Assessment of Inundation Risk from Sea Level Rise and ... · Assessment of Inundation Risk from Sea Level Rise and Storm Surge in Northeastern Coastal National Parks Angelica Murdukhayeva†,

Assessment of Inundation Risk from Sea Level Rise andStorm Surge in Northeastern Coastal National Parks

Angelica Murdukhayeva†, Peter August†, Michael Bradley†, Charles LaBash†, and NigelShaw‡

†University of Rhode IslandDepartment of Natural Resources Science1 Greenhouse RoadKingston, RI 02881, [email protected]

‡National Park ServiceNortheast Region15 State StreetBoston, MA 02109, U.S.A.

ABSTRACT

Murdukhayeva, A.; August, P.; Bradley, M.; LaBash, C., and Shaw, N., 0000. Assessment of inundation risk from sealevel rise and storm surge in northeastern coastal national parks. Journal of Coastal Research, 00(0), 000–000. CoconutCreek (Florida), ISSN 0749-0208.

Sea level rise and an increase in storm frequency and intensity are two major impacts expected to result from climatechange in coastal ecosystems. Coastal national parks have many low-lying areas that are at risk from inundationresulting from these impacts. To help park managers meet their goal of preserving resources, we developed amethodology to evaluate risk of inundation from sea level rise and storm surge at sentinel sites, areas of importance fornatural, cultural, and infrastructural resources. We selected the most recent, readily available, and appropriategeospatial tools, models, and data sets to conduct case studies of our coastal inundation risk assessments in twonortheastern coastal national parks—Cape Cod National Seashore, MA, and Assateague Island National Seashore, MD/VA. We collected elevation data at sentinel sites using real-time kinematic global positioning system (RTK GPS)technology. We used three modeling approaches: modified bathtub modeling; the Sea Level Affecting Marshes Model(SLAMM); and the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model to assess the likelihood ofinundation at sentinel sites. Cape Cod’s sentinel sites, which in many cases occurred in high-elevation settings, werefound to be less vulnerable to inundation than were Assateague Island’s sentinel sites, which were distributed in low-lying areas along the barrier beach island. This inundation risk assessment methodology can be applied to other coastalareas and to the same coastal parks at different times as more accurate elevation data sets and updated sea level riseprojections become available.

ADDITIONAL INDEX WORDS: Coastal elevations, LIDAR, GPS, modeling, climate change.

INTRODUCTIONIn coastal ecosystems, accelerated sea level rise and an

increase in storm frequency and intensity are two major

impacts expected to result from climate change (Ashton,

Donnelly, and Evans, 2008; Bender et al., 2010; Harvey and

Nicholls, 2008). In the next century, the rate of global sea level

rise is anticipated to be several times higher than that

measured over the past century (Cazenave and Nerem, 2004;

Church and White, 2006; Overpeck and Weiss, 2009; Pfeffer,

Harper, and O’Neel, 2008; Rahmstorf, 2007). The U.S.

northeast coast experiences a rate of relative sea level rise

greater than the global average because of substantial regional

variations in glacial isostatic adjustment effects and oceano-

graphic processes (Sallenger, Doran, and Howd, 2012; Tami-

siea and Mitrovica, 2011). Along the U.S. Atlantic coast, the

highest rates of subsidence occur from southern Massachusetts

to Virginia (Engelhart, Peltier, and Horton, 2011), and

predicted changes in ocean circulation, driven by climate

change, could potentially add meters of dynamic sea level rise

along the northeast coast during the next century (Hu et al.,

2009; Yin, Schlesinger, and Stouffer, 2009). Furthermore, the

frequency and extent of severe coastal storms are expected to

increase (Bender et al., 2010), and large surge levels may cause

significant damage to coastal infrastructure and alteration of

ecosystems (Irish et al., 2010; Kirshen et al., 2008; Lin et al.,

2010; McInnes et al., 2003).

Our objective was to explore how widely accessible data and

tools can be used for highly localized assessments of inundation

risk. As case studies, we modeled inundation risk from

predicted sea level rise and storm surge at 97 sentinel sites in

two coastal, northeastern U.S. national parks—Cape Cod

National Seashore (CACO), and Assateague Island National

Seashore (ASIS)—using a variety of data sources, predicted

scenarios, and physical models.

The definitions of two terms used throughout this article are

essential to understanding the project. Sentinel sites are

DOI: 10.2112/JCOASTRES-D-12-00196.1 received 2 October 2012;accepted in revision 25 January 2013; corrected proofs received13 March 2013.Published Pre-print online 2 April 2013.� Coastal Education & Research Foundation 2013

Journal of Coastal Research 00 0 000–000 Coconut Creek, Florida Month 0000

locations of natural or cultural resources or critical infrastruc-

ture of special importance to the National Park Service (NPS).

Examples include piping plover habitat, historic buildings, and

bridges. At risk refers to the likelihood of inundation because of

sea level rise or storm surge. However, some sites and

associated resources predicted to be at risk of inundation

would not necessarily be negatively affected by it. For example,

a building may be unaffected by a brief period of inundation

from storm surge. In other cases, natural or cultural resources

or infrastructure may be severely affected if inundated.

Therefore, risk (of inundation) does not equate to ‘‘impact’’ in

our study. In either case, however, an understanding of the

vulnerability of each sentinel site to inundation will allow park

resource managers to develop appropriate management and

mitigation strategies for these sites and areas of concern.

Coastal zone scientists have used a variety of quantitative

models to assess the effect of sea level rise and storm surge on

coastal inundation and flooding. Several hydrodynamic models

are used to model inundation from rising sea levels. Examples

include MIKE 21 and MIKE FLOOD (Sto. Domingo et al.,

2010), ADCIRC (Lin et al., 2010), LISFLOOD-FP (Lewis et al.,

2011), and ANUGA (Van Drie, Milevski, and Simon, 2010). The

U.S. Federal Emergency Management Agency (FEMA) relies

on a Geographic Information Systems (GIS) model, HAZUS-

MH, to estimate the physical, economic, and social impacts of

large-scale flooding events (Scawthorn et al., 2006a,b). Other

GIS-based methods have been applied as well (Brown, 2006;

Hennecke and Cowell, 2000). A thorough review of sea level

and storm surge inundation models is provided in Murdu-

khayeva (2012).

Parkwide and regional-scale assessments of sea level rise

and storm surge risk have been conducted previously in these

two national parks (CACO, ASIS). The Coastal Vulnerability

Index (CVI) technique was applied at Cape Cod National

Seashore (Hammar-Klose et al., 2003) and at Assateague

Island National Seashore (Pendleton, Williams, and Thieler,

2004). The CVI method combines a number of physical

variables to classify the relative vulnerability of 1.5-km

shoreline segments to sea level rise effects. Gutierrez,

Williams, and Thieler (2007) studied potential shoreline

changes from sea level rise along the United States. Mid-

Atlantic coast. The CVI and shoreline-change assessments

provide a useful and robust overview of coastal vulnerability at

landscape scales (1.5 km stretches of coast). Our study assesses

risk at specific locations, thus provides a more-resolute

evaluation of inundation.

All vulnerability models and methods rely on elevation data,

which are often limited in their vertical accuracy and cause large

ranges of uncertainty in the results (Gesch, 2009). The National

Elevation Dataset (NED) is the largest-scale, readily available,

topographic data set for the entire United States, but with a

vertical accuracy (root mean square error [RMSE]) of 2.44 m

(Gesch, 2007), it is of little value in assessing inundation risk at

specific sites. Our study relied on two sources of high-accuracy

elevation data for sentinel sites: Light Detection and Ranging

(LIDAR) data and real-time kinematic global positioning system

(RTK GPS) data. Our assessment also integrated output for

three sea level rise scenarios and four storm surge scenarios

from several modeling approaches. For sea level rise, we used a

‘‘bathtub inundation’’ model with tidal–orthometric datum

conversion and the Sea Level Affecting Marshes Model

(SLAMM). For storm surge, we used the Sea, Lake, and

Overland Surges from Hurricanes (SLOSH) model. The proba-

bilities of inundation at sentinel sites were calculated for each

modeled scenario, and an index was developed to estimate

overall inundation likelihood at each sentinel site. This overall

index of inundation likelihood was an additional tool for

prioritizing long-term management and climate change adap-

tation plans in the national parks.

METHODSA goal of this research was to develop a process to assess sea

level rise and storm surge inundation risk at specific locations

in coastal environments. We endeavored to use off-the-shelf

data and technology and readily available models, so the

process could be used by other coastal resource managers in

other locations in the United States. A synopsis of the data and

tools we chose and the sources of additional information for

them are presented in Table 1.

Table 1. Data, tools, and models used in this study and references for further information.

Data/Tool Source Citations

Elevation Experimental Advanced Airborne

Research Lidar (EAARL);

the 3di Technologies, Inc.

Digital Airborne Topographic

Imaging System II (DATIS II),

RTK GPS Surveys

Bonisteel et al., 2009; Bonisteel-Cormier et al.,

2010; Brock et al., 2007; MassGIS, 2005,

performed by Neil Winn (NPS ASIS),

Mark Adams (NPS CACO),

Michael Bradley (URI), and

Angelica Murdukhayeva (URI)

Tidal and orthometric datum values NOAA CO-OPS, NOAA VDatum 3.0 beta NOAA, 2007; NOAA, 2011

SLOSH (Sea, Lake, and Overland Surges

from Hurricanes) model

NOAA NWS Display Version 1.64a

(release date: June 2011)

FEMA, 2003; Jarvinen and Lawrence, 1985;

Jelesnianski, Chen, and Shaffer, 1992

Storm surge interpolation tool Applied Science Associates, Inc. Isaji and Knee, 2009

SLAMM

(Sea Level Affecting Marshes Model)

Warren Pinnacle Consulting, Inc. Version 6.0.1 beta Warren Pinnacle Consulting, 2010

Wetlands U.S. Fish and Wildlife Service National Wetlands Inventory USFWS, 2010

Upland land cover NOAA Coastal Change Analysis Program NOAA, 2006

Local accretion rates J. Lynch, personal communication; NPS, 2009

Journal of Coastal Research, Vol. 00, No. 0, 0000

0 Murdukhayeva et al.

Study AreasWe worked in two coastal parks (Figure 1): Cape Cod

National Seashore (CACO) and the Assateague Island National

Seashore (ASIS). The CACO is located on the outer (eastern)

portion of Cape Cod, Massachusetts. It encompasses 123 km of

ocean, marsh, and inner bay shorelines and contains a variety

of marine, estuarine, and upland, terrestrial ecosystems with

relatively varied topography for the coastal zone. The ASIS is

located on the Delmarva Peninsula and lies within the

boundaries of two states, Maryland and Virginia. It has 60

Figure 1. Regional map with locations of study areas: (A) Cape Cod National Seashore (CACO), and (B) Assateague Island National Seashore (ASIS).


Sea Level Rise and Storm Surge in Northeastern National Parks 0

km of shoreline on an undeveloped barrier island with long

stretches of dunes, beaches, and marshes. These study areas

represented two different types of coastal ecosystem land-

scapes: a peninsular glacial moraine (CACO) and a barrier

island (ASIS).

Data SourcesSentinel sites are locations of natural, cultural, and infra-

structural resources of special importance to the NPS and were

identified by park managers. At CACO, the 63 sentinel site

locations included groundwater-monitoring wells, lighthouses,

visitor centers, and historical monuments and sites, e.g. the

Marconi Site and historic Marindin shoreline survey markers

(Marindin, 1891). At ASIS, sentinel sites were locations of 34

newly installed or frequently used geodetic survey markers,

which are critical for ongoing research on the island geomor-

phology. We measured the elevation of sentinel sites using RTK

GPS (Trimble R8 GNSS), which provided elevation estimates

with average vertical RMSE values of 3.5 cm for CACO and 0.7

cm for ASIS.

Digital elevation models (DEMs) were derived from LIDAR

data obtained by the U.S. Geological Survey (USGS) and the

NPS from aircraft-mounted laser sensors. LIDAR data are

typically accurate to 0.15–1 m (Gao, 2007). The horizontal

resolutions of CACO’s and ASIS’s DEMs were 1 m and 2.5 m,

respectively. The reported vertical accuracy of the DEMs for

CACO and ASIS was 15 cm RMSE. To validate these accuracy

estimates, we calculated the vertical RMSE using high-

accuracy (,2 cm vertical and horizontal error) ground-control

points that were available in each park. For CACO, the vertical

RMSE of the DEM was 0.53 m using 35 existing geodetic

control points. For ASIS, the vertical RMSE was 0.33 m using

1179 control points.

The SLAMM model requires a DEM layer, a slope layer

(derived from the DEM), a detailed land-use layer, parameters

for tidal ranges (National Oceanic and Atmospheric Adminis-

tration [NOAA], 2007), and if known, accretion rates for

marshes. The land-use layer was created by merging the

Coastal Change Analysis Program (C-CAP) data (NOAA, 2006)

and the National Wetlands Inventory (NWI) data (USFWS,

2010) and recoding them to SLAMM categories, as specified by

the SLAMM 6.0.1 Beta Users Manual (Warren Pinnacle

Consulting, 2010). The source data for NWI were aerial

photography (CACO in 1993 and ASIS in 1988), and the source

data for C-CAP were 2006 Landsat imagery. Some model

parameters, such as marsh accretion rates, were obtained from

Surface Elevation Table (SET) data (J. Lynch, personal

communication; NPS, 2009). When these and other parameters

(i.e. beach sedimentation rate, frequency overwash) were

unknown or unavailable, SLAMM’s default settings were used.

Using default parameter settings could strongly sway the rates

of inundation predicted by SLAMM (Scarborough, 2009), but

we were limited by data availability for our case study sites.

Sea Level Rise Scenarios and ModelsThree scenarios were selected to represent the current range

of sea level rise predictions for the year 2100: rises of 0.6 m

(IPCC, 2007), 1 m (Vermeer and Rahmstorf, 2009), and 2 m

(Pfeffer, Harper, and O’Neel, 2008). The scenarios represent

reasonable estimates of low, medium, and high predictions of sea

level rise for this region. For example, the Rhode Island Coastal

Resources Management Council has adopted sea level rise

scenarios in the range of 1–2 m to guide their planning policies

(Gregg, 2010). Elevation data set accuracies also affected our

choice of scenarios; based on the recommendations of NOAA

(2010), vertical accuracies were sufficiently high for ASIS that

inundation mapping could be performed for the scenarios: 0.6 m,

1 m, and 2 m. Only the 1 m and 2 m scenarios could be modeled

for CACO because of the amount of uncertainty in the LIDAR

DEM for this area (vertical RMSE¼0.53 m). These two scenarios

for CACO and all three scenarios for ASIS were modeled using

two different sea level rise models.

The first modeling approach, the bathtub model, involved

creating a planar water surface representing the sea level rise

scenario that is added to the Mean Higher High Water

(MHHW) tidal elevation. Modeling sea level rise or storm

surge beyond the MHHW tidal elevation represents the worst-

case inundation scenario. These water surface elevations were

calculated using VDatum software (NOAA, 2011), which

performs elevation conversions between NAVD88 (an ortho-

metric datum) and tidal datums.

The second approach used to evaluate sea level rise

inundation was the Sea Level Affecting Marshes Model

(SLAMM). Using linear relationships and decision-tree rules,

SLAMM calculates water elevation and computes inundation

and habitat response over large areas. The SLAMM incorpo-

rates the temporal rate of the sea level changes (Mcleod et al.,

2010). The output maps show expected habitat classes and

areas of inundation based on the different rates and magni-

tudes of sea level rise associated with each modeled scenario.

Sentinel-site locations were mapped over the output, and a

change matrix was created for each one to show the shift from

initial land-cover class to the final, predicted land-cover class.

Storm Surge Scenarios and ModelsThe four selected scenarios for storm surge are associated

with the Saffir-Simpson Category 1–4 hurricanes. The SLOSH

model, a forecast model for hurricane-induced water levels for

the Gulf and Atlantic coasts (Jelesnianski, Chen, and Shaffer,

1992), was used in conjunction with the Applied Science

Associates, Inc. (ASA), Inundation Toolbox-Interpolation Tool

(Isaji and Knee, 2009). The SLOSH model displays the

expected surge heights along coasts. Surge heights are not

uniform along the coastline but depend on the hurricane track,

wind speed, and topography and bathymetry at the point where

the storm makes landfall (FEMA, 2003). Storm surge heights

were derived for the Providence, Rhode Island/Boston, Massa-

chusetts, and Ocean City, Maryland, storm basins in SLOSH

and were interpolated to a raster surface of the same extent

and resolution as the DEM, with the ASA Toolbox. Elevations

from the DEM were compared with the elevations from the

storm surge surfaces and probabilities of inundation at sentinel

sites were calculated using the z-score uncertainty technique

(described below). The uncertainty technique incorporated

errors associated with the elevation data sets and known

sources of error that are unique to the water surface modeled by

SLOSH. A site was considered at risk from inundation if it had

an elevation less than or equal to the water surface elevation

that was predicted for that location. To account for topographic



features that might prevent inundation of inland areas, only

raster cells adjacent to the ocean or contiguous with other

inundated cells were included in the risk maps.

ArcGIS 10 software was used for all geospatial data

processing (ESRI, 2011).

Statistical ProceduresProbabilities of inundation for sentinel sites were calculated

for each of the sea level rise scenarios (0.6 m, 1 m, and 2 m) and

storm surge scenarios (Category 1–4 hurricanes). The land

surface elevation and modeled water surface were compared,

and probabilities of inundation at sentinel sites were calculated

using the z-score inundation uncertainty technique described

by NOAA Coastal Services Center (2010a). The standard

normal cumulative-distribution function was used to calculate

probabilities of inundation and the certainty of the prediction

given errors associated with the data and models (Ott and

Longnecker, 2010). Standard scores, or z-scores, were calcu-

lated at each sentinel site using the formula:

z�score ¼ Inundation level� Elevation

RMSETotal; ð1Þ

where RMSETotal is calculated as follows:

RMSETotal ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðRMSEElevationÞ2 þ ðRMSEWaterSurfaceÞ2

q: ð2Þ

The RMSE for LIDAR DEMs is calculated as follows:

RMSEElevation ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii ¼ 1nðxLIDAR;i � xGPS;iÞ2

n

s; ð3Þ

where xLIDAR is the elevation from LIDAR at a single location,

and xGPS is the elevation as determined by GPS in the same

location. The RMSEs for the GPS survey elevations were

reported by Trimble Software. The RMSEs for water surfaces

were reported by VDatum. An area was considered at risk from

inundation if it had an elevation less than or equal to the water

surface elevation predicted for that location and was contigu-

ous with other inundated pixels. Because high topographic

Figure 2. Sea level rise scenarios at Cape Cod, Massachusetts. The blue areas represent areas at risk from inundation in 1-m and 2-m sea level rise scenarios. The

areas in orange represent areas whose elevations are under the 1-m and 2-m water surfaces but are unconnected to the ocean or other areas expected to be

inundated. (Color for this figure is available in the online version of this paper.)



features might prevent inundation of low-elevation areas

further inland, we retained inundated locations that were

contiguous with flooded areas but did not retain inland sites

that were low elevation but were separated from the coast by a

landscape feature higher than the inundation elevation (see

Figure 2).

Descriptive statistics were calculated for each risk estimate

variable (e.g. probability of inundation with 1 m sea level rise,

inundation depth at 2 m sea level rise), and the variables were

tested for normality using the Shapiro-Wilk normality test.

Because data were usually nonnormally distributed, we

performed pairwise comparisons using the nonparametric

Wilcoxon signed rank test, and where there were three or

more groups in a comparison, we used the Kruskal-Wallis rank

sum test. A principal components analysis (PCA) was used to

reduce the large number of risk measures to a smaller number

of variables to develop a composite measure of inundation risk

at sentinel sites. All analyses were conducted using the

statistical software package R (R Development Core Team,

2011).

RESULTS

Inundation MappingEstimates of the extent of sea level rise using the bathtub

model are shown for selected parts of CACO (Figure 2) and

ASIS (Figure 3). Expected storm surge heights from Category

1–4 storms were modeled for CACO and ASIS (Table 2). Figure

4 demonstrates how differences in expected surge heights

translate to inundation-zone mapping in the northern extent of

the CACO. Total areas at risk from sea level rise and storm

surge inundation scenarios are presented in Table 3.

Elevations of Sentinel SitesThe elevations of sentinel sites that were obtained from RTK

GPS and those from LIDAR-derived DEMs were significantly

different (Table 4). At CACO, the LIDAR elevations were lower

than RTK GPS elevations were and had a mean difference of

1.19 6 0.10 m (mean 6 1 standard error [SE]). At ASIS, the

LIDAR elevations were often higher than RTK GPS elevations

were and had a mean (6 SE) difference of 0.29 6 0.04 m. For all

of our probability calculations, we used the RTK GPS–derived

elevations for sentinel sites.

Probabilities of InundationMean probabilities of inundation for each park’s set of

sentinel sites are reported in Tables 5 and 6. The complete list

of sentinel sites and their individual probabilities of inundation

can be found in Murdukhayeva (2012). As expected, inundation

risk increased as sea level rise height and storm intensity

increased. The mean probabilities of inundation at CACO were

significantly different for the two sea level rise scenarios

(Wilcoxon signed rank, V¼ 0, p , 0.001). Similarly, for ASIS,

the mean probabilities of inundation are significantly different

for the three sea level rise scenarios (H¼66.02, df¼2, p ,

0.0001). The mean probabilities of inundation for the four

storm surge scenarios are significantly different for both CACO

(H¼44.51, df¼3, p , 0.0001) and ASIS (H¼96.52, df¼3, p ,

0.0001).

Storm surge inundation risk increased dramatically with

increasing Category storms. Sentinel sites at Provincetown

Airport, Pleasant Bay Marsh, and Wellfleet Harbor,

Figure 3. Sea level rise scenarios near Verrazano Bridge, Assateague Island: (A) 0.6 m, (B) 1 m, and (C) 2 m. (Color for this figure is available in the online version

of this paper.)

Table 2. Storm surge heights (above MHHW) predicted by SLOSH.

Storm Class

Surge Height (m)

CACO ASIS

Category 1 0.34–1.52 0.43–1.77

Category 2 0.91–3.20 0.73–3.02

Category 3 1.34–5.33 2.56–4.30

Category 4 1.80–6.07 4.15–5.55



Massachusetts, are at risk under Category 3 and 4

hurricane scenarios. Along the entire length of Assateague

Island, sentinel sites are at high risk. Under the Category 2

storm surge scenario, 22 sentinel sites have .75% proba-

bility of inundation, and under the Category 3 and 4

scenarios, all 34 sentinel sites have .75% chance of

inundation (Table 6).

Habitat Changes Predicted by SLAMM

The SLAMM model predicted habitat classes under

selected scenarios of sea level rise by 2100. Change matrices

(Tables 7 and 8) show the number of sentinel sites in each

habitat class initially and their expected conversions because

of sea level rise. At CACO, only two scenarios were modeled

because the vertical accuracy of the LIDAR data (the primary

Figure 4. Inundation expected from storm surges at Cape Cod, Massachusetts: (A) Category 1, (B) Category 2, (C) Category 3, and (D) Category 4. (Color for this

figure is available in the online version of this paper.)



input driving the model) was inadequate to support the 0.6-m

scenario. In the Undeveloped dry land category, 10 sentinel

sites experienced conversions to the Transitional marsh

category, one sentinel site converted to Estuarine beach, and

one sentinel site converted to Open ocean after 2 m of sea level

rise (Table 7). In ASIS, many sentinel sites were in the

Undeveloped dry land class (24 of 34 total sites [71%]) and

experienced conversions to Transitional marsh (n¼ 9; 38%),

Estuarine beach (n ¼ 1; 4%), Ocean beach (n ¼ 3; 13%), and

Open ocean (n ¼ 11; 46%) with 2 m of sea level rise. Four

sentinel sites that started in the Irregularly flooded marsh

category converted to Salt marsh after 2 m of sea level rise

(Table 8).

Following the recommendation of Scarborough (2009),

similar land cover classes were aggregated for ease of

interpretability. The 11 possible SLAMM categories were

Table 3. Extent of inundation (in hectares) within each study area under bathtub model sea level rise scenarios and SLOSH hurricane scenarios.

Study Area

Sea Level Rise Scenario (m) Hurricane Class

0.6 1 2 Category 1 Category 2 Category 3 Category 4

CACO — 4423 5378 2720 3508 4234 5012

ASIS 4541 6224 8211 2607 6693 8147 8332

Table 4. Summary of sentinel site elevations. Differences between RTK GPS and LIDAR elevations were computed on a pairwise basis at each sentinel site.

Standard error values account for variability of elevations among sentinel sites and do not account for measurement errors. Test of the hypothesis that the

mean difference ¼ 0 was done using a Paired Sample Wilcoxon Test.

Study Area (n)

RTK GPS

(mean 6 SE m)

LIDAR

(mean 6 SE m)

Absolute Difference

(pairwise mean 6 SE m)

Test of Mean Pairwise Differences

V p

CACO (63) 13.20 6 1.45 12.02 6 1.46 1.19 6 0.10 1999 ,0.0001

ASIS (34) 1.39 6 0.07 1.65 6 0.07 0.29 6 0.04 39 ,0.0001

Table 5. Mean (6SE) probabilities of inundation at sentinel sites in each bathtub modeling scenario. The number of sites where the probability of inundation

exceeds 0.75 is shown in parentheses.

Study Area (n)

Inundation Scenario

0.6 m 1 m 2 m

CACO (63) — 0.064 6 0.029 (3) 0.179 6 0.047 (11)

ASIS (34) 0.130 6 0.51(3) 0.485 6 0.067 (11) 0.950 6 0.035 (32)

Table 6. Mean (6SE) probabilities of inundation at sentinel sites using the SLOSH storm scenarios. The number of sites where the probability of inundation

exceeds 0.75 is shown in parentheses.

Study Area (n)

Hurricane Class

Category 1 Category 2 Category 3 Category 4

CACO (63) 0.000 6 0.000 (0) 0.013 6 0.012 (1) 0.051 6 0.022 (1) 0.114 6 0.034 (5)

ASIS (34) 0.162 6 0.047 (2) 0.700 6 0.066 (22) 0.980 6 0.009 (34) 0.999 6 0.001 (34)

Table 7. CACO SLAMM conversion matrix. Numbers in parentheses indicate sentinel sites converted to each class in each inundation scenario (1 m and 2 m).

Initial Category

(No. of sentinel sites)

Predicted Category (1 m, 2 m sea level scenarios)

Dry Land

Developed

Dry Land

Undeveloped

Nontidal

Swamp

Transitional

Marsh

Salt

Marsh

Estuarine

Beach

Rocky

Intertidal

Irregularly

Flooded Marsh Open Ocean

Dry land developed (12) (12, 12)

Dry land undeveloped (46) (40, 34) (5, 10) (1, 1) (0, 1)

Nontidal swamp (1) (1, 0) (0, 1)

Transitional marsh (0)

Salt marsh (0)

Estuarine beach (0)

Rocky intertidal (2) (1, 0) (1, 2)

Irregularly flooded marsh (2) (1, 2) (1, 0)

Open ocean (0)



Table 8. ASIS SLAMM conversion matrix. Numbers in parentheses indicate sentinel sites converted to each class in each inundation scenario (0.6 m, 1 m, 2

m).

Initial Category

(No. of sentinel sites)

Predicted Category (0.6 m, 1 m, 2 m sea-level scenarios)

Dry Land

Developed

Dry Land

Undeveloped

Inland

Fresh Marsh

Transitional

Marsh

Salt

Marsh

Estuarine

Beach

Tidal

Flat

Ocean

Beach

Irregularly

Flooded Marsh

Estuarine

Open Water

Open

Ocean

Dry land developed (1) (1, 1, 1)

Dry land undeveloped (24) (8, 2, 0) (7, 7, 9) (0, 1, 1) (8, 11, 3) (1, 3, 11)

Inland fresh marsh (1) (1, 1, 0) (0, 0, 1)

Transitional marsh (1) (1, 1, 0) (0, 0, 1)

Salt marsh (1) (1, 1, 0) (0, 0, 1)

Estuarine beach (1) (1, 1, 0) (0, 0, 1)

Tidal flat (0)

Ocean beach (1) (1, 1, 1)

Irregularly flooded marsh (4) (0, 1, 4) (4, 3, 0)

Estuarine open water (0)

Open ocean (0)

Figure 5. The SLAMM initial conditions and model output at Assateague Island. Eleven possible land-use classes in the study area (top panels), and land-use

classes aggregated into five broad groups (bottom panels). (Color for this figure is available in the online version of this paper.)



reclassified into five land-cover categories for mapping appli-

cations: Upland, Forested wetland, Marsh, Beach, and Open

water. Figure 5 shows initial conditions and a 1-m sea level rise

output with the original classification scheme (top panels) and

with the aggregated classification scheme (bottom panels).

Figure 6 shows an area east of Calfpen Bay, Virginia (ASIS),

which showed great changes over the modeled scenarios; there

was a large increase in Marsh, Transitional marsh, and Open

Figure 6. The SLAMM output at Assateague Island. Eleven possible land-use classes in this study area aggregated into five broad groups for ease of

interpretation. (Color for this figure is available in the online version of this paper.)



water areas. This area showed some of the greatest changes in

the two parks studied.

Maps with park-scale views of initial land-cover classes and

model-output land-cover classes for each sea level rise scenario

are found in Murdukhayeva et al. (2012).

Overall Inundation IndexThe three models yielded several measures of inundation

risk. We used PCA to reduce the large set of correlated risk

variables to a set of uncorrelated variables or principal

components (Table 9). The first principal component (PC1)

explained 63% of the total variation in risk measures at CACO

and 58% of the total variation in risk measures at ASIS. In all

cases, inundation probabilities had a negative loading, and

RTK GPS elevations had a positive loading on PC1 (Table 9).

Nearly all variables had similar loading values on PC1, thus

PC1 represents a ‘‘size’’ effect (August, 1983) and can be used

as an index of the overall risk of inundation. The other

principal components had high loadings on only one or two

variables and reflected specific risk factors. Furthermore, they

did not predict overall variation well and, therefore, were not

candidates for an overall risk index. The range of PC1 values

for each park was separated into five quintiles. Large, positive

PC1 scores for sentinel sites indicated that inundation was very

unlikely; large, negative PC1 scores indicated that inundation

was very likely (Table 10). Overall inundation index classifi-

cation at sentinel sites was mapped for CACO and ASIS (Figure

7).

DISCUSSION

Quality of the LIDAR DataThe best LIDAR data that were available to us were not as

accurate as expected and this affected how they could be used

in the models. The metadata for each of the two LIDAR-derived

DEMs reported a 15 cm vertical RMSE. To validate these

accuracy estimates, we calculated the vertical RMSE using

ground control points of high quality and accuracy (,2 cm

vertical and horizontal accuracy) collected using survey-grade

GPS. Our estimates of the RMSE of the LIDAR data were two

to three times larger than the RMSE values reported in the

metadata. This result had many implications in our study. We

were unable to reliably model the 0.6-m sea level rise scenario

at CACO based on the uncertainly of the LIDAR DEM used in

the modeling (NOAA, 2010). These results suggest that it is

prudent to independently test the accuracy of DEMs used in

inundation modeling.

Quality of the RTK GPS DataThe high accuracy of the elevations measured with RTK GPS

was an important asset for this project. The GPS field surveys

allowed us to collect accurate elevations of sentinel sites that

were hidden from LIDAR signals and sensors (NOAA CSC,

2010b). For example, we surveyed elevations of groundwater

monitoring wells located in forests with heavy canopy cover.

Furthermore, because these elevations were measured with

great accuracy using the RTK GPS (RMSE values of 0.6–8.7

cm), inundation probabilities could be calculated with greater

certainty. The application of RTK GPS is a promising solution

to elevation uncertainty issues in sea level rise inundation risk

assessments. It is important to note, however, that RTK GPS

protocols require operating a GPS base station at a location

that has been surveyed to within a few millimeters. For this

reason, a network of accurate geodetic control sites within 5 km

is a requirement for RTK GPS measurement at sentinel sites

(Murdukhayeva 2012).

The use of high accuracy RTK GPS data helped minimize the

error in estimating inundation risk. Because the error

associated with each sentinel site elevation was low, there

Table 9. Principal components analysis of risk variables. Class loadings for the first three principal components are provided for each variable. The GPS

elevation refers to site elevation as surveyed by GPS; C1_depth to C4_depth refer to the depth of inundation from Category 1–4 storms; Prob_60cm, Prob_1m,

and Prob_2m refer to probabilities of inundation given sea level rise scenarios of 60 cm, 1 m, and 2 m; SLAMM_60cm, SLAMM_1m, and SLAMM_2m refer to

conversion predictions (yes or no) by SLAMM given sea level rise scenarios of 60 cm, 1 m, and 2 m.

Variable

CACO ASIS

PC1 PC2 PC3 PC1 PC2 PC3

GPS elevation 0.37 0.23 0.09 0.42 0.09 �0.11

C1_depth �0.37 �0.23 �0.09 �0.39 �0.04 0.10

C2_depth �0.37 �0.23 �0.09 �0.33 0.46 �0.04

C3_depth �0.37 �0.23 �0.08 �0.40 0.20 �0.10

C4_depth �0.37 �0.23 �0.08 �0.37 �0.18 �0.06

Prob_60cm �0.19 0.47 �0.53 �0.28 �0.36 �0.45

Prob_1m �0.23 0.49 �0.34 �0.33 �0.25 �0.20

Prob_2m �0.27 0.30 0.41 �0.28 0.17 0.74

Slamm_60cm — — — �0.04 �0.70 0.42

Slamm_1m �0.25 0.39 0.23 — — —

Slamm_2m �0.29 0.17 0.59 — — —

% Variation explained 62.7 23.5 7.3 57.8 16.4 9.8

Cumulative variation (%) 62.7 86.3 93.6 57.8 74.2 84.0

Table 10. Inundation index class and corresponding PC1 raw scores. The

PC score classes were based on quintile categories of the data.

Inundation Index Class CACO ASIS

Very likely �5.54 to �1.87 �5.55 to �1.74

Likely �1.87 to �0.56 �1.74 to �0.43

As likely as not �0.56 to 0.60 �0.43 to 0.45

Unlikely 0.60 to 1.62 0.45 to 1.66

Very unlikely 1.62 to 5.56 1.66 to 6.00



was less uncertainty associated with each modeled scenario.

Many of the sentinel sites had probabilities of inundation of

either 0 (very unlikely) or 1 (very likely) when the RTK GPS

elevation values for the sentinel sites were used. Murdukhaye-

va et al. (2012) found that this was not the case with

probabilities of inundation calculated using elevations at

sentinel sites based on LIDAR data, rather than the more

accurate RTK GPS; many probabilities of inundation were in

the range of 0.25 to 0.75 (rather than 0 or 1). Using higher-

accuracy elevation data for sentinel sites resulted in greater

certainty regarding the likelihood of inundation.

Habitat-Change Modeling

The SLAMM maps with aggregated land-cover classes can

provide useful information for assessing habitat transition

under various sea level rise scenarios. In presenting the

SLAMM output in maps, however, it is important to recognize

the uncertainty of habitat predictions because of uncertainty in

input elevation and land cover. The SLAMM model is

constrained by the quality of the data driving it (e.g. LIDAR-

derived DEMs). It was not possible to integrate high-accuracy

RTK GPS elevations into the model to enhance the results.

Figure 7. Overall inundation index for sentinel sites at CACO (left) and ASIS (right). Blue points indicate sites where inundation is very unlikely, and red points

indicate sites where inundation is very likely. (Color for this figure is available in the online version of this paper.)



Another important baseline data set for the SLAMM model is

initial land-use classification. In this study, initial land-use

conditions were obtained, in part, from National Wetlands

Inventory maps created using aerial photography from 1988

(ASIS) and 1993 (CACO). Those maps did not reflect changes

during the past two decades. Furthermore, there is no way to

quantitatively determine the uncertainty associated with a

SLAMM prediction. These factors and others limit the utility of

the output results (Kirwan and Guntenspergen, 2009; Scar-

borough, 2009), particularly for park managers developing

mitigation strategies. Higher-resolution elevation data and

current land use data sets are becoming more widely available

and should address these deficits.

For the risk assessment, we were first interested in

predicting changes from land to open water (i.e. inundation).

The SLAMM results indicated that CACO had one site (1-m

scenario) and three sites (2-m scenario) convert to open water,

whereas, at ASIS, four sites (1-m scenario) and 12 sites (2-m

scenario) are predicted to convert to open water. The bathtub

model predicts greater amounts of inundation (Table 6). At

CACO, three (1-m scenario) and 11 sites (2-m scenario) have

probabilities of inundation .75%, and at ASIS, 11 (1-m

scenario) and 32 (2-m scenario) sites have probabilities of

inundation .75%. This difference in results supports the

argument for bathtub models over estimate inundation

(Mcleod et al., 2010; NOAA, 2010), but it may also indicate

that SLAMM underestimated inundation. An important

finding of our study was that the use of multiple methods

provided us a robust range of inundation possibilities across

plausible sea level scenarios.

Inundation ModelingBathtub modeling is a relatively straightforward technique

that can be used with off-the-shelf geospatial data, but the

resulting inundation surfaces can be inaccurate (Mcleod et al.,

2010; Poulter and Halpin, 2008). Typically, that inaccuracy

derives from subtle land barriers that are not discerned in the

DEM and can impede inland water flow. Similarly, structures

that permit water flow under the topographic surface, such as

culverts below roads, can contribute to error in modeled

inundation surfaces. Future human modifications of the

landscape as adaptation measures to sea level rise are not

included in simple bathtub models nor are changes to coastal

geophysical processes because of sea level rise, for example

breeching of barrier beaches that would significantly change

tidal dynamics of lagoon systems behind the barrier (Anthony

et al., 2009). By using high-quality LIDAR data for our DEMs

and by excluding pixels that were not connected to the coast,

our bathtub model results can serve as a preliminary

assessment of potential inundation regions in these study

areas.

One of the most valuable products of this assessment is the

range of storm surge heights modeled by SLOSH (Table 4).

Those surge heights can be used for predictions of hurricane

impacts in the near future. For example, the SLOSH model

predicted extensive inundation in Provincetown, Massachu-

setts, and the salt marsh sites in Cape Cod, Massachusetts. The

potential damage to a sentinel-site resource under conditions of

storm surge inundation varies depending on the nature of the

site. Buildings or historical artifacts could face extensive

damage, whereas certain habitats or hard infrastructure

(roads, geodetic monuments) might not be damaged at all

during a brief period of inundation. Therefore, it is important

that the NPS evaluate risk for each site and develop mitigation

plans accordingly.

Implications for Sentinel SitesFor the most part, Cape Cod’s sentinel sites were located

inland, on relatively high elevations; 39 of the 63 sentinel sites

(62%) had elevations .5 m above the MHHW, and, as a result,

many sites are not at risk from sea level rise or storm surge

inundation. For example, the base of Highland Lighthouse

(Truro, Massachusetts) is located at 39.34 m NAVD88 or 38.41

m above MHHW (Figure 8). Although we understand that

inundation modeling does not identify those sites .5 m above

the MHHW as at risk, they could face other impacts of

changing coastal patterns (erosion, etc.). Several sites that

were found to be at risk are very close to current tide levels. For

example, one sentinel site at CACO, a culvert near Wellfleet

Harbor, was almost submerged at high tide on our survey trip

(Figure 8). One polyvinyl chloride (PVC) survey marker in

Pleasant Bay and National Geodetic Survey (NGS) monuments

at the Provincetown airport are at risk as well. These findings

agree closely with the results of the Hammar-Klose et al. (2003)

Coastal Vulnerability Index assessment. In their study, 1.5-km

segments of shoreline were ranked as being of low to high

vulnerability, using an index that combined geological and

physical variables. The high-vulnerability sentinel sites, as

ranked by the composite Inundation Index we derived from the

PCA, tend to appear immediately inland of those shoreline

segments with a high CVI vulnerability rank.

At Assateague Island, all of the sentinel sites were at low

elevations ,2.6 m. Many of them are at risk from sea level rise

and storm surge from large hurricanes. The CVI assessment

and spatial pattern of the overall Inundation Index at ASIS

correspond with each other (Pendleton, Williams, and Thieler,

2004). Disagreement occurs at points near the Chincoteague

Inlet, Virginia. The CVI rated that shoreline as low vulnera-

bility because of high accretion rates, and the Inundation Index

rated sentinel sites in that area as high vulnerability because of

the low elevations.

It is inappropriate to compare the frequency of inundation at

sentinel sites between the national parks in our study because

resource managers at CACO and ASIS chose very different

kinds of sites to serve as sentinel sites. The CACO sites

consisted of a variety of historical and natural resources, as

well as important infrastructure. The ASIS sentinel sites were

monumented reference locations at low elevations. If the CACO

resource managers had, like those at ASIS, selected only

sentinel sites used to monitor sea level rise then those two sets

of sentinel sites would have had more comparable landscape

placement, and the frequency of inundation could have been

quite similar despite the very different geomorphology of the

two parks.

Conclusions and Directions for Future WorkThis study assessed inundation risk at 97 sentinel sites

located in two northeastern U.S. coastal national parks.

Rigorous revalidation of the LIDAR-derived data inputs was



essential to making appropriate use of the models. The

methodology we used can be applied at other coastal parks

and can be repeated at the same parks with improved data,

alternate scenarios, or additional sentinel sites. We used three

different modeling approaches because we were interested in

inundation and habitat changes under various sea level rise

scenarios as well as inundation from storm surges. Each model

provided unique information that served our management

needs. Depending on the resource issues and management

needs of other coastal sites, all three models may or may not be

required.

Our choice of models was driven by the goal to make our

methods transportable to other coastal settings and imple-

mentable using off-the-shelf data. Accurate elevation data,

such as that provided by LIDAR technology, are an essential

requisite for inundation modeling. These analyses could not be

performed with coarse data, such as the NED. All the models

suffer from the naı̈ve assumption that coastal geomorphology

does not change as sea level rises or storm surge overwashes

inland features. As coastal barrier/lagoon systems breach or

dune systems wash away, inundation dynamics will change,

and those changes are not accounted for in the models we have

chosen.

Models of sea level rise and storm surge are continually being

refined, and inundation probabilities can be recomputed as new

models are developed. Scenarios for sea level rise are evolving

as new data from satellite altimetry and ice-melt studies are

acquired. The U.S. National Weather Service updates SLOSH’s

storm-surge predictions for regional basins following large

hurricane events. The LIDAR data are being acquired for large

regions of the United States coast to evaluate sea level rise and

storm-surge risk. For example, the USGS recently completed

Figure 8. (Top) Sentinel site with low relative likelihood of inundation. Highland Lighthouse in North Truro, Massachusetts. (Bottom) Low-lying sentinel site at

Cape Cod National Seashore. A culvert near a bike path in Wellfleet, Massachusetts. (Color for this figure is available in the online version of this paper.)



an extensive LIDAR acquisition for coastal counties of the

northeast United States. The use of RTK GPS technology to

collect accurate elevations at sentinel sites is a promising

undertaking that will improve predictions of inundation risk

based on different possible scenarios and models. The growing

network of stable, permanent, and accurate geodetic control

sites to form a backbone of base station locations makes it

possible to greatly expand the areas where rapid collection of

high-resolution elevation data with RTK GPS are possible

(Murdukhayeva et al., 2012).

POSTSCRIPTWe visited 11 sentinel sites at ASIS immediately following

Hurricane Sandy to determine the accuracy of our inundation

modeling. Peak surge was 29 October 2012 at 1730. The Ocean

City, Maryland, tide gauge (1.5 km from the closest sentinel

site) recorded a surge elevation of 1.4 m NAVD88. We inspected

each sentinel site for signs of inundation, such as sand scouring

and evidence of wrack. Seven sites were at elevations above 1.4

m, and of those, five (71%) were not inundated. The two that did

flood were very close to the surge elevation (1.5 m and 1.7 m).

No sentinel sites above 1.9 m were inundated. Four sites were

below the surge elevation, and of these, three (75%) were

flooded. The site that did not flood was within a meter or two

(horizontally) of the wrack line. The mean (6SE) PC1

inundation index score for flooded sentinel sites was 0.59 6

0.48 (n¼5), and the mean (6SE) PC1 score for sites that did not

flood was 2.97 6 0.94 (n ¼ 6). The flooded sites had a

significantly lower inundation index (i.e. higher risk of

inundation) than did nonflooded sites (Wilcoxon two-sample

test, V¼ 26, p¼ 0.025).

ACKNOWLEDGMENTSThis research was supported by NPS Task Agreement

J4531090800—Assessing Inundation Risk From Sea Level

Rise and Storm Surge in Coastal National Parks Using High

Accuracy Geodetic Control—administered by the North

Atlantic Coast Cooperative Ecosystem Studies Unit at the

University of Rhode Island Coastal Institute. We gratefully

acknowledge the technical and logistic support we received

from the Northeast Coastal and Barrier Network of the NPS

Inventory and Monitoring Program. The project has benefited

from the contributions and ideas of Tim Smith, Rob Thieler,

Donald Cahoon, Kelly Knee (Applied Science Associates), Neil

Winn, Marc Albert, Mark Adams, Charles Roman, YQ Wang,

Chris Damon, Roland Duhaime, Sarah Stevens, Dennis

Skidds, Galen Scott, Tiffany-Lane Davis, Heather Grybas,

Doug Marcy, and many others. The thoughtful comments by

three anonymous reviewers significantly improved the clarity

of the paper.

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