.01 0.30 0.05

14CTB-25M(B) 0–18 18 0.800 0.06 0.01 0.26 0.03

14CTB-25M(B)* 0–12, 15–19 15 0.780 0.04 0.01 0.20 0.03

*Indicates samples analyzed without sand.

For procedure, reference Smith and others (2013); Smith and others (2015).

Table 5. Constant flux and sedimentation model results for the cores.

[cm, centimeters; n, number of samples; r2, coefficient of determination; MAR, mass accumulation rate; MAR estimate, g cm-2 y-1; MAR error, standard 

error (g cm-2 y-1); LSR, linear sedimentation rate; LSR estimate, cm y-1; LSR error, standard error (cm y-1)]
21

observed at Ocean City Inlet, Maryland, and Wachapreague, Virginia, respectively. In comparison, work 

by Zaremba and others in the more interior portion of the island showed that composite overwash deposition from Hurricane Irene and Hurricane Sandy (10–50 cm) would be equivalent to 40 to 200 years of 

vertical marsh accretion from organic matter accumulation. The additional inorganic sediment supplied 

by overwash is thus inferred to aid in the maintenance of elevation of these back-barrier marshes. This 

inorganic contribution allows the marshes to keep up with sea-level rise. 

Alternate Approaches to Extending Remote-Sensing Observations Using Satellite Data

Observationally based and the model-based and barrier-island estuarine-wetland physicalchange assessments rely on remote-sensing methods that can be applied to aerial or satellite imagery 

in order to derive variables required to develop or update barrier-island and wetland assessments in 

the future. Specifically, we require that both long-term and storm-event changes in shorelines and 

sandlines to be resolved by the remote-sensing data and methods. Comparison of the results from the 

aerial imagery (fig. 19A; Guy, 2015a, c) and satellite analyses ( fig. 19B; Bernier and others, 2015) 

illustrates similarities and minor differences in details and location of sandlines and shorelines (fig. 

19C) due to changes in resolution and sensor properties. However, there are advantages in using the 

lower resolution satellite imagery that are well suited to automated land cover classification analysis 

(fig. 3), which is required for sandline detection, and Landsat images are acquired much more frequently than higher resolution aerial imagery. Because our assessment methods are framed statistically as either a linear regression estimation or BN, there is no intrinsic reason to exclude additional 

data sources just because they have reduced resolution, and we conclude that we could utilize Landsat 

imagery effectively. 

Future Applications

Assessment of estuarine-shoreline and sandline change at two timescales can be related to existing assessments of the open-ocean shoreline and dunes. Specifically, the existing open-ocean long-term 

shoreline-change data (Himmelstoss and others, 2010) were used as an input to predictions of both estuarine-shoreline- and sandline-change assessments. Oceanographic drivers from existing dune vulnerability assessments (Doran and others, 2012) were used as inputs to the prediction of changes associated 

with Hurricane Sandy’s landfall. Thus, the value of the model-based over the observation-based assessment is that future scenarios, where the input variables may be altered, can be examined. These future 

scenarios could include short-term (days) forecasts of approaching storms to support storm-impact 

response, as well as exploring hypothetical scenarios to support longer term planning purposes. This 

capability provides a way to test new hypotheses and improve our broad understanding of the evolution 

of coastal systems from the ocean to estuaries. Furthermore, the model structure provides a tool for assessing future resilience and sustainability. Assessments of future conditions could incorporate resource 

management plans that alter parts of the barrier-island systems, demonstrating the impact on the broader 

system as it responds to storms and sea-level rise. 

Barrier islands and their associated wetlands, interior habitat, and shorelines will keep changing, 

either gradually or episodically. This means that coastal land managers will need to continue to adapt 

their management plans and make short-term and long-term decisions that respond to actual or expected 

changes. Our observational and analysis effort demonstrates a capability that can be maintained in the 

existing locations to provide updates on actual shoreline, sandline, and geological characteristics, as 
22

Figure 19. Imagery, shorelines, and sandlines derived from (A) aerial (1-m resolution) and (B) satellite (30-m resolution) 

sources, and comparison (C) of digitized sandlines and shorelines. Image sources are (A) color-infrared digital orthophoto 

quarter quadrangle (DOQQ) imagery acquired April 12, 1989, and (B) Landsat 5 imagery acquired April 28, 1989.
23

well as to update model prediction accuracy and applicability. The capability can also be applied at other 

barrier-island locations, creating consistent and relevant guidance to address problems that are common 

across many barrier-island systems. The requirements for maintaining or extending this capability are 

the following:

• new data to provide updates on actual changes and to test existing models (fig. 20, top);

• research and data targeted at improving prediction accuracy (fig. 20, middle);

• research to identify where insufficient model accuracy and/or insufficient process understanding 

limits the applicability of predictions (fig. 20, middle); and

• updated models that are more accurate and more applicable to landscape management (fig. 20, 

bottom).

Figure 20. Elements required to maintain and extend barrier-island estuarine-wetland physical-change analysis include 

updated observations of changes in features (top), associated data to tie these changes to physical processes (middle), and 

updated models that address coastal management decisions (bottom) (m, meter). 
24

Conclusions

An assessment for estuarine-shoreline and barrier-island-sandline change along the Virginia, 

Maryland, and New Jersey coasts has been completed, documenting both the impacts of Hurricane 

Sandy and the long-term changes, which includes the impacts of past storm events. We demonstrated 

that observational data can be assimilated into predictive models that demonstrate the important interconnections between ocean-facing, barrier interior, and estuarine response to long-term processes and 

storms. Improved understanding of what processes are important in these interconnections has been 

interpreted through comparison to geologic data that resolve the actual events and processes responsible 

for the observed changes in the barrier-island features. A variety of remote-sensing data sources can be 

utilized to develop or update estuarine-shoreline and barrier-island-sandline assessments. It is likely that 

future expansions of this approach will utilize coarser resolution, but frequently sampled, Landsat imagery because of the applicability of objective methods to classify land cover types and estuarine features. 

This effort has produced data and analysis that have been made broadly available through previous 

publications, including the following:

• remotely sensed measures of shorelines and barrier-island-sandline history from the past several 

decades through the period immediately following Hurricane Sandy’s landfall (Bernier and others, 

2015; Guy, 2015c, d); 

• sediment cores and sedimentary and geophysical analysis to extract sediment and depositional 

properties (Smith and others, 2015; Bishop and others, 2016b; Zaremba and others, 2016a);

• observationally based assessment of long-term and short-term changes in estuarine shorelines and 

barrier-island sandlines (this report); and

• model-based assessment of long-term and short-term changes in estuarine shorelines and barrierisland sandlines (this report).

The estuarine-shoreline and barrier-island-sandline assessments extend two approaches developed 

previously that assessed the long-term change in the ocean-facing shoreline (Himmelstoss and others, 

2010) and the response of coastal dunes to extreme storms (Doran and others, 2012). Because the new 

approach shares some of the same drivers for assessing storm response, it is possible to integrate this 

new approach with the existing assessments. Doing so would provide a new decision-support tool that 

could be used to guide restoration activities and assess vulnerability and resilience of ecosystems and 

communities that depend on barrier islands and their estuaries for coastal protection, recreation, and 

commerce. This could help achieve broader national objectives of the U.S. Geological Survey to serve 

“the Nation by providing reliable scientific information to describe and understand the Earth; minimize 

loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; 

and enhance and protect our quality of life” (Buxton and others, 2013). 
25

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29

Appendix 1. BN Models

This appendix contains a text-formatted version of the Bayesian network (BN) models that can be 

saved in a file with .dne file extension and opened with Netica (Norsys Software Corp, 2012). The data 

from Terrano and Smith (2015) can be used to train and run the BN using Netica software and application interfaces. 

Shoreline Change

// File created by PlantN at USGS using Netica 5.05 on Oct 14, 2016 at 

16:41:54.

bnet HSandyNSM_18Jan2016_both5_noCP {

AutoCompile = TRUE;

autoupdate = TRUE;

whenchanged = 1476477690;

visual V1 {

defdispform = BELIEFBARS;

nodelabeling = TITLE;

NodeMaxNumEntries = 50;

nodefont = font {shape= “Arial”; size= 9;};

linkfont = font {shape= “Arial”; size= 9;}

;

windowposn = (34, 0, 1150, 716);

scrollposn = (3, 0);

resolution = 72;

	magnification = 1.2;

drawingbounds = (1403, 779);

showpagebreaks = FALSE;

usegrid = TRUE;

gridspace = (6, 6);

NodeSet Node {BuiltIn = 1; Color = 0x00E1E1E1;};

NodeSet Nature {BuiltIn = 1; Color = 0x00F8EED2;};

NodeSet Deterministic {BuiltIn = 1; Color = 0x00D3CAA6;};

NodeSet Finding {BuiltIn = 1; Color = 0x00C8C8C8;};

NodeSet Constant {BuiltIn = 1; Color = 0x00FFFFFF;};

NodeSet ConstantValue {BuiltIn = 1; Color = 0x00FFFFB4;};

NodeSet Utility {BuiltIn = 1; Color = 0x00FFBDBD;};

NodeSet Decision {BuiltIn = 1; Color = 0x00DEE8FF;};

NodeSet Documentation {BuiltIn = 1; Color = 0x00F0FAFA;};

NodeSet Title {BuiltIn = 1; Color = 0x00FFFFFF;};

PrinterSetting A {

 margins = (1270, 1270, 1270, 1270);

 };

};
30

node Island_width {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (90, 420, 600, 900, 1400, 4300);

parents = ();

title = “Barrier Width (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (480, 78);

 height = 4;

 };

};

node Z_Max {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (1, 3.5, 5, 6, 8, 50);

parents = (Island_width);

title = “Barrier Height (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (210, 96);

 height = 3;

 link 1 {

 path = ((392, 83), (283, 91));

 };

 };

};

node Frontshore_1800_2013_LRR {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-15.5, -2.5, -0.5, 0.5, 5, 28);

parents = (Island_width, Z_Max);

title = “LT Ocean-front Change (m/y)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);
31

visual V1 {

 center = (120, 294);

 height = 5;

 link 1 {

 path = ((398, 128), (214, 239));

 };

 link 2 {

 path = ((187, 146), (145, 237));

 };

 };

};

node Estuary_width {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 500, 1400, 4300, 7000, 12000);

parents = (Island_width, Z_Max);

title = “Estuary Width (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (714, 198);

 height = 9;

 link 1 {

 path = ((568, 122), (623, 151));

 };

 };

};

node Bathy_Mean {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-7, -2, -1.5, -1, -0.5, 1.5);

parents = (Estuary_width, Island_width, Z_Max);

title = “Estuary Depth (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (702, 372);

 height = 1;

 link 3 {

 path = ((283, 136), (622, 327));

 };

 };

};
32

node Backshore_1800_2013_LRR {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-32, -10, -0.5, 0.5, 10, 15);

parents = (Island_width, Z_Max, Estuary_width, Frontshore_1800_2013_LRR, 

Bathy_Mean);

title = “LT Backshore Change (m/y)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (414, 264);

 height = 7;

 };

};

node Beach_waves_mean {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 0.5, 0.6, 0.75, 1, 2);

parents = ();

title = “Beach HS Wave Height (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (300, 600);

 height = 10;

 };

};

node Frontshore_HS_NSM {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-150, -45, -0.5, 0.5, 10, 112);

parents = (Frontshore_1800_2013_LRR, Beach_waves_mean, Z_Max, Island_width);

title = “HS Oceanfront Change (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (114, 486);

 height = 6;

 };

};
33

node Bay_waves_mean {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 0.1, 0.15, 0.5, 0.7, 1);

parents = (Estuary_width, Bathy_Mean, Beach_waves_mean);

title = “Bay HS Wave Height (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (570, 624);

 height = 2;

 link 1 {

 path = ((697, 248), (586, 574));

 };

 };

};

node Backshore_HS_NSM {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-310, -10, -0.5, 0.5, 10, 308);

parents = (Frontshore_1800_2013_LRR, Z_Max, Island_width, Estuary_width, 

Bathy_Mean, Backshore_1800_2013_LRR, Frontshore_HS_NSM, Bay_waves_mean, Beach_

waves_mean);

title = “HS Backshore Change (m)”;

whenchanged = 1476477690;

belief = (0.2, 0.2, 0.2, 0.2, 0.2);

visual V1 {

 center = (408, 450);

 height = 8;

 link 2 {

 path = ((238, 146), (381, 400));

 };

 };

};

ElimOrder = (Island_width, Z_Max, Frontshore_1800_2013_LRR, Estuary_width, 

Bathy_Mean, Backshore_1800_2013_LRR, Beach_waves_mean, Frontshore_HS_NSM, Bay_

waves_mean, Backshore_HS_NSM);

};
34

Sandline, Long-Term

// File created by PlantN at USGS using Netica 5.05 on Nov 30, 2016 at 

15:18:03.

bnet wetlandPhysicalChange_LT_Sandl {

AutoCompile = TRUE;

autoupdate = TRUE;

whenchanged = 1480537083;

visual V1 {

defdispform = BELIEFBARS;

nodelabeling = TITLE;

NodeMaxNumEntries = 50;

nodefont = font {shape= “Arial”; size= 9;};

linkfont = font {shape= “Arial”; size= 9;};

windowposn = (8, 5, 1397, 674);

resolution = 72;

	magnification = 1.41421;

drawingbounds = (1403, 786);

showpagebreaks = FALSE;

usegrid = TRUE;

gridspace = (6, 6);

NodeSet Node {BuiltIn = 1; Color = 0x00E1E1E1;};

NodeSet Nature {BuiltIn = 1; Color = 0x00F8EED2;};

NodeSet Deterministic {BuiltIn = 1; Color = 0x00D3CAA6;};

NodeSet Finding {BuiltIn = 1; Color = 0x00C8C8C8;};

NodeSet Constant {BuiltIn = 1; Color = 0x00FFFFFF;};

NodeSet ConstantValue {BuiltIn = 1; Color = 0x00FFFFB4;};

NodeSet Utility {BuiltIn = 1; Color = 0x00FFBDBD;};

NodeSet Decision {BuiltIn = 1; Color = 0x00DEE8FF;};

NodeSet Documentation {BuiltIn = 1; Color = 0x00F0FAFA;};

NodeSet Title {BuiltIn = 1; Color = 0x00FFFFFF;};

PrinterSetting A {

 margins = (1270, 1270, 1270, 1270);

 };

};

node Island_width {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 750, 1500, 4300);

parents = ();

title = “Barrier Width (m)”;

whenchanged = 1480537071;

belief = (0.3333333, 0.3333333, 0.3333333);

visual V1 {
35

 center = (108, 48);

 height = 1;

 };

};

node Z_Max {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 3, 6, 10);

parents = (Island_width);

title = “Barrier Height (m)”;

whenchanged = 1480537071;

belief = (0.3333333, 0.3333333, 0.3333333);

visual V1 {

 center = (108, 168);

 height = 2;

 };

};

node Beach_waves_max {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (0, 0.5, 1.5, 2.75);

parents = ();

title = “Ocean Wave Height (m)”;

whenchanged = 1480537071;

belief = (0.3333333, 0.3333333, 0.3333333);

visual V1 {

 center = (348, 48);

 height = 4;

 };

};

node Frontshore_1800_2013_LRR {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-16, -0.5, 0.5, 30);

parents = (Island_width, Z_Max, Beach_waves_max);

title = “LT Ocean Shoreline Change (m/y)”;

whenchanged = 1480537071;

belief = (0.3333333, 0.3333333, 0.3333333);
36

visual V1 {

 center = (108, 312);

 height = 3;

 };

};

node Sandline_HS_NSM {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-346, -50, -2, 2, 210);

parents = (Z_Max, Island_width, Frontshore_1800_2013_LRR, Beach_waves_max);

title = “HS Sandline Change (m)”;

whenchanged = 1480537071;

belief = (0.25, 0.25, 0.25, 0.25);

visual V1 {

 center = (348, 174);

 height = 6;

 };

};

node Sandline_1989_2013_LRR {

kind = NATURE;

discrete = FALSE;

chance = CHANCE;

levels = (-56, -0.5, 0.5, 120);

parents = (Island_width, Z_Max, Frontshore_1800_2013_LRR, Sandline_HS_NSM, 

Beach_waves_max);

title = “LT Sandline Change (m/y)”;

whenchanged = 1480537071;

belief = (0.3333333, 0.3333333, 0.3333333);

visual V1 {

 center = (348, 312);

 height = 5;

 };

};

ElimOrder = (Island_width, Z_Max, Beach_waves_max, Frontshore_1800_2013_LRR, 

Sandline_1989_2013_LRR, Sandline_HS_NSM);

};
Plant and others—Barrier-Island and Estuarine-Wetland Physical-Change Assessment after Hurricane Sandy—Open-File Report 2017–1157

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20171157