Journal of Environmental Studies and Sciences

, Volume 2, Issue 2, pp 111–130

Using a boundary organization approach to develop a sea level rise and storm surge impact analysis framework for coastal communities in Maine

Authors

    • Bowdoin College, Environmental Studies
  • Maryellen Hearn
    • Bowdoin College, Environmental Studies
  • Krista Bahm
    • Bowdoin College, Environmental Studies
  • Eileen Johnson
    • Bowdoin College, Environmental Studies
Article

DOI: 10.1007/s13412-011-0056-6

Cite this article as:
Camill, P., Hearn, M., Bahm, K. et al. J Environ Stud Sci (2012) 2: 111. doi:10.1007/s13412-011-0056-6

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 stakeholders 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 changeSea level riseCoastCommunityInfrastructureMarshAdaptationBoundary organizationImpact

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; Preston et al. 2011), often in high-population metropolitan or tourist areas with economic significance (Gornitz et al. 2001; Suarez et al. 2005; Kirshen et al. 2008; Hansen 2010; Frazier et al. 2010; Neumann et al. 2010; Hunt and Watkiss 2011), or in sandy coastlines with the potential for significant erosion and migration of barrier islands (Pilkey and Cooper 2004). These approaches often involve top–down, one-way flows of information from scientific communities to policy arenas—the “pipeline model” of information dissemination described by Cash and Moser (2000).

As such, significant opportunities remain for helping communities better understand and manage risks associated with SLR and storm surges (Moser and Ekstrom 2011) as illustrated by the New England region of the northeastern USA. A significant fraction of populations in this region live in smaller municipalities in which site-specific SLR impact and vulnerability analyses have not yet been conducted. In New England, 324 census-designated, subcounty municipalities—85% of which have populations less than 50,000—border the coastline (US Census Bureau 2010). Maine’s 8,400 km of coastline includes coastal counties that account for 73% of the state population (Moser 2005) and 23% of the population lives within 1 km of the coast (Lam et al. 2009). Many New England communities are situated along steep, erosion-resistant rocky shorelines that create a false sense of security even though potential SLR and storm surge can have significant impacts on roads, infrastructure, and coastal wetlands. Moreover, adaptation planning 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 often faced with resource and technical constraints, and they are often saddled with more pressing issues than climate warming, such as the provision of resources for recreation, public safety, and water, energy, and infrastructure protection (Tribbia and Moser 2008). As a result, the impacts of slow-onset threats like SLR are often understudied, and efforts to develop long-term adaptation strategies languish (Moser 2005; Tribbia and Moser 2008). In Maine, coastal assessments of SLR impacts have been identified as an important goal of the State of Maine Climate Adaptation Plan (Maine Department of Environmental Protection 2010), but the range of technical capacities throughout the state at both the regional planning and local level, and the role of the home rule form of government in Maine (which empowers municipalities to pass laws that are local in nature so long as they do not preempt state law) present particular challenges in developing coordinated efforts between and among state agencies and the local level. These concerns suggest that many communities in Maine and elsewhere in New England remain largely underprepared in terms of planning for SLR impacts.

The ways in which institutions and knowledge sharing have been traditionally structured is part of the reason why effective impact and vulnerability assessments have not been widely implemented, as noted by Tribbia and Moser (2008): “To date, coastal managers insufficiently benefit from the available scientific information on coastal impacts of climate variability and change and sea-level rise, as it exists in largely untapped scientific journals, few experts are ever consulted, and relevant research institutions are not yet linked into the ‘management on the ground.’” Recent theoretical developments on institutional analysis and knowledge systems offer models for effectively integrating science-based information on potential SLR impacts and community planning at the local level (Guston 2001; Cash et al. 2003; Folke et al. 2005; Moser 2005; Lebel et al. 2006; Moser et al. 2008; Tribbia and Moser 2008). As noted by McNie (2007) and Weichselgartner and Kasperson (2010), scientists may not be producing the right kinds of information required by decision makers. Understanding how scientific information relates to the ways issues are valued and framed and which options local communities may consider most important are significant departures from traditional interactions between science and policy (Cash et al. 2003; Michaels 2009). Importantly, information must be salient to the needs of decision makers and legitimized with respect to stakeholder values and beliefs. Sustainability science advocates for the inclusion of stakeholders throughout the research process to ensure that outcomes are relevant to and actionable by stakeholders (Kates et al. 2001; Cash et al. 2003; Folke et al. 2005; Silka 2010).

Boundary organizations are considered to be an effective means of developing communication, trust, and capabilities between communities of experts and communities of decision makers (Guston 2001; Cash et al. 2003; Folke et al. 2005; McNie 2007; Sarewitz and Pielke 2007; Tribbia and Moser 2008). Originally conceived as a way to bridge science and policy, boundary organizations possess several key attributes, including (1) the ability to promote active, iterative, and inclusive communication among stakeholders that facilitates coproduction of knowledge; (2) translation of ideas that are readily shared and understood by both sides; (3) the ability to mediate conflicts and to ensure that all viewpoints are represented; and (4) the ability to digest original research that decision makers may not have the capacity or time to accomplish (Guston 2001; Cash et al. 2003; Tribbia and Moser 2008; Michaels 2009). Importantly, having local data available allows researchers and planners to collaboratively address sea level rise impacts in a manner more removed from the highly politicized arguments about climate change. Residents and decision-makers can engage in discussions about observable local impacts, as local data are easier to apply and act upon (Dempsey and Fisher 2005).

Colleges and universities can serve as effective boundary organizations for addressing climate adaptation. Cash and Moser (2000) argue that boundary organizations are important for mediating not only science and policy but also the efforts of actors across different scales. In the state of Maine, general recommendations for climate adaptation have been promulgated at the state level (Maine Department of Environmental Protection 2010), whereas implementation often lies with local decision makers at the municipal level. Bowdoin College (Brunswick, Maine) has a long tradition of collaborative research with state agencies, local communities, and nonprofit organizations, which helps build trust across these scales and ranges of institutions. This experience allows the College to serve as a focal point intermediate to the state and municipal levels in facilitating a multiscale collaborative network interested in SLR. Guston (2001) adds that boundary organizations are effective when they play a role that is difficult to achieve by groups on either side of the boundary. Maine state agencies offer the vision and expertise to carry out SLR impact analyses, but they often do not have the extensive, site-specific knowledge that local decision makers possess about their own communities or the resources to implement municipal-specific analyses. Consequently, the state adaptation report recommends “develop[ing] and disseminat[ing] tools that will allow local and regional planning authorities to initiate their own adaptation planning process” (Strategy A.4.1). Local decision makers possess detailed understanding of community needs but often require assistance with technical and data support in developing SLR impact methodologies. As a potential boundary organization, Bowdoin College is able to provide the expertise and resources (in terms of faculty and student commitment, scientific and geographic information system (GIS) capacity, and established community networks) to combine the vision of the state climate adaptation planning recommendations with the knowledge and needs of local municipalities to develop a general methodology that can be implemented across coastal Maine communities. Finally, by involving students in the SLR impact analysis process, colleges and universities can make education an important collateral benefit.

Here, we describe a project carried out by researchers at Bowdoin College in consultation with stakeholders representing state, regional, and local entities. These stakeholders included the Maine Department of Environmental Protection and State Planning Office, the Maine Geological Survey, and other local-level governmental staff, elected officials, as well as nongovernmental stakeholders to begin the process of developing a simplified, generalizable approach for determining potential impacts of SLR and storm surge on coastal Maine communities. Our team of authors served in the dual capacity as the scientific team developing the impact assessment methodology as well as the boundary organization responsible for engaging key stakeholders and building their interests and expertise into the methodology. We used the towns of Brunswick and Harpswell, Maine, as case studies to address the following questions: (1) What are the technical needs of key institutional stakeholders interested in a community focused SLR impact assessment methodology? (2) How might we define a simplified framework for impact analysis based on readily available data methods easily transferable to coastal communities in Maine? (3) What are the challenges of gathering information from stakeholders across local and regional scales relevant to SLR impact analysis? (4) What informational and conceptual gaps remain, and what are possible ways to remediate them? (5) What are the challenges in developing capacity among local decision makers in terms of assimilating and utilizing information? (6) What are the broader implications of this case study for the development of national and subnational climate adaptation protocols?

Methods

Study region

Our study area included the municipalities of Brunswick and Harspwell, Maine (Fig. 1). The towns border each other and have extensive coastlines along the Atlantic Ocean, specifically within the Gulf of Maine and Casco Bay. Harspwell (population 5,000) is composed of contiguous land in Cumberland County, as well as Great, Orr’s and Bailey Islands and over 200 uninhabited islands. With nearly 350 km of coast, Harpswell has more shoreline than any other municipality in Maine (CES 2011). Brunswick (population 21,000) is comprised of contiguous land in Cumberland County and has approximately 110 km of coastline. We focused primarily on the effects of SLR on land and infrastructure that currently borders the Atlantic Ocean, excluding for simplicity areas that are bordered by tidal rivers.
https://static-content.springer.com/image/art%3A10.1007%2Fs13412-011-0056-6/MediaObjects/13412_2011_56_Fig1_HTML.gif
Fig. 1

Location of the towns of Brunswick and Harpswell, Maine. Light gray areas Atlantic Ocean, dark gray areas neighboring municipalities. The spatial extent of intertidal ecosystems is also shown (shellfish distribution map adapted from Mason Webber 2009 and Maine Department of Marine Resources 2010)

Characteristic of much of New England, the coastlines of Brunswick and Harpswell are predominantly rocky and steep (see supplementary materials, Fig. S1 for a LIDAR map of elevation). The bedrock geology of this region consists primarily of Precambrian metasedimentary and volcanic rocks of the Casco Bay Group (Osberg et al. 1985). Surficial geology is characterized by sandy glacial–marine outwash deposits (especially in Brunswick), glacial–marine silts and clays, tills, and bedrock with thin glacial sediment cover (Thompson and Borns 1985).

Boundary organization approach

In the summer of 2010, the director of the Maine Coastal Program approached the College regarding the possibility of analyzing the impacts of sea level rise on coastal infrastructure. This collaboration grew from two earlier projects carried out by Bowdoin College staff on (1) the development of a water quality monitoring site inventory maintained by volunteer monitoring organizations along the coast of Maine and (2) long-term collaborations with the Maine State Planning Office, local communities in midcoast Maine, and local and national nongovernment organizations (NGOs) in the development of a conservation plan as part of the Sagadahoc Rural Resources Initiatives [hyperlink: http://www.maine.gov/spo/landuse/docs/ConservationBlueprint_March2010.pdf] for the communities of Sagadahoc County and the towns of Harpswell and Brunswick, Maine.

The initial phase of the project consisted of discussions among the Bowdoin research team, Maine State Planning Office, Maine Department of Environmental Protection, and the Maine Geological Survey (MGS; Table 1). This initial stakeholder group was composed predominantly of representatives from state agencies in an effort to understand the vision and needs of existing state-level adaptation planning efforts. As the boundary organization, the Bowdoin team’s role was to provide research capabilities and to determine how collaboration could facilitate the objectives of the 2010 Report to the Maine Legislature on climate adaption, and meet the needs of local communities. Preliminary scoping discussions were followed up with face-to-face, structured, focus group meetings, where needs and methodologies were specified to the Bowdoin research team. Primary needs included addressing recommendation B.1.1.2 of the climate adaptation report regarding coastal infrastructure vulnerability (Maine Department of Environmental Protection 2010), which included the development of methods to assess SLR impacts on coastal infrastructure (roads, buildings, piers). Coastal wetlands were identified as an important second priority based on the conservation value of marshes and the economic importance of the commercial and recreational shellfishery. Previously implemented methodologies by the MGS (Slovinsky and Dickinson 2009) were described, including processing protocols for LIDAR data and tide-based projections of coastal marsh habitat changes for the city of Scarborough, Maine (see “Elevation modeling using LIDAR” section below; Table 1). These scoping and information-gathering meetings represented a two-way flow of information as the project was formulated, but the Bowdoin research team’s role shifted to that of an information recipient as the state agencies described the state adaptation plan and potentially useful methodologies. It is important to note that there was a change in the Maine governorship halfway through this project, which created a political shift in priorities for state agencies. Combined with the general shift in public attitudes towards climate science over the past few years, the changing political climate in Maine impacted how we structured the research and engaged the community. Specifically, we attempted to make the analysis nonpolitically charged and broadly acceptable to the general public. As described below, we framed SLR as a process that we know is already impacting Maine communities without emphasizing the scientific evidence for human vs. natural causes. In addition, we structured the analysis to highlight the kinds of economic impacts from SLR and storm surge that would interest most community members, including roads, buildings, land, piers, and shellfishing grounds.
Table 1

Project phase 1: initial stakeholder engagement, knowledge generation, and flows of information

Date

Meeting/activity

Community Stakeholders/partners

Purpose

Outcome

Information flow Bowdoin ⇔stakeholders

6/29/10

Initial scoping

Director Maine Coastal Program, Director Land Use Team, Climate Manager, Maine DEP, Bowdoin Environmental Studies Program Director and Program Manager

Project feasibility

Agreement that the Bowdoin-state partnership would be a worthwhile collaboration

8/19/10

Proposed scope of work submitted

Maine State Planning Office and Maine DEP

Project proposal development

Comments received to focus project scope

9/13/10

Initial meeting

Maine State Planning Office and Maine DEP

Government staff provided Bowdoin research team with an overview of the plan and the specific dimensions of the climate inventory

Identification of needs from stakeholders

9/26/10

Presentation by Maine Geological Survey

Maine Geological Survey

Presentation of earlier SLR analysis for town of Scarborough, Maine

Protocol from MGS project was adopted for the current project for the purpose of consistency with other adaptation planning initiatives, including processing of LIDAR data, identification of high and low marsh.

Fall, 2010

Ongoing consultation

Maine Geological Survey, NOAA

Data gathering on tides, historical storm surges

Feedback to improve methodology, staff from MGS continued throughout the project to provide technical input on the project as well as data

The second phase of the project involved a series of structured, focus-group collaborations with a broader group of stakeholders that focused on methodology development and soliciting feedback from local municipal decision makers (Table 2). Stakeholders included elected officials, local governmental agencies, town planners, NGOs, consultants, and citizens groups who have played an active role in local and regional planning in the greater Brunswick and Harpswell region. The goal at this stage was to determine what local knowledge and data sources existed and to ensure that we were including in our methodology the kinds of information and analyses that local stakeholders cared about most. We held meetings in several venues and used several formats to engage as many people as possible, including public presentations at Bowdoin, presentations in the towns of Harpswell and Brunswick, and meetings with other adaptation planners in the Casco Bay region of Midcoast Maine.
Table 2

Project phase 2: methodology development and initial dissemination and solicitation of feedback from stakeholders

Date

Meeting/activity

Community stakeholders/partners

Purpose

Outcome

Information flow Bowdoin ⇔stakeholders

10/2/10

Casco Bay Climate Adaptation Roundtable

Maine state agencies, federal EPA, Casco Bay Estuary Project, Greater Portland Council of Governments, and Maine Cooperative Extension

Discussion of this and other adaptation projects throughout Maine

Affirmation of the value of this project

11/22/10

Initial public presentation

Maine State Planning Office and Maine DEP

Initial dissemination of work; initial solicitation of feedback

Feedback to improve methodology and issues analyzed

1/31/11

Presentation to town of Brunswick

Director of Planning and Development Town of Brunswick, Brunswick Town Councilor, Director of Communications and Government Relations The Nature Conservancy, Director of Land Use Planning Maine State Planning Office, Engineer from Wright-Pierce, The ClimateProject.org Board of Directors, Republicans for Environmental Protection (REP.org), Maine DEP, Maine Geological Survey, Topsham Conservation Commission, Planner for the Greater Portland Council of Governments

First formal public presentation of research to the general public

Feedback to improve methodology and issues analyzed

2/8/11

Presentation to town of Harpswell

Harpswell Conservation Commission, Board Members and the Executive Director of the Harpwell Heritage Land Trust

First formal public presentation of research to the town of Harpswell

Comments received focusing primarily on tidal marshes and implications for conservation lands and conservation planning

5/19/11

Presentation at Manoment Climate Adaptation Symposium

60 Representatives from state agencies, local communities and NGOs within the Sagadahoc Region, Project Manager for Climate Change and Energy Manomet Center for Conservation Sciences

First formal public presentation of research to the town of Brunswick

Dissemination of research and methodology to the general public; feedback received on methodology

DEP Maine Department of Environmental Protection

After incorporating stakeholder feedback into the analysis, the final phase of the project was focused on disseminating the results and methodology to stakeholders and the general public through a series of public presentations at local, state, and national meetings (Table 3). This stakeholder community involved individuals and groups from both the state and local levels.
Table 3

Project phase 3: dissemination of results and methodology to stakeholders and the general public

Date

Meeting/activity

Community stakeholders/partners

Purpose

Outcome

Information flow Bowdoin ⇔stakeholders

3/16/11

Poster Presentation at Maine Water Conference

Faculty and students from Maine colleges and universities, state agency representatives

Second formal public presentation of research to the general public

Dissemination of research and methodology to the general public

5/13/11

Poster presentation at Community Partnership Symposium at the Brunswick Public Library

Bowdoin College faculty students and staff, community members, elected officials from local municipalities

Third formal public presentation of research to the general public

Dissemination of research and methodology to the general public

May, 2011

Ongoing consultation

Maine State Planning Office

Inquiry with Bowdoin GIS staff on time involved in LIDAR processing

Dissemination of methodology to state agencies

6/24/11

Poster presentation at the Association for Environmental Studies and Sciences

Faculty and students from colleges and universities worldwide

Fourth formal public presentation of research to the general public

Dissemination of research and methodology to the general public

Coastal impact analysis

We identified data sources widely available to coastal communities in Maine and used these data to develop a simplified, 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, and storm surge data; (2) assessments of potentially impacted coastal property value and piers associated with public access and local fisheries; (3) transportation network analysis to determine potential weak links threatened by SLR and storm surges; and (4) potential impacts on coastal intertidal ecosystems.
https://static-content.springer.com/image/art%3A10.1007%2Fs13412-011-0056-6/MediaObjects/13412_2011_56_Fig2_HTML.gif
Fig. 2

Conceptual model of the GIS-based framework. Columns primary data needs and analyses, rows data type

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 (Lowe et al. 2009, Anthoff et al. 2010; Hallegatte et al. 2011) and newly published scientific information with upwardly revised SLR projections (Rahmstorf 2007; Füssel 2009; Vermeer and Rahmstorf 2009; Jevrejeva et al. 2010; Nicholls and Cazenave 2010). The low scenario is consistent with the State of Maine planning guidelines (Maine Department of Environmental Protection 2006) and falls within the ranges projected in the AR4 assessment of the IPCC (0.18–0.79 m; IPCC 2007). The US EPA, in cooperation with the Maine State Planning Office (during the Baldacci administration), advised the medium and high sea level rise scenarios for town planning (personal communication).

Storm surges augment SLR and increase damages associated with land inundation. Modeling storm surges at the small-community level is problematic given the need for physical models that consider detailed, site-specific factors like wave dynamics, complex subsurface bathymetry, the distribution of islands and bays, storm intensity, and storm tracks. These kinds of process models require resources and technical expertise that are not widely available to local decision makers (Moser 2005), so we chose a simpler approach of using historical storm surge data obtained through National Oceanographic and Atmospheric Administration (NOAA) and the National Weather Service to define reasonable upper limits of surge. Portland, Maine was the closest available data source with the longest tidal records (1912–present).1 On average, storm surges of 0.91 m (3.0 ft) occur every 5–7 years, and the highest recorded storm surge at high tide in Portland was 1.31 m (4.3 ft) in 1947 (Budd 1980; Cannon 2009). We used these data to define two storm surge scenarios: low = 0.91 m and high = 1.3 m.

We combined the three SLR and two storm surge scenarios to generate a matrix of six potential scenarios for the coastal impact analysis (Fig. 2). Based on these scenarios, we identified parts of the tidal cycle relevant to our impact analysis. For the inundation of land and potential impacts to infrastructure due to SLR, we chose a benchmark of highest annual tide (HAT), which is the highest water elevation likely to affect infrastructure. For impacts caused by SLR plus storm surge, however, we chose a more conservative benchmark of mean higher high water (MHHW), the highest average sea level expected to be achieved per day, given the fact that storms are unpredictable and are likely to occur during times of the year when HAT is not experienced. For impacts to coastal intertidal ecosystems (described below), we also required mean 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 2010 HAT of Cushing Island Station in Casco Bay using the MGS Tide Calculator (HAT = 6.27 ft/1.91 m), and determined 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 SLR impacts (Wu et al. 2008; Gesch 2009) due to the significantly improved spatial resolution and vertical accuracy compared to digital elevation models (DEMs) available through the US Geological Survey’s National Elevation Dataset (NED). DEMs available through the USGS NED have a resolution either of 1 arc-s (approximately 30 m) or 1/3 arc-s (approximately 10 m) and are derived from cartographic contours based upon USGS 7.5-min topographic maps. Studies comparing the vertical accuracy of DEMs with LIDAR indicate that the root mean square error (RMSE) can range to 1.27 m as compared with a RMSE of 0.14 m for LIDAR data. The LIDAR set used as the basis of this analysis has a predicted vertical RMSE of 0.067 m, exceeding the accuracy of comparable nationally available date sets. Field verification of the vertical accuracy of LIDAR data indicated errors in the range of 0.15 m (Slovinsky and Dickson 2006, 2009), comparable to calculated vertical RMSE from other national studies.

Coastline elevations were modeled using LIDAR data collected as part of the Federal Emergency Management Agency’s (FEMA) Map Modernization Program in 2006 (see supplementary materials, Fig. S1 for a LIDAR map of elevation). LIDAR data are now available for the entire coastline of Maine. The original LIDAR dataset was sampled at 0.61-m (2 ft) spacing in State Plane NAD83. A subsequent dataset, provided in LAS (Common LIDAR Exchange Format) had been converted to UTM Zone 19, with units in meters, sampled at 2-m intervals and maintained the NAVD88. These data were used for first return analysis to determine heights of infrastructure such as buildings, piers, and bridges (Fig. 2). All elevation data presented in this study are referenced to the vertical datum NAVD88 and the horizontal datum North American Datum of 1983. For each sea level rise scenario, a spatial dataset was created from the FEMA LIDAR data, which delineates the HAT level under the given scenario. We overlaid these tide elevation scenarios with land and infrastructure data to determine which parcels, buildings, piers, and roads would be directly affected by sea levels at HAT. To analyze storm surge scenarios, we created a spatial dataset that included elevation delineations for all SLR plus storm surge scenarios (Fig. 2).

Impacts on land and coastal infrastructure

Property inundation and value

We used tax assessor databases and digital parcel data from the towns of Brunswick and Harpswell, including the geographic locations of land area, building locations, and property market values (Fig. 2). For the purposes of land valuation, a linear relationship was assumed between parcel acreage and value. The formulas used by tax assessors to calculate parcel-specific land value are complex and idiosyncratic to each municipality, limiting the generality of existing approaches. Previous studies have also estimated the value of land lost to inundation using simplified approximations of the relationship between land area and value similar to ours (Nordhaus 1991, Titus et al. 1991, Bosello et al. 2007). Our approach has one advantage over these previous studies: Studies conducted on regional or national scales often 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 by multiplying each individual parcel value by percent inundation. Building data included spatial footprints of each individual building, building values, and land use codes. For the land inundation vulnerability assessment, we combined parcel data with the LIDAR elevation data to determine the total land acreage submerged and the number of parcels affected under each sea level rise scenario. When combined 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 parcels were designated for conservation, aiding the analysis of how much area currently designated as “marsh area” (according to our tide-determined intertidal habitat zonations described below) coincides with lands under any level of conservation. “Conservation parcels” however, are broadly defined in this case. Conservation lands include any conserved lands through fee or easement and are therefore established as permanent conservation lands. Conservation lands also include parcels that fall within specific tax classification categories that provide reduced tax rates for farmland and forestlands. Although these parcels could experience a reversal in conservation status at any point, these parcels were included as an indicator of potential future permanent conservation protection.

Piers

Data for public and some private piers in Harpswell and Brunswick were provided by The Island Institute, a nonprofit organization in the Gulf of Maine that conducted a statewide inventory of the working waterfront in Maine in 2005 (Conover and Rowan 2007). The data included privately and publicly owned infrastructure and included piers, boat launches, and access points. One of the initial questions raised by state agencies was whether this dataset could be useful for sea level rise analysis at the local level. We imported the spatial locations of waterfront piers and separated piers based on whether they were fixed vs. floating. For the purpose of our analysis, floating piers were assumed to rise with the sea level and were not analyzed for potential inundation. The fixed pier category also included boat launches. The locations of fixed piers were manually inspected against orthophotos (Maine Office of GIS 2009). Because the pier data were originally intended to approximate the location of working waterfronts rather than for precise SLR analysis, we inspected and adjusted each location as necessary. Once adjusted, the elevations from first-return LIDAR were applied to pier locations to determine level of inundation under each scenario.

Transportation

State road data, developed for the planning of emergency routes, were acquired from the Maine Office of GIS (2010). Combining road data with elevation data allowed for the identification of road segments projected to be inundated by SLR and storm surge scenarios (Fig. 2). Once these segments were identified, a network analysis was performed using GIS to determine the lengths and locations of road systems that would become inaccessible beyond points of inundation. We also plotted histograms of the number of roads as a function of the distance of inaccessible road length past the impact (inundation) point to summarize how the distribution of inaccessible road length changes with different SLR and surge scenarios. For ease of analysis, we considered only a subset of SLR and surge scenarios for the road network analysis: (1) low SLR (0.61 m) only, (2) high SLR (2 m) only, (3) low SLR + low surge (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 a simplified methodology adapted from the MGS (Slovinsky and Dickinson 2009). We created a GIS-based spatial model sensitive to the slope of the shoreline and degree of inundation caused by SLR. The zone between MHW and HAT can be classified as high intertidal habitat, often dominated by the high marsh species Spartina patens. The zone between MSL and MHW can be classified as low intertidal 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 shift upslope depending on local topography. Estimating the potential change of these zonations is important in terms of conservation management of coastal marshes, species habitat planning, and the location of commercially and recreationally valuable shellfish species. Although the potential area of mud flats located approximately between mean low water (MLW) and MSL is ecologically and commercially significant to the local shellfishing industry (Fig. 1), we were unable to model changes in this zone due to the inability of LIDAR to reliably map low intertidal elevation and the imprecision and low resolution of existing coastal bathymetric maps. Instead, we compiled information on the harvest rates and economic value of the commercial and recreational shellfish industries for 2010 to assess the potential threat to livelihoods resulting from impacts to low intertidal, mud flat ecosystems.

Following previous work (Titus et al. 1991; Lafever et al. 2007; Cooper et al. 2008; Kuleli 2010), we did not include processes known to be important, such as sediment erosion, 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 to parameterize and run physically based models are often unavailable at a local scale. We recognize the limitations of our simplified approach, but in terms of facilitating rapid, local-scale planning, an easy-to-use equilibrium model of intertidal ecosystem zonation change—followed up by local verification on the ground—is valuable. Moreover, for the 1- and 2-m SLR scenarios, inundation rates are likely to approach or exceed sediment accretion rates in New England (Morris et al. 2002, Ashton et al. 2008), thereby causing the kinds of shifts from high marsh to low marsh and 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 were significant impacts on infrastructure but also important differences between the two municipalities. Between 49 and 144 ha of property (land contained within parcels) were inundated in Brunswick and 96–325 in Harpswell, respectively (Table 4). The number of parcels affected by SLR is greater than 460 in Brunswick and 2,400 in Harpswell, remaining consistent across the three scenarios, indicating that the main effect of SLR is further inundation of impacted parcels rather than the addition of newly impacted parcels. The value of land inundated represents a loss of $1–4 million in Brunswick and $37–140 million in Harpswell, or approximately 0.2–0.4% of total assessed land value in Brunswick and 3.6–13.3% in Harpswell. The number of buildings affected by SLR varies between 27–45 in Brunswick and 210–503 in Harpswell, which corresponds to a total building value of $3–4.5 million in Brunswick and $48–107 million in Harpswell (Table 4). More roads were impacted in Harpswell (24–71) compared to Brunswick (6–12; Table 4). Finally, the majority of fixed piers in both towns would be flooded by even modest levels of SLR (Table 4).
Table 4

Impacts of SLR and storm surge on infrastructure in Brunswick and Harpswell, Maine

Category

SLR Scenario (highest annual tide)

SLR + storm surge Scenario 1 (MHHW)

SLR + storm surge scenario 2 (MHHW)

 

0.61 m

1 m

2 m

0.61 m SLR + 0.91 m surge

0.61 m SLR + 1.31 m surge

2 m SLR + 0.91 m surge

2 m SLR + 1.31 m surge

Number of hectares inundated

 Brunswick (total hectares assessed parcels: 11,408)a

49

73

144

178

204

282

316

 Harpswell (total hectares assessed parcels: 5,827)a

96

153

325

224

284

484

578

Number of parcels affected

 Brunswick (total parcels: 6,828)a

466

477

506

478

487

511

516

 Harpswell (total parcels: 5,042)a

2,404

2,438

2,504

2,448

2,469

2,548

2,583

Land value affected ($ million)

 Brunswick (total assessed land value: 500)a

1.1

2.0

4.0

 Harpswell (total assessed value land: 1,045)a

37

61

140

Number of buildings affected

 Brunswick (total buildings: 4,296)a

27

31

45

32

35

42

52

 Harpswell (total buildings: 2,048)a

210

281

503

278

360

650

781

Building value affected ($ million)

 Brunswick (total assessed improvements: 1,408)a

3

3.5

4.5

3.5

3.6

5.1

6

 Harpswell (total assessed buildings: 758)a

48

61

107

60

76

140

167

Number of roads affected

 Brunswick

6

9

12

9

18

 Harpswell

24

33

71

32

94

Number of piers affected

 Brunswick (total piers: 5)b

4

5

5

4

5

5

5

 Harpswell (total piers: 92)b

47

55

76

50

50

77

83

aData received from Brunswick assessor offices in October 2010 and as geodatabase in 2009. Data for Harpswell is from assessor data base and spatial data base files received in October 2010

bDenotes number of fixed piers only

The 0.91- and 1.3-m storm surge scenarios increased the impact to infrastructure caused by SLR (Table 4). The largest additional impact of surge was observed for land area inundated, buildings affected, building valuation, and number 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 to rise 119% in Brunswick and 78% in Harpswell. The number of buildings affected rose 16% in Brunswick and 55% in Harpswell, and the number of roads affected rose 50% in Brunswick and 32% in Harpswell (Table 4). The addition of surge to the lowest (0.61 m) SLR scenario had an impact comparable to SLR between 1.0- and 2.0-m scenarios, with the exception of land area inundated, which was impacted significantly more by surge (Table 4). We assumed that temporary storm surges do not diminish land valuations, so these impacts were not considered (Table 4).

The road network analysis revealed several important impacts on transportation infrastructure (Figs. 3 and 4). Figure 3 shows the network of roads that become impassible under the different SLR and surge scenarios. The most extreme scenario (2 m SLR + 1.3 m surge) had the greatest impact, blocking access to populated islands in Harpswell with significant tourism and commercial fishery 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 surge scenario, blocking the entire land-based access from Brunswick to Harpswell.
https://static-content.springer.com/image/art%3A10.1007%2Fs13412-011-0056-6/MediaObjects/13412_2011_56_Fig3_HTML.gif
Fig. 3

Map of the road network analysis for four SLR and surge scenarios: (1) low SLR (0.6 m) and low surge (0.91 m), low SLR and high surge (1.3 m), high SLR (2 m) and low surge, and high SLR and high surge. Colored lines roads impacted past flooding impact points. The two inset boxes represent finer-scale maps of Highway 123 (upper left) and Highway 24 (lower right). These are the two primary access points to the town of Harpswell. At these locations (denoted by the red boxes), high SLR and surge cause the entire town of Harpswell to be isolated from Brunswick. For clarity, impacted roads past these points are not shaded in order to highlight other roads made impassible by inundation

https://static-content.springer.com/image/art%3A10.1007%2Fs13412-011-0056-6/MediaObjects/13412_2011_56_Fig4_HTML.gif
Fig. 4

Histograms of the distance of roads past flooding impact points for Brunswick and Harpswell (shown on a logarithmic scale). Vertical bars represent the number of roads in each distance class. A distance class of zero means that these roads were not rendered impassable by road flooding due to the existence of alternate routes around the point of impact. Bars located farther to the right indicate increasing lengths of road made impassible by flooding. a–b Low SLR (0.61 m) only, b–c high SLR (2 m) only, d–e low SLR + low surge (0.91 m), f–g high SLR + high surge (1.3 m)

Histograms showing the total distance of inaccessible roads below flood points indicated that low SLR (0.61 m) had no effect on Brunswick (Fig. 4a). Alternate routes were available for all 21 roads downstream of the four roads flooded as a result of SLR (Table 4). Harpswell fared slightly worse at low levels of SLR, with 24 roads flooded (Table 4), 12 roads losing between 10 and 10,000 m of accessibility, and 97 roads downstream of the flood points unaffected due to the availability of alternate routes (Fig. 4b). At 2 m SLR, both towns were impacted. Twelve roads were flooded in Brunswick (Table 4), and 18 had stretches between 10 and 10,000 m that were inaccessible, with the large majority of inaccessible road length totaling 100–1,000 m (Fig. 4c). Only three roads below the flood points were unaffected due to the availability of alternate routes (Fig. 4c). Harpswell experienced 71 flooded roads (Table 4), with 55 losing segments between 1 and 100,000 m (Fig. 4d). A total of 54 roads in Harpswell below the flood points were unaffected due to the availability of alternate routes (Fig. 4d). Storm surge led to additional loss of road accessibility. With the lowest SLR (0.61 m) and surge (0.91 m) scenarios, four roads in Brunswick lost between 10 and 1,000 m accessibility (Fig. 4e), whereas Harpswell had 26 roads losing between 1 and 10,000 m accessibility (Fig. 4f). The majority of roads (17 for Brunswick and 83 for Harpswell) downstream of flood points were unaffected due to the availability of alternate routes (Figs. 4e–f). Under the highest SLR (2 m) and surge (1.3 m) scenarios, all 21 roads in Brunswick downstream of the flood points were rendered inaccessible to some degree (between 10 and 10,000 m; Fig. 4g). Although Harpswell had 19 roads below the flood points that remained accessible, the majority were impacted (>10–100,000 m; Fig. 4h). The loss of just the two major access points to Harpswell via Highways 123 and 24 accounted for 74,500 m of inaccessible roads spanning all of the major islands of Harpswell (red boxes and insets in Fig. 3).

Impacts of sea level rise on intertidal ecosystems

There is significant potential for changes in intertidal ecosystem habitat (Table 5, Fig. 4). Brunswick currently has 258 ha of intertidal habitat (most of which is low and high marsh), 59 ha of which is under conservation. Harpswell currently has 547 ha of intertidal habitat (mostly salt marshes but also including kelp beds), 27 ha of which is under conservation. Under the 0.61-m SLR scenario, Brunswick loses 57% of its low intertidal habitat but gains 35% more high intertidal habitat (Table 5). Harpswell loses 15% and 23% of low and high intertidal habitats, respectively. For the 1-m SLR scenario, Brunswick loses 67% of its low intertidal habitat but gains 46% more high intertidal habitat (Table 5). Harpswell loses 26% of low and high intertidal habitats. For the 2.0-m SLR scenario, Brunswick loses 63% and 25% of its low and high intertidal habitat, respectively (Table 5). Harpswell loses 15% and 41% of low and high intertidal habitats, respectively. These results suggest that intertidal area does not change monotonically with SLR in these landscapes.
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)

    

0.61 m SLR (%)

1 m SLR (%)

2 m SLR (%)

Brunswick

Low intertidal

126

48

54 (−57)

42 (−67)

47 (−63)

High intertidal

132a

11

177 (+35)

193 (+46)

99 (−25)

Harpswell

Low intertidal

144

16

123 (−15)

106 (−26)

122 (−15)

High intertidal

403a

11

309 (−23)

298 (−26)

239 (−41)

aBased upon OGIS data. Due to errors identifying low marsh in certain sections with LIDAR, current low marsh was inspected against aerial photographs taken at low tide and corrected in portions

This model for land and marsh inundation analysis is based on elevation only and it should be noted that inundation maps do not take into account other factors such as actual land coverage, coastal erosion, wetland accretion, and the impact of coastal protection structures. As a preliminary test of our results, we compared our marsh inundation analysis data with US Soil Service Geographic Database (SSURGO) provided at the county level for Cumberland County from the Natural Resource Conservation Service. There are challenges in using SSURGO data in terms of its overall accuracy. Some datasets originate from the 1980s, and information on the temporal accuracy is generally not available. Additionally, SSURGO data is mapped at a scale that ranges from 1:1,000 to 1:24,000, a level of resolution lower than provided by the LIDAR data (Pantaleoni et al. 2009), which resulted in mismatches in ecosystem classification. Specifically, many of the areas identified as high and low marsh in the SSURGO data also included a significant percentage of water by area (27.2% and 76.9% of the overall marsh area, respectively) pointing to further concerns associated with integrating this dataset into the current analysis. Despite these differences, applying the SSURGO data to our model replicated findings of a dramatic reduction in tidal marsh areas at the different levels of inundation, suggesting that the model results are robust. For high tidal areas specifically, overall area of tidal marsh decreased by 73% under 0.61-m (2-ft) sea level rise scenarios and 98% under a 2-m sea level rise scenario.

Local communities often do not have access to sophisticated datasets or technology such as remote sensing that can be used to verify the characteristics of intertidal areas most at risk. However, local communities can marshal resources in the form of committee members and volunteers who can field verify local conditions. These initiatives are enhanced by the availability and use of data provided by these types of analyses, which can focus these types of community efforts.

Future analysis that includes ground truthing in these areas will be important in terms of assessing the overall impact of sea level rise on marsh habitat. It is also important to point out that inundation levels can differ between the intertidal habitat data (Table 5) and the property data (Table 4) because these are accounted for differently. Specifically, intertidal habitats, such as coastal marshes, often extend beyond the boundaries of the parcels included in the tax assessor databases.

Although we could not account for changes in the mud flat intertidal area (∼MLW < ∼MSL) that characterizes recreational and commercial shellfisheries (Fig. 1), the changes to this zone with SLR could bring about significant economic impacts to this region (Table 6). In 2010, state-wide shellfish harvests totaled 1.26 million kg at a value of $18.3 million. For Brunswick, harvests totaled 47,181 kg at a value of $1.1 million, and for Harpswell, $607,598 in revenue was generated from a harvest of 23,913 kg.
Table 6

Harvest rates and economic value of commercially important shellfish species

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

Soft clams

461,059

12,958,245

28,742

875,538

16,380

506,282

Blue mussels

663,557

2,064,427

NA

NA

2,848

54,631

Oysters

49,254

2,072,608

NA

NA

3,116

14,807

Total

1,257,482

18,379,451

47,181

1,079,167

23,913

607,598

Data courtesy of the Maine Department of Marine Resources

Discussion

Most work to date on SLR and storm surges has focused on analyzing sea level rise in large urban contexts that are high-density and high-risk. This study reveals that, even in communities that are not the most populated or at highest risk, SLR can have a sizeable impact on coastlines, infrastructure, and intertidal ecosystems, thereby affecting local livelihoods (Figs. 3 and 4; Tables 4, 5, and 6). The magnitude of these potential impacts indicates that a rapidly deployable methodology needs to be disseminated widely to local decision makers in coastal Maine municipalities.

Outcomes of the boundary organization approach

The boundary organization approach we adopted in this study enabled us to develop such a methodology by identifying the primary needs of Maine communities interested in assessing potential impacts of SLR and storm surges. Throughout the process, significant stakeholder engagement and feedback was integrated into the process of developing scientific assessments (Tables 1, 2, and 3). Working with stakeholders was critical as a source of feedback, and our community partners made several salient recommendations. The need to assign dollar values to potential impacts and to consider the livelihoods of local people was deemed critical, which we achieved through the analysis of land and building valuations (Table 4) as well as assessing the economic value at stake with potential losses to the shellfish industry and piers used for commercial fishing and other boating activities (Tables 4 and 6). To help communities identify threats to island access, stakeholders recommended the transportation network analysis, through which we identified key bridges and roads that will need to be raised to accommodate future SLR and storm surges (Fig. 4). Stakeholders also emphasized the need to make the results visible and meaningful to residents of the town by focusing efforts on specific, well-known areas in the towns that are valued by locals. In public presentations, we were encouraged to frame the potential outcomes of our storm surge scenarios by making direct comparisons to historical surge events with known impacts, such as the Patriot’s Day storm (http://www.biddefordmaine.org/index.asp?Type=GALLERY&SEC=%7BCD04F870-6BBE-4126-AC3F-526118C8B09E%7D) that impacted southern Maine in April, 2007. Finally, many of the stakeholders were sensitive to the politically charged reality of climate change in terms of how scenario results are presented to the general public. They recommended placing emphasis on how SLR and storm surge scenarios are, in part, based on historic trends in the Gulf of Maine (sensu Gehrels et al. 2002) and less emphasis on the underlying climate change science and associated uncertainties.

The boundary organization model outlined here is one of several possible models for bridging science and policy and scales of governance, as well as cogenerating knowledge, developing participatory collaborations, and operationalizing scientific information. Miller (2001) describes “hybrid management” as a way to coordinate efforts of different groups that are dealing with scientific and political issues simultaneously. Romsdahl (2011) advocates “decision support networks” as a way to (1) increase usefulness of information; (2) improve relationships between knowledge producers and users; and (3) make better decisions. She highlights several case studies such as the Consortium for Atlantic Regional Assessments, which brings together representatives from higher education, decision makers, and municipal- and regional-level assessment teams. Many of the goals of such decision support networks are similar to those presented in our work, suggesting that these approaches are not mutually exclusive: (1) research that is relevant to pending decisions and compatible with existing decision-making approaches; (2) research that is accessible to the relevant decision makers, who are receptive to the results; (3) raising awareness of climate vulnerability in decision making; (4) creating collaborative links and research tools that can bridge science-policy gaps; (5) reducing the reinvention of methods by providing analysis models; and (6) encouraging decision makers to incorporate adaptation perspectives into existing planning approaches (Romsdahl 2011). However, based on previous work (Cash and Moser 2000; Guston 2001) and given the context of institutions, knowledge, scale, and technical capacity for this particular case in coastal Maine, we found the boundary organization approach most attractive. From the outset, we were intentional in bringing together two groups at different levels of governance and with different resources and knowledge sets (Cash and Moser 2000). By providing technical expertise and bringing together the general climate adaptation strategy and technical resources of state planners with the local knowledge base of municipal decision makers, we helped broker the development of a new methodology useful to both sides that would have been difficult to achieve by either side alone (Guston 2001).

Potential impacts of sea level rise and storm surge on local communities in Maine

The outcome of this scientific–stakeholder partnership provides several lessons specific to local decision making in Brunswick and Harpswell. First, the potential economic impact of SLR—even in these relatively steep and rocky coastlines—may be significant. Both Brunswick and Harpswell stand to incur significant economic impacts. Our models indicate that over the next hundred years, Brunswick and Harpswell may lose up to $4–140 million worth of land (Table 4). Several roads and bridges will need to be raised, or alternate routes constructed, in order to maintain accessibility of the entire transportation network (Figs. 3 and 4). In both towns, the potential damages to nonfloating pier infrastructures may result in large repair costs and affect local business and individuals who are dependent on working waterfronts. Building and pier construction guidelines and regulations should take into account projections of SLR. Also, given the economic significance of the shellfish industry (Table 6), emphasis should be placed on the potential loss of mud flats to SLR. Second, the distributive effects of potential impact are important to consider. As SLR increases, we found that the number of parcels affected remains roughly the same while area inundated increased (Table 4), indicating that individual property owners will tend to suffer greater potential losses with SLR rather than the losses being spread among more people. Third, both towns may experience significant loss of important intertidal ecosystems, such as low marsh. Based on the high SLR scenario, Brunswick stands to lose 63% of the intertidal zone commonly inhabited by low marsh species (MSL < MHW), and Harpswell may lose 15% (Table 5). This information and the precise locations of new marshes can assist town planners, local conservation groups, and citizens in the towns with planning future conservation efforts. This is especially important given that only 5–23% of potential marsh land lies within currently delineated conservation parcels (Table 5; Titus et al. 2009). Fourth, our analysis of road inundation reveals that when even a short stretch of road becomes inundated, it can cause widespread transportation problems, such as blocked access to schools, hospitals, or emergency responders (Figs. 3 and 4). In Harpswell, the consequences are more serious, with potential inundation on the two main roads that serve as entrances and exits from the peninsulas and islands to the mainland (Fig. 3).

Implications for national and subnational assessments

Although the stakeholder composition and community context were unique to this study, our approach provides insights that can inform climate adaptation protocols and perspectives. Case studies such as this one from coastal Maine are vital for the integration of local examples into regional and national initiatives (Moser and Ekstrom 2011).

Linkages between federal and local climate adaptation

Two recent reports by the US government—America’s Climate Choices (National Research Council 2010) and the Progress report of the Interagency Climate Adaptation Task Force (White House Council on Environmental Quality 2010)—identify as priorities local climate adaptation planning, strengthening collaborations across federal, state, and local levels, and building resilience to climate change in communities (Moser and Ekstrom 2011). Specific goals and recommendations addressed by our analysis include the coordination of stakeholders and government agencies to develop pilot programs, avoiding duplication of efforts (among municipalities), and leveraging existing capabilities. In promoting the bottom-up development of regional planning, federal agencies can use local case studies to better inform climate adaptation planning nationally, provide better access to information and technical assistance to local decision makers, and support the preparation and evaluation of state and local planning efforts (White House Council on Environmental Quality 2010). The Climate Change Adaptation Taskforce also included eight guiding principles for adaptation: (1) adopt integrated approaches, (2) prioritize the most vulnerable, (3) use best-available science, (4) build strong partnerships, (5) apply risk management methods and tools, (6) apply ecosystem-based approaches, (7) maximize mutual benefits, and (8) continuously evaluate performance.

Our work provides an important first step towards meeting these goals and it offers several insights that can inform federal efforts to support local climate adaptation initiatives. We have identified areas where improved data generation and management, such as census data, can facilitate the process. Based on our initial data assessment, we found a mismatch between the scales of governance and data availability. Governance and tax assessor data in these towns arise and are maintained predominantly at the local level. While federal data, such as the census, are consistent in format, they are often sampled on a scale too broad to be accurate for a local analysis, so we were unable to assess accurately the populations impacted in these towns. State-derived data on land use and soils, while more detailed, are still rather broad in scale and often too coarse in resolution to be used effectively with high-resolution LIDAR data. Local data, as employed in this study, differ in format, level of organization, and completeness from municipality to municipality. For local SLR impact analyses to be successful, more attention is needed on the development of data and data formats that are useful across scales and consistent across municipalities. In addition to data needs, there will need to be consideration of how technical and economic resources are allocated to vulnerable communities as more local impact analyses emerge with the support of state and federal agencies. Specifically, the prioritization of vulnerability at a national scale could emphasize assistance to regions/communities (e.g., coastal Louisiana, Florida, and North Carolina) that differ from those identified at a local scale (e.g., Harpswell, Maine).

Subnational climate adaptation analyses

Our results suggest several lessons that can be applied to other subnational SLR impact analysis and climate adaptation efforts. Successful outcomes include
  • a community-based, iterative research approach can both improve the quality and relevance of the analysis and build stakeholder commitment in the project;

  • boundary organizations can serve as a neutral facilitator between policy making institutions and community members, encouraging trust, and providing needed expertise and resources;

  • the facilitative process of boundary organizations is most effective if there is a specific need for the process being developed and a vested interest in its success by stakeholders.

However, we identified several data and methodological considerations that could make adopting this approach by other municipalities challenging. We emphasized information that is readily available to community decision makers, but as discussed previously, local data differ in format, level of organization, and completeness from municipality-to-municipality. The two municipalities had different systems for collecting and maintaining assessor data, leading to data that varied in formats, quality, and currency. We found that land valuation, in particular, appears to be a nonlinear function of parcel area, but tax assessors currently do not always maintain databases in ways that facilitate fitting models to data, which is why we assumed linearity for simplicity. Such data inconsistencies are inherent given the local governance structure of many communities throughout Maine and New England. Other issues include the integration of disparate datasets. For some communities, integrating digital parcel and soils data with localized data on ownership, location, and status of conserved lands, and the location and types of buildings is currently difficult. Our work also suggests that there may be a disconnect between local decision makers in smaller, more rural towns and the technical capacity available to identify information needs for analyzing data and crafting policy within the framework of land use planning to prepare for climate change. For example, LIDAR data have been collected and will become available for the entire coast of Maine but will require a level of technical expertise to integrate these data into local land use planning datasets. Finally, our methodology for assessing potential changes to intertidal ecosystems (Slovinsky and Dickinson 2009) is based on changes in tidal zonations, and we therefore lack the capacity to predict specific ecosystem types for specific geographic locations.

We offer several suggestions for remediating these challenges. Many communities are beginning to develop GIS capacity, which will be important for long-term planning for climate adaptation. Digital parcel data for most Maine municipalities are currently being developed and should become more widely available. Integration of assessor databases that include information on ownership, assessed value, existence and types of structures, and conservation status with digital parcel data is important for providing municipalities access to the types of analyses presented on our study. We urge local municipalities to discuss ways to adopt consistent methods of data collection, including archiving in digital formats to allow easy integration of multiple datasets. Integration of digital parcel data at the local level with other state level spatial datasets such as land cover type or soil data will be an important next step. The Maine State Planning Office has developed a series of spatial datasets (http://www.maine.gov/spo/landuse/compplans/planningdata.htm) for communities to use as part of the comprehensive planning process. This may be one approach to integrating data pertaining to climate adaptation impacts, which will necessarily draw on local datasets but may best be disseminated at the state level. Moving forward, communities will need to integrate changes due to SLR into assessor data and building codes. Our analyses of potential intertidal ecosystem changes should be followed up by on-the-ground inventories of the spatial distributions of ecosystem types. As our results suggest, such analyses will likely show that urgent changes are needed for coastal conservation planning if intertidal habitat moves to areas that are currently not under conservation easement (Titus et al. 2009). Finally, we outlined one approach to integration of LIDAR data into an analysis that can inform local decision makers on the impacts of sea level rise on local infrastructure. These analyses will become increasingly important for comprehensive planning and capital improvement planning at the local and regional level.

Vulnerability and resilience frameworks

Although there has been considerable recent debate on the relationship between vulnerability and resilience frameworks in the context of climate adaptation (Vogel et al. 2007; Nelson and Adger 2007; Cannon and Müller-Mahn 2010; Miller et al. 2010; Turner 2010; Engle 2011; Nelson 2011), our simplified methodology is an important first step that informs both perspectives. The consideration of risks to infrastructure and livelihoods is key to vulnerability assessment, whereas attention to potential ecological and social states and information integrated across spatial scales is important in the context of resilience (Engle 2011). Our approach is also useful to the extent that vulnerability and resilience approaches are converging around (1) the role of maintaining diversity (ecological, institutional, and livelihood), (2) concerns for cross-scale processes, and (3) the important role of governance (Miller et al. 2010).

Our work offers several insights for building resilience and adaptive capacity in coastal communities. There is a window of time to invest in the types of data that will allow local governments to make decisions on conservation planning, capital improvements, and economic development planning in light of the threats to infrastructure on which local economies rely. For Brunswick and Harpswell, this includes further analysis of changes to intertidal ecosystems and the potential effects on the livelihoods of local fishermen. Several steps can be taken to increase adaptive capacity in these communities. Given that many piers and buildings will be renovated or built before further sea level rise is realized, remapping parcels and shorelines will aid in the construction of future homes, businesses, and piers to reduce vulnerability to SLR and storm surge. Coastal municipalities also face the potential for changing tax revenue as parcels become inundated. Consideration of SLR scenarios can help identify how the overall and distributional effects of the tax burden may shift and what implications this could have in terms of providing essential public services. Transportation corridors identified by the network analysis as the most disruptive in the event of inundation, such as Highways 24 and 123 links between mainland Brunswick and the islands of Harpswell, should be targeted for upgrades by local and state authorities. Finally, land use planners, town officials, private landowners, and NGOs should begin the process of strategizing intertidal ecosystem management, especially in circumstances where marsh ecosystems are expected to migrate from conserved areas to private lands or where ecosystem change is expected to be significant. Shifts in ecosystem services, including potential changes to shellfishing grounds should also be considered to assist local economic decisions about the future prospects of harvests.

Whether or not communities invest resources to shore up vulnerable infrastructure (a vulnerability approach), maintain flexibility 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 by local policy makers and stakeholders. As described by Nelson and Adger (2007), a balance should be struck that considers the acceptable level of risk against the ability of the socioeconomic decisions to maintain the flexibility to respond to future conditions such that responses to vulnerable infrastructure now does not undermine resilience in the future. For example, the expense of maintaining transportation access to Harpswell could limit other responses to SLR and storm surge (what Nelson (2011) calls “loss of response diversity”).

Limitations of the approach

We recognize several potential limitations of our study given (1) the inherent tradeoff between simplification and generality versus physical detail, and (2) issues of data accuracy. This study utilized an equilibrium inundation model of sea level rise and storm surge that has been commonly employed in other studies (Titus et al. 1991; LaFever et al. 2007; Cooper et al. 2008; Kuleli 2010). This analysis does not take into account bathymetry and physical impacts on specific areas of the coastline. Local data on storm surge heights and frequencies are based on historical data from the NOAA and MGS and do not take into account projected changes in storm surge. Additionally, sea level rise will not happen in isolation, but rather in conjunction with changes in precipitation, wind intensity, increased storm surges, river discharge, and storm frequency. Identifying realized changes in intertidal ecosystems requires additional site-specific work for proper characterization of community changes and net accretion rates.

The incorporation of local data provided the opportunity to carry out the analysis at a scale that matched the local communities’ needs, which enhanced the overall accuracy of the analysis. Differences in the format of the data between the two communities resulted in generalization for the purpose of comparing data between communities. Including datasets that were collected at different times and at different levels of resolution, such as the location of infrastructure mapped at a lower resolution than the LIDAR data, may have introduced some inaccuracies. As LIDAR data and familiarity with conducting coastal vulnerability studies become more widespread, we anticipate that techniques used for future data collection will more closely match the underlying accuracy of the LIDAR data and will improve these types of analyses. We were fortunate to have access to a dataset that had previously undergone field verification by the Maine Geological Survey (Slovinsky and Dickinson 2009). Similarly, changes in format of the LIDAR data may have also introduced some errors and future LIDAR datasets provided by the state of Maine will be standardized to avoid this situation. Finally, even at the scale of two town analysis, generalizations in terms of shoreline characteristics were necessary. At a local level, the most effective means of improving accuracy of the overall analysis is field verification. The next phase of this research will be the selection of three pilot areas within the study area to examine and field verify the type and location of marshes.

Conclusion

Helping local decision makers in coastal communities develop the information and tools needed to assess SLR are critical for building the adaptive and resilience capacity. Boundary organizations, such as colleges and universities, can work effectively with communities to gather and create data, conduct preliminary analyses, and facilitate longer-term planning processes in response to SLR and other types of vulnerability assessments and climate adaptation. The availability of consistent data that is locally based but distributed at a state or regional level provides more opportunities for regional planning and reduces the burden on individual communities to maintain datasets. Although the kinds of spatial and assessor data collected in this study will require refinement, the advantage of our approach to SLR analysis is that it highlights areas within a community that may require further investigation in order to best plan for the impacts of climate change. Further research will be required for the ground truthing of marshes and other intertidal ecosystems, assessing the implications for conservation planning, and identifying infrastructure that may need more precise data collection such as the location of bridges and piers at sea level. Given the window of time before significant SLR is realized, by identifying and acting on these data gaps and approaches for expanding capacity, communities will be better positioned to plan for climate change at a local and state level.

Acknowledgments

We wish to thank all of the stakeholders and community partners who made this research possible, especially Cathleen Donovan, Anna Breinich, Carol Tukey, Debbie Turner, Justin Hennessey, Malcolm Burson, Elizabeth Hertz, John Cannon, Steve Dickson, Pete Slovinsky, Heidi Bray, and Doug Marcy. Several Bowdoin students were instrumental in the analyses presented in this paper: Melissa Anson, Tom Marcello, Leah Wang, Woody Mawhinney, and Liza LePage. We thank Ellen Hines and two anonymous reviewers for helpful comments on earlier versions of the manuscript.

Supplementary material

13412_2011_56_MOESM1_ESM.docx (10.3 mb)
Fig. S1LIDAR elevations for Brunswick and Harpswell, Maine relative to the NAVD88 datum (DOCX 10,509 kb)

Copyright information

© AESS 2012