Integrating Coastal Vulnerability and Community-Based Subsistence Resource Mapping in Northwest Alaska Yuri Gorokhovich † , Anthony Leiserowitz ‡ , and Darcy Dugan § † Department of Earth, Environmental, and Geospatial Sciences Lehman College, City University of New York (CUNY) West Bronx, NY 10468, U.S.A. [email protected]‡ Yale University School of Forestry & Environmental Studies New Haven, CT 06405, U.S.A. § Alaska Ocean Observing System Anchorage, AK 99501, U.S.A. ABSTRACT Gorokhovich, Y.; Leiserowitz, A., and Dugan, D., 2014. Integrating Coastal Vulnerability and Community-Based Subsistence 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 estuarine settlements. It is unclear how SLR and erosion might affect coastal subsistence resources because of highly complex ecological interactions. This study integrates physical, anthropological, and survey data to assess coastal vulnerability and to identify areas of concern for local and regional planning and environmental protection. This study analyzes and integrates historical and projected physical coastal changes within the Kotzebue Sound region with (1) a coastal vulnerability 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 identify Kivalina and Deering as particularly vulnerable coastal locations among four studied villages. While the CVI is high in these locations, low erosion rates will not likely have any negative impact on fish and caribou—two of the most important subsistence resource species for these communities. Because of the higher number of identified subsistence resource species, Deering is more resilient than Kivalina to any potential negative coastal impacts. This methodology can be useful 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. INTRODUCTION One 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 received 2 May 2013. Published Pre-print online 29 May 2013. Ó Coastal Education & Research Foundation 2014 Coconut Creek, Florida January 2014 Journal of Coastal Research 30 1 158–169
12
Embed
Integrating Coastal Vulnerability and Community …environment.yale.edu/climate-communication-OFF/files/...Integrating Coastal Vulnerability and Community-Based Subsistence Resource
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Integrating Coastal Vulnerability and Community-BasedSubsistence Resource Mapping in Northwest Alaska
Yuri Gorokhovich†, Anthony Leiserowitz‡, and Darcy Dugan§
†Department of Earth, Environmental, andGeospatial Sciences
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.
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.
Journal of Coastal Research, Vol. 30, No. 1, 2014
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
(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.
Journal of Coastal Research, Vol. 30, No. 1, 2014
Coastal Vulnerability and Subsistence Resources Mapping 167
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.
Journal of Coastal Research, Vol. 30, No. 1, 2014
168 Gorokhovich, Leiserowitz, and Dugan
helped in the collection, analysis, and interpretation of data.
Three anonymous reviewers made considerable improve-
ments to the final manuscript.
LITERATURE CITEDBoruff, B.J.; Emrich, C., and Cutter, S., 2005. Erosion hazard
vulnerability of US coastal counties. Journal of Coastal Research,21(5), 932–942.
Braund, R. and Associates, 2008. Pre-mine subsistence use areasKivalina. Maps 1–17. In: Red Dog Mine Extension, Aqqaluk Project,Draft Supplemental Environmental Impact Statement, Vol. 2,Appendices.
Chambers, R., 2006. Participatory mapping and Geographic Infor-mation Systems: Whose map? Who is empowered and whodisempowered? Who gains and who loses? Electronic Journal onInformation Systems in Developing Countries, 25(2).
Clark, J.R., 1997. Coastal zone management for the new century.Ocean and Coastal Management, 37(2), 191–216.
Craft, C.; Clough, J.; Ehman, J.; Joye, S.; Park, R.; Pennings, S.; Guo,H., and Machmuller, M., 2009. Forecasting the effects of acceler-ated sea-level rise on tidal marsh ecosystem services. Frontiers inEcology and the Environment, 7(2009), 73–78.
Diez, P.G.; Perillo, G.M.E., and Piccolo, M.C., 2007. Vulnerability tosea-level rise on the coast of the Buenos Aires Province. Journal ofCoastal Research, 23(1), 119–126.
Dolan, A.H. and Walker, I.J., 2006.Understanding vulnerability ofcoastal communities to climate change related risks. In: Klein,A.H.F. and Finkl, C.W. (eds.), Proceedings of the 8th InternationalCoastal Symposium, Journal of Coastal Research, Special Issue No.39, pp. 1316–1323.
ESRI. ArcGIS Resource Center, 2013. http://help.arcgis.com/en/arcg isdesktop /10 .0 /he lp / index .html#/Zonal_Stat is t i cs /009z000000w7000000/.
Goodchild, M.F., 1998. Uncertainty: the Achilles heel of GIS? Geo InfoSystems, 8(11), 40–42.
Gornitz, V. and Kanciruk, P., 1989. Assessment of global coastalhazards from sea-level rise. In: Proceedings of the Sixth Sympo-sium on Coastal and Ocean Management ASCE (Charleston, SC,ASCE), pp. 1345–1359.
Gorokhovich, Y. and Leiserowitz, A., 2012. Historical and futurecoastal changes in Northwest Alaska. Journal of Coastal Research,28(1A), 174–186.
Hegde, A.V. and Reju, V.R., 2007. Development of coastal vulnera-bility index for Mangalore Coast, India. Journal of CoastalResearch, 23(5), 1106–1111.
Hinzman, L.; Bettez, N.; Bolton, W.; Chapin, F.; Dyurgerov, M., andFastie, C., 2005. Evidence and implications of recent climatechange in northern Alaska and other arctic regions. ClimaticChange, 72(3), 251–298.
Hughes, P. and Brundrit, G.B., 1992. An index to assess SouthAfrica’s vulnerability to sea-level rise. South African Journal ofScience, 88(6), 308–311.
Huntington, H. and Fox, S., 2005. The changing Arctic: Indigenousperspectives. In: A. M. a. A. Program (ed.), Impacts of a WarmingClimate—Arctic Climate Impact Assessment. Cambridge: Cam-bridge University, pp. 61–98.
Inuit Circumpolar Council, 1992. Principles and Elements for aComprehensive Arctic Policy. Proceedings of the Inuit CircumpolarConference (Inuvik, Canada), 185p.
Magdanz, J.; Utermole, C., and Wolfe, R., 2002. The Production andDistribution of Wild Food in Wales and Deering Alaska (No. 259).Juneau, Alaska: Alaska Department of Fish and Game: Division ofSubsistence, 128p.
McLeod, E.; Poulter, B.; Hinkel, J.; Reyes, E., and Salm, R., 2010. Sea-level rise impact models and environmental conservation: a reviewof models and their applications. Ocean & Coastal Management,53(9), 507–517.
Mittal, A.K., 2009. Alaska Native Villages: Limited Progress HasBeen Made on Relocating Villages Threatened by Flooding andErosion. Report to Congressional Requesters. GAO-09-551.
National Oceanic and Atmospheric Administration (NOAA),2011a.http://www.ngdc.noaa.gov/mgg/bathymetry/maps/maptypes.html. NOAA, 2011b. http://response.restoration.noaa.gov/esi.
Ozyurt, G. and Ergin, A., 2009. Application of sea level risevulnerability assessment model to selected coastal areas of Turkey.Proceedings of the 10th International Coastal Symposium ICS2009. Journal of Coastal Research, Special Issue No. 56, pp. 248–251.
Poppel, B., 2006. Interdependency of subsistence and marketeconomies in the Arctic. In: Glomsrd, S. and Aslaksen, J. (eds.),The Economy of the North. Statistics Norway, pp. 65–80.
Rambaldi, G.; Chambers, R.; McCall, M., and Fox, J., 2006. Practicalethics for PGIS practitioners, facilitators, technology intermediar-ies and researchers. Participatory Learning Action, 54, 106.
Schroeder, R.; Andersen, D.B., and Grant, H., 1987a. Subsistence usearea mapping in ten Kotzebue Sound communities (No. 130).Kotzebue: Alaska Department of Fish and Game, Division ofSubsistence and Maniilaq Association.
Schroeder, R.; Andersen, D.B., and Grant, H., 1987b. Subsistence usearea map atlas for ten Kotzebue Sound communities. ManiilaqAssociation and Division of Subsistence, Alaska Department of Fishand Game, Juneau, Alaska.
Shaw, J.; Taylor, R.B.; Forbes, D.L.; Ruz, M.H., and Solomon, S.,1998. Sensitivity of the Canadian coast to sea-level rise. GeologicalSurvey of Canada Bulletin, 505, 1–114.
Shrestha, H.L., 2006. Using global positioning systems (GPS) andgeographic information systems (GIS) in participatory mapping ofcommunity forest in Nepal. Electronic Journal on InformationSystems in Developing Countries, 25(5), 1–11.
Sieber, R., 2006. Public participation geographic information systems:a literature review and framework. Annals of the Association ofAmerican Geographers, 93(3), 491.
Thieler, E.R. and Hammar-Klose, E.S., 1999. National Assessment ofCoastal Vulnerability to Future Sea-Level Rise: PreliminaryResults for the U.S. Atlantic Coast. U.S. Geological Survey,Open-File Report 99-593.
Thieler, E.R. and Hammar-Klose, E.S., 2000a. National Assessmentof Coastal Vulnerability to Future Sea-Level Rise: PreliminaryResults for the U.S. Pacific Coast. U.S. Geological Survey, Open-File Report 00–178.
Thieler, E.R. and Hammar-Klose, E.S., 2000b. National Assessmentof Coastal Vulnerability to Future Sea-Level Rise: PreliminaryResults for the U.S. Gulf of Mexico Coast. U.S. Geological Survey,Open-File Report 00–179.
Thieler, E.R.; Himmelstoss, E.A.; Zichichi, J.L., and Miller, T.L.,2005. Digital Shoreline Analysis System (DSAS) version 3.0: AnArcGIS� extension for calculating shoreline change. U.S. Geolog-ical Survey Open-File Report 2005-1304.
Tripathi, N. and Bhattarya, S., 2004. Integrating indigenousknowledge and GIS for participatory natural resource manage-ment: state-of-the-practice. Journal on Information Systems inDeveloping Countries, 17(3), 1–13.
U.S. Army Corps of Engineers (USACE). 2006. Alaska Village ErosionTechnical Assistance Program. An Examination of Erosion Issuesin the Communities of Bethel, Dillingham, Kaktovik, Kivalina,Newtok, Shishmaref, and Unalakleet. Alaska District.
Whiting, A., 2006. Native Village of Kotzebue Harvest Survey Program2002–2003–2004: Results of Three Consecutive Years Cooperatingwith Qikiqtagrugmiut to Understand their Annual Catch ofSelected Fish and Wildlife. Kotzebue: Native Village of Kotzebue.
Wolfe, R., 2004. Local Traditions and Subsistence: A Synopsis of 25Years of Research by the State of Alaska (No. 284). Juneau, Alaska:Alaska Department of Fish and Game: Division of Subsistence, pp.1–81.
Wolfe, R.J. and Walker, R.J., 1987. Subsistence economies in Alaska:productivity, geography, and development impacts. Arctic Anthro-pology, 24(2), 56–81.
Journal of Coastal Research, Vol. 30, No. 1, 2014
Coastal Vulnerability and Subsistence Resources Mapping 169