328
Integrated Environmental Assessment and Management — Volume 12, Number 2—pp. 328–344
Published 2015 SETAC
Evaluating the Role of Coastal Habitats and Sea-Level Rise
in Hurricane Risk Mitigation: An Ecological Economic
Assessment Method and Application to a Business Decision
Ecosystem Services
Sheila MW Reddy,*y Gregory Guannel,z Robert Griffin,§ Joe Faries,z Timothy Boucher,k Michael Thompson,#
Jorge Brenner,# Joey Bernhardt,zyy Gregory Verutes,zz§§ Spencer A Wood,z Jessica A Silver,z Jodie Toft,z
Anthony Rogers,ykk Alexander Maas,y## Anne Guerry,z Jennifer Molnar,k and Johnathan L DiMuroyyy
yCentral Science Division, The Nature Conservancy, Durham, North Carolina, USA
zThe Natural Capital Project, Woods Institute for the Environment-Stanford University, School of Environmental and Forest Sciences,
University of Washington, Seattle, Washington, USA
§The Natural Capital Project, Woods Institute for the Environment-Stanford University, Stanford, California, USA
kCentral Science Division, The Nature Conservancy, Arlington, Virginia, USA
#Texas State Chapter, The Nature Conservancy, Corpus Christi, Texas, USA
yyDepartment of Zoology, University of British Columbia, Vancouver, British Columbia, Canada
zzThe Natural Capital Project, Woods Institute for the Environment-Stanford University, Conservation Science Program, World Wildlife
Fund-US, Washington, DC, USA
§§The Natural Capital Project, Woods Institute for the Environment-Stanford University, Stanford, California, USA
kkNicholas School of the Environment, Duke University, Durham, North Carolina, USA
##Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, Colorado, USA
yyyGlobal EH&S and Sustainability, The Dow Chemical Company, Midland, Michigan, USA
(Submitted 15 December 2014; Returned for Revision 20 January 2015; Accepted 15 June 2015)
ABSTRACT
Businesses may be missing opportunities to account for ecosystem services in their decisions, because they do not have
methods to quantify and value ecosystem services. We developed a method to quantify and value coastal protection and
other ecosystem services in the context of a cost-benefit analysis of hurricane risk mitigation options for a business. We first
analyze linked biophysical and economic models to examine the potential protection provided by marshes. We then applied
this method to The Dow Chemical Company's Freeport, Texas facility to evaluate natural (marshes), built (levee), and hybrid
(marshes and a levee designed for marshes) defenses against a 100-y hurricane. Model analysis shows that future sea-level
rise decreases marsh area, increases flood heights, and increases the required levee height (12%) and cost (8%). In this
context, marshes do not provide sufficient protection to the facility, located 12 km inland, to warrant a change in levee design
for a 100-y hurricane. Marshes do provide some protection near shore and under smaller storm conditions, which may help
maintain the coastline and levee performance in the face of sea-level rise. In sum, the net present value to the business of built
defenses ($217 million [2010 US$]) is greater than natural defenses ($15 million [2010 US$]) and similar to the hybrid defense
scenario ($229 million [2010 US$]). Examination of a sample of public benefits from the marshes shows they provide at least
$117 million (2010 US$) in coastal protection, recreational value, and C sequestration to the public, while supporting 12
fisheries and more than 300 wildlife species. This study provides information on where natural defenses may be effective and
a replicable approach that businesses can use to incorporate private, as well as public, ecosystem service values into hurricane
risk management at other sites. Integr Environ Assess Manag 2016;12:328–344. © 2015 The Authors. Published by Wiley
Periodicals, Inc. on behalf of SETAC.
Keywords: Economic valuation
Ecosystem services
Green infrastructure
INTRODUCTION
Financial losses due to natural hazards have been increasing
over the last 30 years (Munich RE 2012). In fact, 2011 had the
largest worldwide losses from natural catastrophes on record—
$380 billion in total, with 37% from storms and flooding
(Munich RE 2012). This trend is expected to continue due to
This article includes online-only Supplemental Data.
* Address correspondence to [email protected]
Published online 29 June 2015 in Wiley Online Library
(wileyonlinelibrary.com).
DOI: 10.1002/ieam.1678
This is an open access article under the terms of the Creative Commons
Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
Natural hazard
Risk management
increasing coastal development, aging infrastructure, degradation of coastal habitats, and climate change (Nicholls and Small
2002; Knutson et al. 2010; UNISDR 2011). Coastal disasters
like the Indian Super Cyclone, Hurricane Katrina, and
Superstorm Sandy have raised public awareness about the
challenge of protecting people and property from future
hazards; at the same time, these disasters have also raised
awareness about the opportunity of addressing this challenge
using habitats (Das and Vincent 2009; Feuer 2012; Fischetti
2012). Studies have shown that natural habitats, such as
marshes, oyster reefs, mangroves, and coral reefs can reduce
erosion, wave heights, and even sometimes reduce surge and
tsunami waves (Gedan et al. 2011; Shepard et al. 2011;
Ferrario et al. 2014). Yet, there is still considerable debate over
the conditions under which habitats can provide coastal
protection (Das and Vincent 2009; Arkema et al. 2013).
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
Businesses and governments, who are responsible for billions of
assets and people in coastal environments, are now increasingly
interested in considering habitats, or natural defenses, in
hazard management (Beck and Shepard 2012; NYS 2100
Commission 2013; Ferrario et al. 2014) or enhance the
effectiveness of built defenses (Cheong et al. 2013). Habitats
can mitigate damage from erosion and floods by acting as
barriers, exerting drag and attenuating wave energy and
heights, absorbing flood waters, and/or accreting soil or
calcium carbonate to build or maintain the land or a reef
structure (Shepard et al. 2011). For example, corals and oysters
form large 3-dimensional reef structures that actively build the
reef through calcium carbonate accretion and, like breakwaters, reduce the amount of wave energy from overtopping
waves (Ferrario et al. 2014). Similarily, the porous but
complex root and branching structures of mangroves attenuate
waves and facilitate soil accretion (Das and Vincent 2009).
Marsh grasses also attenuate waves and absorb flood waters,
while facilitating soil accretion (Shepard et al. 2011; Moller
et al. 2014). However, large or extensive habitats may be
needed to have substantial impacts on hazards and habitats
may have no impact on the largest hazards (Wamsley et al.
2010; Renaud et al. 2013).
Habitats provide additional benefits that built defenses alone
cannot provide. Habitats provide recreational opportunities,
sequester C, and support fisheries and biodiversity (Barbier
et al. 2011). Habitats may have lower maintenance costs
because they are growing and self-maintaining (Ferrario et al.
2014). Moreover, hazard risk management approaches that
use multiple strategies (e.g., natural and built defenses) are
more resilient than strategies that rely on just 1 strategy (Ahern
2011).
Despite increasing evidence that habitats may help provide
coastal protection and additional ecosystem services, built
defenses remain the primary options considered by business,
governments, and other decision makers. The UK and US
governments have made important progress to counter this
trend by providing guidance on how to incorporate ecosystem
services and specifically consider natural defenses or habitats
into hazard management decisions (Penning-Rowsell et al.
2005; Environment Agency for England and Wales 2010; UK
Environment Agency 2014; USACE 2015). This reflects a
broader trend by national governments and international
governmental and nongovernmental organizations to account
for the value of natural capital in accounting and decision
making (MEA 2005; USEPA 2010; UK National Ecosystem
Assessment 2011; UK Department for Environment, Food and
Rural Affairs 2013; World Bank 2014). These efforts have been
supported by the advancement and synthesis of ecosystem
service quantification and valuation methods, models, tools,
and databases (TEEB 2010; Bateman et al. 2011; Kareiva et al.
2011; WBCSD 2011; Bagstad et al. 2013; NESP 2014), some
of which are specific to wetlands (Heal et al. 2005; Brander
et al. 2006; Barbier 2012, 2013; TNC 2015). A key remaining
gap is that businesses do not have methods to evaluate natural
defenses on a project basis using the same biophysical and
economic metrics they use to evaluate built defenses. To date,
few studies have linked habitat’s capacity to reduce flood
heights or erosion to reductions in economic damages or
fatalities (except see Barbier 2007; Costanza et al. 2008; Das
and Vincent 2009; Arkema et al. 2013), and even fewer have
compared habitats to other built defense options (except see
Francis et al. 2011; Ferrario et al. 2014). To our knowledge, no
329
studies have compared natural and built defense options in the
context of a specific business decision.
Businesses evaluate investments, such as the costs and
benefits of built defenses based primarily on their net present
value (NPV), or similar financial metrics. However, businesses
are increasingly looking for methods to help them account for
ecosystem service values that are not included in financial
analyses of NPV, such as the avoided property damage,
business interruption, or capital costs from habitats that
provide coastal protection services. Even though business’s
primary concern is for value to shareholders and owners, they
are also increasingly concerned with accounting for ecosystem
service values to the public, such as recreational values of
coastal habitats, because of reputational benefits, consumer or
community pressure, internal sustainability goals, or external
financing requirements (WBCSD 2011).
In this study, we developed and applied linked biophysical
and economic models of coastal storm protection ecosystem
services, as well as other ecosystem services, provided by
marshes to enable businesses to evaluate natural and built
defense options for hurricane risk mitigation under future
conditions of coastal development and sea-level rise. We applied
these methods to evaluate options for protecting a major coastal
manufacturing facility—The Dow Chemical Company’s Texas
Operations in Freeport, Texas. We show how these methods
enable a business to account for the private value of coastal
storm protection services and evaluate hurricane risk mitigation
options based on traditional NPV metrics. In addition, we show
how qualitative, quantitative, and economic information on
ecosystem services to the surrounding community and impacts
to biodiversity may also provide supporting information to
managers making investment decisions primarily based on
private financial criteria. The result is a replicable method to
account for ecosystem services in cost–benefit analyses of
hurricane risk mitigation options that can be used by Dow and
other businesses at other locations.
STUDY SITE
Dow’s Texas Operations in Freeport, Texas (Figure 1) is
responsible for 20% of Dow’s global sales. The location is
vulnerable to hurricanes because it is located in a low-lying area
along the Gulf of Mexico (Jelesnianski et al. 1992). This area
also has coastal marshes, which are partially protected in the
Brazoria and San Bernard National Wildlife Refuges and
provide important stop-over sites for migratory birds (USFWS
2012). A main levee protects the primary Dow facilities and
the town of Freeport (Figure 1). However, a key part of Dow’s
integrated facility lies outside of this levee. This area, known as
Stratton Ridge, is 12 km inland with an average elevation of
3.8 m. Underground salt domes at Stratton Ridge provide a
critical feedstock to Dow’s Freeport manufacturing plants.
Although large hurricanes capable of flooding Stratton Ridge
are rare, Dow previously considered building levees at Stratton
Ridge to protect against a 100-y storm as a potentially costeffective way to reduce hurricane damage. A 100-y storm in
this area is between a Category 3 and 4 storm on the SaffirSimpson hurricane scale and, on average, may generate 4.4 m
surges and 8 m waves at the coast (USACE 2002). Here, we
advance Dow’s previous hurricane risk analysis by accounting
for 1) the 5 km extent of marsh in front of Stratton Ridge and
the potential loss of this marsh, and 2) a potential increase in
flood heights under a future with projected coastal development and sea-level rise.
330
Integr Environ Assess Manag 12, 2016—SMW Reddy et al.
METHODS
Overview
Our approach enables businesses to incorporate habitats into
natural hazard mitigation decisions by using process- based
models that link forecasts of changes in coastal ecosystems to
changes in ecosystem service production and economic values
(Daily et al. 2009). This modeling approach allowed for the
incorporation of more information on ecological and economic
functions than a benefit transfer approach and is appropriate for
data rich locations such as the United States. In less data rich
locations or resource-constrained studies, benefit transfer
approaches (Richardson et al. 2014) that use the large and
growing literature on the value of wetlands and coastal
protection services in particular may be necessary or more
appropriate (Brander et al. 2006).
We first explored the potential coastal protection services
provided by marshes under different marsh conditions. Using a
representative transect from the Stratton Ridge study area, we
modeled wave attenuation and levee costs associated with a
Category 3 hurricane across a range of values for habitat area
and roughness. Using Dow’s hurricane risk mitigation decision
as an example application to a business, we then evaluated the
private coastal protection value to the business of the 3 coastal
defenses options at Stratton Ridge. A sample of public
ecosystem services and impacts to biodiversity were also
explored to provide supporting information to help the
business understand how coastal defense options may support
both corporate financial and sustainability goals.
The next 2 sections describe the 3 coastal defense options for
Stratton Ridge and the data used to construct the defense
options under future scenarios of coastal development and sealevel rise. The 2 sections thereafter describe the models for
coastal protection services and other ecosystem services and
biodiversity.
Coastal defense options and evaluation criteria
Figure 1. Map of Dow Texas Operations and surrounding coastal habitats in
Brazoria County, TX. The majority of Dow's facilities are protected by an
existing levee (gray area); however, a key part of the integrated facility,
Stratton Ridge (dashed oval), is outside the existing levee system. Biophysical
and economic modeling of built defense, natural defense, and hybrid defense
scenarios over 5 time periods (2006, 2025, 2050, 2075, 2100) focused on
habitats, levees, and properties in the study area shown in red. The 3 parts of
the figure show measured and modeled land cover around Freeport, TX: (A)
current (2006), (B) 2050, (C) 2100. These maps show future losses of total land
area and marsh area due to an increase of sea-level rise to 1.16 m above 2006
levels by 2100 and an increase in developed land by 2050. In the Stratton
Ridge study area (red border), we predict a loss of 23% of the total land area
and a loss of 35% of the marsh area by 2100.
We evaluated natural (marshes), built (levee), and hybrid
(marshes and a levee designed for marshes) defenses designed
for a 100-y hurricane relative to a business-as-usual or baseline scenario over Dow’s 30-y planning horizon. The baseline
scenario has no coastal defenses (levees or marshes) and reflects
Dow’s previous approach to modeling hurricane risk, which did
not incorporate the potential protective capacities of marshes.
The natural defense scenario took into account the role of
existing marshes. The built defense scenario used the baseline
scenario land cover and flood conditions and included a levee
designed to prevent flooding at Stratton Ridge under these flood
conditions. The hybrid defense scenario used the natural
defense land cover and flood conditions and included a levee
design (accounting for any flood elevation reduction due to
natural defenses) to prevent flooding at Stratton Ridge
(Table 1).
We compared the different coastal defense scenarios based
on the net present value (NPV) of coastal protection to Dow
(avoided property damage, business interruption, and levee
costs). These metrics do not include additional costs or impacts
that may be relevant to other aspects of natural hazard
mitigation planning by a business such as risk to human life
(Jonkman and Vrijling 2008) or emergency service costs
(Wolshon et al. 2005), because it is assumed that these are
addressed separately by the business. This approach is
consistent with the FEMA HAZUS model (FEMA 2011),
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
331
Table 1. Coastal defense scenarios
External drivers
Scenario
Sea-level rise
Development
1.16 m by 2100
(above 2006
levels)
2050
Projections
1.16 m by 2100
(above 2006
levels)
2050
Projections
1.16 m by 2100
(above 2006
levels)
2050
Projections
Hazards
Costs
Benefits
None
1. Coastal protection for Dow
2. Coastal
protection
for
community
3. Recreation
4. C sequestration
5. Fisheries
6. Biodiversity
Natural defense:
Existing coastal
marshes (5 km)
Category 1–5
hurricanes
Built defense:
Levee designed for 2050,
assuming marshes are
dry land
Category 1–5
hurricanes
Levee
1. Coastal protection for Dow
construction
Hybrid defense:
Existing coastal marshes
(5 km) and levee designed
for 2050, accounting for
coastal marshes
which is used in the United States, has been adopted
worldwide by emergency managers, and does not model
impacts to emergency service costs or human life. As
supporting information, we also compared the scenarios based
on the NPV of coastal protection (avoided property damage)
and other ecosystem services provided to local and global
communities, and biodiversity. We chose the coproduced
public ecosystem services and metrics for biodiversity based on
the ease and replicability of modeling using data from the area
and based on the anticipated magnitude of the service.
Consequently, the methodology used herein should not be
considered a full accounting of the ecosystem services provided
by marshes. It should be noted that we did not include
potential negative impacts of the levee on marshes (Yin and Li
2001; Hupp et al. 2009; Harvey et al. 2011) and the ecosystem
services they produce. This assumption is based on the fact that
the levees would be located on the already developed industrial
site and on our Sea-Level Rise Affecting Marshes Model
(SLAMM) results (Figure 1) that show that the area in front
and around Stratton Ridge remains dry land through 2100
when sea-level rise is 1.16 m above 2006 levels. These results
suggest that even with marsh migration the levee should not
impact marshes within the time frame of our analysis.
The process to estimate the 3 metrics for each defense option
involved 6 steps. First, we modeled future land use-land cover
(LULC) conditions in the Stratton Ridge study area based on
projected sea-level rise and coastal development. Second, we
modeled flood heights from Category 1–5 hurricanes and a 100y hurricane in the Stratton Ridge study area. Third, we used the
flood water elevation for the 100-y hurricane to compute the
height and cost of a levee at the Stratton Ridge facility under the
built and hybrid defense scenarios. Fourth, we used flood water
elevations for the Category 1–5 hurricanes to estimate property
Category 1–5
hurricanes
1. Coastal protection for Dow
Levee
construction 2. Coastal protection for
3.
4.
5.
6.
community
Recreation
C sequestration
Fisheries
Biodiversity
damage to Dow and the local community, as well as business
interruption to Dow. Fifth, we estimated the monetary values to
the public or biophysical quantities for coproduced public
ecosystem services from the marshes: recreation (monetary
value), C sequestration (monetary value), fisheries (number of
species, relative impact), and biodiversity (number of species,
quality). Finally, sixth, we calculated the discounted sum of
costs and benefits to Dow and the public, and summarized
nonmonetary metrics. All monetary estimates were adjusted to
2010 US$ using the US Bureau of Labor Statistics consumer
price index. We discounted net monetary benefits to Dow using
a private discount rate of 7% based on recent returns on private
investments (OMB 1992) and to the public using a social
discount rate of 3%, which represents the consensus for projects
of this time frame (Weitzman 2001). We also explored the
sensitivity of the results to changes in the discount rate by using
private discount rates of 7% 3% (absent further information
on returns to private capital) and by using declining social
discount rates of 4% for years 1 to 5, 3% for years 6 to 25, and 2%
for years 26 to 75, as recommended by a growing body of
literature (Weitzman 2001; Arrow et al. 2014).
Data inputs for coastal defense scenarios with future sealevel rise and coastal development
We represented the baseline scenario and each defense
scenario (natural, built, hybrid) by a series of LULC maps for 5
different time periods (2006, 2025, 2050, 2075, 2100) and a
levee height specification. Each map had a 10 m horizontal
resolution. The LULC maps were created by modeling marsh
growth and migration as a function of sea-level rise and coastal
development projections across the entire coastal zone of
Brazoria County (the county in which Stratton Ridge is
located) using SLAMM 6 (Geselbracht et al. 2011). The inputs
332
Integr Environ Assess Manag 12, 2016—SMW Reddy et al.
to SLAMM included the most current digital elevation maps,
LULC data, location and height of existing levees or dikes,
marsh accretion and erosion rate values at different locations in
Brazoria County, future land development maps, and local sealevel rise projections (Supplemental Data Table S1).
We derived digital elevation models (DEM) for the region
from 2 sources. The first source was the 2006 Texas Water
Development Board LiDAR (LiDAR is a remote sensing
technology that uses light and radar to make high resolution
maps) for Brazoria, Matagorda, and Galveston Counties. We
also used the 2009 US Army Corps of Engineers (USACE)
Joint Airborne LiDAR Bathymetry Technical Center of
Expertise topographic LiDAR data derived from post Hurricane Gustav and post Hurricane Ike surveys. The 2009
elevation data covered Christmas Bay and the barrier beach
areas near Surfside Beach.
We obtained LULC data from the National Wetlands
Inventory from data collected in 2006, 2001, and 1992. The
2006 data covered the majority of the Brazoria County coastal
zone and the entirety of the Stratton Ridge study area. We
represented future development by reclassifying parcels to
developed and dry land in 2050 based on the HoustonGalveston Area Council’s projection for development in 2040
(HGAC 2012). We used a 1 m global sea-level rise projection
in our analysis because it is consistent with other planning
processes (NOAA 2012) and considered highly likely based on
current scientific literature (IPCC 2007, 2013). Adjusting for
the local historic trend of 4.35 mm/y of sea-level rise in the
Freeport area, we projected local sea-level rise of 1.29 m
between 1990 and 2100 or 1.16 m from 2006 to 2100 (the
time range of our study) (Table 2). We identified levee design
heights based on flood elevation projections and the FEMA
requirement for 0.30 m of freeboard above wave-run up
(FEMA 2002).
Coastal protection modeling
Flood modeling. We modeled total flood heights generated by
Category 1–5 hurricanes and a 100-y hurricane for each
defense scenario in each of the 5 time periods using generic
forcing parameters for surge elevation, waves heights and
periods, wind speed, and predicted sea-level increases at the
coast (Tables 2 and 3). Modeled flood heights were defined as
Table 2. SLR values
2006 Baselinec
1990 Baseline
a
b
Year
Global SLR
Local SLR
Localb SLR
1990
0
0
—
2006
—
0.13
0
2025
0.18
0.28
0.15
2050
0.41
0.57
0.44
2075
0.7
0.92
0.8
2100
1
1.29
1.16
SLR ¼ sea-level rise.
a
The global sea-level rise scenario of 1 m by 2100 was produced by scaling up
of the A1B-max scenario from the Special Report on Emissions Scenarios (IPCC
2007).
b
Local sea-level rise accounts for a local historic trend of 4.35 mm/y.
c
Local sea-level rise values with a 2006 baseline were used in habitat modeling.
Table 3. Forcing parameters for storms
Deep watera
wave
height
(Ho)
Peak
wavea
period
(Tp)
Surge
elevation
at the
coast (S)
Wind
speeda
12 m
13 s
4.4 m
45 m/s
Cat 1
6m
9s
1.4 m
40 m/s
Cat 2
8m
11 s
2.2 m
47 m/s
Cat 3
10 m
12 s
3.5 m
55 m/s
Cat 4
12 m
13 s
5.0 m
65 m/s
Cat 5
15 m
15 s
6.5 m
75 m/s
Storm
100-y
storm
a
Taken from the range presented in USACE (2002) Coastal Engineering
Manual.
Cat ¼ Category.
the sum of surge and wave heights above the terrain elevation
relative to mean sea level (MSL), for a given sea-level rise value.
Tidal components were ignored because Freeport is a microtidal coastal region and hurricanes rarely last longer than a full
tidal cycle nearshore.
We modeled total flood height in the Stratton Ridge study
area using an approach similar to the one currently recommended by FEMA (2007). First, we used DEMs to generate 19
1D transects perpendicular to the 2006 shoreline, from
offshore depths of approximately 15 m to the 9–10 m contour
inland. Then we removed the coastal dunes from each profile,
after determining that they would fail during Category 2 or
greater hurricanes (Judge et al. 2003). Next, we digitally
smoothed each profile to minimize any numerical instability
and populated each transect with appropriate LULC information, for each scenario. From this input data, we then used
computed total flood heights (surge and wave heights) along
each transect, for the different forcing and scenario defined in
the analysis. Finally, we used the flood height on each transect
to generate a 2D flood height surface.
Surge. Theoretical modeling and some limited empirical data
suggest that the effect of coastal marshes on surge is limited
(USACE 2013), highly variable and may only occur when
marshes have a relatively wide footprint (Wamsley et al. 2010).
Consequently, given the relatively small extent of marshes in
the Stratton Ridge study area (total 5 km from coast to Stratton
Ridge) and its level of fragmentation, we assumed that the
marshes have no effect on surge elevations. In addition, in
keeping with the methodology Dow previously used to
estimate surge risk at its Stratton Ridge facility, we estimated
the surge elevation inland using a modified bathtub model. We
modeled the surge SðxÞ along each transect assuming that it is
amplified linearly by a coefficient a starting at the coastline
from an initial value SCoast
8
< ða 1ÞSCoast x þ S
Coast ; x dDow
dDow
SðxÞ ¼
;
ð1Þ
:
aSCoast ;
x > dDow
where x is the cross-shore distance inland from the coastline
and dDow is the approximate orthogonal distance between the
Dow facility and the coast (dDow =12:3km). To account for
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
the effect of sea-level rise on hurricane flood heights, predicted
sea-level rise values were added to the surge profile SðxÞ.
The amplification factor a was developed by Dow using
NOAA’s Sea, Lake, and Overland Surges from Hurricanes
(SLOSH) model (Jelesnianski et al. 1992) and results from
their own and other’s simulations of storm surge in the region
using more complex models that allow for the landward
propagation of surge (Aggarwal 2004). This analysis resulted in
the following values for the amplification factor: a ¼ 0:4 for
SCoast 1:5 m, a ¼ 0:86 for 1:3 < SCoast 3 m, and a ¼ 1:3
for SCoast > 3 m. Comparisons of surge heights at Dow’s
facilities using data from past storms projected using these
amplification factors were in good agreement with previously
measured and modeled surge for this site (Jelesnianski et al.
1992) and sites with similar size habitat (Wamsley et al. 2010;
Gedan et al. 2011).
Waves. Accounting for inland flood depths due to surge
amplification, sea-level rise, and the presence of vegetation, we
modeled wave evolution along each transect, from the
coastline to the limit of inland flooding, using the wave energy
balance equation (Holthuijsen 2010)
1 @H2 Cg
rg
¼ W growth Dbreak Dbottom ;
8
@x
ð2Þ
where r ¼ 1024 kg/m3 is the density of seawater, g ¼ 9.81 m/s2
is the gravitational acceleration, H is the wave height, and Cg is
the speed at which wave energy travels. W growth represents an
input of wind energy to the wave field, which causes waves to
grow and regenerate as they travel inland. We computed
W growth as a function of the wave height, the hurricane wind
speed and the water depth at each cross-shore location
following the method laid out by the FEMA (1998). We
also assumed that wind speeds are not reduced by marshes.
The variables Dbreak and Dbottom represent the dissipation of
wave energy due to wave breaking and bottom friction,
respectively. They are calculated at each cross-shore location x
as (Thornton and Guza 1983)
3
Dbreak
3
sb
¼ pffiffiffi rg 5 H7 ;
32 p gh
Dbottom ¼
Cf s 3 3
pffiffiffi
H ;
16 p sinhkh
ð3Þ
ð4Þ
where b is a breaking coefficient, g is a breaking index, with
default values of 1.0 and 0.78, respectively. The wave radial
frequency s is a function of the wave field peak period, k is the
wavenumber, h is the local water depth at each cross-section x,
and includes surge and sea-level rise values. Finally, Cf is a
friction coefficient.
Physical obstructions on the landscape such as marshes,
forests, and development dissipate wave energy. Their effects
are incorporated in the model by a friction factor, Cf. We
computed the friction factor from the Manning’s n value
associated with different classes of LULC, following the
methodology used by the USACE model STWAVE (Smith
et al. 2001; Cialone and Smith 2007; Wamsley et al. 2009).
Cf ðxÞ ¼
8g
hðxÞ1=3
n2 :
ð5Þ
We generated different Manning’s n values for different
LULC classes by grouping them based on the defined SLAMM
333
Table 4. Land use–land cover categories and corresponding
Manning's n values
Land use–land cover category
Manning's n value
Developed dry land
0.093
Undeveloped dry land
0.030
Nontidal swamp
0.074
Cypress swamp
0.074
Inland fresh marsh
0.055
Tidal fresh marsh
0.055
Transitional salt marsh
0.074
Regularly flooded marsh
0.045
Mangrove
0.074
Estuarine beach
0.030
Tidal flat
0.028
Ocean beach
0.030
Rocky intertidal
0.090
Inland open water
0.020
Riverine tidal open water
0.020
Estuarine water
0.020
Open ocean
0.020
Irregularly flooded marsh
0.045
Inland shore
0.030
Tidal swamp
0.074
habitat categories (Table 4). Note that for the baseline
scenario, we converted all marsh and swamp habitat currently
present in the LULC map to undeveloped dry land.
Economic valuation. We estimated the value of coastal
protection services provided by the marshes and levees to
Dow in each defense scenario in terms of avoided property
damage, avoided business interruption, and avoided levee
costs. We estimated the value of coastal protection services to
the public in terms of avoided property damage only. We
computed these metrics over 30 y by comparing annual costs
incurred by each defense scenario to annual costs computed
under the baseline scenario. For each scenario, we translated
flood heights generated by each storm into property damages
with the standard depth-damage approach used by FEMA and
the USACE in their HAZUS flood modeling (Scawthorn et al.
2006). Using damage estimates for each hurricane category
and the annual rate of occurrence of a hurricane of that
category (Table S2), we calculated the annualized loss
expectancy for Dow and the community in the study area.
Annual rates of occurrence were calculated based on a record
of all hurricanes from 1842 to 2012 that passed along the
400 km stretch of coastline around Freeport, Texas (extending
between Corpus Christi, Texas and Beaumont, Texas). We
also examined the sensitivity of the results to 20% changes in
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Integr Environ Assess Manag 12, 2016—SMW Reddy et al.
the annual rate of occurrence for each hurricane category. We
estimated business interruption for Dow using ratios relating
business interruption to property damage that were previously
developed by Dow (i.e., 1:1 with levee, 1.5:1 without levee).
Finally, we calculated levee costs by developing a generic levee
cost model for earthen levees using cost data from a nearby and
recent earthen levee project and assumed that all costs
occurred in year 1 (Table S3).
Depth-damage functions are a mathematical relationship
between the depth of flood waters in relation to the first floor of
a building, and the amount of damage to the structure and the
building contents that can be attributed to that flood water.
This relationship can be expressed as
D ¼ VðsðyÞ þ cðyÞÞ;
ð6Þ
where D is the total damage to the structure and contents, V is
the market value of the structure, and s and c are the structure
and contents damage ratios for a given flood height above the
first floor elevation y. Both the content damage and structure
damage are expressed as a fraction of the structure value,
leaving the depth-damage function to only rely on flood height
and structure value. Equation 6 is a general mathematical
representation of the location specific depth-damage functions
developed by the USACE (FEMA 2011).
For the Stratton Ridge facility and other commercial and
industrial buildings present along each transect, we used
depth-damage functions developed by the USACE in Galveston, Texas (FEMA 2011) (Table S4). For Stratton Ridge, we
focus on damages to structures only and assume that damages
to contents are negligible, because the structures at Stratton
Ridge are utility buildings with the value being primarily in the
structure. We assumed that those structures are slab on grade
and adjusted flood heights by 1 ft. This is a reasonable
assumption for facilities that are far inland and likely near the
edge or outside of the Flood Insurance Rate Map (FIRM)
Special Flood Hazard Area (SFHA), or the area that is
expected to be impacted by a 100-y flood. No commercial or
industrial facilities were located near the coast in the Stratton
Ridge study area. For residential structures, the most common
types of structures in the Stratton Ridge study area, we used the
depth-damage functions for single-story and multistory (no
basement) damage functions presented in USACE (2003)
(Table S4). Given that we did not have parcel-level
information on the number of floors per residential structure,
we created composite damage functions based on the damage
functions for single-story and multistory buildings without a
basement by weighting the damage ratios based on empirical
estimates of the percentage of building in Texas that are singlestory (72%) and 2-stories or higher (28%). To address the lack
of information on foundations, we used regional summary data
on the distribution of foundation types for pre- and postFIRM
(buildings built before and after flood maps were available)
and the height above grade for the finished first floor for
different foundation types from the HAZUS model (see Tables
S5 and S6) (FEMA 2011). As this data is available at a regional
level and not the parcel level, we estimate an average height
above grade by assuming an equal distribution of preFIRM,
FIRM-A (areas subject to 100-y floods, as determined by
approximate methods), and FIRM-V (areas subject to 100-y
floods and storm-induced waves) buildings and weighting
height by the stated distribution of foundation types. This
yields an average height increase of 1.8 m above grade for all
buildings.
To protect confidential information on the value of Dow’s
property and to make the results more general to industrial
applications, we obtained data on the value of industrial (land
use code F2) structures in Brazoria County and neighboring
Galveston County from the county appraisal district offices
(Table S7). Damages were calculated for the 4 structures at
Stratton Ridge, assuming that the structures are identical. We
used the 95th percentile of structure values to reflect the fact
that the Dow facilities, which are part of the largest integrated
chemical manufacturing facility in the Western Hemisphere,
are extremely high value facilities relative to the industrial
facilities in the area. We also conducted sensitivity analyses
using the 75th and 90th percentile values. For nonStratton
Ridge structures in the study area, we obtained a spatial
inventory of the building stock and property values from the
Brazoria County appraisal district office.
Recognizing that future development may result in new
properties exposed to hurricanes, we estimated the average
value of commercial and residential structures per hectare for
2011 and used this USD/ha figure to construct values for
parcels that are projected to be developed in 2050. We also
made the simplifying assumption that all development takes
place in 2050 and that no development that exists in 2011
reverts to undeveloped land.
Additional ecosystem services provided by marshes
Recreation. To quantify the value of natural habitats for
recreation, we used the InVEST Recreation model (Wood
et al. 2013). Despite the large literature on recreational use
values, the lack of visitation data in many places (including in
Freeport and near the coastal marshes in that region) is a
major challenge for using these recreational use values via
benefit transfer methods. We overcame this challenge and
were able to take advantage of this larger literature and apply
recreational use values (US$/user/day) from the Recreation
Use Values Database by first using the InVEST recreation
model to estimate recreational user days. This model predicts
recreational user-days in 5 km cells based on linear regression
analysis of the relationship between landscape attributes
across space and geotagged photographs downloaded from
the web site Flickr (http://www.flickr.com) (Table S8). To
estimate absolute user-days per grid cell, we used an estimate
of the relationship between the annual density of photograph
user-days and average annual user-days from data on
visitation to 72 Texas State Parks between 2008 and 2012.
We translated user-day estimates into economic values using
estimates of recreational values from the Recreation Use
Values Database for North America (Table S9). The
Recreation Use Values Database for North America contains
2703 estimates of value per user day from 352 studies of
recreational values in the United States and Canada from
1958 to 2006. We selected a subset of values from studies
that had estimated values for recreational activities that are
likely to occur in coastal marsh areas (i.e., fishing, kayaking,
motor boating). We used the mean value for each activity
weighted by the percent of users expected to engage in the
activity based on expert knowledge to calculate a mean value
for all activities.
Carbon. We used the InVEST Blue Carbon model to estimate
C sequestration (mT C/ha/y) by marsh plants to the sediment
(Tallis et al. 2013). To simplify the analysis, we did not model
sequestration to above and below ground biomass, which likely
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
makes the results an underestimate of C sequestration by
marshes. We used habitat-specific C loss or gain rates to
model effects of transitions of marsh habitat to other habitats.
We calculated the social cost of C (SCC)—or the net present
value of the sum of future damages due to increases in
atmospheric C—using one of the “middle” SCC estimates
($22/mT CO2 [or $81/mT C], 3% discount rate) developed by
the federal Interagency Working Group on Social Cost of
Carbon (IWG-SCC 2010).
Fisheries. We qualitatively assessed the fisheries service of
coastal habitats in the study area by modeling the propensity of
a suite of species to respond to projected changes in coastal
habitat area. We identified the most important fisheries species
using commercial and recreational landings data from Texas.
We determined which coastal habitats are used by these
species by querying the scientific literature and fisheries or fish
biology databases. Finally, we scored the potential species
response by direction of change (increasing or decreasing) and
the expected magnitude of the change (very low, low,
medium, high, or very high). We did not estimate the
economic value of enhanced fisheries production services
from marshes because this would require quantitatively
modeling the increase in fish catch due to coastal marshes,
which was outside the scope of this study.
Biodiversity. We used the InVEST 3.0 Biodiversity Model
(Tallis et al. 2013) to quantify trends in habitat quality. Habitat
quality was estimated by modeling the effect of local threats on
existing habitats. The main inputs to the biodiversity model
were LULC data from SLAMM, data on 12 habitat threats, a
habitat threat sensitivity table, and conservation management
areas data. We used our estimated changes in habitat quality, as
well as the number of bird species supported by coastal
habitats, as an indicator of the intrinsic biodiversity value of the
coastal habitats.
RESULTS AND DISCUSSION
Potential protection provided by marshes along a
representative transect
Marshes reduce wave heights, and hence total water levels,
by dissipating wave energy via a bottom friction coefficient
derived from a corresponding Manning’s n value (see Waves
above). Marshes also have the potential to reduce surge
depending on the hurricane strength and orientation and the
amount of marsh present (Wamsley et al. 2010). In this
section, we illustrate how surge and wave height reduction by
marshes can help reduce the total water levels and the size of
levees. In this example, we model how the largest marsh
footprint east of the Stratton Ridge study area could reduce
total water levels induced by a Category 3 hurricane in 2050
(where sea level has risen by 0.44 m). We find that marshes
would have the potential to reduce wave heights by 33%, on
average, across the transect (Figure 2A). At the landward
limit of the marshes (14 km inland), the waves are reduced
by 40% compared to their values in the absence of marshes
(Figure 2A). However, as waves propagate further inland,
the effect of the marsh decreases as waves are regenerated by
winds and the waves become depth limited. Consequently, at
17 and 20 km inland, or 3 and 6 km landward of the edge of
the marsh, the observed reduction in wave height decreases
to 2%.
335
Reduction in wave height, and thus in total water level,
translates into a reduction in levee costs; however, in the
Stratton Ridge application, levee cost reductions are not
significant (Table 5). Under current-day marsh conditions, if a
levee was built inside the marsh area shoreward of the Stratton
Ridge facility (an option that might not allow marshes to
migrate inland and survive in the long term as sea level rises),
the wave reduction services supplied by this habitat would
decrease construction costs by approximately $1.5 million
(2010 US$) (values anywhere between $1 and $2 million
[2010 US$]) (Figure 3). If the levee was built at the landward
edge of the marsh close to the Stratton Ridge facility, the cost
reduction would be approximately $0.5 million (2010 US$)
(Figure 3).
As the distance inland and away from the landward edge of
the marsh increases, the wave attenuation properties of the
marsh decreases thereby translating into decreasing benefits in
terms of avoided levee costs. For levees built further than
20 km from the shoreline, the presence of the wetlands does
not have any effect on levee cost. Note that the cost does not
decrease monotonically moving landward because the difference in flood depth does not decrease monotonically even
though total flood depth does decrease monotonically. This
pattern is a result of the fact that the wave evolution and wave
regeneration equations are nonlinear. The local bathymetry
and topography also influence wave height and any potential
increase in wave height due to regeneration.
We evaluated the relative importance of marsh coverage and
uncertainty in the physical characteristics of marshes by recomputing profiles of wave height using different n values and
marsh coverage configurations. We found that using a higher n
value, or assuming that the marsh is not fragmented, yields
large and similar reduction in wave height of approximately
45%, on average, over the marsh footprint (Figure 2B). The
lower water levels computed using a nonfragmented marsh, or
a higher n value, also reduced levee construction costs by more
than $1.5 million (2010 US$), if such a structure were built
inside the marsh (Figure 3). On the other hand, using a uniform
n value corresponding to regularly flooded marshes (Table 4)
yields wave height values similar to what was obtained when
marshes where not taken into account (Figure 2). At any value
for Manning’s n, we find that the role of marshes is greatest near
the coast and their protective role decreases further inland,
with marshes in this simulation having no impact on levee
construction costs in regions farther than 20 km inland
(Figure 3).
In addition to reducing wave height, marshes can also reduce
surge height. However, following USACE (2013) and
recognizing that marshes in Freeport are relatively small in
size, fragmented, and the Dow facility is far from the coastline,
we ignored the impact of marshes on storm surge. However,
Wamsley et al. (2010) found that, under some circumstances,
marshes can reduce surge height by up to 5.6 cm/km. Using the
same transect east of Dow’s facility as above, this translates
into 30 cm reduction in surge height at the edge of the marsh
area, given its level of fragmentation. Assuming existing marsh
coverage characteristics, we found that this reduction of surge
had a limited impact on wave height and reduced levee height
by nearly 30 cm, because wave heights are nearly identical.
This level of reduction is much higher than what marshes
directly fronting the facility will achieve, because they are only
5 km wide in total, on average. Furthermore, as mentioned in
Wamsley et al. (2010) and in USACE (2013), it is unlikely that
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Figure 2. Profiles of wave height along a sample transect for different wetland conditions. (A) Profiles of wave height in the presence and absence of vegetation,
using the different average Manning coefficients Mn presented in Table 4. (B) Profiles of wave height assuming that the wetland is not fragmented and covers
the whole topographic profile from X 200 m to X 14 km, or has a high Mn of 0.074, the average Mn values used in the analysis, a minimum Mn value of 0.045
or that there is no vegetation. (C) Submerged topographic profile and location of the wetland.
marshes reduce surges by that high amount for most storms.
Consequently, given the extreme nature of some of the storms
considered in this analysis, we hereafter assume that marshes
have negligible impact on surge.
In summary, this initial analysis exercise shows that marshes
have the ability to reduce water levels and levee construction
costs for a Category 3 hurricane conditions. However, the
ability of marshes to influence total water level and levee
construction costs landward of their footprint (their far-field
effect) decreases as flood waters depths decrease and distance
from the marsh increases. In addition, this analysis has shown
that results are highly dependent on the exact footprint
delineation of the marshes and the Manning’s n value used to
compute the friction coefficient. Similar results were found by
(Guannel et al. 2014) in Galveston Bay, using actual physical
parameter values (e.g., diameter, height and density of various
marsh types) instead of n.
Evaluation of coastal defense options at Stratton Ridge
We applied our flood modeling and economic valuation
methods, together with modeling of other ecosystem services
and biodiversity, to evaluate 3 coastal defense options (built,
natural, and hybrid) at Stratton Ridge. Here, we present the
results on the area and type (that affects Manning’s n values) of
natural defenses in the future; flood heights, levee heights, and
costs in the future and across defense scenarios; the total value
of coastal protection (avoided storm damage and levee costs) to
Dow in the future and across defense scenarios, and the
quantity and value of coproduced public ecosystem services
from natural defenses.
Effects of future sea-level rise and development on natural
defenses. Our results predict a 23% decrease in the total land
area (30 728 acres) in front of Stratton Ridge by 2100 relative
to 2006 levels, with the greatest losses occurring in the second
half of the century (Figure 1). Marshes may migrate inland
with sea-level rise (Figures 1B and 1C), although there is some
uncertainty over whether soil types or the speed of sea-level
rise will limit marsh migration. Inundation of marshes and
conversion of marshes to bare mud flats may result in a 9%
decrease in marsh area by 2050 and a 35% decrease by 2100,
relative to 2006 levels (Figures 1B and 1C). The accelerated
loss of land in the second half of the century can be explained
both by the increased rate of sea-level rise and the increased
vulnerability of the coastline to erosion. Marsh-covered
coastlines are less vulnerable to erosion than bare mud flats.
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
337
Figure 3. Changes in levee costs for different levee positions and wetland conditions. (A) Levee costs as a function of distance from the shore, assuming that
there are no wetlands, or that wetlands are present or fully restored to obtain a full coverage on their footprint. (B) avoided construction cost as a function of
distance from the shore, due to the presence of wetlands as they currently are or if wetlands are restored to obtain full coverage.
In contrast to sea-level rise, coastal development has a minimal
impact on marshes because regional planners project development to occur primarily on dry land.
Flood modeling, levee design, and levee cost. Flood modeling
predicts that only floods from large (Category 3–5) storms may
impact Stratton Ridge in all time periods, but smaller storms
(Category 2) do not impact Stratton Ridge in the first half of
the century (2006, 2025, and 2050) (Figure 4). However, flood
heights from all storms are expected to increase over time as sea
level rises meaning that in the second half of the century (2075,
2100) both large storms (Category 3–5) and smaller (Category
2) storms are expected to impact Stratton Ridge (Figure 4). A
regression analysis of the effect of sea-level rise and marsh area
on flood heights from Category 3–5 hurricanes showed that a
10% increase in sea-level rise may increase flood heights by
1.8% (95% confidence: 1.8%, 1.9%), whereas a 10% increase in
marsh area may decrease flood heights by 0.2% (95%
confidence: 0.2%, 0.3%) (Table S10). The small and decreasing
size of the existing marsh (5 km) partly explains why the
marshes make such a minor contribution to wave attenuation.
Note that we also considered a scenario where all dry land was
restored to marshes, creating a 10 km extent of marsh in front
of Stratton Ridge. This scenario showed similar effects on flood
heights as the scenario based on existing marsh; therefore, it
was not considered further. An additional factor affecting the
wave attenuation results is the fact that, in the short-term,
Stratton Ridge is only inundated during large hurricanes
(Category 3–5) and Equation 5 shows that greater water
depths associated with surges from large hurricanes reduce
the degree to which habits can attenuate waves. Finally, as
mentioned previously, the influence of the marsh decreases
with distance from its landward edge, so the wave attenuation
benefits of the marsh are diminished by the time waves reach
the facility, which is more than 5 km away from the marsh’s
edge.
Flood modeling for a 100-y storm (between a Category 3 and
4 storm) suggests that the small effect of marshes on flood
heights (reduction of 0.1 m in 2006 and <0.1 m in 2050)
would not warrant a change in levee design height, whereas the
effect of sea-level rise may warrant a change in levee design
height (Table 5). The natural defense scenario only reduces
flood heights from a 100-y storm by 3% in 2006 and 2025 and,
by 2050, natural defenses have no effect on flood height
(Table 5). However, sea-level rise may increase flood heights
by 12% (0.4 m) from 3.1 m in 2006 to 3.5 m in 2050 under the
baseline scenario (Table 5). Based on these modeled flood
heights, a 3.4 m levee would be needed to protect against a
100-y storm under 2006 conditions for the baseline scenario
(Table 5). This levee would cost $26 million (2010 US$) to
Dow to build in 2014 (Table 5). In contrast, accounting for sealevel rise, a levee designed for a 100-y storm in 2050 under the
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Figure 4. Maximum flood heights (m) at Stratton Ridge (12 km inland) across defense scenarios (baseline [marshes absent and assumed to be undeveloped dry
land] and natural defense]) and storm categories, by years.
baseline scenario should be 12% higher, which would increase
costs to Dow by $2 million (2010 US$) (8%) (Table 5).
Value of coastal protection to Dow: Avoided storm damages,
business interruption, and levee costs. In the absence of a
protective levee, we project that property damage and business
interruption to Dow from single storm events will increase in
the future due to sea-level rise and coastal development
(Table S5 and Figure 5). For example, we estimate a single
Category 3 storm to result in $476 million (2010 US$) in
property damage and $714 million (2010 US$) in business
interruption for Dow under the baseline scenario under
current conditions; however, in 2100 property damage from
a Category 3 storm may be $684 million (2010 US$) and
business interruption may be $1.03 billion (2010 US$).
Importantly, Category 1 and 2 storms are not projected to
have any impact on the facility under 2006, 2025, 2050, and
2075 conditions; however, by 2100, Category 2 storms may
begin to have minor impacts on the facility due to sea-level rise,
with $11 million (2010 US$) in estimated property damage
and $17 million (2010 US$) in business interruption costs.
The small effect of marshes on flood heights means that
marshes in the natural defense and hybrid defense scenarios
have a limited ability to protect Dow against property damages
and business interruption from Category 3 and 4 storms and
provide no protection against Category 5 storms at Stratton
Figure 5. Avoided costs (property damage, business interruption, and levee costs) relative to the baseline (marshes absent and assumed to be undeveloped dry
land) scenario across defense scenario and hurricane storm category, by year, at Stratton Ridge (12 km inland). Natural defenses provide protective value by
avoiding costs from hurricanes; however, the protective value from natural defenses in Freeport are much smaller than those from built and hybrid defense
scenarios that include levees because the large hurricanes that reach the Stratton Ridge facility reduce the marshes ability to attenuate waves.
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
339
Table 5. Flood heights and levee costs associated with a 100-y hurricane for different coastal defense scenarios
Flood height (m)
Levee cost (millions 2010 US$)
Cost savings from habitat
2006
2050
2006
2050
2006
2050
Added costs
from SLR (%)
Dry land (baseline)
3.1
3.5
26
28
—
—
8
Natural defense
3.0
3.5
26
28
0
0
8
Scenario
SLR ¼ sea-level rise.
Ridge (Figure 5). In contrast, levees in the built defense and
hybrid defense scenarios prevent property damage and business interruption from Category 3 storms, because flood
heights from these storms are below the design height of the
levee (Figure 5). Levees also provide some protection to Dow
against Category 4 and 5 storms; however, some damage may
still occur due to overtopping of the levee (Figure 5). For
hurricanes smaller than Category 3, floods do not reach
Stratton Ridge so there are costs for the levee but no avoided
damages (Figure 5).
We estimate that the current annual loss expectancy (AEL)
(total expected property damage and business interruption to
Dow from hurricanes in a given year, calculated as the sum
over each hurricane category 1–5 of the product of the losses
from a single hurricane event of that category and the annual
rate of occurrence of each category) is $53 million (2010 US$),
or 4.4% of the total potential losses ($1215 million [2010 US
$]), under the baseline scenario. Built and hybrid defense
scenarios may reduce Dow’s AEL to $35 million and $34
million (2010 US$), whereas the natural defense scenario
would only reduce Dow’s AEL to $52 million (2010 US$)
(Table 6). With future sea-level rise, we predict Dow’s AEL
under the baseline scenario to increase 28% from $53 million
to $68 million (2010 US$) by 2100 (Table 6).
Our method of calculating the annual rate of occurrence of
storms based on the historic record of all hurricanes within
400 km of Freeport may have introduced bias into our AEL
estimates. Not accounting for how storm-forcing parameters
decrease with distance from the center of the storm bias our
AEL estimates upward. In contrast, not accounting for a
potential increase in the frequency and intensity of storms due
to climate change (Knutson et al. 2010) may bias our AEL
estimate downward. Analysis of the sensitivity of AEL to
20% changes in the annual rate of occurrence of storms
suggests that the AEL may range from $43 million to $64
million (2010 US$) under the baseline scenario for the current
time period (Table 6). This range is nonoverlapping with the
range for the built defense ($28–$42 million [2010 US$]) and
hybrid defense scenario ($27–$41 million [2010 US$]), but it
is overlapping with the range for the natural defense scenario
($42–$63 million [2010 US$]) (Table 6).
We estimate that the NPV over the next 30 years of coastal
protection to Dow (defined as the discounted sum of avoided
property damages, business interruption, and levee costs) from
the built defense scenario is $217 million (2010 US$), the
hybrid defense scenario is $229 million (2010 US$), and the
natural defense scenario is $15 million (2010 US$) (Figure 6).
These results show that coastal protection provided by marshes
to Dow, although small in this case, can be accounted for using
the same metrics that are used to evaluate built defenses.
At Freeport, the 5 km total of marsh in the natural defense
scenario provide $15 million in coastal protection to Dow,
reflecting model results showing that the property damage and
business interruption costs with marshes are lower than under
the baseline scenario. The value of coastal protection in the
hybrid defense scenario is higher than in the built defense
scenario because not only do marshes reduce property damage
and business interruption, they also reduce levee heights and
costs (although the modeled levee design changes are small
enough that they would not be implemented at Freeport).
The ordering of the NPV of the coastal defense options is not
sensitive to changes in the private discount rate; however, as
expected, lower discount rates increase the NPV of the coastal
defense options (4%: built, $312 million; hybrid, $329 million;
natural, $21 million), whereas higher discount rates decrease
the NPV of the coastal defense options (10%: built, $160
million; hybrid, $168 million; natural, $11 million). The lack
of an effect of the choice of discount rates on the ordering of the
coastal defense options can be explained by the large differences between the NPV of each of the options as well as the
fact that these values do not include maintenance costs. If the
built defense option had higher maintenance costs than the
natural defense option, which could be the case because
habitats are somewhat self-maintaining, we might expect to
see an increase in the value of the natural defense option and a
decrease in the value of the built defense option. However, it is
unlikely that this would make up the approximately $200
million difference in the value between the options.
A sensitivity analysis of coastal protection values to
industrial property values shows that for lower value properties
(75th percentile) the value of coastal protection from the
natural defense scenario (<$1 million [2010 US$]) remains
positive but are smaller whereas the value from the built
defense ($20 million [2010 US$]) and hybrid defense ($20
million [2010 US$]) scenarios becomes negative due to the
relatively high cost of the levee as compared to the avoided
property damage. Coastal protection values from all scenarios
remain positive but are smaller by 1 to 2 orders of magnitude
when the 90th percentile value for industrial property values is
chosen instead of the 95th percentile (built defense: $3
million, hybrid defense: $4 million, natural defense: $2 million
[2010 US$]). This suggests that natural defenses that can be
maintained at little to no cost could be beneficial to property
owners, such as small businesses with lower value property for
which more expensive measures like levees are not warranted
financially, but natural defenses would likely not substitute for
built defenses that can protect against large hazards (Renaud
et al. 2013).
Value of coastal protection to the public and additional public
ecosystem services provided by marshes. Beyond coastal protection values provided to Dow, we estimate that marshes in
the study area could provide at least $117 million in benefits to
local and global communities over the next 30 years (Figure 6).
Recreation accounts for the majority of the benefits ($115
million [2010 US$]), whereas protection of the local
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Table 6. Annual loss expectancy and sensitivity to annual rates of occurrence of storms
Millions 2010 US$
Percent of potential losses
Annual rate of occurrence
Annual rate of occurrence
20%
Historic
20%
20%
Historic
20%
Dry land (baseline)
43
53
64
3.5
4.4
5.3
Natural defense
42
52
63
3.5
4.3
5.2
Built defense
28
35
42
2.3
2.9
3.5
Hybrid defense
27
34
41
2.2
2.8
3.4
Dry land (baseline)
44
55
66
3.6
4.5
5.4
Natural defense
43
54
65
3.5
4.4
5.3
Built defense
29
36
43
2.4
3.0
3.5
Hybrid defense
28
35
42
2.3
2.9
3.5
Dry land (baseline)
47
58
70
3.9
4.8
5.8
Natural defense
44
55
66
3.6
4.5
5.4
Built defense
30
37
45
2.5
3.0
3.7
Hybrid defense
27
34
41
2.2
2.8
3.4
Dry land (baseline)
50
63
75
4.1
5.2
6.2
Natural defense
50
62
75
4.1
5.1
6.2
Built defense
32
40
47
2.6
3.3
3.9
Hybrid defense
31
39
47
2.6
3.2
3.9
Dry land (baseline)
54
68
81
4.4
5.6
6.7
Natural defense
53
66
79
4.4
5.4
6.5
Built defense
33
42
50
2.7
3.5
4.1
Hybrid defense
33
41
49
2.7
3.4
4.0
Scenario
2006
2025
2050
2075
2100
For results using depth-damage functions with structure and contents, see Table S2.
community from hurricanes makes a minor contribution ($4
million [2010 US$]). Due to the loss of marsh from sea-level
rise and development, we project net C emissions beginning in
14 years, which would result in a cost to society of $2 million
(2010 US$). Using declining social discount rates results in
somewhat higher public benefits from recreation ($118 million
[2010 US$]), similar public benefits from coastal protection
($4 million [2010 US$]), and higher costs to society from net C
emissions ($4 million [2010 US$]). In contrast, we estimate
that all of the 12 marine and estuarine associated fisheries
species may benefit from trends in sea-level rise. This positive
result for fisheries may mask some potential negative effects,
because the species we considered are primarily associated
with salt marsh and estuarine habitats and would not be
affected by losses of upland freshwater marshes. In addition to
fisheries species, 301 bird species (USFWS 2013) and
numerous other species such as the American Alligator, the
Texas Diamondback Terrapin, Kemp’s Ridley Sea Turtle, the
Gulf Salt Marsh Snake, mollusks, sea grasses, and reeds
(TPWD 2013) occur in marshes around Freeport. In fact,
Freeport often reports one of the highest bird species counts in
the United States (Audubon Society 2013). However, we
predict that increases in threats to marshes may result in a
decline in habitat quality, especially for transitional and
regularly flooded marshes (Figures S2 and S3). This trend in
habitat quality may negatively affect biodiversity.
The biophysical conditions at Stratton Ridge make it
unlikely that explicit consideration of marshes will alter
Dow’s risk mitigation strategy based on benefits that accrue
to Dow alone. However, if Dow accounted for ecosystem
Habitats and Hurricane Risk Mitigation—Integr Environ Assess Manag 12, 2016
Figure 6. Net present value (NPV) of the defense scenarios at Stratton Ridge
(12 km inland). The hybrid defense scenario has the greatest value followed by
the built defense scenario and the natural defense scenario. The hybrid
defense scenario is $11 million (2010 US$) greater than the built defense
accounting for values to Dow only, and $130 million (2010 US$) greater than
the built defense scenario accounting for values to Dow and the public. Note
that C sequestration values over this time period were negative (and small)
due to the loss of the marsh habitat.
services to the public and support for biodiversity to meet
corporate sustainability goals, the hybrid defense scenario may
be the preferred scenario over the built defense scenario
because it provides more value to Dow and the community,
while also supporting fisheries and wildlife (Figure 6).
CONCLUSIONS
The integrated ecological economic assessment approach
that we developed and applied here provides businesses new
tools and insights to integrate ecosystem services into hurricane
risk mitigation. Modeling of coastal protection services showed
that sea-level rise and habitat loss will increase flood heights
and levee costs in the future, but that natural defenses may
reduce overall flood height and levee costs by attenuating
waves. Large, rough marshes (e.g., those that include shrubs
and trees) provide the greatest protection against small to
medium size storms near the coastline. At Dow’s Stratton
Ridge facility (12 km inland), protection provided to Dow by
marshes against a large storm (100-y) does not warrant a
change in Dow’s levee design; however, increases in flood
heights due to sea-level rise may warrant a change in levee
design. Moreover, marshes in front of the levees may
help maintain the coastline in the face of sea-level rise and
also provide additional benefits to the public, including
recreation and support for fisheries and biodiversity. These
results suggest that a hybrid defense option may be the best
option to meet Dow’s needs while also benefiting the public
and nature.
This study made 4 advances to the research and practice of
ecosystem services and coastal risk mitigation. First, it
developed an integrated ecological economic method to
quantify and value hurricane protection based on sea-level
rise and different LULC projections in a region of interest.
Second, it developed and advanced replicable open-source
models (available at http://www.naturalcapitalproject.org) for
assessing the role of natural habitats in coastal protection as
well as other ecosystem services from those habitats. Third, by
analyzing the coastal protection ecosystem service model, it
provided new insights into what habitat and site characteristics
may result in the highest coastal protection values, which could
341
be useful for screening. Fourth, it demonstrated the use of
these models in a specific business decision.
The study, however, is limited in that it took a relatively
simple approach to modeling flooding and the value of public
ecosystem services provided from marshes. Future research
could address the flood modeling limitations by modeling
surge and waves using more advanced hazard and hydrodynamic models and using site-specific habitat information
(e.g., plant diameter, height and density of various marsh
types). Other LULC scenarios could be modeled that account
for the potential migration of mangroves into the area with
climate change and evaluate the implications (negative and
positive) for ecosystem services and biodiversity. Our results
also suggest that future studies should focus on areas where
habitats have the greatest potential, namely in maintaining
shorelines against erosion associated with waves, smaller
storms, and sea-level rise in areas with habitats that have a
great extent (e.g., Louisiana marshes) or have high roughness
(transitional marshes with trees, mangroves). For the purpose
of informing the business decision for coastal defenses, we only
included a sample of public ecosystem services and biodiversity metrics as supporting information. These services were
selected because they could be reliably modeled using land
cover data as inputs and because we anticipated that they
would be relatively large in magnitude. Further research could
provide a more comprehensive accounting of the public
ecosystem services and biodiversity. In particular, the growing
body of literature on green-gray or hybrid infrastructure should
more fully consider the potential negative impacts of gray
infrastructure on habitats, recognizing that regulations may not
exist or may not fully protect wetlands and other resources
from direct and indirect impacts (Stavins and Jaffe 1990). Such
studies may be particularly important to help identify
opportunities for private-public partnerships to enhance
community resilience to coastal hazards (Stewart et al.
2009). This work could draw on the growing body of work
exploring the value of public goods provided by wetlands
(Brander et al. 2006) and best practices guides for incorporating this information into cost benefit analysis for coastal
protection strategies (Penning-Rowsell et al. 2005).
Building the tools and case study evidence base for assessing
the role of natural habitats in coastal protection is an important
step, but integrating natural defenses into hazard mitigation
decisions will require addressing additional challenges. We
identified 2 such business integration challenges during this
research that may be common to accounting for other
ecosystem services. First, businesses typically compare alternative investments by measuring the return on investment in
terms of increased cash flows, but the return on investment for
habitat protection or levees is measured as avoided damages or
costs. Even though a levee investment does not increase cash
flows, it may be warranted if there is high certainty that it will
avoid costs, especially via avoiding potential business interruption that would reduce cash flows. Currently, we do not
have the same level of certainty for natural defenses. This leads
to the second challenge. Only technologies that achieve
specific standards (Harry and Schroeder 2006) may be
considered as potential solutions in certain business processes.
Field and experimental validation of the coastal protection
values of habitats may help address this challenge, although
some uncertainty may still remain due to the natural variability
in habitat coverage, physical characteristics, and, hence,
functions. Accounting for the additional benefits provided
342
by habitats to local communities and biodiversity may provide
incentives to include habitat protection or restoration as part of
an integrated risk management plan even if the certainty of
coastal protection is not extremely high.
More cross-sectoral research efforts and business applications are needed to overcome the challenges we highlighted
and to identify cases where coastal protection values from
habitats are significant. Using methods we developed here,
businesses may find cases where unilaterally investing in
habitat protection makes sense as part of an integrated
coastal risk management plan, especially if the additional
benefits to the public and biodiversity are considered.
Alternatively, it may make more sense for businesses to lead
multistakeholder or private–public investments in habitats,
such as supporting the use of National Recreation Areas
designed for recreation and storm buffering benefits (NPCA
2013). Fundamental attributes of businesses, such as lower
transaction costs (Coase 1937) and information advantages
(Barney 1991), may position businesses to be natural leaders
for multistakeholder investments. Ultimately, we expect that
businesses that apply these types of tools and build awareness
of ecosystem services in their corporate cultures will have
better business outcomes and better outcomes for society and
nature.
Acknowledgment—We thank Doug Whipple, Judy Gunderson, and Danny Ramirez from Dow for their advice and
contributions; Jon Fisher, Mike Beck, Chris Shepard, and Matt
Miller from TNC for review of the project; and Jonathan
Clough, Marco Propato, Amy Polaczyk from Warren Pinnacle
Consulting for their application of the SLAMM.
SUPPLEMENTAL DATA
Figure S1. Flood modeling transects and an example of a
Manning’s n values raster created from a SLAMM output
(note: the dark rectangle in the ocean area is outside of the
study area).
Figure S2. Percentage change in habitat quality from current
(2006) to (A) 2050 and (B) 2100.
Figure S3. Maps of habitat quality in the coastal zone of
Brazoria County and the Stratton Ridge study area near
Freeport, TX: (A) 2006, (B) 2050, (C) 2100. White indicates
currently developed land and land expected to be developed by
2040. Dark gray indicates estuarine and marine waters.
Table S1. Parameters for SLAMM.
Table S2. Damage summary by hurricane category and
annual loss expectancy across scenarios and years.
Table S3. Earthen levee cost model.
Table S4. Depth-damage functions: Structural and contents
damage ratios for industrial/commercial and residential
structures.
Table S5. Gulf of Mexico distribution of foundation types
(%).
Table S6. Gulf of Mexico height of foundation by type.
Table S7. Summary statistics for the assessed value of
industrial structures in Galveston and Brazoria counties.
Table S8. Estimate of recreational photograph-user days
under current (2006) conditions.
Table S9. Mean values and percent of users by recreational
activity.
Table S10. Regression estimates of flood heights at Stratton
Ridge and calculated estimates of impacts of marsh on flood
heights by hurricane category using regression model
coefficients.
Integr Environ Assess Manag 12, 2016—SMW Reddy et al.
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