Integrating Coastal Vulnerability and Community …environment.yale.edu/climate-communication-OFF/files/...Integrating Coastal Vulnerability and Community-Based Subsistence Resource

Integrating Coastal Vulnerability and Community-BasedSubsistence Resource Mapping in Northwest Alaska

Yuri Gorokhovich†, Anthony Leiserowitz‡, and Darcy Dugan§

†Department of Earth, Environmental, andGeospatial Sciences

Lehman College, City University of New York(CUNY)

West Bronx, NY 10468, [email protected]

‡Yale UniversitySchool of Forestry & Environmental StudiesNew Haven, CT 06405, U.S.A.

§AlaskaOcean Observing System

Anchorage, AK 99501, U.S.A.

ABSTRACT

Gorokhovich, Y.; Leiserowitz, A., and Dugan, D., 2014. Integrating Coastal Vulnerability and Community-BasedSubsistence Resource Mapping in Northwest Alaska. Journal of Coastal Research, 30(1), 158–169. Coconut Creek(Florida), ISSN 0749-0208.

Subsistence resources are critical for indigenous communities in the Kotzebue Sound region of NW Alaska. Global sea-level rise (SLR) and coastal erosion are likely to create unfavorable and hazardous conditions for coastal and estuarinesettlements. It is unclear how SLR and erosion might affect coastal subsistence resources because of highly complexecological interactions. This study integrates physical, anthropological, and survey data to assess coastal vulnerabilityand to identify areas of concern for local and regional planning and environmental protection. This study analyzes andintegrates historical and projected physical coastal changes within the Kotzebue Sound region with (1) a coastalvulnerability index (CVI); (2) community-based participatory GIS maps of community subsistence resources; and (3)representative surveys of local communities to determine the importance of each type of resource. The results identifyKivalina and Deering as particularly vulnerable coastal locations among four studied villages. While the CVI is high inthese locations, low erosion rates will not likely have any negative impact on fish and caribou—two of the most importantsubsistence resource species for these communities. Because of the higher number of identified subsistence resourcespecies, Deering is more resilient than Kivalina to any potential negative coastal impacts. This methodology can beuseful in other coastal areas where subsistence resources play a major part in people’s lives.

ADDITIONAL INDEX WORDS: Coastal, subsistence resources, vulnerability, community-based GIS, spatial analysis,sea-level rise.

INTRODUCTIONOne of the tools that can help identify vulnerable coastal

areas is the coastal vulnerability index (CVI) developed by

Thieler and Hammar-Klose (1999, 2000a, b). This is a combined

index of six physical parameters related to the coast. Diez,

Perillo, and Piccolo (2007) noted that the lack of socioeconomic

indicators restricts CVI application; however, few studies have

made an attempt to include socioeconomic indicators in the

method. Hegde and Reju (2007) used a modified CVI expression

by including population as one of the variables; Ozyurt and

Ergin (2009) ranked several factors of human activity (such as

land degradation, river flow regulation, etc.) and added them to

the standard CVI equation; and Boruff, Emrich, and Cutter

(2005) combined county-based socioeconomic characteristics

with the CVI for the conterminous U.S. coast.

Using Geographic Information Systems (GIS), we combined

the CVI with community-based participatory maps of subsis-

tence resources. This study demonstrates a new integrated

methodology that can be developed and employed in coastal

areas where subsistence resources are a large part of people’s

lives and that interact with natural climate related hazards

such as sea-level rise (SLR), flooding, and coastal erosion. This

situation is particularly relevant in the Arctic.

Climate-related changes in the Northwest Arctic Borough

(NAB) (located in NW Alaska) have been summarized in

several previous reports and articles, including documentation

of the impacts on the native Inupiaq way of life (Hinzman et al.,

2005; Huntington and Fox, 2005; Whiting, 2006). These

impacts affect subsistence resources used by native Inupiaq

people for food, ethnic traditions, and education. Poppel (2006)

found that among Arctic Inuit, Alaska has a majority of

households that depend on traditional food. A similar situation

exists along the coasts of Chukotka and the Arctic Ocean in

Russia, Canada, and Greenland, where any impact on

subsistence resources from climate change directly affects the

lives of many indigenous communities.

Significant areas of employment in the NAB are located

within the public sector and seasonal work. Other important

economic activity is associated with the Red Dog Zinc Mine

located 130 km north of Kotzebue in the De Long Mountains of

the western Brooks Range; however, the majority of the

population is involved in subsistence activities. As Wolfe and

DOI: 10.2112/JCOASTRES-D-13-00001.1 received 2 January 2013;accepted in revision 19 March 2013; corrected proofs received2 May 2013.Published Pre-print online 29 May 2013.� Coastal Education & Research Foundation 2014

Coconut Creek, Florida January 2014Journal of Coastal Research 30 1 158–169

Walker (1987) state, ‘‘. . . it is a relatively hidden component of

Alaska’s economy, unmeasured in the state’s indices of

economic growth or social welfare and neglected in the state’s

economic development policy’’ (p.56).

SLR, flooding, and coastal erosion have been identified as

particularly important concerns because they have direct

impacts on the coastal settlements located on Kotzebue Sound

(Figure 1). One village, Kivalina, located on a barrier island,

has experienced especially dramatic impacts. The current cost

of coastal protection in Kivalina has reached US$1.3 million,

and future investments in coastal protection and possibly

relocation could be as high as US$125 million (USACE, 2006)

for this one community. Other vulnerable settlements in the

region include Kotzebue, Selawik, and Deering. Compared to

Kivalina, these communities have experienced relatively minor

coastal erosion and less dramatic impacts on village infra-

structure but nonetheless also depend on a variety of key

subsistence resources located within the coastal zone.

While the direct impacts of coastal dynamics on these

settlements can be assessed relatively easily by engineering

surveys, the impact on subsistence resources is much harder to

assess. Subsistence resources were defined by the Inuit

Circumpolar Conference (1992) as ‘‘a highly complex notion

that includes vital economic, social, cultural and spiritual

dimensions’’ (Poppel, 2006) and are widely distributed along

the coast in both inland and seaward directions. In this paper,

however, we more narrowly refer to subsistence as resources

related to hunting, fishing, and gathering.

Many subsistence resources are seasonal (e.g., caribou,

berries, seals, etc.) and respond rapidly to environmental and

ecological changes. Thus, an assessment of the potential

impacts of SLR and coastal erosion on subsistence resources

requires data on where these resources are geographically

located and, crucially, harvested or gathered by local people,

especially when obtained from community-based research

(Dolan and Walker, 2006). GIS modeling and analysis can be

used to integrate diverse geographic data, including historic

and projected physical changes in coastlines (Gorokhovich and

Leiserowitz, 2012) and the locations of various subsistence

resources to identify potentially vulnerable areas (Mittal,

2009), which can provide valuable information for regional

coastal management and planning (Clark, 1997).

Schroeder, Andersen, and Grant (1987a, b) mapped subsis-

tence resources in the region in 1985–86. These subsistence

maps, however, are not in GIS format and are now more than

25 years old. Braund and Associates (2008) conducted a more

recent subsistence resources survey for the village of Kivalina

as part of the Environmental Impact Statement related to the

nearby Red Dog Mine.

Mixed scales, temporal intervals, and data sources create

challenges for any spatial analysis. Because the purpose of the

proposed study was to integrate coastal vulnerability with

subsistence resources, the available data had to be ‘‘normal-

ized’’ or ‘‘homogenized.’’ In GIS analysis, this technique is

called resampling; related techniques are interpolation and

extrapolation, depending on data condition and type. While

this method creates uncertainty within the selected smallest

spatial units because of the data aggregation (Goodchild, 1998),

it is applicable to large areas as long as these areas reveal the

spatial variability of studied attributes for spatial analysis.

This study developed a GIS-based index of coastal vulnera-

bility (CVI) to SLR and coastal erosion along Kotzebue Sound

and investigated its interaction with subsistence resources

using map overlays. The CVI was created by resampling and

combining various GIS data as an input to the model.

Subsistence resources were mapped using community-based

participatory GIS mapping in each of four studied settlements:

Kivalina, Kotzebue, Selawik, and Deering.

Participatory mapping has become increasingly used to

engage local communities in scientific research and local

planning. The process involves researchers working closely

with local community members in workshops or individual

interviews to map important aspects of the community.

Participatory mapping includes sketch mapping, scale map-

ping, and transect walking (Chambers, 2006). Participatory

mapping can be designed to meet the practical needs of local

people as well as to assist in planning by governments,

agencies, or others (Rambaldi et al., 2006; Sieber, 2006). This

framework allows residents to participate both as contributors

and as users of the knowledge (Shrestha, 2006; Tripathi and

Bhattarya, 2004).

To identify the relative importance of subsistence resources

categories (e.g., caribou, berries, fish, etc.) for each community,

representative surveys of each community were conducted.

These survey results were subsequently used to develop value-

based weighting in the GIS model to identify those areas

where projected impacts of SLR and coastal erosion over-

lapped with highly valued subsistence resources. This combi-

Figure 1. Area of study.

Journal of Coastal Research, Vol. 30, No. 1, 2014

Coastal Vulnerability and Subsistence Resources Mapping 159

nation of coastal and subsistence resources mapping in GIS

provides an example that can be replicated in other geographic

areas where the speed of analysis, simplicity, and low cost

matters.

METHODS AND DATADeveloping an integrated set of GIS-based vulnerability

maps for this region, however, is complicated because of the

heterogeneity of available data (i.e. in terms of scale and

temporal and spatial resolution). The NAB is a remote region

that has received little attention from federal and state geologic

and cartographic organizations. Traditional quadrangle maps

by the United States Geological Survey (USGS) exist for this

region only at a 1:63,360 scale, compared to a 1:24,000 scale for

the lower 48 states. Available bathymetric maps for Kotzebue

Sound produced by the National Oceanographic and Atmo-

spheric Agency (NOAA) were compiled between 1944 and 1976

at a scale of 1:500,000 and 1:100,000,000. More recent

bathymetric maps were compiled in 1986 at a 1:250,000 scale

(NOAA, 2011a). The same situation occurs with digital

elevation models (DEM).

Coastal geomorphology, critical for any analysis of

vulnerability to SLR and coastal erosion, is available at

the coarse scale of 1:250,000 (NOAA, 2011a). The only

data available for temporal analysis of coastline changes

at a scale less than 1:63,360 were aerial photos from the

1950s, 1980s, and 2003, which were recently released by

National Park Service (NPS); however, these data have

different scales and sources that make temporal analysis

uncertain and difficult as well (Gorokhovich and Leiser-

owitz, 2012).

GIS ApproachGIS techniques depend on available data and their quality.

GIS data might be collected or produced by modeling. This

presents challenges to the analytical process and selection of

criteria for analysis unless all data are homogeneous in terms

of their origin and scale.

The choice of GIS technique in this study was dictated by two

factors: (1) use of an index model (i.e. CVI) and (2) the

heterogeneity in data scales, origin, and accuracies. Figure 2

shows an example of discrepancies between Landsat imagery

(used for the community mapping of subsistence resources), the

shoreline from the National Elevation Dataset (NED), and the

shoreline from Environmental Sensitivity Index (ESI) datasets

(used for geomorphology analysis).

The shoreline from Landsat imagery is a result of a composite

process that combines multiple high-quality images. Some

Landsat scenes have clouds, some have noise; the composite

process improves the quality. The final image has resolution of

30 m, and the datum is WGS84. The shoreline from NED was

derived by GIS processing. Its resolution is about 60 m (2 arc-

seconds), and the datum is NAD83.The ESI dataset is a vector

dataset (conforming to National Map Accuracy Standards at

scale 1:250,000) with the datum NAD27.

According to U.S. National Map Standards, ESI data at scale

1:250,000 produce a horizontal error equal to 6127 m (or

absolute error¼ 254 m). Comparison of the DEM produced by

NED and the ESI shoreline found that the maximum

horizontal displacement between shorelines can be up to 270

m. This makes the cumulative error equal to 524 m.

Being an index model, CVI favors the raster data format

because a multiple data overlay works faster and more

efficiently in raster GIS. As Figure 2 shows, the heterogeneity

of datasets causes horizontal displacement that prevents data

from being analyzed in the same manner. For example, it is

impossible to assign the geomorphologic type from ESI data to

the shoreline along with data on erosion/accretion unless both

shorelines align spatially. This limitation requires the spatial

adjustment of all datasets because no single dataset is more

accurate than the others because of differences in data

preparation (i.e. NED is a model, ESI is a cartographic product,

and Landsat is a composite image). Any additional change of

this data (e.g., digital correction) will add more uncertainty

unless corrections include data with higher resolution. Unfor-

tunately, higher resolution data do not yet exist for the area of

study. One GIS method that can make this kind of spatial

adjustment is called resampling. To implement resampling,

data has to be rasterized, i.e. converted into raster grids

consisting of pixels (cells) organized in rows and columns. A

critical issue in this process is the selection of the minimal

spatial unit (i.e. pixel or cell size).

Because the maximum absolute error was found to be 524

m, 500 m was selected for the pixel size in this proposed GIS

framework. Even the ESI dataset (with the highest resolu-

tion of any of these datasets) contains an absolute error.

Because of its 1:250,000 scale, the ESI dataset alone would

require a pixel size of 200–300 m. Because this study

integrated multiple datasets with different resolutions,

however, all data (i.e. subsistence resources, shorelines

obtained from aerial photography, and NED and ESI

coastlines) were conservatively converted into raster grids

with a pixel size of 500 m. Assuming that most horizontal

Figure 2. Discrepancies in accuracies of digital data as shown by combining

three datasets.


160 Gorokhovich, Leiserowitz, and Dugan

displacement and variability occurs within 500 m, pixels of

this size can be considered a uniform unit with known spatial

uncertainty.

Figure 3 illustrates the results of resampling (using a 500-m

grid) for a small selected shoreline segment in Kotzebue Sound.

It also displays the associated data for analysis within each

pixel of the grid: shoreline (i.e. depth ¼ 0) from bathymetric

data (used in estimation of the underwater slope), shoreline

transects to measure coastal erosion using aerial photos, and

examples of two shorelines (one from the 1950s and the other

from the 1980s). The shoreline transect data in this figure were

part of a separate study (Gorokhovich and Leiserowitz, 2012)

that produced erosion/accretion rates for each transect. The

transects were used in the resampling process, and erosion

rates were averaged for each resampled pixel. More specific

descriptions of the transect data obtained from the analysis of

aerial photography from the 1950s, 1970s, 1980s, and 2003 and

the values of coastal erosion/accretion obtained from the

transects can be found in Gorokhovich and Leiserowitz

(2012). After resampling, each grid pixel was assigned a

geomorphologic code from the ESI data, an average coastal

erosion/accretion value, and an underwater slope value.

Detailed descriptions of historical shoreline changes measured

from shoreline transects can be found in Gorokhovich and

Leiserowitz (2012).

CVI ModelModeling coastal vulnerability to SLR requires extensive use

of GIS and various spatial data. Because of data heterogeneity,

most researchers use an index-based approach (e.g., Gornitz

and Kanciruk [1989], Hughes and Brundrit [1992], Shaw et al.

[1998], Thieler and Hammar-Klose [1999, 2000a, b], and

Boruff, Emrich, and Cutter [2005]).

The essence of any index-based approach to vulnerability

assessment is a classification of the risk factors (e.g., coastal

processes associated with SLR) and conditions at risk (e.g., vital

coastal resources) based on their importance or sensitivity. The

GIS technique (vector overlay or raster map algebra) is then

used to integrate this spatial data, resulting in a spatial index

representing a range of low to high vulnerability.

Because the final index is based on spatially distributed data

categorized by ranks and normalized by weight values assigned

to each dataset, the composite index helps to identify conditions

of low to high vulnerability. A set of studies of coastal

vulnerability conducted for the USGS (Thieler and Hammar-

Klose, 1999, 2000a, b) constructed the following CVI model

(based solely on physical parameters):

CVI ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðabcdef Þ=6

p;

where a ¼ geomorphology, b ¼ coastal slope, c ¼ relative SLR

rate, d¼ shoreline erosion/accretion rate, e¼mean tide range,

and f¼mean wave height.

The researchers then assigned ranks to each of these six

variables (Table 1) following the work of Thieler and Hammar-

Klose (1999).

For this study, the geomorphology variable was derived from

the ESI dataset developed by NOAA (NOAA, 2011b), Office of

Response and Restoration, Emergency Response Division for

Northwest Arctic (2002), Western Alaska (2003), and the North

Slope (2005). This dataset was developed to identify sensitive

coastal resources before the occurrence of an oil spill and to

establish priorities and cleanup strategies in advance. Coastal

geomorphology attribute data for Alaska was developed in 2002

with USGS and NOAA data from 1:250,000 scale maps. Fifteen

geomorphologic categories were identified within Kotzebue

Sound (Table 2) with prevailing salt- and brackish-water

marshes, wetlands, and mixed sand and gravel beaches.

Geomorphologic categories from ESI maps were matched with

similar geomorphological categories in Table 1 and were

assigned a numeric rank for the CVI.

Coastal slope data were obtained from the National Ocean

Survey (NOS) at 1:160,000 scale. According to NOS documen-

tation: ‘‘This data complies with International Hydrographic

Organization (IHO) Publication 44 accuracy standards or those

used at the date of the surveys. Bathymetric contour intervals:

10 m to maximum depth, supplemented by 2 meter intervals.’’

In 1986, the NOS produced digital maps at 1:250,000 scale in

PDF format. Using ArcScan, the maps were converted into GIS

format (shapefiles) and then converted in continuous bathy-

metric slope surfaces using a GIS interpolation technique. This

dataset covers the entire study area as a continuous surface

with a pixel size of 500 m. Slope values within 500 m from the

shoreline were used in the ranking process. Table 1 provides

the ranking system (1–5) for slope categories.

Shoreline erosion/accretion was estimated using NPS aerial

imagery from the 1950s, 1970s, 1980s, and 2003. Shorelines

were derived for each period and were compared using USGS

Digital Shoreline Analysis System (Thieler et al., 2005)

integrated with ArcGIS 10 software. In Kotzebue Sound, the

Figure 3. Illustration of the ‘‘resampling’’ process in GIS with examples of

associated data.



study by Gorokhovich and Leiserowitz (2012) found mean

erosion rates of�0.12 to�0.08 m/y from 1950 to 2003. During

the resampling process, transect values for each pixel were

averaged using ArcGIS function Zonal Statistics (ESRI,

ArcGIS Resource Center, 2013). Obtained values of erosion/

accretion were assigned ranks according to Table 1.

Three CVI variables, SLR, wave height, and tidal range were

not used in the model because they were considered constant,

considering their miniscule spatial variability within a rela-

tively small area of Kotzebue Sound and the lack of spatially

distributed data showing spatial and temporal variability of

these parameters along the Kotzebue Sound shoreline; there-

fore, the CVI index was calculated as

CVI ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðabdÞ=3

p;

where a¼ geomorphology, b¼ coastal slope, and d¼ shoreline

erosion/accretion rate (m/y). After all three datasets were

assigned CVI ranks, they were converted into 500-m grids and

combined in GIS as raster layers.

Community-Based Participatory GIS Mapping ofSubsistence Resources

Our subsistence resources maps were constructed in collab-

oration with local experts, including community elders,

hunters, fishers, and gatherers using interactive GIS tools.

Public workshops in Deering, Kivalina, Kotzebue, and Selawik

were conducted to (1) identify key climate change concerns and

community-reported vulnerabilities; (2) map village infrastruc-

ture, subsistence-use areas, transportation routes, cultural

sites, observed climate change impacts, and community-

identified vulnerabilities; and (3) educate community members

about the current and projected climate change impacts in the

Arctic, Alaska, and NAB.

An early study by Schroeder et al. (1987a) documented

subsistence areas specific to the Kotzebue Sound region of

Alaska. More recent studies in the region include the

production and distribution of subsistence food in several

villages (Magdanz, Utermole, and Wolfe, 2002) and an update

to the Kotzebue Sound harvest areas (Whiting, 2006) and the

Kivalina area (Braund and Associates, 2008). The Alaska

Department of Fish and Game also completed a statewide

subsistence survey assessment, looking at subsistence in

Alaska over the past 25 years (Wolfe, 2004).

Groundwork for the participatory mapping workshops began

with a visit to each village in the summer of 2008. This first trip

established local connections, introduced the project to the

community, and developed a study plan in cooperation with

local leaders, including exploration of whether the research

process or final products could assist with other community

projects. Researchers were also taken by local residents in

boats along the coastline in Kivalina and through part of the

river and lake system near Selawik to collect GIS data points

for georeferencing and to document coastal erosion. GIS data

points were collected with a Trimble ProXH receiver and later

processed with differential correction to be used for georefer-

encing of the aerial images of the area. Coastal erosion was

documented by digital video and photography.

The organization of the mapping workshops was discussed,

including the optimal scale of the maps, the extent of coverage

around the village needed to capture subsistence areas, and

whom to invite. We found that participants (predominantly

Inupiaq Eskimo) greatly preferred false-color Landsat imagery

to USGS 1:24,000 quadrangles and cartographic maps by the

U.S. Fish and Wildlife Service. In fact, participants were

universally delighted with these images, had no difficulties

interpreting false color, and found them far more intuitive as a

representation of the landscapes that they knew intimately

through years of personal experience.

The research team returned in January 2009 to conduct the

mapping workshops. This timing was chosen to maximize

participant attendance, as local residents—particularly hunt-

ers—are often out on the land hunting and fishing during other

times of year. Participants were identified and invited by local

leaders in the tribal or city government or by the environmen-

tal coordinator in each village. Some of these names had been

assembled through conversations with other hunters and

Table 1. Ranking of coastal vulnerability index in the USGS model (Thieler and Hammar-Klose, 1999).

Ranking of Coastal Vulnerability Index (CVI)

Variable 1, Very low 2, Low 3, Moderate 4, High 5, Very High

Geomorphology Rocky, cliffed

coasts

Medium cliffs Low cliffs,

glacial drift

Cobble beaches,

estuary, lagoon

Barrier beaches, sand beaches,

salt marshes, mud flats, deltas

Coastal slope (%) .0.115 0.115–0.055 0.055–0.035 0.035–0.022 ,0.022

Relative sea-level change (mm/y) ,1.8 1.8–2.5 2.5–3.0 3.0–3.4 .3.4

Shoreline erosion/accretion (m/y) .2.0 1.0–2.0 �1.0–þ1.0 �1.1–�2.0 ,�2.0

Mean tide range (m) .6.0 4.1–6.0 2.0–4.0 1.0–1.9 ,1.0

Mean wave height (m) ,0.55 0.55–0.85 0.85–1.05 1.05–1.25 .1.25

Table 2. Coastal geomorphologic categories within Kotzebue Sound

(summarized from ESI data) and associated ranks.

ESI Category

Total Length along

the Coast (m)

CVI

Rank

Salt- and brackish-water marshes, wetlands 1,402,744.8 5

Mixed sand and gravel beaches 645,942.5 5

Gravel beaches 142,871.4 3

Sheltered rocky shores 103,181.3 2

Tundra cliffs 80,478.8 2

Exposed tidal flats 71,465.5 5

Exposed scarps and steep slopes in clay 44,807.5 2

Fine- to medium-grained sand beaches 37,848.4 5

Sheltered tidal flats 23,513.8 3

Exposed rocky shore 11,132.3 2

Coarse-grained sand beaches 5210.3 3

Scarps and steep slopes in sand 4539.2 3

Exposed wave-cut platforms in bedrock 4112.3 2

Exposed solid man-made structures 952.6 2



elders during the June trip to villages. Invitations were issued

by local contacts—by phone in Selawik, by paper flyer delivered

to the home in Kivalina, and by VHF radio in Deering. Eight to

10 hunters and elders were invited to each workshop, which

was held in the city building, tribal council building, or school.

Participants were all male except in the village of Deering,

where two women participated.

Workshops were structured with an introduction to the

project and a brief overview of climate change science, followed

by the group mapping session. Mapping categories included

transportation routes by snow machine and all-terrain vehicles

(ATV), subsistence areas (usually the six to eight most

harvested species depending on the village), cultural sites such

as ancestral graveyards or cultural camps, and observed

climate change impacts. Workshops lasted 1.5 to 2 hours.

For data collection, the project tested a new GIS technology,

which employed a laptop on which participants could draw

directly onto the screen with a special digital pen. These marks

were instantaneously digitized and saved as ‘‘layers’’ in GIS.

Because only one person could use the pen at a time and not

everyone felt comfortable marking on the laptop screen, the

image was projected onto the wall at a large scale so that

everyone in the room could follow along. As participants

described use areas, they often stood next to the screen and

pointed to specific areas on the projected map as researchers

followed along with the stylus (Figure 4). Mistakes were easily

erased and redrawn. It was also possible to zoom in and out if

precision required a smaller or larger scale. This function was

utilized frequently.

Cultural sites were typically depicted in points, transporta-

tion routes in lines, and subsistence areas with polygons.

Researchers usually started discussion with open-ended

questions such as ‘‘Where do you usually go to hunt caribou?’’

and asked participants to indicate the extent of their typical

hunting area over the past decade. Final drawings were stored

in ArcGIS shapefile format.

The group setting of the mapping exercise provided an

opportunity for consensus, stimulated discussion, and often

prompted memories of additional locations that did not emerge

during the first period of meetings. There were very few

instances in which participants had differing opinions; these

were resolved quickly and smoothly. During the course of the

workshops, there were occasionally individuals that observed,

but did not participate, but this was rare. All attendees at the

workshops were compensated with US$100 (cash).

After all the shapefiles were collected from communities, the

data were edited to match the Landsat coastline where needed

and to connect line segments in order to make contiguous

boundaries of subsistence resource categories. Figure 5 shows

an example of a subsistence resources map for Kivalina village.

The final subsistence resource data were assigned a geographic

projection and rasterized to 500-m-pixel size to comply with the

rest of the GIS data for analysis. Because subsistence resource

users from different settlements share some hunting/gather-

ing/fishing grounds, GIS data overlap and, after being

combined, represent a complex mosaic.

It should be noted that the subsistence resources categories

used in the GIS analysis have fewer species types than the

community surveys. While the participatory GIS data reflects

specifics of subsistence resource for each settlement (for

example, Selawik inhabitants do not hunt for seal or sheep),

the community survey was done in a uniform way to

understand broader differences between settlements. Table 3

contains a list of subsistence resources types that were mapped

during the participatory sessions and associated ranks as-

signed after analysis of the community surveys.

Figure 4. Process of community-based participatory mapping using digital

pens.

Figure 5. An example of the subsistence resources map for Kivalina village

obtained from participatory GIS mapping.



Ranking Subsistence Resources via Community SurveysWe then conducted a representative survey of all four

communities to assess public observations of local environ-

mental change, climate change awareness, risk perceptions,

policy preferences, and self-assessed importance of specific

village infrastructure, cultural sites, and local subsistence

resources. Telephone interviews (n¼163) were conducted from

11 June to 28 June 2010 by Craciun Research, using

professional interviewers speaking one or more local languag-

es, though most interviews were conducted in English. The

remaining interviews (n ¼ 223) were conducted in person by

Craciun Research interviewers who traveled to Kotzebue,

Selawik, Kivalina, and Deering. One-on-one interviews were

conducted during the period from 26 October to 3 November

2010 with individuals who were not originally contacted at

home by telephone.

Specific QuestionsFirst, to assess the importance of different subsistence

resources potentially at risk because of SLR and coastal

erosion, respondents were asked the following (randomized)

questions: ‘‘Rising sea levels and coastal erosion could make

subsistence hunting, fishing, and gathering more difficult. How

big of a loss would it be to your family if your village no longer

had access to each of the following—very big, big, medium,

small, or no loss at all?’’ (The access questions referred to

caribou, moose, seals, beluga or other whales, fur-bearing

animals, fish, birds, bird eggs, berries.)

Second, to assess the importance of different village

infrastructure and facilities potentially at risk because of

SLR and coastal erosion, respondents were asked the

following (randomized) questions: ‘‘Rising sea levels and

coastal erosion could also damage some villages. How big of a

loss would each of the following be to your family—very big,

big, medium, small, or no loss at all?’’ (The loss questions

referred to the medical clinic, school, airstrip, graveyard,

store, shoreline road, barge dock, electricity, fuel tanks, and

their personal fishing camp.)

Ranking ProcessThe subsistence resource data were subsequently assigned

ranks and were combined in GIS for each studied village (i.e.

Kivalina, Kotzebue, Deering, and Selawik). Ranks were

constructed by calculating the mean value of each subsistence

resource (not including a few ‘‘don’t know’’ responses), then

calculating the range from the most important to least

important resource for each village. The range was then

divided into five equal ranks, and the species assigned a

ranking corresponding with their location in the range. Table 3

shows subsistence resource categories and associated ranks.

For example, in Kivalina, whales were considered the most

important resource and moose the least important resource,

with means ranging from 1.44 (whales) to 2.05 (moose) on a

scale ranging from 1 (very big loss) to 4 (no loss). Thus, the

range between the most and least important resource was 0.61.

Divided by five, the ranks were constructed in 0.12 increments.

As a result, in Kivalina, rank 1 included whales, caribou, and

fish; rank 2 included seals, berries, and birds; rank 3 included

eggs and fur-bearing animals; rank 4 was empty; and rank 5

included only moose. The same process was used to calculate

rankings for subsistence species and village infrastructure

independently for each village—each village was assigned

importance rankings based solely on their own preferences.

Thus, in Kivalina, rank 1 resources were whales, caribou, and

fish. By contrast, in Kotzebue, rank 1 resources were fish and

berries.

Integration of CVI with Highly Important SubsistenceResources

Using GIS, we integrated CVI index and subsistence

resources data to identify critical coastal areas that will be

affected by erosion because of SLR. Results of a previous study

by Gorokhovich and Leiserowitz (2012) concluded that ‘‘future

erosion rates may be as high as�0.16–0.6 m/yr for the period

2000–2049 and �0.42–1.65 m/yr for the period 2050–2100.’’

These erosion rates correspond to CVI . 3 (i.e. high and highest

vulnerability). Therefore, potentially vulnerable areas of

subsistence resources will be at coastal locations where critical

CVIs and highly important subsistence resource overlap. In

this analysis, we omitted such subsistence resources as trails,

camp, and wood-gathering locations, concentrating only on

food-related categories. Table 3 shows the subsistence catego-

ries that were used in the GIS analysis and associated ranks.

Ranks of ‘‘importance’’ for subsistence resources for each

area of the four villages were combined to identify areas with

high and low ‘‘importance.’’ Each surveyed village identified a

different number of resources and ranks. For example,

Kivalina residents identified five resources (seal, fish, caribou,

berries, sheep) with ranks from one to four while Selawik

identified only three types of resources (fish, caribou, bird eggs)

with ranks from one to three, etc. Using the GIS tool Raster

Calculator, we selected areas with a CVI greater than three

(high and very high vulnerability) and areas where subsistence

resources were ranked 1 and 2 (two highest ranks of

importance). After the selection in GIS, the resulting map

showed areas where high to very high vulnerable coastal

segments coincide with ‘‘important’’ subsistence resources.

Table 3. List of subsistence resources categories that were collected during

participatory GIS mapping and community surveys and their associated

ranks used in GIS model.

Village/Resource Kivalina Kotzebue Selawik Deering

Beluga 5 3

Berries 2a 1 2

Caribou 1 2 1 1

Camps, cultural sites Xb X X

Eggs 5 3 3

Fish 1 1 1 1

Furbearers 4 3

Moose 4 5

Musk Ox

Seal/Ugruk 2 4 2

Sheep 4

Trails X X X X

Water fowl 3 3

Wood X

a Numbers refer to ranks used in GIS analysisb X is used for the subsistence resources that were mapped but not used in

analysis.



RESULTS AND DISCUSSIONCommunity-Based Participatory GIS Mapping

The list of various subsistence resources that were mapped

by community-based GIS method are in Table 4. Data differ in

terms of number of species per each coastal community and

reflects the results of discussions and meetings with local

hunters and village representatives; therefore, boundaries are

not exact. Because the participatory GIS mapping was

conducted in groups, each boundary was discussed by the

participants and thus has limited, but at least somewhat

controlled, uncertainty. Because hunters/fishermen/gatherers

from different settlements share subsistence resources, the

GIS data have multiple overlaps between shapefiles. In such

cases where these overlaps occur between subsistence resourc-

es categories of the same rank, they ‘‘double’’ the importance of

the subsistence area because they are utilized by people from

two or more settlements.

Ranking of Subsistence Resources and CommunitySurveys

From a census of all available households, 389 (n ¼ 389)

residents were interviewed by telephone or in-person (Table 5).

The Council of American Survey Research Organizations

response rate for the telephone interviews was 63%.

Overall, subsistence resources remain very important to all

four communities, where large proportions of local diets are

still harvested directly from the local environment (Table 5).

Fish was the highest rated subsistence resource in all

communities, except Kivalina, which rated the potential loss

of caribou as ‘‘very big.’’ Kotzebue and Selawik rated fish,

berries, and caribou as their most important resources. Deering

rated fish, caribou, berries, and seals as most important, while

Kivalina rated caribou, then whales, fish, berries, and seals as

most important (a ‘‘very big loss’’). After reflecting on the

geographic, ecological, and cultural differences between these

communities, however, some species were considered more

important by some villages than others. For example, seals and

whales were rated as a more important subsistence species in

Deering and Kivalina (located closer to the ocean) than in

Selawik (located inside a broad delta wetland), which alone

gave a high rating to moose.

Kotzebue considered the airstrip, which provides access to

the outside world, as the single most important facility (a ‘‘very

big loss’’) (Table 6), followed by fuel tanks and electricity.

Selawik considered electricity, fuel tanks, and the airstrip as

their most important facilities. Deering considered electricity,

fuel tanks, the store, the shoreline road, and the school as their

most important facilities (.75% saying these would be ‘‘a very

big loss’’). Finally, Kivalina considered the airstrip, electricity,

and fuel tanks as most important. A crucial point is that all of

these communities are accessible only by airplane, boat, four-

wheel ATVs, or snowmobile, where roads provide limited

access to only the villages or the immediate environs. Access to

Table 4. Areas surveyed.

Location

Number of

Residents Surveyed

% of Total Area

Residents Surveyed

Kotzebue 248 64

Selawik 44 11

Deering 37 10

Kivalina 60 15

Total 389 100

Table 5. Importance of subsistence resources based on percentage of

responses obtained in each community.

Kotzebue Selawik Deering Kivalina Total

Fish

Very big loss 71 82 89 70 74

Medium loss 14 7 3 12 12

Small loss 6 - 3 8 5

No loss 6 9 3 5 6

Don’t know 3 2 3 5 3

Caribou

Very big loss 70 77 87 78 74

Medium loss 9 7 - 3 7

Small loss 10 7 8 8 9

No loss 9 7 - 8 8


Berries

Very big loss 71 80 78 65 72



No loss 7 7 5 8 7


Seals

Very big loss 59 39 78 65 60


Small loss 10 18 5 5 10

No loss 13 21 3 10 12

Don’t know 5 14 - 10 6

Birds

Very big loss 57 66 65 57 58

Medium loss 23 14 22 22 21

Small loss 12 7 11 8 11

No loss 6 9 3 8 6

Don’t know 3 5 - 5 3

Beluga, or other whales

Very big loss 56 41 65 73 58



No loss 15 21 8 7 14

Don’t know 5 21 - 5 6

Other fur-bearing animals

Very big loss 55 66 35 50 54

Medium loss 19 18 38 18 21

Small loss 13 - 11 10 11

No loss 9 9 3 10 8

Don’t know 4 7 14 12 6

Eggs

Very big loss 50 57 68 48 52

Medium loss 21 11 14 28 20

Small loss 12 11 16 3 11

No loss 13 14 3 12 12

Don’t know 4 7 - 8 4

Moose

Very big loss 54 71 46 35 52

Medium loss 20 16 19 28 21

Small loss 13 5 19 20 14

No loss 10 7 11 10 10


Total 248 44 37 60 389



energy, as represented by the local fuel tanks and electricity,

were consistently rated highly.

Coastal Vulnerability and Subsistence ResourcesFigure 6 shows the CVI index distribution along the

Kotzebue Sound shoreline. The range of this index is from 1

(very low) to 5 (very high).The construction of this index was

based on long-term temporal changes on the coast obtained

from aerial photos and thus includes the impact of SLR

together with other meteorological and geologic factors. A

developed CVI is a tool that can be integrated with other

natural or socio-demographic variables to assess different

aspects of coastal vulnerability. In this study, it was integrated

with subsistence resources.

In the study area, three variables (SLR, wave height, and

tidal range) have miniscule spatial variability within Kotzebue

Sound. Because these three variables did not vary within our

study site, the overall approach is still applicable to other

locations, in which these variables do vary. The method,

however, can be used worldwide where coastal vulnerability

needs to be integrated with the risk to subsistence resources.

The analysis finds that out of four studied settlements

(Kivalina, Kotzebue, Deering, and Selawik), Kivalina and

Deering are the most vulnerable, primarily attributable to

their locations on the coast at river mouths and high ranking of

subsistence resources at risk. Kivalina is located on a highly

dynamic barrier island at the mouth of the Wulik River, and

Deering is located on the gravel spit at the mouth of the

Inmachuk River. Both communities are situated at the mouths

of relatively small- or moderate-sized rivers and identified fish

Table 6. Importance of facilities by community.

Kotzebue Selawik Deering Kivalina Total:

Airstrip

Very big loss 78 71 70 73 76



No loss 9 5 11 7 9


Fuel tanks

Very big loss 74 73 84 72 74



No loss 7 7 3 8 7


Electricity

Very big loss 74 75 84 70 74


Small loss 4 5 - 5 4

No loss 9 5 - 7 7


School

Very big loss 69 66 76 75 70


Small loss 5 - 3 3 4

No loss 11 7 - 5 8

Don’t know 4 9 5 10 5

Store

Very big loss 71 57 78 63 69

Medium loss 11 18 14 15 12


No loss 11 7 - 8 9

Don’t know 3 14 5 5 5

Medical clinic

Very big loss 70 61 70 62 68


Small loss 6 - - 10 5

No loss 11 5 5 12 10


Personal fishing camp

Very big loss 67 64 68 55 65


Small loss 5 7 - 8 5

No loss 15 11 8 15 14

Don’t know 5 11 5 7 6

Shoreline road

Very big loss 65 50 78 58 64

Medium loss 15 21 14 12 15


No loss 7 14 - 8 7


Barge dock

Very big loss 63 55 41 52 58



No loss 11 14 30 18 14

Don’t know 5 7 14 10 7

Graveyard

Very big loss 57 48 49 58 55

Medium loss 12 27 22 12 15

Small loss 9 9 14 15 10

No loss 17 11 8 10 14


Total 248 44 37 60 389

Figure 6. CVI for Kotzebue Sound.



and caribou as their primary subsistence resource (Table 6).

Future coastal dynamics in both locations will most likely lead

to substantial ecological changes. While the exact times and

places where these changes will occur and how ecosystems and

key subsistence resources will respond are difficult to predict,

the changes will undoubtedly occur.

The combination of the number of subsistence resources

species from surveys for each village can serve only as a proxy

of the ‘‘richness’’ or ‘‘abundance’’ of subsistence resources.

There are many other scientifically based ecological indicators

such as vegetation density, spatial distribution of species,

fragmentation, etc. Moreover, subsistence resource data were

used in the analysis as ‘‘thematic maps’’ that lack spatial

variability. Spatial variability is difficult to collect with highly

dynamic and changing subsistence resources, especially fish

and caribou. GIS analysis, however, can provide useful

information for the preliminary interpretation of results by

combining maps of critical CVI and subsistence resources.

Coastal stretches where high and very high CVIs and the

number of highly ranked species coincide are the most

vulnerable to erosion and are likely to have an impact on

important subsistence resources. Figure 7 shows this indicator

combined with high (4) and very high (5) CVI, an indicator of

the ‘‘abundance’’ of species and the combination of subsistence

resources with ranks 1 and 2.

While the focus of this study was coastal vulnerability

attributable to coastal erosion and SLR, other factors, such as

landslide activity and permafrost melting, can, and most likely

will, exacerbate existing geologic conditions along the Kotzebue

Sound coastline and inland areas. Figure 8 shows a large

landslide (mudflow) on Selawik River recorded by authors

during their fieldwork in 2008. Local informants reported that

this single landslide dramatically changed the hydrology of the

river channel, including filling it with sediment loads that

lasted several months. Some also reported ecological changes,

such as the decreasing availability of fresh-water fish in

traditional areas. It is also likely that turbid waters from this

landslide could reach the coast and negatively influence other

coastal habitat.

Permafrost melting and landslides are not only attributable

to SLR or coastal erosion, climatic variations in temperature

can also dramatically change soil structure and cause land

deformations. As one of these examples (the landslide on

Selawik River, Figure 8) demonstrates, these deformations can

change even seasonal ecologic conditions on rivers by increas-

ing river turbidity and changing stream hydrology. Figure 8

shows that the landslide body almost blocks the river channel,

and these changes in the channel geometry affect hydrologic

regime. Temperature, however, is not part of the CVI index

methodology related to the coastline and thus was not

considered in this study.

Because local subsistence fishing is a closely timed seasonal

activity, any disruption in a given season can have a significant

impact on local communities. Increases in permafrost thawing

will also cause additional ground and surface water input in the

local hydrologic system and could cause increased risks of

flooding. Thus, these larger-scale changes in local climatic

conditions (warming) can lead to large-scale changes in

geomorphology, which have their own impacts on local

communities and coastal zones apart from rising sea levels

and coastal erosion.

CONCLUSIONSWhen spatial data are not available in consistent format in

terms of scale and data type, they can be converted to raster

format and resampled to represent spatially uniform structure.

The size of the smallest selected spatial unit should be large

enough to accommodate spatial inaccuracy between datasets.

This can be done by measuring spatial discrepancies in GIS

using manual or automated methods. A resampling technique

also includes data conversion from vector to raster; final

applications should use raster analytical techniques.

An integrated-developed GIS model based on the CVI and

ranked subsistence resource provides a useful tool for local

administrators and coastal managers. It shows that long-

term coastal development influenced by SLR and coastal

geomorphology in Kotzebue Sound results in erosion of

significant coastal length. Kivalina and Deering are the most

vulnerable to the effect of this process. These results suggest

that decision makers in the region should focus their

immediate attention on Kivalina and Deering locations to

develop short- and long-term coastal management plans,

including possible relocation.

The combination of the high and very high (CVI . 3)

vulnerability index with an indicator of subsistence resource

abundance (Figure 7) shows that between the two highly

vulnerable villages, Kivalina uses fewer subsistence resources

(at least from the survey results) than Deering. Therefore, if

coastal dynamics negatively affects at least one of the main

Figure 7. Integrated high and very high (CVI . 3) index, indicator of the

‘‘abundance’’ of subsistence resources and combination of subsistence

resources with ranks 1 and 2.



resources in Kivalina, it will have much higher negative effect

on their community than in Deering.

Subsistence resources mapping and surveys indicated that

fish and caribou (except in Kotzebue where it is fish and

berries) are the top-ranked species for local communities. Low

coastal erosion rates (0.16–1.65 m/y) are unlikely to affect these

resources to the extent that they will considerably diminish;

however, future studies should address the following ques-

tions. Will species adapt by migrating landward? How fast this

can happen? Can we develop a cumulative indicator that will

include temporal-spatial responses of ecological coastal habitat

to continuing erosion? Existing modeling approaches, for

example, the Sea Level Affecting Marshes Model (Craft et al.,

2009; McLeod et al., 2010) can be further developed and

applied. In this case, changes in temperature and vegetation

will play significant roles and should be considered in models.

The GIS model constructed in this study integrates hetero-

geneous (in terms of scale, time, and type) spatial data, a public

survey, and participatory GIS data, and demonstrates substan-

tial value beyond studies that only model physical processes,

such as SLR and coastal erosion, or consider only community

infrastructures at risk. This study explicitly integrates physical

and social variables, including direct consultation with affected

local communities and elicitation of their most valued local

resources. At the same time, integration of disparate datasets

can also increase the imprecision of the results; therefore, they

should be used only as guidance and extended by more specific

local studies if intended to be used for decision making.

ACKNOWLEDGMENTSWe thank our many research collaborators and residents in

the NAB who participated in surveys for their invaluable

support, including the NAB, Kotzebue IRA, Kivalina IRA,

City of Kivalina, Maniilaq, U.S. Fish & Wildlife Service, NPS,

and the Alaska Department of Fish & Game. Special thanks

to Alex Whiting for his help with data collection in Kotzebue

and comments on this manuscript. Financial support from

NOAA Sectoral Applications Research Program (SARP)

Figure 8. Landslide (mudflow) on the Selawik River, 12 June 2008.



helped in the collection, analysis, and interpretation of data.

Three anonymous reviewers made considerable improve-

ments to the final manuscript.

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