Using a boundary organization approach to develop a sea level rise and storm surge impact analysis framework for coastal communities in Maine Philip Camill & Maryellen Hearn & Krista Bahm & Eileen Johnson Published online: 20 January 2012 # AESS 201 Abstract Sea-level rise impact assessments are urgently needed by local planners to make informed decisions about adaptation and vulnerability. Most assessments to date, however, focus on large urban centers, coastlines of economic significance, or involve physical or economic modeling expertise that may be expensive or unavailable to town planners. Despite the large number of small coastal communities in the USA, few methodologies have been developed based on locally available data and expertise. Our research team at Bowdoin College served as a boundary organization working with community stake- holders to identify and meet their needs in developing a simplified, inexpensive methodology based on widely available data to assess sea level rise (SLR) and storm surge impacts on coastal Maine communities. We used two municipalities, Brunswick and Harpswell, as case studies. LIDAR maps were used in a geographic information system framework to model SLR scenarios (projected for the year 2100) of 0.61 (2 ft), 1, and 2 m. Storm surge scenarios based on historical data were modeled additively to SLR projections. We analyzed the potential impacts of SLR and storm surge changes on land acreage, buildings, transportation networks, piers, and coastal marshes. Coastal Maine communities may face substantial impacts to land, infrastructure, intertidal ecosystems, and livelihoods. We identify issues in existing data and governance structures that make implementing this simplified analysis challenging, and we suggest recommendations for overcoming them. Our work provides a useful framework for assessing vulnerability and resilience at the municipal level and the development of subnational adaptation protocols. Keywords Climate change . Sea level rise . Coast . Community . Infrastructure . Marsh . Adaptation . Boundary organization . Impact Introduction Coastlines worldwide are at risk from the impacts of sea level rise (SLR) and storm surge in coming decades as a result of climate warming. The AR4 report of the Intergovernmental Panel on Climate Change (IPCC; 2007) projected global SLR increases of 0.18–0.59 m from 1990 to 2100, depending on emissions scenarios, but new research suggests that SLR will likely fall within a higher range of 0.5–1.9 m (Rahmstorf 2007; Füssel 2009; Vermeer and Rahmstorf 2009; Jevrejeva et al. 2010; Nicholls and Cazenave 2010). One study of the last interglacial period (the Eemian), during which global temperatures were 1–2°C warmer than today, suggested a 95% probability that global sea level was 6.6 m higher than present due to contributions from polar ice (Kopp et al. 2009). Faced with this changing understanding and upward revision of SLR projections, coastal decision makers urgently need access to the kinds of information and tools necessary for assessments of impact, vulnerability, and resilience (Miller et al. 2010). Coastal assessments have traditionally been applied at global, national, and local scales (Nicholls and Mimura 1998; Yohe and Schlesinger 1998; Walsh et al. 2004; Cooper et al. 2008; FitzGerald et al. 2008; Hopkinson et al. 2008; Hallegatte et al. 2011; Electronic supplementary material The online version of this article (doi:10.1007/s13412-011-0056-6) contains supplementary material, which is available to authorized users. P. Camill (*) : M. Hearn : K. Bahm : E. Johnson Bowdoin College, Environmental Studies, 6700 College Station, Brunswick, ME 04011, USA e-mail: [email protected]J Environ Stud Sci (2012) 2:111–130 DOI 10.1007/s13412-011-0056-6 2
Transcript
Using a boundary organization approach to develop a sealevel rise and storm surge impact analysis frameworkfor coastal communities in Maine
Philip Camill & Maryellen Hearn & Krista Bahm &
Eileen Johnson
Published online: 20 January 2012# AESS 201
Abstract Sea-level rise impact assessments are urgentlyneeded by local planners to make informed decisions aboutadaptation and vulnerability. Most assessments to date,however, focus on large urban centers, coastlines ofeconomic significance, or involve physical or economicmodeling expertise that may be expensive or unavailable totown planners. Despite the large number of small coastalcommunities in the USA, few methodologies have beendeveloped based on locally available data and expertise.Our research team at Bowdoin College served as aboundary organization working with community stake-holders to identify and meet their needs in developing asimplified, inexpensive methodology based on widelyavailable data to assess sea level rise (SLR) and stormsurge impacts on coastal Maine communities. We used twomunicipalities, Brunswick and Harpswell, as case studies.LIDAR maps were used in a geographic informationsystem framework to model SLR scenarios (projected forthe year 2100) of 0.61 (2 ft), 1, and 2 m. Storm surgescenarios based on historical data were modeled additivelyto SLR projections. We analyzed the potential impacts ofSLR and storm surge changes on land acreage, buildings,transportation networks, piers, and coastal marshes. CoastalMaine communities may face substantial impacts to land,infrastructure, intertidal ecosystems, and livelihoods. Weidentify issues in existing data and governance structuresthat make implementing this simplified analysis challenging,
and we suggest recommendations for overcoming them. Ourwork provides a useful framework for assessing vulnerabilityand resilience at the municipal level and the development ofsubnational adaptation protocols.
Community . Infrastructure .Marsh . Adaptation . Boundaryorganization . Impact
Introduction
Coastlines worldwide are at risk from the impacts of sealevel rise (SLR) and storm surge in coming decades as aresult of climate warming. The AR4 report of theIntergovernmental Panel on Climate Change (IPCC; 2007)projected global SLR increases of 0.18–0.59 m from 1990to 2100, depending on emissions scenarios, but newresearch suggests that SLR will likely fall within a higherrange of 0.5–1.9 m (Rahmstorf 2007; Füssel 2009; Vermeerand Rahmstorf 2009; Jevrejeva et al. 2010; Nicholls andCazenave 2010). One study of the last interglacial period(the Eemian), during which global temperatures were 1–2°Cwarmer than today, suggested a 95% probability that globalsea level was 6.6 m higher than present due to contributionsfrom polar ice (Kopp et al. 2009).
Faced with this changing understanding and upwardrevision of SLR projections, coastal decision makersurgently need access to the kinds of information and toolsnecessary for assessments of impact, vulnerability, andresilience (Miller et al. 2010). Coastal assessments havetraditionally been applied at global, national, and localscales (Nicholls and Mimura 1998; Yohe and Schlesinger1998; Walsh et al. 2004; Cooper et al. 2008; FitzGerald etal. 2008; Hopkinson et al. 2008; Hallegatte et al. 2011;
Electronic supplementary material The online version of this article(doi:10.1007/s13412-011-0056-6) contains supplementary material,which is available to authorized users.
P. Camill (*) :M. Hearn :K. Bahm : E. JohnsonBowdoin College, Environmental Studies,6700 College Station,Brunswick, ME 04011, USAe-mail: [email protected]
J Environ Stud Sci (2012) 2:111–130DOI 10.1007/s13412-011-0056-6
Preston et al. 2011), often in high-population metropolitan ortourist areas with economic significance (Gornitz et al. 2001;Suarez et al. 2005; Kirshen et al. 2008; Hansen 2010; Frazieret al. 2010; Neumann et al. 2010; Hunt and Watkiss 2011),or in sandy coastlines with the potential for significanterosion and migration of barrier islands (Pilkey and Cooper2004). These approaches often involve top–down, one-wayflows of information from scientific communities to policyarenas—the “pipeline model” of information disseminationdescribed by Cash and Moser (2000).
As such, significant opportunities remain for helpingcommunities better understand and manage risks associatedwith SLR and storm surges (Moser and Ekstrom 2011) asillustrated by the New England region of the northeasternUSA. A significant fraction of populations in this regionlive in smaller municipalities in which site-specific SLRimpact and vulnerability analyses have not yet beenconducted. In New England, 324 census-designated,subcounty municipalities—85% of which have populationsless than 50,000—border the coastline (US Census Bureau2010). Maine’s 8,400 km of coastline includes coastalcounties that account for 73% of the state population (Moser2005) and 23% of the population lives within 1 km of thecoast (Lam et al. 2009). Many New England communitiesare situated along steep, erosion-resistant rocky shorelinesthat create a false sense of security even though potentialSLR and storm surge can have significant impacts on roads,infrastructure, and coastal wetlands. Moreover, adaptationplanning at the local level is often considered advantageous(Keskitalo 2008; Moser et al. 2008; Romieu et al. 2010;Hunt and Watkiss 2011), but local decision makers are oftenfaced with resource and technical constraints, and they areoften saddled with more pressing issues than climatewarming, such as the provision of resources forrecreation, public safety, and water, energy, and infrastructureprotection (Tribbia and Moser 2008). As a result, the impactsof slow-onset threats like SLR are often understudied, andefforts to develop long-term adaptation strategies languish(Moser 2005; Tribbia and Moser 2008). In Maine,coastal assessments of SLR impacts have been identifiedas an important goal of the State of Maine ClimateAdaptation Plan (Maine Department of EnvironmentalProtection 2010), but the range of technical capacitiesthroughout the state at both the regional planning andlocal level, and the role of the home rule form ofgovernment in Maine (which empowers municipalitiesto pass laws that are local in nature so long as they do notpreempt state law) present particular challenges indeveloping coordinated efforts between and among stateagencies and the local level. These concerns suggest thatmany communities in Maine and elsewhere in NewEngland remain largely underprepared in terms ofplanning for SLR impacts.
The ways in which institutions and knowledge sharinghave been traditionally structured is part of the reason whyeffective impact and vulnerability assessments have notbeen widely implemented, as noted by Tribbia and Moser(2008): “To date, coastal managers insufficiently benefitfrom the available scientific information on coastal impactsof climate variability and change and sea-level rise, as itexists in largely untapped scientific journals, few expertsare ever consulted, and relevant research institutions are notyet linked into the ‘management on the ground.’” Recenttheoretical developments on institutional analysis andknowledge systems offer models for effectively integratingscience-based information on potential SLR impacts andcommunity planning at the local level (Guston 2001; Cashet al. 2003; Folke et al. 2005; Moser 2005; Lebel et al.2006; Moser et al. 2008; Tribbia and Moser 2008). Asnoted by McNie (2007) and Weichselgartner and Kasperson(2010), scientists may not be producing the right kinds ofinformation required by decision makers. Understandinghow scientific information relates to the ways issues arevalued and framed and which options local communitiesmay consider most important are significant departures fromtraditional interactions between science and policy (Cash et al.2003; Michaels 2009). Importantly, information must besalient to the needs of decision makers and legitimized withrespect to stakeholder values and beliefs. Sustainabilityscience advocates for the inclusion of stakeholders through-out the research process to ensure that outcomes are relevantto and actionable by stakeholders (Kates et al. 2001; Cash etal. 2003; Folke et al. 2005; Silka 2010).
Boundary organizations are considered to be an effectivemeans of developing communication, trust, and capabilitiesbetween communities of experts and communities ofdecision makers (Guston 2001; Cash et al. 2003; Folke etal. 2005; McNie 2007; Sarewitz and Pielke 2007; Tribbiaand Moser 2008). Originally conceived as a way to bridgescience and policy, boundary organizations possess severalkey attributes, including (1) the ability to promote active,iterative, and inclusive communication among stakeholdersthat facilitates coproduction of knowledge; (2) translationof ideas that are readily shared and understood by bothsides; (3) the ability to mediate conflicts and to ensure thatall viewpoints are represented; and (4) the ability to digestoriginal research that decision makers may not have thecapacity or time to accomplish (Guston 2001; Cash et al.2003; Tribbia and Moser 2008; Michaels 2009). Impor-tantly, having local data available allows researchers andplanners to collaboratively address sea level rise impacts ina manner more removed from the highly politicizedarguments about climate change. Residents and decision-makers can engage in discussions about observable localimpacts, as local data are easier to apply and act upon(Dempsey and Fisher 2005).
112 J Environ Stud Sci (2012) 2:111–130
Colleges and universities can serve as effective boundaryorganizations for addressing climate adaptation. Cash andMoser (2000) argue that boundary organizations areimportant for mediating not only science and policy butalso the efforts of actors across different scales. In the stateof Maine, general recommendations for climate adaptationhave been promulgated at the state level (Maine Departmentof Environmental Protection 2010), whereas implementa-tion often lies with local decision makers at the municipallevel. Bowdoin College (Brunswick, Maine) has a longtradition of collaborative research with state agencies, localcommunities, and nonprofit organizations, which helpsbuild trust across these scales and ranges of institutions.This experience allows the College to serve as a focal pointintermediate to the state and municipal levels in facilitatinga multiscale collaborative network interested in SLR.Guston (2001) adds that boundary organizations areeffective when they play a role that is difficult to achieveby groups on either side of the boundary. Maine stateagencies offer the vision and expertise to carry out SLRimpact analyses, but they often do not have the extensive,site-specific knowledge that local decision makers possessabout their own communities or the resources to implementmunicipal-specific analyses. Consequently, the state adap-tation report recommends “develop[ing] and disseminat[ing] tools that will allow local and regional planningauthorities to initiate their own adaptation planning pro-cess” (Strategy A.4.1). Local decision makers possessdetailed understanding of community needs but oftenrequire assistance with technical and data support indeveloping SLR impact methodologies. As a potentialboundary organization, Bowdoin College is able to providethe expertise and resources (in terms of faculty and studentcommitment, scientific and geographic information system(GIS) capacity, and established community networks) tocombine the vision of the state climate adaptation planningrecommendations with the knowledge and needs of localmunicipalities to develop a general methodology that canbe implemented across coastal Maine communities. Finally,by involving students in the SLR impact analysis process,colleges and universities can make education an importantcollateral benefit.
Here, we describe a project carried out by researchers atBowdoin College in consultation with stakeholders repre-senting state, regional, and local entities. These stake-holders included the Maine Department of EnvironmentalProtection and State Planning Office, the Maine GeologicalSurvey, and other local-level governmental staff, electedofficials, as well as nongovernmental stakeholders to beginthe process of developing a simplified, generalizableapproach for determining potential impacts of SLR andstorm surge on coastal Maine communities. Our team ofauthors served in the dual capacity as the scientific team
developing the impact assessment methodology as well asthe boundary organization responsible for engaging keystakeholders and building their interests and expertise intothe methodology. We used the towns of Brunswick andHarpswell, Maine, as case studies to address the followingquestions: (1) What are the technical needs of keyinstitutional stakeholders interested in a communityfocused SLR impact assessment methodology? (2)How might we define a simplified framework forimpact analysis based on readily available data methodseasily transferable to coastal communities in Maine? (3)What are the challenges of gathering information fromstakeholders across local and regional scales relevant toSLR impact analysis? (4) What informational andconceptual gaps remain, and what are possible ways toremediate them? (5) What are the challenges indeveloping capacity among local decision makers interms of assimilating and utilizing information? (6)What are the broader implications of this case studyfor the development of national and subnational climateadaptation protocols?
Methods
Study region
Our study area included the municipalities of Brunswickand Harspwell, Maine (Fig. 1). The towns border each otherand have extensive coastlines along the Atlantic Ocean,specifically within the Gulf of Maine and Casco Bay.Harspwell (population 5,000) is composed of contiguousland in Cumberland County, as well as Great, Orr’s andBailey Islands and over 200 uninhabited islands. Withnearly 350 km of coast, Harpswell has more shoreline thanany other municipality in Maine (CES 2011). Brunswick(population 21,000) is comprised of contiguous land inCumberland County and has approximately 110 km ofcoastline. We focused primarily on the effects of SLR onland and infrastructure that currently borders the AtlanticOcean, excluding for simplicity areas that are bordered bytidal rivers.
Characteristic of much of New England, the coast-lines of Brunswick and Harpswell are predominantlyrocky and steep (see supplementary materials, Fig. S1for a LIDAR map of elevation). The bedrock geology ofthis region consists primarily of Precambrian metasedi-mentary and volcanic rocks of the Casco Bay Group(Osberg et al. 1985). Surficial geology is characterized bysandy glacial–marine outwash deposits (especially inBrunswick), glacial–marine silts and clays, tills, andbedrock with thin glacial sediment cover (Thompsonand Borns 1985).
J Environ Stud Sci (2012) 2:111–130 113
Boundary organization approach
In the summer of 2010, the director of the Maine CoastalProgram approached the College regarding the possibilityof analyzing the impacts of sea level rise on coastalinfrastructure. This collaboration grew from two earlierprojects carried out by Bowdoin College staff on (1) thedevelopment of a water quality monitoring site inventorymaintained by volunteer monitoring organizations along thecoast of Maine and (2) long-term collaborations with theMaine State Planning Office, local communities inmidcoast Maine, and local and national nongovernmentorganizations (NGOs) in the development of a conser-vation plan as part of the Sagadahoc Rural ResourcesInitiatives [hyperlink: http://www.maine.gov/spo/landuse/docs/ConservationBlueprint_March2010.pdf] for the com-munities of Sagadahoc County and the towns of Harpswelland Brunswick, Maine.
The initial phase of the project consisted of discussionsamong the Bowdoin research team, Maine State PlanningOffice, Maine Department of Environmental Protection, andthe Maine Geological Survey (MGS; Table 1). This initialstakeholder group was composed predominantly of represen-tatives from state agencies in an effort to understand thevision and needs of existing state-level adaptation planningefforts. As the boundary organization, the Bowdoin team’srole was to provide research capabilities and to determinehow collaboration could facilitate the objectives of the 2010Report to the Maine Legislature on climate adaption, andmeet the needs of local communities. Preliminary scopingdiscussions were followed up with face-to-face, structured,focus group meetings, where needs and methodologies werespecified to the Bowdoin research team. Primary needsincluded addressing recommendation B.1.1.2 of the climateadaptation report regarding coastal infrastructure vulnerability(Maine Department of Environmental Protection 2010),
Fig. 1 Location of the towns ofBrunswick and Harpswell,Maine. Light gray areas AtlanticOcean, dark gray areas neigh-boring municipalities. Thespatial extent of intertidalecosystems is also shown(shellfish distribution mapadapted from Mason Webber2009 and Maine Departmentof Marine Resources 2010)
which included the development of methods to assess SLRimpacts on coastal infrastructure (roads, buildings, piers).Coastal wetlands were identified as an important secondpriority based on the conservation value of marshes and theeconomic importance of the commercial and recreationalshellfishery. Previously implemented methodologies by theMGS (Slovinsky and Dickinson 2009) were described,including processing protocols for LIDAR data and tide-based projections of coastal marsh habitat changes for the cityof Scarborough, Maine (see “Elevation modeling usingLIDAR” section below; Table 1). These scoping andinformation-gathering meetings represented a two-way flowof information as the project was formulated, but theBowdoin research team’s role shifted to that of an informa-tion recipient as the state agencies described the stateadaptation plan and potentially useful methodologies. It isimportant to note that there was a change in the Mainegovernorship halfway through this project, which created apolitical shift in priorities for state agencies. Combined withthe general shift in public attitudes towards climate scienceover the past few years, the changing political climate inMaine impacted how we structured the research and engagedthe community. Specifically, we attempted to make theanalysis nonpolitically charged and broadly acceptable tothe general public. As described below, we framed SLR as aprocess that we know is already impacting Mainecommunities without emphasizing the scientific evidencefor human vs. natural causes. In addition, we structuredthe analysis to highlight the kinds of economic impactsfrom SLR and storm surge that would interest mostcommunity members, including roads, buildings, land,piers, and shellfishing grounds.
The second phase of the project involved a series ofstructured, focus-group collaborations with a broader group ofstakeholders that focused on methodology development andsoliciting feedback from local municipal decision makers(Table 2). Stakeholders included elected officials, localgovernmental agencies, town planners, NGOs, consultants,and citizens groups who have played an active role in localand regional planning in the greater Brunswick and Harpswellregion. The goal at this stage was to determine what localknowledge and data sources existed and to ensure that wewere including in our methodology the kinds of informationand analyses that local stakeholders cared about most. Weheld meetings in several venues and used several formats toengage as many people as possible, including publicpresentations at Bowdoin, presentations in the towns ofHarpswell and Brunswick, and meetings with other adaptationplanners in the Casco Bay region of Midcoast Maine.
After incorporating stakeholder feedback into the analysis,the final phase of the project was focused on disseminating theresults and methodology to stakeholders and the generalpublic through a series of public presentations at local, state,T
able
1Project
phase1:
initial
stakeholderengagement,kn
owledg
egeneratio
n,andflow
sof
inform
ation
Date
Meetin
g/activ
ityCom
mun
ityStakeho
lders/partners
Purpo
seOutcome
Inform
ation
flow
Bow
doin
⇔stakeholders
6/29
/10
Initial
scop
ing
DirectorMaine
Coastal
Program
,DirectorLand
Use
Team
,Clim
ateManager,Maine
DEP,
Bow
doin
Env
iron
mentalStudies
Program
DirectorandProgram
Manager
Project
feasibility
Agreementthat
theBow
doin-state
partnership
wou
ldbe
aworthwhile
collabo
ratio
n⇔
8/19
/10
Propo
sedscop
eof
work
subm
itted
Maine
State
Plann
ingOfficeandMaine
DEP
Project
prop
osal
developm
ent
Com
mentsreceived
tofocusprojectscop
e⇔
9/13
/10
Initial
meetin
gMaine
State
Plann
ingOfficeandMaine
DEP
Gov
ernm
entstaffprov
ided
Bow
doin
research
team
with
anov
erview
oftheplan
andthespecific
dimension
sof
theclim
ateinventory
Identificationof
needsfrom
stakeholders
⇐
9/26
/10
Presentationby
Maine
GeologicalSurvey
Maine
GeologicalSurvey
Presentationof
earlierSLRanalysis
fortownof
Scarborou
gh,Maine
Protocolfrom
MGSprojectwas
adop
tedforthe
currentprojectforthepu
rposeof
consistency
with
otheradaptatio
nplanning
initiatives,includ
ing
processing
ofLID
ARdata,identificationof
high
andlow
marsh.
⇐
Fall,20
10Ong
oing
consultatio
nMaine
GeologicalSurvey,
NOAA
Datagatheringon
tides,historical
storm
surges
Feedb
ackto
improv
emetho
dology,stafffrom
MGS
continuedthroug
hout
theprojectto
prov
ide
technicalinpu
ton
theprojectas
wellas
data
⇐
J Environ Stud Sci (2012) 2:111–130 115
Tab
le2
Project
phase2:
metho
dology
developm
entandinitial
dissem
inationandsolicitatio
nof
feedback
from
stakeholders
Date
Meetin
g/activ
ityCom
mun
itystakeholders/partners
Purpo
seOutcome
Inform
ation
flow
Bow
doin
⇔stakeholders
10/2/10
Casco
Bay
Clim
ateAdaptation
Rou
ndtable
Maine
stateagencies,federalEPA
,Casco
Bay
Estuary
Project,Greater
Portland
Cou
ncil
ofGov
ernm
ents,andMaine
Coo
perativ
eExtension
Discussionof
thisandother
adaptatio
nprojectsthroug
hout
Maine
Affirmationof
thevalueof
thisproject
⇔
11/22/10
Initial
public
presentatio
nMaine
State
Plann
ingOfficeandMaine
DEP
Initial
dissem
inationof
work;
initial
solicitatio
nof
feedback
Feedb
ackto
improv
emetho
dology
andissues
analyzed
⇔
1/31
/11
Presentationto
townof
Brunswick
Directorof
Plann
ingandDevelop
mentTow
nof
Brunswick,
BrunswickTow
nCou
ncilo
r,Directorof
Com
mun
ications
andGov
ernm
ent
Relations
The
NatureCon
servancy,Directorof
LandUse
Plann
ingMaine
State
Plann
ing
Office,
Eng
ineerfrom
Wrigh
t-Pierce,
The
Clim
ateProject.org
Board
ofDirectors,
Repub
licansforEnv
iron
mentalProtection
(REP.org),Maine
DEP,
Maine
Geological
Survey,
Top
sham
Con
servationCom
mission
,Plann
erfortheGreater
Portland
Cou
ncilof
Gov
ernm
ents
Firstform
alpu
blic
presentatio
nof
research
tothegeneral
public
Feedb
ackto
improv
emetho
dology
andissues
analyzed
⇔
2/8/11
Presentationto
townof
Harpswell
HarpswellCon
servationCom
mission
,Board
Mem
bers
andtheExecutiv
eDirectorof
the
Harpw
ellHeritage
LandTrust
Firstform
alpu
blic
presentatio
nof
research
tothetownof
Harpswell
Com
mentsreceived
focusing
prim
arily
ontid
almarshes
andim
plications
for
conservatio
nland
sandconservatio
nplanning
⇔
5/19
/11
Presentationat
Manom
entClim
ate
AdaptationSym
posium
60Representatives
from
stateagencies,
localcommun
ities
andNGOswith
inthe
Sagadahoc
Region,
Project
Manager
for
Clim
ateChang
eandEnergyManom
etCenterforCon
servationSciences
Firstform
alpu
blic
presentatio
nof
research
tothetown
ofBrunswick
Disseminationof
research
and
metho
dology
tothegeneralpu
blic;
feedback
received
onmetho
dology
⇔
DEPMaine
Departm
entof
Env
iron
mentalProtection
116 J Environ Stud Sci (2012) 2:111–130
and national meetings (Table 3). This stakeholder communityinvolved individuals and groups from both the state and locallevels.
Coastal impact analysis
We identified data sources widely available to coastalcommunities in Maine and used these data to develop asimplified, GIS-based methodology (Fig. 2) that included(1) the extent of land inundation for three SLR scenarios(0.61, 1.0, and 2.0 m) based on LIDAR, tide gage, andstorm surge data; (2) assessments of potentially impactedcoastal property value and piers associated with publicaccess and local fisheries; (3) transportation networkanalysis to determine potential weak links threatened bySLR and storm surges; and (4) potential impacts on coastalintertidal ecosystems.
Sea level rise and storm surge scenarios
We chose three sea level rise scenarios for the period 1990–2100: (1) low=0.61 m (2 ft), medium=1 m, and high=2 m,following previously published impact assessments (Loweet al. 2009, Anthoff et al. 2010; Hallegatte et al. 2011) andnewly published scientific information with upwardlyrevised SLR projections (Rahmstorf 2007; Füssel 2009;Vermeer and Rahmstorf 2009; Jevrejeva et al. 2010;Nicholls and Cazenave 2010). The low scenario isconsistent with the State of Maine planning guidelines(Maine Department of Environmental Protection 2006) andfalls within the ranges projected in the AR4 assessment ofthe IPCC (0.18–0.79 m; IPCC 2007). The US EPA, incooperation with the Maine State Planning Office (duringthe Baldacci administration), advised the medium and highsea level rise scenarios for town planning (personalcommunication).
Storm surges augment SLR and increase damages associ-ated with land inundation. Modeling storm surges at the small-community level is problematic given the need for physicalmodels that consider detailed, site-specific factors like wavedynamics, complex subsurface bathymetry, the distribution ofislands and bays, storm intensity, and storm tracks. These kindsof process models require resources and technical expertisethat are not widely available to local decision makers (Moser2005), so we chose a simpler approach of using historicalstorm surge data obtained through National Oceanographicand Atmospheric Administration (NOAA) and the NationalWeather Service to define reasonable upper limits of surge.Portland, Maine was the closest available data source with thelongest tidal records (1912–present).1 On average, storm
Tab
le3
Project
phase3:
dissem
inationof
results
andmetho
dology
tostakeholders
andthegeneralpu
blic
Date
Meetin
g/activ
ityCom
mun
itystakeholders/partners
Purpo
seOutcome
Inform
ation
flow
Bow
doin
⇔stakeholders
3/16
/11
PosterPresentationat
Maine
Water
Con
ference
Faculty
andstud
entsfrom
Maine
collegesandun
iversities,state
agency
representativ
es
Secon
dform
alpu
blic
presentatio
nof
research
tothegeneralpu
blic
Disseminationof
research
and
metho
dology
tothegeneralpu
blic
⇒
5/13
/11
Posterpresentatio
nat
Com
mun
ityPartnership
Sym
posium
atthe
BrunswickPub
licLibrary
Bow
doin
College
faculty
stud
entsand
staff,commun
itymem
bers,elected
officialsfrom
localmun
icipalities
Third
form
alpu
blic
presentatio
nof
research
tothegeneralpu
blic
Disseminationof
research
and
metho
dology
tothegeneralpu
blic
⇒
May,20
11Ong
oing
consultatio
nMaine
State
Plann
ingOffice
Inqu
irywith
Bow
doin
GIS
staffon
time
invo
lved
inLID
ARprocessing
Disseminationof
metho
dology
tostateagencies
⇒
6/24
/11
Posterpresentatio
nat
theAssociatio
nfor
Env
iron
mentalStudies
andSciences
Faculty
andstud
entsfrom
colleges
andun
iversitiesworldwide
Fou
rthform
alpu
blic
presentatio
nof
research
tothegeneralpu
blic
Disseminationof
research
and
metho
dology
tothegeneralpu
blic
⇒
1 Data available online at http://tidesandcurrents.noaa.gov/data_menu.shtml?stn=8418150%20Portland,%20ME&type=Tide%20Data
surges of 0.91 m (3.0 ft) occur every 5–7 years, and thehighest recorded storm surge at high tide in Portland was1.31 m (4.3 ft) in 1947 (Budd 1980; Cannon 2009). We usedthese data to define two storm surge scenarios: low=0.91 mand high=1.3 m.
We combined the three SLR and two storm surgescenarios to generate a matrix of six potential scenariosfor the coastal impact analysis (Fig. 2). Based on thesescenarios, we identified parts of the tidal cycle relevant toour impact analysis. For the inundation of land andpotential impacts to infrastructure due to SLR, we chose abenchmark of highest annual tide (HAT), which is thehighest water elevation likely to affect infrastructure. Forimpacts caused by SLR plus storm surge, however, wechose a more conservative benchmark of mean higher highwater (MHHW), the highest average sea level expected tobe achieved per day, given the fact that storms areunpredictable and are likely to occur during times of theyear when HAT is not experienced. For impacts to coastalintertidal ecosystems (described below), we also requiredmean high water (MHW) and mean sea level (MSL)datums. Using the North Atlantic Vertical Datum of 1988(NAVD88) reference height of 0 ft, we calculated the 2010HAT of Cushing Island Station in Casco Bay using theMGS Tide Calculator (HAT=6.27 ft/1.91 m), and deter-
mined the heights of MHHW (4.59 ft/1.40 m), MHW(4.16 ft/1.27 m), and MSL (−0.32 ft/-0.10 m).
Elevation modeling using LIDAR
LIDAR is an increasingly popular tool for mapping SLRimpacts (Wu et al. 2008; Gesch 2009) due to thesignificantly improved spatial resolution and verticalaccuracy compared to digital elevation models (DEMs)available through the US Geological Survey’s NationalElevation Dataset (NED). DEMs available through theUSGS NED have a resolution either of 1 arc-s(approximately 30 m) or 1/3 arc-s (approximately10 m) and are derived from cartographic contours basedupon USGS 7.5-min topographic maps. Studies comparingthe vertical accuracy of DEMs with LIDAR indicate that theroot mean square error (RMSE) can range to 1.27 m ascompared with a RMSE of 0.14 m for LIDAR data. TheLIDAR set used as the basis of this analysis has a predictedvertical RMSE of 0.067 m, exceeding the accuracy ofcomparable nationally available date sets. Field verificationof the vertical accuracy of LIDAR data indicated errors in therange of 0.15 m (Slovinsky and Dickson 2006, 2009),comparable to calculated vertical RMSE from othernational studies.
Fig. 2 Conceptual model of the GIS-based framework. Columns primary data needs and analyses, rows data type
118 J Environ Stud Sci (2012) 2:111–130
Coastline elevations were modeled using LIDAR datacollected as part of the Federal Emergency ManagementAgency’s (FEMA) Map Modernization Program in 2006(see supplementary materials, Fig. S1 for a LIDAR map ofelevation). LIDAR data are now available for the entirecoastline of Maine. The original LIDAR dataset wassampled at 0.61-m (2 ft) spacing in State Plane NAD83.A subsequent dataset, provided in LAS (Common LIDARExchange Format) had been converted to UTM Zone 19,with units in meters, sampled at 2-m intervals andmaintained the NAVD88. These data were used for firstreturn analysis to determine heights of infrastructure suchas buildings, piers, and bridges (Fig. 2). All elevation datapresented in this study are referenced to the vertical datumNAVD88 and the horizontal datum North American Datumof 1983. For each sea level rise scenario, a spatial datasetwas created from the FEMA LIDAR data, which delineatesthe HAT level under the given scenario. We overlaid thesetide elevation scenarios with land and infrastructure data todetermine which parcels, buildings, piers, and roads wouldbe directly affected by sea levels at HAT. To analyze stormsurge scenarios, we created a spatial dataset that includedelevation delineations for all SLR plus storm surgescenarios (Fig. 2).
Impacts on land and coastal infrastructure
Property inundation and value We used tax assessordatabases and digital parcel data from the towns ofBrunswick and Harpswell, including the geographic loca-tions of land area, building locations, and property marketvalues (Fig. 2). For the purposes of land valuation, a linearrelationship was assumed between parcel acreage andvalue. The formulas used by tax assessors to calculateparcel-specific land value are complex and idiosyncratic toeach municipality, limiting the generality of existingapproaches. Previous studies have also estimated the valueof land lost to inundation using simplified approximationsof the relationship between land area and value similar toours (Nordhaus 1991, Titus et al. 1991, Bosello et al. 2007).Our approach has one advantage over these previousstudies: Studies conducted on regional or national scalesoften must rely on national averages for land value,whereas we were able to use parcel-specific land values.The value of land lost to inundation was calculated bymultiplying each individual parcel value by percentinundation. Building data included spatial footprints ofeach individual building, building values, and land usecodes. For the land inundation vulnerability assessment, wecombined parcel data with the LIDAR elevation data todetermine the total land acreage submerged and the numberof parcels affected under each sea level rise scenario. Whencombined with elevation data, the building data were
analyzed to determine which buildings were affected (i.e.,contacted) by water under each sea level rise scenario.
Assessor data were also used to determine which parcelswere designated for conservation, aiding the analysis ofhow much area currently designated as “marsh area”(according to our tide-determined intertidal habitat zona-tions described below) coincides with lands under any levelof conservation. “Conservation parcels” however, arebroadly defined in this case. Conservation lands includeany conserved lands through fee or easement and aretherefore established as permanent conservation lands.Conservation lands also include parcels that fall withinspecific tax classification categories that provide reducedtax rates for farmland and forestlands. Although theseparcels could experience a reversal in conservation status atany point, these parcels were included as an indicator ofpotential future permanent conservation protection.
Piers Data for public and some private piers in Harpswelland Brunswick were provided by The Island Institute, anonprofit organization in the Gulf of Maine that conducteda statewide inventory of the working waterfront in Maine in2005 (Conover and Rowan 2007). The data includedprivately and publicly owned infrastructure and includedpiers, boat launches, and access points. One of the initialquestions raised by state agencies was whether this datasetcould be useful for sea level rise analysis at the local level.We imported the spatial locations of waterfront piers andseparated piers based on whether they were fixed vs.floating. For the purpose of our analysis, floating piers wereassumed to rise with the sea level and were not analyzed forpotential inundation. The fixed pier category also includedboat launches. The locations of fixed piers were manuallyinspected against orthophotos (Maine Office of GIS 2009).Because the pier data were originally intended to approximatethe location of working waterfronts rather than for preciseSLR analysis, we inspected and adjusted each location asnecessary. Once adjusted, the elevations from first-returnLIDAR were applied to pier locations to determine level ofinundation under each scenario.
Transportation State road data, developed for the planningof emergency routes, were acquired from the Maine Officeof GIS (2010). Combining road data with elevation dataallowed for the identification of road segments projected tobe inundated by SLR and storm surge scenarios (Fig. 2).Once these segments were identified, a network analysiswas performed using GIS to determine the lengths andlocations of road systems that would become inaccessiblebeyond points of inundation. We also plotted histograms ofthe number of roads as a function of the distance ofinaccessible road length past the impact (inundation) pointto summarize how the distribution of inaccessible road
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length changes with different SLR and surge scenarios. Forease of analysis, we considered only a subset of SLR andsurge scenarios for the road network analysis: (1) low SLR(0.61 m) only, (2) high SLR (2 m) only, (3) low SLR+lowsurge (0.91 m), and (4) high SLR+high surge (1.3 m).
Potential impacts of sea level rise on intertidal ecosystems
Changes in intertidal ecosystems were analyzed using asimplified methodology adapted from the MGS (Slovinskyand Dickinson 2009). We created a GIS-based spatialmodel sensitive to the slope of the shoreline and degreeof inundation caused by SLR. The zone between MHW andHAT can be classified as high intertidal habitat, oftendominated by the high marsh species Spartina patens. Thezone between MSL and MHW can be classified as lowintertidal habitat, often dominated by low marsh taxa (e.g.,Spartina alterniflora) or kelp beds (e.g., Lamanaria spp.).Changing inundation with SLR causes these zones to shiftupslope depending on local topography. Estimating thepotential change of these zonations is important in terms ofconservation management of coastal marshes, specieshabitat planning, and the location of commercially andrecreationally valuable shellfish species. Although thepotential area of mud flats located approximatelybetween mean low water (MLW) and MSL is ecolog-ically and commercially significant to the local shellf-ishing industry (Fig. 1), we were unable to modelchanges in this zone due to the inability of LIDAR toreliably map low intertidal elevation and the imprecisionand low resolution of existing coastal bathymetric maps.Instead, we compiled information on the harvest rates andeconomic value of the commercial and recreationalshellfish industries for 2010 to assess the potential threatto livelihoods resulting from impacts to low intertidal,mud flat ecosystems.
Following previous work (Titus et al. 1991; Lafever etal. 2007; Cooper et al. 2008; Kuleli 2010), we did notinclude processes known to be important, such as sedimenterosion, accretion, and dynamic hydrology (Reed 1994;Ashton et al. 2008; FitzGerald et al. 2008; Akumu et al.2010), since the data and technical expertise required toparameterize and run physically based models are oftenunavailable at a local scale. We recognize the limitations ofour simplified approach, but in terms of facilitating rapid,local-scale planning, an easy-to-use equilibrium model ofintertidal ecosystem zonation change—followed up by localverification on the ground—is valuable. Moreover, for the1- and 2-m SLR scenarios, inundation rates are likely toapproach or exceed sediment accretion rates in NewEngland (Morris et al. 2002, Ashton et al. 2008), therebycausing the kinds of shifts from high marsh to low marshand low marsh to open water that have been observed
recently in modern ecosystems and the sediment record(Donnelly and Bertness 2001).
Results
Impacts of sea level rise on land and coastal infrastructure
For the low, medium, and high SLR scenarios, there weresignificant impacts on infrastructure but also importantdifferences between the two municipalities. Between 49and 144 ha of property (land contained within parcels) wereinundated in Brunswick and 96–325 in Harpswell, respec-tively (Table 4). The number of parcels affected by SLR isgreater than 460 in Brunswick and 2,400 in Harpswell,remaining consistent across the three scenarios, indicatingthat the main effect of SLR is further inundation ofimpacted parcels rather than the addition of newly impactedparcels. The value of land inundated represents a loss of$1–4 million in Brunswick and $37–140 million inHarpswell, or approximately 0.2–0.4% of total assessedland value in Brunswick and 3.6–13.3% in Harpswell. Thenumber of buildings affected by SLR varies between 27–45in Brunswick and 210–503 in Harpswell, which corre-sponds to a total building value of $3–4.5 million inBrunswick and $48–107 million in Harpswell (Table 4).More roads were impacted in Harpswell (24–71) comparedto Brunswick (6–12; Table 4). Finally, the majority of fixedpiers in both towns would be flooded by even modest levelsof SLR (Table 4).
The 0.91- and 1.3-m storm surge scenarios increased theimpact to infrastructure caused by SLR (Table 4). Thelargest additional impact of surge was observed for landarea inundated, buildings affected, building valuation, andnumber of roads impacted. Compared to the high (2.0 m)SLR scenario alone, the addition of the high-surge (1.3 m)scenario caused the number of land hectares inundated torise 119% in Brunswick and 78% in Harpswell. Thenumber of buildings affected rose 16% in Brunswick and55% in Harpswell, and the number of roads affected rose50% in Brunswick and 32% in Harpswell (Table 4). Theaddition of surge to the lowest (0.61 m) SLR scenario hadan impact comparable to SLR between 1.0- and 2.0-mscenarios, with the exception of land area inundated, whichwas impacted significantly more by surge (Table 4). Weassumed that temporary storm surges do not diminish landvaluations, so these impacts were not considered (Table 4).
The road network analysis revealed several importantimpacts on transportation infrastructure (Figs. 3 and 4).Figure 3 shows the network of roads that becomeimpassible under the different SLR and surge scenarios.The most extreme scenario (2 m SLR+1.3 m surge) had thegreatest impact, blocking access to populated islands in
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Harpswell with significant tourism and commercialfishery industries. Two roads (Highways 24 and 123)were of particular concern (shown as insets in Fig. 3).These roads flooded under the highest SLR and surgescenario, blocking the entire land-based access fromBrunswick to Harpswell.
Histograms showing the total distance of inaccessible roadsbelow flood points indicated that low SLR (0.61 m) had noeffect on Brunswick (Fig. 4a). Alternate routes were availablefor all 21 roads downstream of the four roads flooded as aresult of SLR (Table 4). Harpswell fared slightly worse at lowlevels of SLR, with 24 roads flooded (Table 4), 12 roadslosing between 10 and 10,000 m of accessibility, and 97roads downstream of the flood points unaffected due to theavailability of alternate routes (Fig. 4b). At 2 m SLR, bothtowns were impacted. Twelve roads were flooded inBrunswick (Table 4), and 18 had stretches between 10 and10,000 m that were inaccessible, with the large majority ofinaccessible road length totaling 100–1,000 m (Fig. 4c). Onlythree roads below the flood points were unaffected due to theavailability of alternate routes (Fig. 4c). Harpswell experi-
enced 71 flooded roads (Table 4), with 55 losing segmentsbetween 1 and 100,000 m (Fig. 4d). A total of 54 roads inHarpswell below the flood points were unaffected due to theavailability of alternate routes (Fig. 4d). Storm surge led toadditional loss of road accessibility. With the lowest SLR(0.61 m) and surge (0.91 m) scenarios, four roads inBrunswick lost between 10 and 1,000 m accessibility(Fig. 4e), whereas Harpswell had 26 roads losing between 1and 10,000 m accessibility (Fig. 4f). The majority of roads(17 for Brunswick and 83 for Harpswell) downstream offlood points were unaffected due to the availability ofalternate routes (Figs. 4e–f). Under the highest SLR (2 m)and surge (1.3 m) scenarios, all 21 roads in Brunswickdownstream of the flood points were rendered inaccessible tosome degree (between 10 and 10,000 m; Fig. 4g). AlthoughHarpswell had 19 roads below the flood points that remainedaccessible, the majority were impacted (>10–100,000 m;Fig. 4h). The loss of just the two major access points toHarpswell via Highways 123 and 24 accounted for 74,500 mof inaccessible roads spanning all of the major islands ofHarpswell (red boxes and insets in Fig. 3).
Table 4 Impacts of SLR and storm surge on infrastructure in Brunswick and Harpswell, Maine
a Data received from Brunswick assessor offices in October 2010 and as geodatabase in 2009. Data for Harpswell is from assessor data base andspatial data base files received in October 2010b Denotes number of fixed piers only
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Impacts of sea level rise on intertidal ecosystems
There is significant potential for changes in intertidalecosystem habitat (Table 5, Fig. 4). Brunswick currentlyhas 258 ha of intertidal habitat (most of which is low andhigh marsh), 59 ha of which is under conservation.Harpswell currently has 547 ha of intertidal habitat (mostlysalt marshes but also including kelp beds), 27 ha of whichis under conservation. Under the 0.61-m SLR scenario,Brunswick loses 57% of its low intertidal habitat butgains 35% more high intertidal habitat (Table 5).Harpswell loses 15% and 23% of low and high intertidalhabitats, respectively. For the 1-m SLR scenario, Bruns-wick loses 67% of its low intertidal habitat but gains 46%more high intertidal habitat (Table 5). Harpswell loses26% of low and high intertidal habitats. For the 2.0-mSLR scenario, Brunswick loses 63% and 25% of itslow and high intertidal habitat, respectively (Table 5).Harpswell loses 15% and 41% of low and high intertidal
habitats, respectively. These results suggest that intertidalarea does not change monotonically with SLR in theselandscapes.
This model for land and marsh inundation analysis isbased on elevation only and it should be noted thatinundation maps do not take into account other factorssuch as actual land coverage, coastal erosion, wetlandaccretion, and the impact of coastal protection structures.As a preliminary test of our results, we compared our marshinundation analysis data with US Soil Service GeographicDatabase (SSURGO) provided at the county level forCumberland County from the Natural Resource Conserva-tion Service. There are challenges in using SSURGO datain terms of its overall accuracy. Some datasets originatefrom the 1980s, and information on the temporal accuracyis generally not available. Additionally, SSURGO data ismapped at a scale that ranges from 1:1,000 to 1:24,000, alevel of resolution lower than provided by the LIDAR data(Pantaleoni et al. 2009), which resulted in mismatches in
Fig. 3 Map of the road networkanalysis for four SLR and surgescenarios: (1) low SLR (0.6 m)and low surge (0.91 m), lowSLR and high surge (1.3 m),high SLR (2 m) and low surge,and high SLR and high surge.Colored lines roads impactedpast flooding impact points. Thetwo inset boxes represent finer-scale maps of Highway 123(upper left) and Highway 24(lower right). These are the twoprimary access points to thetown of Harpswell. At theselocations (denoted by the redboxes), high SLR and surgecause the entire town ofHarpswell to be isolated fromBrunswick. For clarity, impactedroads past these points are notshaded in order to highlightother roads made impassibleby inundation
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ecosystem classification. Specifically, many of the areasidentified as high and low marsh in the SSURGO data alsoincluded a significant percentage of water by area (27.2%and 76.9% of the overall marsh area, respectively) pointingto further concerns associated with integrating this datasetinto the current analysis. Despite these differences, applyingthe SSURGO data to our model replicated findings of adramatic reduction in tidal marsh areas at the different levelsof inundation, suggesting that the model results are robust. Forhigh tidal areas specifically, overall area of tidal marshdecreased by 73% under 0.61-m (2-ft) sea level rise scenariosand 98% under a 2-m sea level rise scenario.
Local communities often do not have access to sophis-ticated datasets or technology such as remote sensing that
can be used to verify the characteristics of intertidal areasmost at risk. However, local communities can marshalresources in the form of committee members and volunteerswho can field verify local conditions. These initiatives areenhanced by the availability and use of data provided bythese types of analyses, which can focus these types ofcommunity efforts.
Future analysis that includes ground truthing in theseareas will be important in terms of assessing the overallimpact of sea level rise on marsh habitat. It is alsoimportant to point out that inundation levels can differbetween the intertidal habitat data (Table 5) and theproperty data (Table 4) because these are accounted fordifferently. Specifically, intertidal habitats, such as coastal
Fig. 4 Histograms of thedistance of roads past floodingimpact points for Brunswick andHarpswell (shown on alogarithmic scale). Vertical barsrepresent the number of roads ineach distance class. A distanceclass of zero means that theseroads were not renderedimpassable by road flooding dueto the existence of alternateroutes around the point ofimpact. Bars located farther tothe right indicate increasinglengths of road made impassibleby flooding. a–b Low SLR(0.61 m) only, b–c high SLR(2 m) only, d–e low SLR+lowsurge (0.91 m), f–g highSLR+high surge (1.3 m)
Table 5 Current low and high intertidal habitat area and potential habitat changes under the three SLR scenarios
Town Habitat type Current area (hectares) Conservation area (hectares) Potential area (hectares) (% change)
a Based upon OGIS data. Due to errors identifying low marsh in certain sections with LIDAR, current low marsh was inspected against aerialphotographs taken at low tide and corrected in portions
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marshes, often extend beyond the boundaries of the parcelsincluded in the tax assessor databases.
Although we could not account for changes in the mudflat intertidal area (∼MLW<∼MSL) that characterizesrecreational and commercial shellfisheries (Fig. 1), thechanges to this zone with SLR could bring about significanteconomic impacts to this region (Table 6). In 2010, state-wideshellfish harvests totaled 1.26 million kg at a value of$18.3 million. For Brunswick, harvests totaled 47,181 kg at avalue of $1.1 million, and for Harpswell, $607,598 inrevenue was generated from a harvest of 23,913 kg.
Discussion
Most work to date on SLR and storm surges has focused onanalyzing sea level rise in large urban contexts that arehigh-density and high-risk. This study reveals that, even incommunities that are not the most populated or at highestrisk, SLR can have a sizeable impact on coastlines,infrastructure, and intertidal ecosystems, thereby affectinglocal livelihoods (Figs. 3 and 4; Tables 4, 5, and 6). Themagnitude of these potential impacts indicates that a rapidlydeployable methodology needs to be disseminated widelyto local decision makers in coastal Maine municipalities.
Outcomes of the boundary organization approach
The boundary organization approach we adopted in thisstudy enabled us to develop such a methodology byidentifying the primary needs of Maine communitiesinterested in assessing potential impacts of SLR and stormsurges. Throughout the process, significant stakeholderengagement and feedback was integrated into the processof developing scientific assessments (Tables 1, 2, and 3).Working with stakeholders was critical as a source offeedback, and our community partners made several salientrecommendations. The need to assign dollar values topotential impacts and to consider the livelihoods of localpeople was deemed critical, which we achieved through theanalysis of land and building valuations (Table 4) as well asassessing the economic value at stake with potential losses
to the shellfish industry and piers used for commercialfishing and other boating activities (Tables 4 and 6). Tohelp communities identify threats to island access, stake-holders recommended the transportation network analysis,through which we identified key bridges and roads that willneed to be raised to accommodate future SLR and stormsurges (Fig. 4). Stakeholders also emphasized the need tomake the results visible and meaningful to residents of thetown by focusing efforts on specific, well-known areas inthe towns that are valued by locals. In public presentations,we were encouraged to frame the potential outcomes of ourstorm surge scenarios by making direct comparisons tohistorical surge events with known impacts, such as thePatriot’s Day storm (http://www.biddefordmaine.org/index.asp?Type=GALLERY&SEC=%7BCD04F870-6BBE-4126-AC3F-526118C8B09E%7D) that impacted southernMaine in April, 2007. Finally, many of the stakeholderswere sensitive to the politically charged reality of climatechange in terms of how scenario results are presented to thegeneral public. They recommended placing emphasis onhow SLR and storm surge scenarios are, in part, based onhistoric trends in the Gulf of Maine (sensu Gehrels et al.2002) and less emphasis on the underlying climate changescience and associated uncertainties.
The boundary organization model outlined here is one ofseveral possible models for bridging science and policy andscales of governance, as well as cogenerating knowledge,developing participatory collaborations, and operationalizingscientific information. Miller (2001) describes “hybridmanagement” as a way to coordinate efforts of differentgroups that are dealing with scientific and political issuessimultaneously. Romsdahl (2011) advocates “decision sup-port networks” as a way to (1) increase usefulness ofinformation; (2) improve relationships between knowledgeproducers and users; and (3) make better decisions. Shehighlights several case studies such as the Consortium forAtlantic Regional Assessments, which brings togetherrepresentatives from higher education, decision makers, andmunicipal- and regional-level assessment teams. Many of thegoals of such decision support networks are similar to thosepresented in our work, suggesting that these approaches arenot mutually exclusive: (1) research that is relevant to
Table 6 Harvest rates andeconomic value of commerciallyimportant shellfish species
Data courtesy of the MaineDepartment of MarineResources
Species State-wide Brunswick Harpswell
Kg Value ($) Kg Value ($) Kg Value ($)
Hard clams and razor clams 83,612 1,284,171 18,439 203,629 1,570 31,878
pending decisions and compatible with existing decision-making approaches; (2) research that is accessible to therelevant decision makers, who are receptive to the results; (3)raising awareness of climate vulnerability in decisionmaking; (4) creating collaborative links and research toolsthat can bridge science-policy gaps; (5) reducing thereinvention of methods by providing analysis models; and(6) encouraging decision makers to incorporate adaptationperspectives into existing planning approaches (Romsdahl2011). However, based on previous work (Cash and Moser2000; Guston 2001) and given the context of institutions,knowledge, scale, and technical capacity for this particularcase in coastal Maine, we found the boundary organizationapproach most attractive. From the outset, we wereintentional in bringing together two groups at different levelsof governance and with different resources and knowledgesets (Cash and Moser 2000). By providing technicalexpertise and bringing together the general climate adaptationstrategy and technical resources of state planners with thelocal knowledge base of municipal decision makers, wehelped broker the development of a new methodology usefulto both sides that would have been difficult to achieve byeither side alone (Guston 2001).
Potential impacts of sea level rise and storm surge on localcommunities in Maine
The outcome of this scientific–stakeholder partnershipprovides several lessons specific to local decision makingin Brunswick and Harpswell. First, the potential economicimpact of SLR—even in these relatively steep and rockycoastlines—may be significant. Both Brunswick andHarpswell stand to incur significant economic impacts.Our models indicate that over the next hundred years,Brunswick and Harpswell may lose up to $4–140 millionworth of land (Table 4). Several roads and bridges will needto be raised, or alternate routes constructed, in order tomaintain accessibility of the entire transportation network(Figs. 3 and 4). In both towns, the potential damages tononfloating pier infrastructures may result in large repaircosts and affect local business and individuals who aredependent on working waterfronts. Building and pierconstruction guidelines and regulations should take intoaccount projections of SLR. Also, given the economicsignificance of the shellfish industry (Table 6), emphasisshould be placed on the potential loss of mud flats to SLR.Second, the distributive effects of potential impact areimportant to consider. As SLR increases, we found that thenumber of parcels affected remains roughly the same whilearea inundated increased (Table 4), indicating that individualproperty owners will tend to suffer greater potential losseswith SLR rather than the losses being spread among morepeople. Third, both towns may experience significant loss of
important intertidal ecosystems, such as low marsh. Basedon the high SLR scenario, Brunswick stands to lose 63% ofthe intertidal zone commonly inhabited by low marsh species(MSL<MHW), and Harpswell may lose 15% (Table 5). Thisinformation and the precise locations of new marshes canassist town planners, local conservation groups, and citizensin the towns with planning future conservation efforts. Thisis especially important given that only 5–23% ofpotential marsh land lies within currently delineatedconservation parcels (Table 5; Titus et al. 2009). Fourth,our analysis of road inundation reveals that when even ashort stretch of road becomes inundated, it can causewidespread transportation problems, such as blockedaccess to schools, hospitals, or emergency responders(Figs. 3 and 4). In Harpswell, the consequences are moreserious, with potential inundation on the two main roadsthat serve as entrances and exits from the peninsulas andislands to the mainland (Fig. 3).
Implications for national and subnational assessments
Although the stakeholder composition and communitycontext were unique to this study, our approach providesinsights that can inform climate adaptation protocols andperspectives. Case studies such as this one from coastalMaine are vital for the integration of local examples intoregional and national initiatives (Moser and Ekstrom 2011).
Linkages between federal and local climate adaptation
Two recent reports by the US government—America’sClimate Choices (National Research Council 2010) and theProgress report of the Interagency Climate Adaptation TaskForce (White House Council on Environmental Quality2010)—identify as priorities local climate adaptationplanning, strengthening collaborations across federal, state,and local levels, and building resilience to climate changein communities (Moser and Ekstrom 2011). Specific goalsand recommendations addressed by our analysis include thecoordination of stakeholders and government agencies todevelop pilot programs, avoiding duplication of efforts(among municipalities), and leveraging existing capabilities.In promoting the bottom-up development of regionalplanning, federal agencies can use local case studies tobetter inform climate adaptation planning nationally,provide better access to information and technicalassistance to local decision makers, and support thepreparation and evaluation of state and local planningefforts (White House Council on Environmental Quality2010). The Climate Change Adaptation Taskforce alsoincluded eight guiding principles for adaptation: (1) adoptintegrated approaches, (2) prioritize the most vulnerable,(3) use best-available science, (4) build strong partner-
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ships, (5) apply risk management methods and tools, (6)apply ecosystem-based approaches, (7) maximize mutualbenefits, and (8) continuously evaluate performance.
Our work provides an important first step towardsmeeting these goals and it offers several insights that caninform federal efforts to support local climate adaptationinitiatives. We have identified areas where improved datageneration and management, such as census data, canfacilitate the process. Based on our initial data assessment,we found a mismatch between the scales of governance anddata availability. Governance and tax assessor data in thesetowns arise and are maintained predominantly at the locallevel. While federal data, such as the census, are consistentin format, they are often sampled on a scale too broad to beaccurate for a local analysis, so we were unable to assessaccurately the populations impacted in these towns. State-derived data on land use and soils, while more detailed, arestill rather broad in scale and often too coarse in resolutionto be used effectively with high-resolution LIDAR data.Local data, as employed in this study, differ in format, levelof organization, and completeness from municipality tomunicipality. For local SLR impact analyses to be success-ful, more attention is needed on the development of dataand data formats that are useful across scales and consistentacross municipalities. In addition to data needs, there willneed to be consideration of how technical and economicresources are allocated to vulnerable communities as morelocal impact analyses emerge with the support of state andfederal agencies. Specifically, the prioritization of vulnerabil-ity at a national scale could emphasize assistance to regions/communities (e.g., coastal Louisiana, Florida, and NorthCarolina) that differ from those identified at a localscale (e.g., Harpswell, Maine).
Subnational climate adaptation analyses
Our results suggest several lessons that can be applied toother subnational SLR impact analysis and climate adaptationefforts. Successful outcomes include
& a community-based, iterative research approach canboth improve the quality and relevance of the analysisand build stakeholder commitment in the project;
& boundary organizations can serve as a neutral facilitatorbetween policy making institutions and communitymembers, encouraging trust, and providing neededexpertise and resources;
& the facilitative process of boundary organizations ismost effective if there is a specific need for the processbeing developed and a vested interest in its success bystakeholders.
However, we identified several data and methodologicalconsiderations that could make adopting this approach by
other municipalities challenging. We emphasized informa-tion that is readily available to community decision makers,but as discussed previously, local data differ in format, levelof organization, and completeness from municipality-to-municipality. The two municipalities had different systemsfor collecting and maintaining assessor data, leading to datathat varied in formats, quality, and currency. We found thatland valuation, in particular, appears to be a nonlinearfunction of parcel area, but tax assessors currently do notalways maintain databases in ways that facilitate fittingmodels to data, which is why we assumed linearity forsimplicity. Such data inconsistencies are inherent given thelocal governance structure of many communities through-out Maine and New England. Other issues include theintegration of disparate datasets. For some communities,integrating digital parcel and soils data with localized dataon ownership, location, and status of conserved lands, andthe location and types of buildings is currently difficult.Our work also suggests that there may be a disconnectbetween local decision makers in smaller, more ruraltowns and the technical capacity available to identifyinformation needs for analyzing data and crafting policywithin the framework of land use planning to preparefor climate change. For example, LIDAR data havebeen collected and will become available for the entirecoast of Maine but will require a level of technicalexpertise to integrate these data into local land useplanning datasets. Finally, our methodology for assessingpotential changes to intertidal ecosystems (Slovinsky andDickinson 2009) is based on changes in tidal zonations, andwe therefore lack the capacity to predict specific ecosystemtypes for specific geographic locations.
We offer several suggestions for remediating thesechallenges. Many communities are beginning to developGIS capacity, which will be important for long-termplanning for climate adaptation. Digital parcel data formost Maine municipalities are currently being developedand should become more widely available. Integration ofassessor databases that include information on ownership,assessed value, existence and types of structures, andconservation status with digital parcel data is importantfor providing municipalities access to the types of analysespresented on our study. We urge local municipalities todiscuss ways to adopt consistent methods of data collection,including archiving in digital formats to allow easyintegration of multiple datasets. Integration of digital parceldata at the local level with other state level spatial datasetssuch as land cover type or soil data will be an importantnext step. The Maine State Planning Office has developed aseries of spatial datasets (http://www.maine.gov/spo/landuse/compplans/planningdata.htm) for communities to use as partof the comprehensive planning process. This may be oneapproach to integrating data pertaining to climate adaptation
impacts, which will necessarily draw on local datasets butmay best be disseminated at the state level. Moving forward,communities will need to integrate changes due to SLR intoassessor data and building codes. Our analyses of potentialintertidal ecosystem changes should be followed up byon-the-ground inventories of the spatial distributions ofecosystem types. As our results suggest, such analyseswill likely show that urgent changes are needed forcoastal conservation planning if intertidal habitat movesto areas that are currently not under conservationeasement (Titus et al. 2009). Finally, we outlined oneapproach to integration of LIDAR data into an analysisthat can inform local decision makers on the impacts ofsea level rise on local infrastructure. These analyses willbecome increasingly important for comprehensive planningand capital improvement planning at the local andregional level.
Vulnerability and resilience frameworks
Although there has been considerable recent debate on therelationship between vulnerability and resilience frame-works in the context of climate adaptation (Vogel et al.2007; Nelson and Adger 2007; Cannon and Müller-Mahn2010; Miller et al. 2010; Turner 2010; Engle 2011; Nelson2011), our simplified methodology is an important first stepthat informs both perspectives. The consideration of risks toinfrastructure and livelihoods is key to vulnerabilityassessment, whereas attention to potential ecological andsocial states and information integrated across spatial scalesis important in the context of resilience (Engle 2011). Ourapproach is also useful to the extent that vulnerability andresilience approaches are converging around (1) the role ofmaintaining diversity (ecological, institutional, and liveli-hood), (2) concerns for cross-scale processes, and (3) theimportant role of governance (Miller et al. 2010).
Our work offers several insights for building resilienceand adaptive capacity in coastal communities. There is awindow of time to invest in the types of data that will allowlocal governments to make decisions on conservationplanning, capital improvements, and economic develop-ment planning in light of the threats to infrastructure onwhich local economies rely. For Brunswick and Harpswell,this includes further analysis of changes to intertidalecosystems and the potential effects on the livelihoods oflocal fishermen. Several steps can be taken to increaseadaptive capacity in these communities. Given that manypiers and buildings will be renovated or built before furthersea level rise is realized, remapping parcels and shorelineswill aid in the construction of future homes, businesses, andpiers to reduce vulnerability to SLR and storm surge.Coastal municipalities also face the potential for changingtax revenue as parcels become inundated. Consideration of
SLR scenarios can help identify how the overall anddistributional effects of the tax burden may shift and whatimplications this could have in terms of providing essentialpublic services. Transportation corridors identified by thenetwork analysis as the most disruptive in the event ofinundation, such as Highways 24 and 123 links betweenmainland Brunswick and the islands of Harpswell, shouldbe targeted for upgrades by local and state authorities.Finally, land use planners, town officials, private land-owners, and NGOs should begin the process of strategizingintertidal ecosystem management, especially in circum-stances where marsh ecosystems are expected to migratefrom conserved areas to private lands or where ecosystemchange is expected to be significant. Shifts in ecosystemservices, including potential changes to shellfishinggrounds should also be considered to assist local economicdecisions about the future prospects of harvests.
Whether or not communities invest resources to shore upvulnerable infrastructure (a vulnerability approach), maintainflexibility in future options and the ability to guide social/economic/ecological transformations (a resilience approach),or some combination of the two will be a decision made bylocal policy makers and stakeholders. As described by Nelsonand Adger (2007), a balance should be struck thatconsiders the acceptable level of risk against the abilityof the socioeconomic decisions to maintain the flexibilityto respond to future conditions such that responses tovulnerable infrastructure now does not undermine resiliencein the future. For example, the expense of maintainingtransportation access to Harpswell could limit other responsesto SLR and storm surge (what Nelson (2011) calls “loss ofresponse diversity”).
Limitations of the approach
We recognize several potential limitations of our studygiven (1) the inherent tradeoff between simplification andgenerality versus physical detail, and (2) issues of dataaccuracy. This study utilized an equilibrium inundationmodel of sea level rise and storm surge that has beencommonly employed in other studies (Titus et al. 1991;LaFever et al. 2007; Cooper et al. 2008; Kuleli 2010). Thisanalysis does not take into account bathymetry and physicalimpacts on specific areas of the coastline. Local data onstorm surge heights and frequencies are based on historicaldata from the NOAA and MGS and do not take intoaccount projected changes in storm surge. Additionally, sealevel rise will not happen in isolation, but rather inconjunction with changes in precipitation, wind intensity,increased storm surges, river discharge, and storm frequency.Identifying realized changes in intertidal ecosystems requiresadditional site-specific work for proper characterization ofcommunity changes and net accretion rates.
J Environ Stud Sci (2012) 2:111–130 127
The incorporation of local data provided the opportunityto carry out the analysis at a scale that matched the localcommunities’ needs, which enhanced the overall accuracyof the analysis. Differences in the format of the databetween the two communities resulted in generalization forthe purpose of comparing data between communities.Including datasets that were collected at different timesand at different levels of resolution, such as the location ofinfrastructure mapped at a lower resolution than the LIDARdata, may have introduced some inaccuracies. As LIDARdata and familiarity with conducting coastal vulnerabilitystudies become more widespread, we anticipate thattechniques used for future data collection will more closelymatch the underlying accuracy of the LIDAR data and willimprove these types of analyses. We were fortunate to haveaccess to a dataset that had previously undergone fieldverification by the Maine Geological Survey (Slovinskyand Dickinson 2009). Similarly, changes in format of theLIDAR data may have also introduced some errors andfuture LIDAR datasets provided by the state of Maine willbe standardized to avoid this situation. Finally, even at thescale of two town analysis, generalizations in terms ofshoreline characteristics were necessary. At a local level,the most effective means of improving accuracy of theoverall analysis is field verification. The next phase of thisresearch will be the selection of three pilot areas within thestudy area to examine and field verify the type and locationof marshes.
Conclusion
Helping local decision makers in coastal communitiesdevelop the information and tools needed to assess SLRare critical for building the adaptive and resilience capacity.Boundary organizations, such as colleges and universities,can work effectively with communities to gather and createdata, conduct preliminary analyses, and facilitate longer-term planning processes in response to SLR and other typesof vulnerability assessments and climate adaptation. Theavailability of consistent data that is locally based butdistributed at a state or regional level provides moreopportunities for regional planning and reduces the burdenon individual communities to maintain datasets. Althoughthe kinds of spatial and assessor data collected in this studywill require refinement, the advantage of our approach toSLR analysis is that it highlights areas within a communitythat may require further investigation in order to best planfor the impacts of climate change. Further research will berequired for the ground truthing of marshes and otherintertidal ecosystems, assessing the implications for con-servation planning, and identifying infrastructure that mayneed more precise data collection such as the location of
bridges and piers at sea level. Given the window of timebefore significant SLR is realized, by identifying and actingon these data gaps and approaches for expanding capacity,communities will be better positioned to plan for climatechange at a local and state level.
Acknowledgments We wish to thank all of the stakeholders andcommunity partners who made this research possible, especiallyCathleen Donovan, Anna Breinich, Carol Tukey, Debbie Turner,Justin Hennessey, Malcolm Burson, Elizabeth Hertz, John Cannon,Steve Dickson, Pete Slovinsky, Heidi Bray, and Doug Marcy. SeveralBowdoin students were instrumental in the analyses presented in thispaper: Melissa Anson, Tom Marcello, Leah Wang, Woody Mawhinney,and Liza LePage. We thank Ellen Hines and two anonymous reviewersfor helpful comments on earlier versions of the manuscript.
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