Climate Change Vulnerability Assessment 

for the Chugach National Forest and the 

Kenai Peninsula

United States Department of Agriculture

Forest 

Service Research Station

General Technical Report

PNW-GTR-950

May 

2017
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Editors

Gregory D. Hayward is a population and community ecologist, U.S. Department 

of Agriculture, Forest Service, Alaska Region, PO Box 21628, Juneau, AK 99802-

1628; Steve Colt is a professor of economics, University of Alaska Anchorage, 

Institute of Social and Economic Research, 3211 Providence Drive, Anchorage, 

AK 99508; Monica L. McTeague is a research ecologist, formerly with the University of Alaska Anchorage, Alaska Natural Heritage Program; and Teresa N. 

Hollingsworth is a vegetation and community research ecologist, U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station, Boreal Ecology 

Cooperative Research Unit, P.O. Box 756780, Fairbanks, AK 99775-6780.

Cover: Bear Glacier, Alaska. Photo by Isaac Hayward.
Climate Change Vulnerability Assessment 

for the Chugach National Forest and the 

Kenai Peninsula

Gregory D. Hayward, Steve Colt, Monica L. McTeague, and 

Teresa N. Hollingsworth, editors

U.S. Department of Agriculture, Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-950

May 2017
ii

Abstract

Hayward, Gregory H.; Colt, Steve; McTeague, Monica L.; Hollingsworth, 

Teresa N., eds. 2017. Climate change vulnerability assessment for the Chugach 

National Forest and the Kenai Peninsula. Gen. Tech. Rep. PNW-GTR-950. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 340 p.

This assessment evaluates the effects of future climate change on a select set of ecological systems and ecosystem services in Alaska’s Kenai Peninsula and Chugach 

National Forest regions. The focus of the assessment was established during a 

multi-agency/organization workshop that established the goal to conduct a rigorous 

evaluation of a limited range of topics rather than produce a broad overview. The 

report explores the potential consequences of climate change for: (a) snowpack, 

glaciers, and winter recreation; (b) coastal landscapes and associated environments, (c) vegetation, (d) salmon, and (e) a select set of wildlife species. During 

the next half century, directional change associated with warming temperatures 

and increased precipitation will result in dramatic reductions in snow cover at 

low elevations, continued retreat of glaciers, substantial changes in the hydrologic 

regime for an estimated 8.5 percent of watersheds, and potentially an increase in the 

abundance of pink salmon. In contrast to some portions of the Earth, apparent sealevel rise is likely to be low for much of the assessment region owing to interactions 

between tectonic processes and sea conditions. Shrubs and forests are projected 

to continue moving to higher elevations, reducing the extent of alpine tundra and 

potentially further affecting snow levels. Opportunities for alternative forms of 

outdoor recreation and subsistence activities that include sled-dog mushing, hiking, 

hunting, and travel using across-snow vehicles will change as snowpack levels, 

frozen soils, and vegetation change over time. There was a projected 66-percent 

increase in the estimated value of human structures (e.g. homes, businesses) that 

are at risk to fire in the next half century on the Kenai Peninsula, and a potential 

expansion of invasive plants, particularly along roads, trails, and waterways.

Keywords: climate change, salmon, glaciers, wildlife, seascapes, vegetation, 

snow, scenarios.
iii

Summary 

Background 

We assembled a team of 33 federal resource managers, research scientists, and 

economists, as well as professionals working in the private sector, to examine the 

potential consequences of climate change for an extensive portion of south-central 

Alaska. This collaborative effort, which represents 12 agencies and organizations, 

developed organically as agencies and groups learned of an initial collaboration by 

the University of Alaska Anchorage, and the Chugach National Forest. The resulting assessment examines a limited set of biophysical and economic consequences 

of a changing climate over the next five decades across landscapes that extend from 

the Copper River Delta on the east, through Prince William Sound to the western 

Kenai Peninsula on the west, and north to the spine of the Chugach Range. Priority 

issues were determined early in the process by an interagency group and university personnel; these issues were based on critical problems facing the Chugach 

National Forest, the specific expertise that could be assembled to address them, and 

the relevance of issues to stakeholders. Important elements influencing the scope of 

the assessment include the broad collaboration among diverse partners, the deliberate choice to include economists in the assessment, and the choice to significantly 

limit discussion to six broad areas that are of keen interest to people of southcentral Alaska: (1) snow and ice (glaciers and ice fields), (2) coasts and seascapes, 

(3) salmon, (4) vegetation, (5) wildlife, and (6) infrastructure. Within these broad

areas of investigation, the scope of assessment was further limited, recognizing that

future assessments could build from this foundation.

The Chugach/Kenai region occurs within two climatic regions, Cook Inlet and 

south-central Alaska (Shulski and Wendler 2007). The Cook Inlet climatic region 

represents a subarctic area in transition between a maritime and continental climate, 

with moderate temperatures compared to regions in Alaska’s interior. Precipitation 

is substantially lower than in maritime regions, with a growing season longer than 

100 days at lower elevations. The south-central climatic region is under a strong 

maritime influence, with high annual precipitation, very frequent cloud cover, and 

moderate temperatures (Shulski and Wendler 2007: 27–31). Both regions experience highly variable weather across a range of years, particularly with respect to 

the timing and amount of precipitation and the depth of snowpack at low elevations 

(Bieniek et al. 2014). This variability is a consequence of variation in broad-scale 

ocean circulation patterns and the proximity of the assessment area to the Gulf of 

Alaska (Hartmann and Wendler 2005). 

We assembled a team 

of 33 federal resource 

managers, research 

scientists, and economists, as well as 

professionals working 

in the private sector, to 

examine the potential 

consequences of 

climate change.
iv

Long-term measurements of air temperature, sea surface temperatures, and 

patterns of polar ice all confirm the intense warming of earth’s climate over the 

past 60 years (e.g., IPCC 2014, Melillo et al. 2014, MEA 2005). Over the last 100 

years, high-latitude regions have warmed more substantially than the rest of the 

Earth. In fact, during the last decade, much of Alaska has warmed twice as rapidly 

as the global average (Chapin et al. 2014, Stewart et al. 2013, Wendler and Shulski 

2009). This pattern of warming is far more pronounced in winter and spring than 

in autumn or summer. For instance, the Cook Inlet climate region has warmed 

0.7 °C over the century when looking at average annual temperature (see Shulski 

and Wendler 2007: 141); however, the average temperature in January and February 

during this period was 1.7 °C higher than a century earlier. To ask questions about 

future climate conditions and potential consequences for the biophysical environment and for ecosystem services, we took a scenarios approach, in conjunction with 

downscale climate modeling, to paint multiple pictures of future climate conditions. 

Downscaled projections for regions such as south-central Alaska, which experience 

high interannual and decadal variability, tend to result in significant uncertainty for 

the first 10 to 20 years of projections, higher confidence for the next 30 years, and 

less certainty after 50 or 60 years (Hawkins and Sutton 2009). Therefore, we limited our assessment to exploring potential outcomes for the next five to six decades. 

Then, to create two scenarios, we used the average of five global climate models 

(GCMs) that have been shown to function best for this region.

Potential Future Climate 

Most climate models predict that high latitudes will experience a much larger rise 

in temperature than the rest of the globe through the remainder of the 21st century. 

However, the coastal location of the assessment area, with complex weather patterns and tortuous topography, results in patterns of change dissimilar to arctic 

Alaska (SNAP 2015). Temperature profiles are expected to warm by about 3 °C in 

the next 50 years. Winter temperature change is expected to be most extreme. In 

the warmest coastal areas, average temperatures in the coldest month of the year 

are predicted to rise from only slightly above freezing to well above freezing, or 

approximately 4.5 °C above current temperatures. Moreover, these warm temperatures will spread inland toward Cordova, Valdez, and Seward, with above-freezing 

months of January dominating across all coastal regions, and in some areas as much 

as 20 miles inland. Many rivers will shift from a below-freezing to above-freezing 

temperature regime. Across the region, winter temperatures are expected to warm 

by about 3 to 3.5 °C. 
v

Across the assessment area, the date of spring thaw is expected to come earlier. 

Large areas of coastal and near-coastal land are projected to shift from early spring 

thaw to the “rarely freezes” category. This shift is likely to correspond to lack of 

winter snowpack and an altered hydrologic cycle. Farther from the coast and at 

higher elevations, spring thaw is projected to occur from 3 to 10 days earlier, on 

average. Autumnal changes are projected to be slightly greater than those seen in 

spring, with the freezing point shifting to noticeably later in the year in just a single 

decade. For example, an increase in growing season from about 200 days to about 

230 days may be expected in Palmer, Anchorage, Wasilla, and Kenai; and an even 

more substantial increase may be seen in Seward, Valdez, and Cordova.

The projected decadal trend is toward greater precipitation in both January and 

July and a potential increase in the frequency and intensity of strong storms (Graham and Diaz 2001). However, model results for precipitation are less robust than 

those for temperature, in part because precipitation is intrinsically more geographically and temporally variable. A shift in the percentage of precipitation falling as 

snow, particularly at low and mid elevations, will alter the annual hydrologic profile. 

Potential Biophysical and Economic Change 

Context—

The assessment area is composed of three relatively distinct sociopolitical regions. 

The Municipality of Anchorage and the Kenai Peninsula Borough are each organized as single political jurisdictions equivalent to counties, whereas the Prince 

William Sound region includes the independent cities of Whittier, Valdez, and 

Cordova, as well as the predominantly Native villages of Tatitlek and Chenega. 

Each of the three regions represent distinct social and cultural settings with substantially different demographic and economic characteristics. Anchorage is home 

to more than 40 percent of Alaskans and is the dominant source of demand for 

recreation and tourism on the Chugach National Forest and on the Kenai Peninsula. 

The Kenai Peninsula Borough is a rural area with about 60,000 residents and a 

limited road system. In contrast, the Chugach census subarea has very little private 

land and fewer than 7,000 residents, and has limited connection to the population 

center; for instance, Cordova, Tatitlek, and Chenega are not connected by road to 

the rest of the state, but are served by the state-run ferry system. The relatively 

high median household income in the Chugach census subarea (the Prince William 

Sound region) stems primarily from oil-industry employment at the Valdez terminal 

of the Trans-Alaska Pipeline. Despite this concentration of high-wage private-sector 

jobs, the Prince William Sound region is much more dependent than the other two 

regions on fishing and local government for employment. There is one actively 

An increase in growing 

season from about 

200 days to about 230 

days may be expected 

in Palmer, Anchorage, 

Wasilla, and Kenai.
vi

fished limited-entry permit for every 10 employed residents in Prince William 

Sound, compared to 1 per 20 employed in the Kenai Peninsula and 3 per 1,000 in 

Anchorage. Subsistence and personal-use fish harvest are important components of 

household consumption and well-being for many people, particularly in the Kenai 

and Prince William Sound portions of the assessment area. Harvest and use of wild 

native species represents a significant component of the culture across all three 

regions, but occurs within different social, economic and cultural contexts. Tourism 

and recreation are also important to the economies of all three regions (Colt et al. 

2002, Crone et al. 2002, Fay et al. 2005). An estimated 500,000 people recreate on 

the Chugach National Forest each year (USDA FS 2014). 

The Chugach/Kenai assessment area covers a region in which physical and ecological features reflect incredible geological/physical disturbance. Tectonic forces, 

glacial scouring, and the influence of annual snow result in regionwide patterns of 

directional change in topography and ecology. At the last glacial maximum, the vast 

majority of the assessment area was under ice (Heusser 1983, Kaufman and Manley 

2004). The current vegetation represents the outcome of glacial retreat followed by 

species recolonization. Over the last 14,000 years, directional change has dominated 

the assessment area and continues today. Deglaciation progressed in Prince William 

Sound sufficiently to expose low-lying areas by 9,000 BP, resulting in colonization by coastal tundra and sedge tundra (Ager 2001, Heusser 1983). Conifers first 

become apparent about 2,700 BP. Coastal rain-forest tree species migrated from 

southeast Alaska (where they persisted through the Holocene) following the prevailing storm tracks northwestward along the gulf coast and across Prince William 

Sound. This migration of conifer tree species appears to have required thousands 

of years to travel hundreds of kilometers. About 2,000 BP, coastal rain-forest tree 

species developed forest communities (Heusser 1983: 349).

Potential biophysical and economic changes—

Characteristics of snowpack and glaciers significantly influence a broad array of 

ecological and economic features of the Chugach/Kenai assessment area. Therefore, 

modeling future patterns of snowpack was made a key step in the assessment, 

influencing most other analyses. However, modeling snow depth from downscaled 

climate models is extremely difficult. Useful approaches to develop scenarios to 

understand snowpack include evaluating snow-day fraction (SDF) (the proportion 

of days when precipitation is expected to fall as snow), and snow-water equivalent 

(SWE) (the water yield from existing snowpack). 

Snow-day fraction and snow-water equivalent are projected to decline most 

in late autumn (October to November) and at lower elevations. Snow-day fraction 

is projected to decrease by 23 percent from October to March (the winter period) 

Snow-day fraction is 

projected to decrease 

by 23 percent from 

October to March (the 

winter period) between 

sea level and 500 m of 

elevation.
vii

between sea level and 500 m of elevation. From sea level to 1000 m, the winter SDF 

is projected to decrease by 17 percent. Averaged across the cool season, SWE is 

expected to decrease at elevations below 1000 m owing to increased temperature, 

but increase at higher elevations owing to increased precipitation. Compared to 

the period of 1971–2000, the percentage of the landscape that is snow-dominant in 

2030–2059 is projected to decrease, and the percentage of rain and snow being codominant (transient hydrology) is projected to increase from 27 to 37 percent. Most 

of this change is at lower elevations. 

Glaciers in the region have been losing mass during the past century. However, 

the dynamics of glaciers, particularly whether they advance or retreat at a particular 

time, is extremely complex (see chapter 3). Glaciers in the region are currently 

losing about 6 km3

 of ice per year; half of this loss comes from Columbia Glacier 

(Berthier et al. 2010). Over the past decade, almost all glaciers surveyed within the 

region are losing mass (with one exception), including glaciers that have advancing 

termini (Larsen et al. 2015). In the future, glaciers not calving into the ocean will 

retreat and shrink at rates equivalent or higher to current rates of 3 m/year at their 

termini (Larsen et al. 2015). As an example of the potential rate of glacier retreat, 

Columbia Glacier will likely retreat another 15 km and break into multiple tributaries over the next 20 years before stabilizing. Other tidewater glaciers have uncertain 

futures, but will likely not advance significantly in coming decades.

Changes in the timing and depth of snowpack and changes in glacier extent 

will directly and indirectly influence recreation and tourism. Within the assessment 

period, the portion of the winter with sufficient snow at trailheads for skiing and 

snowmobile recreation will decrease significantly in some areas. Viewsheds along 

roads and trails, and areas visible from marine recreation such as cruise ships, will 

change as glaciers recede and the season of snowpack decreases. 

In contrast to most coastal areas around the globe, negative impacts of sealevel rise on ecological systems and ecological services are unlikely during the 

assessment period. As a consequence of tectonic processes associated with active 

faults and isostatic rebound in response to glacial retreat, the effective sea level has 

dropped or remained relatively stable in many areas. Furthermore, coasts within the 

assessment area experience extreme fluctuation in marine inundation because of 

large tides. Marine systems and economic infrastructure are therefore unlikely to 

be threatened by climate-change-induced sea-level rise. 

Although effective sea level may not change significantly, ecological characteristics of the coast will change. Prince William Sound receives up to 50 percent of 

its freshwater discharge from glacial runoff, indicating that changes to the region’s 

tidewater glaciers will have profound effects on the coastal environment (e.g., 

Negative impacts 

of sea-level rise on 

ecological systems and 

ecological services 

are unlikely during the 

assessment period. 
viii

composition of the Alaska coastal current, relative salinity, and pH). Changes in 

the frequency of harmful algal blooms, the extent of eelgrass beds, and the abundance of food for migrating shorebirds are all features likely to change with highly 

uncertain outcomes.

Five species of Pacific salmon use the freshwater systems of the assessment 

area and represent critical ecological, economic, and cultural resources. Salmon 

from south-central Alaska support commercial fisheries with an estimated economic impact of $587 million in revenues and 7,944 jobs for Alaskans (see chapter 

5). Of the 2.5 million days of recreational fishing by anglers in Alaska, 72 percent 

occurred in south-central Alaska, with salmon being the primary target species. 

Based on two variables, percentage of glacial cover and an index of spring snowpack, 61 of the 720 watersheds (8.5 percent) were classified as being vulnerable 

to climate change because of projected reductions in snowpack over the next 60 

years and the resulting change in the timing and amount of runoff. This analysis 

and qualitative evaluation of the status of freshwater systems in the assessment 

area suggest that the largely intact aquatic systems of the region, with extremely 

diverse conditions for salmon, are likely to be largely resilient to changing climate 

during the assessment period, particularly in the short term. Some evidence based 

on our analysis of long-term salmon abundance in the region suggests the potential 

for increases in salmon abundance in the future, particularly pink salmon. The 

consequences of changing marine conditions, however, are particularly uncertain. 

Some authors expect substantial reductions in quality marine habitat, particularly 

for sockeye salmon (Abdul-Aziz et al. 2011). Although this assessment is largely 

oriented toward the terrestrial/freshwater environment, the potential for change in 

marine systems to influence salmon cannot be overlooked.

Salmon contribute significantly to the lifestyle and character of the region. 

The importance of salmon to commercial fisheries, sport fisheries, subsistence 

lifestyles, and personal-use fisheries outlined in the assessment hint at the critical 

role of salmon to the region. The complex relationships between salmon abundance, 

salmon harvest, total value of the commercial catch, and the price of salmon make 

it extremely difficult to assess the consequences of changing climate on economic, 

social, and cultural systems. Some evidence suggests that potential increases in 

pink salmon abundance resulting from warmer temperatures might be attenuated 

by lower prices, with uncertain effects on harvester earnings. The commercial 

industry has a long history of adapting to both abrupt and gradual changes. The 

effects of changes in salmon abundance or the timing of runs will influence subsistence and personal-use harvesters differently than commercial harvesters. Current 

Salmon from southcentral Alaska support 

commercial fisheries 

with an estimated 

economic impact 

of $587 million in 

revenues and 7,944 

jobs for Alaskans.
ix

policy gives subsistence fisheries priority during times of shortage, providing some 

resilience to changes in abundance. However, changes in run timing and reductions 

in abundance would likely increase tensions between participation in subsistence, 

cash jobs, and even school schedules. Personal-use salmon fishing in the region 

focuses completely on sockeye salmon and accounts for 8 percent of total sockeye 

harvest. Hence, unlike other harvest users, the potential for subsistence users to 

substitute other species is small, thus these users appear to be the most vulnerable 

to a significant decline in sockeye salmon biomass resulting from adverse ocean 

conditions or warmer air temperatures operating through the freshwater ecosystem 

because they would not benefit from an increase in pink salmon. 

Climate change often affects vegetation both directly and indirectly, manifesting in a strong visible shift in terrestrial environments. The assessment area 

supports nine broad vegetation cover types, with coastal temperate rain forest 

dominated by Sitka spruce and transition boreal forest dominated by black and 

white spruce being most widespread. The largely intact nature of ecological systems results in non-native plant species occurring on a very small portion of the 

assessment area (0.04 percent), whereas 53 rare or imperiled native plant species 

were identified. Models examining potential changes in the distribution of vegetation based on the direct effects of climate change suggest that, of ten land cover 

types, coastal temperate rain forest will retain most of its current distribution and 

will expand westward, while subalpine shrub and alpine tundra are likely to decline 

as forest and shrublands move upward in elevation. The area within a suitable 

climate envelope for alpine tundra land cover may decline by 87 percent by 2060. 

The consequences of climate change for rare plants differ dramatically across 

taxa depending on the factors currently limiting their distribution and the current 

geographic location of the taxa within the assessment area.

The effects of climate change on disturbance regimes, both fire and insects, 

will indirectly affect vegetation. In addition, disturbance regimes, fire in particular, 

directly affect humans. The interactions among fire, insect disturbance, and vegetation are important to quantify. Models suggest that while fire will remain rare in 

coastal rain forest communities, the potential for fire is likely to increase on the 

western Kenai. As a result, the combined influence of property development (e.g., a 

53-percent increase in the number of private structures) and changing climate will

increase the vulnerability of the built landscape to fire. The value of structures at

risk to fire is projected to grow by 66 percent on private lands by 2065. The projected value of structures in landscapes with high to extreme fire risk may approach

$3.8 billion in 2065 (based on 2014 dollars). Defoliating insects that periodically

Coastal temperate 

rain forest will retain 

most of its current 

distribution and will 

expand westward, while 

subalpine shrub and 

alpine tundra are likely 

to decline as forest 

and shrublands move 

upward in elevation.
x

cause extensive defoliation of conifer and deciduous trees and shrubs have shifted 

their distribution northward over the past several decades. Rising winter temperatures may facilitate more frequent defoliation events and reductions in tree and 

shrub vigor. 

The assessment area supports a broad range of wildlife ranging from spectacular flocks of migrating shorebirds and waterfowl to large mammals such as brown 

bear and moose. We limited our assessment of the potential wildlife response 

to shorebirds (see chapter 4) and three species of ungulate; moose, caribou, and 

Sitka black-tailed deer. Current distributions of these three ungulate species in the 

assessment area are artifacts of glacial history and translocations (moose, Sitka 

black-tailed deer) and reintroduction (caribou) in the 20th century. All three species 

hold cultural, social, and economic values but differ substantially in their distribution and ecology—hence, they together represent a focused case study into the 

array of responses that may be realized for wildlife in the region. About 10,000 

moose are well distributed throughout the assessment area, mostly on the western 

Kenai Peninsula and around Anchorage. Their distribution is likely to increase 

in the near term owing to continued post-introduction colonization of the Prince 

William Sound area, afforestation of the Kenai lowlands and alpine tundra, and 

increasing fires on the western Kenai Peninsula. About 1,000 caribou are distributed on the western side of the Kenai Peninsula in four herds. Their distribution is 

likely to decrease in response to afforestation of alpine tundra and increases in fire. 

About 20,000 black-tailed deer occur in the Sitka spruce forest along Prince William Sound. Their distribution is likely to increase owing to declining snow depths 

along the coast and continued post-introduction dispersal onto the Kenai Peninsula. 

Over the longer term, scenarios for distribution and abundance of these ungulates 

becomes uncertain because of the expected introduction of novel pathogens and 

their interaction with changing ecological drivers. 

This climate vulnerability assessment highlights a limited subset of changes 

in socioeconomic and biophysical conditions expected to occur in the Chugach/

Kenai region as a consequence of a warming climate. Beyond the specific results 

for icefields and glaciers, salmon and ungulates, and vegetation and coastlines, the 

assessment demonstrates several broader principles. These broad messages emerge 

from other assessments but are worthy of note because they provide a useful generalized framework for evaluating climate change that may be valuable when one is 

considering resource management and other social responses. First, this assessment 

illustrates the relationship between global patterns and local responses; it demonstrates that context matters. Second, the assessment shows the value of taming 
xi

the firehose of information on climate change—it acknowledges the limitations 

of human focus and the value of narrowing the conversation. Finally, by honestly 

acknowledging the significant uncertainty in long-term scenarios, the assessment 

highlights the ultimate value of reducing the driver of climate change—the emission of greenhouse gases—to address long-term risks. 

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xiii

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 2014. Climate change impacts in 

the United States: the third national climate assessment. Washington, DC: U.S. 

Global Change Research Program. 841 p. http://s3.amazonaws.com/nca2014/

low/NCA3_Climate_Change_Impacts_in_the_United%20States_LowRes.

pdf?download=1. (27 May 2016).

Millennium Ecosystem Assessment [MEA]. 2005. Ecosystems and human 

well-being: synthesis. Washington, DC: Island Press. 137 p. http://www.

millenniumassessment.org/documents/document.356.aspx.pdf. (27 May 2016).

Shulski, M.; Wendler, G. 2007. The climate of Alaska. Fairbanks, AK: 

University of Alaska Press. 208 p.

Scenarios Network for Alaska and Arctic Planning [SNAP]. 2015. Regional 

climate projections. Fairbanks, AK: University of AlaskaFairbanks. https:// 

www.snap.uaf.edu. (23 September 2015). 

Stewart, B.C.; Kunkel, K.E.; Stevens, L.E.; Sun, L.; Walsh, J.E. 2013. Regional 

climate trends and scenarios for the U.S. National Climate Assessment: Part 

7. Climate of Alaska. Tech. Report NESDIS 142-7. Washington, DC: U.S.

Department of Commerce, National Oceanic and Atmospheric Administration.

60 p. http://www.nesdis.noaa.gov/technical_reports/NOAA_NESDIS_Tech_

Report_142-7-Climate_of_Alaska.pdf. (27 May 2016).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2014. Assessment 

of the ecological and socio-economic conditions and trends: Chugach National 

Forest, Alaska. R10-MB-787. Anchorage, AK: U.S. Department of Agriculture, 

Forest Service, Alaska Region. 333 p.

Wendler, G.; Shulski, M. 2009. A century of climate change for Fairbanks, 

Alaska. Arctic. 62(3): 295–300. 
xiv

Contents

1 Chapter 1: Introduction

Gregory D. Hayward, Steve Colt, Monica L. McTeague, and 

Teresa N. Hollingsworth

2 Focus of Assessment

4 Constraints on the Assessment

4 History of the Assessment

4 Uncertainty in a Resource Planning Environment

6 Temporal Scale and Uncertainty 

7 Characteristics of the Chugach National Forest and the Kenai 

Peninsula Assessment Area

7 Climatic Setting 

8 Physical and Ecological Setting

11 Social, Economic, and Cultural Setting

16 Literature Cited

21 Chapter 2: Climate Change Scenarios

 Nancy Fresco and Angelica Floyd

21 Summary

21 Introduction

22 Development of Climate Scenarios

23 Changes in Temperature

24 Changes in Precipitation

26 Literature Cited

29 Chapter 3: Snow and Ice

Jeremy S. Littell, Stephanie McAfee, Shad O’Neel, Louis Sass, 

Evan Burgess, Steve Colt, and Paul Clark

29 Summary

30 Introduction

31 Climate Change and Its Effects on Snow and Ice

33 Impacts of Climate Change Effects on Snow and Ice

36 Future Snow Response to Climate Change

36 Strengths and Limitations of Climate Modeling for Snow Impacts

38 Projected Effects of Climate Change on Snow-Day Fraction in the 

Chugach National Forest

43 Projected Effects of Climate Change on Snow-Water Equivalent in 

South-Central Alaska

48 Projected Effects of Climate Change on Snowpack Vulnerability in 

South-Central Alaska

52 Limitations: Caveats and Uncertainty

53 Current and Future Ice and Glacier Response to Climate Change

56 Case Study: Monitoring the Retreat of Exit Glacier
xv

58 Case Study: Evaluating Glacier Change By Using Remote Historical 

and Current Remote-Sensing Tools

63 Snow and Ice: Effects on Ecosystem Services

63 Introduction and Conceptual Framework

65 Affected Ecosystem Services

67 Maintaining Ecosystem Services in the Face of Climate Change

68 Maintaining Recreation and Tourism Subsystems

68 Consequences of Potential Change in Snow and Glacier for 

Recreation Infrastructure

69 Impacts to Facilities

69 Purpose or Draw to the Facility

70 Access to Facilities

70 Adaptive Capacity

71 Literature Cited

79 Chapter 4: Seascapes

 Andrew Erickson, Claire Colegrove, and Aaron Poe

79 Summary

80 Introduction

81 Chugach Seascapes

83 Abiotic Effects of Climate Change

83 Sea-Level Change

87 Glacial Changes

89 Ocean Acidification

90 Consequences to Biotic Communities

90 Harmful Algal Blooms

91 Eelgrass Beds

94 Shorebirds

97 Conclusions

98 Literature Cited

107 Chapter 5: Salmon

Mark Chilcote, Angela Coleman, Steve Colt, Peter Kirchner, 

Gordie Reeves, Dan Rinella, Eric Rothwell, and Steve Zemke

107 Summary

108 Introduction

110 Overview of Climate Change Impacts on Salmon

114 Salmon Diversity and Production Cycles

117 Components of Salmon Vulnerability Assessment

118 Watershed Vulnerability

122 Case Study: Monitoring Lake Ice

124 Watershed Vulnerability—Findings
xvi

131 Salmon Population Dynamics

134 Salmon Population Dynamics—Findings 

137 Fish Runs and Fisheries

139 Fish Runs and Fisheries—Findings 

143 Effects on Salmon Ecosystem Services

146 Commercial Salmon Harvest

149 Sport Salmon Harvest

150 Subsistence Salmon Harvest

150 Personal-Use Salmon Harvest

151 Ecosystem Services Summary of Findings

151 Literature Cited

163 Chapter 6: Vegetation

Teresa Hollingsworth, Tara Barrett, Elizabeth Bella, Matthew Berman, 

Matthew Carlson, Paul Clark, Robert L. DeVelice, Greg Hayward, John 

Lundquist, Dawn Magness, and Tobias Schwörer

163 Introduction

164 Ecological Setting

165 Summary of Historical Vegetation Patterns

166 The Future Is Contingent On the Past: Historical Changes in Vegetation

166 Long-Term Vegetation History

168 Short-Term Vegetation History (~10,000 Years BP)

171 Summary: Current Vegetation 

173 Case Study: Historical and Current Patterns of Spruce Beetle Outbreaks 

and Long-Term Consequences on Vegetation

176 Context for Climate Change: Current Vegetation Patterns and Plant 

Distributions 

176 Current Disturbance Regimes

178 Current Distribution of Biomes and Land Cover Types 

183 Current Distribution of Spruce Species

185 Current Distribution of Rare Species

186 Current Status of Non-Native Species

191 Case Study: Using Repeat Field Measurements to Detect Change in 

Forests of the Chugach/Kenai Region

194 Summary: Future Vegetation 

195 Examining the Future: Potential Vegetation Change

196 Climate-Envelope Modeling: Using Current Climate Niches to 

Evaluate Potential Changes in the Future

197 Biomes to Organisms: Future Scenarios of Change 

200 Projected Changes in Vegetation

217 Uncertainty, Economics, and Invasive-Species Management 

225 Potential Scenarios for Insect Populations Under a Warming Climate
xvii

228 Vulnerability to Fire in the Kenai Peninsula Wildland-Urban Interface

230 The Study Region

232 Current Vulnerability to Wildfire

238 Projecting Future Vulnerability to Wildfire

243 Discussion

246 Conclusion

247 Examining the Effects of Vegetation Change on Recreation 

Infrastructure on the Chugach National Forest

248 Adaptive Capacity, Increased Management 

248 Interpreting the Patterns of Change

250 Literature Cited

265 Chapter 7: Moose, Caribou, and Sitka Black-Tailed Deer

John M. Morton and Falk Huettmann

265 Summary

265 Introduction

267 Moose: Current and Historical Distribution

273 Caribou: Current and Historical Distribution

275 Sitka Black-Tailed Deer: Current and Historical Distribution

277 Future Distributions

278 Regional Ecological Niche Models for 2009 Versus 2069 (Decades) 

283 Proximate Drivers of Future Distributions

286 Climate Effects on Wildlife Diseases

289 Conclusion 

290 Literature Cited

297 Chapter 8: Conclusion

Gregory D. Hayward

297 Context Matters

300 Taming the Firehose

300 Adaptation and Greenhouse-Gas Emissions

301 Literature Cited

303 Acknowledgments

303 English Equivalents

304	 Appendix 1: Common and Scientific Names

307 Appendix 2: Modeling Climate Change

325 Appendix 3: Estimated Number and Economic Value of Salmon 

Produced from Watersheds of the South-Central Alaska Study Area 

330 Appendix 4: From Biomes to Species: Methods for Modeling Future 

Vegetation Distribution

337 Appendix 5: Modeling and Projecting Development Status and 

Structure Value of Kenai Peninsula Property
xviii

With temperatures at about -10 °C, a musher and his team sprint down a long straightaway at the edge of Anchorage during the 2012 World Championship Sled Dog Race. The race was canceled in 2015 and 2016 because of insufficient snow 

resulting from warm temperatures and rain.

Patrician Hayward
1

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Gregory D. Hayward,1 Steve Colt,2 Monica L. McTeague,3 and 

Teresa N. Hollingsworth4

Long-term measurements of air temperature, sea surface temperatures, and patterns 

of polar ice mass all confirm the intense warming of earth’s climate over the past 

60 years (e.g., IPCC 2014, MEA 2005, Melillo et al. 2014). Scientific consensus 

that fossil fuels contribute to global climate change comes from a combination of 

physical-system science, long-term measurements of temperature and atmospheric 

carbon dioxide (CO2), and paleoproxy reconstructions of past climate. Although 

global patterns of climate change have been discussed for decades, their positive 

and negative consequences have recently become more obvious. Examples include 

sea-level rise in certain portions of the globe that is threatening communities and 

agriculture (IPCC 2014); Arctic villages near seacoasts that are being undercut by 

wave action as permafrost thaws and coastal geography changes (ADEC 2010); 

increased CO2 and lengthening growing seasons that have increased agricultural 

productivity for some crops in some locales (Rosenzweig and Hille 1998). 

As the potential consequences of rapid, directional climate change become 

more apparent, individuals, communities, and nations have begun to consider 

what actions to take—a process often called “climate adaptation”—in response to 

changing climate. Likewise, land and resource management agencies are developing responses to perceived threats to resource values. Coordinated, effective action, 

however, requires understanding how the physical and biological environment will 

respond to climate change and how these biophysical changes will affect ecosystem 

services. This report, crafted as a climate vulnerability assessment (Glick et al. 

2011), represents an important step toward developing effective climate change 

adaptation for land and resource management agencies and the public with respect 

to the Kenai Peninsula and Chugach National Forest region of south-central Alaska. 

Our goal is to examine the potential response of several important features and 

resources of the Kenai/Chugach region to changing climate over the next 30 to 50 

years and to consider the potential consequences of these changes for associated 

social and economic systems.

Chapter 1: Introduction 

1 Wildlife ecologist, U.S. Department of Agriculture, Forest Service, Alaska Region, 

Anchorage, AK 99504. 

2 Professor of economics, University of Alaska Anchorage, Institute of Social and Economic Research, Anchorage, AK 99503. 

3 Biologist, University of Alaska Anchorage, Alaska Center for Conservation Science, 

Alaska Natural Heritage Program, Anchorage, AK 99508. 

4 Research ecologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station, Fairbanks, AK 99775.

Coordinated, effective 

action requires 

understanding how 

the physical and 

biological environment 

will respond to climate 

change and how these 

biophysical changes 

will affect ecosystem 

services. 
2

GENERAL TECHNICAL REPORT PNW-GTR-950

Focus of Assessment

A climate vulnerability assessment can best aid resource managers and society in 

making decisions when it is focused on important ecosystem services (MEA 2005). 

Ecosystem services are the benefits that people obtain from ecosystems, such as 

food, clean water, timber, regulation of floods, outdoor recreation, and spiritual 

values associated with the environment. How will changes in the delivery of ecosystem services, the availability of resources, and physical conditions experienced 

by individuals and communities influence the lives of people in the immediate and 

distant future? The Kenai/Chugach assessment area (fig. 1-1) occurs in a region 

undergoing change as a consequence of major ongoing physical dynamics—tectonics, glaciation, and extreme snowfall.

Regardless of any climate forcing by industrial society, these dynamics result 

in significant directional change that will influence social decisions. As will be 

outlined below, ice sheets have been receding for millennia, and mega-earthquakes 

Figure 1-1—The Chugach National Forest and Kenai Peninsula assessment area within south-central Alaska.
3

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

have periodically stirred the landscape—the Kenai/Chugach is a landscape whose 

very essence is change, and much of that change is directional at the scale of the 

entire assessment area over any reasonable time frame. Understanding the potential 

consequences of climate change demands that we consider the potential influence 

of human-caused (greenhouse-gas-induced) climate change in the context of an 

inherently dynamic region, regardless of human-induced climate forcing.

This assessment is written with the goal of informing decisions by resource 

managers and the public. Two features define its scope. First, unlike many vulnerability assessments that focus on natural resource management, we broaden our 

scope by evaluating several social and economic outcomes of climate change. 

Second, rather than examining a plethora of resource elements, we narrow our 

scope by limiting our discussion to areas that are of particular concern to people 

of the region.

Of keen interest to natural resource managers in south-central Alaska are 

six topics: (1) snow and ice (glaciers and ice fields), (2) coasts and seascapes, (3) 

salmon, (4) vegetation, (5) wildlife, and (6) infrastructure. We begin by asking how 

a changing climate may influence particular physical and ecological features across 

these topic areas. The consequences of climate change are examined from the perspective of scenarios (potential futures). Our assessment then attempts to ask how 

climate-driven changes in the physical/ecological characteristics of south-central 

Alaska might influence several ecosystem services and associated economic activities. Integrating potential social/cultural consequences into the assessment is an 

important but difficult task because of the inherent uncertainty in climate scenarios 

and projected responses of physical/ecological elements. However, considering 

potential social and economic outcomes, even in light of considerable uncertainty, 

provides managers a view through a different lens that helps them prioritize their 

adaptation options.

Limiting the set of assessment topics helped authors explore particular 

resources and ecosystem services more deeply. However, bounding the assessment 

necessarily left many important topics unaddressed. We considered this outcome 

desirable because we see this vulnerability assessment as an initial examination 

of climate change consequences that will lead to future assessments, which will 

explore topics more deeply depending on the needs of managers and the public. 

Therefore, this is the first step in an iterative collaboration among resource 

managers and scientists intended to foster understanding of the complex outcomes 

of a changing climate.
Figure 1-2—Alternative approaches to addressing uncertainty in 

natural resource management (after Peterson et al. 2003: 365). 

4

GENERAL TECHNICAL REPORT PNW-GTR-950

Constraints on the Assessment

History of the Assessment

This assessment began with a desire by managers of the Chugach National Forest 

to understand how climate change may be influencing the resources managed by 

the forest and how it will affect users of the vast landscape administered by the 

Chugach. Recognizing the importance of understanding potential social, cultural, 

and economic consequences of biophysical changes occurring on the land, the 

Chugach partnered with the University of Alaska’s Institute of Social and Economic Research to produce a modest narrative report that integrates this range of 

consequences. Soon, other agencies learned of the assessment, and an interagency 

effort developed with an all-lands perspective extending from the western Kenai 

Peninsula eastward through the Copper River Delta region. This organic development brought together a rich array of scientists and practitioners excited about 

collaboration. The resulting assessment benefits from their breadth of perspectives 

and expertise, from the expanded geographic scope, and from the integration of 

scientists with practitioners. Readers will recognize variations in tone and style 

resulting from the diversity of participants in our collaboration. We offer this report 

as a tool for learning about climate change in a portion of Alaska from a range of 

perspectives. 

Uncertainty In a Resource Planning Environment

Resource management requires the art of taking action despite uncertainty. Limitations of knowledge, temporal and spatial variation in resource conditions, changing 

socioeconomic dynamics, and limited understanding of future resource needs all 

contribute to an environment of uncertainty. As a result, resource managers have 

developed planning approaches that aid in identifying acceptable decisions in the 

face of this uncertainty (fig. 1-2).

Adaptive 

Management

Scenario

Planning

Optimal

Control Hedging

Controllable

Controllability

Uncontrollable

Low

High

Uncertainty
5

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Climate change adds to the uncertainty associated with natural resource decisionmaking. Furthermore, several features of climate change differ from most factors that lead to uncertainty in resource management. Climate change is global, it is 

long-term, and it cannot be managed directly or effectively through local or regional 

action. Consequently, the tools to address uncertainty in most natural-resourceplanning problems may not be effective for addressing uncertainties associated with 

climate change. For example, adaptive management (Walters 1986) is a planning tool 

advocated for addressing uncertainty in natural resource management (Julius et al. 

2013, Tompkins and Adger 2004). Active adaptive resource management employs 

models that identify dominant uncertainties, develops management experiments to 

examine them, and relies on feedback to gain knowledge and revise management to 

more effectively meet management goals (Walters 1986). However, the long-term 

nature of climate change suggests that feedback from management experiments will 

likely occur too slowly to improve management decisions.

As an alternative, some practitioners suggest that scenario planning may be 

more effective, and a rich literature is developing around this approach (e.g., Knapp 

and Trainor 2013, Peterson et al. 2003, Rickards et al. 2014). Understanding the 

use of scenarios in planning may be illustrated most easily through an example 

from everyday life. Decisions regarding the purchase of insurance, such as life or 

homeowner’s insurance, illustrate the pragmatic use of scenarios in planning. When 

considering the purchase of life insurance, most people envision several potential 

futures, each representing a different “story” describing what may happen in the 

future. None of the stories is a forecast, and often the probability of one or another 

occurring is unclear. The ultimate decision regarding purchase of the insurance 

policy occurs after integrating the insights that come from considering the various 

stories. 

Understanding of probabilities plays a minor role in the decision because management of risk is the actual goal. Instead, the insights generated by the scenarios 

result in thinking that would not occur otherwise. The use of scenarios in resource 

management in the context of climate change is very similar. 

In this assessment, we use the philosophy of scenario planning to help decisionmakers and the users of public lands make better choices despite the uncertainty of 

how resources, ecosystem services, and other characteristics of south-central Alaska 

will change as a result of changing climate. We develop “story lines” that outline the 

potential conditions that will be experienced in the future. These stories are intended 

to motivate innovative thinking about the interaction between decisions and future 

conditions. Therefore, when we describe potential snow conditions or streamflow, 

we are not making forecasts or projections. Rather, we use an understanding of 

When we describe 

potential snow conditions or streamflow, 

we are not making 

forecasts or projections. Rather, we are 

painting a picture, or 

scenario, that is one 

plausible rendering of 

the future. 
6

GENERAL TECHNICAL REPORT PNW-GTR-950

the current physical and ecological system, along with background on history and 

current trends, to paint a picture, or scenario, that is one plausible rendering of the 

future. That scenario is neither the only nor the “best” illustration of the future. The 

value of the scenario is the degree to which it helps the reader recognize that the 

future will be different from the present (possibly similar to a subset of scenarios), 

and therefore that planning must consider potential alternative futures.

Our approach to scenarios begins by examining a range of climate trajectories that in turn generate several climate scenarios. The entire assessment builds 

on these climate scenarios. Because the various chapters in this report examine 

different physical and biological resources and ecosystem services, each employs 

the climate scenarios differently. However, in all cases, the intent is to stimulate an 

analysis that considers potential outcomes in a changing landscape. In many cases 

we illustrate only one scenario—one potential future. When it is employed, this 

single-scenario approach is chosen for simplicity and clarity in communication.

Temporal Scale and Uncertainty 

Employing scenarios to examine climate change necessarily requires consideration 

of future conditions. Climate change models can produce nonintuitive shifts in 

uncertainty as scenarios are considered for different periods in the future. In this 

assessment, we explicitly consider scenarios in the context of agency planning 

horizons; planning generally covers 10 to 20 years but considers the legacy left to 

future generations. Hence, we examine outcomes in the next 10 to 20 years, but also 

conditions 50 years in the future. How these time horizons influence uncertainty is 

a bit complex, but we outline the basics here.

Our assessment employs downscaled projections from climate models as a 

foundation for developing physically consistent, place-based scenarios for the future 

(see chapters 2 and 3, along with app. 1, for more details). The downscaled projections for regions such as south-central Alaska, which experience high interannual 

and decadal variability, tend to result in significant uncertainty for the first 10 to 20 

years of projections, higher confidence for the next 30 years or so, and less certainty after 50 or 60 years (Hawkins and Sutton 2009). In some cases, the near-term 

uncertainty (first decade or two) results from what might be called model “wind 

up.” The downscaled model develops a set of initial conditions or a baseline as it 

begins—this results in an initial climate that is different than what actually occurs 

(owing to regional climate variability, for example) and thus “uncertainty” in the 

results. Following this “wind up” period, these models tend to produce more stable 

results based on the basic responses of the global climate model (sometimes referred 

to as a general circulation model; hereafter GCM) that forms their backbone, and 

Regions such as 

south-central Alaska 

tend to result in 

significant uncertainty 

for the first 10 to 20 

years of projections, 

higher confidence for 

the next 30 years or so, 

and less certainty after 

50 or 60 years 
7

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

the largest source of uncertainty is model-to-model parameterization (for example, 

the ways that internal feedbacks are handled or the fundamental temperature sensitivity to greenhouse-gas concentrations). After 50 years or so, however, uncertainty 

in social (government policy) response to climate change begins to become a major 

driver in the outcome of the GCMs (owing to the magnitude of greenhouse-gas 

emissions) and therefore uncertainty increases. Additional uncertainty that results 

from “model uncertainty” is described in more detail in appendix 1. In this assessment, we explicitly address uncertainty by considering the time scales important 

to decisionmaking and by using them to calculate future scenarios that are resilient 

to the uncertainty associated with decadal climate variability and model variability 

(Littell et al. 2011, Snover et al. 2013).

Characteristics of the Chugach National Forest and 

the Kenai Peninsula Assessment Area

Climatic Setting 

The climate in south-central Alaska is subarctic, with short, cool summers and long 

winters. Cloud cover is frequent through the summer, particularly after mid-June, 

and temperatures rarely exceed 26.7 °C. Winter snowpack, even near sea level, 

can extend from October through May. Winters have periods of deep cold but also 

periods with temperatures well above freezing. Extensive coastline, in combination 

with complex topography resulting from mountain ranges extending north-south 

and east-west, result in extremely complex weather patterns and a mixture of continental and maritime influences. Precipitation, snowpack, and temperature maps 

from Blanchet (1983), along with climate descriptions from Davidson (1996) and 

DeVelice et al. (1999), provide some detail regarding differences in climate among 

three portions of the Kenai/Chugach assessment area.

In the Kenai Mountains portion of the Kenai Peninsula, the climate is transitional between maritime and continental, with mean annual temperatures of 3.9 °C 

at low elevations and -6.7 °C at upper elevations. The annual precipitation ranges 

from 50 to 200 cm with a mean maximum snow pack of 50 to 300 cm, depending on elevation and location. Climate at the Cooper Lake Hydroelectric Project 

weather station on the Kenai shows a decline in monthly precipitation from January 

through June, followed by an abrupt increase in precipitation from July through 

September. There is a brief period of relative drought in June. This dry period 

reduces fuel moisture and increases fire frequency in the Kenai Mountains.

Storm tracks tend to move in a counterclockwise pattern from the Gulf of 

Alaska into Prince William Sound, resulting in abundant precipitation and cool, 
8

GENERAL TECHNICAL REPORT PNW-GTR-950

but not cold, temperatures. The lands around Prince William Sound feature mean 

annual temperatures ranging from 4.4 °C at shoreline to 0 °C at upper elevations. 

Mean annual precipitation ranges from 200 cm at sea level to more than 760 cm at 

some upper elevation locations. The mean maximum snowpack ranges from 150 to 

400 cm depending on location and elevation. Precipitation at the Main Bay weather 

station in the Sound exceeds 200 mm for each month of the year.

In the Copper River Delta area, mean annual temperature varies from 1.1 to 

5.6 °C. Average precipitation ranges from 200 cm at the seashore to 500 cm farther 

inland. The mean maximum snowpack ranges from 25 to 200 cm, with depth 

increasing with distance from the seashore. Strong continental winds, which drain 

the Alaska interior in the winter, flow out of Copper River Canyon, cooling the 

temperatures in this area. Climate measurements at the Federal Aviation Administration (FAA) weather station at Cordova are similar in overall pattern to those 

found at Main Bay in western Prince William Sound. However, monthly precipitation at the Cordova FAA station ranges between 125 to 450 cm, while it is between 

250 to 650 cm at Main Bay, demonstrating the increased precipitation farther west 

in the Sound. 

The northern portion of the assessment area, represented by the high Chugach 

and Saint Elias Mountains, features cold, wet summers and winters. The annual 

precipitation occurs mainly as snow at elevations above 2500 m. The snow accumulations range up to 800 cm annually.

The southern and eastern coasts of the Kenai Peninsula have a maritime climate 

characterized by heavy precipitation falling as snow in the higher altitudes (up to 

10 m on the ice fields). The Kenai Mountains create a partial rain shadow for the 

western, particularly northwestern, peninsula (Ager 2001).

Physical and Ecological Setting

The Chugach/Kenai assessment area covers a region whose physical and ecological 

characteristics reflect incredible geological and physical disturbances. Tectonic 

forces, glacial scouring, and the influence of annual snow produce a legacy of 

disturbance that results in regionwide patterns of directional change in topography 

and ecology. Episodic mega-earthquakes along with broad-scale subsidence result 

in periodic resetting of plant succession and rearranging of plant communities, 

while the steady progression of the region from almost complete glacial cover to the 

current interglacial condition results in the steady colonization of exposed land by 

plants and animals and a migration of biota through the region that is still occurring 

today. In this section, we provide a brief introduction to the directional patterns of 

ecological change experienced in the region over the past ten or more millennia—a 

The northern portion 

of the assessment 

area, represented by 

the high Chugach and 

Saint Elias Mountains, 

features snow 

accumulations 

that range up to 

800 cm annually.
9

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

changing ecological canvas that informs us of the potential consequences of humaninduced climate change. 

As described by Plafker et al. (1992), mega-earthquakes resulting from the 

sudden shifting of the Pacific and North American plates every 400 to 1,300 years 

result in instantaneous movement of shorelines by up to 11.3 m. The lateral and vertical shift in the Earth’s crust simultaneously eliminates and creates conditions for 

saltwater marsh landscapes and intertidal zones, while drowning forest communities. 

The consequences of large quakes are clear in environmental legacies—the presence 

of terraces along shorelines of islands such as Middleton and Montague Islands, and 

of stands of dead trees in coastal areas of the Kenai/Chugach assessment area. The 

periodic nature of large quakes and associated subsidence results in cyclic patterns 

of vegetation succession along coastal areas. In contrast, the retreat of glaciers since 

their maximum extent 10,000 to 14,000 years ago has led to strong directional (rather 

than cyclic) changes in geomorphology, hydrology, and ecology. 

At the last glacial maximum, the vast majority of our assessment area was under 

ice. Nunataks appear to have occurred on Knight, Montague, and Hinchinbrook 

Islands, resulting in isolated terrestrial refugia in Prince William Sound (Heusser 

1983). These sites would not have supported trees and likely few shrub species 

persisted. The western Kenai Peninsula, in the snowshadow of the Kenai Mountains, 

appears to have maintained several large biological refugia including sites in the 

northwest Kenai Mountains, the upland between Skilak and Tustumena Lakes, and 

in the Caribou Hills north of Homer (Reger et al. 2007). Other refugia in the Copper 

River basin and Talkeetna Mountains, along with low passes in the Alaska Range, 

provided sources for species to establish in newly exposed terrestrial habitat. Hence, 

the current vegetation represents the outcome of glacial retreat followed by species 

recolonization. Over the past 14,000 years, directional change dominated the assessment area and continues today. These directional processes began earlier on the 

western Kenai than around the Sound. Earlier deglaciation and substantial refugia 

(that occurred in a variety of life zones) west of the Kenai Mountains facilitated 

more rapid plant migration than in the Sound. Retreating ice on the Kenai allowed 

the expansion of birch (Betula sp.) and herb tundra beginning 14,200 years ago. 

Early postglacial vegetation included dwarf birch (Betula nana), alder (Alnus spp.), 

willow (Salix spp.), grasses (Poaceae), sage (Artemisia), herbs, and ferns. Boreal 

spruce, likely white spruce (Picea glauca) from refugia, along with paper birch 

(Betula papyrifera) was present 8,500 years ago and began expanding significantly 

about 5000 BP on the Kenai (Ager 2001, Ager et al. 2010, Jones et al. 2009). By 

about 2,900 BP, mountain hemlock (Tsuga mertensiana) and Sitka spruce (Picea 

sitchensis) began invading the eastern and northern valleys of the Kenai Mountains. 

At the last glacial 

maximum, the vast 

majority of our 

assessment area was 

under ice ... Hence, 

current vegetation 

represents the 

outcome of glacial 

retreat followed by 

species recolonization. 

Over the past 14,000 

years, directional 

change dominated the 

assessment area and 

continues today. 
10

GENERAL TECHNICAL REPORT PNW-GTR-950

Deglaciation progressed in Prince William Sound sufficiently to expose lowlying areas by 9,000 BP, resulting in colonization by coastal tundra and sedge 

tundra (Heusser 1983). In many areas, alder established early following deglaciation 

and persisted for more than 1,000 years before tundra again dominated in areas 

such as College Fjord. Conifers first become apparent about 2,700 BP. Coastal rain 

forest tree species migrated from southeast Alaska (where they persisted through 

the Holocene) following the prevailing storm tracks northwestward along the gulf 

coast and across Prince William Sound. This migration of Sitka spruce, mountain 

hemlock, black cottonwood (Populus balsamifera ssp. trichocarpa), yellow-cedar 

(Callitropsis nootkatensis), and western hemlock (Tsuga heterophylla) appears to 

have required thousands of years to travel hundreds of kilometers. About 2,000 BP, 

alder pollen declined and western hemlock and associated coastal rain forest species 

developed forest communities (Heusser 1983: 349).

Although the preceding summary of transition from Pleistocene ice cover to 

contemporary vegetation is portrayed as a unidirectional conversion, the dynamic 

nature of the region is further demonstrated by short-term changes that have also 

been observed in records of environmental history. Periods of glacial advance 

occurred 3,200 and 2,500 BP and again quite recently with the Little Ice Age, 

resulting in glacial advances and subsequent retreat (Jones et al. 2009). Although 

not as obvious in the glacial record, significant warm periods occurred. Patterns of 

high temperatures in the Northern Hemisphere during the Medieval Warm Period 

(about 950 to 1100 CE) appear similar to those of the late 20th century (1961–1990), 

and the rate of increase was comparable to that of the past couple decades (Mann 

et al. 2008, 2009). Figure 1-3 illustrates both the variability in global temperatures 

(note the Medieval Warm Period [~1000 CE] and the Little Ice Age [centered about 

1700 CE]) over the past 1,700 years and the unique nature of the pattern in the last 

couple of decades.

Clearly, the physical and biological systems of the Kenai/Chugach have experienced radical change in the past, prior to the dramatic climate shifts being explored 

in this assessment. The vegetation currently occurring in the region is different 

from the past, and resulted from directional change that began with the exposure 

of land following the last glacial maximum (fig. 1-4). This tapestry of change 

represents critical context for interpreting the scenarios for future dynamics the 

region may experience as a result of human-induced climate change in the next half 

century. Strong abiotic drivers—ice, snow (depth and slides), tectonics, and geology interacting with climate, the historical legacy of species colonization, and the 

formation of new vegetation communities—have resulted in the environment that 
Figure 1-3—Pattern of surface air temperatures for the northern Hemisphere over the past 1,700 years (based on data published in 

Mann et al. 2008). 

11

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

people use across the Chugach/Kenai region today. This document seeks to explore 

the character of this environment in the future as a consequence of continued but 

accelerated climate change. 

Social, Economic, and Cultural Setting

The assessment area is composed of three relatively distinct regions. The Municipality of Anchorage and the Kenai Peninsula Borough are each organized as single 

political jurisdictions equivalent to counties, whereas the Prince William Sound 

region includes the independent cities of Whittier, Valdez, and Cordova, as well 

as the predominantly Native villages of Tatitlek and Chenega. These communities 

comprise the Chugach census subarea, a geographic area with no regional government (fig. 1-5). 

Each of the three regions represent distinct social and cultural settings with 

substantially different demographic and economic characteristics (table 1-1). 

Anchorage is home to more than 40 percent of Alaskans and is the dominant source 

of demand for recreation and tourism on the Chugach National Forest and on the 

Kenai Peninsula. The Kenai Peninsula Borough is a rural area with about 60,000 

residents, with four major population centers—Kenai (population 7,100), Homer 

(population 5,003), Soldotna (population 4,163), and Seward (population 2,693)—
12

GENERAL TECHNICAL REPORT PNW-GTR-950

Figure 1-4—Illustration (adapted from Ager 2001) of postglacial vegetation histories from three sites located in different climatic 

regimes across the northern Kenai Peninsula and northwest Prince William Sound: (1) Hidden Lake, in the partial precipitation shadow 

of the Kenai Mountains, (2) Tern Lake peat section, north-central Kenai Mountains, near the boundary between transitional and 

maritime climate types, and (3) Golden, a peat section from a coastal maritime climate. Holocene climate trends for the southern coast 

of Alaska (modified from Heusser et al. 1985 as cited by Ager 2001) show the coincidence between relatively warm, dry climate and the 

spread of boreal-forest plants during the early Holocene and cool, wet climate and the development of coastal forest vegetation along the 

coast of Prince William Sound and the eastern Kenai Peninsula during the late Holocene.

TERN LAKE

PEAT SECTION

(THIS PAPER)

GOLDEN,

WEST PRINCE 

WILLIAM SOUND

(HEUSSER 1960, 1983) 

SOUTH COASTAL

ALASKA CLIMATE

TRENDS (HEUSSER 

AND OTHERS 1985)

1400 1800 2200

12 13 14 15 16

1

2

3 ? ? ? ? ? ? ? ? ? ?

4

5

6

7

8

9

10

11

12

13

14RADIOCARBON YEARS B.P. × 103

HIDDEN LAKE

(AGER 1983 AND

UNPUBLISHED DATA)

Boreal spruce with

minor Sitka spruce,

mountain hemlock

Boreal spruce,

paper birch,

alder

Mixed boreal spruce and 

Sitka spruce, paper birch,

mountain hemlock

Alder, boreal spruce, 

paper birch

Alder, shrub birch

 soapberry, herbs, ferns

Alder and ferns with

minor boreal spruce,

soapberry

Soapberry, ferns

Mountain hemlock,

Sitka spruce,

alder, crowberry,

sphagnum

Mean annual

precipitation, mm

Alder, mountain

hemlock, Sitka spruce

Alder, sedge, fern

Willow, alder, sedge

Alder, fern

Alder-willow

Poplar-willow

Shrub birch–

herb tundra

Herb tundra

Precipitation

July temperature

Mean July temperature, °C
Figure 1-5—Administrative setting of the assessment area illustrating the context of the Kenai Peninsula Borough, Municipality of 

Anchorage, and Chugach census subarea in Alaska, 2013.

13

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Homer

Kenai

Valdez

Cordova

Chenega

Tatitlek Whittier

Anchorage

Municipality

Matanuska-Susitna

Borough Valdez-Cordova

Census Area

Kenai Peninsula

Borough

Homer

Kenai

Valdez

Cordova

Chenega

Tatitlek Whittier

Anchorage

Municipality

Kenai Peninsula

Borough

o

ALASKA

Climate vulnerability assessment area

Alaska boroughs and census areas

supporting most of the population but a significant number of residents dispersed 

along the limited road system. In contrast, the Chugach census subarea has very 

little private land and fewer than 7,000 residents, most of whom are concentrated 

in Cordova and Valdez. Cordova, Tatitlek, and Chenega are not connected by road 

to the rest of the state, but are served by the state-run ferry system known as the 

Alaska Marine Highway.

The relatively high median household income in the Chugach census subarea 

(Prince William Sound region) stems primarily from oil industry employment at the 

Valdez terminal of the Trans-Alaska Pipeline. Despite this concentration of highwage private sector jobs, the Prince William Sound region is much more dependent 

on fishing and local government for employment than the other two regions. There 

is one actively fished limited entry permit for every 10 employed residents in the 

Sound, compared to one per 20 employed in the Kenai Peninsula and three per 

thousand in Anchorage.
14

GENERAL TECHNICAL REPORT PNW-GTR-950

Subsistence is an important component of household consumption and wellbeing for many people, particularly in the Kenai and Prince William Sound portions 

of the assessment area. Harvest and use of wild native species represent a significant component of the culture across all three regions, but occur within different 

social, economic and cultural contexts. Fay et al. (2005) summarized the results of 

a major subsistence study covering Prince William Sound communities affected by 

the Exxon Valdez oil spill: 

The study found strong evidence of the continuing importance of subsistence 

harvests and uses of fish and wildlife resources in the study communities. Virtually 

every household in each community used subsistence resources and the vast majority engaged in harvest activities and was involved in sharing. Harvest quantities in 

the 1997–1998 study year as estimated in usable pounds were substantial, ranging 

from 179 pounds per person in Cordova to 577 pounds per person in Chenega Bay. 

Tatitlek’s annual harvest was 406 pounds per person, though in 1988–1989 the 

per-person annual harvest was 644 pounds. Harvests were also diverse, with the 

average household using 15 or more different kinds of resources in the study 

communities (Fay et al. 2005: 73).

Table 1-1—Economic and demographic characteristics illustrating significant differences in the social 

and economic environment across three portions of the assessment area

Feature

Municipality 

of Anchorage

Kenai Peninsula 

Borough

Chugach census 

subarea

Population and housing:

Population on July 1, 2014 300,549 57,212 6,707 

Population on July 1, 2011 295,920 56,623 6,733 

Average annual growth 2011–2014 (percent) 0.5 0.3 -0.1

Occupied housing units in 2010 107,332 22,161 2,676

Employment and income:

Residents employed in 2013 130,673 23,909 3,152 

Private sector (percent) 85 80 74

Local government (percent) 8 14 20

State government (percent) 7 6 6

Median household income (dollars) 77,454 61,793 91,338 

Fishing and subsistence:

Number of limited entry permit holders who fished 388 1,097 334 

Estimated ex-vessel value of fish harvested (dollars) 46,630,382 136,807,046 62,137,013 

Federal rural subsistence priority? No Yes Yes, except Valdez

Sources: ADCCED 2015 and ADLWD 2015.
15

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Personal-use fish harvests—harvests by Alaska residents for personal use and 

not for sale or barter (Fall et al. 2014, Utermohle 1999)— also are significant to 

many households throughout and beyond the study region.5 In 2012, total personaluse salmon harvests in the Chugach-Kenai region were 781,132 fish, 69 percent of 

which came from the Kenai River dip net fishery and 18 percent from the Chitina 

Subdistrict dip net fishery (Fall et al. 2014) (table 1-2). Anchorage and Kenai Borough residents harvested about two-thirds of the total, with an average harvest of 

1.4 fish per person. The personal-use and subsistence activities connect individuals, 

families, family groups, and communities to specific landscapes, often resulting in 

an intimate understanding of natural resources and important connections to place. 

The annual calendar for many residents is organized around the timing of natural 

events (e.g., salmon returns) and longstanding traditions associated with the timing, 

methods, location, processing, and use of native plants and animals.

5 From a legal and management perspective, “personal use” fisheries differ from “subsistence” fisheries depending on determinations by the Alaska Board of Fish. According to 

the Alaska Department of Fish and Game, “Subsistence uses of wild resources are defined 

as ‘noncommercial, customary and traditional uses’ for a variety of purposes.” http://www.

adfg.alaska.gov/index.cfm?adfg=fishingsubsistence.main.

6 Because tourism is not a defined industry for statistical reporting purposes, it is not 

possible with current data to determine tourism employment or income for specific regions 

in Alaska.

Table 1-2— Salmon harvest from Chugach-Kenai region personal-use 

fisheries, 2012

Sockeye salmon Other salmon Total salmon

Lower Cook Inlet 137 1,757 1,894

Upper Cook Inlet:

Kenai River dip net 526,992 8,243 535,235

Kasilof River dip net 73,419 2,229 75,648

Other Upper Cook Inlet 29,195 695 29,890

 Upper Cook Inlet subtotal 629,606 11,167 640,773

Cook Inlet total 629,743 12,924 642,667

Chitina Subdistrict dip net 136,441 2,024 138,465

Total Chugach-Kenai region 766,184 14,948 781,132

Tourism and recreation are important to the economies of all three regions 

(Colt et al. 2002, Crone et al. 2002, Fay et al. 2005).6 An estimated 500,000 people 

recreate on the Chugach National Forest each year; much of this use occurs in the 

summer, but snow-based winter recreation is becoming increasingly popular as 

well (USDA FS 2014). A total of 145 commercial recreation special-use permits 

Personal-use and 

subsistence activities 

connect individuals, 

families, family groups, 

and communities to 

specific landscapes, 

often resulting in an 

intimate understanding 

of natural resources 

and important 

connections to place. 
16

GENERAL TECHNICAL REPORT PNW-GTR-950

have been issued for 2016 on the Chugach, of which 134 are for outfitting and 

guiding services (CNF 2016). Recreation on the Chugach is supported by a system 

of facilities, roads, and trails across the eastern Kenai Peninsula, Prince William 

Sound, and the Copper River Delta region. This infrastructure includes more than 

100 recreation sites, about 840 km of trails, and just over 145 km of roads. Many 

facilities are most popular during a specific time of year when conditions are best 

for fishing, hunting, boating, mountain biking, snowmachining (snowmobiling), 

or backcountry skiing, to name a few activities. More information on recreation 

settings, opportunities, and use levels can be found in the Forest Plan Revision 

Assessment (USDA FS 2014: chapter 3).

Literature Cited

Ager, T.A. 2001. Holocene vegetation history of the northern Kenai Mountains, 

south-central Alaska. In: L. Gough; R. Wilson., eds. Geologic studies in Alaska 

by the U.S. Geological Survey, 1999. Professional Paper 1633. Denver, CO: 

U.S. Department of the Interior, Geological Survey: 91–107. 

Ager, T.A.; Carrara, P.E.; McGeehin, J.P. 2010. Ecosystem development in 

the Girdwood area, south-central Alaska, following late Wisconsin glaciation. 

Canadian Journal of Earth Sciences. 47: 971–985. 

Alaska Department of Commerce, Community, and Economic Development 

[ADCCED]. 2015. Alaska community database online. [Database]. http://

commerce.state.ak.us/cra/DCRAExternal/. (15 October 2015).

Alaska Department of Environmental Conservation [ADEC]. 2010. Alaska’s 

climate change strategy: addressing impacts in Alaska. A report from the 

adaptation advisory group to the sub-cabinet. Draft final report. Juneau, AK: 

Alaska Department of Environmental Conservation. 88 p.

Alaska Department of Labor and Workforce Development [ADLWD]. 2015.

Alaska population overview 2013 estimates. http://laborstats.alaska.gov/pop/

estimates/pub/popover.pdf. (15 October 2015).

Blanchet, D. 1983. Chugach National Forest environmental atlas. Report No. 124. 

Anchorage, AK: U.S. Department of Agriculture, Forest Service, Chugach 

National Forest.

Chugach National Forest [CNF]. 2016. Special Uses Database System. [Database]. 

Anchorage, AK: U.S. Department of Agriculture, Forest Service. (26 January 

2016).
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Colt, S.; Martin, S.; Mieren, J.; Tomeo, M. 2002. Recreation and tourism in 

south-central Alaska: patterns and prospects. Gen. Tech. Rep. PNW-GTR-551. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 78 p.

Crone, L.K.; Reed, P.; Schaefers, J. 2002. Social and economic assessment of the 

Chugach National Forest area. Gen. Tech. Rep. PNW-GTR-561. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 108 p.

Davidson, D.F. 1996. Ecological hierarchy of the Chugach National Forest. 

Unpublished administrative paper. On file with: Chugach National Forest, 

161 East 1st Ave., Anchorage, AK 99501. 7 p.

DeVelice, R.L.; Hubbard, C.J.; Boggs, K.; Boudreau, S.; Potkin, M.; Boucher, 

T.; Wertheim, C. 1999. Plant community types of the Chugach National Forest: 

southcentral Alaska. Tech. Pub. R10-TP-76. Anchorage, AK: U.S. Department of 

Agriculture, Forest Service, Chugach National Forest. 375 p.

Fall, J.A.; Braem, N.M.; Brown, C.L.; Evans, S.S.; Hutchinson-Scarbrough, 

L.; Ikuta, H.; Jones, B.; La Vine, R.; Lemons, T.; Marchioni, M.A.; Mikow, 

E.; Ream, J.T.; Sill, L. A. 2014. Alaska subsistence and personal use salmon 

fisheries 2012 annual report. Tech. Paper No. 406. Anchorage, AK: Alaska 

Department of Fish and Game, Division of Subsistence. 54 p.

Fall, J.A.; Utermohle, C.J. 1999. Subsistence harvests and uses in eight 

communities ten years after the Exxon Valdez oil spill. Tech. Paper 252. 

Juneau, AK: Alaska Department of Fish and Game, Division of Subsistence. 

624 p. 

Fay, G.; Colt, S.; Schwoerer, T. 2005. Sustainable economic development for the 

Prince William Sound region. Anchorage, AK: National Wildlife Federation. 

http://www.iser.uaa.alaska.edu/Publications/2005_09-SustainableEconomic

DevelopmentForPWS.pdf. (15 October 2015).

Glick, P.; Stein, B.A.; Edelson, N.A., eds. 2011. Scanning the conservation 

horizon: a guide to climate change vulnerability assessment. Washington DC: 

National Wildlife Federation. 168 p.

Hawkins, E.; Sutton, R. 2009. The potential to narrow uncertainty in regional 

climate projections. Bulletin American Meteorological Society. 90: 1095–1107.

Heusser, C.J. 1983. Holocene vegetation history of the Prince William Sound 

region, south-central Alaska. Quaternary Research. 19: 337–355. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Heusser, C.J.; Heusser, L.E.; Peteet, D.M. 1985. Late-Quaternary climatic 

change on the American Pacific coast. Nature. 315: 485–487.

Intergovernmental Panel on Climate Change [IPCC]. 2014. Summary 

for policymakers. In: Field, C.B.; Barros, V.R.; Dokken, D.J.; Mach, K.J.; 

Mastrandrea, M.D.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; Estrada, Y.O.; Genova, 

R.C.; Girma, B.; Kissel, E.S.; Levy, A.N.; MacCracken, S.; Mastrandrea; P.R.; 

White, L.L. eds. Climate change 2014: impacts, adaptation, and vulnerability. 

Part A: global and sectoral aspects. Contribution of working group II to the 

fifth assessment report of the Intergovernmental Panel on Climate Change. 

Cambridge, United Kingdom and New York, NY: Cambridge University Press: 

1–32.

Jones, M.C.; Peteet, D.M.; Kurdyla, D.M.; Guilderson, T. 2009. Climate and 

vegetation history from a 14,000-year peatland record, Kenai Peninsula, Alaska. 

Quaternary Research. 72: 207–217. 

Julius, S.H.; West, J.M.; Nover, D.; Hauser, R.; Schimel, D.S.; Janetos, A.C.; 

Walsh, M.K.; Backlund, P. 2013. Climate change and U.S. natural resources: 

advancing the nation’s capability to adapt. Issues in Ecology. 18: 1–17.

Knapp, C.N.; Trainor, S.F. 2013. Adapting science to a warming world. Global 

Environmental Change—Human and Policy Dimensions. 23: 1296–1306. 

Littell, J.S.; Kerns, B.K.; Cushman, S.; McKenzie, D.; Shaw, C.G. 2011. 

Managing uncertainty in climate-driven ecological models to inform adaptation 

to climate change. Ecosphere. 2: 102. doi:10.1890/ES11-00114.1.

Mann, M.E.; Ahang, A.; Hughes, M.; Bradley, R.S.; Miller, S.K.; Rutherford, 

S.; Ni, R. 2008. Proxy-based reconstructions of hemispheric and global surface 

temperature variations over the past two millennia. Proceedings of the National 

Academy of Sciences of the United States of America. 105: 13252–13257.

Mann, M.E.; Woodruff, J.D.; Donnelly, J.P.; Zhang, Z. 2009. Atlantic 

hurricanes and climate over the past 1,500 years. Nature. 460: 880–883. 

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 2014. Climate change impacts 

in the United States: The third national climate assessment. Washington, DC: 

U.S. Global Change Research Program. 841 p.

Millennium Ecosystem Assessment [MEA]. 2005. Ecosystems and human 

well-being: synthesis. Washington, DC: Island Press. 137 p.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Peterson, G.D.; Cumming, G.S.; Carpenter, S.R. 2003. Scenario planning: a tool 

for conservation in an uncertain world. Conservation Biology. 17: 358–366.

Plafker, G.; Lajoie, K.R.; Rubin, M. 1992. Determining recurrence intervals of 

great subduction zone earthquakes in southern Alaska by radiocarbon dating. 

In: Taylor, R.E.; Long, A.; Kra, R.S., eds. Radiocarbon after four decades: an 

interdisciplinary perspective. New York: Springer-Verlag: 436–452. 

Rickards, L.; Wiseman, J.; Edwards, T.; Biggs, C. 2014. The problem of 

fit: scenario planning and climate change adaptation in the public sector. 

Environment and Planning C: Government and Policy. 32: 641–662.

Reger, R.D.; Sturmann, A.G.; Berg, E.E.; Burns, P.A.C. 2007. A guide to the 

Late Quaternary history of northern and western Kenai Peninsula, Alaska. 

Guidebook 8. Anchorage, AK: Alaska Department of Resources, Division of 

Geological and Geophysical Surveys. 112 p.

Rosenzweig, C.; Hillel, D. 1998. Climate change and the global harvest: potential 

impacts of the greenhouse effect on agriculture. Oxford, United Kingdom: 

Oxford University Press. 324 p.

Snover, A.K.; Mantua, N.J.; Littell, J.S.; Alexander, M.A.; McClure, M.M.; 

Nye, J. 2013. Choosing and using climate-change scenarios for ecological-impact 

assessments and conservation decisions. Conservation Biology. 27: 1147–1157.

Tompkins, E.L.; Adger, W.N. 2004. Does adaptive management of natural 

resources enhance resilience to climate change? Ecology and Society. 9: 10. 

http://www.ecologyandsociety.org/vol9/iss2/art10/. (15 October 2015).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2014. Assessment 

of the ecological and socio-economic conditions and trends: Chugach National 

Forest, Alaska. R10-MB-787. Anchorage, AK: Alaska Region. 334 p. 

Walters, C.J. 1986. Adaptive management of renewable resources. New York: 

MacMillan Publishing Company. 374 p.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Lost Lake, near Seward, Alaska.

Frank Kovalchek
21

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Chapter 2: Climate Change Scenarios

Nancy Fresco 1 and Angelica Floyd 2

Summary

• Downscaled climate projections developed by the Scenarios Network for

Alaska and Arctic Planning (SNAP) are useful for examining potential

changes in a range of climate variables, and have been used to develop

quantitative and qualitative stories regarding climates that may be experienced across the assessment area in the future.

• In this chapter, we examine basic SNAP projections, including mean and

extreme monthly temperature and precipitation for July and January; the

timing of thaw and freeze; and the expected monthly proportions of snow

versus rain (“snow-day fraction”).

• Overall, the assessment area is expected to become warmer in the middle

of this century, with earlier springs, later autumn, a longer growing season,

and shorter, less-severe winters.

• Some increases in precipitation are likely, but overall snowfall will decrease

owing to higher temperatures, particularly in the late autumn (October

to November), and at lower elevations. The snowline will move higher in

elevation and farther from the coast. This change in snow dominance will

also be explored in other chapters of this report.

Introduction

Alaska’s climate has undergone rapid changes. Substantial warming has occurred 

at high northern latitudes over the last half century. Most climate models predict 

that high latitudes will experience a much larger rise in temperature than the rest 

of the globe through the 21st century; however, the geographic location of the 

assessment area, in a coastal region with complex weather patterns and tortuous 

topography, results in patterns of change dissimilar to arctic Alaska (SNAP 2015). 

To understand the impacts of climate change in the Chugach National Forest/Kenai 

Peninsula region, these changes must be examined in the context of the dynamic 

nature of the region.

1 Network coordinator, Scenarios Network for Alaska and Arctic Planning, University of 

Alaska Fairbanks, 930 Koyukuk Drive, Fairbanks, AK 99775.

2 Spatial analyst, Scenarios Network for Alaska and Arctic Planning, University of Alaska 

Fairbanks, 930 Koyukuk Drive, Fairbanks, AK 99775.
22

GENERAL TECHNICAL REPORT PNW-GTR-950

Development of Climate Scenarios

Much of the climate modeling for this project uses datasets downscaled or derived 

by the Scenarios Network for Alaska and Arctic Planning (SNAP) (http://www/

snap.uaf.edu), a program within the University of Alaska. The SNAP program is 

a collaborative network that includes the University of Alaska; state, federal, and 

local agencies; nongovernmental organizations; and industry partners. SNAP provides access to scenarios of future conditions in Alaska and other Arctic regions 

for planning by communities, industry, and land managers. For this effort, we 

chose a set of models that perform particularly well in south-central Alaska. For 

additional detail, including discussion of model uncertainty, see app. 2 and http://

www.snap.uaf.edu.

SNAP climate projections are based on downscaled outputs from five global 

climate models (GCMs) that were selected, based on regional accuracy, from 

the 15 GCMs used by the Intergovernmental Panel on Climate Change (IPCC) 

when preparing its fourth assessment report, which was released in 2007 (IPCC 

2007, Walsh et al. 2008). SNAP scaled down these coarse GCM outputs to 771 m 

resolution, using baseline climatology grids (1971–2000) from PRISM (parameterelevation regressions on independent slopes model). This effort employed CMIP3 

models because those were the most recent available at the onset of the project. 

These results focus on the A2 greenhouse gas emissions scenario as defined by the 

IPCC. Although the IPCC’s most recent report, the fifth assessment report (AR5) 

(IPCC 2013) refers to four Representative Concentration Pathways (RCPs) rather 

than the scenarios described in the Special Report on Emissions Scenarios (SRES) 

published in 2000, the slightly older model outputs used in this analysis are still 

relevant within the new framework (Fussel 2009). The A2 scenario outputs fall 

between those of RCP 6 (a mid-range pathway in which emissions peak around 

2080, then decline) and RCP 8.5, the most extreme pathway, in which emissions 

continue to rise throughout the 21st century (Rogelj et al. 2012). For the purposes of 

comparison, some results from the slightly more optimistic A1B scenario are also 

shown in appendix 2.

Temperature and precipitation values are expressed as monthly means for 

decadal time periods (current, 2020s, 2040s, and 2060s). This averaging helps 

smooth the data and reduce the effects of model uncertainty so that a clear trend 

emerges, facilitating comparison among decades. However, some uncertainty 

does occur across broader timeframes, owing in part to the influence of the 

Pacific Decadal Oscillation (PDO) and other long-term, broad-scale climate 

patterns (Bieniek et al. 2014, Walsh et al. 2011). Uncertainty is discussed further 

in appendix 2. 
23

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

January and July data were selected to highlight changes in the most extreme 

months of winter and summer. Changes in shoulder season characteristics and timing are also biologically and culturally important, and are captured via assessment 

of freeze and thaw dates. 

Changes in Temperature

Modeled data for the current decade show that temperatures in the coldest month of 

the year (January) range from a mean decadal average of approximately -20 °C in 

the mountains to slightly above freezing along the coastline south of Cordova and 

Valdez. In the hottest month, July, the mean decadal average temperatures (15 °C) 

are found in low-lying inland areas, while the coolest temperatures are again found 

at the mountain peaks, where averages are well below freezing (-7 °C).

These temperature profiles are expected to change over time, with all areas 

warming by about 3 °C in the next 50 years. Areas with July temperatures below 

freezing are unlikely to undergo significant glacial melting, although it should be 

noted that daily highs will exceed mean values, and that direct solar radiation can 

drive effective temperatures above recorded air temperature.

Winter temperature change is expected to be even more extreme (fig. 2-1). 

Average temperatures in the coldest month of the year are predicted to rise from 

only slightly above freezing in the warmest coastal areas to well above freezing, 

or approximately 4.5 °C. Moreover, these warm temperatures will spread inland 

toward Cordova, Valdez, and Seward, with above-freezing Januaries dominating 

across all coastal regions of the Chugach, and some areas as much as twenty miles 

inland. Many rivers shift from a below-freezing to above-freezing temperature 

regime. Across the region, winter warming is expected to be approximately 3 to 

3.5 °C. Although the greatest impact of summer warming may be in the coldest 

regions of the Chugach, where snow and glaciers will be most influenced, the greatest winter impacts may be in the warmest coastal and near-coastal regions, where a 

shift is underway between winters with seasonal mean temperatures below freezing 

to winters in which the mean temperature across December, January, and February 

is above freezing. Although this shift does not preclude significant frost and snowfall, it does imply a change in the duration and prevalence of snowpack and ice.

Areas with mean January temperatures above freezing may still experience 

days or even weeks of freezing temperatures, and daily lows are likely to be significantly cooler than mean values. However, it is unlikely that significant ice formation would occur in such areas, particularly given the fact that sea water freezes at 

approximately -2 °C rather than at 0 °C. For brackish water, intermediate freezing 

temperatures are the norm.
Figure 2-1—January temperatures for the 2010s, 2020s, 2040s, and 2060s for the A2 emissions scenario.

24

GENERAL TECHNICAL REPORT PNW-GTR-950

Changes in Precipitation

The projected decadal trend is toward greater precipitation in both January and July. 

However, model predictions for precipitation are less robust than those for temperature, in part because precipitation is intrinsically more geographically variable. In 

addition, although precipitation is predicted to increase, inferring the hydrologic 

status of soils, rivers, or wetlands based on this greater influx of water is problematic. Increases in temperature (and associated evapotranspiration) may more than 

offset increases in precipitation, yielding a drying effect. Changes in seasonality 

and water storage capacity can also affect the hydrologic balance. Furthermore, 

a shift in the percentage of precipitation falling as snow can drastically alter the 

annual hydrologic profile.

Although current SNAP models do not directly address storm frequency, 

the literature suggests that climate-change-driven increases may be occurring in 

the frequency and severity of storm events in the Gulf of Alaska and Bering Sea 

(Graham and Diaz 2001, Terenzi et al. 2014). 
25

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Model results: freeze, thaw, and warm season length— 

SNAP interpolates monthly temperature and precipitation projections to estimate 

the dates at which the freezing point will be crossed in spring and fall. The intervening time period is defined as “summer season length.” It should be noted that 

these dates do not necessarily correspond with other commonly used measures 

of “thaw,” “freeze-up,” and “summer season.” Some lag time is to be expected 

between mean temperatures and ice conditions on lakes or in soils. Different plant 

species begin their seasonal growth or leaf-out at different temperatures. However, 

analyzing projected changes in these measures over time can serve as a useful 

proxy for other season-length metrics.

Across the assessment region, date of thaw in spring is expected to come 

earlier. Large areas of coastal and near-coastal land are projected to shift from early 

spring thaw to the “rarely freezes” category. This is likely to correspond with lack 

of winter snowpack and an altered hydrologic cycle. Primarily frozen areas are 

expected to shrink significantly. Elsewhere, changes are projected to occur as a 

shift of 3 to 10 days, on average. For example, the A2 scenario shows spring thaw 

occurring in Soldotna and Kenai around April 4 in the current decade, but in late 

March by the 2060s.

Autumnal changes are, overall, projected to be slightly greater than those seen 

in spring, with the date at which the running mean temperature crosses the freezing 

point shifting noticeably later in just a single decade. Major changes in warm-season length include incursion of the “rarely freezes” zone as far as 20 miles inland; 

an increase from about 200 days to about 230 days for Palmer, Anchorage, Wasilla, 

and Kenai; and an even more drastic increase for Seward, Valdez, and Cordova.

Future snow response to climate change—

SNAP data, based on downscaled GCM outputs, do not directly model snowfall 

as a separate quantity from overall precipitation, measured as rainfall equivalent. 

However, for the purposes of this project, SNAP researchers used algorithms 

derived by Legates and Bogart (2009) to estimate snowline and create contour maps 

depicting the probability of snow versus rain during winter months. The implications of this modeling—as well as other applications of SNAP data to snow and 

ice conditions—are explored in other chapters. However, a summary of snow-dayfraction outputs is provided here.

A rapid change in snowline is expected over time. This change is illustrated in 

figure 2-2 by the change in geographic locations at which an estimated 90 percent 

of winter precipitation will fall as snow (fig. 2-2). Although inter-year variability 

in snowline is expected to be high in the next 10 to 20 years, the modeled snowline 

shifts well inland from Valdez. By 2040, many areas are predicted to receive less 

A rapid change in 

snowline is expected 

over time. 
Figure 2-2—Projected snowline for the A2 emissions scenario for the current decade and a 50-year outlook.

26

GENERAL TECHNICAL REPORT PNW-GTR-950

than 30 percent of winter precipitation as snow, and by the 2060s the snowline (as 

defined by the 90 percent contour) is predicted to shift to the highest peaks. 

To assess the snowline during the coldest season, as opposed to the winter as 

a whole, we also examined the projected snowline for the month of January alone. 

Results show that for many areas that typically experience almost all January 

precipitation as snow, this pattern may shift in coming decades. By the 2060s, 

Anchorage, Kenai, Soldotna, Wasilla, and Palmer may have only intermittent 

snow cover, even in the coldest month of the year.

Literature Cited 

Bieniek, P.A.; Walsh, J.E.; Thoman, R.L.; Bhatt, U.S. 2014. Using climate 

divisions to analyze variations and trends in Alaska temperature and 

precipitation. Journal of Climate. 27: 2800–2818.
27

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Fussel, H.M. 2009. An updated assessment of the risks from climate change based 

on research published since the IPCC Fourth Assessment Report. Climatic 

Change. 97: 469–482.

Graham, N.E.; Diaz, H.F. 2001. Evidence for intensification of North Pacific 

winter cyclones since 1948. Bulletin of the American Meteorological Society. 

82: 1869–1893.

Intergovernmental Panel on Climate Change [IPCC]. 2007. In: Solomon, S.; 

Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, 

H.L. eds. Climate change 2007: the physical science basis. Contribution of

working group I to the fourth assessment report of the Intergovernmental Panel

on Climate Change. Cambridge, United Kingdom, and New York: Cambridge

University Press. 996 p.

Intergovernmental Panel on Climate Change [IPCC]. 2013. In: Stocker, T.F.; 

Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, 

Y.; Bex, V.; Midgley, P.M., eds. Climate change 2013: the physical science 

basis. Contribution of working group I to the fifth assessment report of the 

Intergovernmental Panel on Climate Change. Cambridge, United Kingdom, 

and New York: Cambridge University Press. 1535 p.

Legates, D.R.; Bogart, T.A. 2009. Estimating the proportion of monthly 

precipitation that falls in solid form. Journal of Hydrometeorology. 10: 

1299–1306.

Rogelj, J.; Meinshausen, M.; Knutti, R. 2012. Global warming under old and 

new scenarios using IPCC climate sensitivity range estimates. Nature Climate 

Change. 2: 248–253.

Scenarios Network for Alaska and Arctic Planning [SNAP]. 2015. Regional 

climate projections. Anchorage, AK: University of Alaska Anchorage. 

https://www.snap.uaf.edu. (23 September 2015).

Terenzi, J.; Jorgenson, M.T.; Ely, C.R. 2014. Storm-surge flooding on the 

Yukon-Kuskokwim Delta, Alaska. Arctic. 67: 360–374.

Walsh, J.E.; Chapman, W.L.; Romanovsky, V.; Christensen, J.H.; Stendel, M. 

2008. Global climate model performance over Alaska and Greenland. Journal 

of Climate. 21: 6156–6174.

Walsh, J.E.; Overland, J.E.; Groisman, P.Y.; Rudolf, B. 2011. Ongoing climate 

change in the Arctic. AMBIO. 40: 6–16.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Skilak Glacier, Kenai National Wildlife Refuge, Alaska.

U.S. Fish and Wildlife Service
29

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Snow: Jeremy S. Littell 1 and Stephanie McAfee1 

Glaciers: Shad O’Neel,1 Louis Sass,1 and Evan Burgess1

Economic service evaluation: Steve Colt 2

Recreation evaluation: Paul Clark 3 

Summary

• Temperature and precipitation are key determinants of snowpack levels.

Therefore, climate change is likely to affect the role of snow and ice in the

landscapes and hydrology of the Chugach National Forest region.

• Downscaled climate projections developed by Scenarios Network for

Alaska and Arctic Planning (SNAP) are useful for examining projected

changes in snow at relatively fine resolution using a variable called “snowday fraction (SDF),” the percentage of days with precipitation falling as

snow.

• We summarized SNAP monthly SDF from five different global climate

models for the Chugach region by 500 m elevation bands, and compared

historical (1971–2000) and future (2030–2059) SDF. We found that:

▪ Snow-day fraction and snow-water equivalent (SWE) are projected

to decline most in late autumn (October to November) and at lower

elevations.

▪ Snow-day fraction is projected to decrease 23 percent (averaged across

five climate models) from October to March, between sea level and

500 m. Between sea level and 1000 m, SDF is projected to decrease

by 17 percent between October and March.

▪ Snow-water equivalent is projected to decrease most in autumn

(October and November) and at lower elevations (below 1500 m), an

average of -26 percent for the 2030–2059 period compared to 1971–

2000. Averaged across the cool season and the entire domain, SWE is

Chapter 3: Snow and Ice

1 Jeremy S. Littell is a research ecologist; Stephanie McAfee was a postdoctoral fellow, 

Shad O’Neel is a research geophysicist, Louis Sass is a physical scientist, and Evan Burgess 

was a research scientist, U.S. Department of the Interior, Geological Survey, Alaska 

Climate Science Center, Anchorage, AK 99508. McAfee currently is an assistant professor, University of Nevada–Reno, Reno, NV 89557, and Burgess is currently an assistant 

research professor, University of Utah, Department of Geography, Salt Lake City, UT 

84112.

2 Professor of economics, University of Alaska Anchorage, Institute of Social and Economic Research, Anchorage, AK 99503.

3 Recreation and trails program manager, U.S. Department of Agriculture, Forest Service, 

Chugach National Forest, Anchorage AK 99501.
30

GENERAL TECHNICAL REPORT PNW-GTR-950

projected to decrease at elevations below 1000 m because of increased 

temperature, but increase at higher elevations because of increased precipitation.

• Compared to 1971–2000, the percentage of the landscape that is snowdominant in 2030–2059 is projected to decrease, and the percentage in

which rain and snow are co-dominant (transient hydrology) is projected to

increase from 27 to 37 percent. Most of this change is at lower elevations.

• Glaciers on the Chugach National Forest are currently losing about 6 km3

of ice per year; half of this loss comes from Columbia Glacier (Berthier

et al. 2010).

• Over the past decade, almost all glaciers surveyed within the Chugach

have lost mass (with one exception), including glaciers that have advancing

termini (Larsen et al. 2015).

• Glaciers that are not calving into the ocean are typically thinning by

3 m/year at their termini (Larsen et al. 2015).

• In the future, glaciers not calving into the ocean will retreat and shrink

at rates equivalent to or higher than current rates of ice loss (Larsen et al.

2015).

• Columbia Glacier will likely retreat another 15 km and break into multiple

tributaries over the next 20 years before stabilizing.

• Other tidewater glaciers have uncertain futures, but likely will not advance

significantly in coming decades.

• These impacts will likely affect recreation and tourism through changes in

reliable snowpack and access to recreation and viewsheds.

Introduction

Climate change can be expected to affect where, when, and how much snow and ice 

occur on the terrestrial landscape. Changes in temperature and precipitation alter 

the fundamental physical processes that govern the buildup and melt of snowpacks, 

the growth or decline of glaciers, and the timing and quantity of important hydrologic processes such as streamflow. However, the impact of climate change on snow 

and ice depends on what time frame is considered; how local weather and climate 

respond to hemispheric or global changes in temperature and precipitation; and, at 

finer scales, how these changes play out over the complex and rugged topography of 

the region. Some of these changes are intuitive, but the complex interaction between 

topography, elevation, and broad-scale weather patterns may lead to some unexpected dynamics for both snow and glaciers.
31

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

In this chapter, we discuss the mechanisms by which climate affects snow and 

ice in the Chugach National Forest and surrounding region. We also synthesize 

available scientific literature and data to characterize plausible impacts of climate 

change on snow and ice in the future.

Climate Change and Its Effects on Snow and Ice

Climate—the statistics of weather over time (usually 30 years or more)—is determined by the combination of temperature, precipitation, wind, the nature of storms, 

atmospheric pressure, and other factors characteristic of a place. Climate also 

includes the interannual to decadal (and longer) variability in those characteristics 

and the regional to global mechanisms that cause it. However, what is “characteristic” is changing rapidly in ways that are explainable only by global climate change, 

that is, those trends in climate that are significantly influenced by anthropogenic 

greenhouse-gas emissions. Projecting possible climate impacts on snow and ice 

processes requires understanding the mechanisms by which weather and climate 

affect snowpack and glaciers.

Snowpack—

In places where snow and ice were historically common, changes in climate can be 

expected to affect snowpack development, distribution, and melting as temperature 

increases, as well as the timing and quantity of precipitation change. Increasing 

temperature affects snowpack directly by affecting both the seasonal timing of 

snowmelt and the period of the year that is cool enough to promote snowpack 

accumulation. First, as temperature during fall, winter, and spring increases, there 

is increased likelihood that storms will coincide with above-freezing temperatures, 

and the proportion of precipitation that falls as rain instead of snow increases. 

Second, as spring temperatures increase, the timing of spring melt is pushed earlier 

in the year. In places where storms historically occurred at temperatures near 

freezing, a small increase in temperature can result in relatively large decreases in 

snowfall as the form of precipitation changes to rain. In contrast, in places where 

storms historically occurred at temperatures well below freezing, the impact is 

proportionally less. Rain-on-snow events may also increase with temperature, but 

are difficult to predict and model. Furthermore, despite increased temperature, 

increased precipitation may result in substantial increases in snow at high elevations 

where precipitation was less abundant in the past but future temperatures are rarely 

expected to be above freezing. Therefore, at colder locations where temperature is 

consistently below freezing (usually at higher elevations), increased future precipitation could result in increased snowpack.
32

GENERAL TECHNICAL REPORT PNW-GTR-950

Glaciers—

Glaciers are the result of a climate that consistently produces more snowfall during winter than can be melted in summer. The surplus of snow accumulates over 

decades to millennia and eventually compacts into ice. As the ice deepens, the 

glacier’s immense weight causes the ice to flow downhill until the ice reaches 

lower elevations, which are warmer and receive less snowfall, thus allowing the 

excess glacial ice to melt. A glacier can maintain a constant size and shape if the 

net gain of snow in the upper accumulation zone of the glacier perfectly offsets 

the net amount of ice lost in the lower ablation zone (melt zone). If the amount of 

melt exceeds the amount of snow accumulation, the mass budget of the glacier 

becomes negative and the glacier will shrink, adding that water to streamflow, and 

eventually, the oceans. The size of glaciers is thus inextricably linked to the relative amounts of snowfall and melt—two terms that are expected to change with a 

changing climate. 

Glacierized basins (i.e., ice-covered currently as opposed to glaciated, or 

historically ice-covered) produce 2 to 10 times more runoff than similarly sized, 

non-glacierized basins (Mayo 1984). When compared to ice-free basins, basins with 

only a few percent of basin ice coverage exhibit notable differences in streamflow 

at all time scales. Given two identical neighboring basins, with the sole exception of one basin having 20 percent ice cover, cumulative annual streamflow will 

be higher in the glacierized basin, and the annual streamflow will have a longer 

period of higher flow, owing to continued release of water after basin snow cover 

is melted. Daily streamflow will exhibit diurnal variations, even in the absence 

of snow, resulting from melt. Historically, higher glacial coverage in a watershed 

translates to increased runoff rates, later timing of peak streamflow in late summer, and decreased interannual variability (Fountain and Tangborn, 1985, Jansson 

et al. 2003, O’Neel et al. 2015). Meanwhile, water clarity, stream temperature, and 

streambed stability all decrease (Fleming 2005, Hood and Berner 2009, Milner 

and Petts 1994). 

Glaciers in the Chugach region receive an exceptional amount of snow each 

winter (estimated at greater than 3000 mm water-equivalent precipitation averaged 

over the region) and are also subjected to exceptional amounts of melt in summer. 

They must flow exceptionally fast to offset the high mass turnover and therefore are 

relatively quick to respond to climate variability and change. 

Tidewater glaciers—those that calve icebergs into the ocean—are not only 

controlled by climate; they are also sensitive to changing ocean temperatures and 

fjord shape. These controls are powerful enough to affect a glacier’s mass balance 

by promoting additional ice loss through iceberg calving. Subtle changes—perhaps 

Glaciers in the Chugach 

region receive an 

exceptional amount of 

snow each winter and 

are also subjected to 

exceptional amounts of 

melt in summer. They 

must flow exceptionally fast to offset the 

high mass turnover and 

therefore are relatively 

quick to respond to 

climate variability and 

change. 
33

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

in climate or glacier shape—can cause the glacier to accelerate, which leads to 

more iceberg calving and more acceleration, and hence a feedback loop that causes 

the glacier to lose far more ice than climate would allow alone. This is referred 

to as a “rapid” retreat (e.g., Meier and Post 1987). On the other hand, a similarly 

sized change in climate may yield no response at a different stage of the tidewater 

glacier advance-retreat cycle (Post et al. 2011). Columbia Glacier in the Chugach is 

typical of this process; it has lost 155 km3 of ice in the past three decades but less 

than 10 percent of this loss has been due to climate (O’Neel 2012, Post et al. 2011, 

Rasmussen et al. 2011). In concept, rapid retreats continue to affect glacier mass 

balance after a retreat from deep ocean water. The retracted geometry (removal 

of ablation area) favors positive mass balance, and mass gains are likely even in 

a climate unsupportive of widespread mass gain/advance for land-terminating 

glaciers (Post et al. 2011). 

Calving dynamics are the reason for the wide range of tidewater glacier 

behaviors currently occurring in Prince William Sound and will be responsible 

for a complex future pattern of glacier change in the Chugach (Larsen et al. 2015). 

Glaciers in the Chugach that do not terminate in the ocean are not subject to these 

interactions. When one is reviewing current and projecting future changes in 

Chugach glaciers, it is important to distinguish between tidewater glaciers and all 

others (Arendt et al. 2002, Larsen et al. 2015). How climate and tidewater dynamics are affecting glaciers now and how they may affect glaciers in the future will 

be discussed in following sections.

Impacts of Climate Change Effects on Snow and Ice

Streamflow timing and volume—

Collectively, the expected changes in snow and ice will have impacts on the 

hydrology of systems both within and downstream from mountains and glaciers 

(O’Neel et al. 2015). These hydrologic changes can in turn have significant 

impacts on—and be influenced by—terrestrial, riparian, and coastal ecosystems. 

Geology and geography, along with the physical and ecological changes in watersheds, affect the response of hydrography to climate change, so responses can 

differ significantly from watershed to watershed within a region. There are also 

strong ice-ocean-ecosystem linkages and feedbacks, including nutrient delivery 

and primary productivity, which likely have implications for fish, marine mammal, and bird populations. This illustrates the importance of an interdisciplinary 

approach and modeling to understand climate change impacts in complex systems.

Neal et al. (2010) and Hill et al. (2015) estimated that 43 percent (370 km3

/year 

of 870 km3/year) of the runoff flowing into the Gulf of Alaska is from glaciers 
34

GENERAL TECHNICAL REPORT PNW-GTR-950

in southeast Alaska, a volume comparable to that of the Mississippi River basin 

despite being seven times smaller in area. Freshwater delivery to the ocean affects 

ocean circulation, sea-level change (Larsen et al. 2007), and possibly also hydropower resources. For example, the Alaska coastal current, which flows north from 

the Gulf of Alaska, delivers more fresh water via marine supply than is supplied to 

the Arctic Ocean by the two largest rivers in the region (Weingartner et al. 2005.) 

Climate warming eventually influences the net mass balance of land-terminating 

glaciers and thus the seasonal timing and amount of streamflow in streams dependent on them (Jansson et al. 2003), but the glacier volume buffers the streamflow 

response—there is a smooth increase with glacier melt, then a decrease in response 

to declining volume. Runoff increases until glacier contribution decreases, and then 

runoff decreases. In much of Alaska, the current status of such river systems is 

unknown because the relative position of watersheds in the evolution of glacier melt 

and hydrologic delivery (runoff) is unclear. Changes in runoff depend on complex 

seasonal evolution that is itself a function of details of glacier structure (firn, piping, 

water saturation and ponds and channels, and bedrock geometry). These factors 

affect downstream flows via their influences on the diurnal timing and withinseason variability in streamflow. A study of monthly flow for nine rivers in Canada 

(Fleming 2005) indicates that nonglacial basins have a freshet peak with comparatively long persistence into summer. As little as 2 percent of ice cover in a basin 

is enough to transfer a hydrograph to glacial basin dynamics. Glacierized basins 

have a much larger freshet relative to baseflow, and higher flows persist longer. 

In Alaska, comparison of a continental glacier (Gulkana Glacier) with a coastal, 

land-terminating glacier (Wolverine Glacier) suggests that a coastal glacier has 

comparatively high fall flow, and larger peaks the rest of time (O’Neel et al. 2014). 

Projecting future streamflow in glacierized basins is difficult. Precipitation amount 

and timing, temperature, and local topography and glacier morphology all affect 

dynamics of glaciers and thus the streamflow from them. But changes in glacier 

shape are difficult to predict (Jost et al. 2012). Cumulative mass balance at Gulkana 

Glacier steadily decreased, (-25 m in area-average thickness since the 1960s), while 

Wolverine Glacier had an increased rate of mass-balance gain in the 1980s but a 

rapid decrease since then, thus mass losses have been proportionally less on the 

coast (-16 m) (O’Neel et al. 2014). Coastal glaciers have fared better historically 

because they have a different seasonal climate (more precipitation and less summer 

heat), but the slope of the decrease in mass balance is similar over the last 20 years. 

Coastal glaciers are probably more vulnerable over the long term because they have 

a temperature regime closer to 0 °C than do those in the interior.

Climate warming 

eventually influences 

the net mass balance 

of land-terminating 

glaciers and thus the 

seasonal timing and 

amount of streamflow 

in streams dependent 

on them.
35

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Role of glaciers in oceanography, marine ecosystems—

Glacier mass balance and effects on streamflow are not the only expected impacts 

of climate change associated with glaciers. For example, the surfaces of glaciers 

have been shown to support microbial ecosystems. Atmospheric deposition of 

nutrients, the resulting primary and heterotrophic production at the glacier surface, 

microbial activity underneath the glacier ice (Skidmore et al. 2000), and hillslope 

runoff combine to result in large material contributions to the marine environment. 

Heterotrophic carbon in glacier runoff (Hood et al. 2009) is nearly that of some 

boreal forest runoff (glacial dissolved organic carbon [DOC] = 12–18 kg C/ha/year; 

boreal forest DOC export 22–86 kg C/ha/year). The runoff flux from glaciers to 

streams or ocean is therefore large, and is bioavailable, including nutrients (phosphorus), micronutrients (iron), and contaminants (mercury and others). However, 

much as with glacial changes, the flux response is locally variable—biochemistry 

and turbidity vary widely in streams dominated by glacial runoff (Hood and Berner 

2009). Riverine biodiversity increases with basin glacierization (Jacobsen et al. 

2012). Despite this variability, it is important to recognize the substantial input of 

organic nutrients from glaciers, a characteristic that was recognized only recently 

(Hood et al. 2009).

Glacier runoff also affects near-shore ecology in part because of the input of 

nutrients including organic matter to the system. Euphausiids and zooplankton can 

thrive in glacier-dominated fjords (Arimitsu et al. 2012), as do coastally adapted 

birds (Mehlum and Gabrielsen 1993). Diving seabirds forage on upwelled crustaceans and thus have high fidelity to glacial habitat. Tidewater glaciers provide 

refuge from predation for seals, and pups born near calving glaciers have short 

weaning times (Blundell et al. 2011, Herreman et al. 2009, Womble et al. 2010). The 

effects of glacial turbidity on light penetration affect the vertical migration of fish. 

In clear water, sunlight penetrates >100 m and moonlight penetrates <50 m, but in 

sediment-laden water sunlight penetrates less than 50 m. As a consequence, mesopelagic fishes are nearer the surface during daylight hours. Consequently, forage 

fish plausibly spend more daylight hours at the surface and are therefore possibly 

more available to birds. 

For fjord glaciers, warming and melting result in changes to coastal (baroclinic) 

current through changes in physical oceanography. These result in effects for the 

whole circulation pattern in the fjord, which changes the rate of iceberg production, 

forage-fish survival and productivity, and the timing and structure of currents. 

Beyond their influence on individual fjords, glaciers play an important role in 

delivering fresh water to the Gulf of Alaska. Where tidewater (calving) glaciers 

have a direct connection to the ocean environment, there is a direct interaction, 
36

GENERAL TECHNICAL REPORT PNW-GTR-950

through melting of ice below sea level. This “submarine melt” has been shown to 

be capable of melting the majority of ice delivered to the calving front from 

upstream during summer months. In the Chugach region, the ocean is normally 

warm enough to melt ice 5 or 6 months a year, and freshwater contribution to the 

marine environment peaks in autumn. In such environments, there are enormous 

amounts of submarine melt, as all tidewater glaciers are grounded below sea level 

in the ocean (Bartholomaus et al. 2013, Motyka et al. 2013). 

Future Snow Response to Climate Change

Strengths and Limitations of Climate Modeling for Snow Impacts

One tool available for assessing the plausible impacts of climate change on snow 

and ice is future climate modeling. Global climate models (GCMs) take advantage 

of modern computing capacity to simulate historical and future climate from “first 

principles”—knowledge of the physical properties and behavior of the atmosphere, 

ocean, land surface, and other factors as well as how they interact to affect climate. 

It is worth noting that the climate modeling community has recently transitioned 

from the CMIP3 group of climate models used in the fourth IPCC assessment 

(AR4) to the next generation, or CMIP5 group of models, which generally have 

finer resolution and (slightly) more advanced treatment of the climate system. 

Knutti and Sedlacek (2013) concluded that CMIP3 and CMIP5 can be considered 

realizations of the same probability spectrum of plausible climate scenarios, and 

at the time of this writing, CMIP5 downscaled climate output for Alaska did not 

exist. Given the mid-century focus of this assessment, the scenarios presented in 

this chapter should be broadly consistent with CMIP5 models with the higher RCP 

emissions scenarios (4.5, 6.0, or 8.5) because those do not diverge appreciably from 

A2 until the 2050s or 2060s. Despite modern computing capability, however, the 

atmospheric resolution of GCM simulations is commonly performed at about 0.5 

to 1.0 degrees latitude (roughly 35 to 70 mi [56 to 113 km]), though some models 

exceed this resolution. This limits the local processes that can be resolved by these 

models. For example, rugged topography such as in the Chugach National Forest 

might result in an elevation difference of 1500 m or more at the scale of one or two 

cells in a climate model. 

To make climate model output more applicable to finer landscape features, a 

process called “downscaling” can be used (see chapter 2). The many approaches 

to downscaling differ in complexity. Whether increasing complexity is advantageous depends on the question. For example, understanding future monthly or 

seasonal changes in temperature and precipitation averaged across several climate 

models may not require the same level of detail as understanding daily responses 

Global climate models 

take advantage of 

modern computing 

capacity to simulate 

historical and 

future climate from 

“first principles”—

knowledge of the 

physical properties 

and behavior of the 

atmosphere, ocean, 

land surface, and other 

factors as well as how 

they interact to affect 

climate. 
37

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

in climatic extremes, which may require complex statistical relationships between 

historical gridded data and GCM cells. In Alaska, the Scenarios Network for 

Alaska and Arctic Planning (SNAP) uses an approach called the “delta” method to 

relate gridded historical climate information (PRISM) to the expected CHANGE 

for each GCM. That is, the difference between the GCM historical climate and 

the GCM future climate is calculated, and that change (or “delta”) is added to (for 

temperature) or multiplied by (for precipitation) the historical value. 

The delta method, while less complex than some other approaches, has a 

straightforward and easy-to-understand method for dealing with climate model 

bias. The bias in a climate model is the degree to which it is too warm or cold (wet 

or dry) compared to measured climate in the historical record. For example, mountain ranges that are too small to be resolved in the model create real rainshadows 

that the model cannot “see,” resulting in too much modeled precipitation in the 

rainshadow and not enough in the mountains. Bias correction uses historical data 

to estimate the correction of such model error. The delta method does not explicitly 

model the error—instead it takes the simulated historical climate and the simulated 

future climate from the GCM and uses the difference between the two to estimate 

the change expected by the model in the future. This is an indirect control of bias, 

but it is straightforward and effective, and does not result in substantial loss of 

information for monthly or seasonal questions.

SNAP’s future projections come from five different GCMs (CCCMACGCM3.1 t47, GFDL-CM2.1, MPI-ECHAM5, MIROC3.2 medres, and 

UKMOHadCM3) that have been evaluated for their fidelity to Alaskan climate 

(a “reanalysis,” i.e., not directly from weather station data) (Walsh et al. 2008) 

during the instrumental period (1958–2000). Deltas for each of these GCMs have 

been computed and applied to gridded, interpolated historical climate (PRISM) 

at 30-arc-second resolution (similar to 800 m resolution, about 771 m at 60° north 

latitude). This results in more localized (downscaled) estimates of historical and 

future temperature and precipitation. However, the consequences of changes in 

precipitation and temperature for snowpack at a location as fine as 800 m are 

complex. Snowpack is affected by other factors that are not commonly downscaled 

to such fine resolution, and without this information, climate models cannot 

simulate snowpack at local scales. For example, the difference in snow accumulation on one 800-m pixel compared to a neighboring pixel is a product of elevation, 

orientation to prevailing winds, wind effects on redistribution, vegetation, and the 

variations in storm track from year to year. Elevation and aspect are fixed—they do 

not change appreciably over the time frame important for such questions. However, 

year-to-year differences in wind redistribution of snow and storm tracks can affect 
38

GENERAL TECHNICAL REPORT PNW-GTR-950

the two neighboring pixels differently. It is also critical to remember that changes

in temperature and precipitation at the two pixels are effectively the same, though 

downscaling could result in different absolute values.. Downscaled climate output 

gives us the ability to examine those changes, but there is considerable local information that climate models do not “see”—they do not resolve the wind differences 

and topography for the two example pixels. Therefore, “forecasts” of an 800-m 

snowpack for a given year or even a given decade are beyond the scope of such 

work. However, the changes from historical snowpack to future snowpack over the 

duration of the “climatology” (30-year period) average out the factors that result in 

large year-to-year and pixel-to-pixel differences and focus instead on the trends to 

be expected given changes in temperature and precipitation.

In the following analyses, we have chosen to focus on the midterm impacts 

of climate change (SRES Scenario A2) over a 30-year period in the future—2030 

to 2059. This is a sufficiently long time span that the averages of temperature and 

precipitation (“climatology”) are comparatively robust to interannual and decadal 

variation in climate, but sufficiently near in the future that it has bearing on management time horizons considered between now and the 2030s. For this initial 

analysis, we have chosen to analyze impacts for 500-m elevation bands, which 

avoids confounding the results too much with local differences. The changes in the 

projections below should therefore be considered “averages” with some variability 

to be expected within the elevation bands and the 30-year period resulting from 

topographic and interannual variability in factors that affect snowpack.

We focus on three aspects of how snow may change in the future: SDF, SWE, 

and snowpack vulnerability. Snow-day fraction addresses the changes that could 

reasonably be expected in the proportion of precipitation that falls as rain versus 

snow now and in the future. Snow-water equivalent addresses the consequences of 

changes in both precipitation and SDF for snow accumulation on the land surface 

during the cool season. Finally, snowpack vulnerability addresses the proportion of 

precipitation entrained in the snowpack during the cool season.

Projected Effects of Climate Change on Snow-Day Fraction in 

the Chugach National Forest

McAfee et al. (2013) developed models of decadal SDF for all of Alaska at 800 m.4

Snow-day fraction is the ratio of days with precipitation falling as snow to the total 

number of precipitation days. For example, an SDF of 30 percent means that of the 

4 Data: http://www.snap.uaf.edu/data.php#dataset=historical_monthly_snow_day_

fraction_771m; User’s guide: http://www.snap.uaf.edu/files/data/snow_day_fraction/

snow_fraction_data_users_guide.pdf.
39

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

days with measurable precipitation, on 30 percent of those days the precipitation fell 

primarily as snow. A projected change of -20 percent SDF would result in a future 

value of 10 percent SDF, but would represent a decrease of 67 percent of the historical value.

Here, we present a summary of SDF for the Chugach National Forest based on 

data and projections developed by McAfee et al. (2013). They developed decadal 

historical data (1900–1909, 1910–1919, etc., to 2009) and future projections (2010–

2019, 2020–2029, etc., to 2099) for different future greenhouse-gas emissions scenarios (B1, A1B, and A2 SRES emissions scenarios, e.g., Nakicenovic et al. 2000). 

Given the 2030–2059 projected future timeline for this assessment, we chose to use 

the future climates derived from scenario A2, which result in similar temperature 

changes as A1B until about the middle of the 21st century, after which they result in 

more warming than A1B. Recent emissions are comparable to the trajectory of both 

A1B and A2 scenarios, so we elected not to consider B1 scenarios. We used an historical benchmark climatology of 1971–2000, and thus averaged the three decadal 

800-m-resolution downscaled estimates of SDF for the 1970s, 1980s, and 1990s. We

did the same for the downscaled projected values for the 2030s, 2040s, and 2050s.

We subtracted the historical data from the future projections to estimate the change

in SDF. All analyses to this point were done for the whole state of Alaska.

By using the project domain for the Chugach National Forest Vulnerability 

Assessment and the digital elevation model associated with the SNAP products, 

we developed eight 500-m elevation bands for analysis, from sea level (0 m) to 

>3000 m (fig. 3-1; table 3-1). We calculated the mean historical (1971–2000) and

projected future (2030–2059) SDF for the elevation bands (i.e., over all pixels in

each elevation band).

Snow-day fraction changes by elevation band—

The results of this analysis are summarized in table 3-1 and figure 3-2. In the text 

that follows, the comparisons described are between the historical SDF and the 

five-model mean future SDF. Individual model projections may be more or less 

than the five-model average (see fig. 3-2). When the range of model projections 

includes the historical mean, it is less clear that the projected changes are distinguishable from the historical variability. In no case is the five-model future mean 

greater than the historical mean; in a few cases, notably in May below 2000 m and 

July at elevations above 2000 m, the GCM with the highest future SDF exceeds the 

historical mean. 

In most months at all elevations, the five-model mean indicates projected 

decreases in SDF. These decreases are most pronounced at lower and mid elevations 

Projected decreases 

in snow-day fraction 

are most pronounced 

at lower and mid 

elevations during 

late autumn and 

early winter (October 

through December).
Figure 3-1—Elevation bands used in snow-day fraction analysis for the Chugach National Forest. 

40

GENERAL TECHNICAL REPORT PNW-GTR-950

Table 3-1—Elevation bands, area, and snow-day fraction for the Chugach National Forest

Elevation band Pixels Area 

Historicala

snow-day fraction 

(October—March)

Projectedb

snow-day fraction 

(October—March)

Changec

(October—March)

 Meters Square kilometers - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - -

0 14,612 8686 38.1

1–500 22,361 13 292 56.7 29.5 -22.7

501–1000 14,865 8836 71.1 47.6 -16.0

1001–1500 9,725 5781 80.6 62.5 -12.1

1501–2000 2,541 1511 86.4 72.8 -9.7

2001–2500 971 577 91.7 80.2 -7.3

2501–3000 368 219 95.4 87.3 -4.7

>3000 44 26 97.8 92.7 -2.8

a

 1970–1999 cool season average. b

c

 [(Projected—historical)/projected] × 100.
Figure 3-2—Historical (1971–2000) and projected (2030–2059) changes in mean monthly snow-day fraction by elevation band for 

the domain of the Chugach National Forest Vulnerability Assessment. Months are in “hydrologic year” order, October to September. 

climate models.

41

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

(2000 m and less) in the late autumn/early winter (October, November, and December). For elevation bands at 2000 m and below, the projected (2030–2059) model 

with the highest SDF is less than the historical (1971–2000) means for these months. 

Decreases in these elevations differ with month (fig. 3-3) and elevation (fig. 3-2), but 

are higher in October (mean -13 percent, model range -6 to -24 percent) and November (mean -12 percent, model range -4 to -25 percent) than in December (mean -4 

percent, range -2 to -8 percent). Differences in October are evident at elevation 

bands above 2000 m, but the projected changes decrease as elevation increases (fig. 

3-2). For elevation bands at 1500 m and below, there also appears to be a decline in 

February SDF (around -13 percent average, model range -36 to -2 percent), although 
Figure 3-3—2030–2059 changes in HUC-12 level mean snow-day fraction relative to historical (1971–2000) for selected months: (A) 

October, (B) November, (C) February, and (D) March. The maps are focused on the domain of the Chugach National Forest Vulnerability 

October, and at lower elevations (<500 m) in (B) November. Note that the percentage values in the maps are raw declines, not percentages of percentages, such that a decrease from 60 to 30 percent and from 30 to 0 percent would get the same change but result in different 

absolute values. HUC = hydrologic unit code.

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GENERAL TECHNICAL REPORT PNW-GTR-950

February has one of the largest ranges of projected future responses of any month, 

particularly at 2500 m and below (fig. 3-2).

The difference between historical and future SDF, as well as the disagreement 

among climate models, initially decreases with increasing elevation. However, 

models agree more on warm-season (April to September) changes below 2000 

m than they do on cool-season (October to March) changes. At elevations above 
43

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

2500 m, models agree more on cool-season changes than they do on warm-season 

changes. 

Projected Effects of Climate Change on Snow-Water Equivalent 

in South-Central Alaska

Snow-water equivalent is the amount of water entrained in a given volume of 

snowpack. Snowpacks with identical depth but different densities have different 

water content. Snow-water equivalent is a way of putting snow depths and densities, 

which differ considerably, on a consistent hydrologic footing.

Using the same scenarios as for SDF, we used historical and future gridded 

precipitation5 to estimate precipitation totals and projected changes for the key cool 

season months October to March. Snowpack obviously can accumulate in southcentral Alaska, particularly at the highest elevations, earlier in the autumn and later 

in spring than these months, but this is a comparatively standard hydrologic season 

that comprises the bulk of the snowiest months. For each month, we multiplied 

the SDF by precipitation to estimate total maximum SWE. Local processes, such 

as wind redistribution, sublimation from the surface or tree canopies, and melt 

could well affect the actual SWE, so these should be interpreted as estimates of the 

climatically determined component of SWE.

Snow-water equivalent changes projected using this methodology indicate different responses at different elevations (fig. 3-4) across the cool season and substantial differences across months (table 3-2). Averaged across the cool season, SWE 

would be projected to decline most in autumn (October and November) and at lower 

elevations (below 1500 m), an average decrease of 26 percent for the 2030–2059 

period compared to 1971–2000, with the largest decreases at lower elevations and 

in October. In contrast, from December to March at elevations above 1000 m, the 

five-GCM average SWE is projected to increase by an average of 12 percent, with 

the largest increases at highest elevations in January and February. Below 500 m, 

SWE is projected to decrease in all months except January and March, which have 

model-projected increases (table 3-2). For the cool season as a whole, the five-model 

GCM average projects decreases in SWE at elevations below 1000 m and increases 

above 1500 m (fig. 3-5; table 3-3). Agreement across GCM models is reasonably 

good at the lowest and highest elevations—most of the models agree on decreases 

in monthly SWE for October through March at the lowest elevations (<1000 m) and 

increases at the highest elevations (>2500 m). However, at mid elevations, some 

models project decreases and some increases (table 3-3; fig. 3-6). 

 5 Data: http://www.snap.uaf.edu/data.php#dataset=historical_derived_

precipitation_771m.
Figure 3-4—Historical (1971–2000) and projected (2030–2059) mean monthly snow-water equivalent (SWE) by elevation band for the 

domain of the Chugach National Forest Vulnerability Assessment. Months are in “hydrologic year” order, October to September. Blue 

models. Seasonal decreases in SWE are consistent with snow-day fraction, including decreases in autumn at elevations of 1500 m and 

below, and possible increases in the winter months at elevations above 1500 m.

44

GENERAL TECHNICAL REPORT PNW-GTR-950
Table 3-2—Historical snow-water equivalent (SWE) (1971–2000) and percentage of change (five-GCM average, 2030–2059) 

elevation band

Month

October November December January February 

Elevation 

band

Historical 

SWE Change

Historical 

SWE Change

Historical 

SWE Change

Historical 

SWE Change

Historical 

SWE Change

Hi

Meters Millimeters Percent Millimeters Percent Millimeters Percent Millimeters Percent Millimeters Percent Mi

0 (sea level) 47 -45 84 -34 109 -8 105 -4 94 -24 

1–500 58 -38 93 -22 117 -1 107 7 91 -13 

501–1000 155 -29 177 -13 215 4 184 11 156 -5 

1001–1500 274 -20 247 -8 293 5 250 12 216 1 

1501–2000 426 -9 307 -4 393 6 317 15 285 8 

2001–2500 684 0 443 -1 575 8 475 18 412 13 

2501–3000 758 6 465 2 603 9 492 19 438 17 

>3000 787 10 457 4 603 9 489 20 423 20 

GCM = global climate model/general circulation model.

45
Figure 3-5—Historical (1971–2000, top) 

and projected future (2030–2059, mean 

March snow-water equivalent (SWE), 

with percentage of change (bottom).

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GENERAL TECHNICAL REPORT PNW-GTR-950

Table 3-3—Historical snow-water equivalent 

range for October to March season

October through March

Elevation 

band

Historical 

SWE Change

Model 

range

Meters Millimeters Percent

0 (sea level) 509 -20 -36 to -4

1–500 539 -11 -22 to +1

501–1000 1035 -4 -13 to +7

1001–1500 1502 0 -9 to +10

1501–2000 1998 5 -7 to +16

2001–2500 2968 9 -5 to +21

2501–3000 3143 11 -4 to +25

>3000 3123 13 -3 to +20

GCM = global climate model/general circulation model.
emissions scenario: (A) CGCM 3.1; (B) GFDL CM 2.1; (C) ECHAM 5; (D) HadCM3; and (E) Miroc 3.2 MedRes. Note that the 

percentage values in the maps are the percentage of change from historical SWE. 

47

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula
48

GENERAL TECHNICAL REPORT PNW-GTR-950

Projected Effects of Climate Change on Snowpack 

Vulnerability in South-Central Alaska

Snow-water equivalent projections used in conjunction with precipitation projections allow calculation of an index of snowpack vulnerability (indicated by changing exposure to melt) to climate change (see Elsner et al. 2010 and Mantua et al. 

2010 for details). This index is the ratio of April 1 SWE to the total precipitation 

between October 1 and March 31. Values of less than 0.1 (that is, 10 percent of the 

precipitation was entrained in snowpack on April 1) indicate a “rain-dominant” 

hydrology. Values between 0.1 and 0.4 indicate a “transient” hydrology, in which 

the annual hydrologic cycle is partially driven by rain and partially by snowpack. 

Values greater than 0.4 indicate a “snow-dominant” hydrology, in which snowmelt 

strongly affects the timing of peak flow.

We used two separate datasets to evaluate snowpack vulnerability. First, the 

University of Washington Climate Impacts Group (UWCIG 2012) developed historical (1950–2000) and future (2030–2059) temperature and precipitation outputs 

from the same five GCMs as Walsh et al. (2008), downscaled to 0.5° (about 65 km) 

over a domain of the entire North Pacific, and used them as input to the variable 

infiltration capacity (VIC) model (e.g., Liang et al. 1994) to estimate SWE. However, they developed these outputs for the SRES A1B emissions scenario, which 

arguably results in slightly less warming by the middle of the 21st century than scenario A2 used for the SNAP data above. Although the 0.5° products are ultimately 

too coarse to allow small (e.g., 12-digit hydrologic unit code [HUC]) watershed 

calculation and comparison, these projections can give a regional perspective on 

snowpack vulnerability using independent methods.

Second, we calculated the same snowpack vulnerability index for areas within 

the Chugach Vulnerability Assessment by using the calculations for SWE in the 

previous section in conjunction with SNAP’s precipitation projections to calculate 

snowpack vulnerability index for the same gridded surfaces in the snow fraction 

and SWE analyses above, allowing smaller watershed comparisons. 

In both cases, we calculated the snow vulnerability index (April 1 SWE/

October to March total precipitation) for a 2030–2059 time period. Compared to 

historical data, the results from the UWCIG (2012) data averaged across all five 

future models for the Chugach vulnerability assessment domain suggest a decrease 

in the percentage of the landscape that is snow-dominant and an increase from 8 

to 13 percent transient (63-percent increase) and an increase from 0 to 3 percent 

rain-dominant (table 3-4). 
49

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 3-4—Changes in landscape fraction of snowpack vulnerability index 

classes for the Chugach National Forest estimated from coarse (0.5°) 

downscaled GCMs

Snow-dominanta Transientb Rain-dominantc

Percent

Historical 92 8 0

CCCMA-CGCM3.1 t47 76 16 8

MPI-ECHAM5 76 18 5

GFDL-CM2.1 76 18 5

UKMOHadCM3 84 16 0

MIROC3.2 medres 87 8 5

Five-model average 84 13 3

a

 April 1 SWE / ONDJFM PPT > 0.4.

b

 April 1 SWE / ONDJFM PPT between 0.1 and 0.4.

c

 April 1 SWE / ONDJFM PPT < 0.1.

Note: Rows may not add to 100 percent because of rounding.

Figure 3-7 shows the historical and projected future distribution of the index for 

each climate model using the SNAP data and the SWE calculated here. According 

to the finer downscaling approach that SNAP used, the historical condition of the 

HUC 12 watersheds Chugach vulnerability assessment domain was about 73-percent snow-dominated (>40 percent of October to March precipitation entrained in 

snowpack) and 27-percent transient (between 10 and 40 percent by area, with no 

rain-dominated watersheds. The five-model average future distribution is projected 

to be about 63-percent snow-dominated and 37-percent transient, still with no raindominated watersheds (table 3-5). The five GCMs vary considerably in their future 

proportion of the landscape in transient versus snow-dominated watersheds (fig. 

3-7; table 3-5), with a lower estimate of snow-dominant watersheds at 55 percent 

(CCCMA-CGCM3.1 t47) and a higher estimate at 67 percent (UKMOHadCM3).

Of the 551 HUCs in the domain, 4 percent (23) shift from snow-dominated to 

transient, while none shifts from transient to rain-dominated or from transient to 

snow-dominated. Among historically transient HUCs, the average change in snowpack vulnerability index is about -0.04, but among the historically snow-dominated 

HUCs the average change is 0. This value, however, is misleading—the comparatively large increases (+0.4 to +0.8) in the historically most snow-dominated HUCs 

(at higher elevations and with SVI > 0.55; see fig. 3-8) cancel out the changes in 

other snow-dominated HUCs. For example, in figure 3-8, lower elevation HUCs 

become closer to rain-dominant, but below about 1200 m, a large number of HUCs 

becomes a class away from becoming transient.
CGCM 3.1. Note that “red” is transitional, in which precipitation is a mix of rain and snow during the cool season, and orange indicates 

that precipitation is moving toward being rain-dominant.

50

GENERAL TECHNICAL REPORT PNW-GTR-950
51

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Figure 3-8—Snowpack vulnerability index for each Chugach National Forest domain hydrologic unit code by elevation for historical 

and projected future periods. 

Table 3-5—Changes in landscape fraction of snowpack vulnerability 

downscaled GCMs

Snow-dominanta Transientb Rain-dominantc

Percent

Historical 73 27 0

CCCMA-CGCM3.1 t47 55 45 0

MPI-ECHAM5 58 42 0

GFDL-CM2.1 64 36 0

UKMOHadCM3 67 33 0

MIROC3.2 medres 65 35 0

Five-model averaged 63 37 0

a

 April 1 SWE / ONDJFM PPT > 0.4. b

 April 1 SWE / ONDJFM PPT between 0.1 and 0.4. c

 April 1 SWE / ONDJFM PPT < 0.1.

d

 Five-model averages are not the average of the rows above, but are calculated for each pixel in the domain, 
52

GENERAL TECHNICAL REPORT PNW-GTR-950

Limitations: Caveats and Uncertainty

There are several important limitations on the future SDF, SWE, and snow vulnerability index projections. First and foremost, weather stations with long, complete, 

and well-documented historical climate observations are sparse in Alaska, especially above 500 m in elevation. The equations developed by McAfee et al. (2013) 

to estimate SDF from temperature data and the hydrologic modeling done by 

UWCIG (2012) were constructed almost exclusively from observations below 500 

m because this is the only information available. In addition, the historical observations underlying them are sparser than a comparable area in more populated parts 

of North America. For example, for SDF, this translates to less certainty in the 

relationship between observed temperature and the probability of snow at higher 

elevations, particularly under conditions near freezing (0 °C). However, given that 

these higher elevations are areas with less projected absolute change in this analysis 

and are historically colder, this limitation probably does not affect interpretation 

of the results very much. If anything, the projections are likely to be conservative 

because the actual lapse rate in coastal areas is likely to be, at least when annually 

averaged, shallower than the gridded climatology-assumed environmental average 

lapse rate of 6.5 °C. Given the topography of the region and the lack of weather 

station data applicable to understanding the interactions between topography and 

storms, the spatial variability of the projections is also undetermined. The aggregation of the pixel values to watersheds and over multiple decades is a partial hedge 

on this uncertainty.

Second, near-term decadal-to-interdecadal climate variability is not well 

predicted, even though the climate models of the AR4 generation often simulate 

realistic variability at those time scales. In fact, decadal prediction is cutting-edge 

science in the most recent generation of climate models and is an active area of 

research. But it is likely that temperature trends projected for a future decade could 

be above or below the future observations because of natural climate variability. We 

have used a 30-year climatology in both analyses that should, given current knowledge, be relatively robust to such variations. In addition, the fact that the projection 

window (2030–2059) is before uncertainty regarding future emissions begins to 

exceed that of models or variability increases our confidence in these projections.

Third, the elevation bands used for the analyses are relatively broad. Under 

average environmental conditions, the temperature difference across 500 m of 

elevation is often around 3.3 °C and sometimes considerably more in drier climates 

or in some seasons. These elevation bands are used as averages across the study 

domain, and conditions at a location within an elevation band could be quite different from the average depending on local factors associated with topography, sea 

Weather stations with 

long, complete, and 

well-documented 

historical climate 

observations are 

sparse in Alaska, 

especially above 

500 m in elevation. 
53

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

ice, etc., and broad-scale factors such as the pixel or HUC’s position east or west of 

Prince William Sound.

Finally, this analysis is not based on an exhaustive approach to future climate 

scenarios—these are plausible scenarios based on global climate models that have 

reasonable skill in simulating historical observed climate in Alaska at relatively 

broad scales. The process of downscaling them provides more physically tailored 

responses, but it does not resolve some local features and processes that are known 

to be important in the development and melting of snowpack. The strength of the 

projections is therefore at coarser spatial scales—watershed to regional, rather than 

pixel-by-pixel.

For these reasons, the projections presented here should not be viewed as predictions, but rather scenarios of the best available projected future conditions given 

current knowledge, capability, and resources.

Current and Future Ice and Glacier Response to Climate Change 

Since 1950, Alaska has warmed 2 °C in winter and 1 °C in summer (Arendt et 

al. 2009). Although decadal climate variability explains some of this change, an 

increase in temperature is certain, is occurring throughout Alaska’s weather station 

network, and is expected to continue with climate change (Stewart et al. 2013). 

Increases in temperatures have likely led to increased melt but have also led to 

higher elevation freezing levels and hence conversion of precipitation to rain from 

what historically would have been snow. Precipitation overall (rain and snow) is 

expected to increase slightly in the future, though it is not clear if this is happening 

currently. Only 17 percent of meteorological stations show an increase in precipitation; all others show no change (Arendt et al. 2009). 

These changes in climate have contributed to a widespread loss of ice from 

glaciers throughout Alaska. Statewide, Alaska glaciers are losing 65 km3 of ice 

per year on average, meaning that glaciers are losing far more mass to melt than 

they are able to gain though snowfall (Arendt et al. 2013, Larsen et al. 2015). This 

volume of ice lost annually is equivalent to more than a year of discharge on the 

Copper River. The rate of mass loss from year to year is not steady, however; variations in summertime temperatures led to annual losses of up to 125 km3

 in 2004 

and even a mass gain of 15 km3 in 2008 (Arendt et al. 2013).

Chugach National Forest glaciers are currently losing about 6 km3 of ice per 

year, which is equivalent to melting a uniform 60 cm of ice across all glaciers in the 

Chugach (Berthier et al. 2010). However, these changes are not uniform (fig. 3-9). 

All non-calving glaciers within the Chugach are losing mass. Most of these glaciers 

are also retreating, and typically thinning at glacier termini by about 3 m/year 

Statewide, Alaska 

glaciers are losing 65 

km3 of ice per year on 

average, meaning that 

glaciers are losing far 

more mass to melt 

than they are able to 

gain though snowfall. 

This volume of ice lost 

annually is equivalent 

to more than a year 

of discharge on the 

Copper River. 
54

GENERAL TECHNICAL REPORT PNW-GTR-950

(fig. 3-9) (Larsen et al. 2015). These changes are consequence of warming summer 

temperatures (Larsen et al. 2015).

Changes in tidewater terminus positions are more complex. Since the 1950s, 10 

glaciers have retreated more than 0.5 km, only Harvard Glacier has advanced more 

than 0.5 km, and the rest have displayed relatively little change (McNabb and Hock 

2014). The length and pace of these retreats far outweighs the advances. In the last 

decade, Harvard, Yale, and McCarty Glaciers have gradually advanced despite losing mass overall (Larsen et al. 2015, McNabb et al. 2014). Most other glaciers have 

recently stabilized at retreated positions (McNabb and Hock 2014), but some fully 

retreated tidewater glaciers have continued to retreat up onto bedrock (therefore 

ceasing to function as tidewater glaciers), while others have begun a re-advance. 

Because most of these retreated glaciers still appear to be losing mass, it is more 

likely that they will remain close to their stabilized positions or retreat in the near 

future (warming climate) and less likely that these glaciers would re-advance. 

Because Columbia Glacier is responsible for half of the Chugach glacial ice 

loss, its future evolution must be considered separately. The volume of Columbia 

Glacier has declined by approximately 50 percent in the past 35 years, in one of 

the largest scale calving retreats ever observed. Future iceberg calving is likely 

to remain significantly lower than peak levels (O’Neel et al. 2013) because of a 

large-scale reduction in ice thickness across the entire glacier. The glacier is bedded 

below sea level 15 km or more upstream of the current terminus, and best projections suggest about 20 years of continued retreat (Pfeffer et al. 2015).

O’Neel et al. (2014) analyzed mass balance and streamflow data from Gulkana 

and Wolverine Glaciers to show that both are losing mass as a result of stronger 

summer ablation. In the continental climate (Gulkana Glacier), positive streamflow 

anomalies arise primarily from negative annual mass balance anomalies. In the 

more complex maritime climate (Wolverine Glacier), streamflow has multiple drivers, including melt, and highly variable rainfall and snow accumulation. Although 

it is common to assume that discharge varies proportionally to annual mass balance 

for heavily glacierized basins, our data show that, in maritime climates, discharge 

is less coupled to annual mass balance than the delivery of mass balance to outlet 

streams as summer streamflow.
Figure 3-9—Volume of ice lost from glaciers in the Chugach National Forest. Surveyed glaciers are colored; unsurveyed glaciers are 

shown in white. Brick to green color indicates mass loss; blue indicates mass gain. Note that short survey time frames (2009–2012) do 

not yet capture trends for some of these glaciers (Larsen et al. 2015). 

55

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula
56

GENERAL TECHNICAL REPORT PNW-GTR-950

Case Study: Monitoring the Retreat of Exit Glacier 

Deborah Kurtz, resource management specialist, National Park Service, 

Kenai Fjords National Park

Glaciers are a sensitive indicator of climate change. As temperatures warm 

or precipitation decreases, a threshold can be reached in which glacial ice is 

lost faster than it is replenished. This phenomenon results in a reduction of the 

ice mass; the surface elevation decreases as ice thins and the area diminishes 

as the ice margins melt or calve off. For many who observe glaciers, this is 

most apparent in the retreat of a glacier, resulting in an overall decrease in its 

length. During the Little Ice Age, a period (circa 1550 to 1850) of cool conditions in the Northern Hemisphere, many glaciers reached their recent maximum extent, and most have retreated since then. General trends in historical 

retreat rates can be reconstructed through physical clues in the landscape and 

analysis of historical photography. Past terminus positions can be determined 

based on recessional moraines, landscape features that were deposited during 

temporary stationary periods amidst an overall period of retreat.

Researchers at Kenai Fjords National Park have used a combination of 

these techniques to document the retreat and changes in the geometry of Exit 

Glacier.6

 Past terminus positions evident from recessional moraines were 

identified by Ahlstrand (1983), Wiles (1992), and Cusick (2001) using a combination of photogrammetry and field techniques such as tree-core analysis and 

radiocarbon dating. These recessional moraines date back to Exit Glacier’s 

1815 Little Ice Age maximum position. A series of aerial photography and 

satellite imagery beginning in 1950 provides additional documentation of the 

glacier’s position. Until the mid-1900s, Exit Glacier was a piedmont glacier, 

meaning that its ice extended beyond the valley walls and spilled out into the 

relatively flat and unconfined valley floor. From photo documentation, we 

know that the glacier’s shape changed dramatically from 1950 when it was a 

piedmont glacier to 1974 when the thinner, more pointed shape that is characteristic of a valley glacier was first documented. 

In 1980, Kenai Fjords National Park was established, and park staff 

began direct observation of changes to the terminus (fig. 3-10). Photographic 

evidence reveals that, from 1983 to 1993, Exit Glacier experienced an advance 

and the glacier lengthened by 75 m (see footnote 6). A recessional moraine 

6 Tetreau, M. 2005. Exit Glacier recent history in simple terms. Unpublished report. 

On file with: Kenai Fjords National Park, 411 Washington Street, Seward, AK 99664. 
57

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Figure 3-10—Pattern of glacier recession at Exit Glacier, Kenai Fjords National Park, from 1815 to 2009. 

resulting from the decade-long advance is visible on the outwash plain today. The 

glacier began retreating again in 1995. In 2006, park staff began documenting 

annual terminus positions with a global positioning system (GPS) and calculating 

annual rates of retreat. These data documented a recent shift in seasonal glacier 

movement as well. Although there was net annual retreat for these years, Exit 

Glacier advanced slightly during the winters 2005–2006 through 2008–2009. 

Beginning in winter 2009–2010, Exit Glacier has been retreating year-round.

Exit Glacier’s overall trend of retreat is consistent with the retreat of glaciers 

around the world (WGMS 2008). Changes to glacier lengths, documented at Exit 

Glacier by the change in terminus position, appear in response to past climate 

conditions and mass-balance changes with a response time on the order of 

decades. Climate is not the only factor influencing terminus positions. Geometry, 

basal topography, slope, aspect, and microclimates also contribute to changes. 

The intermittent advance that was documented at Exit Glacier in the 1980s is not 

unusual among glaciers. ■
58

GENERAL TECHNICAL REPORT PNW-GTR-950

Case Study: Evaluating Glacier Change By Using Remote 

Historical and Current Remote-Sensing Tools

Linda Kelly, GIS specialist, USDA Forest Service, Chugach National Forest

Landscape photographs taken by early explorers and historical aerial photography provide records to evaluate multi-decade to century-long change 

in the surface area of individual glaciers. However, evaluating change in 

glacial cover for an entire region such as south-central Alaska over this same 

historical period represents a significant challenge. I explored existing maps, 

aerial photography, and geographic information system (GIS) tools to examine 

changes in surface area occupied by glaciers across the assessment area. After 

thorough evaluation, I found that existing information precluded estimating 

change with reasonable certainty at this broad spatial extent. Here I document 

my investigation to assist further investigation of glacier change.

The Randolph Glacier Inventory (RGI) (Pfeffer et al. 2014) version 3.0, 

released in 2013, represents a reliable source for estimating the current extent 

of glaciers in the assessment area (http://www.glims.org/RGI/). This GIS 

product is a global inventory of glacier outlines, supplemental to the Global 

Land Ice Measurements From Space (GLIMS) initiative. Glacier outlines 

were developed using satellite imagery. Uncertainty is estimated about ±5 

percent based on comparisons with alternative inventories. To estimate glacier 

expansion or decline, I sought a source, or combination of source data, to map 

historical glacier extent for comparison with the RGI. I examined:

• Chugach National Forest timber type mapping which includes cover

of icefields and snowfields (source data: 1:15,840 aerial photography

dated from the 1950s–1970s)

• Chugach National Forest Geology GIS layer: source data 1:250,000

paper map, prepared by the U.S. Geological Survey (USGS) Branch of

Alaska Geology, 1985.

• Chugach National Forest Landsystems GIS layer: source data 1:63,360

USGS 15-minute quadrangle maps, 1975, 1978, 1982, and 1983.

These three sources were rejected owing to their limited extent of mapping within the assessment area. I also evaluated the National Hydrography 

Dataset (NHD) ( http://nhd.usgs.gov/) a digital vector dataset containing water 

features, including glaciers, maintained by the USGS for the National Map 

program. For Alaska, the source data was mapped at 1:63,360 scale. Source 

date for the NHD depends on the production date of the initial line work and 
59

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

whether this line work was updated when Digital Line Graph files were created by USGS. Therefore, the vintage of the line work for Alaska varies from 

the 1950s to the present. Examination of the USGS topographic base maps 

used to form the NHD layer in the November 2012 product suggested that they 

result from aerial photographs taken in 1950 and 1957. My evaluation indicated that this GIS layer represented the most promising source to compare 

with the Randolph product.7

Therefore, I used the NHD (November 2012) and RGI (April 2013) to 

compare the area extent of glaciers and produce a display that illustrates 

areas of potential glacier change (fig. 3-11). The NHD data were expected to 

reflect a glacial area extent from an earlier time than the RGI, with a time span 

assumed to represent 50 to 60 years. 

The Chugach National Forest 1950 and 1959 (1:15,840) black-and-white 

aerial photography set and 2008–2009 4-band orthophotography (60-cm resolution) were used to verify a sample of watersheds representing the greatest 

degree of change measured between RGI and NHD.

To examine potential sources of error in the comparison, the map in figure 

3-11 was used to select areas of glacier change to validate with a backdrop 

of photography. The analysis suggested potential sources of error leading to 

unreliable estimates of glacier expansion and loss. The most significant source 

of error displayed as examples in figures 3-12 and 3-13 represent errors in 

mapping glacier boundaries in the NHD. The area of glacial extent is less 

credible in the NHD than in RGI. 

In conclusion, using differences in NHD and RGI to detect changes in 

the extent of glacial boundaries to measure effects of climate change should 

proceed with caution and careful validation that employs alternative sources 

such as aerial photographs. Mapping employed in NHD failed to include 

some glacial features that were large enough to meet standards for the size 

of features that should have been captured. In addition, the finer detail of 

other features was simplified, such that the area mapped as glacier was less 

extensive, leading to potential errors in estimates, particularly of increased 

glacier cover in NHD/RGI comparisons. The standard of the NHD feature 

capture was not consistent across the study area. On the other hand, RGI 

7The NHD data were from a data download from USGS NHD in November 2012. The 

Randolph Glacier Inventory data have since been used to update glacier features in NHD, 

replacing the previously mapped areas of glacier polygon features in the Waterbody dataset. 

Any current NHD downloads would no longer allow this type of comparison.
Figure 3-11—Difference in the spatial extent of glaciers mapped using the Randolph Glacier Inventory (RGI) and National Hydrography Dataset (NHD

are classified by the extent to which RGI indicates that ice is present and NHD indicates that ice is not present. Interpretation of this difference might sumay be advancing, which has been documented for Harvard and Mears Glaciers but is questionable in other places. Map by Linda Kelly, USDA Forest 

60

Columbia Glacier

Yalik Glacier

Tonsin

C

Power Creek Watershed

Chenega Glacier

Meares Glacier

Harvard Glacier

0 12.5 25 50 Miles

0 40 80 20 Kilometers

Percentage

of change

Noice 0 5 
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Climate Change Vulnerability Assessment for the Chugach National Forest and Kenai Peninsula

Figure 3-12—Tarr Glacier watershed, illustrating an approach to validating differences in 

glacial extent, measured using Randolph Glacier Inventory (RGI) and National Hydrography 

Dataset (NHD) data: (A) overlaying a photography base, and (B) with the data only. The differences between the mapped glacial margins illustrate an occurrence in which a programmatic 

comparison falsely suggests glacier expansion resulting from mapping errors. Map by Linda 

Kelly, USDA Forest Service.

more frequently misclassified glacial features 

along rocky ridges and 

very steep slopes, particularly shadowed slopes, 

which NHD tended to 

correctly interpret as 

rock in areas where I 

compared both datasets 

to photographic sources. 

My evaluation of NHD 

and the resulting comparison of NHD with RGI 

correctly identified the 

three cases of advancing tidewater glaciers: 

Harvard and Surprise 

Glaciers in the College 

Fjord area and Mears 

Glacier in north-central 

Prince William Sound. 

This suggests some value 

in cautious use of these 

tools to examine glacier 

change. Using NHD and 

RGI to detect recent ice 

expansion was mainly 

useful for selecting areas 

for further examination. I 

caution against estimating

A

B
Figure 3-13—Pigot Glacier watershed, illustrating an approach to validating differences in glacier extent measured using 

Randolph Glacier Inventory (RGI) and National Hydrography Dataset (NHD) maps employing black-and-white photography 

from 1996. The RGI maps 15 percent more glacial extent than the NHD, but this difference largely results from errors in NHD, 

in which there was a failure to map some glacial features. Map by Linda Kelly, USDA Forest Service.

62

GENERAL TECHNICAL REPORT PNW-GTR-950

differences in the two datasets for broad measures of increase in glacial extent. 

In conjunction with validation, local areas can be evaluated. 

Comparison of NHD and RGI information can adequately detect retreat 

along the margins and valley edges of glaciers. Based on my broad evaluation 

of glacial extent from NHD and RGI, the greatest loss of ice surface area in the 

domain of our assessment was associated with Columbia Glacier, Miles and 

Allen Glacier in the Copper River system, and Bear Glacier in Kenai Fjords 

■
Figure 3-14— Estimation of ice loss resulting from comparison of Randolph Glacier Inventory (RGI) and National Hydrography 

loss illustrated by comparing RGI and NHD in this case is valid. 

63

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Snow and Ice: Effects on Ecosystem Services

Introduction and Conceptual Framework

In this section, we consider how the findings discussed above—especially higher 

elevation average snowlines and fewer average snow days—might affect the ecosystem services related to tourism, recreation, and visitation of the study area. 

Our approach is to treat the natural resource interaction with humans in their 

roles as producers and consumers as a complex social-ecological system (SES). 

Snow- and ice-dependent tourism and recreation are a subsystem within this SES. 

So, too, are specific activities such as heli-skiing. This kind of analysis is relatively 
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GENERAL TECHNICAL REPORT PNW-GTR-950

new. Previous analyses (notably Haufler et al. 2010) have considered the implications of climate change on broader ecosystem services. However, as one of the few 

published papers focusing on the human dynamics of the tourism industry notes, 

“While tourism and the environment has been studied extensively... the concept 

of resilience as a means to understanding the impact of disturbances or stress on a 

system has rarely been used...” (Becken 2013).

Although that paper uses resilience rather than vulnerability as the organizing 

concept, the general point about such analyses being relatively new still applies.

It is challenging to isolate the effects of climate change on the SES and relevant 

subsystems because they are affected by numerous other shocks, stresses, and 

forces of change. As Becken (2013) put it, “The emphasis on present and future climatic disturbances allows for a focused analysis; however, it is important to note 

that tourist destinations experience a wide range of other stress factors simultaneously” (Becken 2013) (emphases added).

Some of these other stress factors include global and national market forces and 

prices, changing technology and preferences (e.g., the rise of snow bicycling), and 

key decisions taken by major industry players (cruise lines, Alaska Railroad) and 

government agencies.

The stability landscape concept (Walker et al. 2004) provides a useful framework for this discussion. Each subsystem is currently within a relatively stable 

state known as a basin of attraction. Each basin has a single “low point” toward 

which the subsystem tends absent any disturbance. The latitude (L) of the basin is 

a measure of how much the subsystem can be disturbed before it leaves the basin. 

For example, summer boating and sea kayaking in Prince William Sound have 

very wide latitude with respect to warmer temperatures. The resistance (R) of the 

system is a measure of how sensitive it is to perturbation. For example, if snow at 

high elevations remains dry despite average temperature increasing by 4 °C, this 

would be high resistance. Finally, the precariousness (Pr) of the subsystem is a 

measure of how close it is to a tipping point or threshold. For example, a ski area 

that has had several mediocre seasons because of economic recession might have 

very low cash reserves, and thus be precariously close to going out of business after 

a bad snow year.

These concepts make more sense when combined into a summary such as this 

one: social-ecological systems can be close to, or far away from, important thresholds (Pr). They can be easy or hard to change (R). The range of dynamics that can 

be accommodated while still retaining basically the same system can be large, or 

small (L). (Walker 2004: 7)
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

The disturbances affecting the stability landscape are also usefully characterized as “slow” vs. “fast” changes (Carpenter and Turner 2000). The Alaska economy and its tourism industry are subject to the “fast” influences of crude oil prices, 

national or global economic recession, and weather. Climate change operating to 

raise the snowline over 50 years is a “slow” change, as is an aging and growing 

resident population. Similarly, humans can respond with “fast” adaptations such 

as postponing a trip or drawing on financial reserves. But over “slow” time scales, 

regions within Alaska, and Alaska itself, can become significantly less unique or 

less preferred as a destination by both residents and tourists. 

Affected Ecosystem Services 

The snow- and ice-related ecosystem services most likely to be affected in ways 

that influence the recreation and tourism subsystems of the Chugach-Kenai SES 

are:

• Reliably deep snow

• Reliably dry snow

• Reliably accessible snowpack

• Stable (meaning storm-free) weather.

As a general proposition, there is one general threshold of greatest interest; the

change from snow to rain or from sub-freezing to above-freezing temperatures. 

Deep snow—

The dearth of snow during the 2014–2015 winter season demonstrates that the presence or absence of snow has significant economic and social consequences for the 

people and businesses of the Chugach-Kenai SES. Ski areas were shut down (Edge 

2014, 2015) and backcountry skiing was limited or nonexistent (Hollander 2014). 

Dog races were moved (Alaska Dispatch News 2015).

Although it is generally recognized that snowfall is volatile, and most businesses can shrug off an occasional bad snow season, as long-term averages change 

or as expectations change, people may begin to substitute other options for snowdependent activities in specific places. For example, Hatcher Pass, approximately 

50 km north of Anchorage, can be thought of as the place where Anchorage skiers 

may go when all else fails. It is a good example of economic substitution within a 

range of specific ecosystem services in specific places. It costs more time and fuel 

to get there, so only the more ardent skiers make the trip. However, it may be that 

Hatcher is the “substitute of last resort” for some people; even the fear that it may 

be dry could cause further substitution out of Alaska altogether.

The dearth of snow 

during the 2014–2015 

winter season demonstrates that snow 

levels have significant 

economic and social 

consequences for the 

people and businesses 

of the Chugach-Kenai 

region.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Dry snow—

Dry snow is the ecosystem service that supports powder skiing and arguably 

separates Alaska in the marketplace from the Pacific Northwest, and certain other 

skiing destinations. There is some evidence that heli-skiing is already shifting 

northward or out of the Kenai Peninsula. The Chugach Powder Guides website 

(http://www.chugachpowderguides.com/trips) lists only the Girdwood/Alyeska and 

Seward/Pacific Coast areas as specific skiing zones. Although there is currently no 

direct evidence to support the proposition, it seems reasonable to speculate that as 

the study area snowpack becomes wetter on average, it will be less desirable as a 

destination for both Alaska residents and nonresident visitors. 

Reliable access to snowpack—

This ecosystem service is a function of the elevation of snowline and whether 

existing trailheads provide access to snow. People can walk to reach skiable terrain 

(as they famously do in New Hampshire) but snowmachines cannot travel long 

distances over dry land, and regulations limit snowmachine use when snowpack is 

shallow. The findings above suggest that access to snow could become a concern 

as the snowline rises. Existing trailheads could become “stranded” below sufficient 

snow for snowmachine access. Users would naturally seek out other access points 

that still connect with snow, resulting in potential crowding and other consequences.

An obvious adaptation response is to extend trailhead access to reach higher 

snowlines. Although this may be impractical for existing trailheads, new ones could 

be planned over a 10- to 20-year horizon to accommodate an ascending snowline.

Storms and storm-free weather—

Storm frequency and intensity could also negatively affect visitation. Tour operators 

must build potential storm-related interruptions into their planning and revenue 

projections, much as businesses must plan for a certain percentage of bad debts, or 

concert promoters must plan for cancelled shows. Insurance markets could emerge 

or expand to address these concerns, with the overall effect being an increase in 

the cost of supplying “good-weather experiences.” There could also be a decrease 

in demand if customers are forced to bear the risk of cancellation or postponement. 

Both producers and consumers of recreation and tourism experiences will share 

the burden of disruptions. Exact allocation will depend on market conditions, but 

the overall effect of more storms and extreme weather will likely be to reduce the 

quantity of tourism excursions and experiences, and to increase the prices paid. 

Access to snow could 

become a concern as 

the snowline rises. 

Existing trailheads 

could become 

“stranded” below 

sufficient snow for 

recreational access. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Substitution in the face of change—

Within limits, there is substantial scope for substitution of locations and activities 

within south-central Alaska. In this respect, the latitude (L) of the stability landscape 

is reasonably wide for winter recreation and tourism as a regional or statewide activity and business sector. Backcountry skiers and snowmachiners can migrate north, 

seeking drier or more accessible snow. Snowmachiners in particular may simply 

go higher within existing terrain, assuming they can still gain initial access to the 

snowpack. Some people will substitute hiking for skiing. However, there will be a 

loss of quality or recreation value; if not, these shifts would already have happened. 

Furthermore, some substitution among recreation opportunities may also be negatively influenced by changing climate. For instance, a shift from skiing to rafting 

may be limited if changes in precipitation reduce streamflow patterns such that the 

season of rafting is constrained.

If the quality and cost of recreational opportunities in the Chugach-Kenai region 

shift in ways that favor other winter recreation areas closer to large population 

centers, then some nonresident tourists are less likely to make the long trip to Alaska 

and more likely to fly to places like Utah. Similarly, some Alaska residents—referred 

to by economists as those “at the margin”—may substitute a backcountry ski trip 

in British Columbia for a ski trip within the Chugach-Kenai region. Although these 

kinds of substitutions may be relatively rare, each one will have a much larger economic impact than simply shifting recreation locations within south-central Alaska. 

Maintaining Ecosystem Services in the Face of Climate Change

Many of the same measures to stabilize infrastructure that are currently used, such 

as erosion control, will be needed all the more under wetter, warmer scenarios. 

Therefore, the consequences of climate change further reinforce the rationale for 

existing management strategies for trail maintenance. However climate change in the 

form of more rain may overwhelm existing practices; hence one might say that current methods to control erosion may leave the trail system and other infrastructure 

more vulnerable to damage (an example might be the Resurrection Trail near Exit 

Glacier).

It is possible that some activities on the forest could be managed more flexibly 

if the goal was to maximize ecosystem services from snow. For example, the current 

alternating-year openings of the Resurrection Trail system to snowmachines might 

be adjusted to reflect snow conditions: if there is a good snow year, there could be a 

special opening for snowmachines during a nonmotorized year, and vice versa. This 

kind of regime is already practiced for personal use and commercial fisheries.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Maintaining Recreation and Tourism Subsystems

When specific ecosystem services (snow) cannot be retained because of climate 

change, it may still be possible for the human activities and the associated economic 

livelihoods to shift, just as species can potentially move with shifting habitat. There 

are already mechanisms (e.g., cash reserves) available to accommodate short-term 

“shocks” to snow-dependent activities. Such mechanisms are mentioned in the tourism literature as being important to operators. For example, Biggs (2011) reported 

that based on survey data:

• Reef tourism enterprises indicate that financial and marketing support are

the most important actions that government can take to support enterprises

in the face of a large shock.

• Snowmaking is a longer term reaction to uncertain snowfall, which of

course depends on water resources and sufficiently low temperatures.

Adding summer-activity infrastructure is another strategy already adopted

by many U.S. ski areas. One could perhaps think of the underlying “ecosystem service” as terrain rather than snow.

The tourism industry and resident recreation patterns have changed dramatically in the Chugach-Kenai SES during the past 20 years (Colt et al. 2002). 

These changes reflect shifting socioeconomic driver variables and an upsurge in 

entrepreneurial effort directed at providing nature-based tourism as a commercial 

product. The rapid deployment of people and capital seems to be a hallmark of these 

activities. Tourism businesses and their employees can and do move in response to 

changing conditions. Although it is probably outside the management purview of 

the Forest Service to directly assist with this process as it is carried out by individuals, there may be a scope for easing transitions and accommodating change by 

focusing more on forest users and tourism businesses and less on the ecosystem 

services themselves. One example of this approach might be a more flexible fee 

structure for special-use permits that recognizes the increased economic risk of 

running a snow-based business in the region.

Consequences of Potential Change in Snow and Glacier for 

Recreation Infrastructure

Changes to snow and ice, of all the biophysical changes evaluated in this vulnerability assessment, have the greatest potential to affect the condition of, and 

demand for, Chugach National Forest recreation infrastructure, particularly changes 

in snowfall and snowpack. Almost all the developed recreation facilities, which 

include cabins, campgrounds, day-use sites, trailheads, and the roads and trails 

Almost all the developed recreation facilities 

are found between 

sea level and 1500 m 

of elevation, where 

projected changes to 

snow-day fraction, 

snow-water equivalent, 

and snowpack 

vulnerability are 

the greatest.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

that provide access to them, are found between sea level and 1500 m of elevation, 

where projected changes to SDF, SWE, and snowpack vulnerability are the greatest. 

In Prince William Sound and the Copper River Delta, all recreation sites, trails, 

and roads are located between 0 and 500 m in elevation, with most close to sea 

level. Currently, recreational use on the Chugach is managed as snow-free (May 1–

November 30) and snow-based (December 1–April 30) seasons. Where over-snow 

motorized vehicles are allowed, there must be adequate snow levels and conditions 

to prevent damage to vegetation and soils. 

Impacts to Facilities 

Snow and ice have resulted in damage to facilities in the past, including two cabins 

that sustained structural damage during heavy snowfalls in the winter of 2011–

2012. Scenarios described above suggest that at elevations below 1500 m, snow 

may put less pressure on structures across the Chugach, especially cabins along the 

coastline in Prince William Sound and the Copper River Delta. At the same time, 

a decrease in SDF, especially in October and November, may extend the season of 

use for snow-free activities on trails that remain snow-free for a longer period of 

time. Trails popular for hiking, mountain biking, and pack and saddle use may also 

be vulnerable to ruts, trail widening, and other impacts to trail tread owing to a 

longer period of muddy conditions if rain replaces snow more often during the year. 

Where models project a possible change from snow-dominant to transient hydrology, mostly along the coastline in Prince William Sound and in the Copper River 

Delta area, these changes may effect trail and trail bridge infrastructure depending 

on how nearby streamflow is affected. 

Purpose or Draw to the Facility 

Facilities that primarily support snow-based recreation or include glacier viewing 

would see the biggest change resulting from projected declines in snow days, SWE, 

and greater snowpack vulnerability, especially early and late in the winter season. 

The Turnagain Pass facilities are the clearest example, as the two parking areas see 

more use in the winter as a backcountry skiing and snowmachining destination. 

Although skiers could still use the site to access higher elevations on foot, snowmachines could not do the same. Approximately 32 km of trails on the Chugach are 

exclusively snow trails, all below 1500 m in elevation. These trails may see less use, 

especially where motorized use is currently popular. Also, local volunteers have 

started to groom Russian River and Trail River campgrounds for Nordic skiing in 

the winter, an activity that would see a shorter season and more inconsistent conditions throughout the winter. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

The Spencer Glacier Whistle Stop in the Kenai Mountains and Childs Glacier 

Campground along the Copper River were developed primarily for glacier viewing. 

Looking at projections in glacial retreat and thinning, these sites could face a similar situation as the Begich, Boggs Visitor Center (BBVC), where viewing Portage 

Glacier from the theater was the main draw. The glacier has been retreating for 

decades and is no longer visible from the BBVC. Because of this, as well as many 

other factors, visitation to the BBVC has declined from over 300,000 in the 1990s to 

around 70,000 in 2013. 

Almost all the campgrounds and day-use sites, including picnic areas, campsites, trailheads, and boat ramps, are adjacent to the Seward, Sterling, Portage 

Glacier, and Copper River Highway. Turnagain Pass, at milepost 68 of the Seward 

Highway, is the highest point on this road system at an elevation of just over 300 m. 

Campgrounds and most day-use sites are primarily used in the snow-free season, 

especially between Memorial Day and Labor Day. Thus, the type and amount of use 

at these facilities is unlikely to see significant changes, though the shoulder seasons 

of use could potentially be extended later in the fall. 

Access To Facilities

Similar to changing patterns of the use of recreation sites, access to and from sites 

that depend on adequate snow conditions will be the most adversely affected, 

though no facilities and only about 32 km of trail are used exclusively for snowbased recreation. On the other hand, where deep snowpack limits access or 

increases the challenge of using a facility, the season of use may expand. Cabins in 

Prince William Sound and Copper River Delta areas may be easier to access and 

could see an increase in use with less snow, though snow is not the only limiting 

factor for use of these facilities. For instance, it still may not be desirable to be out 

in Prince William Sound in winter months when weather and seas are unpredictable. The cabins along Resurrection Pass Trail are popular in winter both for skiers 

and snowmachiners; poor snow conditions make access by these means more 

difficult or impossible.

Adaptive Capacity

Management of most recreation facilities on the Chugach National Forest will 

be able to adapt to projected changes in snow and ice, because very few are used 

exclusively for snow-based activities, and the vast majority of facilities are used 

more heavily in the snow-free months, especially between May to September. It is 

difficult to anticipate potential trends of snow-free activities, though, because multiple factors help make facilities popular at a given time during the year, and current 

Management of most 

recreation facilities on 

the Chugach National 

Forest will be able to 

adapt to projected 

changes in snow and 

ice, because very few 

are used exclusively 

for snow-based 

activities.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

understanding of the behavior of recreationists is insufficient to make reasonable 

predictions. Thus, just being snow-free may not necessarily increase use. Overall, 

it is likely that facilities supporting winter, snow-based recreation will see a more 

significant decline in use than any corresponding increase in the use of infrastructure supporting snow-free recreation. 

The least adaptable infrastructure would be motorized snow trails, because 

these are not used when snowpack is limited or inconsistent. At Spencer Glacier 

and Childs Glacier, summer recreation may still be popular and the potential to use 

facilities there does not change, but they may not have the same allure, and visitors 

may be less likely to spend the money and effort to get to these remote locations.

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Alaska Dispatch News. 2015. Knik 200’s a go; Norton Sound 450 is canceled. 

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Arendt, A.A.; Echelmeyer, K.A.; Harrison, W.D.; Lingle, C.S.; Valentine, V.B. 

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Becken, S. 2013. Developing a framework for assessing resilience of tourism sub 

systems to climatic factors. Annals of Tourism Research. 43: 506–528.

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Picturesque Cove on the west bank of Culross Passage, Prince William Sound, Alaska.

Phil Hayward
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Andrew Erickson,1 Claire Colegrove,1 and Aaron Poe 2

Summary

• The Chugach National Forest/Kenai Peninsula coastline stretches for

6260 km, including islands and Prince William Sound.

• Coasts and marine environments within the assessment area receive

heavy human use from tourism, fishing, and local economic activity.

Understanding how coastal environments will be affected by climate

change is important for land management planning.

• Important abiotic effects of climate change on the coastal environment

include sea-level change, glacial changes, and ocean acidification.

• The current trend for the region shows sea-level decline at rates of up to

a fraction of an inch per year, resulting in changes to tidal marshes and

barrier islands.

• Prince William Sound receives up to 50 percent of its freshwater discharge

from glacial runoff, indicating that changes to the region’s tidewater glaciers will have profound effects on the coastal environment.

• Cold ocean temperatures make Alaska’s oceans particularly vulnerable to

ocean acidification. Cold arctic waters dissolve carbon dioxide (CO2) readily, which results in acidification. Biotic communities in Prince William

Sound, the Copper River Delta, and the Kenai coast may be resilient (over

the short term). Systems in this region experience highly variable physical

and chemical conditions owing to seasonal freshwater influx.

• Important biotic effects of climate change include harmful algal blooms,

changes to eelgrass beds, and effects on shorebirds.

• Warmer waters and glacial melt may exacerbate harmful algal blooms,

which pose a threat to shellfisheries in the assessment area.

• Eelgrass beds are abundant within the assessment area and are susceptible

to changes in water depth and to human-caused disturbances.

• Prince William Sound and the Copper River Delta, two of the most visited

stopover locations for migrating shorebirds, are vulnerable to loss of mudflats caused by sea-level change.

Chapter 4: Seascapes

1 Alaska representatives, Defenders of Wildlife, Anchorage, AK 99501.

2 Science coordinator, U.S. Department of the Interior, Fish and Wildlife Service, Aleutian 

and Bering Sea Islands Landscape Conservation Cooperative, Anchorage, AK 99503.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Introduction

The assessment area is predominantly a coastal landscape. Its 6260-km shoreline 

encompasses Prince William Sound and numerous islands, connecting the upland 

forest ecosystem to the Pacific Ocean through rocky beaches, marsh tidelands, 

eelgrass beds, and tidewater glaciers. As an important part of the Chugach/Kenai 

landscape, the coastal ecosystem deserves significant attention from the region’s 

scientists and policymakers. 

Although this dynamic coastal landscape (Harwell et al. 2010) is accustomed 

to extremes in physical conditions—it frequently experiences powerful storms, 

extremely variable wave action, seasonal changes in weather, and many other 

stressors—climate change presents the potential continuing directional change in 

the ecosystem (Haufler et al. 2010). Climate-induced abiotic changes (including 

ocean acidification, increased air and ocean temperatures, altered precipitation 

patterns, and dynamic sea-level conditions) are likely to affect coastal environments 

(Ainsworth et al. 2011, Melack et al. 1997). As outlined elsewhere in this assessment (see chapter 3), snow-and glacier-covered portions of the assessment area are 

currently experiencing altered temperature and precipitation patterns, resulting 

in changes to seasonal snow cover and glacial extent. The directional changes in 

abiotic factors are likely to affect biotic characteristics of the Chugach coastal 

ecosystem, including species diversity and distribution, and introduction of pioneering or exotic species into climate-disturbed areas (Haufler et al. 2010). 

This chapter provides a primer on the issues and environmental conditions 

affecting the coastal ecosystems of the assessment area. We examine an array of 

climate-induced changes in ocean and coastal conditions, including abiotic and 

biotic effects of climate change. This discussion is predicated on the understanding that human use of the assessment area, notably Prince William Sound and the 

eastern Kenai Peninsula, primarily occurs within the marine environment. The 

coast serves as the primary attraction to visitors to the region and supports those 

who make a living from the marine environment. Given the importance of the coast 

and its varied environments to tourists, fishermen, and the biota of both land and 

sea, examining potential influences of climate change is critical to the larger assessment. However, the chapter’s scope is limited by the rapidly developing scientific 

understanding of the coastal ecosystem and our capacity to distill the complex 

biological, physical, and chemical processes into a useful tool for land managers. 

Our intent is not to comprehensively examine the broad array of environmental 

processes affecting the Chugach coast. Instead, we aim to draw attention to several 

changes of which land managers and the public should be aware, and to identify 

issues for which more comprehensive and rigorous evaluation could be beneficial. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Chugach Seascapes

The coasts are among the most accessible and visited parts of the Chugach National 

Forest. Since its creation in 1907, a central focus of the Chugach has been to 

provide visitors with the opportunity to experience unrivaled landscapes, wildlife 

encounters, and recreational opportunities (USDA FS 2011). But because the high 

mountain ranges and inaccessible terrain constrain land transportation throughout 

the forest, the traditional point of access has been along the coast. Ferries, cruise 

ships, smaller private vessels, and aircraft provide access throughout the assessment 

area for residents and visitors (Poe and Greenwood 2010). Tourism, fishing, and 

recreation are the dominant uses of public lands in the assessment area (Poe et al. 

2010b), and a majority of this use, particularly when sightseeing from cruise ships is 

included, stems from the marine environment. 

To conceptualize changes occurring over such a vast and diverse coast, 

we divide the coastal ecosystem into three geographical areas, which we call 

“seascapes” (fig. 4-1). A seascape consists of more than just the beach—it is the 

capillary zone where upland forests transition to the ocean. The spatial extent of a 

seascape varies, but typically it is considered to be a large area in which conditions 

in the ocean and conditions on upland habitats affect each other. Each of the three 

seascapes has unique physical and biological characteristics, ecological processes, 

and human environments (LCCN 2014). The aesthetic quality of seascapes, and 

their enjoyment by visitors in ocean craft, result from characteristics of seascapes 

extending far inland (e.g., upland glaciers, forested mountainsides). 

The western seascape consists of the upper Kenai Peninsula, beginning in the 

high alpine peaks of the Chugach Mountains and extending 240 km southwest. The 

Kenai Peninsula is separated from the mainland of Alaska by Cook Inlet to the west 

and Prince William Sound to the east. The Kenai is widely recognized for its stunning scenery, world-class fishing, wildlife viewing, and outdoor recreation (USDA 

FS 2002). 

The central seascape consists of the western, northern, and eastern coasts of 

Prince William Sound, including the many islands within the Sound. This seascape 

is characterized by shallow straits, long fjords, protected bays, diverse tidal zones, 

and forested shores. The area is home to a diverse biological community that 

includes seabirds, shorebirds, fish (including all five Pacific species of salmon), 

sea otters, harbor seals, Stellar sea lions, orcas, and gray and humpback whales. 

Cruise ships of all sizes travel through the Sound, offering tourists the chance to see 

glaciers and wildlife. Commercial, sport, and subsistence fishing are all significant 

economic drivers of the Prince William Sound human environment (Harwell et al. 

2010, Jewett and Duffy 2007). 

The coasts are among 

the most accessible 

and visited parts of 

the Chugach National 

Forest. 
Figure 4-1—Geographic areas of south-central Alaska used to organize discussion of seascapes in the Chugach National Forest/

Kenai Peninsula assessment area.

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GENERAL TECHNICAL REPORT PNW-GTR-950

The northwestern portion of the Prince William Sound seascape sees the highest degree of human use on the Chugach itself, with most of the access via small, 

privately owned motorized boat. There is also commercial outfitter and guide 

activity that accesses forest land in this region by small motorized boat and kayak. 

This activity is authorized by the forest under special-use permit; in comparison to 

overall vessel use, these users represent only about 10 percent of the total small boat 

traffic in the region (Poe at al. 2010b). Subsistence harvest, primarily of fish and 

marine species, occurs throughout this region, but occurs most frequently in the 

vicinity of the communities of Cordova, Chenega Bay, Tatitlek, and Whittier (Poe 

et al. 2010a). Downloadable map galleries and datasets are available at http://www.

fs.usda.gov/detail/chugach/landmanagement/planning/?cid=stelprdb5139741. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

The eastern seascape consists of the Copper River Delta, which stretches across 

283 280 ha and drains an area of 68 635 km2. The delta is extremely large and 

one of the most significant wetland complexes on North America’s Pacific Coast. 

The delta seascape’s most distinct ecological features are its barrier islands, which 

create shallows that support large populations of marine invertebrates and provide a 

haul-out and nesting ground for marine mammals and birds. In spring, the delta is a 

globally significant staging ground for 16 to 20 million migratory birds (USDA FS 

2014). Nearly the entire Pacific Coast population of dunlins and western sandpipers 

rely on the delta for habitat (Bishop et al. 2000). Other important bird species in the 

delta seascape include knots, Canada geese, and swans (Isleib and Kessel 1973). 

In the following sections, we highlight some of the broad abiotic and biotic 

effects of climate change that are likely to alter the Chugach’s seascapes. 

Abiotic Effects of Climate Change

As outlined earlier in this assessment (chapters 2 and 3), the climate of south-central 

Alaska has warmed and is expected to continue warming during the next 20 to 40 

years, leading to higher winter and summer temperatures, reductions in snowpack 

at lower elevations; reductions in glacial mass, volume, and spatial extent; and a 

longer growing season (Haufler et al. 2010, Keyser et al. 2000, Larsen et al. 2007). 

As a result of the changing climate, abiotic conditions within the Chugach’s seascapes are expected to change, which in turn will affect seascape biological communities and human uses (Haufler et al. 2010). We focus on three important abiotic 

effects: sea-level change, glacial changes, and ocean acidification. 

Sea-Level Change

Alaska has more than 71 000 km of shoreline, more than twice that of the lower 

48 states combined (Glick et al. 2010). With such a large coast, Alaska as a whole 

is exposed to the potential consequences of long-term sea-level rise. But unlike 

coastal regions in much of the rest of the country, the coasts across the assessment 

area are unlikely to experience large sea-level rises (Clark et al. 1978, Dean 2009, 

Haufler et al. 2010). 

Despite the global trend of rising seas, the sea level in this region has been falling at rates of up to a fraction of an inch per year. The decreasing sea levels in the 

assessment area are largely the result of long-term patterns of isostatic rebound and 

geologic activity. The decrease is also caused, in part, by ocean circulation shifts in 

response to changes in wind stress at the eastern boundary of the North Pacific.

Isostatic rebound occurs when the melting of glaciers decreases the pressure 

exerted by ice on the land, triggering uplift in the land (Larsen et al. 2005). In the 

Despite the global 

trend of rising seas, 

the sea level in this 

region has been 

falling at rates of up 

to a fraction of an 

inch per year. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

assessment area, glaciers that dominate the seascapes have been diminishing in 

extent and mass for thousands of years (Heusser 1983, Jones et al. 2009), resulting in long-term uplift of the underlying land. Studies conducted on the Kenai 

Peninsula show regional isostatic rebound rates of 1.0 cm/year, and peak uplift 

rates for southeast Alaska exceed 3.0 cm/year. The rate of isostatic rebound for the 

Kenai Peninsula is three times faster than global average sea-level rise; throughout 

southeast Alaska, isostatic rebound is occurring ten times faster than sea-level rise. 

Geologic activity and resulting tectonic movement is also a major contributor to local changes in sea level in the region. South-central Alaska experienced 

dramatic tectonic movement associated with the historic 9.2-magnitude earthquake 

in 1964 (Haufler et al. 2010). As a result, some areas of the coast sank while others 

experienced significant uplift. Because the Chugach/Kenai region contains both 

areas of uplift and subsidence, local changes in sea level are likely to vary along the 

coastline. For example, the Copper River Delta is subsiding at approximately 1.2 

mm/year (Garrett et al. 2014). 

Although isostatic rebound and geologic activity have mitigated the effects of 

global sea-level rise in the Gulf of Alaska since the mid-1970s (Bromirski et al. 

2011), the trend toward stable or falling sea level may not continue in the extreme 

long term. Isostatic rebound slows once glaciers have melted substantially, tectonic 

uplift can be reversed by another major earthquake, and it is likely for the Northeast 

Pacific circulation to change again, all of which would result in a period of sea-level 

rise substantially faster than the global average (Bromirski et al. 2011). 

The potential consequences of changing sea level are especially apparent in the 

Copper River Delta. The delta seascape contains a large percentage of all the tidal 

marshes in south-central Alaska. Tidal marshes can occur wherever there is flat 

land at sea level (Frohne 1953). Three elements are required for tidal marsh formation: (1) the input of tidal waters, (2) sediment deposition, and (3) protection from 

ocean wave and ocean-current erosion (Boggs et al. 2008). Tidal marshes and the 

adjoining mudflats are one of Alaska’s most important habitats as staging areas for 

millions of migrating shorebirds, geese, and swans. The marshes and mudflats also 

support species like dusky Canada goose (Branta canadensis occidentalis), Western 

sandpiper (Calidris mauri), and dunlin (Calidris alpina), whose conservation status 

is of concern. 

Changes in relative sea level have a dramatic effect on tidal marshes and other 

coastal ecosystems. Along a subsiding coastline, tidal marshes may migrate inland, 

inundating formerly nontidal sites such as forests or peatlands. At the same time, 

tidal communities along an outer marsh may erode or drown completely. Tidal 
Figure 4-2—Coastal dunes on Egg Island, Copper River Delta, Alaska, illustrating vegetation stabilizing the dune structure.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

marshes where coastal areas are stable or subsiding appear to be some of the most 

vulnerable habitats to sea-level rise in the Chugach National Forest. 

Barrier islands within the assessment area may also be vulnerable to the effects 

of sea-level change. In other parts of Alaska, these islands are also threatened by 

coastal erosion and inundation as a result of changes in frequency and intensity 

of coastal storms (Meehan et al. 2012). Barrier islands are sandy coastal islands 

separated from the mainland by an estuary or bay (fig. 4-2). They are uncommon 

in southern Alaska and typically occur near large river deltas, such as the Copper River Delta (Boggs 2000, DeVelice and Juday 2007, Hayes and Ruby 1994). 

Although barrier islands are created by processes similar to those that created spits, 

they are unique in that barrier island separation from the mainland reduces access 

to their shorelines by predators such as brown bears and wolves. Consequently, barrier islands provide protected haul-outs for harbor seals, stopover feeding grounds 

for migrating shorebirds, and habitat for a variety of bird species, including the 

glaucous-winged gull (Larus glaucesens) and dusky Canada goose (Sowls et al. 

1978). 

M.Bishop
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GENERAL TECHNICAL REPORT PNW-GTR-950

The barrier islands of the Copper River Delta range up to 2 km in width and 13 

km in length and typically rise less than 9 m above sea level (Thilenius 1990) (fig. 

4-3). Sand and silt are delivered to the coast by the Copper River, where the sediment is transferred to the marine environment and deposited on the deltas. Longshore currents, which generate waves that strike beaches obliquely, move sediment 

parallel to these currents. Waves redistribute sediment across the beach profile, and 

wind erodes depositional features and transports the sand downwind. High-waveenergy environments suspend silt and transport it to lower energy depositional 

environments. Consequently, sand forms beaches and dunes along the high-energy 

seaward side of islands, and silt forms tidal marshes and tide flats along the leeward, low-energy estuary side of islands (fig. 4-4). 

During storms, portions of barrier islands and spits are often inundated and 

subjected to wave action known as overwash. Sand is transported from the beach 

and deposited further inland on the island or spit. Depending on the severity of 

the storm, overwash may affect the front portion of the landform or completely 

breach low portions. In the latter case, sediment is deposited on the back side of the 

landform as a washover fan (Ritter 1986). 

Distinct landform and vegetation patterns are common among barrier islands. 

Low-gradient beaches emerge from the ocean and transition to sparsely vegetated 

dunes, taller back dunes dominated by herbaceous vegetation, and wetlands in the 

low areas between the dunes. On the landward side of the tall back dunes, elevation tapers toward the estuary, where vegetation grades to uplifted tidal marshes, 

tidal marshes, and tide flats. Pioneer species such as American dunegrass (Leymus 

mollis) stabilize the sand with roots that penetrate more than 1 m to the water table 

(Boggs 2000, DeVelice and Juday 2007). Species and plant association diversity 

increases with dune stability. Herbaceous associations include fireweed (Chamerion 

angustifolium), beach strawberry (Fragaria chiloensis), American dune grass/

boreal yarrow (Leymus mollis/Achillea borealis), and Nootka lupin (Lupinus nootkatensis). 

Loss of barrier island habitat from climate-induced sea-level change is difficult 

to predict; projections must account for local trends of tectonic uplift and subsidence, the potential for seismic repositioning of the shoreline, and glacial rebound. In 

general, barrier islands represent dynamic habitats capable of repositioning, growing, and shrinking in response to changing conditions. 
Figure 4-3—Distribution of barrier islands along the Copper River Delta, Alaska.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Glacial Changes

The second major abiotic effect of climate change in the Chugach seascape is glacial change. Alaska’s glaciers are one of the main attractions for tourists in Alaska. 

Cruise ships and charter boats bring thousands of visitors to view tidewater glaciers 

each year. Glaciers provide remote recreational opportunities, including world-class 

ice and alpine climbing, skiing, and glacier trekking, while also serving an important ecological function in the Chugach seascape (Timm et al. 2014) (see chapter 3). 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Melting glaciers drive the Alaska coastal current, bring nutrients to the ocean, and 

control the hydrology of many river ecosystems (Astrom et al. 2014). 

Although a small number of Alaska’s glaciers are growing (their termini are 

extending), taken as a whole, the state’s glaciers are experiencing a loss of between 

40 and 70 Gt/year (Kaser et al. 2006). The first statewide survey of glacier volume 

change completed in 2002 estimated an ice loss of 21 km/year from the 1950s to the 

mid-1990s, and a rate of volume loss that is expected to double in the next 5 years 

(Markon et al. 2012). 

The most dynamic glaciers are the low-lying tidewater glaciers (Larsen et 

al. 2007). The coastal ecosystems created by the interface of glacier runoff and 

marine environment located at the terminus of tidewater glaciers result in highly 

productive, heterotrophic systems (Hood and Scott 2008). Changes in climate affect 

glaciers in complex ways. Melting of ice or accumulation of snow in either the 

accumulation zone or the terminus result in mass balance changes that influence 

most interactions of the glacier with the physical and biotic environment (O’Neel 

et al 2015). In the past, the pattern of glacier growth and decline, or modulation, 

appears to have occurred primarily at temporal scales of many decades to centuries 

for most glaciers, superimposed upon the millennium-scale dynamics of glacial 

and interglacial periods. Human-induced climate change affects this balance, and 

modeling (see chapters 2 and 3) shows the zone of accumulation pushed to higher 

elevations. At the highest elevations, precipitation increases substantially and temperatures stay below freezing for much of the year, thus it is possible for glaciers to 

increase in mass balance as a consequence of high accumulation of snow at upper 

elevations. The ultimate status of each glacier depends on the long-term outcome of 

the balance between accumulation at high elevations and loss at lower elevations. 

The Prince William Sound coastal region is particularly influenced by climate 

change because such a significant portion of its freshwater discharge, about 50 

percent, is derived from glacial runoff (Neal et al. 2010). The iron supplied by the 

glacial dust suspended in this freshwater discharge is critical to phytoplankton 

production in this region. Changes in the dynamics of this ecosystem have implications for nutrient delivery that affects primary productivity and subsequent fish, 

shellfish, and marine bird and mammal populations. Currently, krill and plankton 

thrive in glacier-dominated fjords; this productivity stems from the nutrient content 

of the sediment loads derived from upstream glacial action. Seabirds and harbor 

seals feeding at tidewater glacier termini show a high fidelity to glacial habitat 

because of the availability of food and sea-ice haul-outs to protect themselves from 

predators. 

Melting glaciers drive 

the Alaska coastal 

current, bring nutrients 

to the ocean, and 

control the hydrology 

of many river 

ecosystems. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

These same areas are also a focal point for marine recreation and tourism, 

particularly with respect to tidewater glaciers (Poe at al. 2010b), where higher levels 

of human use overlap with some species of conservation concern (Suring and Poe 

2010). Decreases in the biological productivity of these systems as they become 

less influenced by the presence of glacier ice will have significant implications for 

species and likely also for their desirability as recreation and tourism destinations 

(O’Neel et al. 2015). However, as glaciers retreat, they also open up new beaches 

and recently denuded terrain as new potential recreation locations; this has been 

observed in the vicinity of Columbia Glacier. 

In south-central Alaska, in contrast to areas that support continental glaciers, 

understanding the affect of climate change on glaciers is complicated by the fact 

that glaciers in different situations respond differently to the same regional changes 

in climate. Elevation, association with ice fields, and whether a glacier’s terminus 

is in tidewater affect how the glacier will respond to temperature and precipitation 

changes. 

Ocean Acidification

The third major abiotic effect of climate change affecting Chugach seascapes is 

ocean acidification. The world’s oceans play the dominant role in global dynamics 

of the carbon cycle through the uptake and chemical processing of carbon dioxide 

(CO2). Through biological and chemical processes, oceans absorb nearly a third of 

the CO2 emitted every year (Wackernagel et al. 2002). Although ocean capture of 

CO2 has buffered the terrestrial world from more significant atmospheric warming, 

CO2 in the oceans moves into a dynamic system involving both biotic and abiotic 

pathways, with a small portion being sequestered in sediments as plants or animals 

sink to the ocean floor and get buried. Most of the CO2 remains dissolved in the 

surface seawater, forming carbonic acid. Over the past 250 years, as atmospheric 

CO2 has increased, the pH of the ocean has decreased by 0.1 units (from 8.2 to 

8.1), corresponding to a 30 percent increase in acidity, with projected increases at a 

rate of 0.5 to 1.0 percent per year. Changes in acidity have major consequences for 

marine life by reducing the availability of carbonate ions that many marine organisms use to build shells and external skeletons. The colder temperatures of Alaska’s 

oceans make them particularly vulnerable to ocean acidification. The Arctic Ocean 

has had high concentrations of CO2 dissolved in it since historical times. This is due 

to physical processes such as cooling of the relatively fresh surface water, which 

causes this surface water to sink below the surface toward the bottom of the ocean. 

When this colder, denser water sinks, it sequesters atmospheric CO2 in the Arctic 

Ocean. This results in the Arctic Ocean having a much lower pH and lower concentrations of carbonate ions compared to other oceans of the world.

Through biological and 

chemical processes, 

oceans absorb nearly 

a third of the carbon 

dioxide emitted every 

year.
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GENERAL TECHNICAL REPORT PNW-GTR-950

The assessment area experiences an extremely complex pattern of ocean 

currents, freshwater input, and tidal movements. As a result, spatial and temporal 

variation in pH (and other physical and chemical features of the water) is extreme. 

Consequently, the organisms within Prince William Sound, along the Copper River 

Delta/marine interface, and along the Kenai coast experience high variability in 

physical and chemical conditions. It is difficult to predict how this inherent variability will interact with directional changes in pH to influence marine life over the 

short term. Over the long term, organisms will be exposed to more acidic conditions with resulting changes in food webs. 

Consequences to Biotic Communities

The abiotic changes in the assessment area are likely to affect biotic characteristics 

of the coastal ecosystem. This section highlights some of the effects of climate 

change on biotic communities within the Chugach seascapes. We focus on three: 

harmful algal blooms (HABs), which are an emerging threat within the Alaska 

coastal environment, changes to eelgrass beds, and effects on shorebird populations. 

Harmful Algal Blooms

Harmful algal blooms involving toxic phytoplankton have recently emerged as a 

threat to commercial and subsistence shellfisheries in parts of Alaska (Anderson 

et al. 2000). In addition to the potential influence of climate change, non-climate 

drivers like point and non-point pollution influence harmful algal blooms. 

There are several mechanisms by which climate changes could increase the 

threat of harmful algal blooms in the coastal areas of the assessment area. Warmer 

coastal waters, rapid melting of glaciers (resulting in iron-rich dust blowing into 

ocean waters that provides a critical micronutrient for algae), and ocean acidification may favor organisms that do not have calcium carbonate shells, including 

dinoflagellates and diatoms, over more beneficial types of plankton (Gao and 

Campbell 2013). 

Beyond the public health threat of HABs, health problems associated with 

phytoplankton have also occurred in humpback and right whales and a number of 

bird species, including northern fulmar, great cormorant, herring gull, common 

tern, common murre, Pacific loon, and sooty shearwater. Also associated with algal 

blooms is mortality of sea lions, seals, sea otters, dolphins, sperm whales, minke 

whales, and large numbers of birds, including grebes, gulls, cormorants, American 

avocets, loons, and sooty shearwaters. 

Harmful algal blooms 

involving toxic 

phytoplankton have 

recently emerged as 

a threat to commercial 

and subsistence 

shellfisheries in 

parts of Alaska.
Figure 4-5—Sea stars feeding on mussels in bed of eelgrass (Zoster sp.).

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Eelgrass Beds

Eelgrass (Zostera marina) appears in abundant meadows and beds throughout 

Alaska where sand and mud substrates occur in sheltered estuarine environments 

(fig. 4-5). Mundy (2005) described eelgrass as one of the primary sources of food 

in the northern Gulf of Alaska along with phytoplankton, macroalgae, and detritus. 

Macroalgae and eelgrass are the primary groups providing biomass to the nearshore zone, followed closely by shallow and deep infauna, deep epibenthos, and 

herbivorous zooplankton. The rich and varied eelgrass environment provides significant primary production and stability for sediments as well as varied substrate, 

cover, and food for invertebrates and vertebrates (Cowardin et al. 1979; Dean et al. 

1998, 2000; McRoy 1970). Within Prince William Sound—described by Harwell 

et al. (2010) as “a semi-enclosed fjord estuary on the southern coast of Alaska—

eelgrass beds are an important component of the nearshore ecosystem (Dean et 

al. 1998). The diverse biota supported by the extensive vegetation and associated 

detritus are comprised of microfaunal species (foraminifera, ciliates, and other 

protozoans) (Mundy 2005) and meiofauna (nematodes, harpacticoid copepods, 

and turbellarians) (Feder and Paul 1980a, 1980b). Some of the more recognizable 

macroinvertebrates include gastropods, bivalves, polychaetes, and amphipods living 

in and among the dense rhizome masses, and on and among the leaves (Jewett et al. 

David Ward, U.S. Geological Survey
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GENERAL TECHNICAL REPORT PNW-GTR-950

1999). Dean et al. (1998) reviewed the literature documenting the rich assemblage 

of invertebrates, fish, and birds that use eelgrass beds for food and shelter, including economically important species of crabs and fish. Hughes et al. (2014) provided 

an extensive list of the fish and key invertebrates that use eelgrass beds as nursery 

grounds at critical life stages. Johnson et al. (2003) considered eelgrass beds to be 

essential fish habitat that serves as nursery grounds both for salmon and key species 

of groundfish. 

Many shorebirds, sea ducks, and seabirds make use of the nutrition contained 

in eelgrass beds during different seasons. Perhaps the best-documented example 

is from the Izembek Lagoon located at Cold Bay on the Alaska Peninsula, where 

nearly the entire world’s population of Pacific brant and Taverner’s Canada geese 

(Ward et al. 1997) use the lagoon’s eelgrass meadows. Use of Prince William Sound 

eelgrass beds by birds is less documented, but the beds are no doubt used extensively by resident birds and by species making their way north and south on annual 

migrations. Marine mammals and land mammals also forage in eelgrass beds on 

abundant prey in certain locations. More specifically, sea otters and harbor seals 

forage in eelgrass beds (Ward et al. 1997), and, at low tide in the Izembek Lagoon, 

brown bears have been seen foraging in the exposed eelgrass beds.3

Potential consequences of climate change on eelgrass ecosystems may be 

inferred from past research. Potential stressors include changes in water temperature, salinity, pH, and depth. Studies conducted in Izembek Lagoon (Biebl and 

McRoy 1971) demonstrated remarkable resilience of eelgrass beds to broad fluctuations in temperature. Sharp declines in photosynthesis were not observed until 

temperatures of 30 to 35 °C were reached. Biebl and McRoy (1971) distinguished 

between subtidal and tidepool forms of eelgrass beds, with the tidepool form 

having a capacity to withstand 35 to 40 °C before photosynthesis declined. The 

study further indicated that, although eelgrass survived well in freshwater for up to 

10 days without visible damage, the plants died in 24 hours when exposed to four 

times the normal salinity of seawater (Biebl and McRoy 1971). Based on these studies, it appears unlikely that eelgrass beds in Prince William Sound will be severely 

affected by anticipated climate change effects of increased marine temperature (e.g., 

Abdul-Aziz et al. 2011) and changing salinities. 

Eelgrass favors the soft sediments of shallow, protected lagoons and is excluded 

from large river deltas and glacial fjords (Hall 1988). Changes in salinity resulting from either freshwater intrusion from melting glaciers or increasing salinity 

3 Ward, D.H. 2015. Personal communication. Research wildlife biologist, U.S. Department 

of the Interior, Geological Survey, Alaska Science Center, 4210 University Dr., Anchorage, 

AK 99508.

It appears unlikely 

that eelgrass beds 

in Prince William 

Sound will be severely 

affected by anticipated 

climate change effects 

of increased marine 

temperature and 

changing salinities. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

in areas that may be isolated and affected by rising temperatures may have little 

effect, owing to the high tolerance of the eelgrass to changes in both decreased and 

increased salinity. 

Less is known about the response of eelgrass to changes in pH. Changing pH 

will influence the broader eelgrass system; however, it is not yet clear what the 

effects will be on dominant fauna such as calcareous invertebrates or the softbodied invertebrates that inhabit eelgrass beds. The abundant invertebrates that 

use eelgrass for cover and food, and that are less tolerant to short- and long-term 

changes in pH, may respond most directly to climate change and indirectly influence eelgrass systems. 

Eelgrass is susceptible to changes in water depth. Water clarity affected by turbidity and depth of light attenuation affects the bathymetric distribution of eelgrass. 

Shaughnessy et al. (2012) reported the eelgrass depth range to be 0.9 to -1.6 m in the 

sheltered Izembek Lagoon with respect to the mean lower low water (MLLW). In 

a study conducted near Juneau, Alaska, Harris et al. (2008) found the distribution 

of eelgrass to be from 2.0 to -2.8 m. relative to MLLW. In Denmark, the historical 

distribution for eelgrass was 5.6 to 11 m depth, with the recent distribution reduced 

to between 2.5 and 8 m depth in sheltered and exposed areas, respectively (Baden et 

al. 2003). However, significant changes in effective sea level are not expected over 

the next 40 years as a result of climate change (see “Sea-Level Change” on page 

83). Should water levels rise or descend substantially in the assessment area, it will 

most likely be due to tectonic activity. Post-glacial rebound is likely to ameliorate 

significant relative sea-level rises in Alaska (NOAA Climate Program Office 2012). 

For the immediate future, submergence of the eelgrass beds to an unfavorable depth 

because of climate change effects is unlikely. 

The most significant and immediate threats to eelgrass beds are from more 

immediate anthropogenic sources such as dredging, vessel groundings and other 

disturbances, and pollution (Ward et al. 1997), although at least one example of 

eelgrass beds polluted by the Exxon Valdez oil spill demonstrated rather remarkable 

resilience and recovered relatively quickly (Dean et al. 1998). However, based on 

the studies following the oil spill, the species occurring with eelgrass are likely to 

be more susceptible to pollution within this habitat. It is quite possible that the community could be recolonized by zoochore and hydrochore dispersal of invertebrates 

and their larvae from within the Sound if the eelgrass beds remain largely intact. 

Eelgrass meadows and beds, with their apparent resilience, will likely continue 

to provide habitat and nourishment for the associated invertebrates, fish, birds, and 

wildlife that use the resource despite changes in marine conditions anticipated over 

the next 40 years. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Shorebirds

Shorebirds are a dominant ecological taxonomic group associated with the 

Chugach/Kenai seascapes, and they provide significant ecosystem services to 

people. The coastline of Prince William Sound, and particularly the Copper River 

Delta, is one of the most visited stopover locations for birds migrating to northern 

breeding grounds in spring. The Chugach/Kenai coastline is separated from the valleys of interior Alaska by sheer slopes of the Chugach and Kenai mountain ranges, 

and that restrictive topography combined with climate and biogeography make it 

an ideal place for shorebirds to feed and rest, owing to abundant food and feeding 

habitat at a critical location along the Pacific flyway migratory route. An estimated 

5 million shorebirds visit the Copper River Delta each spring, the largest concentration in the Western Hemisphere (Alaska Shorebird Group 2008). More than 73 different species of shorebirds—about a third of the world’s shorebird species—have 

been documented in Alaska, and most move through the Chugach/Kenai region.

Globally, shorebird populations have declined significantly since 2000 (Alaska 

Shorebird Group 2008). In Alaska, shorebirds face potential threats as well as 

positive changes to their habitats. These include sea-level change, glacial retreat, 

and associated changes in hydrology; uplifted marshes; increased storm activity; 

potential changes in food availability resulting from changes in ocean temperature; 

ocean acidification; and phenological shifts that may decouple food abundance 

from the arrival of the migratory birds. Key shorebird species of current conservation concern in the region, with further vulnerability compounded by climate 

change, include black oystercatcher (Haematopus bachmani), surfbird (Calidris 

virgata), red-necked phalarope (Phalaropus lobatus), western sandpiper (Calidris 

mauri), and red knot (Calidris canutus roselaari). 

Several anticipated climate-related changes could affect shorebird populations 

along the Chugach National Forest shoreline. One of the clear concerns for western 

sandpiper is the potential inundation of intertidal mudflats, which could eliminate 

both feeding and nesting habitat. Because of their low gradient and location at the 

transition from ocean to land, intertidal mud flats are susceptible to the slightest 

shift in sea level. A significant increase in sea level would inundate large areas 

of current mudflats. The availability of this critical habitat type, therefore, would 

depend on the rate of mudflat formation in areas currently existing as upland 

habitat. This interplay between sea-level rise and mudflat status is especially 

complex in the assessment area. Increased storm surges influence the dynamics 

of mudflats; large storms can inundate substantial portions of tidal mudflats, 

potentially reducing the food supply across large proportions of foraging areas. 

More than 73 different 

species of shorebirds—

about a third of the 

world’s shorebird 

species—have been 

documented in Alaska, 

and most move 

through the Chugach/

Kenai region.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Bird migration is also likely to be affected by climate change. Changes in ocean 

and air temperatures can cause phenological changes, prompting birds to arrive 

in their feeding stopovers when food is least abundant, which can lead to many 

shorebirds not making the final shift to their breeding grounds. Changes in ocean 

conditions such as shifts in temperature or acidity can also affect food availability 

in areas where it has previously been plentiful for shorebirds (Visser and Both 

2005). 

Fewer than 11,000 black oystercatchers are estimated to remain worldwide and 

over half are known to nest in Alaska, particularly concentrated around Prince 

William Sound (Morrison et al. 2006, Tessler et al. 2007). The species is listed as 

a species of high concern in the Alaskan, U.S., and Canadian shorebird conservation plans (Alaska Shorebird Group 2008, Brown et al. 2001, Donaldson et al. 

2000). Black oystercatchers exhibit strong breeding-site fidelity, which makes their 

reproduction particularly sensitive to environmental changes (Andres 1998) and to 

potential disturbance of shorelines by human use (Poe et al. 2009). Black oystercatchers nest in a restricted area between the high-tide line and coastal vegetation, 

or on islets just above high tide. Consequently, nests are vulnerable to surging 

storm tides. Other potential interactions with climate change include changes in 

abundance of dominant food species (mollusks and bivalves) that may decline in 

abundance if marine pH declines significantly. 

Following the Exxon Valdez oil spill, black oystercatcher populations were 

examined carefully because they appeared to be one of the species to recover most 

slowly (Andres 1997, Murphy and Mabee 2000). The life history of black oystercatchers, particularly low recruitment of young, suggests relatively long recovery 

periods following major mortality events. The long lifespan of the species provides 

a buffer against the loss of reproduction in particular years, with overall population 

growth most dependent on adult mortality. This large shorebird demonstrated its 

resilience to the major ecological disturbance following the Exxon Valdez spill. Furthermore, the species demonstrated an ability to disperse into, occupy, and increase 

in new habitat following the development of open shore habitat on Middleton Island 

resulting from the 1964 earthquake (Gill et al. 2004). 

Several of the many species of shorebirds that use the region for migratory 

stopover habitat are worth highlighting: these include the surfbird, red-necked 

phalarope, western sandpiper, and red knot. The surfbird also has a relatively 

small global population estimated at 70,000 (Senner and McCaffery 1997). More 

than three-fourths of all surfbirds breed in Alaska, north of the assessment area 

(Senner and McCaffery 1997). Prince William Sound coastline provides one of the 

most important staging grounds for surfbirds during spring migration. Migrating 
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GENERAL TECHNICAL REPORT PNW-GTR-950

surfbirds depend on herring spawn and mollusks for food during critical stopovers 

during their long migration along the Pacific coast of North and South America 

(Brown et al. 2001). Both of these prey types underwent steep declines in availability after the Exxon Valdez oil spill (e.g., Jewett et al. 1999, Shigenaka 2014, 

Thorne and Thomas 2008). Pacific herring spawning around Prince William Sound 

historically has occurred in mid-April when water temperatures reach around 4 °C 

(Cooney et al. 2001). The eggs are attached to underwater vegetation near shore. 

Once deposited, many herring eggs experience immediate mortality from heavy 

wave action and smothering (Cooney et al. 2001). An increase in large waves from 

storm surges could increase herring spawn mortality. Studies have shown that the 

majority of egg deposition in Prince William Sound occurs around northern Montague Island and a few eastern and northern sites (Norcross et al. 2001). In a study 

completed on northern Montague Island in 1994, scientists found that surfbirds 

accounted for a large percentage of the birds observed in the herring spawning 

areas, and their arrival occurred shortly after spawning (Bishop and Green 2001). If 

the herring spawn earlier, triggered by warmer ocean temperatures, surfbirds and 

other migratory herring-spawn feeders in this area, like black turnstones (Arenaria 

melanocephala), must adjust their migration timing to take advantage of this food 

source. The potential phenological mismatch is made especially possible by herring 

spawn’s short incubation period of just 2 to 3 weeks (Norcross et al. 2001). Phenological plasticity in surfbirds and other shorebirds is not well understood, especially 

as it relates to the timing of migratory stopovers. Potential responses to changes 

in the timing of stopover food availability include shifts in the timing of migration 

by entire species, broadening of the range of migration dates within species, and 

changes in bird abundance resulting from changes in migratory stopover food 

availability. Any shift or variability in herring spawning dates could lead to variability in the areas used by surfbirds during migratory stopover and in the timing 

of stopovers, reducing their peak numbers. 

Red-necked phalaropes are Holarctic breeders with populations that nest on the 

Copper River Delta (Brown et al. 2001). The breeding status of these birds makes 

them stand out among shorebirds in the region. These shorebirds use marine areas 

during migration and staging, and freshwater ponds of the delta during breeding. 

There is no clear estimate of the population breeding on the delta, but a majority of 

ponds have at least one and up to 10 pairs breeding on the pond shores. The birds 

feed on freshwater invertebrates immediately prior to and during the breeding 

season. Freshwater invertebrates will be more sensitive to near-term increases in air 

temperature and changes in precipitation than marine invertebrates. Therefore, red 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

phalaropes may be more exposed to changes in the phenology of invertebrates than 

will other shorebirds in the region. 

Although Western sandpipers have a large population of an estimated 3.5 million birds, there are concerns that the species is declining (Farmer and Wiens 1999, 

USSCP 2004). Over a million western sandpipers use the Copper River Delta as a 

migratory stopover in spring (Bishop et al. 2000). An abundance of food and secure 

resting habitat is critical to assist western sandpipers in reaching their breeding 

grounds in western Alaska. Little is known about the distribution of the Alaskan 

population of red knots; however, it is suspected that the entire population uses 

the delta during migration. Red knots use the mixed sand/mud areas of the barrier 

islands of the delta. Although these areas may be susceptible to increases in storm 

intensity and frequency, it is unclear whether the spatial extent of barrier islands 

is likely to change substantially over time. In the 1970s, red knot numbers were 

estimated to be about 40,000 on the Copper River Delta (Isleib and Kessel 1973). 

Currently the C. c. roselaari subspecies of red knot is estimated at below 20,000, 

which would indicate they are undergoing a significant decline. 

Conclusions

The coastal ecosystem is an important part of the Chugach/Kenai assessment area, 

and policymakers and scientists are learning more about the potential effects of 

global climate change on the seascapes of the region. Abiotic factors, particularly 

glacial changes and ocean acidification, are likely to affect the assessment area 

(see chapter 5 for more details). Major biotic effects of climate change, including 

harmful algal blooms, changes to eelgrass beds, and consequences for shorebirds 

will change and influence other biota and users of the assessment area. Perhaps the 

most compelling conclusion from this chapter, one that does not arise directly from 

the review, is the high uncertainty involved in developing scenarios for seascape 

futures. In particular, the Chugach/Kenai region—where substantial glacier change 

(see chapter 3) influences both sea level and isostatic rebound, and influences freshwater hydrology but also marine currents and chemistry—is particularly difficult to 

imagine into the future. Uncertainty, a ubiquitous partner in any climate assessment 

(see chapters 1 and 3) is particularly apparent for this dynamic region, where land 

meets sea and the dearth of understanding of marine ecosystems becomes apparent 

to managers of terrestrial and freshwater systems. 

Over a million western 

sandpipers use the 

Copper River Delta as 

a migratory stopover 

in spring.
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GENERAL TECHNICAL REPORT PNW-GTR-950

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Sockeye salmon (Oncorhynchus nerka), Tern Lake, Kenai Peninsula, Alaska.

Ron Neilbrugge
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Chapter 5: Salmon

Mark Chilcote,1 Angela Coleman,1 Steve Colt,2 Peter Kirchner,3 Gordie Reeves,4

Dan Rinella,5 Eric Rothwell,6 and Steve Zemke1

Summary

• About 15 percent of the world’s Pacific salmon (hatchery and wild fish

combined), originate in the assessment area.

• Production of wild and hatchery salmon from the south-central Alaska

assessment area supports commercial fisheries with a $587 million economic impact and 7,944 jobs to Alaska.

• Of the 2.5 million days of recreational fishing by anglers in Alaska, 72 percent of this effort occurred in south-central Alaska, with salmon being the

primary target species.

• Across the south-central Alaska assessment area, 720 watersheds were classified based on two variables, percentage of glacial cover and an index of

spring snowpack (i.e., the proportion of October–March precipitation that

remains as snowpack on April 1).

• Based on climate scenarios for the next 60 years, the snowpack indices for

watersheds at lower elevations are projected to decline because of warmer

temperatures. In contrast, snowpack indices in higher elevation watersheds

are expected to increase because of increased precipitation.

• Sixty-one of the 720 watersheds (8.5 percent) were classified as being vulnerable to climate change because of projected reductions in snowpack over

the next 60 years.

• The productivity for 58 percent of the 234 salmon populations analyzed for

recruitment performance increased with warmer air temperatures. For the

remaining populations (42 percent), warmer temperatures were associated

with the opposite population response, reduced productivity. Overall, pink

1 Mark Chilcoate is a fish biologist, Andrea Coleman is a hydrologist, and Steve Zemke is 

a fish biologist (retired), U.S. Department of Agriculture, Forest Service, Alaska Region, 

Anchorage, AK 99504.

2 Professor of economics, Institute of Social and Economic Research, University of Alaska, 

Anchorage, AK 99503.

3 Physical scientist, U.S. Department of the Interior, National Park Service, Southwest 

Alaska Inventory and Monitoring Network, Anchorage, AK 99501.

4 Research fish ecologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Corvallis, OR 97331.

5 Program aquatic ecologist, Alaska Natural Heritage Program, Alaska Center of Conservation Science, University of Alaska, Anchorage, AK 99503.

6 Hydrologist, Bureau of Reclamation, Pacific Northwest Region, Boise, ID 83706.
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salmon production in Prince William Sound is projected to increase by 

26 percent over the next 70 years in association with a warming scenario; 

however, the projected responses for individual populations were highly 

variable. 

• Results from population dynamic modeling for chum salmon project a

decrease in numbers under a warming scenario; however, there was a very

high degree of uncertainty associated with this finding.

• An analysis of the number of fish caught in fisheries and counted in

streams found that, for seven of the eight groups of salmon evaluated,

warmer temperatures in past years were associated with more fish.

• Should apparent linkages between temperature found with fishery catch

and total run-size continue into the future, we speculate that warming conditions brought on by climate change would result in more salmon in the

study area, at least for the next 50 years.

• For the two resident species examined, Dolly Varden char and rainbow

trout from the upper Kenai River, no association was found between air

temperature and fish abundance.

Introduction

Salmon are a key feature of the biological, social, and economic landscapes of 

south-central Alaska, and hence are a defining ecosystem service for this region. 

For example, from 2009 to 2013, the return of hatchery and wild salmon to study 

area watersheds has averaged 99.3 million per year based on an analysis of fishery 

harvest and impact rates estimated by the Alaska Department of Fish and Game 

(ADFG) (see app. 3). To put this in a Pacific-wide context, we estimate the total 

production of Pacific salmon to be 654 million hatchery and wild salmon per year 

based on our analysis of information provided by Ruggerone et al. (2010), thus 

study-area salmon production represents about 15 percent of the total (appendix 3). 

As a point of clarification, we use the term “hatchery” salmon to refer to fish that 

begin life in a hatchery and that are released into the wild after growing for a period 

of time in that hatchery environment. 

The economic impact of commercial salmon fisheries to this region is $587 

million annually with an associated 7,944 jobs (appendix 3). Of the 2.5 million days 

that residents and nonresidents spend sport fishing in Alaska, about 72 percent of 

this activity occurs within south-central Alaska, much of it devoted to fishing for 

salmon (Southwick Associates et al. 2008). Bowker (2001) found that Alaskans 

devoted more trips per capita to fishing as the primary purpose of a trip than to any 

other outdoor activity. In addition to the recreational and commercial importance of 
lakes where they are born and eventually die, the other being the ocean where they rapidly grow. 

there is considerable variability, both in terms of the amount of time spent in the two environments 

and in terms of how many years it takes to complete their life cycle. (Illustration courtesy of Su Kim, 

NOAA Fisheries.)

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

fishing, the unique existence of subsistence and personal-use fisheries in Alaska are 

additional evidence that salmon play a fundamental cultural and food supply role 

for many residents of the state. Salmon are a symbol of regional pride. 

In addition to their economic and cultural importance, salmon are an interesting group of fish to evaluate with regard to climate change. Unlike many fish species, salmon are born in freshwater, migrate to the ocean where they grow to adult 

size, then return to freshwater to spawn and die (fig. 5-1). Given that the effects of 

climate change are likely different between freshwater and marine environments, 

a species whose life history exposes them to both environments should be an 

In Alaska, salmon 

are a symbol of 

regional pride. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

informative indicator of the net impact of climate change across a diverse range of 

ecological conditions. In addition, there is considerable variation among salmon 

species with regard to the amount of time needed in freshwater and marine environments to complete their life cycle (fig. 5-2).

Figure 5-2—Pink salmon have the shortest life history among Pacific salmon; only 2 years occur 

from egg to spawning adult. The other four species of Pacific salmon require two to three times as 

long to complete their life cycle. In terms of the relative time spent in fresh water, coho have the 

greatest freshwater component (62 percent), whereas chum salmon have the least (17 percent). There 

are significant ecological differences among these species as a result of this life-history diversity. 

7

6

5

4

3

2

1

0

Years for life cycle

Species

Pink Chum Sockeye Coho Chinook

Marine

Fresh water— juvenile

Fresh water— spawn

Overview of Climate Change Impacts on Salmon

Climate change is bringing about changes to both the freshwater and marine 

environments that sustain salmon. In general, the response of salmon is complexly 

intertwined with innate capacities for adaptation through the combined mechanisms 

of phenotypic plasticity and genetic change, which provide resilience (Bryant 2009, 

Crozier et al. 2008, Ficke et al. 2007). Here we review the important environmental 

changes resulting from climate change affecting salmon in the assessment area.

Within the freshwater portion of the salmon life cycle, increases in water 

temperature, which are the result of precipitation falling as rain rather than snow 

as well as warmer air temperatures, are thought to potentially affect salmon in a 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

variety of ways. These include earlier hatching of fry because of warmer incubating 

temperatures (Holtby 1988, Schindler and Rogers 2009); increased growth rate and 

younger age of smoltification for those species with an extended freshwater-rearing 

life history (Bryant 2009, Rich et al. 2009, Russell et al. 2012); increased metabolic 

rates that make survival more difficult during seasonal periods of low food supply (Mangel 1994, Mantua et al. 2010); and earlier downstream migration that 

may result in a mismatch with optimum initial survival conditions in the marine 

environment (Crozier et al. 2008). Because the winter-period water temperatures in 

most of the streams in the study area are only slightly above freezing, even small 

increases in water temperature will substantially increase the rate of development 

for incubating eggs. This could result in the timing of hatching and fry emergence 

being shifted up to a month earlier and possibly being no longer paired with the 

timing of optimal conditions that affect subsequent survival of young. 

Streamflows are expected to increase during the time of year when salmon 

eggs are incubating in gravel spawning beds (Battin et al. 2007, Shanley and Albert 

2014). Such changes may be adverse if physical scouring of gravel and the associated mortality of incubating salmon eggs is proportional to the size and frequency 

of anticipated high-flow events (Goode et al. 2013, Leppi et al. 2014). However, 

the magnitude of such impacts on salmon are likely influenced by other factors as 

well, such as sediment input (Neupane and Yager 2013), geomorphic features of the 

channel (McKean and Tonina 2013), basin geology, and the structure of the stream 

network (Tague and Grant 2009, Tague et al. 2008). For example, McKean and 

Tonina (2013) found that the risk of scour from potentially higher flows was limited 

because of the geomorphic setting (low gradient and wide valley) areas where 

salmon spawn. Ice scouring of salmon spawning sites, with the associated loss of 

incubating eggs and pre-emergent fry, occurs under present conditions in Alaska 

(Doyle et al. 1993). It is possible with warming conditions and less ice formation 

during winter months that this impact would be lessened. A shift of stream water 

supply from snow to rain because of warming would result in less flow during 

spring and more flow during winter months. This could reduce access to some 

streams by adult salmon on summer spawning migrations. However, it is possible 

that increased flows during winter might increase the availability of overwinter 

habitat and enhance the survival of juvenile salmon during this life-history period. 

Studies from the Pacific Northwest predict the loss of snowpack resulting from 

climate change to increase salmonid growth in streams that are currently cold and 

to decrease salmonid growth in streams that are currently warm (Beer and Anderson 2011, Crozier and Zabel 2006). In Alaska, given its inherently colder climate 

and cooler stream temperatures, we would generally expect the growth rate of 
112

GENERAL TECHNICAL REPORT PNW-GTR-950

juvenile salmon to increase with moderate climate warming. However, simulations 

for coho salmon in south-central Alaska’s Chuitna River demonstrate the complexity inherent in predicting climate change impacts, even for a single species within a 

single watershed (Leppi et al. 2014). In this study, coho populations within the three 

subwatersheds evaluated were projected to increase or decrease depending on each 

subwatershed’s habitat conditions and the specifics of the climate change scenarios 

examined. Similarly, Kovach et al. (2014) found that the observed response of two 

salmonid species to 31 years of climatic variation was not the same even though 

they occurred in the same habitat. Kovach et al. (2014) made the point that growing empirical evidence from other studies indicates that climate change can have 

dramatically different consequences among salmonid populations, even those that 

are geographically located near each other.

Climate change will also affect the marine environment in ways that are 

expected to affect salmon. However, the assessment of these impacts is complicated 

by naturally occurring cycles in ocean conditions associated with ocean temperature and atmospheric patterns (Johnstone and Mantua 2014). Periods of relatively 

warmer sea-surface water temperatures, expressed as positive values for the Pacific 

Decadal Oscillation (PDO) index, are associated with higher numbers of Alaskan 

salmon (Mantua et al. 1997). However, this association is not uniform across all 

populations of northeast Pacific salmon. For example, positive PDO values (warmer 

conditions) are associated with fewer salmon returning to the watersheds of Washington and Oregon, the reverse of what has occurred for Alaskan stocks (Hare et al. 

1999).

Overall, the impacts of climate on the marine environment are thought to have 

contributed to relative increases in pink (Oncorhynchus gorbuscha) and chum 

(Oncorhynchus keta) salmon, and to decreases in coho (Oncorhynchus kisutch) and 

Chinook (Oncorhynchus tshawytscha) salmon observed in the Pacific Ocean during 

the past 80 years (Irvine and Fukuwaka 2011). However, these changes are difficult 

to detect at a local level because of the presence of unrelated natural cycles of low 

and high production that are typical of most salmon populations and which often 

occur at decadal or longer time periods (Rogers et al. 2012). Further, these production cycles may be asynchronous among regions, species, and populations (Beamish 

2012, Noakes and Beamish 2009). 

Against this backdrop of the recent past, climate change is expected to push 

certain environmental parameters, such as sea surface temperatures in the North 

Pacific Ocean, above the range of previously observed variability by mid-century if 

not sooner (Overland and Wang 2007). Although such changes may benefit Alaskan salmon initially, it is difficult to predict the long-term consequences of these 

Climate change can 

have dramatically 

different consequences 

among salmonid 

populations, even 

those that are geographically located 

near each other.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

unprecedented ecological conditions. For example, Pierce (2004) modeled the possible changes in zooplankton and phytoplankton dynamics associated with climate 

change and concluded that, by 2090, some regions of the North Pacific will lose 

their traditional springtime plankton bloom, to be replaced by even production yearround. This could have an adverse impact on pink and chum salmon smolts, whose 

transition to the marine environment is timed to capitalize on plankton blooms. 

Alternatively, this consistent food availability may make the timing of the marine 

transition less critical, which could benefit salmon if warmer conditions shorten the 

time needed for growth and development during the freshwater stage.

Another factor relates to changes in spatial extent and location of the conditions 

preferred by different salmon species in the marine environment. Abdul-Aziz et al. 

(2011) examined this question by using the A1B (medium) emissions scenario. They 

projected that high-seas habitat for the five salmon species would decrease by as 

much as 86 percent for Chinook salmon to as low as 29 percent for chum salmon by 

2100. Downscaling these broad scenarios to potential impacts on specific salmon 

populations is not possible. Results of this modeling do suggest that a northward 

shift in high-seas distribution of salmon is likely. Adbul-Aziz et al. (2011) also 

speculated that this shift in thermal conditions may favor the northerly spread of 

some other species and life histories such as steelhead (anadromous form of rainbow trout [Oncorhynchus mykiss]). 

In addition to sea temperatures, other factors affected by climate change 

influence salmon growth and survival in the marine environment. These include 

changes in salinity, ocean currents, prey quality, interspecific competition, 

predator-prey relations, and acidity. Of these, the expectation that increased anthropomorphic CO2 emissions will cause increased ocean acidity is one of the greatest 

concerns (Mathis et al. 2014). Since preindustrial times, acidification of the world’s 

oceans has increased by 30 percent likely because of anthropomorphic causes 

(Mathis et al. 2014). As atmospheric CO2 increases, some of the most dramatic 

increases in acidity are expected in Arctic and subarctic regions (Steinacher et al. 

2009). These changes are anticipated to have an adverse impact on marine ecosystems, particularly for organisms like mollusks and crabs that must form calcareous shells (Mathis et al. 2014). For example, acidic conditions are lethal to some 

petropods (planktonic mollusks), causing the complete dissolution of their shells 

in 48 hours (Orr et al. 2005). Petropods are an important food source for juvenile 

pink salmon, comprising up to 60 percent of their diet in some years (Armstrong et 

al. 2005). Should petropods be adversely affected by ocean acidification and not be 

replaced by other suitable prey, their disappearance would likely cause a decrease in 

the numbers of pink salmon and perhaps other salmon species as well. 
Figure 5-3—Number of wild pink salmon returning to Prince William Sound from 1962 to 2010 (solid line) and 

the average of monthly values for the Pacific Decadal Oscillation (PDO) from November through March for the 

years 1960 to 2010 (dashed line); both are expressed as 5-year moving averages. 

114

GENERAL TECHNICAL REPORT PNW-GTR-950

Salmon Diversity and Production Cycles

Salmon abundance, and therefore their relationship with people, changes over 

both short and long time scales. To illustrate this point, over a 50-year period the 

numbers of wild pink salmon returning to Prince William Sound streams have 

experienced highs and lows, but without any apparent long-term trend in population 

size (fig. 5-3). Particularly from 1960 to 1990, this pattern closely tracked values for 

the PDO. Although associations between PDO and salmon population cycles such 

as these have been well documented in a variety of other studies (Hare et al. 1999, 

Mantua et al. 1997), the mechanism behind this association is not well understood, 

nor are the other interactions involved in regulating salmon production (Rogers 

et al. 2012). Regardless of the mechanisms driving salmon production cycles, the 

important message is that such variations in salmon populations are a natural 

feature of their biology.

25

20

15

10

5

0

1.5

1.0

0.5

0

0.5

-1.0

-1.5

Number of pink salmon (millions)

PDO index

1960 1970 1980 1990 2000 2010

Year

Pink salmon

PDO
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Although pink and chum salmon have similar life histories, the other three 

salmon species in this area (chinook, coho, and sockeye [Oncorhynchus nerka] 

salmon) are not only quite distinct from each other, but also quite different from 

pink and chum salmon. This wide diversity in life histories among salmon species 

means that variations in abundance will be controlled by different factors (Bryant 

2009). 

For example, pink salmon migrate directly to the marine environment after 

they hatch in fresh water. In contrast, coho salmon spend from 1 to 3 years in fresh 

water, growing to a much larger size, before they migrate to the ocean. Once in the 

ocean, their pattern is different as well. Pink salmon rear for a little more than 1 

year before returning to spawn invariably as 2-year-olds, and coho spend from 1 

to 2 years at sea before returning to spawn in mixed age classes. Such differences 

mean that coho will be more sensitive to year-round habitat quantity and quality in the freshwater environment, which will control how many coho smolts are 

produced (Bisson et al. 2009). In contrast, because pink salmon enter the marine 

environment as a very small and vulnerable fish, near-shore abundance of food 

and predators will be major factors influencing their survival and subsequent adult 

run-size (Armstrong et al. 2005). 

In addition to the diversity that exists among salmon species, considerable 

variation occurs among populations of each species and among individuals within 

any population. Relative to other bony fishes, salmon exhibit a large degree of 

genetic and phenotypic diversity (Crozier et al. 2008, Schindler et al. 2010, Waples 

1991) and can adapt to changing conditions rapidly (Healey and Prince 1995, Quinn 

et al. 2001). This genetic and phenotypic diversity has facilitated persistence of 

salmon in highly dynamic environments (Waples et al. 2009) and is a key to their 

persistence in the future (Mangel 1994). Conceptually, climate-related changes 

will likely intensify the dynamic nature of the environment that salmon occupy, 

and maintaining adaptive capacity may require an even higher level of within- and 

among-population diversity. Unfortunately, knowledge of genetic variation and 

the ability of natural populations to respond adaptively to environmental change is 

limited, and the rate and extent of climate change is uncertain (Geinapp et al. 2008). 

Despite this lack of knowledge, we can say that this region’s salmon are probably better prepared to adapt to climate change because they have less exposure to 

additional stressors of habitat degradation and fragmentation that have contributed 

to the depletion of many populations to the south (Nehlsen et al. 1991). The region’s 

watersheds have received relatively little impact from such activities as logging, 

mining, agriculture, stream channelization, water pollution, and damming, and, as 

Genetic and phenotypic diversity has 

facilitated persistence 

of salmon in highly 

dynamic environments 

and is a key to their 

persistence in the 

future. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

a result, they retain an assortment of complex and accessible spawning and rearing 

habitats in a relatively intact state. This watershed complexity ranges from small 

streams and ponds to large rivers and lakes, and is complemented by a diversity 

of hydrologic and thermal regimes associated with different water supplies (e.g., 

glacial, snow-dominated, transitional, spring-fed). 

This habitat diversity, in turn, supports a high level of population and lifehistory diversity that contributes to the resilience of a region’s salmon and the 

fisheries that rely on them (Schindler et al. 2010). For example, sockeye salmon in 

the Bristol Bay region have adapted to a similar range of habitats by optimizing 

behavioral and physiological traits like timing of spawning, egg size, and the size 

and shape of spawning adults, resulting in a stock complex comprised of hundreds 

of distinct spawning populations, each adapted to its own spawning and rearing 

environment (Hilborn et al. 2003). Superimposed on this localized adaptation is 

variation within each spawning population, such as differences in the number of 

years spent rearing in fresh water and at sea, resulting in a high degree of biological 

complexity (Hilborn et al. 2003, Schindler et al. 2010). These layers of biocomplexity lead to asynchronous patterns of productivity among the various stocks because 

differences in habitat and life history lead to different population responses despite 

exposure to the same prevailing environmental conditions (Rogers and Schindler 

2008). For example, a year with low streamflows might negatively affect populations that spawn in small streams but not nearby populations that spawn in lakes 

(Hilborn et al. 2003). 

The resilience afforded by biocomplexity can break down when habitats are 

degraded or when the genetic diversity that allows salmon to use the full complement of available habitats is diminished. The loss of habitat diversity and associated 

loss of population diversity has contributed to declines of once-prolific salmon fisheries, including those in the Sacramento (Lindley et al. 2009) and Columbia Rivers 

(Bottom et al. 2005, Moore et al. 2010). Although there are specific areas of human 

impact on salmon habitat within the assessment area, they are minor compared to 

the much larger area in which the habitat has not been altered.

The influence of hatchery-produced salmon, particularly pink salmon, on 

the resilience of local populations is unknown. Hatchery fish mixing with wild 

populations, as has been noted by Brenner et al. (2012) for certain pink and chum 

salmon populations within Prince William Sound, could homogenize and disrupt 

the adaptive genetic character of natural salmon populations. How much such an 

impact could affect the capacity of these populations to respond to climate change 

is unknown.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Components of Salmon Vulnerability Assessment 

In this chapter, we present three analyses that examine the character and scale of 

environmental changes on salmon. The findings from these analyses expand existing understanding of the impact of climate change on salmon populations in southcentral Alaska. Collectively, this information provides an informed perspective, or 

scenario, regarding the potential ecological future for salmon in this region to guide 

development of adaptation and management strategies. The evaluations presented 

here were developed by a group of managers and scientists (hereafter identified as 

the Salmon Team).

The three analyses presented here—watershed vulnerabililty, salmon population dynamics, and fish runs and fisheries—are not a comprehensive assessment of 

the impact of future climate conditions on salmon, but are intended to supplement 

existing and future information. They present tangible findings from three analytical approaches that to our knowledge represent the first applications of such methodologies to the study area and to associated physical and biological datasets. 

Watershed vulnerability identified vulnerable watersheds based on potential 

changes in hydrology such as streamflow and water quality (i.e., temperatures and 

turbidity) resulting from climate change as tied to dominant water delivery processes (snow, rain, glaciers). Based on patterns of snowfall from a projected climate 

scenario, the Salmon Team identified watersheds in the region that would have the 

greatest chance of experiencing significant change in hydrology and associated 

processes. In particular, the expectation was that some watersheds would have 

higher winter flows and a diminished snowpack because of more winter precipitation falling as rain vs. snow. Further, associated spring runoff was expected to start 

earlier and be lower in volume as snowpack levels are reduced. Water temperatures 

would be expected to increase to varying degrees year-round. Hydrologic changes 

along these lines have already been documented in snowmelt-dominated watersheds across western North America, including coastal Alaska, where the timing of 

snowmelt-derived streamflow has advanced by up to a month during the latter half 

of the 20th century (Stewart et al. 2005). 

To assess salmon population dynamics, the team examined existing data 

on salmon population abundance to develop insights into potential responses of 

salmon to climate change over the next 50 years. This analysis examined 30 years 

of salmon return records to determine if a relationship existed between annual 

air temperature and annual salmon production. After the team found evidence for 

such a relationship, air temperatures expected under global warming were used to 

develop a scenario for salmon production in the future.

Based on patterns 

of snowfall from a 

projected climate 

scenario, the Salmon 

Team identified 

watersheds in the 

region that would 

have the greatest 

chance of experiencing 

significant change 

in hydrology and 

associated processes.
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GENERAL TECHNICAL REPORT PNW-GTR-950

The third analysis, fish runs and fisheries, was an outgrowth of the second. 

This analysis responded to the lack of detailed population-by-population data for 

many salmon species across much of the evaluation area. Although the spawner and 

recruit abundance data needed to apply the salmon population dynamics methodology (the second analysis) were available for pink and chum salmon populations of 

Prince William Sound, comparable recruitment data for coho, chinook, and sockeye 

salmon were extremely limited, and, for Dolly Varden char (Salvelinus malma) and 

rainbow trout, virtually nonexistent. 

Although pink and chum salmon represent a large portion of the region’s fisheries resource, contributions from the other species are also important. Therefore, 

to inform our evaluation for coho, chinook, and sockeye, we examined annual 

estimated catch or total run-sizes from a broader geographic area. Unlike the case 

for recruitment data, a time series of catch and run-size estimates are available 

for many of the recreational and commercial fish species in the study area. The 

shortcoming of these data is the large statistical area (e.g., Prince William Sound) 

sampled and therefore the limited geographic resolution for annual production. 

Each analysis covers many different populations. As a result, biologically important 

variability among individual populations cannot be measured. 

For this third analysis, we performed a retrospective evaluation to determine 

whether annual variations in fish numbers corresponded with past variations 

in climatic indices related to air temperature. Our objective was to determine if 

there was a relationship between numbers of fish and conditions associated with 

warmer temperatures, and whether this relation, if found, was positive or negative. 

Our intent was to use the results as a basis to develop future scenarios of trends in 

salmon abundance.

Watershed Vulnerability 

Temperature and flow regimes are important attributes of salmon habitat, and 

both are expected to change in response to climate change. As a framework for 

evaluating climate-related changes in salmon habitat across the Kenai/Chugach 

region, we developed a spatially specific watershed classification system based on 

the dominant source of water in a given watershed. We used sixth-level watersheds 

(hydrologic unit code [HUC] 12) as our unit of classification. The sixth-level watershed boundaries are based on the Watershed Boundary Dataset. This system is tied 

to key watershed factors in a manner that facilitates the evaluation of changes in 

air temperature and the amount and timing of precipitation under various climate 

change scenarios. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

The watershed classification system we devised was based on two variables: (1) 

the proportion of the HUC that is covered by glaciers, and (2) a snowpack index, 

expressed as the proportion of total precipitation falling from October through 

March that is entrained in snowpack on April 1. The proportion of the watershed 

covered by glacial ice was selected as an indicator of the relative impact of glaciers 

on a watershed. Although the impact of glaciers on the hydrology of a watershed is 

most directly influenced by glacier volume relative to watershed size, estimating 

glacier volume is very difficult. For example, Synder (1993) found that 50 percent 

of the runoff in the Tanana watershed is produced by glaciers, which themselves 

occupy only 5 percent of the watershed area. For our analysis, we used glacial area 

because of the challenge of measuring volume, and assumed that glacial area served 

as a proxy for the functionally more accurate metric of glacial volume. We classified watersheds into three categories based on glacial cover: glacial when greater 

than 10 percent of the watershed area is functionally glacier-covered, transitional 

glacial when 1 to 10 percent of the watershed area is glacier-covered, and clearwater when less than 1 percent of the watershed area is covered by glaciers. As 

described by Pelto (2008), watersheds with greater than 10 percent of their area 

covered by glaciers function in a similar fashion and are largely driven by glacial 

processes. Transitional glacial watersheds are less dominated by glaciers and also 

represent watersheds susceptible to change in the near term from climate change. 

Clearwater systems are effectively free from glacial influence.

For the snowpack index, three classes were defined similar to those described 

by Mantua et al. (2010) and also used by the Alaska Climate Science Center vulnerability assessment for snow and ice (see chapter 3). Snow-dominant watersheds 

were defined as those with 40 percent or more of the October through March 

precipitation entrained as snow on 1 April. Transitional snow watersheds were 

those in which the snowpack on April 1 contained more from 10 to 40 percent 

of the winter’s precipitation. Those watersheds with less than 10 percent of the 

winter’s precipitation remaining as snowpack on April 1 were assigned to the raindominant classification. 

We identified nine watershed categories based on a matrix of all possible 

combinations of the three watershed classes and three snowpack index classes, as 

shown in table 5-1. The hydrological dynamics of these watershed categories are 

distinctive. Probably the best understood are those that are free of glacial influence 

(i.e., clearwater: all three entries in the first column of table 5-1).
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GENERAL TECHNICAL REPORT PNW-GTR-950

Clearwater/snow-dominant (CS) watersheds contain streams that drain 

high-elevation watersheds with robust snowpack levels that provide water throughout summer. Discharge fluctuates with air temperature throughout summer, and 

water temperatures are relatively cold. Peak flows occur in early summer (June/

early July); however, some of the watersheds will also have high flows (a second 

but lesser peak) associated with autumn rainfall. Some of these streams will shift 

toward transitional in coming decades. 

Clearwater streams in transitional-snow (CT) watersheds more frequently 

occur at moderate elevations, where peak flows occur in May and June, which is 

generally earlier than for snow-dominant systems. These watersheds characteristically have a second rainfall peak flow in autumn, which in some cases may be 

greater than the peak flow in May–June. Both summer and winter water temperatures are generally warmer than those of snow-dominant systems. Some of these 

watersheds will shift toward rain-dominant in upcoming decades. The peak flows 

in spring, fed by melting snow, will diminish, and the timing of maximum discharge will shift to autumn. 

Clearwater/rain-dominant (CR) watersheds contain streams that drain low 

elevations with little or no snow accumulation during winter months. Precipitation 

enters streams soon after it falls, and discharge spikes with periods of rain throughout the year. Year-round, water temperatures are expected to be warmer than in 

snow-dominant systems.

Watersheds in the transitional glacial group (TS, TT, and TR) represent a 

group with uncertain future dynamics, because it is not clear how modest glacial 

influence might hydrologically ameliorate snowpack losses. Glacial systems in 

general are characterized by later peak flows (July and August) corresponding to 

the highest air temperatures. During summer melt, there is often a strong diurnal 

pattern of flow, in which discharge peaks after the warmest part of the day, then 

falls during the night to a minimum in the morning. The greater the contribution of 

glaciers to watershed flow, the more this diurnal flow pattern will be observed. The 

presence of glacial cover results in a longer period of cooler water and extended 

Table 5-1—Nine watershed categories defined for the assessment region based 

on a matrix of glacial cover and snowpack index classes (see chapter 3) 

Snowpack index

Glaciers

Clearwater Transitional glacial Glacial

Snow-dominant CS TS GS

Transitional snow CT TT GT

Rain-dominant CR TR GR
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

flows during summer months, particularly in the snow-transitional and rain-dominant watersheds (TT and TR). 

Finally, watersheds that fall within the glacial class (GS, GT, and GR) are 

believed to function similarly in that they are largely controlled by annual and 

longer term cycles of glacial processes, with peak flows in July and August and 

diurnal flow variation during summer months.

In addition to the glacial and snowpack variables, the presence of lakes in the 

drainage and the occurrence of groundwater both affect the temperature and flow 

characteristics of streams (Webb et al. 2008). Lakes, especially if they are large 

relative to the stream system, can buffer extremes in water temperature and flow, 

trap sediment, and buffer the intensity of flood events. A substantial number of 

watersheds in the study area have lakes; however; they differ in size and number, 

which makes it very difficult to develop a formulated approach to evaluating their 

impact. 

A similar issue exists with regard to the effect of groundwater on watershed 

character. Groundwater is known to have a strong influence on stream temperature 

and other characteristics (Webb and Nobilis 2007). Groundwater flow occurs 

primarily as localized flow controlled by the permeability of aquifer materials 

and surface topography. Alluvium in river valleys, glaciofluvial deposits, and the 

coastal lowlands make up the most productive aquifers within the study area (e.g., 

Carmen River, Williwaw Creek, parts of Copper River Delta). 

Groundwater systems are thought to be inherently more resistant to future, 

climate-change-induced shifts in water temperature and flow. As a result, significant groundwater input will buffer potential change in hydrology as a result of 

climate-induced changes in snowpack. The relative importance of this cushion for 

salmon production and survival will vary among watersheds and across regions, but 

the difficulty of estimating groundwater input confound attempts to estimate the 

effect. 

Groundwater systems 

are thought to be 

inherently more 

resistant to future, 

climate-changeinduced shifts in water 

temperature and flow. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Case Study: Monitoring Lake Ice

Chuck Lindsay, physical scientist, and Peter Kirchner, physical scientist, 

U.S. Department of the Interior, National Park Service, Southwest Alaska 

Inventory and Monitoring Network, Anchorage, AK 99501

Lake ice cover is a key component of cold-region ecosystems, influencing 

lacustrine physical and chemical systems (e.g., temperature profile, dissolved 

gas concentration), biological productivity (e.g., zooplankton abundance), 

and human well-being (e.g., ice-jam flooding, subsistence, and recreational 

opportunities). Trends observed in historical records from across the Northern Hemisphere over the last 150 years show that duration of lake ice cover 

has become shorter by an average of 12 days per century during 1846–1995 

(Magnuson et al. 2000). Projections of continued warming in the northern 

high latitudes indicate later ice cover formation and earlier break-up (Prowse 

et al. 2011).

In this study, we use remotely sensed data to track the timing of ice 

formation and break-up on large lakes in the western portion of the study area 

(fig. 5-4). Moderate spatial resolution (250 m) and high temporal frequency 

(1 day) provided by the Moderate Resolution Imaging Spectroradiometer 

(MODIS) allow frequent observation of our region, which is characterized 

by prolonged cloud cover and little infrastructure. We use imagery from the 

MODIS Aqua and Terra satellites to monitor lake ice dynamics across the 

national parks and wildlife refuges in southwest Alaska (Reed et al. 2009).7

The lakes studied on the Kenai Peninsula include two of the largest lakes on 

the peninsula, Skilak and Tustumena Lakes, and a cluster of small lakes near 

the Swanson River/Swanson Lakes area, referred to as the northern Kenai 

lakes (fig. 5-4).

Typically, the lakes in this study did not completely freeze (>90-percent 

ice cover) until mid-November (northern Kenai lakes) to mid-January (Skilak 

and Tustumena Lakes), and in some years, Skilak (in 2003) and Tustumena 

(in 2003, 2013, and 2014) did not completely freeze. Mean freeze start dates 

(>10-percent ice cover) range from early November (northern Kenai Lakes) to 

mid-December (Skilak and Tustumena Lakes) and show an interannual variability of up to 105 days (1 standard deviation [SD] = 10 to 29 days; fig. 5-4). 

7 Kirchner, P.; Lindsay, C.; Shepard, T. [N.d.]. Lake ice freeze and break-up dates 

for southwest Alaska, 2001–2016. Manuscript in preparation. Natural Resource Report. 

Fort Collins, CO: U.S. Department of the Interior, National Park Service.

Trends observed in 

historical records 

from across the 

Northern Hemisphere 

over the last 150 years 

show that duration 

of lake ice cover has 

become shorter by 

an average of 12 days 

per century during 

1846–1995.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

climate of the southwest Alaska region during fall and early winter, which 

oscillates between warm and cold temperatures over several weeks.

In contrast, break-up occurs more rapidly than freeze-up, and the timing 

of break-up end dates (<10-percent ice cover) does not differ as greatly with an 

interannual variability of up to 31 days (1 SD = 7 to 8 days for lakes that com-

(Skilak and Tustumena Lakes) to early May (northern Kenai lakes). Adrian et 

than thawing. Thus, changes in the timing of break-up are better indicators of 

broad-scale climate patterns than are changes in the timing of freeze-up. The 

relatively stationary nature of break-up dates observed on lakes on the Kenai 

Peninsula supports this concept.

Figure 5-4—Lake ice image and time series of freeze and break-up dates for selected lakes on the Kenai Peninsula. Background 

image shows location and stages of break-up on April 10, 2014: (A) northern Kenai lakes, completely frozen (92-percent ice); (B) 

Skilak Lake, undergoing break-up (79-percent ice); and (C) Tustumena Lake, completed break-up (8-percent ice). Line graphs 

show the number of days per winter that each location had more than 10-percent ice cover, and bar charts show periods of complete ice cover with triangles to indicate freeze and break-up.
124

GENERAL TECHNICAL REPORT PNW-GTR-950

Watershed Vulnerability—Findings

Using available geographic information system (GIS) data and information on 

snowpack from the analysis outlined in chapter 3, we categorized each HUC 

based on current (1971–2000) glacial cover and precipitation class (snow/rain). The 

distribution of watersheds across the two gradients of glacial extent and snowpack 

indicates that, within the Kenai/Chugach assessment area, the clearwater/snowdominant (CS) watershed category was the most common (260 watersheds), closely 

followed by glacial-dominant/snow-dominant (GS), with 251 watersheds assigned 

In contrast to the shortened duration of lake ice cover across the Northern 

Hemisphere over the last 150 years, the 13-year pattern of freeze-up and 

break-up dates in this study does not show an obvious trend. One pattern that 

is evident is that lake ice cover is closely related to air temperature. During 

the four years with the highest locally recorded mean annual air temperatures 

(2003, 2005, 2013, and 2014), Skilak and Tustumena Lakes did not completely 

freeze (>90 percent ice cover) or froze for only a short period (fig. 5-4). This 

pattern is less noticeable but is still evident in the duration of ice season at 

the northern Kenai Lakes. The winters of 2002–2003 and 2004–2005 were 

significantly warmer than average (>2 SD) and coincided with El Niño 

events. Benson et al. (2000) and Robertson et al. (2000) also recognized that 

large-scale atmospheric and oceanic conditions like the El Niño–Southern 

Oscillation and the Pacific Decadal Oscillation (associated with higher winter 

and spring temperatures since the late 1970s) have significant impacts on the 

timing of lake ice formation and break-up.

There have only been a few detailed studies that describe future changes 

in the timing of lake ice cover. Dibke et al. (2011) simulated lake ice response 

to future climate in 2040–2079 using a global climate model (CGCM3) and an 

upper level emission scenario (SRES A2). Their results propose that freeze-up 

will be later by 5 to 20 days and break-up will be earlier by 10 to 30 days. 

Lakes in Pacific coastal areas of North America saw the largest projected 

changes, while lakes in the Alaskan interior were less affected. 

Future reductions in the duration of lake ice cover on the Kenai Peninsula 

would likely be associated with thinner ice, reduced albedo, warmer lakewater temperatures, increased turbidity, increased light input, and decreased 

opportunities for winter recreation, ice fishing, and trapping. ■
Figure 5-5—Association between proportion of glacier coverage and snowpack for 720 watersheds (6th-level HUCs) in 

the Chugach/Kenai assessment area for recent time period (1971–2000). Snowpack indices greater than 0.40 indicate 

a snow-dominant watershed; values between 0.40 to 0.10 indicate a transitional snow-rain watershed; and those less 

than 0.10 are rain-dominant watersheds. These snow-rain watershed classifications intersect with three possible glacier 

categories in which glacial proportions greater than 0.10 indicate a glacier-dominant system; glacier proportions from 

0.10 to 0.01 indicate a glacial transition watershed; and those with less than 0.01 glaciers indicate a clearwater system.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 5-2—Classification of 720 watersheds in the assessment region based 

on glacial cover and snowpack for current conditions (1971–2000) and glacial 

coverage and snowpack projected from a climate scenario for the period 

2030–2059

Snowpack index

Time

period

Glaciers

Clearwater Transitional glacial Glacial

Snow-dominant Current 260 74 251

Future 212 65 247

Transitional snow Current 113 17 5

Future 161 26 9

Rain-dominant Current 0 0 0

Future 0 0 0

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Snowpack index

0.01 0.10 1.00

Glacial proportion for watershed

to this category (table 5-2). No watersheds were identified for the rain-dominant 

categories (CR, TR, and CR). Therefore, only six of the possible nine watershed 

categories occur under present conditions (fig 5-5). 
colored yellow, orange, and red. 

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GENERAL TECHNICAL REPORT PNW-GTR-950

There was a strong spatial pattern in the distribution of watershed categories 

across the assessment area. Clearwater/snow-dominant watersheds (CS) were confined to the northwestern portion of the region (fig. 5-6). Glacial/snow-dominant 

watersheds (GS) occur throughout the mountainous portions of the assessment area. 

Watersheds assigned to the transitional snow categories are most frequent along 

southern coastline and especially common for watersheds that ring Prince William 

Sound.
Figure 5-7—Percentage of change in snowpack index for 720 watersheds in the Chugach National 

Forest/Kenai Peninsula assessment area from current conditions to modeled snowpack six decades 

in the future.

120

100

80

60

40

20

0

Number of watersheds

Percentage of change in snowpack index

-20 -16 -12 -8 -4 0 4 8 12 16 20

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

To evaluate the impact of climate change on hydrologic function, we sought to 

develop a vulnerability index combining the influence of glacier cover and changing snowpack to indicate which watersheds were likely to experience significantly 

different hydrographs in the future. We used snowpack index values projected for 

the years 2030 to 2059 to establish a future scenario (see chapter 3). The difference 

between current and projected values for the snowpack index ranged from a reduction of 20 percent at one extreme to an increase of 14 percent at the other. However, 

a majority of the watersheds had shifts in the snowpack index that fell within the 

interval of -10 to +6 percent change from current conditions (fig. 5-7). Decreases in 

the snowpack index were associated with lower elevation watersheds of the eastern 

portion of the Kenai Peninsula and coastal areas (fig. 5-8). Increases in the snowpack index were most frequently associated with higher elevation, more mountainous portions of the assessment area.
Figure 5-8—Relationship among watersheds expected to experience an increase (blue), no change (white), and decrease (green) in 

snowpack during the next six decades based on modeling of snowpack (see chapter 3). 

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GENERAL TECHNICAL REPORT PNW-GTR-950

Watersheds expected to experience an increase in snowfall under climate 

change scenarios did not move to a different hydrologic category; these watersheds were already in the snow-dominant group. However, the classification of 

61 of the 720 watersheds changed from snow-dominant to transitional as a result 

of expected decreases in the snowpack index (table 5-2). The largest number of 

changes occurred for clearwater (glacier-free) watersheds, where 48 changed from 

snow-dominant (CS) to transitional snow (CT). The climate scenario projections for 

the snowpack index in no instance resulted in a watershed falling into any of the 

rain-dominant categories. 

A shortcoming of our analysis was that we were unable to model glacial 

changes to compliment the snowpack analysis. A suitable analysis of potential 

glacier retreat is not available across the 720 HUCs. Therefore, at this point, our 

watershed vulnerability index reflects only the influence of changes in expected 

snowpack with the clear recognition that changes in glacier cover, particularly in 

those systems with less than 10 percent cover, are likely to influence many characteristics of stream function and therefore habitat for fish. 

the 720 watersheds 

changed from 

snow-dominant to 

transitional as a result 

of expected decreases 

in the snowpack index.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

We coined the term “vulnerable watersheds” to represent the watersheds that 

shifted category under the climate scenario modeling. The term as applied here is 

primarily meant to identify those places where changes in hydrologic processes 

are expected to be the most significant, and the potential disruption to the ecology 

of salmon populations the greatest. However, it is important to emphasize that a 

“vulnerable watershed” does not necessarily mean that the watershed is at risk; our 

intention with this label was to flag locations where we believe substantial change 

in watershed function may occur in the future. We are not predicting that salmon 

populations will increase or decline, only that hydrologic conditions will change 

significantly. Furthermore, we emphasize that our scenario for hydrographic change 

does not currently include the influence of receding glaciers and the associated 

changes in stream conditions. A model of glacier retreat, even a simple model, will 

be necessary to add this feature to the analysis.

All the vulnerable watersheds identified in our analysis represented locations 

where the snowpack index changed from a current value greater than 0.40 (snowdominant) to a value less than 0.40 (transitional snow) under the climate scenario 

projection. The geographic distribution of these watersheds across the study area 

was highly structured, with the majority ringing the mainland shoreline of Prince 

William Sound (yellow, orange, and red watersheds in fig. 5-6). A number also 

occurred along the southern coastline of the Kenai Peninsula as well as a few in 

the vicinity of the Copper River Delta. We have singled out these watersheds on 

the basis of our analysis that these are the locations at which we expect significant 

shifts in the hydrograph to occur under climate change. Watersheds not so identified are still expected to be affected, but to a lesser degree. In terms of impacts on 

salmon, the impact thresholds may be more or less sensitive than the levels we have 

picked to identify vulnerable watersheds. 

In addition to snowpack and glacier conditions, we intended to assess whether 

hydrological functionality might be mitigated by the stabilizing effect of groundwater or the existence of lakes within the stream network. Both lakes and groundwater 

play key roles in stream function; however; we found it impossible to quantify such 

effects in a spatial framework based on available geographic information. Locations 

and volume of groundwater are poorly documented, although there seems to be an 

association with peripheral streams that occur within large glacial outwash plains. 

The effect of lakes on watersheds classified as vulnerable was also difficult to 

quantify. However, the fact that lakes comprised no more than 5 percent of the total 

area for these vulnerable watersheds led us to believe that the “lake effect” on the 

hydrologic process for these specific watersheds was likely minor. We concluded, 

given the uncertainty related to groundwater flow and the relatively small size of 
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GENERAL TECHNICAL REPORT PNW-GTR-950

lakes in relation to watershed area, that the mitigating impacts of these features on 

the watersheds we classified as vulnerable could not be demonstrated. Therefore, 

the vulnerable watersheds identified in our analysis were not adjusted for the effect 

of either groundwater or lakes. This outcome does not mean that the effect of 

groundwater or lakes is generally not important to hydrologic processes. It is simply 

that the hydrologic impacts of these two features are not included in the broad 

analysis to identify vulnerable watersheds in the assessment area. 

As illustrated in figure 5-6, most of the vulnerable watersheds were located 

within Prince William Sound, which is a very large producer of pink and chum 

salmon. Given this observation, we explored whether or not vulnerable watersheds 

were disproportionately associated with these two salmon species. To investigate 

this question of species proportionality, we used a classification of streams based 

on the aquatic ecosystems associated with quality habitat for four salmon species: 

Chinook, coho, pink-chum, and sockeye-coho salmon (USDA FS 2014). We labeled 

the classes “aquatic ecosystem types.” When streams classified based on ecosystem 

types were matched with the hydrologic vulnerability score, we found that none 

was disproportionately classified as vulnerable (table 5-3). 

Table 5-3—The proportion of four aquatic ecosystem types (USDA FS 2014) 

classified as vulnerable or not vulnerable based on the hydrologic vulnerability 

of watersheds in the Chugach/Kenai assessment area 

Watershed 

assignment Chinook Coho Pink-Chum Sockeye-Coho Number

Vulnerable 0.04 0.13 0.71 0.12 56

Not vulnerable 0.05 0.15 0.63 0.17 347

Although the greatest number of vulnerable watersheds are associated with 

pink-chum salmon ecosystems, pink-chum ecosystems are by far the most common 

represented in the area evaluated. Vulnerable watersheds appeared to be projected 

across this region in a manner that is proportional to the occurrence of each of the 

four salmon-based ecosystem types, and watershed vulnerability did not appear 

biased toward any particular species group. Note, however, that this particular 

comparison focused on watersheds of Prince William Sound and the eastern Kenai 

Peninsula, and did not include watersheds from the eastern and southwest portion 

of the climate vulnerability assessment area.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Salmon Population Dynamics 

In the second analysis, the Salmon Team developed empirical models based on 

associations between observed air temperatures and salmon recruitment over the 

past 30 years for 234 different salmon populations. We then used these associations 

(model parameters) to project future population responses under two climate change 

scenarios. This analysis employed a statistical rather than mechanistic model. 

Therefore, the output does not examine how or why air temperature is related to 

salmon production. Rather, the model demonstrates whether an association between 

the two was present. 

Our model focused on adult salmon returns measured during annual surveys 

from 234 sites throughout the assessment area. We measured how much of the 

observed annual variation in adult return was explained by two predictors: the 

number of fish that spawned in the parent year (spawners) and an index of annual 

air temperature at one of four monitoring stations in the region. We sought to 

determine whether warmer years were associated with the subsequent number of 

returning salmon. 

We selected air temperature as the predictor variable (rather than sea surface 

temperature or other environmental measures associated with climate change) 

because estimates of air temperature were available from downscaled climate models for the next 70 years. Such estimates were needed to project how salmon production would change in the future. We made a decision not to use other environmental 

variables more closely associated with salmon production (e.g., PDO index) because 

we lacked an objective means for estimating future values. 

We used the magnitude and sign (positive or negative) of the temperature 

parameter estimated for each salmon population to postulate future fish abundance 

with the underlying assumption that temperature responses observed over the past 

30 years would predict abundance over the next 70. Values for projected temperature increases were based on output from two climate change scenarios (a1B and 

a2), downscaled sufficiently to represent conditions at the four temperature monitoring stations used in the recruitment modeling. 

Findings are summarized to illustrate the proportional increase or decrease in 

average salmon abundance under the two climate scenarios for each population in 

70 years. Variation in climate change responses among populations were examined 

for the possibility of geographic and species-specific patterns. 

Estimated numbers of salmon spawning in each of the last 20 to 30 years 

were compiled from Alaska Department of Fish and Game (ADFG) publications 

including Begich and Pawluk (2011), Hochhalter et al. (2011), Shields and Dupuis 

(2012), and Botz et al. (2013). Additional information concerning the annual 
132

GENERAL TECHNICAL REPORT PNW-GTR-950

estimates of pink and chum salmon spawning in streams of Prince William Sound, 

as well as fishery catch rates for wild fish in this area, were provided by R. Brenner 

(ADFG, Cordova). 

Analyzing salmon abundance data for the Prince William Sound area was 

complicated by the large number of hatchery fish returning to this area; in many 

cases, counts of spawning salmon include hatchery fish that have strayed into the 

spawning streams. To estimate how many spawners were wild fish produced in the 

survey streams, we needed to remove hatchery fish from the counts. To estimate 

the proportion of hatchery fish in each stream, we used results from a model of 

pink salmon developed by Brenner et al. (2012) and separate ADFG observations 

of marked hatchery fish on the spawning grounds for chum salmon.

Of the 234 populations with data, the large majority were either pink salmon 

(199 populations) or chum salmon (27 populations). Sockeye, Chinook, and coho 

salmon were poorly represented, with data available for only 5, 2, and 1 populations, respectively. Fixed-wing aerial surveys by ADFG staff were used to count 

numbers of pink and chum salmon; numbers of other species were estimated at 

fish-counting weirs either visually or using split-beam sonar. 

For each population, we used the structure of the Ricker recruitment model to 

determine if the number of fish returning in a given year (response variable) could 

be explained by two predictor variables, the number of parental spawners, and an 

index of air temperature. To do this, we modified a standard Ricker recruitment 

model (Ricker 1954) to include a second environmental variable such as reported 

by Chilcote et al. (2011) and as illustrated in equation 1: 

ln(Rt) = ln(St) + ln(α) – βSt

 + γE (1)

where: 

Rt is the number of returning wild salmon that were produced from parents that 

spawned in year t (response variable), 

St is the number of salmon that spawned in year t (first predictor variable), and 

E is the air temperature index (second predictor variable). 

In addition, α (alpha), β (beta), and γ (gamma) are model parameters that 

describe the form of the recruitment curve. For this analysis we are most focused on 

the value estimated for gamma, which is effectively the temperature-related multiplier of the number of recruits produced. A gamma estimated as positive indicates 

a positive relationship between air temperature and salmon production, whereas a 

negative gamma indicates the opposite. We estimated equation 1 parameters (alpha, 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

beta, and gamma) for each population via nonlinear regression using DataFit®

software (Oakdale Engineering, Oakdale, Pennsylvania).8

We examined a number of forms of air-temperature data available online from 

the Western Region Climate Center at http://www.wrcc.dri.edu/. First, rather than 

using temperature from just one index site, our evaluation included data from four 

south-central Alaska weather stations that bracketed the study area: Cold Bay 

(55°12' N, 162°43' W), Alyeska (60°58' N, 149°08' W), Cordova (60°30' N, 143°30' 

W), and Yakutat (59°31' N, 139°40' W). Second, we used monthly average air 

temperatures for January, April, July, and October to evaluate whether patterns in 

salmon production were tied to conditions at a particular time of year (i.e., winter, 

spring, summer, and fall). 

Finally, because salmon life histories span several years, and it was not known 

which life stage would be most sensitive to temperature, we retarded and advanced 

the temperature time series relative to the spawning year to determine if there was 

a lag in the effect of temperature. Sensitive stages, for example, might be during 

the fall/winter when the eggs were in the gravel or in spring when the smolts first 

reached the ocean, which could occur shortly after hatching for pink and chum 

salmon or 2 to 3 years later in the case of coho salmon. 

To summarize, for each population we attempted to fit temperature data from 

each of four weather stations, for the months of January, April, July, and October, 

and with four different time lags (data series shifts of -1, 0, +1, and +2 years). 

Effectively, 64 different temperature datasets were fit to spawner and recruit 

abundance data for each population. This modified the γE term in equation 1 such 

that it became: 

γEijt + lag (2)

where γ represents gamma as before, but E becomes one of 64 temperature values 

with i = 1 to 4 for the weather stations Cold Bay, Alyeska, Cordova, and Yakutat, 

respectively; j = 1 to 4 for the months of January, April, July, and October, respectively; t = the year the parent spawned; and lag = values from -1 to +2. 

For each population, we fit equation 1 for all possible temperature datasets as 

were identified in equation 2. For each model, we calculated a score for the corrected Akaike’s information criterion (AICc) (Burnham and Anderson 2002) and 

ranked the models based on AICc scores. We selected the model with the lowest 

score as the best representation of recruitment performance of the population. 

8 The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
134

GENERAL TECHNICAL REPORT PNW-GTR-950

To project salmon production for future climate change scenarios, we employed 

temperature projections for the period 2060–2069 from SNAP (see chapter 2) for 

the months of January, April, July, and October at each of the four weather stations 

for each of two climate scenarios—a2 and a1B. We estimated the number of salmon 

recruits for each population using the best recruitment model (see above) using the 

future temperature estimates. The number of spawners (first predictor variable in 

equation 1) was set to equal the average number of spawners observed for the population. Baseline (or current) salmon recruitment was estimated using the identical 

abundance of spawners but air temperature from the average value for the associated weather station from 1977–2012. This time period corresponds to the period 

used to fit recruitment curves. We then expressed the expected population response 

under future climate as the proportional change as represented in equation 3: 

Pchange = (Rscenario – Rbase)/Rbase (3)

where: 

Pchange = proportional change in salmon production with positive values indicating 

proportional increase in salmon production and negative values indicating proportional decrease in salmon production, 

Rscenario = the number of recruits predicted under climate scenario conditions, and 

Rbase = the number of recruits expected under average temperature conditions of 

base period 1977 to 2012.

Salmon Population Dynamics—Findings 

We were unable to find parameter solutions to the nonlinear model for 38 of the 234 

populations. The 196 populations with suitable models included 173 pink salmon 

and 16 chum salmon populations. Model fits were also obtained for two chinook 

populations, four sockeye populations, and one coho population. 

Pink salmon abundance increased an average (geometric mean) of 26 percent under both the a2 and a1B climate scenarios (table 5-4). Populations varied 

substantially in modeled response to temperature increase, including a number of 

populations in which production levels were projected to decline (fig. 5-9). One 

interpretation of this variability is that it expresses differences in habitat complexity 

or genetic diversity among pink salmon populations; a conceptual model for Prince 

William Sound pink salmon not unlike that proposed by Hilborn et al. (2003) and 

Schindler et al. (2010). We did not detect any geographic ordering of responses 

across the evaluation area (fig. 5-9). To confirm that the mean positive increase 

we resolved for pink salmon was significant, we evaluated estimates of salmon 

Pink salmon abundance increased an 

average (geometric 

mean) of 26 percent 

under both the a2 and 

a1B climate scenarios.
Figure 5-9—Index of production change, expressed as the natural log of Pchange +1 (see text), for 173 pink salmon populations 

based on 70-year climate scenario projections under the a1B model; each bar represents a population and is displayed left to right 

in a sequence that approximately represents an east to west counterclockwise sweep through Prince William Sound.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 5-4—Modeled response of salmon recruitment for five species in response 

to increased air temperatures associated with climate scenario models a2 and 

a1B for the period 2060 to 2069 (expressed as proportional increase or decrease)

Salmon species

Number of 

populations

Proportional 

production change Binomial significance test

a1B a2 a1B a2

Pink 173 + 0.26 + 0.26 < 0.001 < 0.001

Chum 16 - 0.41 - 0.34 0.07 0.07

Sockeye 4 - 0.16 - 0.12 — —

Chinook 2 - 0.33 - 0.3 — —

Coho 1 + 1.17 + 0.87 — —

4.0

3.0

2.0

1.0

0

-1.0

-2.0

-3.0

-4.0

Index of production change

Pink salmon populations from eastern to western Prince William Sound
Figure 5-10—Index of change in recruitment expressed as the natural log of Pchange +1 (see text), for 

16 chum salmon populations based on 70-year climate scenario projections under the a1B climate 

change model. Each bar represents a population and is displayed left to right in a sequence that 

approximately represents an east to west counterclockwise sweep through Prince William Sound.

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GENERAL TECHNICAL REPORT PNW-GTR-950

response using a binomial test and found that our departure from a random 50/50 

distribution of plus and minus values was statistically significant (p < 0.001) (table 

5-4).

Chum salmon abundance declined under both the a2 and a1B climate scenarios

(table 5-4) with the decline ranging from 34 to 41 percent (table 5-4). The analysis 

included only 16 populations and therefore is not as robust as the pink salmon 

evaluation. Indeed, a binomial test provided only modest evidence, supporting the 

conclusion of declining recruitment across populations (11 out of 16: p = 0.07). 

Unlike pink salmon, however, a geographic pattern in the response of chum salmon 

populations is suggested. Recruitment in populations from the eastern and central 

portion of Prince William Sound generally decreased in the future, while most from 

the western Sound were projected to increase (fig. 5-10). 

4.0

3.0

2.0

1.0

0

-1.0

-2.0

-3.0

-4.0

Index of production change

Chum salmon populations from eastern to western Prince William Sound

Chum salmon abundance declined under 

both the a2 and a1B 

climate scenarios 

with the decline 

ranging from 34 to 

41 percent.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Few populations of sockeye, coho, and Chinook salmon had time-series data 

to support modeling future recruitment. Our models suggested negative responses 

to warming conditions for both sockeye and Chinook, whereas the single coho 

population showed a positive response (table 5-4). The limited number of populations studied for these species preclude making any general statements regarding 

potential response to climate change under this modeling framework.

Our findings were based on the premise that the relationship between air 

temperature and salmon recruitment in the past can be used to project future 

salmon spawners. Therefore, our results should not be viewed as predictions about 

the future production of salmon, but rather one of many possible scenarios that may 

occur. With that caveat, our population analyses and scenario modeling suggest that 

the production of pink salmon may increase as the region warms over the next 70 

years. In contrast, this same analysis for chum salmon suggests a negative response 

to warming climate, but the evidence for a decline was weaker. As noted earlier, 

historical data for sockeye, Chinook, and coho is limited, and our modeling provides inadequate insight into how these species will respond to climate change. 

Fish Runs and Fisheries

Our third analysis explored broad-scale relationships between fish abundance and 

climate using fish catch and run-size estimates (table 5-5). Objective estimates of 

run-size were regarded as most reliable but were not available for some geographic 

areas. Therefore, we relied on commercial or recreational fishery catch data for a 

subset of our analysis (table 5-5). We assumed that harvest was proportional to total 

run-size. We restricted our analysis to from 1986 to 2012 to establish a baseline or 

current condition. This time frame was selected as a compromise between accepting all possible datasets, including those with too few years for meaningful analysis, and restricting the analysis to only those few datasets with a longer time series.

Using these sources of information, we were able to compile abundance-related 

data for 10 groups of fish from the Prince William Sound and Cook Inlet portions 

of the assessment area. In most cases, the scale of the information was for the entire 

area (e.g., Prince William Sound). However, representative data were also used that 

were specific to fish from the intensively managed Kenai River watershed. 

We used three climate indices in this analysis. Two were derived from average monthly temperatures recorded at the Cold Bay and Yakutat weather stations 

as described previously in “Salmon Population Dynamics.” However, unlike the 

previous analysis, we also examined an annual winter-season temperature index 

calculated by averaging the sequential monthly temperatures from November 

to March. For example, the 1990 winter average was calculated as the mean of 
Table 5-5—Fish abundance data for ten groups of fish from south-central Alaska used to examine potential 

relationships between run size and air temperature 

Species Area Data type Years Source

Chum salmon 

 (wild fish only)

Prince William Sound Total run-size 1986–2012 Brenner (2015)a

Pink salmon 

 (wild fish only)

Prince William Sound Total run-size 1986–2011 Brenner (2015)a

Sockeye salmon Kenai River Total run-size 1986–2012 Shields and Dupuis (2012)

Sockeye salmon Prince William Sound Commercial fishery catch 1986–2012 Botz et al. (2014)

Chinook salmon 

 early run

Kenai River Total run-size 1986–2012 ADFG (2014), Begich and 

 Pawluk (2011) 

Chinook salmon 

 late run

Kenai River Total run-size 1986–2012 ADFG (2014), Begich and 

 Pawluk (2011);

Coho salmon Cook Inlet Commercial fishery catch 1986–2012 Shields and Dupuis (2012)

Coho salmon Prince William Sound Commercial fishery catch 1986–2012 Botz et al. (2013)

Dolly Varden char Kenai River Recreational fishery catch 1986–2009 Begich and Pawluk (2011)

Rainbow trout Kenai River (upstream 

 from Skilak Lake)

Recreational fishery catch 1986–2009 Begich and Pawluk (2011)

a Brenner, R. 2015. Personal communication. Fishery biologist, Alaska Division of Fish and Game, P.O. Box 115526, Juneau, AK 99811.

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GENERAL TECHNICAL REPORT PNW-GTR-950

monthly averages for November and December of 1989, and January through March 

of 1990. We used the notations winCB and winYk to represent the indices for 

Cold Bay and Yakutat, respectively. The SATARC (surface air temperature) index 

described by Johnstone and Mantua (2014) was the basis for the third index used 

in our evaluation. The authors derived SATARC from temperature data collected at 

51 stations around the margin of the northeast Pacific Ocean. Using supplemental 

information referenced by Johnstone and Mantua (2014) we generated a winter 

version of this index (winSATARC) by averaging SATARC values for the months of 

November through March. 

Our rationale for focusing on the winter time frame was twofold. First, we 

expected winter temperatures to have a strong influence on snowfall accumulation and subsequent patterns of stream discharge and temperature. These physical 

factors may have a strong influence on variations in salmon production from year 

to year. Second, winter air temperatures for this region are strongly associated 

with sea surface temperatures (SST) (Johnstone and Mantua 2014), and the greatest 

contrasts in these SST from year to year occur during the winter months (Mantua 

2001). Not only did we hope to take advantage of these larger contrasts to resolve 

temperature associations with fish abundance, we were also aware that correlations 

between annual fluctuations in PDO, an index of SST, and salmon production as 

well as other physical factors such as streamflow, flood risk, and snowpack have 

been demonstrated by others (Mote et al. 2003). 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

We matched temperature time series with the corresponding start and end 

dates for fish abundance data (either total run-size or fishery catch) and computed 

a correlation coefficient, r, for each fish run. We were interested in the direction 

(positive vs. negative) rather than the size of resulting correlations. A negative 

correlation indicated that, for the period of record, increased air temperatures were 

associated with lower fish abundance estimates, while a positive value meant the 

reverse.

In addition to examining the influence of temperatures during the year of 

the adult return, we also applied a range of lag periods to match other portions of 

salmon life history. For example, a pink salmon that spawned in 1990 (year = t) 

would have been an incubating embryo during the winter/spring of 1989 (year = 

t – 1). Therefore, to capture the pink salmon life cycle, we matched the temperature 

index sequences for year t and year t – 1. We applied this temperature lag to all 

of the fish abundance datasets, employing a range of lag periods to match the life 

cycle of the species involved. Although the pink salmon life cycle is fixed at 2 

years, life cycle length for other species can vary considerably within species. To 

account for within-species variability, we selected a generic life cycle that spanned 

all commonly observed ages of adult return in this portion of Alaska: Chinook 

salmon = 6 years, sockeye and chum salmon = 5 years, coho salmon = 4 years. 

We assumed a 5-year cycle for Dolly Varden char, cutthroat trout (Oncorhynchus 

clarkia), and rainbow trout. 

Fish Runs and Fisheries—Findings 

Fish run size and temperature demonstrated a broad range of relationships within 

and among fish species. Correlation coefficients varied from -0.51 (PWS sockeye 

salmon, winCB, lag-3) to 0.53 (early run Kenai Chinook, winCB, lag-2) (table 5-6). 

Across all 10 groups, there was no consistent pattern of positive or negative correlations with temperature. Chum salmon, Kenai sockeye, both Kenai Chinook runs, 

and Cook Inlet coho abundance were positively related to air temperature, based 

on the results of a binomial test for statistical departure from the null hypothesis of 

equal positive and negative temperature associations. From 1986 to 2012, warmer 

temperatures were associated with more fish and cooler temperatures were associated with fewer fish. 

Sockeye salmon from Prince William Sound were the only group showing a 

consistent negative response to temperature, suggesting asynchronous production 

between Kenai and Prince William Sound sockeye. Asynchronous production 

cycles among populations of salmon over long periods (500 years) have been 

reported in the case of sockeye salmon returning to different watersheds on the 

Alaska Peninsula (Rogers et al. 2012). 

Sockeye salmon from 

Prince William Sound 

were the only group 

showing a consistent 

negative response to 

temperature, suggesting asynchronous 

production between 

Kenai and Prince 

William Sound 

sockeye.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Table 5-6—Relation between fish abundance and winter temperature indices winCB (Cold 

Bay), winYk (Yakutat), and winSATARC (surface air temperature index from Johnstone and 

Mantua 2014) for ten groups of fish (correlation coefficient), where a grey shading indicates 

negative correlation coefficients

Group Time lag winCB winYk winSATARC

Chum salmon—Prince William Sound -4 0.01 -0.09 -0.05

-3 0.18 0.24 0.10

-2 0.15 0.22 0.05

-1 0.11 0.17 0.01

0 0.07 0.25 0.23

Pink salmon—Prince William Sound -1 -0.28 0.03 -0.09

0 0.33 0.18 0.16

Sockeye salmon—Kenai -4 -0.20 0.21 0.02

-3 0.10 -0.09 -0.21

-2 0.35 0.13 -0.12

-1 0.30 0.08 0.14

0 0.30 0.29 0.38

Sockeye salmon—Prince William Sound -4 -0.47 0.04 -0.03

-3 -0.51 -0.10 -0.20

-2 -0.36 -0.03 -0.18

-1 -0.18 -0.07 -0.15

0 -0.19 0.16 0

Chinook salmon—Kenai early run -5 0.15 0.13 0.07

-4 0.17 -0.23 -0.17

-3 0.50 0.28 0.38

-2 0.53 0.08 0.15

-1 0.23 0.27 0.29

0 0.25 -0.09 0.17

Chinook salmon—Kenai late run -5 -0.24 0.11 0

-4 0.03 0.12 0.11

-3 0.41 0.35 0.33

-2 0.51 0.28 0.30

-1 0.52 0.40 0.49

0 0.33 0.24 0.40

Coho salmon—Cook Inlet -3 0.33 0.15 0.02

-2 0.20 0.18 0.13

-1 0.48 -0.06 -0.13

0 0.23 -0.05 0.13

Coho salmon—Prince William Sound -3 0 0.13 -0.03

-2 -0.11 0.21 0.13

-1 0.35 0.17 0.13

0 0.22 -0.05 0.11

Dolly Varden—Upper Kenai -4 -0.17 -0.38 -0.17

-3 0.17 -0.30 0.01

-2 0.04 -0.14 -0.08

-1 -0.06 0.29 0.18

0 -0.33 -0.18 -0.30

Rainbow trout—Upper Kenai -4 -0.12 0.09 0.20

-3 -0.21 0.02 0.03

-2 -0.42 0.01 -0.10

-1 0.03 0.33 0.18

0 -0.31 -0.17 -0.38
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Coho salmon were the only other species for which Kenai versus Prince 

William Sound groupings could be compared. In this case, the pattern of fish 

abundance appeared positively correlated with air temperature, although only the 

Kenai group demonstrated strong evidence of a positive correlation between the 

abundance of spawners and previous air temperatures.

Numbers of early- and late-run Kenai River Chinook were both positively associated with air temperature. However, there appeared to be a difference between the 

two groups in terms of the lag period that was associated with the largest correlation coefficients. The best correlations were found using a -3-year lag for the early 

run and a -1-year lag for the late run. Although these correlations may be spurious, 

it is also possible that they indicate different temperature impacts acting on the 

survival of Chinook, which occur at a later life stage in the late run (-1 lag) than the 

early run (-3 lag). 

With the exception of Chinook, we did not find any evidence that a particular 

lag period was consistently associated with the highest correlation coefficients. Had 

there been a particular temperature-critical stage in the life history of one of these 

groups, we expected to find that reflected as consistently higher correlation coefficients for a specific lag period matching the age at the critical life history stage. 

That we found evidence of this only in the case of Chinook may mean that the logic 

behind this expectation was flawed, that there was significant mixing of age groups 

in individual runs (except for pink salmon which are all the same age at return), or 

that the data were not sufficient to detect this feature.

The relationship between temperature and fish abundance suggested in this 

analysis is dissimilar to those obtained in the previous section (see “Salmon Population Dynamics—Findings”) based on population dynamics modeling. In particular, 

we found that chum salmon abundance was positively associated with temperature 

in this section but negatively associated with temperature in the former section, 

and that pink salmon abundance was unrelated to temperature in this section but 

positively associated with temperature for the majority of runs in the former. 

The reason for these inconsistencies may simply be that the methods used to 

derive the estimates are so different that forcing a comparison will invariably yield 

meaningless outcomes. Another possibility is that the time series used in each was 

not long enough to establish a reliable association with observed air temperatures. 

We found some evidence to support the latter explanation. 

Total run-size estimates for wild pink and chum returning to Prince William 

Sound are available for years prior to 1977, but not for the other fish groups evaluated in this report. For pink salmon, the full data set runs from 1960 to 2011 and for 

chum salmon it runs from 1970 to 2012. When we repeated the temperature-fish 
142

GENERAL TECHNICAL REPORT PNW-GTR-950

abundance correlation analysis of this section for this longer dataset, the results 

were different and seemed more consistent in terms of which life history stage was 

most sensitive to temperature variations.

For this longer time series, all of the associations tested between fish abundance 

and temperature were positive, indicating that across all combinations, warmer conditions were associated with more fish (table 5-8). For both chum and pink salmon, 

there was strong evidence for a positive relationship (more positive correlations than 

expected by chance, p < 0.05). In addition, for chum salmon the highest correlations 

for all three temperature indices occurred for the -3 year lag; corresponding to the 

first winter of ocean residence. For pink salmon, the picture was less clear. For 

two of the three indices, the largest correlation occurred for the 0 lag time, which 

represents the last winter of ocean residence. 

Table 5-7—Number of positive and negative correlations between fish 

abundance and air temperature indices for each of 10 groups of fish (p-value 

associated with binomial tests for equal proportions of positive and negative 

correlations)

Fish abundance dataset

Correlation count

Statistical 

Positive Negative probability

Chum salmon—Prince William Sound 13 2 0

Pink salmon—Prince William Sound 4 2 0.23

Sockeye salmon—Kenai Peninsula 11 4 0.04

Sockeye salmon—Prince William Sound 2 13 0

Chinook salmon—Kenai Peninsula (early run) 15 3 0

Chinook salmon—Kenai Peninsula (late run) 17 1 0

Coho salmon—Cook Inlet 9 3 0.05

Coho salmon—Prince William Sound 8 4 0.12

Dolly Varden—Upper Kenai Peninsula 5 10 0.09

Rainbow trout—Upper Kenai Peninsula 8 7 0.20

Table 5-8—Relationship between chum and pink salmon estimates of total 

run-size and temperature indices for an extended time series of 1970–2012 

(chum) and 1960–2011 (pink) (p-value associated with binomial tests for 

equal proportions of positive and negative correlations)

Group Time lag winCB winYk winSATARC

Chum salmon (1970–2012) -4 0.31 0.19 0.17

-3 0.33 0.33 0.28

-2 0.21 0.17 0.06

-1 0.06 0.27 0.19

0 0.26 0.28 0.25

Pink salmon (1960–2011) -1 0.07 0.28 0.27

0 0.49 0.31 0.22

For this longer 

time series, all of 

the associations 

tested between fish 

abundance and 

temperature were 

positive, indicating 

that across all 

combinations, warmer 

conditions were 

associated with 

more fish.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

It appears that, over the long term, both pink and chum salmon numbers are 

greater when temperatures are warmer. For pink salmon in particular, the positive 

relationship is different from the ambivalent result for the shorter 1986–2011 period. 

We are unable to evaluate relationships over this longer period for the other salmon 

because the necessary data were not available. 

Examining a longer time series for pink and chum salmon, we found that the 

fish abundance was more likely to have a positive association with the temperature 

indices (tables 5-7 and 5-8). If this association represents a biologically significant 

linkage with temperature, and if this linkage remains into the future as temperatures increase, we expect an increase in the number of fish for each of these groups. 

The exceptions were the results for Prince William Sound coho salmon, which was 

somewhat ambiguous, and the negative temperature association found for Prince 

William Sound sockeye salmon. 

The two non-salmon fish, Dolly Varden char and rainbow trout from the upper 

Kenai River, demonstrated no association between fish abundance and temperature 

(table 5-7). Because the life history of neither population includes an ocean phase, 

all of the potential temperature effects would occur in fresh water. It is possible 

that the lack of association between fish numbers and temperature in this case 

could mean purely freshwater species in this region are less susceptible to annual 

temperature variations than those that are anadromous, such as salmon. If this were 

true, perhaps these species would be less affected by the warmer temperatures of 

the future. The corollary to this hypothesis might be that the primary driver of the 

linkage between temperature and fish abundance was associated with the ocean 

environment. However, it is also possible the catch of these two freshwater species in recreational fisheries, which is the basis for the data used in our analysis, is 

influenced by other unique factors such as annual variations in the clarity of water 

conditions, how many salmon eggs are accessible to predation, and stream temperature. Finally, it is also possible that the nature of the temperature-fish abundance 

associations for these two groups, along with Prince William Sound coho salmon, 

is such that their detection requires the analysis of data over a longer time period, 

similar to pink salmon. 

Effects on Salmon Ecosystem Services

In this section, we summarize how salmon harvested in the assessment area are 

used by people and how the benefits provided by these fish—the ecosystem services from salmon—might be affected by the climate scenarios discussed in the 

preceding sections.

It appears that, over 

the long term, both 

pink and chum salmon 

numbers are greater 

when temperatures 

are warmer. 
144

GENERAL TECHNICAL REPORT PNW-GTR-950

Salmon in the assessment area are caught by commercial fishers, by sport 

anglers, by subsistence users, and by Alaska residents participating in “personal 

use” fisheries. As discussed in appendix 3, between 29 million and 104 million 

salmon were caught annually during the period of 2009 through 2013; the average 

was 61 million fish. Based on 2011 and 2012 data, more than 96 percent of all 

salmon were caught for commercial purposes (table 5-9 and fig. 5-11). However, 

the breakdown differs significantly between sockeye and pink salmon, the two 

most important species when measured by volume or market value. Sockeye, 

which typically have a commercial value per fish exceeding five times that of pink 

salmon, are highly valued by sport, subsistence, and personal-use fishers in the 

assessment area. Pink salmon are not.

Also apparent from these data is the overwhelming importance of personal-use 

sockeye salmon fisheries to Alaska residents living in the assessment area. More 

than 44,000 permits were issued in recent years (2011–2012) to households participating in the Cook Inlet and Chitina Subdistrict personal-use dip net fisheries. 

Personal-use fisheries are dominant because almost the entire Anchorage and Kenai 

Peninsula regions are classified as the “Anchorage-Matsu-Kenai Nonsubsistence 

Area” by the Alaska Joint Board of Fisheries and Game (Fall et al. 2014). The 

personal-use fisheries are undoubtedly used by many households from the urban 

areas of Anchorage and surrounding communities who consider their fishing to 

be for “noncommercial, customary, and traditional uses.” In other words, for many 

Table 5-9—Distribution of commercial, sport, subsistence, and 

personal-use harvest of salmon based on average salmon catch from 

2011 and 2012 harvest years in the assessment area

Sockeye Pink All salmon

Number of fish:

 Commercial 8,108,500 30,855,000 42,484,500

 Sport 529,826 47,820 904,588

 Subsistence 9,212 1,621 12,817

 Personal use 767,438 5,077 784,028

 Total 9,414,976 30,909,517 44,185,933

Share of fish:

Percent

 Commercial 86.1 99.8 96.1

 Sport 5.6 0.2 2.0

 Subsistence 0.1 0 0

 Personal use 8.2 0 1.8

 Total 100.0 100.0 100.0

Note: personal use includes the Chitina Subdistrict dip net fishery.

Between 29 million 

and 104 million salmon 

were caught annually 

during the period of 

2009 through 2013; 

the average was 61 

million fish.
Figure 5-11—Relative numbers of salmon caught in the commercial, sport, and personal-use fisheries 

within the assessment area based on average 2011 and 2012 data. Subsistence use is not shown owing 

to low values. Personal-use numbers include the Chitina Subdistrict dip net fishery. Source: author 

calculations using data from ADFG (2014, 2015) and Fall et al. (2013, 2014). 

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

100

90

80

70

60

50

40

30

20

10

0

Share of total harvest (percent)

Sockeye Pink

Species

All salmon

Personal use

Sport

Commercial

people the personal-use fishery provides the same benefits as an officially designated subsistence fishery.

In addition to providing direct benefits to people, salmon also play an important role in nutrient cycling that benefits upstream ecology (Post 2008). We do not 

consider these services in greater detail.

We now consider some further aspects of the commercial, sport, subsistence 

and personal-use salmon fisheries in relation to the findings presented earlier in this 

chapter. We consider two broad scenarios. First, the ecological impact of warming 

conditions may lead to more fish, especially pink salmon in Prince William Sound. 

Second, substantial variations in response to climate change among populations and 

species will mean declines in fish abundance may occur, with a focus on sockeye 

salmon. These two scenarios, which represent very different outcomes, represent 

useful scenarios to examine because of the substantial uncertainty in future salmon 

populations based on the combined effects of freshwater and marine life history 

stages.
Figure 5-12—Volume of commercial salmon harvested from the combined Prince William Sound and Cook Inlet fisheries. 

Source: author calculations using raw data from the Alaska Commercial Fisheries Entry Commission. http://www.cfec.state.

ak.us/fishery_statistics/earnings.htm.

146

GENERAL TECHNICAL REPORT PNW-GTR-950

In discussing these scenarios, we focus in this section on social and economic 

factors and outcomes. The findings about salmon abundance reported above relate 

to what we may call the biophysical subsystem. There is also what we may call 

the social-economic subsystem, which exerts significant influence on the overall 

salmon-related social-ecological system (SES). The two subsystems interact directly 

through annual fish harvest management and catch effort, and indirectly through 

actions that affect fish habitat.

Commercial Salmon Harvest

The Alaska commercial salmon harvest fluctuates significantly over time (fig. 

5-12). This fluctuation results in part from numerous factors affecting total salmon 

biomass (which is, of course, not directly measurable). People directly influence 

some of these factors. Hatcheries—which accounted for about 37 million Prince 

William Sound pink salmon in 2015 (PWSSC 2015)—provide one salient example 

of this direct influence. Habitat alteration operates over longer time scales to affect 

biomass through biophysical channels.

350

300

250

200

150

100

50

0

Salmon harvest (million pounds)

Year

1975 1980 1985 1990 1995 2000 2005 2010
147

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Changes in fish biomass do not translate directly into changes in jobs, income, 

or profits to fish harvesters and others engaged in the seafood industry. There are 

several important intervening steps in the process that bear separate consideration 

when determining the social and economic vulnerability of the commercial fishing 

industry to climate change. Indeed, the social-economic subsystem exhibits its own 

sensitivity, exposure, and adaptive capacity, all of which determines the extent to 

which changes in biophysical abundance ultimately affect both total harvests and 

the allocation of those harvests among competing groups.

The first linkage is from biomass abundance to commercial harvest. This 

connection is mediated by the behavior of harvesters and fishery managers. Alaska 

fish harvesters have a long history of adapting their harvesting efforts to different 

places, different species, different technologies (such as fish traps or weirs), and 

different “rules of the game.” If salmon abundance shifts primarily geographically, 

harvests could plausibly shift as well.

The second link is from harvest volume to initial harvest value (“ex-vessel 

value”). This link depends critically on the price per pound received by harvesters. 

Ex-vessel price has fluctuated at least as much as harvest volume during the past 40 

years, with prices sometimes doubling or falling by half during 2- or 3-year periods. 

Volatile prices are determined by numerous factors, most of which are not directly 

affected by climate change. These include foreign exchange rates, shifts in consumer tastes, and the abundance and prices of other salmon—both wild (Russian) 

and farm-raised substitutes (chiefly farmed Atlantic salmon, which now accounts 

for two-thirds of total world salmon supply (Knapp 2013)).

Figure 5-13 shows, for the combined Prince William Sound–Cook Inlet salmon 

fishery, how salmon volume, price, and ex-vessel value have changed since 1975. 

The simple correlation coefficient for volume and value is only 0.3. However, when 

only the past 20 years are considered, the correlation between volume and value 

is actually much stronger (r = 0.8). The correlation between harvest volume and 

price during this 20-year period is slightly negative (r = - 0.3), indicating that high 

volumes might depress the price. If this is true, then the increase in pink salmon 

volume resulting from warmer temperatures might be attenuated by lower prices, 

with uncertain effects on harvester earnings.

There are additional links in the social-economic subsystem between exvessel or gross value, and measures of net economic benefit such as profits to fish 

harvesters or wages to participants in the processing industry (many of whom live 

in Alaska). In the short run, variable inputs can be adjusted, and in the longer run 

technological innovation could take place, all directed at reducing costs. Fundamental institutional innovation is also possible (Ulmer and Knapp 2005). For example, 

Changes in fish 

biomass do not 

translate directly 

into changes in jobs, 

income, or profits to 

fish harvesters and 

others engaged in the 

seafood industry.
Figure 5-13—Salmon volume, price, and value relative to 1975 for the combined Prince William Sound and Cook Inlet fisheries. 

Values for 1975 are set equal to 1.00. Source: author calculations using raw data from Alaska Commercial Fisheries Entry Commission. http://www.cfec.state.ak.us/fishery_statistics/earnings.htm.

148

GENERAL TECHNICAL REPORT PNW-GTR-950

the current system for salmon is based on limited entry into the fishery but unlimited rights to catch fish during openings. This system could be changed to a catchshare system such as the one now used for halibut, under which each harvester must 

own the harvesting rights to a specific number of fish.

The commercial pink salmon harvests in the assessment area are mostly of 

hatchery-raised fish. The overwhelming factor affecting the profitability of hatcheries is the return rate of adult fish from the ocean back to the hatchery. Because 

hatcheries have high fixed costs, they are vulnerable to complete shutdown if 

returns drop below some minimum threshold. Conversely, under a scenario of 

increased biomass associated with warmer temperatures, hatcheries could enjoy 

major economic benefits because their fixed costs are already covered so that 

additional fish contribute directly to net income.

Next, we consider the second scenario of possible declines in some fish populations and species resulting from the large variability in expected responses to 

ecological changes brought on by a warming climate. 

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0

Index (1975 = 1.0)

1975 1980 1985 1990 1995 2000 2005 2010 2015

Volume

Price

Value

Year
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

For sockeye salmon, if climate change results in a decline in numbers, the 

impact could be quite negative for the Alaska commercial industry. There are three 

reasons for this concern. First, the findings reported in this chapter are neutral-tonegative regarding any countervailing positive effects of warmer air temperatures 

on sockeye abundance. Second, it is possible that warming temperatures may correlate with reduced fishery catches of sockeye, even though the supporting data upon 

which this finding is based are quite limited. Third, sockeye are far more valuable 

(per fish) than pinks and cannot be easily augmented by hatchery technology. The 

average annual ex-vessel value of sockeye from the assessment area was about $70 

million during the period from 2009 to 2013. A 40 percent decline in abundance 

could result in about $30 million of lost ex-vessel value, which equals 18 percent of 

total commercial salmon ex-vessel value. 

Sport Salmon Harvest

The economic importance of sport fishing to the Alaska economy is well documented (Colt and Schwoerer 2009, Fay et al. 2007, Haley et al. 1999, Northern 

Economics 2009). As discussed in appendix 3, total spending by anglers in 2007 

on sport and personal-use fishing activities in south-central Alaska was about $1 

billion. This spending supported 11,535 jobs and generated $386 million of labor 

income (Southwick Associates 2008). These numbers are based on all species and 

include spending on the personal-use fisheries. Salmon constituted 74 percent of all 

fish caught by these groups (ADFG 2014), and the annual number of salmon caught 

was about 900,000 (sport) and 800,000 (personal use). Assuming that sport anglers 

spent three times as much per fish as personal-use dip netters, a rough estimate of 

spending on sport-fishery salmon harvesting is about $575 million, which would 

support about 6,600 total jobs. This calculation also yields the figure of $636 per 

fish for expenditures on sport fishing for salmon.

Because sockeye account for 59 percent of total sport-fishery salmon, by 

applying the figure of $636 per fish we can estimate that the assessment area sport 

sockeye fishery generates about $337 million of angler spending and supports about 

3,900 jobs. Even if the total sockeye abundance were to drop by 40 percent, and 

even if that decline were allocated proportionately to sport fishing, it is unlikely 

that sport anglers would reduce their effort and spending by 40 percent. Nonresidents in particular are arguably paying for a fishing experience rather than a certain 

number of fish. However, even if sport-fishing effort and spending were cut by only 

20 percent the result would be a loss of more than $67 million of spending and 777 

fishing-related jobs. When considering these potential losses of jobs and spending, 

A rough estimate of 

spending on sportfishery salmon 

harvesting is about 

$575 million, which 

would support about 

6,600 total jobs. 
150

GENERAL TECHNICAL REPORT PNW-GTR-950

keep in mind that the spending by Alaska residents would likely be redirected to 

other activities, and not lost from the economy altogether.

Subsistence Salmon Harvest

Only about 9,000 sockeye salmon—less than one-tenth of 1 percent—are caught by 

participants in officially designated subsistence fisheries (table 5-9). Because this 

number is so small, and because subsistence fisheries must be given a preference in 

times of shortage, it is reasonable to project that even if sockeye abundance were to 

fall by 20 or even 40 percent the salmon allocations to official subsistence fisheries 

could and likely would remain the same.

A potentially more important effect of warming for these fisheries could be 

changes in run timing. There is already some tension in some Alaska communities 

between participation in subsistence and participation in cash jobs and school (Colt 

et al. 2003). To the extent that participation in subsistence provides cultural benefits, it will be important for institutions such as schools and employers to attempt 

to accommodate shifts in the scheduling of subsistence fishing.

Personal-Use Salmon Harvest

The personal-use salmon fishery is completely focused on sockeye salmon, and 

accounts for about 8 percent of the total sockeye harvest from the assessment area 

(including the Chitina Subdistrict dip net fishery). Although some participants may 

derive cultural and educational benefits from participating, many if not most are 

seeking to fill their freezers with high-quality protein. It appears that personal-use 

dip net fishers currently have no interest in substituting pink salmon for sockeye. It 

also seems less likely that fishery managers would reallocate sockeye salmon from 

commercial or sport harvests to maintain the size of the personal-use fisheries. It is 

also the case that personal-use participants are not interested in nor directly affected 

by the commercial price of sockeye salmon.

For all these reasons, the personal-use fishery appears to be the most vulnerable 

to a significant decline in sockeye salmon biomass that result from ecological shifts 

brought on by climate change. Conversely, it would not benefit from an increase in 

pink salmon biomass. There were 44,125 permits issued for personal-use fisheries 

(average of 2011 and 2012 data) (Fall et al. 2013, 2014). That number equates to 

17.77 fish caught per permit. Assuming an average weight of 6.55 pounds (ADFG 

2015), each personal-use permit holder (typically one household) harvested 116.4 

pounds of sockeye salmon in 2011 and 2012. If this harvest were to be reduced 

by 20 or by 40 percent, participating households would see declines of 23 or 46 

pounds, respectively.

The personal-use 

fishery appears to be 

the most vulnerable to 

a significant decline 

in sockeye salmon 

biomass that result 

from ecological shifts 

brought on by climate 

change.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Ecosystem Services Summary of Findings

As this chapter has emphasized, warmer air temperatures may be associated with 

more pink salmon, while changes in sockeye abundance are much less certain 

and may be negative. There are likely to be many ways by which the commercial 

fishing industry can adjust to these possible changes in the abundance or location 

of salmon. The industry has a long history of adapting to both abrupt and gradual 

changes. The sport-fishing industry would likely have a more difficult time dealing with a major decline in sockeye salmon abundance simply because it is more 

focused on one species, and each fish is associated with significant spending and 

employment. However, reduced spending by Alaska residents on sport fishing 

would likely shift to other activities but would remain within the regional economy. 

Officially designated subsistence salmon fisheries in the assessment area could 

easily be maintained because they are currently allocated very few fish. (Of course, 

this conclusion would not be valid for other areas of Alaska with major subsistence 

fisheries, such as the Yukon River watershed.) Finally, the personal-use fisheries, 

which are exclusively focused on harvesting sockeye salmon by dip net, appear 

to be most vulnerable to a warming climate. A potential increase in pink salmon 

abundance would likely provide no increase in ecosystem services to this fishery. 

However, a decrease in sockeye abundance in the assessment area could translate 

directly into significant losses of food protein for more than 40,000 Alaskan 

households. 

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GENERAL TECHNICAL REPORT PNW-GTR-950

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Fall color above Eklutna Lake, Chugach Mountains, Alaska.

Patricia Hayward
Chapter 6: Vegetation

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Teresa Hollingsworth,1 Tara Barrett,2 Elizabeth Bella,3 Matthew Berman,4 Matthew 

Carlson,5 Paul Clark,6 Robert L. DeVelice,7 Greg Hayward,8 John Lundquist,9

Dawn Magness,10 and Tobias Schwörer11

Introduction

A challenging task is to assess current vegetation patterns across a gradient that 

extends from marine intertidal communities along the Prince William Sound and 

Kenai Peninsula coastlines, to coniferous forests and alpine tundra at higher elevations. Assessing future vegetation patterns is even more difficult. However, because 

vegetation is one of the most critical biotic components of terrestrial systems, 

describing the patterns of plant communities and species in the assessment area 

is a necessary initial step toward understanding the effects of climate change. 

Subsequent steps include projecting future patterns in these plant groups and a 

synthesis of expected changes in the vegetation patterns and potential alterations 

of ecosystem services.

Climate strongly influences plant species distributions at broad spatial scales, 

while species interactions, disturbance patterns, and soil conditions generally drive 

plant distributions at the local level (GBIF 2015, Pearson et al. 2003). With dramatic 

increases in mean annual temperature in Alaska in the last century (Wendler et 

al. 2012) and continued increases in growing-season length (SNAP 2012) (see 

1 Research ecologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station, Fairbanks, AK 99775.

2 Research forester, U.S. Department of Agriculture, Forest Service Pacific Northwest 

Research Station, , Wenatchee, WA 98801.

3 Ecologist, U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, , Soldotna, 

AK 99669.

4 Professor of economics, University of Alaska, Institute of Social and Economic Research, 

Anchorage, AK 99508.

5 Professor and Alaska Center for Conservation Science director, University of Alaska, 

Anchorage, AK 99508.

6 Recreation and trails program manager, U.S. Department of Agriculture, Forest Service, 

Chugach National Forest, Anchorage AK 99501.

7 Vegetation ecologist, U.S. Department of Agriculture, Forest Service, Chugach National 

Forest, Anchorage, Alaska 99501.

8 Wildlife ecologist, U.S. Department of Agriculture, Forest Service, Alaska Region, 

Anchorage, AK 99504.

9 Forest entomologist, U.S. Department of Agriculture, Forest Service, Alaska Region, 

State and Private Forestry, Anchorage, AK 99501.

10 Fish and wildlife biologist, Kenai National Wildlife Refuge, U.S. Department of the 

Interior, Fish and Wildlife Service, Soldotna, AK 99669.

11 Research associate, University of Alaska Anchorage, Institute of Social and Economic 

Research, Anchorage, AK 99508.
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chapter 3), plant distributions in this region are expected to change. Climate change 

is expected to affect plant species that are both of ecological significance (e.g., 

dominant trees) and of conservation concern (i.e., rare species and invasive species). 

Many examples are accumulating of distribution changes in response to climate or 

of spatial mismatches between optimal climates and the current climate the species 

experiences (e.g., McLane and Aitken 2012). 

Climate change affects the distribution of plant species in multiple ways. It can 

cause rapid range contraction, in which direct climate effects (conditions being too 

hot, too dry, too wet, or too cold) result in large-scale mortality. It can also cause 

slow range contraction, in which direct climate effects affect successful regeneration and establishment of seedlings of long-lived species. Climate change modifies 

interactions among species in both simple and complicated ways. For instance, 

climate alters competitive ability by differentially affecting growth, mortality, and 

regeneration for co-occurring (sympatric) species. Recent examples of large-scale 

tree mortality in North America (Abella and Fornwalt 2014, Bentz et al. 2010, Berg 

et al. 2006, Cudmore et al. 2010, Mitton and Ferrenberg 2012) suggest that climaterelated alterations in disturbance regimes—particularly fire and insects—are even 

more important factors in rapidly shifting distributions than are direct physiological 

impacts from climate change. 

Ecological Setting

The Copper River Delta on the eastern side of the assessment area comprises the 

largest wetland on the west coast of North America—an area of rapid wetland 

succession occurring as a result of multiple disturbance processes, which include 

flooding by one of the largest rivers along the west coast and changes in saltwater 

influence stemming from tectonic uplift (the 1964 earthquake) and gradual subsidence (Plafker et al. 1992). Prince William Sound dominates the center of the study 

area, supporting 4,355 islands (those larger than 100 m2) that result in varying 

degrees of biological isolation and challenges for species movement and establishment since the last glacial maximum. Like the islands of Prince William Sound, 

the Kenai Peninsula, which marks the western portion of the assessment area, is 

partially isolated from mainland biota and experiences substantial differences in 

climate from east to west as result of a north-south mountain range that intercepts 

the generally east-to-west flow of storm systems. As a consequence, the Kenai 

currently supports coastal rain forest along its eastern shores and transitional boreal 

forest on northwestern lowlands, with montane and alpine habitats in between. 

Elevations in the assessment area range from sea level along the extensive coast 

to Mount Marcus Backer in the Chugach Mountains at 4016 m.
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Understanding the potential influences of climate change on vegetation in 

south-central Alaska is impossible without some underlying knowledge of current 

vegetation patterns within the context of the directional development of vegetation that has occurred over the past 3,000 to 14,000 years following the last glacial 

maximum. In this chapter, we take a four-pronged approach to addressing vegetation change within the context of climate change: 

(1) We begin with a broad overview of environmental history and a description

of historical vegetation patterns, with particular emphasis on these patterns in

relation to climate change in the past.

(2) We follow the historical overview by reviewing current vegetation patterns.

This includes linking current patterns to past climate, abiotic factors, and

succession.

(3) We then outline scenarios of future vegetation cover.

(4) To close, we emphasize the potential consequences of a changing climate on

wildfire, which is one of the most important vegetation disturbance agents

in this system. Throughout the second and third sections of this chapter, we

employ a hierarchical approach, looking at current and future patterns in

biome-level vegetation, landcover types, coniferous spruce trees, rare species,

and invasive species. As in the other chapters of this assessment, we close with

consideration of the potential consequences for ecological services resulting

from changes in physical and ecological conditions.

Summary of Historical Vegetation Patterns 

• Vegetation across the assessment area is dynamic—the region has exhibited

directional vegetation change for thousands of years. Except for limited glacial refugia found on the Kenai Peninsula and on a few nunataks in Prince

William Sound, vegetation across the assessment area developed during

the past 10,000 to 14,000 years of glacial retreat, with relatively similar

patterns of primary succession and eventual development of forest on sites

capable of supporting tree growth.

• Vegetation development from bare ground, or primary succession, which

can be observed near recent areas of glacial retreat, illustrates the process

of directional vegetation development that has occurred throughout the

region since the last glacial maximum.

• Conifer forests were rare east of the Kenai Mountains until about 2,000

years ago. Sitka spruce woodlands likely formed on the eastern side of the

Kenai Peninsula and on islands throughout the Sound beginning 3,000

Understanding the 

potential influences 

of climate change on 

vegetation in southcentral Alaska is 

impossible without 

some underlying 

knowledge of current 

vegetation patterns.
166

GENERAL TECHNICAL REPORT PNW-GTR-950

years ago, but well-developed temperate coastal rain forests became common only in the last 2,000 years. Boreal forest to the west of the Kenai 

Mountains formed earlier by migration of species from ice-free refugia. 

Boreal forests spread across the western Kenai about 4,600 years ago.

• Migration of plants via seed and vegetative reproduction occurred from

source populations in southeast Alaska, small areas of glacial refugia

throughout the assessment area, and for boreal species, from forests to

the north.

• Historically, fire has not played a major role in the ecosystems of coastal

temperate rain forests. In the boreal region of the Kenai, fire was historically important and was tightly linked to historical vegetation patterns.

The Future Is Contingent On the Past: Historical 

Changes in Vegetation

Climate ultimately determines the potential distribution of vegetation at broad 

spatial scales (Cain 1944, Woodward 1987). However, ecological processes, regional 

climate variation, geographic barriers to dispersal, and soil factors all act to influence the realized distribution of species, thereby determining their historical and 

current geographic distribution (Kruckeberg 2002). Almost complete glacial cover 

during the last glacial maximum, followed by gradual deglaciation over the past 

12,000 to 20,000 years, and more rapid ice lost since the end of the Little Ice Age 

(circa 1850) created a geologically “new” landscape that was quickly colonized by 

short-lived species that can thrive post-disturbance (fig. 6-1). Below, we examine 

the short- and long-term ecological history of the assessment area to illustrate how 

climate resulted in directional change that has occurred for millennia and to provide 

context from which to evaluate potential future changes resulting from humaninduced climate change in the future. 

Long-Term Vegetation History

Inspection of the fossil record reveals that dramatic changes in vegetation occurred 

repeatedly in the past in response to changes in global climate. Particularly striking 

was the occurrence of temperate hardwoods such as oak, hickory, beech, chestnut, 

elm, and sweetgum in interior and south-central Alaska during the Miocene (17 

to 14.5 mya). Following an abrupt transition from the extremely warm Miocene 

period, temperatures oscillated and tree species diversity was extremely high compared to the present. The region supported a range of conifers including Douglasfir, redwood, pine (related to lodgepole pine), and firs (Ager 2007). Tree species 

diversity declined during glacial/interglacial cycles of the Pliocene (2 to 6 mya), 
Figure 6-1—Extent of glacial ice at different periods in the past, demonstrating the directional decline in glacier cover during the past 

20,000 years or more and the current extent of glaciers. Source: Alaska PaleoGlacier Atlas (version 2) (Manley and Kaufman 2016).

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

resulting in the loss of hemlock, fir, and pine from interior forests (Ager 2007). The 

last time temperatures in Alaska were warm enough to support extensive forest in 

the interior was 125,000 years ago during the last interglacial—a warm period that 

lasted about 20,000 years and represented one of many brief warm periods in the 

past 2 million years. During this period, the mean annual temperature of interior 

Alaska was 2 °C warmer than today (fig. 6-2) (Ager 1997, 2007). During the most 

recent glaciation (ending about 27,000 to 12,000 years ago), sea level dropped about 

125 m below its current level, and ice extended far onto the continental shelf of 

the current Gulf of Alaska (Ager 2007: 9–18). Vegetation in nearby regions that 
Figure 6-2—Illustration of dramatic variation in long-term global temperatures that influenced vegetation in 

the assessment area. Figure presents general trends in Pacific Ocean seawater temperature from oxygen isotope 

measurements in marine fossils. Source: Ager (1997).

Major global

warming

Major 

glaciation

) Glaciation

 southern

 Alaska

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

20,000,000

Temperature Trends

(from marine oxygen isotope data)

Warmer Colder Age (years) Geologic age

PLEISTOCENE

PLIOCENE

Late

MIOCENE Middle

Early

168

GENERAL TECHNICAL REPORT PNW-GTR-950

were not overrun by glaciers 13,000 years ago appears to have been dominated by 

species present in contemporary wet and mesic meadows and Empetrum heathlands 

(Peteet and Mann 1994).

Short-Term Vegetation History (~10,000 Years BP)

The vast majority of land in the assessment area was covered with ice for much of 

the past 100,000 years, setting the stage for a dynamic change in vegetation and 

resulting in the array of tundra, shrublands, wetlands, boreal forest, and coastal rain 

forest we observe today (Ager 2007, Jones et al. 2009). Deglaciation, which began 

earlier on the western Kenai Peninsula than on Prince William Sound and the 

Copper River Delta, set the stage for vegetation development. However, proximity 

of plant seed sources, prevailing storm tracks and wind (dispersing some seeds), 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

topography, and climate gradients all strongly influenced this pattern. Several small 

areas within the assessment area remained ice-free during the last glacial period. 

Such areas are called refugia. Identified biological refugia include several mountaintop nunataks (exposed land) on Hinchinbrook, Montague, and Knight Islands, 

and more extensive areas on the western Kenai, including the far northern lowlands, 

an area between Skilak and Tustumena Lakes, and portions of the Caribou Hills 

north of Homer (Ager 2001, Heusser 1983). The significance of refugia is that, postglaciation, these areas often served as seed sources for colonization and continue to 

disproportionately harbor rare and widely disjunct populations (Carlson et al. 2013). 

However, most plants are believed to have established from dispersal events from 

outside the region, notably from southeast Alaska.

Mountain ranges interact with prevailing storm tracks that typically bring 

westerly winds, strongly influencing the pattern of plant dispersal. The Kenai 

Mountains, forming a north-south spine dividing the peninsula, and Chugach 

Mountains forming the northern boundary of the assessment area, were particularly 

important. Several authors have suggested that coastal rain forest species migrated 

from coastal Canada or southeast Alaska following deglaciation, and that this 

dispersal was dependent on prevailing winds (Ager 2001, Jones et al. 2009). Other 

dispersal routes of plants into the assessment area appear to have been along the 

Copper River drainage and low passes in the Talkeetna and Chugach Mountains.

Broad climatic gradients appear to have been maintained through much of 

the post-glacial vegetation development. The strong moisture and temperature 

gradients that led to very wet conditions throughout Prince William Sound and 

the Copper River Delta, along with mild summer and winter temperatures in this 

same region, structured the resulting vegetation. A rapid decline in precipitation 

and more extreme summer and winter temperatures occur from east to west. As a 

consequence, substantially different patterns of vegetation recolonization occurred 

on the eastern and western side of the Kenai Mountains (Ager 2007, Heusser 1983, 

Jones et al. 2009). In most areas throughout the assessment area, coastal and sedge 

tundra initially occupied newly exposed land. In the western Kenai and Anchorage 

regions, where present-day boreal forest types occur, post-glacial vegetation transitioned from tundra to shrub birch, alder, and willow, followed by establishment of 

white spruce by 8,500 BP and black spruce by 4,600 BP (Ager 2001, Anderson et 

al. 2006). The source of spruce and other trees is uncertain—some trees may have 

remained in refugia on the Kenai, or the boreal forest trees moved southward from 

the interior through low passes in the Talkeetna Mountains (Ager 2007, Jones et al. 

2009). Regardless of their origins, boreal forest trees were present 8,500 years ago 

and boreal forests began forming 4,600 years ago in the western Kenai.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Forest development occurred much later east of the Kenai Mountains in the 

broad region from the Delta through western Prince William Sound. Deglaciation 

occurred later in this region because ice thickness was greater and melting slower, 

resulting in development of vegetation beginning as late as 10,000 to 9,000 years 

ago in portions of the Sound as compared to 100,000 years ago on the western 

Kenai (Heusser 1983). Cooler summer temperatures and higher precipitation also 

supported alder, sedge, and fern communities that persisted for long periods. In 

some areas, alder appeared to dominate large areas for more than 1,000 years (Ager 

2007, Heusser 1983). Mountain hemlock and Sitka spruce moved in from the east 

around 3,000 years ago (Ager 2001), and forest communities of hemlock and spruce 

did not develop in the western Sound until about 2,000 years ago (Heusser 1983). 

Currently, Sitka spruce continues to migrate westward along the Gulf of Alaska 

on Kodiak Island at a rate estimated at about a mile per century (Griggs 1934), and 

can be found along the northeast coast of the Alaskan Peninsula. Western hemlock 

currently has not migrated into Cook Inlet (Heusser 1983), but Sitka spruce has, and 

reaches its northern limit near Palmer, Alaska (Viereck and Little 2007). An expansion of black spruce in the Kenai lowlands may have followed the end of the Little 

Ice Age in the 1850s (Berg et al. 2009). 

Fire and defoliating and wood-boring insects also influenced past forest conditions in the assessment area. Historically, fires were not uncommon in the western 

Kenai. During the early and mid Holocene, estimated mean fire-return intervals on 

landscapes in the Kenai lowlands ranged from 77 years during birch/willow/cottonwood phases to 138 years during shrub/herb tundra phases. White spruce and black 

spruce forests experienced fire-return intervals of about 80 and 130 years, respectively (Anderson et al. 2006). Historically, fires were notably infrequent in the 

eastern portion of the assessment area. Since the establishment of coastal rain forest 

dominated by Sitka spruce and hemlock about 3,000 years ago, fire has played little 

role in ecosystem dynamics. 

During the past couple centuries, human ignitions have become more important 

in the western portion of the assessment area. Although Alaska Native peoples have 

been present in south-central Alaska for thousands of years, there is no evidence 

that they used fire as a land management tool. Gold miners set fires to clear land for 

prospecting, particularly in the Kenai Mountains, and seem to have unintentionally 

created extensive moose habitat.12

Major fires occurred on the western Kenai during a number of years beginning 

in the late 1800s (Lutz 1960 as cited by Morton et al. 2006). The basic cause for 

12 Boraas, A. 2013. Personal communication. Professor of anthropology, Kenai Peninsula 

College, Soldotna, AK 99669.

Fire and defoliating and 

wood-boring insects 

also influenced past 

forest conditions.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

these fires was attributed to railroad activity igniting 95 fires between 1932 and 

1953. The drought conditions following the 1912 Katmai Volcano eruption also 

contributed to fire behavior by creating favorable weather for burning. Holbrook 

(1924) reported that “the region has been visited by numerous fires and most of the 

better grade of timber has been burned.” He mapped about 12 000 ha of burned 

area on the forest. These large fires included the Resurrection Creek watershed 

covering 4050 ha. Following World War II, several large fires occurred in the 

western Kenai. Fires in 1947 and 1969 burned 125 000 and 34 800 ha, respectively. 

From 1990 throught 2012, about 57 000 ha of forest burned. Near the end of the 20th

century, an average of 66 wildfires occurred on the peninsula each year, most being 

very small (fig. 6-3).

The historical influence of insects on forest structure and composition was 

more dramatic west of the Kenai Mountains—the boreal forest region rather than 

the coastal region. In the boreal forests, over time, defoliators periodically erupt 

and remove the majority of leaves across large areas. Similarly, in the recent past, 

spruce bark beetle represents a dominant disturbance in the boreal forest, with a 

mean return interval of around 50 years on the Kenai Peninsula (Berg and Anderson 2006). Based on tree-core evidence, Berg et al. (2006) found that an outbreak of 

spruce beetle occurred on the Kenai in the late 19th century. The late-20th-century 

outbreak appears to be representative of past spruce mortality events and indicates 

that beetles represent an important part of the ecological history of the western 

Kenai Peninsula. 

Summary: Current Vegetation 

• There are nine vegetation cover types within the assessment area.

• Spruce is the dominant conifer in the assessment area, with Sitka spruce

dominating in the temperate rain forest and white and black spruce dominating in the boreal forest.

• Across the assessment area, there are 53 rare-to-imperiled plant species that

are found in eight different habitat types.

• 159 taxa of non-native species have been found within the assessment area,

occupying approximately 0.04 percent of the total area of the region.

• Fire continues to be rare in the temperate coastal rain forest. However,

in the western Kenai where boreal (and transition-boreal) vegetation

occurs, large fire events, although uncommon, are an important factor in

structuring the current vegetation pattern. Fire events are often linked to

broad climatic patterns, although human activity has also played a role in

promoting fire in the assessment area.
human infrastructure on the peninsula. Source: KPBOEM (2004).

172

GENERAL TECHNICAL REPORT PNW-GTR-950
173

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Case Study: Historical and Current Patterns of Spruce 

Beetle Outbreaks and Long-Term Consequences on 

Vegetation

John Lundquist, supervisory forest entomologist, Chugach National Forest

The history of spruce beetle outbreaks on the Kenai Peninsula was reconstructed for a period extending from the 1770s to the mid 1990s by using 

tree-ring growth-pulse profiles (Berg and Anderson 2006, Berg et al. 2006). 

Based on these chronologies, Berg and his colleagues found that sites differed 

in specific outbreak histories, but that distinct widespread outbreaks occurred 

in the 1810s, 1870s, 1910s, 1970s, and 1990s (fig. 6-4). Outbreaks in the 1810s, 

1910s, and 1970s were mild (they killed a low proportion of trees in any single 

area) but extensive, creating a diversity of forested and nonforested patches 

across the landscape. Because these outbreaks affected forest patterns across 

broad areas, they helped maintain landscape heterogeneity, and, presumably, 

reduced the chances of future regional catastrophes caused by various disturbance agents, including spruce beetles or wildfire. 

The 1880s and 1990s outbreaks were exceptions. The earlier outbreak 

killed so many trees that it reset large contiguous areas of unmanaged spruce 

forests to a common age, reducing the inherent patchy heterogeneity of the 

25

20

15

10

5

0

-5

Severity

Spruce Beetle Activity

18151835185518751895191519351955197519952010

Year

Major outbreaks

Minor outbreaks

1990s

outbreak

Figure 6-4—Intensity of spruce bark beetle infection on the Kenai Peninsula over a 200-year 

period based on a variety of evidence (adapted from Berg et al. 2006).
174

GENERAL TECHNICAL REPORT PNW-GTR-950

forested landscape. Without additional disturbances, from 50 to 70 years would 

be required for the surviving forest to reach crown closure and for individual 

trees to become large enough to be susceptible to spruce-beetle attack. By the 

1990s, vast areas of the forests on the Kenai had grown dense and into relatively 

large trees of susceptible size. In this way, the 1880s outbreak predisposed forests 

on the Kenai Peninsula to the 1990s outbreak, even though the two events were 

separated in time by more than 100 years! 

In addition to forest conditions and the size class of trees, climate also plays 

a role in spruce-beetle outbreak dynamics, but its role may not be entirely obvious. Research based mostly on the 1990s outbreak has led to several hypotheses 

about the cause of changes in beetle behavior (table 6-1), and they are all directly 

or indirectly related to warming temperatures. 

A conceptual ecological model represents a foundation from which to 

discuss the potential influence of a changing climate on bark beetle dynamics, 

their impacts, and the resulting patterns of recovering forest growth. There is 

an inherent limit to the frequency of broad-scale, intense bark beetle mortality 

events, given the substantial lag needed for trees to grow to susceptible size 

classes between catastrophic mortality events. But warming temperatures could 

increase tree growth rates, reducing this inherent time-lag between outbreaks. 

Low winter temperatures could reduce overwinter survival of beetles, slowing 

rates of beetle population growth. Earlier onset of spring could trigger changes 

in the number of annual life cycles, leading to exponential increases in beetle 

Table 6-1— Hypotheses on what caused the 1990s Kenai spruce beetle outbreak

Attack hypothesis Climate condition Spruce beetle response

Winter beetle mortality Moderate winter temperatures and 

abundant snowfall

Low winter mortality

Early spring Early increasing spring temperatures Early emergence, attack, oviposition, and 

egg hatch

2- to 1-year life cycle Early increasing spring temperatures Switch from 2- to 1-year life cycle

Host stress Increasing summer temperatures with 

lower precipitation

Increased host tree susceptibility caused by 

water deficit stress

Frozen root Cold soil, frozen roots while temperatures 

warm in spring

Increased host tree susceptibility caused 

by increased demand for water while the 

amount roots can supply are limited

Growth season length Lengthened growth season Longer time to cause damage
175

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

abundance. Depending on patterns of moisture, changing climate could reduce the probability 

of broad-scale beetle outbreaks if precipitation increases to relieve water stress, and if moisture 

patterns become less variable between years. Alternatively, high variation in precipitation, 

especially if a combination of temperature and precipitation leads to late-summer water stress, 

could result in periodic conditions that promote effective beetle attacks. 

Maps of the statewide distribution of spruce beetles, based on Forest Health Management 

surveys since the 1940s, show that the earliest reports of infestations occurred simultaneously 

at several different locations across south-central Alaska. Foci can be identified around Copper 

River, near Wasilla, on Afognak Island, and near McGrath (fig. 6-5). Subsequently, populations of spruce beetles spread unevenly across the geography of Alaska. The paths they took 

were probably influenced by the topography, climate, elevation, and latitude. In this regard, 

two notable jumps in distribution occurred from 1980 to 1990 and from 1990 to 2000. Another 

notable jump occurred in 2007 and is indicated by the lobe jutting northeastward toward the 

Charlie River at the northeast side of the state. Arguably, the pattern suggests that outbreaks of 

increasing severity migrating northward, which is likely to continue with further warming. ■

* Dates of expansion

are approximate

2007

2000

1990

1980

1970

1960

1950

1940

1920

Long-term expansion of beetle outbreak distribution

Figure 6-5—Historical distribution of reported spruce beetle outbreaks in Alaska, and the large-scale, long-term 

trend of their spread across the state (Lundquist 2009).
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GENERAL TECHNICAL REPORT PNW-GTR-950

Context for Climate Change: Current Vegetation 

Patterns and Plant Distributions 

Current Disturbance Regimes

Study of more recent fire history suggests broad patterns that are similar to the 

long-term record described above. Fire continues to be rare and never covers more 

than a few hectares in the coastal rain forest. As in the past, the transitional boreal 

forests of the western Kenai Peninsula have experienced a relatively complex pattern of fire in response to existing vegetation, human ignition rates, and interannual 

variation in weather. 

Currently, fires are relatively uncommon in the boreal forests of the western 

Kenai. However, when they occur, they are an important part of the disturbance 

ecology because individual events can be large. Natural fire occurs in the Kenai 

boreal biome based largely on climate conditions—uncommonly dry windy conditions, particularly in spring before green-up, result in high rates of spread. Fires in 

1947, 1969, and 2014 resulted in large disturbances—310,000, 86,000, and 194,000 

ac (125 000, 35 000, and 78 500 ha) burned, respectively. Nonforest vegetation also 

experiences fire, but fires in these vegetation types are more difficult to characterize because they leave less-discernible legacies. 

Climate ultimately determines the potential distribution of vegetation at broad 

spatial extents (biomes), while interactions among disturbances, soils, weather, and 

history determine the character of vegetation at finer spatial scales. Recent patterns 

of fire occurrence and the probability of fire differ substantially across the assessment area (fig. 6-6). 

More than 500 000 ha of bark beetle activity was recorded (Werner et al. 

2006) on the Kenai Peninsula during the 1990s, and an estimated 1.6 million ha of 

spruce forest in south-central Alaska was affected. In many stands, tree mortality 

reached 85 to 98 percent of all spruce over 6 inches diameter at breast height. The 

bark beetle episode lasted about 10 years at high intensity. Tree mortality resulting 

from bark beetle attack changed the character of the landscape, setting in motion 

a sequence of changes in the probability of fire and the potential spatial extent of 

individual fire events across the western Kenai. Although fire conditions changed 

rapidly, the level of change was not unique to this bark beetle event. 

Regardless of the historical pattern of bark beetles on the Kenai (see case 

study on page 173), the recent bark beetle event changed fuel characteristics on the 

peninsula, and vegetation development following the event continues to change 

the probability of fire and the potential pattern of disturbance given an ignition. 

The greatest increase in fine fuels is represented by native bluejoint grass (Calamagrostis canadensis) (Wahrenbrock 2009) that increased in extent following 

Climate ultimately 

determines the 

potential distribution 

of vegetation at 

broad spatial extents, 

while interactions 

among disturbances, 

soils, weather, and 

history determine the 

character of vegetation 

at finer spatial scales.
-

177

Climate Change Vulnerability Assessment or the Chugach National Forest and the Kenai Peninsula
178

GENERAL TECHNICAL REPORT PNW-GTR-950

mortality of spruce. Spruce mortality contributed to the accumulation of both fine 

and large fuels. Over time, as trees lose branches and fall, the characteristics of 

fuels, particularly large fuels, are changing. 

In 2004, the Kenai Peninsula Borough released an action plan for fire prevention and protection (KPBPEM 2004), hereafter called the All Lands Action Plan. 

A major focus of this interagency effort was development of a fire hazard and risk 

classification for a large portion of the Kenai Peninsula. The report represents the 

best overview of current conditions on the portion of the assessment area where 

fire is an important ecological and economic agent (fig. 6-7). Other assessments 

of fire hazard or risk have also been produced (e.g., Hansen and Naughton 2013); 

however, the Kenai Borough product seems most relevant to our assessment and 

will be applied below. The U.S. Forest Service has not produced a fire regime map 

nor mapped fire hazard/risk for the area (see recent fire management plan) (USDA 

FS 2014).

Current Distribution of Biomes and Land Cover Types 

Ecological communities can be classified at multiple spatial scales. Biomes are the 

broadest ecological unit and are defined mainly by climate. Different classification schemes have defined slightly different biome delineations for the assessment 

area. Depending on the classification, there are two or three major biomes in the 

assessment area (fig. 6-8), and within these biomes a variety of species occur with 

only minimal overlap in distribution. Coastal temperate rain forest and transitional 

boreal forest in the Kenai lowlands are always delineated as biomes (Nowacki et 

al. 2001). Other classifications also break out alpine tundra in the mountains as a 

distinct biome (Brown et al. 1998, Viereck et. al.1992). 

Land cover types are generalized vegetation classes, such as deciduous or 

evergreen forests that are defined at finer spatial scales than a biome. The spatial 

distribution of land cover types is mediated by climate and other regional factors 

such as dispersal barriers and hydrology. Eleven land cover classes have been 

identified in south-central Alaska using standard remote-sensing approaches (fig. 

6-9; table 6-2). These 11 land cover types include nine vegetation cover types and 

two types free of vegetation. The nine vegetation cover types include deciduous 

forest, evergreen forest, mixed forest, dwarf scrub (including alpine), shrub/scrub, 

grassland/herbaceous, woody wetlands, emergent herbaceous wetlands, and sedge/

herbaceous (table 6-3).

The 11 land cover 

types in south-central 

Alaska include nine 

vegetation cover types 

and two types free of 

vegetation.
KPBOEM (2004).

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Community protection zone

Wildland-urban interface

Property with structures

Wildfire risk

Extreme

High

Moderate

Low

None

0 10 20

Miles
Figure 6-8—Biomes of the Chugach-Kenai climate vulnerability assessment area. The four biomes are aggregates of Davidson (1996) 

ecological subsections.

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GENERAL TECHNICAL REPORT PNW-GTR-950

Biomes

Boreal

Icefield

Rain forest

Transitional

Sitka spruce (Picea sitchensis), mountain hemlock (Tsuga mertensiana), and 

western hemlock (T. heterophylla) are dominant trees in evergreen forests of the 

coastal rainforest biome. White spruce (Picea glauca), black spruce (P. mariana) 

and Lutz spruce (P. × lutzii) are dominant trees in the evergreen forests of the 

transitional boreal biome. Birch (Betula papyrifera var. kenaica and B. neoalaskana), aspen (Populus tremuloides), and black cottonwood (P. balsamifera ssp. 

trichocarpa) are common deciduous trees in the boreal forest. Although black 

cottonwood is the only deciduous species to occur in the rain forest, it is only rarely 

dominant. Diverse shrublands, classified as shrub/scrub, include Sitka alder (Alnus 

viridis spp. sinuata), salmonberry (Rubus spectabilis), multiple species of willow 

(Salix spp.), and bog birch (Betula nana and B. glandulosa). Alpine vegetation, 

generally classified as dwarf shrub, occurs on mountains above about 1,500 ft. 

(450 m) in the absence of glaciers or rock. Some species include Loiseleuria procumbens, Empetrum nigrum, Cassiope stelleriana, Hierochloe alpina, Phleum alpinum, Carex pyrenaica, Artemisia arctica, and Phyllodoce aleutica. Peatlands also 

occur across the assessment area and support dominant vegetation ranging from 
0

0

25 50 100 miles

Land cover classes

Snow/ice/barrenShrublandForestedWaterHerbaceousDeveloped

37.5 75 150 kilometers

ALASKA

Assessment

area

Figure 6-9—Distribution of 11 National Land Cover Database (NLCD) classes across the assessment area. See table 6-2 for details of vegetation classes.

181
182

GENERAL TECHNICAL REPORT PNW-GTR-950

Table 6.2—Descriptions of the 11 land cover types in south-central Alaska

Label Description

Perennial ice/snow All areas characterized by a perennial cover of ice or snow, generally greater than 25 

percent of total cover.

Barren land (Rock/sand/clay) Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, 

glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen 

material. Generally, vegetation accounts for less than 15 percent of total cover.

Deciduous forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent 

of total vegetation cover. More than 75 percent of the tree species shed foliage 

simultaneously in response to seasonal change.

Evergreen forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent of 

total vegetation cover. More than 75 percent of the tree species maintain their leaves all 

year. Canopy is never without green foliage.

Mixed forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent 

of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 

percent of total tree cover

Dwarf scrub Alaska only areas dominated by shrubs less than 20 cm tall with shrub canopy typically 

greater than 20 percent of total vegetation. This type is often co-associated with 

grasses, sedges, herbs, and nonvascular vegetation tundra and may be periodically or 

seasonally wet and/or saturated with water. This type commonly occurs in alpine or 

tundra areas and may contain permafrost.

Shrub/scrub Areas dominated by shrubs less than 5 m tall with shrub canopy typically greater than 

20 percent of total vegetation. This class includes true shrubs, young trees in an early 

successional stage or trees stunted from environmental conditions.

Grassland/herbaceous Areas dominated by grammanoid or herbaceous vegetation, generally greater than 80 

percent of total vegetation. These areas are not subject to intensive management such 

as tilling, but can be used for grazing.

Woody wetlands Areas where forest or shrub land vegetation accounts for greater than 20 percent of 

vegetative cover and the soil or substrate is persistently saturated with or covered with 

water.

Emergent herbaceous wetlands Areas where perennial herbaceous vegetation accounts for greater than 80 percent of 

vegetative cover and the soil or substrate is persistently saturated or covered with 

water.

Sedge/herbaceous Alaska only areas dominated by sedges and forbs, generally greater than 80 percent of 

total vegetation. This type can occur with significant other grasses or other grass-like 

plants, and includes sedge tundra, and sedge tussock tundra and may be periodically or 

seasonally wet and/or saturated. This type may contain permafrost.

forest (black spruce), to shrublands (e.g., Empetrum nigrum, Cassiope stelleriana, 

Vaccinium uliginosum), to herbaceous cover (e.g., Sphagnum fuscum, Carex limosa, 

Trichophorum caespitosum, Eriophorum angustifolium, Andromeda polifolia). 

Depending on the primary composition, peatlands are classified as woody wetlands 

(forest or shrub cover >20 percent) or emergent herbaceous wetlands (perennial 

herbaceous vegetation cover >80 percent).
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-3— Percentage of the assessment 

area occupying 11 land cover types

Percent

Barren land (rock/clay/sand) 24.34

Shrub/scrub 23.87

Perennial ice/snow 19.86

Evergreen forest 14.67

Dwarf shrub 5.89

Deciduous forest 3.89

Woody wetland 3.08

Mixed forest 2.90

Emergent herbacous wetlands 1.42

Grassland/herbaceous 0.06

Sedge/herbaceous 0.03

Current Distribution of Spruce Species

Spruce occur as dominant species in several vegetation types that cover significant 

portions of the assessment area. As dominant taxa, these spruce, which include 

white, black, and Sitka spruce, exert a strong influence on ecosystem composition, 

structure, and function. For example, they can change soil temperature (through 

shading), soil chemistry (through litterfall), and soil turnover rates (through treefall and tip-up mounds) (Alban et al. 1978, Bonan and Shugart 1989, Schaetzl et al. 

1988). They influence fire behavior because of their flammability and architecture 

(Cronan and Jandt 2008). Spruce forests provide habitat for many birds, small 

mammals, insects, and pathogens, and therefore help determine the biodiversity of 

landscapes. Examining trends in distribution of spruce species provides significant 

insight into current and future ecosystem conditions. Here, we review the current 

distribution of black, white, and Sitka spruce across the assessment area to provide 

a context for examining a scenario for changes in their distribution.

The distribution of Alaska’s three spruce species corresponds with two distinct 

forest biomes: Sitka spruce distribution matches the temperate rain forest, and black 

spruce and white spruce distribution reflects the boreal forest (fig. 6-10). A series 

of mountain ranges separates these two biomes along most of the Gulf of Alaska. 

However, the Kenai Peninsula and the forests surrounding Cook Inlet represent a 

rare transition zone between the two biomes lacking the mountain barrier found 

elsewhere. As a geographic area with rapid transition between biomes, but lacking 

a clear physical barrier, the Cook Inlet/Kenai Peninsula region has the potential to 

experience substantial climate change effects, shuffling the distribution of these 

two biomes. 

The distribution of 

Alaska’s three spruce 

species corresponds 

with two distinct 

forest biomes: Sitka 

spruce distribution 

matches the temperate 

rain forest, and black 

spruce and white 

spruce distribution 

reflects the boreal 

forest.
Figure 6-10—The distribution of Sitka spruce indicates the temperate rain forest biome in Alaska, while black spruce and white spruce 

distribution define the boreal forest biome. Note: geographic information system data for species ranges was obtained from Les Viereck 

and corresponds to Viereck and Little (2007).

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GENERAL TECHNICAL REPORT PNW-GTR-950

White spruce

Black spruce

Sitka spruce
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

In the past, spruce has exhibited fairly low potential migration rates. Following 

the most recent deglaciation, it took thousands of years for Sitka spruce to recolonize Prince William Sound from southeast Alaska. Dispersal attributes for Alaska 

spruce species include:

• Minimum seed-bearing age: 10 to 40 years

• Seed type: winged seed 10 to 12 mm long.

• Seed dispersal mode: wind.

• Large seed crop frequency: 2 to 12 years.

• Seed dispersal distance: 30 to 800 m; most seed falls within 100 m.

• Lifespan: varied, from 800 years (Sitka spruce) to about 200 years

(black spruce).

Current Distribution of Rare Species

In contrast to dominant species such as trees, rare plant species are more likely to 

be affected by more subtle and finer spatial-scale environmental change. Additionally, despite their low abundance, rare plant species represent an important 

component of biodiversity and in some cases contribute to ecological resiliency (see 

Mouillot et al. 2013). Rare species themselves, however, are typically quite sensitive 

to ecological perturbations and are vulnerable to extinction (Gaston 1994). 

The concept of “rarity” incorporates multiple spatial, demographic, and 

ecological elements for a given species in a region. Species are classified as rare 

based on their geographic range size, degree of habitat specificity, and population 

size (Rabinowitz 1981). Thus a species may be considered rare if it has a limited 

geographic range (narrowly endemic) even if it is locally abundant. Alternatively, 

a species may be widely distributed but tightly restricted to unusual habitat types 

(e.g., inland sand dunes or serpentine soils), and a rare species may be widespread 

but always occur at chronically low population sizes. Last, species may encompass 

multiple forms of rarity, such as those that are narrowly endemic and are habitat 

specialists. See AKNHP (2014) for a description of rarity ranks used in Alaska. 

The forms of rarity that are most vulnerable to extirpation are those with narrow 

geographic range sizes or those that specialize in habitats that are also likely to be 

affected by resource development.

Patterns of plant rarity in Alaska are notably different from other states. The 

density of rare plants is considerably lower (i.e., 15× lower) relative to Pacific 

Northwest states such as Washington and Oregon. A larger proportion of rare species in Alaska are widely distributed habitat specialists rather than narrow endemics 

(Carlson and Cortés-Burns 2012). Finally, fewer globally rare and federally listed 

plant species are known from Alaska (Carlson and Cortés-Burns 2012). The low 

number of species at risk of extirpation in Alaska is due to a combination of the 

Despite their low 

abundance, rare plant 

species represent an 

important component 

of biodiversity and in 

some cases contribute 

to ecological resiliency.
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GENERAL TECHNICAL REPORT PNW-GTR-950

biogeography of biodiversity and the extent of wilderness, which results in fewer 

threats from habitat conversion, the primary cause of species endangerment (Meffe 

and Carroll 1997, Wilcove and Master 2005). 

Recorded populations of rare plants occur throughout the assessment area, but 

with lower densities in the Kenai Mountains and lowlands, and in the Chugach 

Mountains near Prince William Sound. Fewer recorded plants in these regions are 

likely owing to lower collection intensity and large regions of unsuitable habitat 

(ice fields). A large number of rare plants has been recorded in the Anchorage Bowl 

and adjacent western Chugach Mountains. This high density is partially an artifact 

of very high collection intensity, as well as high topographic, environmental, and 

geologic complexity. 

Fifty-three rare-to-imperiled plant species have been documented in the assessment area by the Alaska Natural Heritage Program (see http://aknhp.uaa.alaska.edu/

botany/rare-plant-species-information/) (table 6-4). The global and regionally rare 

taxa are found in a range of habitat types: four species are restricted to freshwater 

aquatic habitats and seven are associated with intertidal and supratidal habitats. 

Nine species are associated with wetlands; six with woodlands; 17 with meadows; 

two with steppe bluffs; and seven with rock outcrops and alpine slopes. One species 

is found in multiple habitat types. More than half of the rare wetland species are 

associated with neutral, calcareous, or alkaline substrates. Rare taxa are strongly 

represented by the Cyperaceae (14 taxa), Poaceae (seven taxa), and Brassicaceae 

(four taxa). The majority of these species are secure globally, but reach their distributional limits in the Chugach. For example, western fescue (Festuca occidentalis) 

is ubiquitous in the Pacific Northwest and British Columbia, but is known from 

only three populations in Alaska, two of which are on the Kenai Peninsula. Six 

taxa are considered rare globally: Sessileleaf scurvygrass (Cochlearia sessilifolia), 

Harold’s milkvetch (Astragalus robbinsii var. harringtonii), fourpart dwarf gentian 

(Gentianella propinqua ssp. aleutica), Alaska hollyfern (Polystichum setigerum), 

Pacific buttercup (Ranunculus pacificus), and Alaska mistmaiden (Romanzoffia 

unalaschcensis). 

Current Status of Non-Native Species

Invasive species represent one of the greatest threats to ecosystems and economies 

globally and are a challenging issue for land managers at the regional and local 

level. Non-native species with the capacity to form large and self-sustaining populations in new regions (i.e., “invasive”) can displace native plant and wildlife populations, reduce habitat quality, alter ecosystem functions, and reduce the overall 

economic value of the landscape (Pimentel 2009). 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-4—List of rare plant taxa tracked by the Alaska National Heritage Program occurring 

in the assessment area

Family Taxon name State rank Global rank

Asteraceae Agoseris glauca S2S3Q G5

Asteraceae Artemisia dracunculus S1S2 G5

Fabaceae Astragalus robbinsii var. harringtonii S3 G5T3

Brassicaceae Boechera lyallii S1 G5

Brassicaceae Boechera stricta SU G5

Cyperaceae Bolboschoenus maritimus ssp. paludosus S3 GNRTNR

Ophioglossaceae Botrychium virginianum S3 G5

Cyperaceae Carex atratiformis S3 G5

Cyperaceae Carex bebbii S1S2 G5

Cyperaceae Carex deflexa var. deflexa S2S3 G5

Cyperaceae Carex deweyana var. deweyana S2S3 G5

Cyperaceae Carex heleonastes S3 G4

Cyperaceae Carex interior S3 G5

Cyperaceae Carex parryana S2 G4

Cyperaceae Carex phaeocephala S3 G4

Cyperaceae Carex preslii S1 G4

Cyperaceae Carex sprengelii S1 G5?

Orobanchaceae Castilleja hyetophila S2S3 G4G5

Poaceae Catabrosa aquatica S1S2 G5

Brassicaceae Cochlearia sessilifolia S2Q G1G2Q

Crassulaceae Crassula aquatica S1S2 G5

Brassicaceae Draba incerta S3 G5

Cyperaceae Eleocharis quinqueflora S2 G5

Cyperaceae Eriophorum viridicarinatum S2S3 G5

Poaceae Festuca occidentalis S1 G5

Gentianaceae Gentianella propinqua ssp. aleutica S3 G5T2T4

Rosaceae Geum aleppicum ssp. strictum S3 G5T5

Apiaceae Glehnia littoralis ssp. leiocarpa S2S3 G5T5

Poaceae Glyceria striata S3 G5

Cupressaceae Juniperus horizontalis S3 G5

Plantaginaceae Limosella aquatica S3 G5

Caprifoliaceae Lonicera involucrata S3 G4G5

Asparagaceae Maianthemum stellatum S3 G5

Saxifragaceae Micranthes porsildiana S2 G4

Ericaceae Monotropa uniflora S1 G5

Haloragaceae Myriophyllum farwellii S1 G5

Hydrocharitaceae Najas flexilis S3 G5

Orobanchaceae Pedicularis groenlandica S2 G5

Zosteraceae Phyllospadix serrulatus S3 G4

Poaceae Poa macrantha S1S2 G5

Poaceae Poa secunda ssp. secunda S1S2 G5TNR

Poaceae Podagrostis humilis S3 G5

Dryopteridaceae Polystichum setigerum S3 G3

Potamogetonaceae Potamogeton robbinsii S2 G5

Rosaceae Potentilla drummondii S2S3 G5

Ranunculaceae Ranunculus orthorhynchus var. orthorhynchus S2S3 G5T5

Ranunculaceae Ranunculus pacificus S3S4 G3

Hydrophyllaceae Romanzoffia unalaschcensis S3S4 G3

Salicaceae Salix hookeriana S2S3 G5

Poaceae Schizachne purpurascens S2 G5

Amaranthaceae Suaeda calceoliformis S1S2 G5

Cyperaceae Trichophorum pumilum S1 G5

Violaceae Viola sempervirens S1 G5
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GENERAL TECHNICAL REPORT PNW-GTR-950

Although invasive species rank second to outright habitat conversion as a threat 

to biodiversity globally and nationally (Simberloff 2009, Wilcove and Master 2005), 

in Alaska and the circumpolar North, invasive species are not known to have caused 

the degree of damage observed at lower latitudes (Carlson and Shephard 2007, Lassuy and Lewis 2013, Sanderson 2012). 

The more restricted impact of invasive species in Alaska is likely due to a 

range of factors. These may include preemptive colonization of disturbed habitats 

by native ruderal species in relatively young post-glaciation landscapes; high 

former biogeographic exchange between Nearctic and Palearctic floras (cf. Abbott 

and Brochmann 2003) prior to human-aided dispersal (leaving a smaller pool of 

potential species that would be new introductions); shorter growing seasons and 

lower temperatures; and low rates of species introduction by human vectors as a 

consequence of low human population density, minimal agriculture, and a confined 

transportation network. However, human population growth is increasing in Alaska, 

and consequently, the diversity and geographic scope of non-native species is 

expanding. Formerly pristine landscapes are increasingly being threatened (Carlson 

and Shephard 2007). The state of non-native species establishment in Alaska is in an 

early stage with continued geographic expansion. Ecological and economic consequences are expected to increase as certain invasive species expand (Bella 2011, 

Carlson and Shephard 2007, Jarnevich et al. 2014). Evaluation of the scope of threat 

posed by invasives is incomplete.

South-central Alaska is an area with the potential to experience a significant 

impact by invasive species. The region encompasses some of the highest densities of 

people and infrastructure in the state, and the region harbors intact wilderness adjacent to invasive-species sources. Although the diversity and biomass of non-native 

plants are currently low, non-native plants have become an inescapable component 

of many habitats in the region. 

Of about 40,000 invasion records in the assessment area, 159 taxa have been 

documented, encompassing 3128 ha of 11 007 ha surveyed (AKEPIC 2014), which 

represents a very small percentage (0.04 percent) of the total area. Most invasions 

are associated with urban areas and road corridors. Areas without non-native plants 

are generally restricted to habitats off of the anthropogenic footprint (fig. 6-11). 

About 20 percent of weed records in the assessment area are found in the Anchorage 

Bowl. Of the 20 plant species with the capacity for the greatest ecological damage, 

half are known from only 30 or fewer records, and seven species are known from 

more than 100 populations (table 6-5). Seven invasive species are currently geographically restricted to the Anchorage Bowl. Nearly one-third of the documented 

species in the region have not been evaluated for perceived ecological risk.

South-central Alaska 

is an area with the 

potential to experience 

a significant impact 

by invasive species. 
Figure 6-11—Density of non-native plant invasions in the Kenai Peninsula-Chugach National Forest region. Yellow circles indicate densities of all non-native plants. Orange to red circles indicate densities of infestations of species considered moderately to highly invasive. 

Areas that have been surveyed and no non-native species observed are shown as blue points. Also shown are trails, roads, and highways.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Sweetclover (Melilotus albus), orange hawkweed (Hieracium aurantiacum), 

and waterweeds (Elodea canadensis, E. nuttallii, and their hybrids) are considered 

ecologically damaging non-native species that are found in intact ecological communities, as well as in the anthropogenic footprint. Reed canarygrass (Phalaris 

arundinacea) is considered a highly invasive species, which in this region is likely 

composed of mixed Eurasian-American cultivars (a few isolated native Pleistocene 

relict populations persist in interior Alaska at warm springs) (see Jakubowski et al. 

2013). 

Although a few species are expected to be highly damaging in more remote 

natural areas, most non-native plants that have established in these areas tend to 

be those of lower predicted ecological impact. The species that most commonly 
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GENERAL TECHNICAL REPORT PNW-GTR-950

occur outside of the anthropogenic footprint are common dandelion (Taraxacum 

officinale), annual bluegrass (Poa annua), Kentucky bluegrass (Poa pratensis ssp. 

irrigata/ssp. pratensis), common plantain (Plantago major), and disc mayweed 

(Matricaria discoidea). These species are disturbance specialists that are unlikely 

to persist in later successional stages and rarely achieve densities that exclude native 

plants or change ecosystem processes significantly.

Table 6-5—List of highly invasive plant species and number of records in the assessment area

Name Invasiveness rank Number of records

Bromus tectorum L. 78 5

Caragana arborescens Lam. 74 16

Centaurea stoebe L. 86 50

Cirsium arvense (L.) Scop. 76 311

Elodea sp. Michx. 79 30

Fallopia ×bohemica (J. Chrtek & Chrtkov) Bailey 87 1 a

Hieracium aurantiacum L. 79 436

Hieracium caespitosum Dumort. 79 28

Impatiens glandulifera Royle 82 15 a

Lupinus polyphyllus Lindl. 71 227

Lepidium latifolium L. 71 2 a

Lythrum salicaria L. 84 14 a

Melilotus albus Medikus 81 883

Phalaris arundinacea L. 83 1089

Prunus padus L. 74 335 a

Prunus virginiana L. 74 63 a

Rosa rugosa Thunb. 72 2 a

Sonchus arvensis ssp. arvensis L. 73 72

Sonchus arvensis ssp. uliginosus L. 73 6

Vicia cracca ssp. cracca L. 73 913

a

 Species currently restricted to the Anchorage Bowl.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Case Study: Using Repeat Field Measurements to Detect 

Change in Forests of the Chugach/Kenai Region

Tara Barrett, research forester, Pacific Northwest Research Station

Change in plant species composition is often rapid within small areas as individual plants regenerate, grow, and die. Over very large regions, vegetation 

change reflects broad shifts in climate, management, or disturbance regimes. In 

this case study, we look at recent changes in aboveground live-tree biomass and 

discuss possible causes. We used a set of 1,079 forested field plots installed from 

Ketchikan to Kodiak in the period of 1995–2003, then remeasured in 2004–2010, 

to assess recent change in unmanaged forests at a very broad spatial scale (see 

Barrett [2014] for methods).

The Chugach National Forest experienced a recent increase in live-tree 

biomass during the remeasurement period, estimated as an overall 4.5-percent 

increase, which is equivalent to an increase of 501 kg of dry biomass per forest 

acre per year. The 95-percent confidence interval of the live-biomass increase 

ranged from 1.5 to 7.6 percent, indicating uncertainty in the amount of change, but 

providing strong evidence for an increase in biomass. Significant (p-values < 0.10) 

increases of live-tree biomass occurred for Sitka spruce and white spruce species. 

For stands classified by their dominant species, significant increases in biomass 

occurred in the cottonwood, paper birch, western hemlock, and white spruce forest types. No forest type showed a significant decrease in live-tree carbon mass. 

The Chugach National Forest has primarily temperate rain forest tree species 

(western hemlock, Sitka spruce, and mountain hemlock), but its western edge 

does border the boreal forest. To look at change in the larger region surrounding 

the forest, plots were grouped into three ecoregions (fig. 6-12): (1) the Cook Inlet 

Basin ecological section; (2) southeast Alaska; and (3) the western portion of the 

temperate rain forest, referred to here as the Gulf Region. 

The Cook Inlet region, composed primarily of boreal forest species, showed 

no significant change in live-tree biomass overall. The region had an annual turnover in live-tree biomass of about 2.4 percent, and both growth and mortality were 

high. There was modest evidence for an increase in aspen biomass (fig. 6-13); the 

p-value was 0.047 for a two-sided t-test with a null hypothesis that change had not 

occurred, but there were only 26 forested plots with aspen. 

Southeast Alaska also showed no change in within-forest live-tree biomass 

overall, although biomass increases are occurring on gentler slopes, and forest 

area appears to be increasing (Buma and Barrett 2015). There is an estimated 
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GENERAL TECHNICAL REPORT PNW-GTR-950

there is some evidence for a decrease in shore pine (estimated rate of change in 

and a p-value of 0.096 for a test against no change). Shore pine is mostly found 

in the same low site productivity areas as yellow-cedar, a species that has experienced widespread mortality events in the past century, thought to be related to 

climate change (Hennon et al. 2012). Annual turnover of live-tree biomass was 

about 0.6 percent, much lower than the turnover in the western Kenai Peninsula 

In contrast to the other two regions, evidence is quite strong for withinforest live-tree biomass increases in the Gulf Region, where the Chugach 

National Forest is found. Estimated rate of change was an average annual 

increase of 0.8 ± 0.2 percent (p-value < 0.001). This change is primarily driven 

by an average annual increase in Sitka spruce live-tree biomass of 0.9 ± 0.3 

Figure 6-12—Ecoregions (combined ecological sections) used for analysis of biomass change for live trees. Based on Nowacki 

(2001).
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

40

30

20

10

0

-10

-20

-30

Decadal net change, as percentage of initial biomass

Quaking aspen

Black spruce

White spruce

Paper birch

Cottonwood

Sitka spruce

Mountain hemlock

Western hemlock

Western redcedar

Yellow-cedar

Shore pine

Red alder

All species

Southeast Region

Gulf Region

Cook Inlet Region

Is the increase in live-tree biomass related to 

climate?—

Climate affects live biomass growth and mortality in multiple ways. It can change disturbance 

mortality from insects, windthrow, ice storms, 

disease, or fire; it can alter background mortality 

through drought or temperature extremes; and 

Figure 6-13—Net decadal change in live-tree biomass by species and ecoregion from 1,079 plots in unmanaged forest. Plots were 

installed from 1995 to 2003 and were remeasured from 2004 to 2010.

-7

-5

-3

-1

1

3

5

7

Cook Inlet

Region

Gulf Region Southeast

Region

Percentage of initial biomass

Annual mortality

Annual growth

Figure 6-14—Annual growth and mortality for 

live tree biomass by ecoregion. Numbers equal 134 

plots for the Cook Inlet Region, 265 for the Gulf 

Region, and 680 for the Southeast Region. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Summary: Future Vegetation 

• Over a quarter of the land cover in the assessment area is projected to change

by 2060.

• By 2050, expanded suitable habitat is projected for Sitka spruce and decreasing habitat for white spruce.

• Changes in disturbance regimes and antagonistic and mutualistic interactions

are likely to have equal or greater impacts on rare plant species than

are direct effects of climate.

• Anthropogenic and fine-scale habitat variables may be the most important

factors in determining vulnerability to invasive species.

• Successful management of invasive species in the assessment area depends

on the evaluation of sources of uncertainty. These uncertainties include type

and rate of spread, policy irreversibility, efficiency, economic damages, and

spatial considerations.

• Climatic factors will limit the extent to which fire will increase within

the study area.

it can change basic tree growth rates. Increasing levels of CO2 in the atmosphere could also positively affect tree growth. Mortality rates are similar for 

the major species (western hemlock, mountain hemlock, and Sitka spruce) 

between southeast Alaska and the Gulf region, suggesting that the reason for 

the increase in the Gulf region is higher growth rather than reduced mortality. 

Tree ring analysis could more closely examine the potential influence of tree 

growth rates on the estimated biomass increase found in our analysis.

There are likely to be few direct negative consequences from faster tree 

growth, but improved growing conditions for trees may mean that forests are 

encroaching into shrublands or alpine environments. Indirectly, increased 

growth might lead to more opportunities for commercial use of wood for forest products or bioenergy, although the effects on site productivity are likely 

to be modest. Perhaps most importantly, the results suggest that large-scale 

alterations in ecosystem processes suggested by climate model projections 

may already be occurring. Shifts in species composition within a forest, or 

between forest, shrubland, and grassland, can affect everything from aesthetics to wildlife habitat, with complex and wide-ranging consequences that may 

be positive or negative depending on social goals. ■
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

• The combined influence of property development (e.g., a 53 percent increase

in number of private structures) and changing climate (which may increase

the frequency and intensity of fire) on the western Kenai Peninsula will

increase the vulnerability of the built landscape to fire. The value of structures at risk to fire is projected to grow by 66 percent on private lands by

2065. The projected value of structures in landscapes with high to extreme

fire risk may approach $3.8 billion in 2065 (based on 2014 dollars).

• In regions in which insects are currently affecting forest structure and

function, we can expect a continued effect of insects on landscape

fragmentation.

• Changes in vegetation could have impacts on recreation infrastructure in

the assessment area; in particular, the afforestation and spread of invasive

species.

Examining the Future: Potential Vegetation Change 

Insights into future environmental conditions can be informed by considering 

conditions at several levels of biological organization, from organisms to biomes. In 

this section, we look at the results of four modeling exercises to develop scenarios 

for potential future trends for broad biomes, tree distributions, rare plant distributions, and non-native species. Then, we look at potential scenarios in key disturbance regimes (fire and insects) with a specific focus on the effects on vegetation. 

These studies were designed to give managers and scientists some examples of 

future scenarios and vegetation patterns. Because each modeling exercise is unique, 

we acknowledge that each scenario we present (from biome to species) has varying degrees of uncertainty. All scenarios use a fairly robust evaluation of future 

climate based on integration of a variety of climate models. However, the scenarios 

themselves differ in the robustness of the outcome. For example, the biome and 

ecosystem modeling has a particularly strong foundation because the outcome (i.e., 

scenario) is built on previous biome shift modeling in Alaska and uses considerable 

current vegetation modeling that has been peer-reviewed and published extensively 

(Magness and Morton n.d., Wang et al. 2012). On the other hand, rare plant modeling is constrained by a limited number of known plant locations from which to 

draw associations with explanatory variables. Detailed methods for our scenarios 

modeling, as well as an indepth discussion about model confidence and robustness, 

can be found in appendix 4. We begin this section, however, with a brief overview 

of climate-envelope modeling for those less familiar with this common approach to 

developing scenarios for ecological response to shifting climate.
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Climate-Envelope Modeling: Using Current Climate 

Niches to Evaluate Potential Changes in the Future 

Generating scenarios is a rapid and cost-effective method for considering potential 

consequences of climate change. Scenarios can employ climate-envelope modeling to illustrate where suitable conditions for species occur in today’s climate and 

where suitable conditions may occur in future climates. 

Climate-envelope models are used to identify the climate niche occupied by a 

species, land cover type, or biome (Guisan and Thullier 2005, Hamann and Wang 

2006, SNAP and EWHALE 2012). For example, a particular tree species may occur 

in areas with mild rainy winters and cool cloudy summers, such as the case of Sitka 

spruce. By combining an understanding of climatic limits for species or ecological 

units with scenarios for future climate, these models help identify possible shifts in 

geographic distribution of the climate niche as the climate changes. If climate models for areas in southeast Alaska with mild rainy winters and cool cloudy summers 

suggest shifts upward in elevation and northward in direction, models for species 

associated with that climate will show similar distributional responses.

Climate-envelope models provide information about how the distribution of 

the climate space is changing. They have ecological limitations that influence 

interpretation. The future distributions of species or ecosystems may not match 

changing climate conditions. Plants and animals must be able to find and disperse 

into these suitable climate spaces. Other important ecological factors not included 

in climate-envelope models, such as soil properties or competitive interactions, 

may make an area unsuitable for a species even when the climate is favorable. 

Extreme or rare climate events, such as drought or storm events, may be extremely 

important drivers of distribution, but are not represented in downscaled climate 

models, and therefore are not included in the climate-envelope models. Ecological 

transition and species migration may also exhibit considerable time lags. Ecological 

transitions, or regime shifts, often occur suddenly when structuring properties, such 

as climate, exceed a tipping point (Hughs et al. 2013). When disturbance triggers 

these transitions, established trees and plants may hang on in unsuitable climate 

conditions, but not reproduce. Finally, the future climate may not be represented 

by current conditions, and models built based on current conditions cannot explain 

parts of the niche that are not yet available to be described (Williams et al. 2007). 

In summary, climate-envelope models may not predict species distribution when 

the niche space is not completely described or when differences exist between the 

fundamental niche and realized niche. 

Climate-envelope 

models provide 

information about 

how the distribution 

of the climate space 

is changing. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Other uncertainties can be introduced by the modeling approach and available 

data. Models of local climate are particularly difficult to develop in Alaska, where 

digital elevation models are coarse and weather stations are few and biased toward 

lower elevations. Future projects vary across the range of global climate models 

(GCMs) and modeling algorithms (Elith and Graham 2009). Consequently, climateenvelope models should not be considered predictors of the future, but instead the 

foundation for scenarios or hypotheses regarding differential shifts in species or 

land cover distribution. However, climate-envelope models can be useful tools for 

exploring the range of future climate conditions and potential ecological outcomes 

associated with climate change. 

Biomes to Organisms: Future Scenarios of Change 

Biomes— 

Changes to the climate space associated with biomes can provide information 

about broad climatic changes that are likely to influence ecological structure and 

function. We review climate-envelope models in the literature for Alaska (Murphy 

et al. 2010), Alaska and Canada (SNAP and EWHALE 2012), and North America 

(Rehfeldt et al. 2012). 

Land cover types— 

Understanding potential changes in land cover can provide a context within which 

to consider individual species and also lend insight into potential future vistas, 

future fire characteristics, potential changes in future wood fiber resources, and 

other fundamental characteristics of the ecosystems that humans will encounter. 

Therefore, we looked at possible changes in land cover based on climate constraints 

across the Kenai/Chugach analysis area 50 years into the future (called “Change in 

land cover across the Chugach/Kenai Peninsula” throughout the rest of the section). 

Broad land cover types in the assessment area were modeled, and redistributions 

of future climate envelopes estimated for a range of emission scenarios employing 

multiple GCMs. The emission scenarios and GCMs are sources of uncertainty that 

were then used along with model outputs to identify robust trends when compared 

with other lines of evidence (empirical data, published mechanistic models, climateenvelope models from other spatial scales).

Spruce species— 

We used a climate-envelope model to look at potential changes in the distribution 

of Sitka spruce, black spruce, and white spruce (called “Change in spruce distributions” throughout the rest of this section). Western hemlock, mountain hemlock, 

yellow-cedar, quaking aspen, balsam poplar, black cottonwood, Alaska paper birch, 

Kenai birch, and a number of willow and alder species also occur in the assessment 
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GENERAL TECHNICAL REPORT PNW-GTR-950

area, often in association with the three spruce species. By focusing on three dominant tree species, this modeling illustrates the potential varied responses to changing 

climate that may be observed by the wide range of trees that occur in the region.

Rare species— 

We modeled future habitat suitability for three rare plant species in the ChugachKenai region: Aphragmus eschscholtzianus, Papaver alboroseum, and Romanzoffia 

unalaschcensis, based on current ecological niches and future climate scenarios 

(called “Change in habitat suitability for three rare plants” throughout the rest of the 

section). Similarly to the modeling of three spruce species, the results from these 

rare plants illustrate the varied response to climate change that may be expected 

from the much broader group of rare plants that occur in the assessment area.

Invasive plant species— 

Twenty-eight non-native plant species that represented a spectrum of current distributions in Alaska (table 6-6) were selected based on perceived impacts to wetland, 

riparian, coastal, and aquatic habitats that are critical to fish, wildlife, hydrological 

functions, fire regimes, and other ecosystem functions. Three species are currently 

not known in the state; nine species have fewer than 50 known invasions, and 16 

have between 50 to more than 5,000 documented invasions in the state. In general, 

the area of suitable habitat was not projected to change dramatically in the future 

for most species, but the locations of the suitable habitat did change. Results of four 

species are discussed in greater detail: 

1. Cirsium arvense (creeping thistle) is a short-lived perennial that has been

spreading in mixed forb and grasslands in south-central Alaska, particularly

around Anchorage and communities on the Kenai.

2. Waterweeds (Elodea canadensis, E. nuttallii, and their hybrids) are a very

ecologically damaging aquatic species of ponds and slow-moving streams

and rivers that have recently been recorded from a number of lakes and

ponds in south-central Alaska (Lissuzo 2011, see references in Nawrocki et

al. 2011, AKEPIC 2014.

3. Sweetclover (Melilotus albus/M. officinalis) is a biennial to short-lived

perennial legume that has spread widely over the state, particularly on

mineral soils along roads and along river bars. It is capable of fixing atmospheric nitrogen, can alter soil chemistry (Sparrow et al. 1993), is associated

with lower native diversity and higher mortality of willows (Spellman and

Wurtz 2010), and alters plant-pollinator relationships.13

13 Schneller, L.; Mulder, C.H.P.; Carlson, M.L. [N.d.]. Invasive Melilotus albus alters 

plant-pollinator networks in boreal Alaska. Manuscript in preparation.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-6—Species included in habitat suitability models

Common name Scientific name

Alaska 

occurrencea Worldwide occurrenceb

Garlic mustard Alliaria petiolata (M. Bieb.) Cavara & Grande 13 1,192

Cheatgrass Bromus tectorum L. 13 557

Siberian peashrub Caragana arborescens Lam. 57 335

Spotted knapweed Centaurea stoebe L. ssp. micranthos (Gugler) Hayek 50 221

Creeping (Canada) thistle Cirsium arvense (L.) Scop. 328 1,227

Bull thistle Cirsium vulgare (Savi) Ten. 169 1,430

Scotch broom Cytisus scoparius (L.) Link 31 1,138

Elodea (waterweed) Elodea canadensis Michx, E. nuttallii (Planch.) H. 

St. John and hybrids

80* 1,500*

Leafy spurge Euphorbia esula L. 1 369

Knotweed complex (Japanese, 

giant, bohemian) 

Fallopia japonica (Houtt.) Ronse Decr., F. sachalinensis

(F. Schmidt ex Maxim.) Ronse Decr., F. ×bohemica 

(J. Chrtek & Chrtkov) J. P. Bailey, Watsonia.

331 734

Hempnettle (splitlip, brittlestem) Galeopsis bifida Boenn., G. tetrahit L. 334 1,972

Giant hogweed Heracleum mantegazzianum Sommier & Levier 1 322

Hawkweed complex (orange, 

meadow, narrow-leaf)

Hieracium aurantiacum L., H. caespitosum Dumort., 

H. umbellatum L.

2,212 1,114

Hydrilla Hydrilla spp. Rich. (mainly H. verticillata (L. f.) Royle) 0 655

Ornamental jewelweed Impatiens glandulifera Royle 30 496

Oxeye daisy Leucanthemum vulgare Lam. 2,222 2,045

Butter-n-eggs Linaria vulgaris Mill. 742 1,310

Purple loosestrife Lythrum salicaria L. 13 1,513

Sweetclover (yellow or white) Melilotus officinalis (L.) Lam. 2,286 863

Eurasian watermilfoil Myriophyllum spicatum L. 4 599

White waterlily Nymphaea alba L. 0 664

Reed canarygrass Phalaris arundinacea L. 5,142 1,556

European bird cherry Prunus padus L. 272 985

Himalayan blackberry Rubus armeniacus Focke 2 1,247

Cordgrass complex (smooth, 

Atlantic, saltmarsh)

Spartina alterniflora Loisel., S. anglica C.E. Hubbard, 

S. densiflora Brongn., S. patens (Ait.) Muhls

0 971

Common tansy Tanacetum vulgare L. 354 641

Scentless false mayweed Tripleurospermum perforatum (Mérat) M. Lainz 81 261

Bird vetch Vicia cracca L. 912 1,747

a

 Source: AKEPIC (2014).

b

 Source: Subsampled from the Global Biodiversity Information Facility (http://www.gbif.org/).
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GENERAL TECHNICAL REPORT PNW-GTR-950

4. Reed canarygrass (Phalaris arundinacea) cultivars were widely planted in

south-central and southeastern Alaska for roadside stabilization and as forage. This species is particularly threatening to riparian and wetland systems

(Galatowitsch et al. 1999, Lavergne and Molofsky 2004, Miller et al. 2008).

Projected Changes in Vegetation

Biome shifts—

We compared the general results from three biome-scale climate-envelope models 

that forecast future climate conditions in relation to biomes for the assessment area. 

The coastal rain forest biome remains stable in future forecasts in all three models. 

On the Kenai lowlands, the boreal forest biome was forecast to be outside of the 

boreal climate niche in the future. However, the most similar biome climate niche 

relative to future conditions differed among models and ranged from forested to 

nonforest biomes. The Kenai lowlands converted to a climate that was more similar 

to the Aleutian meadows biome in the south and the montane cordillera biome in 

the north by 2060 (Murphy et al. 2010), with some areas similar to the Saskatchewan prairie and grassland biome (SNAP and EWHALE 2012) and the Rocky 

Mountain montane conifer forest biome (Rehfeldt et al. 2012). All three models 

used different biome classifications and spatial extents, changing the biomes available to match future climate conditions. However, the lack of convergence between 

model signals suggests that a dissimilar and open climate niche may develop on 

the Kenai lowlands. The boreal biome north of the coastal rain forest biome on the 

mainland may also be transitional. Two models show this area shifting to climate 

conditions more similar to the southern boreal forests, and to the potential for the 

coastal rain forest biome to expand northward (Rehfeldt et al. 2012, SNAP and 

EWHALE 2012,). When alpine tundra biome was delineated, this biome lost area 

(Rehfeldt et al. 2012) across the assessment area. 

Changes in land cover— 

Our model of land cover change used 10 vegetation categories based on the National 

Land Cover Database (NLCD) system and data (fig. 6-9). Climate envelopes 

representing potential vegetation in 2060 suggested that 19 percent of the land 

area across the assessment area will change (4 percent will shift from forested to 

deforested and 15 percent will shift to a climate niche that suggests afforestation), 

while 82 percent will remain the same (60 percent will remain nonforested and 

22 percent will remain forested) (fig. 6-15). Afforestation occurred mainly in 

subalpine and alpine elevations, though not in all climate projections. Deforestation 

occurred in the Kenai lowlands and around the Caribou Hills north of Homer, 

but deforestation in the Kenai lowlands has less model convergence across the 
Figure 6-15—Deforestation and afforestation forecast in 2069 across the eight climate projections representing five global climate 

models (GCMs) and the five-model average GCM and three emission scenarios

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

climate projections. In other words, the areas of deforestation in the Kenai lowlands 

converted to deciduous forest in some scenarios. These classifications excluded 

glaciers, water bodies, and other areas that currently do not support vegetation.

Across the range of climate projections, 32 to 43 percent of the assessment area 

was forecast to have a climatic envelope shift by 2069. The subalpine zone and 

southwestern Kenai Peninsula were consistently transitional (fig. 6-16). Ice field 

core areas and evergreen coastal forest remained stable. The land cover trajectory 

was less certain in the subalpine zone and had a greater diversity of land cover 

types forecast (fig. 6-17). Although various climate projections produced multiple 

responses across all climate projections there were some consistent land cover 

trajectories (table 6-7; figs. 6-18 and 6-19). This is significant because it points to 

land cover types that are most likely to shift:

Vulnerability 

assessment area

Afforestation

Deforestation

Stable forest

Stable nonforest

2060–2069

8 models agree

4 models agree 0 45 90

Miles

o

Across the range of 

climate projections, 

32 to 43 percent of 

the assessment area 

was forecast to have a 

climatic envelope shift 

by 2069. 
Figure 6-16—Number of the eight climate projections representing five global climate models (GCMs) and the five-model average 

GCM and three emission scenarios that agree that a pixel will remain the same (stable or refugia).

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GENERAL TECHNICAL REPORT PNW-GTR-950

Vulnerability 

assessment area

Afforestation

Deforestation

Stable forest

Stable nonforest

2060–2069

8 models agree

4 models agree 0 45 90

Miles

o

1. Subalpine shrub (dwarf shrub) and alpine tundra (barren) declines and converts

to forest (evergreen, deciduous, and mixed), shrub/scrub, or grassland/herbaceous vegetation. Although there were some regional patterns, the conversion

pathway was uncertain and variable among models.

The alpine conversion trajectory, which results in treeline moving upward 

in elevation, is supported by well-documented recent observations and other 

modeling (e.g., Magness and Morton n.d.). Treeline has risen 10 m per decade 

from 1950 to 1990 on cool aspects of the Kenai Peninsula, and both shrub and 

tree cover increased above 700 m across all aspects (Dial et al. 2007). Climateenvelope models from other spatial scales also support this trajectory. The area 

within a suitable climate envelope for alpine tundra land cover declined by 87 
Figure 6-17—The number of different land cover types forecast in 2060–2069 across the eight climate projections representing five 

global climate models (GCMs) and the five-model average GCM and three emission scenarios.

0 45 90

Miles

o

Forecast Land Cover 

Diversity

Vulnerability 

assessment area

1

2

3

4

5

6

Number of land cover types 

forecast (maximum = 8)

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-7—Pecentage of the assessment area in each land cover type calculated with LANDSAT values, a 

conversion threshold, and maximum index of likelihood

Landsat

1910–1919 

ecological 

fidelity

1910–1919 

maximum 

value

1960–1990 

ecological 

fidelity

1960–1990 

maximum 

value

2060–2069 

ecological 

fidelity

Percent

Barren land (rock/clay/sand) 18 18 11 19 14 13

Deciduous forest 3 4 7 4 6 6

Dwarf shrub 4 2 1 3 2 1

Emergent herbaceous wetlands 2 4 7 2 3 4

Evergreen forest 19 13 5 17 13 19

Grassland/herbaceous 0 1 3 0 1 8

Mixed forest 3 4 6 4 5 12

Perennial ice/snow 26 33 47 31 41 30

Shrub/scrub 21 15 8 15 9 4

Woody wetland 4 5 7 5 7 3
Figure 6-18—Land-cover climate niche forecast using climate data backcast to 1900–1919.

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GENERAL TECHNICAL REPORT PNW-GTR-950

percent by 2060 in south-central Alaska in our model and when tundra was 

modeled as a North American biome. (Rehfeldt et al. 2012). 

2. Deforestation increases, especially on the southwestern Kenai Peninsula where

evergreen converted to grassland/herbaceous. Some empirical and anecdotal

evidence supports current deforestation on the southwestern Kenai in the

Homer area; however, other climate-envelope models provide mixed support for

this trajectory (Magness and Morton n.d., Rehfeldt et al. 2012) (see “Changes

in spruce distribution” on page 206). This suggests that current afforestation

patterns may be a combination of direct climate factors and indirect effects of

climate on disturbance regimes. The widespread bark beetle outbreak in the

1990s was linked to warmer temperatures that shortened the beetle life cycle

from 2 years to 1 year (Berg et al. 2006). Warmer temperatures also increased

tree mortality because the trees were drought stressed. Spruce regeneration has

been severely limited by competition from Calamagrostis canadensis grass

Mixed forest

Moss

Open water

Perennial ice/snow

Sedge/herbaceous

Shrub/scrub

Woody wetland

Vulnerability assessment area

Anthropogenic

Barren land (rock/clay/sand)

Deciduous forest

Dwarf shrub

Emergent herbaceous wetlands

Evergreen forest

Grassland/herbaceous

1910–1919

0 45 90

Miles

o
Figure 6-19—Land-cover climate niche forecast for the 1960–1990 baseline.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

(Boggs et al. 2008). However, it is unclear if spruce could reestablish if the C. 

canadensis cover and spruce seed availability were actively managed. 

3. Coastal rain forest remains across most of its current distribution and expands

westward. Stable coastal rain forest land cover is supported by empirical

evidence. Historically, the rain forest system was robust even during warm periods such as the medieval warm period (e.g. Ager et al. 2010, Gavin et al. 2003).

Biome-scale and other land cover climate-envelope models also support stable

coastal rain forest. Climate conditions similar to those currently associated

with the coastal Pacific maritime biome remain in future climate conditions

(see chapter 2). When the climate envelopes of North American biomes were

considered, coastal hemlock forest was stable in 2060 (Rehfeldt et al. 2012).

When more detailed land cover types were modeled using a climate-envelope

approach on the Kenai Peninsula, the mixed-conifer type associated with Sitka

spruce and mountain hemlock expanded (Magness and Morton n.d.).

Baseline 1960–1990

Mixed forest

Moss

Open water

Perennial ice/snow

Sedge/herbaceous

Shrub/scrub

Woody wetland

Vulnerability assessment area

Anthropogenic

Barren land (rock/clay/sand)

Deciduous forest

Dwarf shrub

Emergent herbaceous wetlands

Evergreen forest

Grassland/herbaceous

0 45 90

Miles

o
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GENERAL TECHNICAL REPORT PNW-GTR-950

4. Kenai Peninsula lowlands remains forested, although some areas convert to

emergent herbaceous wetlands. The Kenai lowlands are a mosaic of black

spruce, white spruce, and deciduous tree species. Forest type is forecast to

transition to mixed forest, but the climate niches of mixed forest, evergreen,

and deciduous forest types overlap. Some empirical evidence and trends support stability of Kenai lowland forest, but there is little evidence that emergent

herbaceous wetlands have increased or will increase. Black spruce forest is

expanding into peatlands as the climate warms and increased evapotranspiration causes drying (Klein et al 2005). The bark beetle disturbance did not cause

deforestation in the Kenai lowlands. Bark beetles thinned white spruce, but

black spruce and deciduous species were the primary tree canopy in which

white spruce was affected (Boucher and Mead 2006). Most other climateenvelope models suggest that forest will remain stable (e.g., see “Changes

in spruce distribution” below). A land cover model forecasts that the climate

niche of mixed forest, deciduous forest and black spruce stands remained

stable (Magness and Morton n.d.).

Changes in spruce distribution— 

Potential future distribution of Sitka, black, and white spruce were examined by 

evaluating current climate conditions associated with established spruce forest 

and considering how the three spruce species would respond to climate conditions 

modeled in the future. We began by developing an imputation model to predict 

the current distribution of spruce species based on climate variables. This model 

effectively predicted the current species distribution, indicating that our model 

approach identified an appropriate set of climate features to predict current distribution of spruce in this region. Results also confirmed that Sitka, black, and white 

spruce occupy distinctly different climates. For instance, compared to black spruce, 

Sitka spruce locations averaged an extra 32 days per year during which the temperature was above 5 °C, had maximum June temperatures that were 2.7 °C cooler, 

had December minimum temperatures that were 8.7 °C warmer, and were found at 

locations with 13 cm more May precipitation. Sitka spruce is characteristic of the 

coastal rain forest. In contrast, white spruce and black spruce can tolerate colder 

winter temperatures and warmer, drier summers. White spruce and black spruce 

occupy similar climate conditions; however, black spruce has a greater tolerance 

for high water tables and nutrient-poor soils, and thus is often found in areas where 

white spruce cannot grow. In addition, because black spruce trees usually don’t 

grow very large, black spruce is relatively unaffected by the spruce beetle outbreaks 

that substantially influence white spruce populations in this region. 

Results confirmed 

that Sitka, black, and 

white spruce occupy 

distinctly different 

climates.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

When projected 50 years into the future, our modeling suggests an expanding 

geographic distribution of suitable habitat for Sitka spruce, and decreasing habitat 

for white spruce (fig. 6-20B). Black spruce is less affected than white spruce, 

although it also shows a decrease in habitat. The geographic pattern of potential 

change in distribution of habitat for these three spruce species is particularly 

interesting. Areas of coastal rain forest at low elevations around Prince William 

Sound continue to support Sitka spruce habitat in the future. This result is consistent with other modeling efforts (see “Change in land cover” on page 200) and with 

an understanding of the relative stability of the coastal rain forest ecosystem across 

the Gulf of Alaska. 

In contrast to the relative stability of Sitka spruce habitat around Prince William Sound, modeled climate envelopes for the three spruces suggest significant 

change on the Kenai Peninsula west of the major mountain spine. Currently, black 

and white spruce (along with Lutz spruce) are the dominant conifers throughout this 

area. However, the climate scenario examined in this investigation suggests a substantial expansion of potential Sitka spruce habitat across the western Kenai peninsula and associated declines in the geographic area of climate suitable for white and 

black spruce (fig. 6-20). Because of the association between climate envelopes for 

Sitka spruce and other coastal rain forest species, western hemlock and potentially 

cedar climate envelopes may also occur on the western Kenai by 2050.

Increased habitat for Sitka spruce makes sense in the context of warmer temperatures and increased precipitation, which is what most of the climate models 

predict for the assessment area. Expanded Sitka spruce (and associated conifers) is 

also consistent with modeling for western hemlock, which shows habitat expanding 

on the Kenai Peninsula and on Kodiak and Afognak Islands (Barrett et al. 2012). 

Future patterns of precipitation represent the factor leading to greatest uncertainty 

in results for Sitka spruce west of the Kenai spine. Models of future climate differ 

substantially in pattern of precipitation. It is possible that increased precipitation 

in the summer would not be enough to offset increased evapotranspiration from 

warmer temperatures. 

The 50-year projection shows a large reduction in white spruce habitat, reducing the most suitable habitat to a small region between Ninilchik and Anchor 

Point. Black spruce habitat south of Tustumena Lake and along Cook Inlet also 

declines, as Sitka spruce habitat increases. However, the climate-envelope modeling 

employed does not account for soil types or wetland conditions. In regions with 

poor soil drainage, it is unlikely that Sitka spruce would displace black spruce. On 

better drained sites, both black spruce and hardwoods fared well relative to white 

spruce and Lutz spruce during past beetle outbreaks. If warming conditions and 
Figure 6-20—Comparison of (A) current distribution of spruce species to (B) projected habitat 50 years in the future. The figure projects 

Sitka spruce habitat displacing white spruce habitat on the western Kenai Peninsula.

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GENERAL TECHNICAL REPORT PNW-GTR-950

Current Species of Inventory Plots

Black spruce

White spruce

Sitka spruce

A

regrowing white, Sitka, and Lutz spruce foster another large beetle outbreak, this 

pattern favoring black spruce is likely to continue. 

In general, the impacts of shifts in spruce species distribution will influence 

land use patterns, economic patterns, and human values associated with disturbance events that kill trees, rather than from gradual spruce inmigration. If white 

spruce and black spruce are displaced by Sitka spruce through competition, then 

effects from this projected shift in habitat will occur slowly. If white spruce is 

displaced because it can no longer tolerate the climate (or becomes more susceptible 

to disease or insects because of changes in climate), effects will be realized much 

faster and over a larger areas.

Hybridization between Sitka spruce and white spruce (producing Lutz spruce) 

is common. Pollen is wind-dispersed, providing an opportunity for rapid hybridization. Consequently, distributional change in these conifers as a result of genetic 
Figure 6-20—Continued.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Most Suitable Species Habitat at Inventory Plots in 50 Years 

Most Similar Neighbors Imputation, A1B Scenario

Black spruce

White spruce

Sitka spruce

B

migration could occur more rapidly than movement through seed dispersal. In 

contrast, there is little evidence for black spruce/white spruce hybridization.

Planting of tree species around developed areas and as a forest practice 

increases the potential for rapid, long-distance migration. Introduction of non-native 

tree species to this area of Alaska will also likely play a role in long-term shifts 

of species distribution. In a review of non-native trees in Alaska, Alden (2006) 

examined 32 sites where non-native trees had been planted on the Kenai and in the 

Matanuska-Susitna Valley, and concluded that potential naturalization of lodgepole pine and Siberian larch was high. These species can outgrow white spruce 

on productive sites, and they demonstrated successful regeneration at many older 

plantings. Furthermore, Alden suggested that balsam fir, another non-native, had 

high potential for naturalization on moister sites in the study region (see “Changes 

in distribution of invasive species” on page 214).
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GENERAL TECHNICAL REPORT PNW-GTR-950

Change in habitat suitability for three rare plants— 

We examined potential changes in geographic distribution of three rare herbaceous 

plants, whose current and future habitat suitability was explored at the statewide 

level previously (Carlson and Cortés-Burns 2012). Modeled outputs from Aphragmus eschscholtzianus, Romanzoffia unalaschcensis, and Papaver alboroseum were 

overlaid on land management boundaries and explored in greater detail here. These 

three species were selected from a larger pool of species because modeled outputs 

were of higher confidence and they represent a range of current distributions.

The overall trend within the assessment area is for a decrease in suitable habitat 

for Aphragmus eschscholtzianus and Romanzoffia unalaschcensis over the next 50 

years and an increase in suitable habitat for Papaver alboroseum (figs. 6-21 and 

6-22, summarized in table 6-8). Despite an overall increase in suitable habitat for 

P. alboroseum, particularly in the northwestern portion of the assessment area, our

modeling results suggest that within the Chugach National Forest there will be an

estimated 31 percent loss of area with habitat suitability greater than 70 percent

(fig. 6-22; table 6-8). Also, of particular note is the estimated 100 percent loss of

area with habitat suitability greater than 70 percent for A. eschscholtzianus within

the Chugach.

Climate variables used to develop the models included mean annual temperature, mean annual precipitation, and growing season length (number of frost-free 

days). Slope and elevation were also included in the models. We found that mean 

annual precipitation was the variable that explained the greatest amount of variation in distribution for all three rare plant species. Elevation was of secondary 

importance for Aphragmus eschscholtzianus and Romanzoffia unalaschcensis, 

with Aphragmus eschscholtzianus associated with intermediate elevations and 

Romanzoffia unalaschcensis associated with low elevations. Slope was the second 

most important explanatory variable for Papaver alboroseum, with populations 

most associated with intermediate to steep slopes. Despite the importance of 

precipitation, geographic patterns between current and future conditions for these 

three species reflects a response to temperature; suitable habitats largely shift to the 

north and to higher elevations for all three species.

The vulnerability of species to a rapidly changing climate encompasses two 

elements: (1) the degree of change in mesoscale climate means and extremes, and 

(2) the intrinsic sensitivity of the species (Moritz and Agudo 2013). Assessing the

spatial shifts in perceived climatic envelope, as we have done, addresses only the

first component of climate vulnerability. These correlative approaches have often

been criticized for lacking a mechanistic underpinning and failing to capture the

spatial variability in climate and other variables at finer spatial scales (Ackerly et al.

Mean annual precipitation was the variable 

that explained the 

greatest amount of 

variation in distribution 

for all three rare plant 

species. 
Figure 6-21—Habitat suitability for (A) Aphragmus eschscholtzianus (year 2010), (B) Aphragmus eschscholtzianus (year 2060), (C) 

Papaver alboroseum (year 2010), (D) Papaver alboroseum (year 2060), (E) (year 2010), (F) 

unalaschcensis (year 2060) in the Chugach-Kenai climate vulnerability assessment area. Warm colors represent potentially suitable 

habitat while cool colors indicate areas in which the species is less likely to occur; the spectrum ranges from red to blue. Areas currently 

-

tal envelope for the species at a coarse ecological grain. The actual distribution will depend on species interactions and other ecological 

211

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula
Figure 6-22—Habitat suitability ≥ 0.7 (shown in red) for (A) Aphragmus eschscholtzianus (year 2010), (B) Aphragmus eschscholtzianus 

(year 2060), (C) Papaver alboroseum (year 2010), (D) Papaver alboroseum (year 2060), and (E) (year 

2010), (F) (year 2060) in the Chugach-Kenai climate vulnerability assessment area. The Chugach National 

Forest is shown in stippling. The green triangles are locations of known occurrences. Areas currently covered by glaciers and open 

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GENERAL TECHNICAL REPORT PNW-GTR-950
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-8—Acreage with habitat suitability >0.7 for APES, PAAL, and ROUN in the assessment area

APES PAAL ROUN

Hectares Percent Hectares Percent Hectares Percent

Within assessment area:

 2010 90,177 1.66 153,247 2.82 35,131 0.65

 2060 34,894 0.64 272,670 5.02 11,294 0.21

Change -55,283 -61.30 +119,423 +77.93 -23,837 -67.85

Within Chugach National Forest 

 outer boundary:

 2010 12,602 0.49 41,076 1.61 15,455 0.60

 2060 0 0 28,177 1.10 6,896 0.27

Change -12,602 -100.00 -12,899 -31.40 -8,559 -55.38

APES = Aphragmus eschscholtzianus Andrz. ex DC.; PAAL = Papaver alboroseum Hultén; ROUN = Romanzoffia unalaschcensis Cham.

2010, Lenoir et al. 2013, Moritz and Agudo 2013). Substrate type and other habitat 

features, such as presence or absence of an overstory, are likely to be important 

niche space parameters for the species modeled here, but could not be included in 

the distribution models. Therefore, the areas delineated as “suitable habitat” encompass only the coarse climatic variables; inclusion of fine-scale surficial geology, soil 

moisture, etc., would likely refine the suitable habitat dramatically. Intrinsic sensitivity, such as physiological limits, degree of phenotypic plasticity, and obligate 

species interactions (e.g., pollinators, mycorrhizal fungi, etc.) are not known and 

therefore estimations of true vulnerability to climate change are limited. Autecological studies of these and other rare species in Alaska would greatly enhance our 

understanding of similarities and differences in vulnerability among these species 

of conservation concern.

Although mean annual temperature and precipitation are accepted in general 

to be the most important niche parameters for vascular plants (see Davis and Shaw 

2001, Hughes 2000, McCarty 2001, Walther et al. 2002, Woodward 1987), it is 

possible that these variables are not important within the scope of the geographic 

region investigated. The habitat suitability outputs produced in these models 

represent a coarse perspective based on a limited number of predictors. Further, 

changes in disturbance regimes and antagonistic and mutualistic interactions, such 

as pollinators, herbivory, and pathogens, are likely to have equal or greater impacts 

on rare plant species than are the direct effects of climate (Adler et al. 2009, Davis 

et al. 1998, Klanderud 2005, Suttle et al. 2007). In any case, the results shown in 

figure 6-22 and summarized in table 6-8 suggest that some rare plant populations 

in the area may be vulnerable to climate change. Some of these species may have 
Figure 6-23—(A) Current and (B) future predicted range for reed canarygrass.

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GENERAL TECHNICAL REPORT PNW-GTR-950

difficulty tracking suitable habitats. For example, Aphragmus eschscholtzianus 

is found at high elevations and Romanzoffia unalaschcensis on islands, and both 

species lack clear migration corridors to track their climate envelopes under future 

scenarios.

Change in distribution of invasive species— 

For ease of interpretation, we discuss results for four taxa (Cirsium arvense, 

Elodea canadensis/E. nuttallii, Melilotus albus/M. officinalis, and Phalaris arundinacea) that differ in their life histories and illustrate the variation in modeled 

outputs. Graphically, we display one example (Phalaris arundinacea) of scenarios 

of potential current and predicted future habitat suitability and documented populations in the assessment area with the highest elevation areas remaining unsuitable 

(fig. 6-23). 

Modeled habitat suitability for creeping thistle (C. arvense) indicates high 

suitability in the Anchorage Bowl, southwestern Kenai Peninsula, and eastern 

Climate change assessment boundary

AKEPIC locations, 2012, Phalaris arundinacea L.

Habitat suitability percent

High: 100

Low: 0

Reed canarygrass (Phalaris arundinacea)—current climate

0 15 30 60 Kilometers

A o
Climate change assessment boundary

AKEPIC locations, 2012, Phalaris arundinacea L.

Habitat suitability percent

High: 100

Low: 0

0 15 30 60 Kilometers

Reed canarygrass (Phalaris arundinacea)—future climate

o

Figure 6-23—Continued.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

B

coastal Prince William Sound. By 2080, highly suitable habitat for creeping 

thistle is projected to move upward in elevation and become more continental. 

The waterweeds group of species displayed a very similar pattern to that of 

creeping thistle, with high current suitability along the western Kenai lowlands 

and Anchorage Bowl. Known locations closely matched current modeled suitable 

habitat. Additionally, a weak arc of mixed high and low suitability extended 

through the Kenai Mountains and into the Chugach Mountains to the eastern 

margin of Prince William Sound. By 2080, suitability was projected to decrease in 

the Kenai lowlands, Anchorage Bowl, and Kenai to Chugach Mountains, with the 

arc of mixed suitability shifting to the northern Chugach Mountains. 

The model for current suitable habitat of sweetclover (M. officinalis) displays a 

moderate correspondence between known invasions and areas of high suitability. 

A number of invasions along the road system on the Kenai Peninsula are in areas 

modeled to be of low habitat suitability. This likely reflects the discontinuity 
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GENERAL TECHNICAL REPORT PNW-GTR-950

among spatial scales, where sweetclover is established in warmer (lower elevation) 

microclimates that are not reflected in the coarser climate data used in the model 

(see Lenoir et al. 2013). Overall, areas of high current suitability are found in the 

Anchorage Bowl and scattered eastward through the Chugach Mountains. Isolated 

areas of high suitability were projected in the Kenai lowlands. The whole region 

is expected to increase in suitability for this species by 2080, particularly at lower 

elevations.

Model performance for reed canarygrass was quite poor (AUC = 0.62) and 

efforts to model its habitat suitability elsewhere in Alaska have been largely unsuccessful (Jarnevich et al. 2014). The lack of model performance is likely due to 

either lack of inclusion of climate or environmental factors that in fact determine 

its distribution, or that this species is a generalist that is able to grow in a very wide 

range of conditions. This plant is the product of a long history of plant breeding, 

including the crossing of North American and Eurasian cultivars (Jakubowski et al. 

2013), and high ecological amplitude is therefore probable. Thus, the entire assessment area with the exception of high elevations is likely vulnerable to establishment 

of this grass. Particular habitats that are moist or wet and open are of greatest risk. 

In addition to Phalaris arundinacea, a number of other taxa such as Prunus 

padus, Linaria vulgaris, Hieracium spp., Galeopsis spp., and Vicia cracca had 

modeled distributions with very little variation in suitability, despite the large 

temperature and precipitation gradients present in the region. As with many invasive species, these plants are capable of persisting in a broad range of habitats, and 

climate-envelope modeling at the regional scale may not offer considerable insights. 

Anthropogenic factors and finer-scale habitat variables are likely to be much more 

important in determining areas of high and low vulnerability to invasion (see 

Jarnevitch et al. 2014)

Currently, the highest diversity and abundance of invasive species are 

associated with the urban centers and travel corridors (table 6-6). Characteristic 

invaders of urban settings include creeping thistle, hempnettles, hawkweeds, oxeye 

daisy, waterweeds, butter-n-eggs, purple loosestrife, European bird cherry, common 

tansy, scentless false mayweed, and bird vetch (see table 6-6 for scientific names). 

A subset of urban species is found along road and trail corridors that act both as 

dispersal routes and low-competition habitats that are highly suitable for non-native 

plant establishment, while other non-natives frequent streamsides and appear to 

travel readily along waterways. Elodea may be transported from high-use urban 

lakes to more isolated lakes, such as to Alexander Lake in the Susitna Valley from 

an invasion in Sand Lake in Anchorage, and to waterways by floatplane or other 

vectors. Last, a small cohort of species has moved further from areas used heavily 

Currently, the highest 

diversity and abundance of invasive 

species are associated 

with the urban centers 

and travel corridors.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

by humans but likely originated from the larger established populations in urban 

centers and road corridors. 

Certainly the majority of non-native plant populations are associated with and 

likely facilitated by disturbance, both anthropological and natural, if there is seed 

source. However, it is never easy to disentangle how much is the effect of propagule 

pressure, reduced competition, and germination/establishment. Future work in 

modeling the spread of invasive species should include examination of opportunities for invasion of non-native species following disturbance, especially facilitated 

by climate change. The importance of dispersal limitation may be difficult to detect 

for most of the species treated here that are habitat generalists, where niche barriers are fuzzy. Dispersal barriers and corridors are not readily identifiable but are 

likely more important in determining regional patterns of invasion (see Elith et al. 

2006, Evangelista et al. 2008). Dispersal limitation may be made clearer by including additional parameters in future models that represent potential barriers such as 

glaciers, elevation, rivers, and human infrastructure. 

Uncertainty, Economics, and Invasive-Species Management 

 It is doubtful whether universal species eradication regardless of cost is 

even possible, or if possible, whether it holds a moral trump card over all 

other priorities such as our children’s health and education.

—Jason F. Shogren

There is evidence that new species are becoming established in the assessment area 

(see “Current Status of Non-Native Species” on page 186). Experience demonstrates 

that the vast majority of new species change the natural and economic system in 

trivial ways. A few introduced or non-native species change systems dramatically 

and therefore alter the ecosystem and have measurable economic consequences. 

These latter species are considered invasive because they have severe ecological, 

economic, or health consequences. On the other hand, some of these invaders can 

result in positive changes to primary productivity and other ecosystem measures 

besides their negative impacts. Mainly driven by the negative consequences, natural 

resource managers have developed management infrastructure focused on detection, control, and, in some cases, eradication of non-native species, but much work 

remains to be done to assess uncertainty related to many aspects of risk management. Current invasive species practices in Alaska are most closely described as 

risk assessment rather than risk management owing to the lack of explicit treatment 

of uncertainty and economic consequences.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Anthropogenic factors will be an important driver of ecosystem change related 

with non-native species causing ecological and economic change in the region. The 

odds of rare-species survival and invasive-species spread and associated damages 

depend on biological factors and climate just as much as on economic factors 

such as relative prices, wealth, and the extent and diversity of landownership. Yet 

optimal management response in part is influenced by the ability to reliably predict 

outcomes, both biophysical and social. As a consequence, resource managers face 

challenging decisions inevaluating the potential benefits of current action (or inaction) in the face of much uncertainty. 

Successful invasive-species management demands an acknowledgment and 

willingness to work with “uncertainty.” For example, sources of uncertainty in 

management relate to our inability to completely understand consequences and benefits of invasive species within native ecosystems, measurement error of biophysical 

and economic impacts, and stochasticity in environmental conditions and processes. The social sciences are inextricably linked to the invasive-species problem, 

providing valuable insights on optimal management under uncertainty (McNeely 

2001, Perrings et al. 2002). Bioeconomics in particular can help managers improve 

their understanding of human drivers shaping future economic behavior through 

targeted incentives, optimizing investments in management actions, or estimating 

economic damages to assess benefits and costs to stakeholders. In addition, affected 

parties often have differing views on both facts and values related to management 

actions, obliging decisionmakers to account for a complex and challenging array of 

social and biophysical factors. 

Integrating social and human dimensions in invasive species management—

This section provides a brief overview of recent research into the integration of 

ecological and socioeconomic dimensions addressing invasive-species management 

challenges. This part also presents evidence from past invasive-species investments 

in Alaska relevant to the list of invasive plants examined in this report. The section 

concludes with a case study assessing the bioeconomics of managing Elodea spp., 

an aquatic invasive weed occurring in Cordova and the Copper River Delta, among 

other locations in Alaska. 

Currently, statewide invasive-species management relies on invasiveness rankings; risk assessments conducted by expert groups to evaluate potential ecological 

impact; biological characteristics; dispersal ability; ecological amplitude; distribution; and feasibility of control (Carlson et al. 2008). The framework helps managers 

assess the relative threat of different non-native plants to Alaska. This ecologically 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

focused evaluation begins from the philosophical foundation that the consequences 

of invasive species are always negative. Because risk of undesirable invasive 

species is as much a question of economics as it is about ecology, there is a need 

to integrate biology with economics to better inform management aimed at risk 

reduction.14 Shogren (2000) cautioned that ignoring the human dimension, particularly people’s differing preferences and values, can lead to excessive expenditures 

on invasive-species management. He further emphasizes that risk assessment can 

be more effective when human dimensions are integrated, resulting in active risk 

management rather than passive risk assessment. 

A step toward such integration is the explicit treatment and analysis of uncertainty, an important aspect of decisionmaking often overlooked within ad hoc 

decisionmaking processes that can lead to delay and larger damages in the long run 

(Leung and Steele 2013, Melillo et al. 2014, Simpson 2008). For example, agencies 

and lawmakers alike have the tendency to delay policy responses while gathering 

information on potential damages. In most situations, a “wait-and-see approach” is 

less than optimal, except in situations of low uncertainty. This stems largely from 

the potential for rapid expansion in the geographic distribution of a recent invader 

and therefore rapid increases in control or eradication efforts if decisions are made 

to take action. The following defines the main sources of uncertainty affecting a 

timely policy response to invasive species, including: (a) type and rate of spread, (b) 

policy irreversibility, (c) efficacy (treatment success), (d) economic damages, and (e) 

spatial considerations as outlined by Sims and Finnoff (2013) and Epanchin-Niell 

and Wilen (2012).15

When a “wait-and-see approach” is optimal— 

A wait-and-see approach may be optimal if a recently detected invasive species 

is expected to spread slowly, with little uncertainty about the reversibility of 

implemented management policy. The length of the optimal delay is determined 

by a combination of the magnitude of uncertainty in spread rate and by policy 

irreversibility. Contrary to intuition, economic uncertainty about damages matters 

less. Also, a moderate rate of spread, with moderate uncertainty about spread, may 

justify a wait-and-see approach in a completely irreversible policy setting. Complete 

irreversibility arises when investments in biocontrol or irreversible infrastructure 

14 Risk reduction can occur through mitigation (reducing or changing the distribution of 

species; reducing the probability of invasion to occur) or through adaptation—the adjustment of human behavior to reduce the consequences of invasions.

15 Currently applied invasiveness ranking could benefit from inclusion of some of the 

variables outlined.

Because risk of 

undesirable invasive 

species is as much a 

question of economics 

as it is about ecology, 

there is a need to 

integrate biology 

with economics.
220

GENERAL TECHNICAL REPORT PNW-GTR-950

are made without the option to recoup parts of the cost when policy fails in the long 

term (Sims and Finnoff 2013).16

However, species that spread quickly with large amounts of uncertainty are 

simply spreading too quickly and unpredictably to allow anything but an immediate 

response.17 Overall, managers often have a variety of less aggressive responses 

available to complement those with irreversible sunk costs. Pursuing these 

continuous control actions may provide information on species density and spread 

that alleviates some of the uncertainty associated with more long-term investments. 

Here, a “go slow” approach may be preferred, in which less aggressive actions 

precede long-term commitments (Sims and Finnoff 2013). Interestingly, the decision 

to treat is less sensitive to uncertainty surrounding the efficacy of treatment, thus 

has less influence on decision outcomes (Saphores and Shogren 2005). 

Estimating economic damages— 

Even though significant work has been done on valuing economic damages of 

invasive plants in the United States, specific studies cannot readily be transferred to 

cases in Alaska. This issue can result in additional economic uncertainty. Often the 

economic effects play out differently in Alaska than in places where the economic 

damages were initially measured. The reasons for the discrepancy are differing 

economic structures and human population densities. For example, many invasive 

species cause measurable economic loss to agriculture by reducing forage yields 

to cattle ranchers (Hirsch and Leitch 1996). Because Alaska’s agricultural sector 

is very small, and most agricultural products are imported, agricultural invasives 

have fairly low economic effects in Alaska on a per capita basis. In the case of 

spotted knapweed, for example, there are limited grazing effects, and most impacts 

are associated with changes to intangible nonmarket values such as wildlife forage, aesthetics, and soil and water conservation. Thus the application of many of 

the economic damages estimated elsewhere are difficult to apply to management 

situations in Alaska. 

The ability to quantify economic externalities of invasive species is greatest 

when the invader has a direct effect on a harvestable resource, because the link 

between ecology and economics can easily be established, and damages can be 

estimated based largely on market data (Barbier 2007, Hiebert 1997). On the other 

16 On the contrary, quarantine programs, trade restrictions, and continuous control actions 

all can be reversed, and parts of the sunk costs can be recouped. Flexibility to cancel existing policies is also important when spread rates decline.

17 Note that large amounts of uncertainty in the spread rate means that the invader shows 

a very wide range of potential spread rates. Consequently, the distribution associated with 

the rate of spread would be a very wide one, with a considerable variance around the mean.
Figure 6-24—Relationship between initial infestation size, eradication success, and effort for 53 

independent infestations of non-native plants in California. Source: Rejmánek and Pitcairn (2002).

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

hand, economic analysis is particularly challenging when there are little or no 

data on human preferences and use of an affected resource, or when the affected 

resource is not traded in the marketplace. A similar situation arises when little is 

known about the ecological effects of the invader, even though the affected resource 

may be marketed (e.g., the effects of waterweed on salmon). Despite the challenge 

of estimating economic damages, the past decade has seen an emergence of economic damage assessments. Transferred carefully to local cases, these examples 

can provide valuable insights for prioritization and decisionmaking (Frid et al. 

2013). 

Spatial considerations— 

A look at spatial considerations shows that the size of an initial invasion matters for 

deciding on the optimal time to treat. Generally, it is optimal to initiate control and 

even eradication when initial invasions are small in size. If an invasion has already 

gained a significant foothold before detection, the expected damages must be large 

to justify eradication (Olson and Roy 2002). Rejmánek and Pitcairn (2002) present 

evidence that successful eradication drops off sharply with increasing initial invasion size, requiring a steep increase in management effort (fig. 6-24). 

90

80

70

60

50

40

30

20

10

0

Eradication success (percent)

Mean effort per infestation (work hours)

0.1 1.0 100 1000

Initial infestation (hectares)

40,000

30,000

20,000

10,000

0

success

effort
222

GENERAL TECHNICAL REPORT PNW-GTR-950

For Alaska, expenses associated with management of invasions in the past 

suggest that management tends to be close to optimal when judged by total invasion 

size. Figure 6-25 presents statewide management investments from 2007–2011 for 

the invasive species listed in this report. Actions mainly targeted small invasions 

of highly invasive plants with little or nothing being spent on invasions that had 

already reached more than 100 ha in size (Schwörer et al. 2014). Because the current 

risk assessment framework does not provide information on the level of uncertainty 

regarding spread or potential ecological limits of these species, little can be said 

about optimality in policy, given other important considerations outlined in this 

section. 

Last, recent research suggests that invasions that are of the same size can have 

very different optimal management policies if they differ in shape (patchy versus 

compact) and location (near landscape boundaries or in the center) (Epanchin-Niell 

and Wilen 2012).18 Physical landscapes are not homogeneous, and the patterns and 

processes of invasion differ across space, particularly where glaciers, mountain 

ranges, or river canyons provide natural barriers to invasion processes. Where to 

initiate action and at what severity become difficult decisions in the spatial context, 

with optimal management paths over time that may not be obvious at first. Economic principles suggest that early and intensive control near the initial invasion 

site is better than later control elsewhere (Wilen 2007). Landscape-explicit research 

of invasion processes and associated long-term optimal management provide novel 

insights into how the topology of an invasion and the landscape itself determine 

the optimal policy path through time. Particularly in the context of highly valued 

patches (where rare species are present) and their relative location to natural boundaries, invasion location and shape and the shape of the landscape become important 

decision variables for optimal management. 

For example, invasions in more compact landscapes warrant more control 

because spread is less constrained, resulting in higher damages compared to landscapes in which there are bottlenecks that constrict the spread. These features can 

be used to reduce long-term containment costs, highlighting the role of landscape 

geometry in invasion control. The occurrence of constrictions was also found to be 

the only feature supporting optimal delays in management action, as it physically 

delays spread. Concerning location, the initial invasion location being centered 

within a landscape generally results in higher long-term damages compared to invasions that start on a landscape edge, where control costs are lower because natural 

boundaries help contain the invasion. Two interesting results by Epanchin-Niell 

18 Do not account for stochastic rare long-distance dispersal events.
400

300

200

100

0

Management expenses (thousands of dollars)

Himalayan balsam

Siberian peashrub

Purple loosestrife

Spotted knapweed

Choke cherry

Bird vetch Lupines Whitesweet clover Reed canarygrass

Acres

0 500 1,000

Orange hawkweed

Bird cherry

Cheatgrass

Rank

84

80

76

72

Figure 6-25—Total management expense by species for 2007–2011 by infestation size and Alaska non-native plant invasiveness ranking. 

Himalayan balsam represents a tight cluster of species that includes Bohemian knotweed, creeping thistle, pepper grass, Rugosa rosa, 

and field sowthistle. Figure developed using data from AKEPIC (2014) and Schwörer et al. (2014).

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

and Wilen (2012) are of particular importance for optimally managing multi-patch 

invasions:

1. Greater management expenses may be optimal for smaller satellite invasions

because their eradication and containment costs are lower.19

2 Optimal management for each patch depends on the entire invasion and landscape. Patches cannot be considered independently, in which case a blanket 

19 This result is supported by Olson and Roy (2002), who suggested that, contrary to 

intuition, it may be optimal to eradicate small patches in cases where marginal cost exceeds 

marginal benefits for eradication.
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GENERAL TECHNICAL REPORT PNW-GTR-950

approach to management is unlikely to be optimal. Many invasions are too 

widespread to justify eradication. Under some circumstances (e.g., existence of 

high-value/rare species patches), it is optimal to eradicate some invasive patches 

while leaving others to spread further. Where there is a large potential for 

spread, it can be optimal to slow or contain widespread invasions when eradication is not justified. 

Elodea invasion in Fairbanks: a case study— 

Until recently, Alaska has been considered free of invasive submerged aquatic 

plants. The discovery of Elodea spp. in Fairbanks in 2010 drew attention to an 

established population in Eyak Lake, Cordova, Alaska, and led to the discovery of 

Elodea in 15 additional water bodies across Alaska. Eleven infested waterbodies are 

habitat to at least one species of Pacific salmon. Resource managers are uncertain 

about Elodea’s effect on the state’s wild salmon resources. Elodea is native to other 

locations in North America at much lower latitudes. None of the literature describes 

the effects of Elodea on Pacific salmon.

The invasion of Chena Slough can serve as an interesting case study owing 

to the availability of historical information on vegetation and the fish population 

pre- and post-invasion, as well as existing economic data on sport fisheries. The 

slough used to be an important breeding and rearing habitat for arctic grayling that 

produced 30 to 50 percent of arctic grayling found in the Chena River. Historically, 

the slough has been used recreationally by sport anglers and canoeists. The grayling 

sport fishery of the Chena River in 1996 was estimated to have a net annual economic value of $1.6 million (in 2011 dollars) (Duffield et al. 2001).

Being located close to Alaska’s second largest city, Fairbanks, Chena Slough 

has experienced extensive flow modifications. A flood-prevention dike built after 

a large flood in 1967 resulted in flow reduction and long-term changes in the flow 

regime conducive to the establishment of aquatic plants. Biologists believe that, 

around the year 2000, the highly invasive aquatic plant Elodea spp. was introduced 

to the Slough. Elodea is now the most dominant vegetation found in the Slough, and 

has altered 30 percent of previous grayling spawning and rearing habitat into dense 

mats of vegetation.20 Over the past decade, catch data indicates that grayling have 

significantly declined compared to a decade earlier. Based on anecdotal information, the Slough was also a popular canoeing destination. Because of the dense 

vegetation, canoeing is no longer enjoyable and water quality has declined. 

20 Larsen, A.S.; Lisuzzo, N.J. [N.d.]. Changes in Arctic grayling spawning and rearing 

habitat in a subarctic Alaskan stream following a decade of flow modifications. Unpublished report. On file with: A.S. Larsen, National Park Service, 4175 Geist Road, Fairbanks, 

AK 99709. aslarsen@nps.gov.

None of the literature 

describes the effects 

of Elodea on Pacific 

salmon.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

This case study uses a bioeconomic simulation model that explicitly accounts 

for inventory and treatment expenditures as well as the amount of avoided damages 

(benefits) related to the management of invasive aquatic vegetation over a 100-year 

period. A benefit-cost framework was used to find the most cost effective of four 

potential management actions including: (a) do nothing, (b) suction dredging, (c) 

herbicide application with a small budget, and (d) herbicide application with a sufficiently large budget.21 Damages were valued in terms of the loss in net economic 

value to production of grayling for the sport fishery in Chena River ($1.01/m2), the 

estimated loss in property values ($0.46/m2 ) (Zhang and Boyle 2010), and the loss 

in net economic value to recreational canoeists ($0.13/m2) (Loomis 2005).

Among the three management options, the application of herbicides with a 

budget large enough to eradicate the vegetation results in the best management 

outcome, reducing much of the variation in long-term cost (fig. 6-26A). In this case, 

benefits of damage reduction outweigh the costs in all 1,000 cases simulated by the 

model. In the simulation with the least favorable outcome, benefits still outweighed 

costs by 1.4. In the highest case, benefits outweigh costs by more than 12 times, 

and on average the mean benefit-cost ratio for the herbicide option equaled 3.7. The 

analysis also shows that management actions without an adequate budget will not 

allow managers to get ahead of the invasion, resulting in higher and more uncertain 

long-term costs as well as lower and more uncertain long-term avoided damages 

(fig. 6-26B). This result clearly demonstrates that, in the early invasion stage, full 

eradication attempts can pay off in securing long-term avoided damages (mean 

annual avoided damages $750,000 after 100 years), whereas if budgets are not sufficient and eradication not successful, long-term control will be necessary, resulting 

in higher long-term costs (mean annual avoided damages are $350,000 after 100 

years). 

Potential Scenarios for Insect Populations Under a 

Warming Climate

Within the forests of Alaska, insects may be among the first responders to varying 

climatic conditions. Small body size and ectothermy make insects more exposed to 

changes in thermal conditions, which result in changes in physiology, development 

rates, and ultimately, population processes like survival, reproduction, and dispersal. The evidence is strong but mostly circumstantial for climate-driven changes to 

occur in the behavior and distribution of a handful of insects in Alaska that interact 

with important trees and shrubs. The following scenarios provide a way to consider 

21 A discount rate of 4 percent was used. For more information about the analysis, contact 

the author.
Figure 6-26—Simulation results showing the mean annual avoided damages (benefits) with 90-percent confidence interval over a 

100-year period related to eradication attempt (A) and control approach (B) herbicide use in Chena Slough, Fairbanks, Alaska. 

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GENERAL TECHNICAL REPORT PNW-GTR-950

the potential influence of insect pests in Alaska’s forests in the future, particularly 

on the Chugach National Forest as climate continues to change.

The incidence and severity of some insect pests will increase. Populations of 

many insects are regulated by temperature, especially in northern climates where 

episodes of cold temperature beyond species survival limits prevent their populations from expanding, or cause ongoing outbreaks to collapse (Bentz et al. 2008). If 

winter temperatures in south-central Alaska increase sufficiently, the incidence of 

insects whose populations are controlled by low temperatures will likely increase, 

provided there are sufficient host resources (Raffa et al. 2008). Warmer winters will 

result in an earlier advent of spring and associated insect mating activity, followed 

by faster larval development. As a consequence, the entire life cycle of some insect 

populations may shorten, leading to more generations per growing season, especially if the growing season is extended. Not only will insect populations increase, 

they will also have a longer period to damage trees (Berg et al. 2006, Werner and 

Holsten 1985, Werner 2007).

Both the frequency and extent of defoliator outbreaks may increase. Hardwood 

tree species in Alaska are associated with a diverse community of leaf-feeding 

insects. Most of these leaf feeders have distinctly cyclic population densities; 

relatively dense populations reoccur periodically. The frequency of high-density 

cycles may be correlated with seasonal temperatures. Some evidence suggests 

900,000

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

0

-100,000

-200,000

-300,000

-400,000

Dollars

A B

5 to 95 percent

± one standard deviation

Mean

0 100 years 0 100 years
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

that a wider amplitude and more frequent swings of population density will make 

predictions less reliable. 

In addition to the direct influence of a warming climate on insect life history, 

climate-change-related stress on host trees may also contribute to insect-related 

damage to tree populations. Some tree species will experience less favorable growing conditions, especially those in environmental ecotones. The resulting reduction 

in vigor will increase susceptibility to attack by insects. 

Forest insect pest ranges will expand northward, including new invasives. 

Species richness is inversely correlated with latitude and elevation (Speight 2003). 

During the past decade, many insect species have shown poleward shifts in range 

(Andrew and Hughes 2005). The distribution of insects with high populationgrowth potential, facultative voltinism, and absence of diapause will expand, 

whereas those with slow development or long life cycles are less likely to do so. In 

addition, nonspecialist herbivorous insects are likely to shift hosts, providing an 

increased potential to shift their range northward. Consequently, the total number 

of new species in the Arctic, including Alaska, is expected to increase as a result of 

range expansions under a warmer climate. Some of these species will be invasive 

insects that will create new forest health issues.

Pest interactions and multiple pest complexes will increase. The incidence of 

multiple pest complexes, in which insects interact with diseases and forest declines, 

will probably increase (Ayres and Lombardero 2000, Thomas et al. 2002, Valladares 2008). As a result, pathogens that currently cause little damage in Alaska 

forests may contribute to increased tree mortality through these new interspecies 

interactions.

Influence of insect pests on vegetation dynamics— 

Several species of defoliators, bud and shoot insects, sap-sucking insects and mites, 

wood borers, seed and cone insects, and bark beetles that currently occur in the 

analysis area will undoubtedly be affected by future changes in climate. Few of 

these, however, would be able to shift the composition or abundance of existing 

forest vegetation. One exception is the spruce beetle (Dendroctonus rufipennis). 

We predict that as long as white spruce or Lutz spruce are major components of 

the forest, spruce beetle will continue to be a major catalyst for vegetation change, 

primarily because it is an eruptive pest that kills its host. The distribution and 

severity of future spruce beetle activity and how it will respond to a changing 

climate will depend on insect abundance and distribution, the abundance and 

geographic locations of vulnerable host trees, and the past disturbance history 

of forested areas at risk. 

We predict that as 

long as white spruce 

or Lutz spruce are 

major components 

of the forest, spruce 

beetle will continue to 

be a major catalyst for 

vegetation change.
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GENERAL TECHNICAL REPORT PNW-GTR-950

The effects of the 1990s spruce beetle outbreak on landscape-scale patterns of 

regeneration, and subsequent recovery processes have been measured and described 

by a range of scientists (Boggs et al. 2008; Boucher and Mead 2006; Holsten et 

al. 1995; Schulz 1995, 2000; van Hees 1992; Werner 1996). These studies offer a 

glimpse of what the vegetation landscape in the analysis area might look like in 

the future. Many factors can affect patterns of injury and recovery. For instance, 

human development in the analysis area may cause the disturbance return interval 

to expand (Berg et al. 2006). Under a changing environment, white spruce trees 

would probably become increasingly mismatched with their local environment, and 

increased stress will enhance their vulnerability to bark beetle attack. Under these 

conditions, endemic populations of spruce beetles would be sustained and would 

continue to kill trees that survived earlier beetle outbreaks, opening the canopy and 

enabling understory tree species to expand. Boucher and Mead (2006) reported that 

the hemlock understory was stimulated and its successional partitioning enhanced 

by the beetle infestation. In this scenario, spruce beetle would act as a catalyst 

enhancing successional partitioning and speeding up forest change. Over the analysis period, we could anticipate an increase in mountain hemlock across landscapes 

in the Kenai Mountains. 

If Sitka spruce eventually displaces white spruce in the western Kenai, a different set of insect pests may become important. One such insect is spruce aphid, a 

largely coastal pest that may expand as its host expands and will probably become 

more common if our changing climate results in milder winters. The spruce aphid 

can by itself cause mortality, but additionally infested trees are stressed in ways 

that can make them more susceptible to bark beetles, which are more commonly the 

proximate cause of death. The mild winter-spruce aphid-spruce beetle interaction is 

a relatively simple example of multiple pest complexes that will likely become more 

prominent as climatic conditions continue to change. 

Vulnerability to Fire in the Kenai Peninsula 

Wildland-Urban Interface

On May 19, 2014, a wildfire of unknown human origin started in a remote area of 

the Kenai Peninsula. By the time the Funny River Fire was brought under control 

more than 2 weeks later, it had torched nearly 81 000 ha of forest land. Because 

most of the burn lies within an undeveloped roadless area of the Kenai National 

Wildlife Refuge between Tustumena Lake and the Kenai River, damage to structures was modest. Only four cabins plus an outbuilding burned this time (NWCG 

2014). Residences in subdivisions along Funny River Road were spared, although 

risk of future damage remained.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

As it happened, the Funny River Fire caused little damage but did illustrate 

two important facts. The first is that fire plays a significant role in the ecology of 

the Kenai Peninsula. Without fire, young hardwood stands providing important 

forage for moose would not exist on the Kenai. Soil charcoal layers from the past 

2,000 years show that the western Kenai Peninsula has experienced a mean fire 

interval of 400 to 600 years (Berg and Anderson 2006). Fire frequency appears 

to vary greatly within the region, however. Shorter fire returns of 80 to 130 years 

have prevailed in black spruce stands, whereas intervals lasted as long as 1,000 

years in upland Lutz spruce forests (Anderson et al. 2006, DeVolder 1999).

A massive suppression response to the Funny River Fire, using as many as 

760 firefighters, was at times ineffective in altering its spread and combustion 

intensity, just as it was during the previous Caribou Hills Fire in 2007, which 

burned more than 22 000 ha and 88 cabins or residences on the Kenai Peninsula 

(Jackinsky 2007, NWCG 2014, Staab 2014,Wahrenstock 2009). The fires themselves cannot be blamed on the massive spruce beetle infestation of the 1990s and 

early 2000s, which killed large percentages of mature spruce trees throughout the 

region. However, stands of beetle-killed spruce may have contributed to the rapid 

spread and intensity of the fires, and they undoubtedly complicated suppression 

efforts. The results of years of bark beetle infestation prompted fire command 

agencies to take a step back and essentially let the fire burn for safety reasons, 

according to Pete Buist of the Alaska Interagency Coordination Center (Holthaus 

2014). “You can’t put people into an area where a bunch of trees have died and 

fallen,” Buist observed.

Berg et al. (2006) inferred from tree-ring patterns that bark beetle outbreaks 

have thinned spruce stands at a mean return interval of 52 years over the past 250 

years; that is, much more frequently than wildfires. Despite the apparent effect 

on fire dynamics when fires do occur, forests in the Kenai Peninsula may have 

experienced 10 or more beetle outbreaks for every cycle of fire (Berg and Anderson 2006). 

The second fact illustrated by the Funny River Fire is that people have largely 

ignored the role of fire on the Kenai Peninsula in their settlement patterns and 

property development choices. Public officials express dismay at the ineffectiveness of fire-suppression activities because people have placed themselves and their 

property at risk. Early in the 20th century, regeneration of hardwood species after 

several large fires created ideal habitat for moose, leading to the establishment 

in 1941 of the Kenai National Moose Range (renamed Kenai National Wildlife 

Refuge in 1980). As the population and local economy grew after the discovery 

of oil on the refuge in 1958, thousands of residences, commercial buildings, and 

A massive suppression 

response to the Funny 

River Fire was at 

times ineffective in 

altering its spread and 

combustion intensity. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

second homes were built in areas historically susceptible to fire. The pattern of fire 

activity also changed, with fires started by human activity greatly exceeding fire 

starts caused by lightning (KPBOEM 2004).

The vulnerability of human lives and property to wildfire is not unique to 

south-central Alaska, and has emerged as a common feature across North America 

in the wildland-urban interface (WUI). Looking to the future, two main factors will 

determine the vulnerability to wildfire in the Kenai Peninsula region. One factor is 

of course the pattern of development of new structures, particularly those located 

in more rural areas outside of established community centers (i.e., the WUI), in 

relation to areas most likely to experience large, destructive wildfires. The other 

factor is climate change, with associated effects on fire-relevant weather extremes, 

and potentially on vegetation and fuel production.

This study makes a preliminary assessment of the vulnerability to the combined 

effects of these two main forces for change. It focuses on defining and estimating 

property values most at risk to wildfire, and making initial long-term quantitative 

projections of future risks. The study combines information drawn from the Kenai 

Peninsula Borough’s property appraisal database with information derived from 

the borough’s community wildfire protection plans (KPBOEM 2014) to obtain a 

spatially explicit baseline estimate of property values at different levels of wildfire 

risk. It then analyzes historical patterns of property development to make a spatially 

explicit projection of future development over the next five decades. The development scenario provides the basis for a quantitative estimate of the future vulnerability of structures to wildfire, considering how climate change may affect wildfire 

risks. The results potentially raise more questions than they answer; the concluding 

section discusses approaches to improve future evaluations.

The Study Region

The Kenai Peninsula Borough is one of 19 organized regional governments in 

Alaska, incorporating 41 473 km2, or about 4.1 million ha in south-central Alaska. 

Most land is owned by the federal or state governments (fig. 6-27). The Kenai Peninsula includes about 2.4 million ha between Cook Inlet and Prince William Sound. 

All but the easternmost reaches of the Kenai Peninsula bordering Prince William 

Sound lie within the Kenai Peninsula Borough. Kenai Peninsula lands outside the 

borough consist largely of rock and ice, with scattered pockets of coastal rain forest, 

no history of fire, and hardly any human residents. The study therefore focuses on 

the 2.2 million ha of the Kenai Peninsula that lie within the Kenai Peninsula Borough. Hereafter in this chapter, the term “Kenai Peninsula” will refer to the portion 

of the peninsula that lies within the borough.
Figure 6-27—Land ownership pattern in the Kenai Peninsula Borough.

231

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

The federal government dominates ownership of Kenai Peninsula lands, 

controlling nearly 70 percent of the land area (fig. 6-27). Most federal lands are 

included within three large conservation units: Chugach National Forest, Kenai 

National Wildlife Refuge, and Kenai Fjords National Park. The state of Alaska 

owns another 16 percent of Kenai Peninsula lands, and 9 percent lies in Native 

ownership (Alaska Native Claims Settlement Act village and regional corporation 

lands and individual Native Allotments). Only about 4 percent of the Kenai Peninsula consists of private property.

C o o

k I n l e

t

Tyonek

Anchor Point

Sterling

Hope

Clam Gulch

Cooper Landing

Kachemak City

Port Graham

Kasilof

Nikiski

Ninilchik

Moose Pass

Nanwalek

Beluga

Kustatan

Kalgin Island

Gray Cliff

Moose Point

Bear Creek

Lowell Point

Happy Valley

Seward

Seldovia

Soldotna

Homer

Kenai

0 10 Miles5

Ownership types

Borough

Federal

Municipal

Native

Native allotment

Private

State

State highway

Major road
232

GENERAL TECHNICAL REPORT PNW-GTR-950

The population of the Kenai Peninsula Borough stood at 55,400 people as of the 

2010 U.S. Census. Nearly all that population lives within the Kenai Peninsula study 

area. Considering the small amount of private property, the density of settlement is 

relatively high, at about 240 km2 of private property. However, the population density is uneven. Slightly more than one-third of residents live in four towns: Kenai 

(pop. 7,100), Homer (pop. 5,003), Soldotna (pop. 4,163), and Seward (pop. 2,693). 

Table 6-9 shows that the total land area within the municipal boundaries of the four 

towns—about 41 000 ha—comprises less than 2 percent of Kenai Peninsula lands. 

Private property within town boundaries comprises only 0.3 percent of total lands.

Table 6-9—Kenai Peninsula land cover area within and outside 

municipal boundaries of the four largest communities (Kenai, Homer, 

Soldotna, and Seward)

Ownership Within town boundaries Outside towns

Acres Percent Acres Percent

Federal 16,824 0.5 3,683,243 99.5

State 42,398 4.9 830,839 95.1

Borough 1,710 2.7 60,830 97.3

Municipal 16,924 95.7 759 4.3

Native 2,923 0.6 490,813 99.4

Private 17,002 7.1 222,442 92.9

 Total 97,782 1.8 5,288,926 98.2

The remaining two-thirds of the population is spread out along the three 

highways in the region: the Seward, Sterling, and Hope Highways. In addition to 

primary residences and commercial and industrial buildings, the peninsula contains 

many recreational cabins and second homes, also mostly spread out along the road 

corridors. The greatest vulnerability to wildfire lies in these corridors of sparsely 

settled recreational lands outside the boundaries of the larger communities.

Current Vulnerability to Wildfire

Vulnerability to wildfire could be defined in many different ways. Even if one 

limits the scope to consequences for people, the study could differ greatly depending on whether the focus is on threats to public safety, potential loss of ecosystem 

services, or damage to the economy. Given the historical and likely future pattern 

of wildfires, settlement patterns, and local economies, potential damage to structures stands out as a salient concern. This study, therefore, concentrates on vulnerability to wildfire of the built environment on the Kenai Peninsula.

This study concentrates on vulnerability 

to wildfire of the built 

environment on the 

Kenai Peninsula.
233

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Approach— 

The Kenai Peninsula Borough’s recently completed community fire plans provide 

the starting point for analyzing vulnerability to wildfire (KPBOEM 2014). The 

19 regional plans were developed to update the earlier comprehensive 2004 study 

(KPBOEM 2004). Among other changes, the revised plans expand the spatial extent 

of the WUI to include all areas near roads, to incorporate areas potentially needing 

access to emergency services. The vast majority of private property lies within 

the expanded WUI definition, termed the Community Wildfire Protection Plan 

(CWPP) zone.

The community fire plans include a vegetation analysis that classifies land 

cover types by fire hazard. The fire hazard rating combines ignition probability, 

fuel load (potential intensity), and speed of spread into a single ordinal category. 

Fire hazard categories of various vegetation types are derived from a detailed classification of species, condition, and size for up to three dominant species, overall 

biomass density, and type of understory. The study maps 464 separate land cover 

types into a set of six hazard categories, ranging from 1 (very low) to 6 (extreme). 

Each land cover type has separate fire hazard ratings derived for spring and summer. The main difference between spring and summer hazards is that herbaceous 

land cover and understory types were assigned a lower hazard rating after green-up 

takes place in early summer.

To focus safety planning efforts, the community fire plans used the vegetationbased fire hazard ratings to identify relatively large planning areas where vulnerability was generally higher. Because the vegetation was mapped at a relatively 

fine scale, one could potentially use the hazard mapping to identify vulnerability 

to fire at a finer scale, down to the level of individual properties. Such a fine-scale 

analysis should be approached with caution, however, for a number of reasons. 

First, vegetation is dynamic, and could change as a result of disturbance such as fire 

or insect infestation, timber harvest, or natural succession. Second, homeowners 

may undertake activities to reduce the risk to structures on their property, such as 

clearing brush and trees around their homes and selecting fireproof roofing materials. Third, vegetation patterns do not in general coincide with property boundaries. 

Different parts of a parcel could have different vegetation, and even relatively small 

parcels of an acre or less might contain vegetation types with different fire hazard 

ratings. Spatial data available are not precise enough to determine where on a parcel 

structures are or will be located. Finally, a property parcel may be dominated by 

vegetation with a high fire hazard but surrounded by low-hazard lands, as, for 

example, an island in a lake. Or the reverse may be true: a low-hazard parcel may 

be adjacent to or even surrounded by high-hazard lands.
234

GENERAL TECHNICAL REPORT PNW-GTR-950

The appropriate spatial analysis should take advantage of the spatial detail 

available in the data without misleading readers by presenting results at a finer than 

meaningful scale. The focus of the current analysis lies not on identifying individual properties at highest risk, but rather to quantify the overall value of structures 

built on properties at high risk within a generalized area. The current study therefore makes use of information at the parcel level to derive an overall vulnerability 

at a broad spatial extent. Keeping this goal in mind, the study makes a number of 

simplifying assumptions. While the land cover types map to six different categories 

for fire hazard, the concern is mainly with the high-hazard lands. All other parcels 

are assigned to the lower risk category. If vegetation that yields a low fire hazard 

dominates a parcel, a potential public safety concern may still exist if access to the 

parcel is through high-hazard vegetation. However, that is an issue for public safety 

access; our focus is on property values. A structure on an individual parcel with 

low hazard vegetation that is surrounded by high-hazard vegetation could likely be 

defended in a wildfire.

Consequently, we define two levels of elevated risk—high and extreme—to 

quantify vulnerability of structures to wildfire. The structures on a parcel are designated at extreme risk if one-half or more of the parcel contains vegetation cover 

given an extreme (6) spring hazard rating. The parcel is designated high risk if half 

or more of the land has either high (5) or extreme (6) spring hazard, but the majority 

is not in the extreme hazard category. For example, a parcel whose vegetation is 

one-third high and one-third extreme spring hazard is assigned a high risk, while 

a parcel whose vegetation is one-sixth high hazard and one-half extreme hazard is 

assigned an extreme risk. The wildfire risk categories use spring rather than summer fire hazard because spring is more inclusive: land cover types with high and 

extreme hazards for summer all have at least as high a spring hazard.

At-risk lands— 

Given this simplification, the basic measures of vulnerability follow from summarizing the land area, parcels by ownership, and value of structures in the different 

risk categories. To start, one should note that while most of the land area is in public 

ownership (fig. 6-28A), most of the individual parcels are in private ownership 

(fig. 6-28B). The analysis is therefore at a coarse scale for public and Alaska Native 

lands, but at a fine scale for private lands, which contain most of the structures. 

For large blocks of contiguous federal land, the Kenai Peninsula Borough property 

appraisal database—the source of all the property parcel data—defines the parcel 

as an entire township (9324 hectares).

Keeping in mind the possible coarse scale of the analysis for public lands, the 

results of tallying up the total land area in the three fire risk categories as defined 
Figure 6-28—Percentage of land ownership in the Kenai Peninsula Borough (A) assessed as distribution of land ownership, 

and (B) assessed by number of parcels per ownership category.

Figure 6-29—Within the Kenai Peninsula Borough, proportion of (A) all land in three fire risk categories, and 

(B) private land in three fire risk categories.

235

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

State (16%)

Private (4%)

Alaska Native (9%)

Borough (1%)

Municipal (0%)

Federal (69%)

Private (89%)

Federal (1%)

State (3%)

Borough (2%)

Municipal (2%)

Alaska Native (3%)

A B

above shows that nearly three-fourth of Kenai Peninsula lands are in the lower risk 

category. Fewer than four percent of the lands are in the extreme-risk category, and 

the remainder—22.6 percent—are in the high-risk category (fig. 6-29A).

However, a much greater share of the higher risk lands are in private ownership. 

Considering only private lands, the share in the extreme-risk category jumps to 23 

percent. Another 39 percent is in the high-risk category, while only 38 percent is at 

lower risk (fig. 6-29B). Most private parcels in these two elevated risk categories are 

larger than 0.81 ha, and 86 percent are larger than 0.2 ha. Clearly, the lands most at 

risk to wildfire are concentrated in private ownership. The primary reason for this 

result is simple geography. The public lands contain mountains and rain forest as 

All lands Private lands

Extreme risk 

(3.9%)

High risk 

(22.6%)

Low to 

moderate risk 

(73.5%)

Low to 

moderate risk 

(38%)

Extreme risk 

(23%)

High risk 

(39%)

A B
Figure 6-30—Relationship between parcel type (with or without structures) and fire risk categories in the Kenai Peninsula Borough for 

(A) private land, and (B) other, not privately owned land.

236

GENERAL TECHNICAL REPORT PNW-GTR-950

well as lowland areas. All the private property is located in the lowlands, which are 

more conducive to development, and most of the lowlands on the Kenai Peninsula 

are located in the drier western part of the Peninsula, where fire hazards are also 

highest. 

Current development status—

Although more than 60 percent of the private lands in the Kenai Peninsula are 

located in areas at high or extreme wildfire risk (fig. 6-29b), the current risk to private property is quite a bit less. Only some of the parcels have structures, and many 

of those structures are small cabins or mobile home additions, and of relatively low 

value. As of the 2014 borough property tax assessment, 43 percent of private parcels 

on the peninsula are vacant, with no structures (fig. 6-30A). Another 28 percent of 

privately owned parcels have structures, but are located in areas with low-to-moderate wildfire risk. Most of these lower risk structures are either in the Seward area, 

or in the more urban parts of Kenai, Soldotna, and Homer. Twenty-nine percent of 

private parcels with higher wildfire risk have structures, slightly more than half of 

which are located on extreme-risk lands (fig. 6-30B).

Relatively few land parcels that are in government or Alaska Native ownership 

have structures, and most of those structures are in areas of lower wildfire risk. 

Development status of

private parcels, 2014

Development status of 

other parcels, 2014

Parcels with extreme fire 

risk with structures (1%)

Parcels with 

high fire risk 

with structures 

(14%)

Parcels with low to 

moderate fire risk 

with structures (28%)

Vacant

parcels

(43%)

Parcels with high fire risk 

with structures (2%)

Parcels with 

extreme fire risk 

with structures 

(15%)

Parcels with low to 

moderate fire risk 

with structures (10%)

Vacant

parcels

(87%)

A B
237

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Only 13 percent of other parcels have structures, of which 10 percent are located 

on lands with low-to-moderate wildfire risk (fig. 6-30b). Structures on public and 

Native lands include public schools and public utilities (municipal and borough 

lands); post offices, visitor facilities, maintenance buildings, and offices (federal 

and state lands); and resort property (Native lands). Many of these structures have 

high appraised values, but few are located in lands with high or extreme wildfire 

risk.

The final step for evaluating the current vulnerability of Kenai Peninsula 

property to wildfire involves translating the number of parcels with structures at 

risk for wildfire summarized in figures 6-27 and 6-28 into property values. The 

Kenai Peninsula Borough’s property tax appraisal database provides a potential, 

though imperfect, means to construct an estimate of property values at risk. The 

borough appraises land values and improvements separately. Improvements include 

driveways, utilities, and other facilities that would not necessarily be at risk of damage from a wildfire, as well as structures. Information on structures including type, 

value, and various characteristics such as year built is available for most, but not all 

properties with structures.

Table 6-10 summarizes the value of Kenai Peninsula structures by fire risk 

category using the information available on structures in the Kenai Peninsula Borough’s 2014 property tax appraisal. The table estimates that structures worth $1.1 

billion are located on parcels at extreme risk to wildfire. Another $1.3 billion worth 

of structures are located in areas with high wildfire risks. Eighty-seven percent of 

the value of these structures at risk lies on private property. On lands with lowto-moderate wildfire risk are $5.6 billion worth of Kenai Peninsula structures, of 

which slightly more than three-fourths are on private property.

The values in table 6-10 do not include oil and gas production and transportation property, which is assessed separately by the state, and is mostly at low risk to 

wildfire. They also do not include personal property such as mobile homes, boats, 

aircraft, recreational vehicles, and other vehicles. Some of the personal property 

Table 6-10—Value of Kenai Peninsula structure by fire risk category, 2014

Private 

ownership

Other 

ownership Total

Million dollars

Extreme fire risk areas 947 175 1,121 

High fire risk areas 1,189 146 1,334 

Low-to-moderate fire risk areas 4,371 1,202 5,573 

 Total value 6,507 1,523 8,029 

Source: Kenai Peninsula Borough property appraisal database.
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GENERAL TECHNICAL REPORT PNW-GTR-950

might easily be moved if threatened by wildfire, while some might not. The values 

represent appraisal information and not taxable property values, because a portion of the value of structures on private lands and most of the structures on other 

lands are not taxed, and the values in the table do not include the value of land. 

Under Alaska state law, local governments are instructed to assess property at 100 

percent of market value. (Alaska Statutes 29.45.110(a)). The Alaska State Assessor’s office annually studies the extent to which local governments are complying. 

In the latest report available, for 2013, the Kenai Peninsula Borough was assessing 

at 93 percent of market value (Office of the State Assessor 2014: 41). In general, 

then, the values in table 6-10 should be considered lower-bound estimates of the 

potential wildfire risk.

Projecting Future Vulnerability to Wildfire

Two main factors will determine how vulnerability to wildfire in the Kenai Peninsula region will evolve over time. Climate change could alter weather conditions 

that affect the probability of ignition, rate of spread, and intensity of wildfires. 

Over the long term, vegetation may respond to changes in temperature or precipitation, with additional effects on quantity and quality of fuels. The pattern of 

future development could also dramatically affect vulnerability to wildfire, as new 

structures are built and improvements to existing structures made in areas that 

may experience large and potentially destructive wildfires. We first discuss effects 

of climate change, then consider development effects.

Effects of climate change— 

Downscaled projections from climate models created by Scenarios Network for 

Alaska Planning (SNAP) show precipitation as well as temperature to increase 

steadily on the Kenai Peninsula over the next 50 years (see chapter 2). Precipitation trends are more uncertain, however, and it is not clear whether increased 

precipitation, if it does occur, will be sufficient to offset the drying effects of 

higher transpiration rates associated with temperature increases during the growing season. 

The longer growing season and possibly increased rainfall could prompt 

changes in vegetation. Climate-envelope studies show a potential shift from white 

spruce to Sitka spruce or white-Sitka hybrid (Lutz spruce) on much of the western 

Kenai Peninsula. Stands dominated by black spruce would remain relatively 

stable. Additionally, large portions of the southwestern Kenai Peninsula between 

Homer and Ninilchik could potentially convert from forest to grassland (see 

“Biome shifts” and “Changes in land cover” on page 200). Actual conversion 

rates are likely to be very slow, however, and may not take place at all until 

Climate change 

could alter weather 

conditions that affect 

the probability of 

ignition, rate of 

spread, and intensity 

of wildfires. 
239

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

a large-scale disturbance. Fire is one type of disturbance that could hasten 

conversion of vegetation. Future bark beetle outbreaks are even more likely 

(Berg and Anderson 2006). 

Given the uncertainties inherent in climate and vegetation projections, the 

information that is available suggests the following inferences for projecting the 

effect of climate change on wildfire hazards. First, much of the area in the southwestern Kenai Peninsula that is projected for potential conversion from forest 

to grassland has recently experienced high spruce mortality from bark beetle 

infestations. This area is currently composed of dead spruce and bluejoint grass 

(Calamagrostis canadensis) and is classified in the KPB Community Wildfire 

Protection Plans as being in the extreme fire hazard state. Second, the distribution 

of land cover types dominated by black spruce—also mostly classified as in the 

extreme fire hazard state—are projected to remain similar to their current distribution. Wildfire hazards in land cover types currently classified as having a high 

hazard—mostly white spruce and mixed forest—will likely remain at least as high 

as they are today over the next 50 years, even if the long-term trend is toward more 

Lutz and Sitka spruce. 

In general, fire hazards will likely increase somewhat from today on all lands. 

Young et al. (2012) analyzed spatially explicit datasets of vegetation, fire occurrence between 1950 and 2010, and downscaled climate estimates between 1970 and 

2000 throughout the boreal forests of Alaska. The climate-fire relationships they 

estimated from the data suggested that fuel drying was the main factor determining 

the pattern of boreal forest burning, rather than the type or amount of fuel. They 

projected that warmer summer temperatures projected for the next several decades 

would increase the frequency of drying conditions, even after considering the effect 

of precipitation increases. One should also note that future population growth and 

continued land development will almost certainly lead to more human-caused ignitions. The combination of warmer summers and increasing ignitions could create 

a greater fire risk in all fuel types than currently (Berg and Anderson 2006). A 

conservative projection of wildfire hazards on lands in the Kenai Peninsula would 

be to assume that future risks remain at least as high as they are currently, with the 

spatial variation in that risk distributed largely as it is today. 

Methods for projecting effects of population growth and land development— 

The Alaska Department of Labor projects population in the Kenai Peninsula 

Borough to grow relatively slowly over the next 30 years, increasing by 16 percent 

from 2012 to 2042 (Howell 2014). Applying the implied annual growth rate of 0.5 

percent per year for 50 years, the borough population would grow to about 73,000 

by 2065—an increase of 32 percent from 2010. Given the high prevalence of 
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GENERAL TECHNICAL REPORT PNW-GTR-950

recreational homes and visitor-serving businesses in the region, however, traditional 

methods of projecting development based on population growth are insufficient 

to project future infrastructure at risk for wildfire in the borough. A reasonable 

projection of structures at risk requires a method that takes into account the distinction between private property and other property, the spatial dispersion of private 

property in relation to wildfire hazard, and forces speeding or impeding land 

development. 

The economic forces determining land development in the region are diverse. 

Before Alaska became a state, commercial fishing dominated the local economy, 

and development was concentrated near the coast. During the initial years after 

statehood, oil and gas development became the leading driver, with greatly 

expanded settlement and infrastructure being built in and near the city of Kenai. In 

recent decades, recreation and tourism activities have come to dominate development patterns, contributing to a much more spatially dispersed infrastructure. All 

these historical drivers of regional development derive from forces operating at 

least at the state level, and for the most part at national or even global scales. That 

makes it difficult to predict how economic drivers will change over the next 50 

years, much less how they will influence development locally on the Kenai Peninsula. 

Instead of trying to predict the population and economy directly, this study 

opts for a scenario approach: a reasonable and informed projection of what could 

happen in the region based on long-term historical spatial patterns of development. 

The scenarios are based on an analysis of spatial patterns of land development on 

the Kenai Peninsula since 1960. The analysis explicitly considers location and land 

characteristics, including wildfire hazards, as factors that could potentially influence development. Testing whether fire hazard and other spatial characteristics 

influence development patterns helps clarify the current fire risk to property, as 

well as improve projections of spatial density of future development and associated 

potential future wildfire risks. 

The main factors analyzed for their effect on development include land ownership status, parcel size, proximity to roads, wetland percentage of the parcel 

area, wildfire risk category, and whether the land lies within or outside municipal 

boundaries of Kenai, Homer, Soldotna, or Seward. The method produces what 

should be considered preliminary projections of potential vulnerability to wildfire 

rather than refined estimates. Its greatest value lies in demonstrating a feasible 

and scientifically credible approach that suggests directions for future research. 

The approach can and probably should be replicated, including additional data that 

could help articulate the spatially explicit drivers of property development and 

The economic forces 

determining land 

development in the 

region are diverse. 
241

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

potential subdivision and sale of land. Specifically, the analysis could consider such 

factors as proximity to or frontage on major rivers, lakes, and protected open space; 

distance to town centers and Anchorage; and additional land cover information and 

associated climate data. A study of the neighboring Matanuska-Susitna (Mat-Su) 

Borough showed that these factors did play a significant role in determining private 

land values, so one might infer that they could influence development patterns as 

well (Berman and Armagost 2013). However, in the Kenai Peninsula Borough, large 

expanses of protected lands in the three main federal conservation units restrict the 

spatial extent of development much more than in the Mat-Su Borough, making the 

location of private lands and road development more important as limiting factors.

To some extent, the approach taken here is similar to that employed by Hansen 

and Naughton (2013). They analyzed the association of spruce bark beetles, recent 

wildfire, and other factors with spatial property values on the Kenai Peninsula. 

However, Hansen and Naughton (2013) included in their study only properties with 

single-family homes, and ignored commercial property, vacant land, and parcels 

with recreational cabins or mobile homes; that is, the vast majority of land parcels. 

They focused on differential property values, controlling for the characteristics of 

structures, so most of the differences they found would be attributed to the value 

of land. Our focus, in contrast, is on which parcels get developed at what time, and 

what, if anything, gets built there.

The analysis proceeded in several steps. The first step estimated a set of 

survival-time equations that explained the timing and location of the first instance 

of a structure appearing on a parcel after 1960. If a structure currently exists on the 

property, or if the estimated equations predicted that it would be developed, then a 

panel regression model explained the value of the structure over time. 

The estimated equations for survival of a parcel in an undeveloped state and the 

value of structures on developed parcels then formed the basis of long-term future 

projections of property at risk on the Kenai Peninsula. Changes in land ownership 

in the region since 1960, not counting transfers among private owners, have been 

determined primarily by the Alaska Statehood Act and the Alaska Native Claims 

Settlement Act (ANCSA) of 1971. Aside from decades-old state land disposals 

and limited transfers of borough land to private ownership in and near established 

communities, relatively little change has occurred in the configuration of public and 

private land since the Alaska National Interest Lands Conservation Act (ANILCA) 

in 1980 created Kenai Fjords National Park. Even less change in landownership 

status seems likely going forward. Therefore, it seems reasonable to assume that 

patterns of development that have become established on private and other lands in 

the region will continue.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Spatially explicit scenarios for Kenai Peninsula property development were 

constructed by taking random draws for the state of development (structure built 

or not) by 2065, based on the estimated equations for development status. Parcels 

presently containing structures were assumed to continue to contain structures. If 

the scenario included a structure on a parcel in 2065, the value was projected from 

the equations explaining historical patterns for that kind of property. Additions, 

remodeling, and replacement of buildings on parcels already developed today were 

also based on the established long-term trends.

Appendix 5 contains details of the modeling of land development, structure 

value, and scenarios, including detailed equation results. Findings are summarized 

below.

Results explaining historical pattern of land development— 

The factor that most explained the likelihood that a structure gets built on private 

property was the parcel’s proximity to a road. Development for a parcel that had 

road frontage or lay within 400 m of a road was nearly three times as likely as for 

a more remote parcel. Larger parcels were more likely to get developed, and those 

with a higher percentage of wetland were less likely to be developed. Parcels within 

the city limits of Kenai, Soldotna, and Homer were more likely to be developed 

relative to lands outside municipal boundaries, but those in Seward were less likely. 

Seward is older than the other communities, and the devastation suffered from the 

1964 earthquake may have impeded development. Areas with high fire risk were 

less likely to be developed, controlling for other factors. Extreme fire risk was 

associated with an even lower potential for development.

Parcels of other ownership types were much more likely to be developed if 

lying within city limits of any of the larger towns. Road frontage increased the 

likelihood of development and wetlands greatly reduced it, as on private lands, but 

other effects differed. Parcels with high fire risk and larger parcels were much less 

likely to be developed, and municipal and state-owned parcels were less likely to be 

developed than borough, Native, and federal parcels.

On average, the results showed that structures in towns were much more 

valuable than those built outside city limits, with those built in Kenai and Soldotna 

being worth the most. The larger towns tended to have larger commercial buildings, 

as well as some multi-family residences. Structures on or near roads were more 

valuable than those built on remote parcels, which presumably tended to be recreational cabins and associated outbuildings. Structures on larger parcels were also 

worth more, controlling for other factors, and the value of structures built on lands 

with high spring fire risk or more wetland area was lower. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Structures built on public and Native lands were more diverse and therefore 

more difficult to explain and predict. Structures on lands within city limits of the 

larger towns were much more valuable than those built outside city limits, and 

structures on or near roads were more valuable than those built on more remote 

parcels. Municipal structures were worth more, which is not surprising given that 

city-owned buildings would include office buildings and public utility structures.

Projected Kenai Peninsula property development in 2065— 

The equations provide a basis for projecting future property vulnerability to wildfire, assuming that the historical pattern of subdivision of private property continues. A number of scenarios were constructed using different random draws from 

the projected probability distribution for development of vacant private property 

and other lands. As it turned out, different sets of random draws produced essentially identical projections of property development. The only visible difference 

among scenarios was the location of a few relatively low-value structures projected 

to materialize on large tracts of public lands with a low probability of development. 

Consequently, the results are reported below for a single representative scenario.

The projection to 2065 included a 53-percent increase in the number of private 

Kenai Peninsula parcels with structures, to about 49,000. The distribution of the 

additional structures among areas with different wildfire risk categories changed 

relatively little, although the number of structures in extreme fire risk areas 

increased at a slightly higher rate: 56 percent (fig. 6-31A). The number of other 

parcels with structures increased at a faster rate—by 72 percent—but the total number of these other structures remained relatively small—fewer than 1,500—and few 

of these structures were built in high or extreme fire risk areas.

Projected values at risk to wildfire in 2065— 

The total value of structures on private lands was projected to increase by 66 percent over the next 50 years. The value of structures on other lands would increase 

somewhat less: about 60 percent. The projected increase in value of structures is 

nearly identical for each wildfire risk category (fig. 6-31B).

Discussion

The analysis of structures potentially at risk to wildfire shows clearly that the vulnerability of the Kenai Peninsula to wildfire is high—several billion dollars—and 

growing (fig. 6-32). A principal reason for the high risk is the dispersed settlement 

pattern, which creates a large WUI. 

The value of structures at risk is projected to grow by 66 percent on private 

lands and 60 percent for other lands between now and 2065. The growth rate in 

Analysis of structures 

potentially at risk to 

wildfire shows clearly 

that the vulnerability 

of the Kenai Peninsula 

to wildfire is high—

several billion dollars—

and growing.
Figure 6-31—Across all three fire risk categories in the Kenai Peninsula Borough, projected change in (A) the number of parcels with 

structures between 2014 and 2065 and (B) value of structures between 2014 and 2065. 

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GENERAL TECHNICAL REPORT PNW-GTR-950

value at risk is twice as fast as projected for the regional population. Two factors 

underlie the faster projected rate of increase in value of structures than in the 

resident population. First, a growing share of the property on the Kenai Peninsula is 

recreational property and second homes. Forces primarily outside the region drive 

the demand for recreation property on the peninsula. These forces have produced 

steady growth, and there is no reason to doubt that the growth will continue. 

Recreational development is more spatially dispersed than that coming from 

population growth, more of which is within towns where the wildfire risk is lower.

The second factor underlying the more rapid growth in value of structures is 

that the structures that are being built are projected to be larger and more valuable 

than before. This is to be expected as the region matures and wealth increases. On 

private lands, the projected increase in the number of parcels with structures is 53 

percent, while the equations project a 66-percent increase in value of structures.

The analysis assumes that the likelihood of large destructive wildfires 50 years 

from now will be similar to those prevailing today. In fact, the wildfire hazard 

Number of parcels with structures

Current and projected number of parcels

with structures, by fire risk category

Current and projected value of parcels

with structures, by fire risk category

50,000

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Private

ownership,

2014

Other

ownership,

2014

Private

ownership,

2065

Other

ownership,

2065

Low-to-moderate fire risk areas

High fire risk areas

Extreme fire risk areas 2014 dollars (millions)

10,000

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Private

ownership,

2014

Other

ownership,

2014

Private

ownership,

2065

Other

ownership,

2065

A B
Figure 6-32—Spatial distribution of high or extreme fire risk (orange), parcels with structures in 2014 (green), and value per parcel of 

structures projected for 2065.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Current structure value at risk

0–0.99

1

Scenario for structure value in 2065

0–99

100–9,999

10,000–49,999

50,000–99,999

100,000–199,999

200,000–499,999

500,000–200,000,000

Water
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GENERAL TECHNICAL REPORT PNW-GTR-950

will probably increase. More property development is likely to create more humancaused ignitions. Warmer temperatures may produce drying conditions more 

frequently than today, even if the total amount of precipitation increases (Young 

et al. 2012).

One interesting finding is that areas with extreme wildfire risk, and to a lesser 

extent areas with high fire risk, were less likely to be developed, controlling for 

other factors. Hansen and Naughton (2013) found that property values were higher 

near previous large wildfires and recent spruce bark beetle attacks. It may appear 

that both these sets of empirical correlations imply some kind of preference of property owners either for or against wildfire hazards, although that is unlikely to be the 

case. It is more likely that these measures correlate spatially with other unobserved 

features that really do matter to people. For example, households seeking recreational homes may prefer riparian areas, which coincidentally have vegetation types 

that are less vulnerable to wildfire.

Conclusion

This study analyzed the combined effects of climate change and property development to construct a preliminary assessment of the vulnerability of Kenai Peninsula 

property to wildfire. It developed a method to define and estimate property values 

most at risk to wildfire based on information drawn from the Kenai Peninsula Borough property appraisal and community wildfire protection plans (KPBOEM 2014). 

It made a spatially explicit projection of future development to 2065 and projected 

property values at risk. The study projected a total value of $1.8 billion in 2065 for 

structures in extreme wildfire risk areas, with an additional nearly $2 billion for 

structures in high-wildfire-risk lands. Private property contains 86 percent of the 

value of these vulnerable structures.

This analysis is preliminary; it demonstrates a feasible method for quantitative 

projections of vulnerability, but leaves out many details. This analysis could be 

extended by including additional spatially explicit elements influencing property 

value, such as distance to towns and to Anchorage, additional climate and vegetation data, and proximity to lakes, streams, and protected open space. Unobserved 

spatial influences could be modeled with spatially correlated error terms, especially 

in the survival analysis for vacant land parcels. The main challenge is computational, with such a large dataset and with private property distributed across relatively narrow bands along road corridors. 

Perhaps the most important element that could improve the analysis involves 

more complete modeling of the wildfire hazards. Detailed analysis of the projected 

downscaled climatology could provide insight into the changing probability of 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

drying conditions and fire weather that combine to produce the most destructive 

wildfires. Vegetation modeling could include dynamics before and after disturbance 

to provide a more dynamic projection of fuels and potential for ignition and wildfire 

spread rates. Finally, the spatial modeling of property at risk could include a formal 

analysis of vegetation and wildfire hazard of adjacent parcels and along critical 

access routes. 

Examining the Effects of Vegetation Change 

on Recreation Infrastructure on the Chugach 

National Forest

The assessment area supports a diversity of recreation settings, ranging from dense 

rain forest to muskegs to alpine tundra, and each recreational setting presents 

unique challenges to managers who are providing access to recreation. For example, 

muskegs can typically be extensively traveled only in the wintertime, whereas 

alpine areas are used both in winter and summer months. This is a strong factor in 

determining the ease of crossing terrain and accessing destinations, and how difficult it will be for land managers to construct and maintain trails in a given area. 

Changes in vegetation can alter scenery, change access, and increase or decrease 

hazards at facilities; all these factors may influence the experience that people have 

at recreation facilities. Of the climate-related vegetation dynamics described in 

this assessment, however, three stand out as having the greatest potential to affect 

recreation infrastructure on the Chugach National Forest: afforestation of subalpine 

and alpine areas, the potential for different and more disease and insect outbreaks, 

and the expansion of aggressive invasive plants. 

One of the key experiences that facilities and trails provide on the Chugach 

is accessing alpine terrain to enjoy relatively easy cross-country travel and enjoy 

sweeping views of the surroundings. Thus, several trails and associated trailheads 

across the forest, along with Palmer Creek Road on the northern Kenai Peninsula, 

are designed to provide recreational access to alpine and subalpine areas. If afforestation continues at current projections, infrastructure built to access alpine lands 

may no longer serve that purpose. However, in the short term (less than 50 years), 

the impacts of afforestation to recreation infrastructure should remain minimal. 

Another potential impact could be through the proliferation of non-native plant 

species, particularly invasives, along trails and at or near facilities, undermining the 

scenic experience of natural landscapes. These are the locations where invasives 

are most likely to proliferate because the common transporters are people, domesticated animals, and equipment using this infrastructure. The assessment notes that 

Three vegetation 

dynamics stand 

out as having the 

greatest potential 

to affect recreation 

infrastructure: 

afforestation of 

subalpine and alpine 

areas, the potential 

for different and 

more disease and 

insect outbreaks, 

and the expansion of 

aggressive invasive 

plants. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

the coastline in Prince William Sound may be particularly susceptible to the spread 

of invasives in the future. The western half of Prince William Sound is managed to 

avoid degrading wilderness character, which includes maintaining natural conditions. Visitors to cabins in the wilderness study area may be particularly sensitive to 

the presence of invasives. One particularly damaging invasive is Elodea. If current 

rates of spread into backcountry lakes continue, this invasive has a strong potential 

to critically limit floatplane access to remote cabins across the Chugach.

Third, any increase in tree mortality from insect or disease outbreaks could 

lead to an increase in the need for logging out trails throughout the year, and 

increase the workload for removing dead trees that become a hazard to visitors at 

developed recreation sites. As with the spread of invasive plant species, high rates 

of tree mortality can also change the scenic qualities that people seek in forested 

landscapes. 

Adaptive Capacity, Increased Management 

Overall, it appears that vegetation on the Chugach will remain relatively stable 

compared to the western Kenai Peninsula, meaning that impacts to recreation 

infrastructure from vegetation changes are not expected to be extensive. Chugach 

National Forest recreation infrastructure should be resilient to changes in vegetation 

over the next 20 to 30 years, and these changes should not significantly affect either 

summer or winter use of facilities and trails. To maintain this resiliency, however, 

management actions may need to increase focus on avoiding the spread of invasives 

near facilities and along trails where most of these species are found, and must be 

responsive to identifying hazards at facilities and logging out trails if there is any 

substantial increase in tree mortality resulting from causes described in this assessment. 

Interpreting the Patterns of Change

This chapter is particularly complex because of multiple scales of observation 

(spatial and temporal), the broad array of factors affecting vegetation distribution, 

and the various modeling approaches used to examine future scenarios. Through 

this array of models, we provide a lens for land managers, residents, and visitors 

to consider potential patterns of change. A simple summary of results illustrates 

the range of responses that may occur in vegetation during the next 50 to 60 years 

(table 6-11). According to our models, the distribution of coastal rain forests and 

Sitka spruce (the main spruce component of coastal rain forests) will be minimally 

affected by projected changes in climate. These two features occupy much of the 
249

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 6-11— Rate and direction of potential vegetation change in the assessment area 

Vegetation type/species High Moderate Low

Direction 

of change

Climatic factor(s) 

affecting future 

distribution

Factors not directly 

climate-related affecting 

future distribution

Coastal rainforest X + Precipitation,

air temperature

Shrubification of lowlands 

fire disturbance, spruce 

bark beetle outbreaks, 

competition with 

graminoids

Kenai lowlands X –

Boreal forest X –

Alpine tundra X – Advancement of treeline

Sitka spruce X + Precipitation,

air temperature

Black spruce X – Fire disturbance

White spruce X – Spruce bark beetle 

outbreaks

Aphragmus eschscholtzianus X – Precipitation,

temperature

Romanzoffia unalaschcensis X – Precipitation

Papaver alboroseum X – Precipitation,

temperature

Creeping thistle X + Human spread

Waterweeds X + Human spread

Sweetclover X + Temperature Human and fire 

disturbance

Reed canarygrass X + Human spread

landscape east of the Kenai spine. Land cover types dominated by white and black 

spruce (western Kenai) are likely to decrease, and in particular, white spruce 

abundance is predicted to decrease because of climatic factors, in combination 

with climate-related increases in spruce bark beetle outbreaks. Of paramount 

concern in the assessment area is potential loss of alpine tundra and associated 

rare species. In contrast, it is likely that changes in climate will increase invasive/

weed species—with greatest concern to generalist species like reed canarygrass.

Together, these varied results suggest a future assessment area with vegetation 

that differs from what is currently on the landscape. However, it is important to 

remember that changes in species distributions and land cover types will lag significantly behind climate. The lag will differ by life form, with long-lived species 

like trees potentially showing the slowest response. Furthermore, disturbances 

such as fire will influence rates of change in species distribution. Although land 

managers likely cannot mitigate the wide variety of vegetation change expected, 

our results aid in identifying land cover types and species most likely to change, 

so that response and action can be prioritized and coordinated.

These varied results 

suggest a future 

assessment area with 

vegetation that differs 

from what is currently 

on the landscape. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

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Moose cow and calves.

Dan Thompson, U.S. Fish and Wildlife Service
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John M. Morton1 and Falk Huettmann2

Summary

• Current distributions of these three ungulate species on the assessment area

are artifacts of glacial history, and of relatively recent translocations (moose,

Sitka black-tailed deer) and reintroduction (caribou) in the 20th century.

• About 700 moose, 20 to 30 caribou, and 2,000 deer are harvested annually in

the assessment area by both recreational and subsistence users.

• About 10,000 moose are well distributed throughout the assessment area,

mostly on the western Kenai Peninsula and around Anchorage. Their distribution is likely to increase in the near term owing to continued postintroduction colonization of Prince William Sound, afforestation of the

Kenai lowlands and alpine tundra, and increasing fires on the western Kenai

Peninsula.

• About 1,000 caribou are distributed on the western side of the Kenai

Peninsula in four herds. Their distribution is likely to decrease in response to

afforestation of alpine tundra.

• About 20,000 black-tailed deer occur in the Sitka spruce forest along Prince

William Sound. Their distribution is likely to increase because of declining

snow depths along the coast and continued post-introduction dispersal onto

the Kenai Peninsula.

• In the longer term, forecasting becomes uncertain because of the expected

introduction of novel pathogens and their interaction with changing ecological drivers.

Introduction

Moose (Alces alces gigas) (Bubenik 1997, Hundertmark et al. 2006), caribou 

(Rangifer tarandus granti) (Bergerud 1978), and Sitka black-tailed deer (Odocoileus 

hemionus sitkensis) (Wallmo 1978) are three ungulate species that will be affected 

by changes in the composition and distribution of vegetation, snow depth, ecological disturbances, interspecific competition, and perhaps new diseases in response to 

a warming climate. Moose are widely distributed over the assessment area, but their 

abundance varies both temporally and spatially, with the largest populations on the 

western Kenai Peninsula and adjacent mainland. Caribou are restricted to the Kenai 

Chapter 7: Moose, Caribou, and Sitka Black-Tailed Deer

1 Supervisory biologist, U.S. Department of the Interior, Fish and Wildlife Service, 

Kenai National Wildlife Refuge, Soldotna, AK 99669.

2 Associate professor, University of Alaska Fairbanks, Department of Biology and Wildlife, 

Institute of Arctic Biology, Fairbanks, AK 99775.
Figure 7-1—Game management units designated by the Alaska Department of Fish and Game for managing moose, caribou, and Sitka 

black-tailed deer populations in the assessment area.

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Peninsula, currently in four herds. Sitka black-tailed deer occur on the mainland 

and on islands in Prince William Sound. Extant distributions of all three species 

are partially artifacts of glacial history, barriers to movement after the last glaciers 

receded (Klein 1965), and translocations and reintroductions in the 20th century 

(Paul 2009).

Moose, caribou, and black-tailed deer are harvested for both subsistence and 

recreational purposes in the assessment area. Population abundance and composition are manipulated primarily through harvest regulations in game management 

units (GMUs) designated by the Alaska Department of Fish and Game. GMUs 15 

and 7 are on the Kenai Peninsula, GMU 14C is on the mainland adjacent to the 

peninsula, and GMU 6 stretches from Prince William Sound through the eastern 

extent of the Copper River Delta (fig. 7-1). Fire management also plays an important 

role in the distribution and abundance of moose on the western Kenai Peninsula.
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Moose: Current and Historical Distribution

Moose have been present in Alaska since mid- to late-Pleistocene times (11,000–

14,000 years BP) (Hundertmark et al. 2003, 2006). They likely survived in relatively small disjunct groups wherever suitable habitat could be found throughout 

this period, when a tundra-steppe community dominated much of Alaska refugia 

(LeResche et al. 1974). With the close of the glacial period and proliferation of 

shrub and forest communities, moose spread via river valleys throughout 90 percent 

of contemporary Alaska (LeResche et al. 1974, Lutz 1960). Very recent extensions 

of moose distribution have occurred in the geographic extremes of Alaska; most 

relevantly in southeast Alaska, where glacial recessions have allowed moose to 

colonize coastal forests (Darimont et al. 2005, Hundertmark et al. 2006, Klein 1965, 

LeResche et al. 1974) as well as by deliberate translocations to the Copper River 

Delta near Cordova (Paul 2009). LeResche et al. (1974) concluded that, in most of 

Alaska, moose numbers have varied dramatically in local areas over the past two 

centuries, largely in response to fire and subsequent forest succession. Historical 

accounts that moose were absent from a particular locale within its range most 

likely reflected only a period of very low moose numbers resulting from a prolonged absence of fires in that area (LeResche et al. 1974), which is likely the case 

for the Kenai Peninsula through most of the 19th century (Lutz 1960). 

The Alaska-Yukon race of moose is widely but patchily distributed in the 

assessment area, consistent with the distribution of vegetation, and, to a lesser 

extent, glaciation. Currently, about 10,000 moose populate the assessment area, 

of which almost 60 percent are on the western side of the Kenai Peninsula (GMU 

15). Moose do not occur on the extreme southern Kenai Peninsula, and are patchily 

distributed along Prince William Sound, presumably because suitable habitats are 

restricted to a few sizable areas where the vegetation is still in the early stages of 

succession, and which occur only in the larger river valleys of the mainland and on 

the terminal moraines of glaciers that have recently receded (Klein 1965). 

Moose are most abundant on the western Kenai Peninsula, where a drier 

climate and an active fire regime produce hardwood browse and less snow, which 

are critical and interacting components for overwinter survival (Peek 1998). 

Moose occur in relatively low densities on the eastern Kenai Peninsula, where the 

mountainous terrain, paucity of hardwood habitats (and browse), and deep snow are 

limiting. Moose are absent from much of the southern coast of the peninsula, which 

is isolated by the Kenai Mountains, the Harding lcefield, and the WosnesenskiGrewingk Glacier complex. Although there is anecdotal evidence that moose may 

have colonized the Kenai Peninsula in the late 1800s, Lutz (1960) provides references that indicate that moose were on the peninsula since the early 1800s and were 

Currently, about 10,000 

moose populate the 

assessment area, 

of which almost 60 

percent are on the 

western side of the 

Kenai Peninsula 
268

GENERAL TECHNICAL REPORT PNW-GTR-950

present in archaeological sites dating to circa 750 BCE. Moose are abundant on the 

mainland immediately north of the Kenai Peninsula, specifically in the Anchorage 

Bowl, where they take advantage of high-quality browse growing in the wildlandurban interface and adjacent military reservation. Genetic evidence suggests that 

populations on the Kenai Peninsula are semi-isolated from the adjacent mainland, 

presumably because of natural and human barriers in and around the 16-km-wide 

isthmus (Wilson et al. 2015).

Moose are sparse over much of the Sitka spruce (Picea sitchensis)-dominated 

landscape adjacent to Prince William Sound. Moose were effectively isolated 

from Prince William Sound by glaciation in the Chugach Mountains and by Miles 

Canyon on the Copper River (Klein 1965). In southeast Alaska, anecdotal evidence 

suggests that moose colonized the lower Stikine River valley at the end of the 19th

century and the Yakutat-Dry Bay area from 1925 to 1935 in response to relatively 

recent deglaciation (Klein 1965); genetic evidence suggests that these populations 

may originate from the Western moose (Alces alces andersoni) (Colson et al. 2014),3

although Hundertmark et al. (2006) suggested that the dividing line is further south 

(58° 45′ N). The extant population on the Copper River Delta (and Berners Bay) 

(Klein 1965) was established by a series of calf transplants between 1948 and 1958 

from the Kenai Peninsula (Burris 1965, Paul 2009) and so belong to the AlaskaYukon race (Hundertmark et al. 2006). The moose population on the western Copper River Delta has responded favorably to uplift and vegetation growth since the 

1964 earthquake, which help sustain a small population on Hinchinbrook Island.4 

A few moose inhabit the Valdez area, occasionally reaching western Prince William 

Sound at Kings Bay via the Nellie Juan River (LeResche et al. 1974). 

Kenai Peninsula (GMU 15)—

Moose populations on the western Kenai Peninsula are managed in three subunits 

(fig. 7-1). GMU 15A includes all of the Kenai lowlands and other habitats north 

of the Kenai River. GMU 15B includes the subalpine shrubs on the Tustumena 

Benchlands and forested habitats between the Kenai and Kasilof Rivers. GMU 15C 

includes the Caribou Hills and other forested areas south of the Kasilof River. 

On the Kenai Peninsula, moose population fluctuations have been historically 

linked to fires and winter browse availability (Franzmann and Schwartz 1998, 

3 Colson, K.; White, K.S.; Hundertmark, K. 2014. Population boundaries and the subspecific divide in southeastern Alaskan moose. Presented at 48th North American Moose 

Conference and Workshop, Girdwood, Alaska.

4 Burcham, M. 2016. Personal communication. Wildlife biologist, U.S. Department of 

Agriculture, Forest Service, Chugach National Forest, Cordova Ranger District, Cordova, 

AK 99574.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Loranger et al. 1991, Oldemeyer et al. 1977, Spencer and Hakala 1964). Moose 

populations typically peak 15 to 25 years post-fire (Loranger et al. 1991, Spencer 

and Hakala 1964), but fires must be severe enough that mineral soil is exposed to 

get maximum regeneration of hardwood browse (Miner 2000). The moose population in GMU 15 probably peaked in 1925 and declined somewhat by 1950 in the 

aftermath of large human-caused fires in 1871, 1891, and 1910 (Chatelain 1952; 

cited in LeResche et al. 1974), which burned much of the Tustumena Benchlands 

(GMU 15B). In the aftermath of these fires, which replaced mature spruce with 

early successional hardwood, wintering habitat for moose was created while 

destroying that of caribou (see “Caribou: Current and Historical Distribution” on 

page 273). Declining moose populations on the western Kenai Peninsula in the 

early 1900s helped justify establishing the Kenai National Moose Range in 1941, 

later to become the Kenai National Wildlife Refuge in 1980. 

The 125 500 ha Kenai Fire in 1947 and the 32 000 ha Swanson River Fire in 

1969, both accidentally caused by humans, set the stage for abundant hardwood 

browse (Oldemeyer et al. 1977) and moose (Loranger et al. 1991) in the Kenai 

lowlands (GMU 15A) in the 1960s through 1980s. Populations in this area peaked 

at 5,300 moose in 1971, with densities as high as 4.4 moose/km2 (Loranger et al. 

1991), but browse quality declined thereafter (Oldemeyer et al. 1977). The moose 

population in GMU 15A is now less than 1,600, a consequence of no significant 

fires for more than 40 years until the 3600 ha Card Street Fire in 2015.

After the 1969 fire, the U.S. Fish and Wildlife Service and subsequently the 

Alaska Department of Fish and Game used three 40-ton LeTourneau tree crushers 

in efforts to create moose browse in GMU 15A (fig. 7-2). From 1970 to 1988, young 

spruce on more than 6500 ha were crushed with limited success and at high financial cost (Miner 2000). The Kenai National Wildlife Refuge initiated a prescribed 

fire program in 1986 to increase the likelihood of softwood being converted to 

hardwood (Miner 2000), but fewer than 2400 ha were successfully burned before 

that program was terminated in the early 2000s.

Since 1985, moose populations have fluctuated on the western Kenai Peninsula 

(GMU 15A, 15B, 15C) between 5,000 and 6,000 animals. This variation, however, 

has not been uniform in distribution. Moose populations have decreased in GMU 

15A, remained stable in GMU 15B, and increased in GMU 15C (Wilson et al. 

2015). These differences can be attributed to changes in the habitat conditions 

in each subunit. Mostly black spruce (Picea mariana) forests within GMU 15A 

have experienced few fires in the last four decades and have continued to mature 

since the last big wildfire in 1969, producing less browse as forest succession has 

progressed. GMU 15B has seen little increase in moose habitat, as there have been 
Figure 7-2—Three LeTourneau tree crushers were used to create 16,000 ac of early successional hardwood on the Kenai National Moose 

Range (now the Kenai National Wildlife Refuge) during the 1970s. This approach to managing large-scale winter moose habitat was 

270

GENERAL TECHNICAL REPORT PNW-GTR-950

no significant fires until very recently. The 2800 ha 2004 Glacier Creek Fire was 

an intense fire on the northeast shoreline of Tustumena Lake, and the 2014 Funny 

River Fire spanned a 80 000 ha fire perimeter of which about 65 percent was actually burned, much of it black spruce and beetle-killed white (Picea glauca) spruce. 

In GMU 15C, most white and Lutz (Picea × lutzii) spruce forests had not 

burned in the past 600 years (Berg and Anderson 2006). In the past two decades, 

however, white and Lutz spruce forests that coincidentally experienced high mortality rates resulting from spruce bark beetle attacks during the late 1980s through the 

1990s have burned. Until the most recent Funny River Fire in 2014, about 57 000 ha 

burned on the Kenai Peninsula since the 1100-ha Windy Point Fire in 1994, twothirds of that south of Tustumena Lake in GMU15C. Not surprisingly, moose have 

increased in GMU 15C from 2,000 in 1992 to 3,200 in 2013. 

Annual harvests in GMU 15 have averaged >500 moose over the past three 

decades, ranging from a high of 884 in 1983 to a low of 388 in 1999. Over this same 

U.S. Fish and Wildlife Service
271

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

period, moose-vehicle collisions on the Kenai Peninsula, mostly along the Sterling 

Highway, have averaged 244 per year, translating to more than 7,100 moose killed 

by vehicles since 1980. Currently, about a third (30 percent) of moose killed by 

humans every year in GMU 15 is a result of vehicle collisions (Morton 2012). 

Kenai Peninsula (GMU 7)—

The moose population on the eastern side of the Kenai Peninsula is managed in 

GMU 7, which includes drainages flowing into the Gulf of Alaska and upper Turnagain Arm, and the Kenai River upstream from the Russian River. Moose densities 

are low relative to GMU 15 on the Kenai Peninsula and are expected to remain so 

unless significant habitat alteration occurs. Widespread spruce bark beetle infestations that began in the 1990s have affected more than 500 000 ha of spruce forests 

on the Kenai Peninsula. Since 2001, infestation rates have decreased as the number 

of uninfected trees becomes scarce (Schulz 2003). The impact of spruce mortality 

and salvage logging efforts will affect the quality of moose habitat over a large 

area, but the nature of the effect remains uncertain, particularly in the Sitka-spruce 

dominated GMU 7, where wildfire has historically not been an ecological driver.5

Although a complete population survey has never been completed, the Alaska 

Department of Fish and Game has assumed that fewer than 1,000 moose reside in 

GMU 7; over the past 20 years, this game management unit has provided about 10 

percent of the annual moose harvest on the Kenai Peninsula. 

Prince William Sound area (GMU 6)—

Moose populations on the northern coast of the Gulf of Alaska and throughout 

Prince William Sound are managed in four subunits of GMU 6 (fig. 7-1). GMU 

6A encompasses the Gulf of Alaska drainages east of Katalla, including Kanak, 

Wingham, and Kayak Islands. GMU 6B encompasses the Gulf of Alaska and Copper River basin drainages west of Katalla and east of the Copper River. GMU 6C 

includes the drainages west of the Copper River and east of the Rude River. GMU 

6D includes all other drainages flowing into Prince William Sound from Cordova 

to Valdez to Whittier. 

Moose populations in most of GMU 6 originated from translocations of calves 

from the Kenai Peninsula, Anchorage, and Matanuska-Susitna areas (Burris and 

McKnight 1973, Paul 2009). During 1949–1958, Cordova residents successfully 

raised 24 captive moose calves and released them on the western Copper River 

Delta in GMU 6C. This small population grew rapidly and expanded eastward into 

5 Potkin, M. 1997. Fire history disturbance study of the Kenai Peninsula mountainous 

portion of the Chugach National Forest. Unpublished report. 75 p. On file with: Chugach 

National Forest, 161 East 1st Ave., Door 8 Anchorage, AK 99501.

Moose populations 

in most of GMU 6 

originated from 

translocations of 

calves from the Kenai 

Peninsula, Anchorage, 

and MatanuskaSusitna areas.
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GENERAL TECHNICAL REPORT PNW-GTR-950

GMU 6B by the early 1960s. Eastward expansion continued into GMU 6A to the 

Bering River area by the late 1960s, and to Cape Yakataga by the mid-1970s. The 

population reached a record high of approximately 1,600 in 1988 (Griese 1990), 

then declined to 1,227 by 1994 as part of a planned reduction (Nowlin 1998). The 

only moose indigenous to GMU 6 are small populations in the Lowe River drainage 

and Kings Bay, numbering about 40 animals total. The current population in GMU 

6 is estimated to be 1,250 moose, and is stable in most areas, but is increasing in 

GMU 6C near Cordova. 

Hunting of the introduced population in 6C began with 25 bulls harvested in 

1960. Harvest began in 6B and 6A during 1965 and 1971, respectively. Moose in 

6A were divided into two populations (east and west of Suckling Hills) during 1977 

and have been managed separately since then. Hunters have harvested 3,800 moose 

during 1965–1998 in GMUs 6A, 6B and 6C. In contrast, total hunting mortality of 

the indigenous moose population in GMU 6D during the same period was about 

40 moose. 

Anchorage area (GMU 14C)—

Part of the assessment area includes a subunit of GMU 14, which encompasses 

drainages north of Turnagain Arm to the Knik River and Knik Glacier and includes 

the Anchorage area. Moose were uncommon in this area before the 1940s. They 

increased in the late 1940s as brushy regrowth replaced mature forests cut or 

burned during the development of Anchorage and the Fort Richardson Military 

Reservation. Numbers increased considerably during the early 1950s, and by the 

late 1950s and early 1960s moose were abundant and have remained so during 

the past four decades because of continued production of browse. Open-canopied, 

second-growth willow (Salix spp.), birch (Betula neoalaskana), and aspen (Populus 

tremuloides) occur on burned-over military lands and on several hundred acres of 

military lands that have been rehabilitated during the past two decades. Extensive 

moose browse occurs in the parks, greenbelts, residential areas, riparian areas, and 

most south-facing slopes in the Anchorage Bowl. Consequently, moose mortality 

from vehicle collisions is high, particularly along the Glenn Highway. From 1994 to 

1999, annual moose-vehicle collisions averaged 156 in the Anchorage Bowl (Garrett 

and Conway 1999), although the annual rate has exceeded 200 in recent years.

Nearly 2,000 moose are currently estimated in GMU 14C, including the Placer 

and Portage River drainages. A record harvest of nearly 500 moose (50 percent 

females) occurred in 1965, but hunters harvested only 18 moose in 1978. Annual 

harvests increased steadily during the late 1980s and early 1990s but began to 

decline in 1992. The mean annual harvest in the past 5 years was fewer than 90 

moose, most of which were taken on military reservations.
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Annual harvest from all GMUs currently approximates 700 moose out of an 

estimated population of 10,000 moose in the assessment area. Collisions with 

vehicles kill more than 400 moose annually in the assessment area.

Caribou: Current and Historical Distribution

Caribou have not penetrated into the southern coastal region of Alaska except on 

the Alaska and Kenai Peninsulas, where they have been restricted almost exclusively to the dryer western sides (Klein 1965). The only caribou in the assessment 

area are on the Kenai Peninsula. The peninsula historically supported an endemic 

caribou subspecies (Rangifer stonei) (Porter 1893, Seton-Karr 1887) that, by all 

accounts, functioned as a woodland population.6 The indigenous caribou were 

extirpated circa 1912, when Andrew Berg shot 13 caribou near Ptarmigan Head 

in the Caribou Hills, the last authenticated report of caribou on the peninsula. 

This extirpation has been attributed to large-scale habitat change brought about by 

both human-caused fires and overharvest by miners and trophy hunters in the 19th

century, but Davis and Franzmann (1979) believed the latter was a “more proximate 

cause of extermination” than the former (also see Collins et al. 2011).

By the early 1950s, biologists from the Kenai National Moose Range (now 

the Kenai National Wildlife Refuge) and the Alaska Game Commission (now the 

Department of Fish and Game) were considering the introduction of caribou to 

the peninsula. The first 15 caribou were captured from the Nelchina herd near 

Glenallen and released at an airstrip near the Chickaloon River in 1965 (Paul 2009). 

Another 29 caribou were inadvertently released at Watson Lake on the East Fork of 

the Moose River in 1966 after the transport vehicle broke down. These two translocations resulted in the establishment of the Kenai Mountain and Kenai Lowland 

herds, respectively. Additional releases of 80 caribou in 1985 and 1986 at Emma 

Lake, Green Lake, Tustumena Glacier Flats, and Caribou Lake eventually became 

the Killey (Twin Lakes) and Fox River herds (Paul 2009) (fig. 7-3). 

The Kenai Mountain Herd ranges from 200 to 400 animals and currently 

numbers about 300. This herd occupies 907 km2 in the Chickaloon River and Big 

Indian Creek drainages on the Kenai National Wildlife Refuge (GMU 15), and 

Resurrection Creek drainages on the Chugach National Forest east of the Sterling 

Highway (GMU 7). 

The Killey River Herd currently numbers about 250 animals and occupies 778 

km2

 in the upper Killey, Funny, and Skilak River drainages on the Kenai National 

6 Palmer, L.J. 1938. Management of moose on the Kenai Peninsula. Research Project 

Report, March, April, May 1938. Unpublished manuscript. On file with: Kenai National 

Wildlife Refuge, 1 Skihill Road, Soldotna, AK 99669. 40 p.

The only caribou in the 

assessment area are 

on the Kenai Peninsula. 
Figure 7-3—Caribou were reintroduced to the Kenai Peninsula from the Nelchina herd in the 1960s and 1980s by the Alaska Department 

of Fish and Game and the U.S. Fish and Wildlife Service. The endemic Stone’s caribou was extirpated from the peninsula circa 1912 by 

overharvesting and loss of habitat resulting from human-caused fires.

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GENERAL TECHNICAL REPORT PNW-GTR-950

Wildlife Refuge (GMU 15). This herd once numbered more than 700 animals 

until three avalanches killed about 200 caribou during two winters in 2001–2003, 

representing about 20 percent of the caribou population on the Kenai Peninsula at 

that time.7

The Fox River Herd contains between 50 and 75 caribou. It occupies 130 

km2 south of the Tustumena Glacier in the Upper Fox River and Truli Creek drainages on the Kenai National Wildlife Refuge (GMU 15). This herd appears to be 

expanding its range eastward into the ice fields of Kenai Fjords National Park; 

small groups have been seen above Exit Glacier in recent years. 

The Kenai Lowland herd numbers between 130 and 150 animals. This herd 

occupies 1425 km2

 in and around the communities of Kenai, Soldotna, and Sterling 

on the western side of the Kenai Peninsula (GMU 15). Of the four caribou herds 

U.S. Fish and Wildlife Service

7 Ernst, R.D.; Spraker, T.; Hall, J.; Selinger, J. 2004. Caribou killed by “unexpected 

predator”—avalanches. Presented at 10th North American Caribou Conference, Girdwood, 

Alaska.
275

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

recognized on the Kenai Peninsula, the Kenai Lowland herd is the only one that 

does not spend time feeding on lichens above treeline in the Kenai Mountains. 

Instead, this herd winters east of the Moose River, feeding on arboreal lichens that 

have regenerated in the spruce forests since the 1947 fire that burned 125 000 ha. 

In the latter part of May, cows from this herd calve near the Cook Inlet, from the 

Kenai Airport through the Kenai Flats and south toward Kasilof along Kalifornsky 

Beach Road. Domestic dogs and vehicle collisions are looming problems as the 

Kenai-Soldotna area becomes urbanized. As many as five caribou have been killed 

in a single vehicle incident on Bridge Access Road (Morton 2007).

The fewer than 1,000 caribou on the Kenai Peninsula continue to redistribute 

themselves. After the last releases of caribou in the 1980s, five distinct herds were 

established. By 2003, wildlife managers recognized that the Twin Lakes herd 

was comingling with the Killey River herd, and the two herds were combined for 

management purposes. In recent years, caribou groups have been observed more 

frequently in the Exit Glacier area of Kenai Fjords National Park, presumably from 

the Killey River herd. Also, in 2013, caribou groups were seen near Carter Lake 

and east of the Seward Highway in Turnagain Pass, presumably from the Kenai 

Mountain herd. Collectively, these observations suggest that caribou are continuing 

to spread eastward on the Kenai Peninsula, occupying (or re-occupying) alpine 

habitat, likely seeking new lichen forage. 

The Kenai Mountain, Killey River, and Fox River herds are open to public 

hunting through the drawing system. The Kenai Mountain herd is also open to 

federal subsistence harvest. The Kenai Lowland herd has been closed to hunting 

since 1993. Current annual harvest from all three hunted herds is 20 to 30 animals.

Sitka Black-Tailed Deer: Current and Historical Distribution

Sitka black-tailed deer (fig. 7-4) are closely related to the larger Columbia blacktailed deer (Odocoileus hemionus columbianus) of the Pacific Northwest, and both 

are considered subspecies of the even larger mule deer (Odocoileus hemionus) of 

the American West (Wallmo 1978). Sitka black-tailed deer are native to the wet 

coastal rain forests of southeast Alaska and north-coastal British Columbia, from 

latitude 53° N to 58° N. They swim well and are found on all southeast islands 

except remote Forrester Island. An estimated 200,000 deer occupy southeast 

Alaska,8

 where deep snow keeps the number of deer on the mainland lower than 

on adjacent islands that generally accumulate lower snowpack (Klein 1965).

8 Merriam, H.R. 1970. Deer fluctuations in Alaska. Presented at Northwest Section of 

The Wildlife Society. Spokane, WA: 5 p.

Sitka black-tailed deer 

are native to the wet 

coastal rain forests of 

southeast Alaska and 

north-coastal British 

Columbia, from latitude 

53° N to 58° N. 
Figure 7-4—Sitka black-tailed deer are not native to the assessment area. They were initially introduced to Prince William Sound from 

southeast Alaska by the Cordova Chamber of Commerce in 1916–1923. They are now harvested for both subsistence and recreational 

purposes in Game Management Unit 6, mostly on the Chugach National Forest. Deer have continued to expand their distribution and are 

likely to colonize the eastern Kenai Peninsula in coming years.

276

GENERAL TECHNICAL REPORT PNW-GTR-950

Sitka black-tailed deer densities on winter ranges have been estimated as high 

as 25 to 75 deer per square kilometer (Hanley 1984); population densities are highest in high-volume old-growth forest. Deer populations tend to fluctuate primarily 

in response to winter weather and to wolf and bear predation. During winter, the 

distribution of deer is influenced by changing snow depth. During extreme snow 

accumulation, deer congregate in old-growth forest stands with high canopy cover 

at lower elevations, and on beaches, where they consume intertidal species. 

Deer have inhabited northern southeast Alaska since their migration from 

southern refugia following the Pleistocene (Klein 1965). The oldest deer bone in 

southeast Alaska was found on the west side of Heceta Island, dated at more than 

8,000 years old (ADFG 2015). Deer likely moved northward up the coast as the glaciers receded and forest became established. Deer have occupied all islands of the 

Alexander Archipelago capable of supporting them, with the exception of remote 

Forrester Island, and occur on the mainland northwest of Juneau. The absence of 

Milo Burcham
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

deer, except through human introduction, on the Queen Charlotte Islands likely 

results from the effectiveness of Hecate Strait as a dispersal barrier. Their access to 

favorable habitat in Prince William Sound and on Kodiak Island has been blocked 

by the exposed coastal area north of Cross Sound, where conditions are not suitable 

for their survival (Klein 1965).

Sitka black-tailed deer were introduced into the assessment area, where they 

have continued to disperse. The Cordova Chamber of Commerce introduced deer 

to Prince William Sound and North Gulf Coast (GMU 6), specifically 24 deer to 

Hawkins and Hinchinbrook Islands during 1916–1923 (Paul 2009) (fig. 7-1). Deer 

were introduced to Kodiak Island (GMU 8) in three transplants, totaling 25 deer, 

during 1924–1934. In 1930, two more deer were captured from Prince of Wales 

Island and released on Long Island. There was little natural movement from Long 

Island to Kodiak, so, in 1934, nine deer were captured in the Rocky Pass area near 

Petersburg and released on Kodiak. Deer were introduced to the Yakutat area in 

1934 (Paul 2009).

The original 24 deer transplanted to Hinchinbrook and Hawkins Islands 

survived and flourished. Being good swimmers, Sitka black-tailed deer spread from 

these islands to the mainland and to other islands in Prince William Sound. Blacktailed deer now occupy most of GMU 6 (Harper 2013) with a population estimated 

to be about 20,000 animals in 2012 (Gibbins 2012). Legal hunting began in 1935 

and, currently, about 2,000 deer are harvested annually in the Prince William 

Sound area (Harper 2013).

Although Hinchinbrook and Hawkins Islands are about 240 air kilometers from 

Anchorage, separated by mountain ranges, glaciers, rivers, inlets, and bays, the 

Alaska Department of Fish and Game received reports of deer in mountain passes 

during the 1990s. Since 2002, a few deer, including both bucks and does, are seen 

in Anchorage, in the Portage and Placer river drainages, and along Turnagain Arm 

(Peninsula Clarion 2003). In recent years, black-tailed deer have also been seen 

infrequently on the Kenai Peninsula near Seward.

Future Distributions

Over the next 50 years, moose, caribou and Sitka black-tailed deer populations 

will persist in the assessment area, although their distributions and abundance will 

vary in response to changing vegetation, fire regimes, and snow dynamics. The 

most salient of the anticipated long-term impacts include afforestation of alpine 

tundra, primarily by mountain hemlock (Tsuga mertensiana); deforestation of the 

southwestern Kenai Peninsula (see chapter 6); and afforestation and reduced snow 

depth along the Prince William Sound coastline (chapter 3). In general, deer and 
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GENERAL TECHNICAL REPORT PNW-GTR-950

moose should respond favorably to predicted changes along Prince William Sound. 

Black-tailed deer in particular will likely continue their westward colonization 

onto the Kenai Peninsula, with contemporary climate warming (including reduced 

snowfall) only accelerating that dispersal. Moose distribution will likely decline 

in response to grassland conversion on the southern Kenai Peninsula, but their 

abundance will likely increase in the near term as black spruce forests in the Kenai 

lowlands approach the historical mean fire-return interval. The Kenai Peninsula has 

already lost about 121 000 ha of alpine tundra over the past 50 years to treeline rise 

(derived from Dial et al. 2007). Consequently, the abundance of caribou will almost 

certainly decline and their genetic diversity likely reduced (Yannic et al. 2013) over 

the long term in response to alpine afforestation. These changes in land cover and 

snow dynamics are incorporated into the following climate envelope modeling used 

to develop scenarios for future distributions of the three ungulate species. 

Regional Ecological Niche Models for 2009 Versus 

2069 (Decades) 

This model effort predicts the potential ecological niche for moose, caribou, and 

Sitka black-tailed deer on the assessment area. This niche is modeled from the 

best publicly available distribution data for each species in Alaska. The algorithms 

used are from the machine learning family, which are high-performing models 

for visualizing relationships (TreeNet; i.e., boosting) and for prediction of species 

distribution (RandomForest; i.e., bagging) (Craig and Huettmann 2009, Lawler et 

al. 2011).

Distribution data for all three ungulate species were generally of poor quality or 

spatially incomplete within the assessment area. Consequently, models were based 

on statewide distributions and then clipped for the assessment area. For moose, 

we compared the year-round presence points from the Gap Analysis Program 

(GAP) (http://akgap.uaa.alaska.edu/) with pseudo-absence points for all of Alaska 

(fig. 7-5A). For caribou, we used the year-round presence polygons in Alaska, and 

subsampled them for presence points (in polygons) and absence points (outside of 

the polygons; fig. 7-6A). For Sitka black-tailed deer, we used the known year-round 

range maps from the Alaska GAP, and subsampled them for presence points (inside 

of the polygons) and absence points (outside of the polygons) in the study area (fig. 

7-7A). For all three species models, we chose to average data by decade to increase 

the robustness of the climate signal. 

The key elements of this approach are the use of available species location data 

in Alaska (1971–2000), high-resolution environmental GIS layers (table 7-1), and 

high-performance modeling algorithms, resulting in model predictions for species 

The abundance of 

caribou will almost 

certainly decline and 

their genetic diversity 

likely reduced.
279

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table 7-1—Environmental predictors used as geographic information system layers in ArcGISTM for modeling 

current and future potential distributions of moose, caribou and Sitka black-tailed deer

Data set 

number Dataset name Units

Time period

(decadal) Pixel size Source

1 Elevation Meters Constant 60 m AK GAP

2 Slope Degrees Constant 60 m AK GAP

3 Aspect Degrees Constant 60 m AK GAP

4 Temperature July Degrees Celsius 2010, 2069 (A2) 2 km SNAP PRISM

5 Temperature January Degrees Celsius 2010, 2069 (A2) 2 km SNAP PRISM

6 Precipitation July Millimeters 2010, 2069 (A2) 2 km SNAP PRISM

7 Precipitation January Millimeters 2010, 2069 (A2) 2 km SNAP PRISM

8 Distance to coast Meters Constant 60 km AK GAP

9 Maximum 1 April snow equivalent Index of snow depth 2010, 2049 (A2) 2 km J. Little

10 NLDC (rescaled) Landcover class 2010, 2069 (A2) 2 km D. Magness

distributions for the present (2000–2009) and future (2060–2069). Despite good 

model performance, the models are limited in several important ways: spatially 

incomplete location data, particularly for the assessment area, to inform, train and 

test the models; lack of relevant high-quality environmental GIS layers for the 

assessment area such as fine-grain vegetation layers; and poor snow-cover data 

for Alaska.

However, the models illustrating current distribution appear to adequately 

explain general trends, and reflect the potential ecological niche for the three 

ungulates (figs. 7-5 through 7-7). These models show a high prediction accuracy 

(AUC >92 percent) based on the potential ecological niche. As expected, the 

potential niches of Sitka black-tailed deer and moose in the assessment area were 

much greater than extant distributions. As discussed earlier, the historical (natural) 

distributions of both species were constrained by topographic and glacial barriers 

in the post-Pleistocene landscape, whereas the current distributions are an artifact 

of translocations by humans and subsequent dispersal. Consequently, the realized 

distributions of both species are geographically small subsets of their potential 

distributions. 

At broad spatial extents, caribou and black-tailed deer do not overlap in either 

realized or potential distributions; caribou are constrained to the western side of 

the Kenai Peninsula, whereas black-tailed deer tend to occupy all other areas of the 

assessment area. At finer grains, more characteristic of habitats, caribou tend to 

prefer alpine tundra, whereas black-tailed deer prefer forests, although the former 

will feed on arboreal lichens within mature forests during winter. Moose are widely 

but patchily distributed over the assessment area, sympatric with deer in coastal 

areas and with caribou on the western Kenai Peninsula.
Figure 7-5—Current and future distributions of moose: (A) Training data for statewide 

distribution of moose from the Alaska Gap Analysis Program (http://akgap.uaa.alaska.edu), 

(B) modeled potential moose distribution on the assessment area in 2000–2009, and (C)

the likelihood of occurrence (0–1) ranging from green (absent) to yellow to orange to red 

(present) as generated by Random ForestTM for the decadal means.

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GENERAL TECHNICAL REPORT PNW-GTR-950
Figure 7-6—Current and future distributions of caribou: (A) Training data for statewide 

distribution of caribou from the Alaska Gap Analysis Program (http://akgap.uaa.alaska.edu), 

(B) modeled potential caribou distribution on the assessment area in 2000–2009, and (C)

the likelihood of occurrence (0–1) ranging from green (absent) to yellow to orange to red 

(present) as generated by Random ForestTM for the decadal means.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula
Figure 7-7—Current and future distributions of Sitka black-tailed deer: (A) Training data for regional 

distribution of deer from the Alaska Gap Analysis Program (http://akgap.uaa.alaska.edu), (B) modeled potential Sitka black-tailed deer distribution on the assessment area in 2000–2009, and (C) modthe likelihood of occurrence (0–1) ranging from green (absent) to yellow to orange to red (present) as 

generated by Random ForestTM for the decadal means.

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GENERAL TECHNICAL REPORT PNW-GTR-950
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Five decades from now (circa 2069), these climate envelope models generally 

suggest diminishing ecological niches within the assessment area, resulting in 

range shifts for all three species, usually northward and toward higher elevations. 

This redistribution pattern translates to shifts inland, away from the coast, particularly for moose and deer. Despite modeling that suggests diminishing potential 

distributions resulting from contemporary climate warming, it seems likely that the 

realized distributions of moose and deer will be more sympatric in the future owing 

to their continued dispersal in the aftermath of 20th-century introductions to Prince 

William Sound.

Proximate Drivers of Future Distributions

Future distributions of moose, caribou, and Sitka black-tailed deer, and particularly 

their local-scale population abundances, will be mediated by mechanistic ecological 

interactions involving vegetation disturbance, predators, competitors, and disease. 

For example, ecological disturbances (e.g., fire and insects; table 7-2) will influence 

forage, cover, and predation. Also, as these three species continue to disperse and 

more fully occupy the assessment area (i.e., their realized distribution), competitive 

interactions are likely between moose and deer, and between moose and caribou. 

Diseases transmitted among species or moving northward could also influence 

populations. 

Moose—

Moose distribution is likely to expand in response to forecasted afforestation of 

coastal areas and alpine tundra in the assessment area, as these will be early successional, albeit not necessarily hardwood. LeResche et al. (1974) suggested that 

four macrohabitats were used by moose in Alaska: climax communities dominated 

by upland willow or birch; lowland bog; and seral communities created by fire 

and by glacial or fluvial actions. Fires that are hot enough to burn to mineral soil 

in boreal Alaska, such as on the western Kenai Peninsula where black and white 

spruce predominate, generally convert conifer stands to hardwood (Miner 2000). 

Good moose habitat occurs for 15 to 25 years after these mineral soil-exposing fires 

(Miner 2000). Hundertmark (2007) found that, between 40° and 60° N latitude, 

mean sizes of winter and summer home ranges of moose in 13 studies remained 

relatively stable at 51 km2. However, in the Yakutat forest in southeast Alaska, mean 

annual home range size was 76 km2

 for females and 125 km2 for males (Oehlers et 

al. 2011), presumably because of relatively poorer browse availability in the coastal 

rain forest than in fire-dominated boreal systems. Future moose abundance in the 

assessment area will likely be driven by the distribution of these habitats and their 

interaction with changing rates of fire and other disturbances.

Five decades from 

now (circa 2069), these 

climate envelope 

models generally 

suggest diminishing 

ecological niches 

within the assessment 

area, resulting in 

range shifts for all 

three species, usually 

northward and toward 

higher elevations. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Anticipated changes in climate are likely to increase the frequency and extent 

of fire on the western Kenai Peninsula (see chapter 6), at least in the near term. The 

official start of the Alaska fire season was changed in 2006 from May 1 to April 

1, largely because of the increasing threat of “pre-green up” grassland fires in the 

aftermath of the spruce bark beetle outbreak on the Kenai Peninsula combined 

with earlier snowmelt. The year before, in 2005, the Tracy Avenue Fire near Homer 

started on April 29, burning 2185 ha in what was described by the Division of Forestry’s director as the “earliest large complex fire in the state’s history.” This and other 

spring fires since then were human-caused, and were started in grasslands composed 

primarily of bluejoint (Calamagrostis canadensis). This is a significant departure 

from fire records kept over the previous half century that show mostly lightningcaused fires started in spruce forests in mid or late summer. Radiocarbon-dated 

soil charcoal and tree-ring counts show that not all spruce on the western Kenai 

Peninsula burns with the same frequency (Berg and Anderson 2006). On the Kenai 

lowlands, where black spruce predominates, a given acre has historically burned 

every 80 years, a statistic called the mean fire-return interval (MRI). In the southern 

part of the Kenai National Wildlife Refuge, where white and Lutz spruce predominate, the MRI is 400 to 600 years. The MRI for white and black spruce stands mixed 

with hardwood is 130 years. Sitka spruce, on the eastern side of the peninsula, has 

essentially no MRI because of the wet climate there. 

Understanding the MRI helps us understand the distribution of moose populations. Spruce stands convert to hardwood when fires are hot enough to burn to 

mineral soil, and moose favor these stands for winter browse 15 to 25 years post-fire. 

But fires and the vegetation response to fires are changing. Roughly 50 percent of 

every acre burned in spruce on the western Kenai Peninsula has been converted to 

hardwood in the past century. However, in the aftermath of the longest spruce bark 

beetle outbreak in North America, not all spruce is regenerating back to spruce or 

converting to hardwood. Much of what was mature white and Lutz spruce forest 

on the southern peninsula is now bluejoint grasslands with few spruce seedlings. 

This has prompted federal, state, and local fire management agencies in the Kenai 

Peninsula Borough to evaluate different treatments for reducing bluejoint in the 

wildland-urban interface (Oja et al. 2004). Consistent with climate-envelope models 

that suggest deforestation on parts of the Kenai (see chapter 6), these spring fires 

may be a mechanism by which a novel grassland ecosystem is maintained in what 

was previously transitional boreal forest. The long-term consequence will likely be 

reduced moose densities. 

Under future warming scenarios, moose in the assessment area may also experience physiological stress. For example, moose in boreal Minnesota, on the extreme 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

southern edge of this species’ range in central North America, have declined since 

their numbers peaked in 1984, coinciding with increased temperatures in September 

and March that resulted in lower reproductive rates and poorer body condition owing 

to heat stress (Murray et al. 2006). Murray et al. (2006) concluded that in areas 

where climate and habitat conditions are marginal, especially where deer act as hosts 

for parasites, moose populations will likely not persist; these are conditions that may 

occur on the assessment area in the future (see discussion below on disease).

Caribou— 

Caribou habitat in the assessment area is anticipated to decrease, primarily in 

response to treeline encroachment into alpine tundra. This rate of treeline rise is 

expected to exceed land exposed from deglaciation (and its subsequent colonization by lichens). The distribution of caribou is ultimately constrained by the need 

for animals to escape or find relief from flying insects in the summer and to find 

food through deep snow or in old forest in the winter (Bergerud 1978). The winter 

survival of caribou populations living in subarctic or northern taiga areas depends 

on the availability of lichen, mostly reindeer lichen (Cladina spp.), which is their 

preferred forage (Bergerud 1978, Helle and Aspi 1983, Paez 1991). Lichen forage is 

constrained by its slow growth rate and by snow that may reduce its availability. 

The only other large mammal that may compete with caribou on the Kenai Peninsula 

is the Dall sheep (Ovis dalli dalli), though the latter prefers graminoids rather than 

lichens.

There is some anecdotal evidence to suggest that the Kenai Mountain and Killey 

River herds may be dispersing further eastward in the Kenai Mountains, but likely 

in response to declining lichen forage. Although warmer winter temperatures and 

reduced snow depths may benefit caribou, the former will likely increase avalanche 

rates. Three avalanches over a 2-year period in 2002–2003 killed about 200 caribou 

in the Killey River herd, representing 20 percent of all caribou on the Kenai Peninsula at that time; this may have been random chance or a harbinger of winters to 

come (Ernst et al. 2004).

Sitka black-tailed deer— 

Even without invoking contemporary climate change, Sitka black-tailed deer are 

likely to continue expanding their range throughout the Sitka-spruce-dominated 

coastal rain forest. The introduction of deer to Prince William Sound by humans in 

contemporary times appears to have accelerated what would have been its “natural” 

northward expansion from southeast Alaska post-Pleistocene. Recent reports of 

deer near Seward suggest that they are likely to spread throughout the eastern 

Kenai Peninsula. 

Even without invoking 

contemporary climate 

change, Sitka blacktailed deer are likely 

to continue expanding 

their range throughout 

the Sitka-sprucedominated coastal 

rain forest. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

Snow depth and its interaction with canopy cover appear to be the ultimate 

driver of Sitka black-tailed deer distribution in the assessment area (Parker et al. 

1999). Moose and deer are not likely to be direct competitors, as the former is 

adapted to deep snow and the latter is constrained by deep snow during winter. 

Moose appear to be better adapted to foraging on hardwood and woody shrub 

browse during winter. In contrast, black-tailed deer forage on evergreen forbs and 

arboreal lichens during winter, and only switch to woody browse such as blueberry (Vaccinium spp.) and hemlock when snow is deep (Hanley et al. 2012). This 

browse alone, however, offers inadequate nutrition, and deer rapidly deplete their 

energy reserves when restricted to such a limited diet. Unlike grazers such as Dall 

sheep, black-tail deer rarely eat grass. Reductions in snow cover and expansion of 

snow-free periods at low elevations will generally favor improved deer habitat (see 

chapter 3 for snow patterns) contributing to expansion of deer distribution, certainly 

along Prince William Sound. 

Climate Effects on Wildlife Diseases

In general, climate affects the health of animals either directly (e.g., thermal-neutral 

zone, heat stress) or, more often, indirectly by influencing the agents, vectors, and 

ecosystems with which animals live and interact (Greifenhagen and Noland 2003, 

Hueffer et al. 2013, Kutz et al. 2012, Murray et al. 2006). The responses of disease 

agents to specific climate changes are difficult to predict. Multiple, differential 

population changes may be reflected in changing biotic-abiotic interactions; i.e., 

a change in the organization of the ecosystem itself. Many disease agents, indeed 

most of concern under conditions of climate change, are protean, meaning they can 

infect multiple species (Greifenhagen and Noland 2003). 

Wildlife diseases can be transmitted directly from animal to animal, or indirectly through vectors. Diseases that are directly transmitted include influenza, 

rabies, canine distemper, tuberculosis, brucellosis, and chronic wasting disease. 

The latter two diseases are of particular concern to moose, caribou, and Sitka 

black-tailed deer, particularly as their distributions expand over the assessment 

area. Darimont et al. (2005), in discussing range expansion by moose into coastal 

areas of British Columbia, suggested that there may be ecological consequences 

such as transmission of disease to native black-tailed deer. Clearly the reverse is 

possible as well: Sitka black-tailed deer that were introduced to Prince William 

Sound continue to expand into habitats currently occupied by both indigenous 

and introduced moose populations. 

Brucellosis is a bacterial disease that can be spread from contact with livestock, 

and can cause weight loss, loss of young, infertility, and lameness in wild cervids. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

In Alaska, Brucella suis is known to occur in both caribou and moose populations 

(Heffer et al. 2013), but is still not known to occur on the Kenai Peninsula.9 However, horses, llamas, alpacas, and even goats have been used by humans in recent 

years to pack gear into the backcountry on the Kenai Peninsula, thereby providing a 

mechanism to spread Brucella abortus and B. melitensis known to occur in livestock. Hueffer et al. (2013) reported that brucellosis in humans has been linked to 

the consumption or processing of raw caribou meat, and infection has been shown 

to be endemic in many caribou and reindeer herds across Alaska and northern 

Canada. They suggest that a warming climate may increase the likelihood that 

Brucella spp. will be transmitted to subsistence user groups in Alaska. 

Chronic wasting disease (CWD) is caused by prions (nonliving protein infectious particles). The most obvious and consistent clinical sign of CWD is weight 

loss over time, hence the name “chronic wasting.” Behavioral changes also occur in 

most cases, including listlessness, lowering of the head, droopy ears, stumbling or 

tremors, and a smell like rotting meat. Once an animal starts manifesting signs of 

CWD, it may be only weeks or months before death. This disease was first documented in captive mule deer at a Colorado research facility in 1967, but, by 1981, 

it was detected in free-ranging mule deer and elk (Cervus canadensis) populations 

in nearby Rocky Mountain National Park. By 2002, CWD had moved eastward to 

white-tailed deer (Odocoileus virginianus) in Wisconsin; by 2011, it was detected 

on the east coast. It is now known to infect wild populations of elk, mule deer, 

white-tailed deer, black-tailed deer, and moose. Currently, CWD does not occur 

in Alaska; however, CWD was confirmed in a road-killed moose from southern 

Alberta in 2013, the first time that moose tested positive for CWD in Canada.

Vectorborne diseases possess a vector stage, usually associated with an insect, 

acarid, mollusk, or crustacean. Each of these is poikilothermic (cold-blooded) 

and is therefore especially sensitive to changes in climatic variables, especially 

temperature and humidity. Disease-causing agents and their vectors are strongly 

affected by weather. Many adult insect vectors and the agents they carry are killed 

by low winter temperatures, so that cycles of disease transmission are interrupted 

and need to be restarted in spring (Greifenhagen and Noland 2003). Consequently, 

warmer winters can increase survivorship and therefore range expansion of insect 

vectors, particularly those that cause arboviral infections. These include mosquitoes 

(responsible for western equine encephalitis, snowshoe hare virus, and West Nile 

virus); Culicoides or biting midges (bluetongue virus and epizootic hemorrhagic 

9 Butler, E.A. 2006. Diseases found on the Kenai National Wildlife Refuge with recommendations for screening and investigations. Unpublished report. 98 p. On file with: Kenai 

National Wildlife Refuge, 1 Ski Hill Rd., Soldotna, AK 99669-2139. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

disease virus); and ticks. The most likely novel insect vectors for vectorborne 

diseases with the potential to kill ungulates in the near term are winter tick (Dermacentor albipictus) and meningeal worm (Parelaphostrongylus tenuis).

Winter ticks feed on ungulates, including moose and caribou. In more severe 

cases, associated with substantial blood and hair loss and distraction from eating, animals starve to death, especially in winter. Winter tick survival rather than 

moose density is probably the major determining factor for outbreaks of severe 

disease. Adult tick survival is enhanced by warm temperatures, low precipitation, 

and absence of snow cover in April. Although winter ticks have not been found 

in Alaska, increasing temperatures associated with a warming climate and the 

occurrence of winter ticks in the Yukon Territory beginning in the 1980s and in the 

Northwest Territories in the 2000s suggest that introduction of this ectoparasite to 

the assessment area is likely. 

Moose have declined in many parts of the eastern United States because of 

meningeal worm, a neurological disease that can be fatal in moose. The white-tailed 

deer is the usual host of this parasitic nematode, and neither is currently known to 

occur in Alaska. Adult meningeal worms live as long threadlike worms in the veins 

and venous sinuses of the cranial meninges of white-tailed deer. Eggs pass to the 

heart and then lungs, then are coughed up, swallowed, and passed into the environment. Larvae are picked up by slugs and snails, and deer become reinfected when 

they incidentally ingest gastropods while foraging. Although meningeal worms 

do not cause serious disease in deer, they can cause severe neurological illness in 

some species such as moose. Moose sickness (meningeal worm infection) has been 

associated with severe moose population declines in New Brunswick, Nova Scotia, 

Maine, and Minnesota (Murray et al. 2006). Warmer summers and lengthening of 

the frost-free period in autumn will likely result in more infections and higher doses 

of worms per infection. Moose and deer can co-exist sympatrically, albeit at lower 

densities (Schmitz and Nudds 1994). In contrast, Murray et al. (2006) cautioned 

that moose will be extirpated where climate and habitat conditions are marginal 

and where deer are abundant and act as reservoir hosts for parasites. The concern 

for ungulates in the assessment area is that meningeal worm may ultimately spread 

to Sitka black-tailed deer. A different species of meningeal worm, P. odocoilei, has 

been found in Columbia black-tailed deer in Oregon, as well as in woodland caribou, mountain goat (Oreamnos americanus) and Dall sheep in Alaska and Canada 

(Mortenson et al. 2006). 

Other diseases known to exist in the assessment area that could affect moose 

populations include hemorrhagic disease (the result of bluetongue virus or epizootic 

hemorrhagic virus), bovine viral diarrhea virus, infectious bovine rhinotracheitis, 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

parainfluenza virus, contagious ecthyma virus, Coxiella burnetii, Leptospira interrogans, Echinococcosis sp., and malignant catarrhal fever virus (Butler 2006). 

Conclusion 

Moose and Sitka black-tailed deer occur in much of the assessment area as a result 

of human introductions into Prince William Sound over the past century. Similarly, 

caribou were re-introduced to the Kenai Peninsula during this same period after 

being extirpated at the start of the 20th century (circa 1913) (Paul 2009). Assuming no significant change in proximate drivers (table 7-2), caribou populations are 

expected to persist in the Kenai Mountains, albeit with a decrease in distribution 

and abundance in the foreseeable future. Moose and black-tailed deer are expected 

to continue colonizing the assessment area regardless of contemporary climate 

change. A warming climate is expected to accelerate this process in the near term, 

but the expected introduction of novel pathogens hosted by both species is likely 

to negatively affect moose abundance. Indeed, in the longer term, anticipating the 

demographics of these three species becomes highly problematic because of the 

uncertainty of how ecological drivers and landscape change may interact in the 

future (table 7-2). 

Table 7-2—Likely responses of three ungulate species to anticipated changes on the Chugach-Kenai 

Peninsula assessment area over the next 50 years

Moose

(n ~ 10,000)

Caribou

(n ~ 1,000)

Sitka black-tailed deer

(n ~ 20,000)

Change Distribution Abundance Distribution Abundance Distribution Abundance

Best guess (assumes no unexpected 

change in mechanistic drivers)

- + - - + +

Higher temperatures ? ? ? ? ? ?

Glacial retreat 0 0 + 0 0 -

Decreased snow depth (particularly at 

lower elevations)

- - 0 + + +

Increased fire frequency and intensity 

on western Kenai Peninsula

+ + 0 - 0 0

Increased activity of spruce bark 

beetle and other forest defoliators

? ? 0 - ? ?

Afforestation of alpine tundra + + - - + 0

Deforestation of southwest Kenai 

Peninsula

- - 0 0 0 0

Afforestation of coastline + ? 0 0 + +

Increased richness and abundance of 

terrestrial exotic invasive plants

? ? 0 - ? ?

New wildlife diseases (brucellosis, 

chronic wasting disease, winter tick, 

meningeal worm) 

0 - 0 - 0 -

Responses: + = increase; - = decrease; ? = uncertain; 0 = no change.

In the longer term, 

anticipating the demographics of these 

three species becomes 

highly problematic 

because of the 

uncertainty of how 

ecological drivers and 

landscape change may 

interact in the future.
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Miner, B. 2000. Forest regeneration and use of browse by moose in large-scale 

wildfires and managed habitat areas, Kenai National Wildlife Refuge, Alaska. 

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Bildfell, R.J.; Green, R.L. 2006. Parelaphostrongylus odocoilei in Columbian 

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A backcountry skier enjoys stark vistas and the winter light of January in the high mountains of south-central Alaska.

Greg Hayward
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Gregory D. Hayward1

This assessment highlights a subset of changes in socioeconomic and biophysical conditions expected to occur in the Chugach National Forest/Kenai Peninsula 

region as a consequence of a warming climate. Accordingly, we provide scenarios 

to stimulate consideration of these future conditions, so that resource users and 

managers can imagine new ecological and social conditions and prepare to adapt. 

However, the assessment also demonstrates several broader principles independent 

of the specific ecological and social trajectories of south-central Alaska. These 

broad messages emerge from other assessments but are worthy of note because they 

provide a useful generalized framework for evaluating climate change. This framework may be valuable when one is considering resource management and other 

social responses. First, this assessment illustrates the relationship between global 

patterns of climate change and local responses; it demonstrates that context matters. 

Second, the assessment shows the value of taming the firehose of information on 

climate change by considering a subset of resource conditions. To help set priorities 

for action, it acknowledges the limitations of human focus and the value of narrowing the conversation. Finally, by evaluating potential changes in the short term 

and honestly acknowledging the significant uncertainty in long-term scenarios, the 

assessment indirectly highlights the ultimate value of reducing the driver of climate 

change—emissions of greenhouse gasses—to address long-term risks. 

Context Matters

Rapid directional climate change resulting from human activities is a global phenomenon (chapter 1). Scientific analyses and public media both provide a continuous stream of examples illustrating the consequences of a warming planet on social 

systems, culture, and the environment. Thoughtful evaluations of future social, economic, and environmental conditions describe unsettling challenges (e.g., Bergoglio 

2015, IPCC 2014). Sea-level rise, dramatic changes in native vegetation, shifts in 

the distribution of flora and fauna, and changes in major disturbance agents such 

as floods, hurricanes, and wildfire all appear to be universal outcomes that many 

people recognize as global consequences of climate change. Awareness is growing 

that, in the desert southwest, a combination of invasive species and altered disturbance regimes are converting old pinyon-pine forests to annual grasslands (Romme 

et al 2012, Spotts 2013), and that sea-level rise in coastal regions of Virginia and 

Florida threatens highly productive estuaries as well as the infrastructure of cities 

Chapter 8: Conclusion

1 Population and community ecologist, U.S. Department of Agriculture, Forest Service, 

Alaska Region, PO Box 21628, Juneau, AK 99802-1628.
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and military installations (Gillis 2014). Alaska is recognized as experiencing some 

of the most dramatic environmental change. Readers of the New York Times and 

other media hear of permafrost melting and the resulting damage to buildings, of 

coastal erosion threatening villages, and of increased peatland fires (e.g., Gillis 

2011, Hirschfeld Davis 2015). 

Although broad awareness and alarm has developed regarding climate-induced 

global challenges, our assessment communicates a humble but important message: 

through example, it demonstrates the critical importance of context in determining 

the local outcomes of climate change. Just as resource management is geographically, ecologically, and socially context-specific, the scenarios described in this 

report demonstrate the importance of place. They show how the unique characteristics of the Chugach/Kenai region will result in social and biophysical outcomes that 

differ significantly from other locations in Alaska and across the globe. 

Many of the catastrophic changes to ecological and social systems expected in 

other regions during the next 30 years are not anticipated in the assessment area. 

Although sea-level rise across the globe alters ecological systems and threatens 

economies, the effective sea level in the assessment area will change little because 

of isostatic rebound—the uplifting of the land following the melting of glaciers (see 

chapters 1 and 4). Although shrub systems are invading large portions of the Arctic 

(Sturm 2001), and desertification in North Africa robs large populations of food 

crops (Verdin 2005), the vast coastal rain forest in the assessment area will likely 

support productive rain forest far into the future. Although salmon stocks in the 

Columbia River basin are experiencing multiple threats from climate change (e.g., 

Isaak 2012), freshwater systems that support salmon reproduction in the assessment 

area are currently intact and diverse. Their significant resilience will likely continue 

to support the freshwater life history of robust salmon populations. 

Local conditions that buffer the negative consequences of changing climate 

for certain biophysical and social features also contribute to potential negative 

consequences—again, local context matters. Anchorage, the largest city in Alaska, 

is located in the northwest corner of the assessment area, and for decades the Kenai 

Peninsula has experienced some of Alaska’s highest rates of population increase. 

Tourism and recreation are major features of the regional economy, including summer cruise ship traffic and winter sports that exploit what once was a dependably 

deep snowpack. The rugged Chugach and Kenai Mountains attract summer visitors 

viewing dramatic glacier-centered vistas, as well as heli-skiers in winter. Snowmachine enthusiasts and others enjoy miles of alpine and subalpine snow-covered 

mountain slopes. Both the glaciers and snowpack, features intimately associated 

with the region’s tourism and recreation, are changing rapidly in response to a 

Just as resource 

management is 

geographically, 

ecologically, and 

socially contextspecific, the scenarios 

described in this 

report demonstrate the 

importance of place. 
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warming climate (see chapter 3, “Snow and Ice”). Cancellation of the ceremonial 

start of the Iditarod dogsled race in 2015 for lack of snow, and ubiquitous photos 

of glaciers in cruise ship advertisements, demonstrate the importance of glaciers 

and snowpack but also the vulnerability of local economies and social activities to 

changes in these physical features (O’Neel et al. 2015). The coastal location of the 

assessment area, in a region that often experiences winter temperatures near freezing, results in high vulnerability of the snowpack. The context of the assessment 

area—its geography (e.g., a wet coastal climate with high mountains) and its social 

environment (high human population oriented toward snow and glacier tourism)—

result in particular vulnerabilities described in previous chapters. 

While local conditions (social, physical, ecological) will largely determine 

the outcomes from a changing climate, changes in neighboring regions and global 

conditions will also be important for some elements of the assessment area. Salmon 

provide a striking example. Although glacier systems and mountain environments 

(which include high-elevation snowpack) will buffer changes in many freshwater 

systems of the region, salmon populations will also respond to changes in ocean 

conditions across extensive portions of the north Pacific. Ocean surface temperatures, pH, and food webs will determine growth and survival of adult salmon and 

the characteristics of stocks returning to the assessment area’s largely intact freshwater systems (e.g., Abdul-Aziz et al. 2011, Mathis et al. 2014). Likewise, global 

fish markets and global tourism (sport fishing) will influence local salmon harvest 

and demand for local fish, with cascading consequences for salmon populations 

and the economic role of salmon in the region. Similarly, interregional and global 

processes influence coastal environments in the assessment area, demonstrating 

the interaction of local and global conditions. Freshwater input from glaciers in 

southeast Alaska is largely responsible for the Alaska Coastal Current, which 

interacts with Prince William Sound to affect marine chemistry and biota (e.g., 

O’Neel et al. 2014). The vast migration of waterfowl using the Copper River Delta 

and other stopover spots throughout the assessment area is supported by processes 

in the Arctic and subarctic breeding grounds and distant wintering areas (chapter 

4). These represent just a few of the many interactions between local and global 

conditions that determine outcomes experienced in the Chugach/Kenai assessment 

area. Hence, while local context is critical for envisioning potential futures, regional 

and local processes must be integrated into scenarios for some elements as demonstrated by our assessment. 

Ultimately, this vulnerability assessment and the scenarios offered represent 

a set of potential futures offered to stimulate deeper consideration of the consequences of climate change for social, cultural, and environmental systems in the 
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Chugach/Kenai region. None of the scenarios offered here—climatic, social or 

biophysical—is likely to be experienced as described in the assessment. This is not 

a failing of the assessment but rather the reality of characterizing an exceptionally 

complex future. The value of the assessment rests in the extent to which it provides 

vision and opens the imagination to potential change. The assessment serves its 

purpose if it stimulates resource users, policymakers, and resource professionals to 

begin carefully considering actions that are appropriate in light of rapidly changing climate. The assessment highlights topics motivating grave concern in other 

locations that need not motivate immediate action in the short-term in this region, 

such as significant changes in coastal rain forest or sea-level rise. It also highlights 

elements of the system that invite attention—changes in snowpack, alpine environments, or fire in the western Kenai, to name just three. 

Taming the Firehose

Vulnerability assessments provide a mechanism to manage the abundance of 

information regarding climate change for a particular region and focus attention on 

a subset of the plethora of changes taking place globally. The flood of information 

on climate change is drowning policymakers, resource management practitioners, 

and the public. A vulnerability assessment focuses attention and synthesizes current 

understanding. If considered carefully, the synthesis can be used to begin setting 

priorities for adaptation and create a common language among partners crafting 

adaptation actions. Multi-organization collaborations should begin by deciding 

what actions not to pursue and where to focus common attention. We suggest that 

this assessment be employed to initiate that collaborative process. 

Adaptation and Greenhouse-Gas Emissions

In response to the opportunity for prioritizing management actions toward various 

social or biophysical elements affected by climate change that are motivated by 

this assessment, I wish to highlight an emergent message. Doing so requires brief 

consideration of the temporal scope of the assessment, which examines relatively 

short-term futures. We chose not to consider a longer time horizon for three reasons. First, to provide a focused treatment of the most important current management considerations, the scope was purposefully limited to a small set of topics. 

In that spirit, we also chose to limit the temporal extent. Second, the assessment 

was motivated, in part, by forest plan revision on the Chugach National Forest. 

Examining change in the next 30 to 50 years served that effort. Finally, and more 

importantly, careful consideration of uncertainty further confirmed consideration of 

 The value of this 

assessment rests in 

the extent to which it 

provides vision and 

opens the imagination 

to potential change. 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

a 30- to 50-year climatology rather than longer term scenarios. Considering longterm change requires a projection window that extends beyond 2060, when uncertainty regarding emissions scenarios (release of greenhouse gasses) exceeds model 

uncertainty, and potential biophysical consequences become highly uncertain. 

The resulting short-term scope of the assessment provides an opportunity to 

motivate constructive dialogue to establish priorities for climate change adaptation 

actions. The shorter temporal scope avoids the less tractable characterization of 

multiple, divergent long-term scenarios, and acknowledges that planning near-term 

adaptation actions based on long-term (uncertain) scenarios will largely lead to 

“no-regrets” decisions that benefit little from specific scenarios. Furthermore, a 

more direct response to risk involves initiating adaptation actions in response to 

short-term scenarios with reasonable certainty while focusing long-term actions on 

the more compelling task of mitigation. Without a radical change in emission levels, 

the long-term trajectory for climate is known—substantial warming will occur. 

Reduction in emissions of greenhouse gases addresses the threat directly and therefore reduces risk regardless of the specifics of long-term scenarios. The Chugach/

Kenai assessment illustrates that short-term consequences of a warming climate are 

unlikely to shatter the social, cultural, or ecological systems of the Chugach/Kenai 

region. Over longer time frames, the changes that occur in such features as coastal 

rain forest and the marine environment are highly uncertain, therefore are begging 

for adaptation actions. The approach to mitigation—a reduction of emissions—can 

be effectively applied while an informed public and enlightened managers make 

short-term, climate-smart adaptation decisions. 

Literature Cited

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impacts on thermal habitats of Pacific salmon (Oncorhynchus spp.) in the North 

Pacific Ocean and adjacent seas. Canadian Journal of Fisheries and Aquatic 

Sciences. 68: 1660–1680.

Bergoglio, J.M. 2015. Encyclical letter Laudato Si’ of the Holy Father Francis: 

on care for our common home. Vatican City: Libreria Editrica Vaticana. 

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Gillis, J. 2011. As permafrost thaws, scientists study risks. New York Times. 

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change in emission 

levels, the long-term 

trajectory for climate 

is known—substantial 

warming will occur. 
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Gillis, J. 2014. The flood next time. New York Times. (January 13). http://www.

nytimes.com/2014/01/14/science/earth/grappling-with-sea-level-rise-sooner-notlater.html. (15 October 2015).

Hirschfeld Davis, J. 2015. Obama’s Alaska visit puts climate, not energy, in 

forefront. New York Times. (August 30). http://www.nytimes.com/2015/08/31/us/

politics/obama-to-urge-aggressive-climate-action-in-visit-to-arctic-alaska.html. 

(15 October 2015).

Intergovernmental Panel on Climate Change [IPCC]. 2014. Summary 

for policymakers. In: Field, C.B.; Barros, V.R.; Dokken, D.J.; Mach, K.J.; 

Mastrandrea, M.D.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; Estrada, Y.O.; Genova, 

R.C.; Girma, B.; Kissel, E.S.; Levy, A.N.; MacCracken, S.; Mastrandrea, P.R.; 

White, L.L., eds. Climate change 2014: Impacts, adaptation, and vulnerability. 

Part A: global and sectoral aspects. Contribution of working group II to the 

fifth assessment report of the Intergovernmental Panel on Climate Change. 

Cambridge, United Kingdom, and New York: Cambridge University Press: 

1–32. http://ipcc-wg2.gov/AR5/images/uploads/WG2AR5_SPM_FINAL.pdf. 

(27 May 2016).

Isaak, D.S.; Horan, D.; Chandler, G. 2012. Climate change effects on stream and 

river temperatures across the northwest US from 1980–2009 and implications for 

salmonid fishes. Climate Change. 113: 499–524.

Mathis, J.T.; Cooley, S.R.; Lucey, N.; Colt, S.; Ekstrom, J.; Hurst, T.; 

Hauri, C.; Evans, W.; Cross, J.N.; Feely, R.A. 2014. Ocean acidification 

risk assessment for Alaska’s fishery sector. Progress in Oceanography. 

http://dx.doi.org/10.1016/j.pocean.2014.07.001. (15 October 2015).

O’Neel, S.; Hood, E.; Bidlack, A.; Fleming, S.W.; Arimitsu, M.L.; Arendt, A.; 

Burgess, E.; Sergeant, C.J.; Beaudreau, A.H.; Timm, K.; Hayward, G.D.; 

Reynolds, J.H.; Pyare, S. 2015. Icefield-to-ocean linkages across the Northern 

Pacific coastal temperate rainforest ecosystem. BioScience. 65: 499–512.

Romme, W.H.; Hayward, G.D.; Regan, C. 2012. A framework for applying the 

historical range of variation concept to ecosystem management. In: Wiens, J.A.; 

Hayward, G.D.; Safford, H.D.; Giffen, C.M., eds. Historical environmental 

variation in conservation and natural resource management. Oxford, United 

Kingdom: John Wiley and Sons: 246–264. Chapter 17.

Sturm, M.; Racine, C.R.; Tape, K. 2001. Increasing shrub abundance in the 

Arctic. Nature. 411: 546–547.
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Spotts, P. 2013. Global warming: Yet another threat to Southwest’s iconic pinyon 

pine? Christian Science Monitor. (February 13). http://www.csmonitor.com/

Environment/2013/0213/Global-warming-Yet-another-threat-to-Southwest-siconic-pinyon-pine. (15 October 2015).

Verdin, J.; Funk, C.; Senay, G.; Choularton, R. 2005. Climate science and 

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Science. 360: 2155–2168.

Acknowledgments 

We thank the following people for their contributions and support: Ronald Britton, 

Sara Boario, Milo Burcham, Erin Cooper, Helen Cortés-Burns, Aimee Delach, 

Karla Dutton, Connie Hubbard, Steven Gray, Erik Jackson, Lara Kelly, Gunnar 

Knapp, Priscilla Lema, Robert Loeffler, Thomas McDonough, Amy Miller, Peter 

Nelson, Mike Novy, Wayne Owen, Aaron Poe, Eric Rothwell, Michael Sheppard, 

and Nils Warnock. Additionally, we thank David Peterson, Martin Phillip, Aaron 

Poe, Amanda Robertson, and Beatrice Van Horne for formal reviews of this report. 

English Equivalents

When you know: Multiply by: To find:

Millimeters (mm) 0.0394 Inches

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Kilometers (km) 1.61 Miles

Hectares (ha) 2.47 Acres

Square meters (m2

) 10.76 Square feet

Square kilometers (km2

) 0.386 Square miles

Cubic kilometers (km3

) 0.2399 Cubic miles

Kilograms (kg) 2.205 Pounds

Degrees Celsius (°C) 1.8 °C + 32

Metric Equivalents 

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters

Feet (ft) .305 Meters

Miles (mi) 1.609 Kilometers

Square miles (mi2

) 2.59 Square kilometers

Acres (ac) 0.405 Hectares

Gigatons (Gt) 0.907 Gigatonnes

Degrees Fahrenheit (°F) .56(°F – 32) Degrees Celsius
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GENERAL TECHNICAL REPORT PNW-GTR-950

Common name Scientific name

Plants:

Alder Alnus Mill.

Alaska bellheather Harrimanella stelleriana (Pall.) Coville

Alaska hollyfern Polystichum setigerum (C. Presl) C. Presl

Alaska mistmaiden Romanzoffia unalaschcensis Cham.

Aleutian mountainheath Phyllodoce aleutica (Spreng.) A. Heller

Alpine azalea Loiseleuria procumbens (L.) Desv.

Alpine sweetgrass Hierochloe alpina (Sw. ex Willd.) Roem. & Schult.

Alpine timothy Phleum alpinum L.

American dunegrass Leymus mollis (Trin.) Pilg.

Annual bluegrass Poa annua L.

Beach strawberry Fragaria chiloensis (L.) Mill.

Black cottonwood Populus balsamifera L. ssp. trichocarpa (Torr. & A. Gray ex Hook.) Brayshaw 

Black crowberry Empetrum nigrum L.

Black spruce Picea mariana (Mill.) Britton, Sterns & Poggenb.

Blueberry Vaccinium spp.

Bluejoint Calamagrostis canadensis (Michx.) P. Beauv.

Bog birch Betula glandulosa Michx.

Bog blueberry Vaccinium uliginosum L.

Bog rosemary Andromeda polifolia L.

Boreal sagebrush Artemisia arctica Less.

Boreal yarrow Achillea millefolium L. var. borealis (Bong.) Farw.

Canadian waterweed Elodea canadensis Michx.

Common dandelion Taraxacum officinale F.H. Wigg.

Common plantain Plantago major L.

Canada thistle Cirsium arvense (L.) Scop.

Crowberry Empetrum L.

Dwarf birch Betula nana L.

Disc mayweed Matricaria discoidea DC.

Eelgrass Zostera marina L.

Fireweed Chamerion angustifolium (L.) Holub

Fourpart dwarf gentian Gentianella propinqua (Richardson) J.M. Gillett ssp. aleutica (Cham. & Schltdl.) 

J.M. Gillett

Grasses: Poaceae

Harold’s milkvetch Astragalus robbinsii var. harringtonii

Kenai birch Betula papyrifera Marshall var. kenaica (W.H. Evans) A. Henry

Kentucky bluegrass Poa pratensis L. ssp. pratensis

Lutz spruce Picea × lutzii Little [glauca × sitchensis]

Mountain hemlock Tsuga mertensiana (Bong.) Carrière

Mud sedge Carex limosa L.

Nootka lupine Lupinus nootkatensis Donn ex Sims

Appendix 1: Common and Scientific Names
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Common name Scientific name

Orange hawkweed Hieracium aurantiacum L.

Pacific buttercup Ranunculus pacificus (Hultén) L.D. Benson

Paper birch Betula papyrifera Marshall

Pyrenean sedge Carex pyrenaica Wahlenb.

Quaking aspen Populus tremuloides Michx.

Reed canarygrass Phalaris arundinacea L.

Reindeer lichen Cladina (Nyl.) Nyl.

Resin birch Betula neoalaskana Sarg.

Sagebrush Artemisia L.

Salmonberry Rubus spectabilis Pursh

Sessileleaf scurvygrass Cochlearia sessilifolia Rollins

Sitka alder Alnus viridis (Chaix) DC. ssp. sinuata (Regel) Á. Löve & D. Löve 

Sitka spruce Picea sitchensis (Bong.) Carrière

Sphagnum Sphagnum fuscum (Schimp.) Klinggr.

Tall cottonbrush Eriophorum angustifolium Honck.

Tufted bulrush Trichophorum cespitosum (L.) Hartm.

White sweetclover Melilotus albus (L.) Lam.

Western waterweed Elodea nuttallii (Planch.) H. St. John

Western fescue Festuca occidentalis Hook.

Western hemlock Tsuga heterophylla (Raf.) Sarg.

White spruce Picea glauca (Moench) Voss

Willow Salix spp.

Yellow-cedar Callitropsis nootkatensis (D. Don) Oerst. ex D.P. Little 

Yellow sweetclover Melilotus officinalis (L.) Lam.

Diseases:

Bovine brucellosis Brucella abortus 

Ovine brucellosis Brucella melitensis

Swine brucellosis Brucella suis

Animals:

Alaska-Yukon race of moose Alces alces gigas

Biting midges Culicoides

Black oystercatcher Haematopus bachmani

Black turnstones Arenaria melanocephala

Caribou Rangifer tarandus granti

Chinook salmon Oncorhynchus tshawytscha

Chum salmon Oncorhynchus keta 

Coho salmon Oncorhynchus kisutch 

Columbian black-tailed deer Odocoileus hemionus columbianus

Cutthroat trout Oncorhynchus clarkii 

Dall sheep Ovis dalli dalli

Dolly Varden trout Salvelinus malma 

Dunlin Calidris alpina
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GENERAL TECHNICAL REPORT PNW-GTR-950

Common name Scientific name

Dusky Canada goose Branta canadensis occidentalis

Elk Cervus canadensis

Glaucous-winged gull Larus glaucescens

Meningeal worm species #1 Parelaphostrongylus tenuis

Meningeal worm species #2 Parelaphostrongylus odocoilei

Mountain goat Oreamnos americanus

Mule deer Odocoileus hemionus

Northwestern moose Alces alces andersoni

Pink salmon Oncorhynchus gorbuscha

Rainbow trout Oncorhynchus mykiss

Red knot (Pacific coast population) Calidris canutus roselaari

Red-necked phalarope Phalaropus lobatus

Sitka black-tailed deer Odocoileus hemionus sitkensis

Sockeye salmon Oncorhynchus nerka 

Spruce bark beetle Dendroctonus rufipennis

Stone’s caribou Rangifer tarandus stonei

Surfbird Calidris virgata

Western moose Alces alces andersoni

Western sandpiper Calidris mauri

White-tailed deer Odocoileus virginianus

Winter tick Dermacentor albipictus
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Appendix 2: Modeling Climate Change

Purposes of This Appendix

The Scenarios Network for Alaska and Arctic Planning (SNAP) provided objective projections of potential climate futures, or scenarios, based on downscaled 

climate models to form the foundation for this assessment. Subsets of the extensive 

SNAP library of climate models were used in different portions of the assessment 

depending on the climate feature of interest and the geographic or temporal scale 

of interest. In many cases, climate scenarios from the SNAP library were used 

in conjunction with other data or models to provide the appropriate set of climate 

variables to characterize potential future conditions.

SNAP data, models, methods, and results are described, in brief, in each section 

of the report in which they are used. This appendix offers expanded background 

and additional maps illustrating outputs examined for this project. 

More extensive detail regarding the climate models is available from SNAP’s 

website at http://www.snap.uaf.edu.

What Is SNAP?

SNAP is a research, modeling, and outreach program centered within the University of Alaska’s International Arctic Research Center. The collaborative network 

includes the University of Alaska; state, federal, and local agencies; nongovernmental organizations; and industry partners. The network provides downscaled 

climate projections and other data to craft scenarios of future conditions in Alaska 

and other Arctic regions for more effective planning by communities, industry, and 

land managers. The network meets stakeholders’ requests for specific information 

by applying new or existing research results, integrating and analyzing data, and 

communicating information and assumptions to stakeholders. SNAP’s goal is to 

assist in informed decisionmaking.

What Information Does SNAP Offer?

Downscaled climate models and associated date delivered by SNAP cover Alaska, 

Alaska–western Canada, polar regions, and other regions spanning the mid 1800s 

to 2100. Datasets include observed historical data, modeled historical data, and 

modeled downscaled projected data out to 2100.

SNAP climate projections are based on downscaled regional global climate 

models (GCMs) from the Intergovernmental Panel on Climate Change (IPCC). 

The IPCC used 15 different GCMs when preparing its fourth assessment report, 

released in 2007. SNAP researchers analyzed how well each model predicted 

This appendix offers 

expanded background 

and additional maps 

illustrating outputs 

examined for this 

project. 
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GENERAL TECHNICAL REPORT PNW-GTR-950

monthly mean values for three different climate variables over four overlapping 

northern regions for the period from 1958 to 2000, and selected the top five.

Each set of SNAP-projected climate data files originates from one of these 

five top ranked GCMs, or is calculated as a five-model average. Each set of files 

also represents one of three greenhouse-gas emission scenarios (B1, A1B, A2), as 

defined by the IPCC.

SNAP datasets include derived products such as monthly decadal averages or 

specific seasonal averages. Basic monthly outputs have also been interpolated or 

interpreted to produce datasets such as mean date of freeze and mean date of thaw 

(representing days on which temperatures are projected to cross the freezing point) 

and snow-day fraction (SDF) (temperature-based projections of the percentage of 

days in a given month in which precipitation, were it to fall, would arrive as snow).

Model Downscaling

GCMs generally provide only broad-scale output, with grid cells typically 1 to 5° 

latitude and longitude. SNAP bias-corrects and downscales these files via the delta 

method by using parameter-elevation regressions on independent slopes model 

(PRISM) baseline gridded climate data (Daly et al. 2008). These grids represent 

mean monthly values for precipitation and temperature. PRISM uses historical 

data from climate stations, a digital elevation model, and other spatial datasets to 

generate gridded estimates of monthly, yearly, and event-based climatic parameters, 

such as precipitation, temperature, and dew point. PRISM baselines represent the 

years 1961–1990 for SNAP’s 2-km-resolution Alaska and Western Canada data, or 

1971–2000 for SNAP’s 771-m Alaska data.

Model Uncertainty

Greenhouse-gas-driven climate change represents a response to the radiative forcing associated with increases in carbon dioxide, methane, water vapor, and other 

gases, as well as associated changes in cloudiness. The projected response varies 

widely among GCMs because climate forcing is strongly modified by feedbacks 

involving clouds, the cryosphere (ice and snow), water vapor, and other features 

whose effects are not well understood. The ability of a model to accurately replicate 

seasonal radiative forcing is a good test of its ability to predict changes in radiative 

forcing associated with increasing greenhouse gases. SNAP models have been 

assessed using back-casting and comparison to historical conditions, and have 

proven to be robust in predicting overall climate trends for the portions of Alaska 

addressed in this assessment. 

SNAP models have 

been assessed 

using back-casting 

and comparison to 

historical conditions, 

and have proven to be 

robust in predicting 

overall climate trends 

for the portions of 

Alaska addressed in 

this assessment.
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Model projections are presented as monthly average values. Although trends 

are relatively clear, precise values for any one year or month for any single model 

cannot be considered reliable weather forecasts. Each model incorporates the 

variability found in normal weather patterns. The downscaling process introduces 

further uncertainty. Although PRISM offers the best available algorithms for linking climate variability to weather station interpolation and digital elevation maps 

(DEMs), the connection incorporates considerable uncertainty. Weather stations are 

sparse in Alaska, which tends to lower model reliability. Even when climate trends 

are directional and consistent, the dominant trend can be obscured by normal ups 

and downs in weather patterns that take place on a monthly, annual, or decadal 

scale. For example, the Pacific Decadal Oscillation (PDO) can temporarily mask 

or exacerbate climate trends (Bieniek et al. 2014, Walsh et al. 2011). GCM outputs 

simulate this normal variability, mimicking an appropriate degree of variability 

across time scales, but the variations cannot be expected to match actual swings. 

Overall, model validation has shown that SNAP projections are more robust for 

temperature than for precipitation. 

Some variability introduced by factors such as the PDO can be dampened by 

using average values across time, space, and GCMs. All three kinds of averaging 

have been used in SNAP downscale models. Averaging increases the reliability of 

projections over temporal scales such as decades, but makes it difficult to make 

predictions about extreme events such as storms or floods. 

Results presented below use model projections averaged across five GCMs. 

However, examining the variability between these five models sheds light on model 

uncertainty. Given this variability, projected fine-scale changes in temperature 

cannot be considered highly significant if they are less than approximately 2.5 °C 

(36.5 °F). This should be kept in mind when interpreting the maps presented in 

this appendix. Precipitation data carry an even higher level of uncertainty. Thus, 

although trends are clear, estimated dates for variables such as freeze, thaw, season 

length, and snowfall should be viewed as approximate and considered over a series 

of years rather than in a single year.

Models Specific to This Project

For this assessment, SNAP used mean (composite) outputs from five GCMs, and 

examined outputs based on midrange (A1B) and more pessimistic (A2) predictions 

of greenhouse gas emissions. Outputs from the A2 scenario, now considered the 

most realistic, were the primary focus in the body of the text, but both A1B and A2 

outputs are shown below.

Overall, model 

validation has shown 

that SNAP projections 

are more robust for 

temperature than for 

precipitation. 
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The projections used in this project were for a range of modeled data. Basic 

climate outputs examined in the introduction reference a baseline time period 

(1971–2000), the current decade (2010s), and future decades (2020s, 2040s, and 

2060s). For the introduction, SNAP provided data on the effects of climate change 

on the following environmental factors: mean and extreme July and January 

temperature; mean and extreme July and January precipitation; timing of thaw and 

freeze; length of unfrozen season; and estimated SDF and snowline. 

Model Results: Temperature and Precipitation

Temperature and precipitation values are expressed as monthly means for decadal 

time periods. For example, July temperature for the A1B emissions scenario for the 

2020s represents the average of 50 SNAP data files (five models × 10 years in the 

decade). This averaging smooths the data, facilitating comparison between decades. 

If examined annually, any of the climate features would exhibit normal variability, 

with some years and seasons being hotter, colder, wetter, or drier than others owing 

to the vagaries of weather, rather than the driving force imposed by increases in 

greenhouse gases. Some variability will occur at a decadal or multidecadal scale, 

owing to the influence of the Pacific Decadal Oscillation.

January and July data were selected for illustration of patterns in temperature 

and precipitation to highlight changes in the most extreme months of winter and 

summer. Changes in shoulder season characteristics are also biologically and 

culturally important, and are captured via assessment of freeze and thaw dates. 

Figure A2-1 shows temperature projections for the current decade. Because 

consequences of the two emissions scenarios have had little time to diverge, A1B 

and A2 outputs are similar. Mean temperatures in the coldest month of the year 

range from approximately -20 °C in the mountains to slightly above freezing along 

the coastline south of Cordova and Valdez. In July, the hottest temperatures (15 °C) 

are found in the Anchorage and Wasilla region, outside the core study area, while 

the coolest temperatures are again found at the mountain peaks, where averages are 

well below freezing (-7 °C).

These temperature profiles are expected to change over time. Summer warming trends can be seen in figure A2-2, which compares July temperatures for the 

current decade with those projected for the 2020s, 2040s, and 2060s, all for the 

A1B emissions scenario. Figure A2-3 offers the same comparison using data from 

the more pessimistic A2 scenario. Both scenarios show a similar pattern across the 

landscape, with all areas warming by about 2 °C (A1B) or 3 °C (A2) in the next 

50 years. 

January and July 

data were selected 

for illustration of 

patterns in temperature 

and precipitation to 

highlight changes 

in the most extreme 

months of winter and 

summer.
Figure A2-1—January and July temperature for the current decade for the A1B and A2 scenarios.

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Areas with July temperatures below freezing are unlikely to undergo significant glacial melting, although it should be noted that daily highs may well exceed 

mean values, and that direct solar radiation can drive effective temperatures above 

recorded air temperature.
Figure A2-2—July temperature change. Maps depict the 2010s, 2020s, 2040s, and 2060s for the A1B emissions scenario.

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Winter temperature change is expected to be more extreme. Figures A2-4 

and A2-5 show projected change for January. As with summer conditions, winter 

temperatures for the two scenarios differ mainly in the rate of change, not in its 

geographic pattern or temporal trends. 

For the A1B scenario, average temperatures in the coldest month of the year are 

predicted to rise from only slightly above freezing in the warmest coastal areas to 

well above freezing, or approximately 4.5 °C. Moreover, these warm temperatures 
Figure A2-3—July temperature projections for the 2010s, 2020s, 2040s, and 2060s for the A2 emissions scenario.

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will spread inland toward Cordova, Valdez, and Seward, with above-freezing 

Januaries dominating across all coastal regions of the Chugach, and some areas as 

much as 20 miles inland. Many rivers are seen shifting from a below freezing to 

above-freezing temperature regime, particularly in the A2 scenario.

Across the region, winter warming is expected to be approximately 3 to 3.5 °C 

for both the A1B and A2 scenario. Although the greatest impact of summer warming may be in the coldest regions of the Chugach, where snow and glaciers hang in 
Figure A2-4—January temperature projections for the 2010s, 2020s, 2040s, and 2060s for the A1B emissions scenario.

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GENERAL TECHNICAL REPORT PNW-GTR-950

the balance, the greatest winter impacts may be in the warmest coastal and nearcoastal regions, where a shift is underway between frozen and unfrozen winters.

Areas with mean January temperatures above freezing may still experience 

days or even weeks of freezing temperatures, and daily lows are likely to be significantly cooler than mean values. However, it is unlikely that significant ice formation would occur in such areas, particularly given the fact that seawater freezes at 

approximately -2 °C rather than at 0 °C. For brackish water, intermediate freezing 

temperatures are the norm. 
Figure A2-5—January temperature projections for the 2010s, 2020s, 2040s, and 2060s for the A2 emissions scenario.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Model predictions for precipitation (fig. A2-6) are somewhat less robust than 

those for temperature, in part because precipitation is intrinsically more variable 

across the landscape. In addition, although precipitation is predicted to increase 

across the landscape, the hydrologic status of soils, rivers, or wetlands is difficult 

to predict because of the influence of factors other than absolute precipitation. 

Increases in temperature may more than offset increases in precipitation, yielding a 

drying effect in some areas. Changes in seasonality and water storage capacity can 

Increases in temperature may more than 

offset increases in 

precipitation, yielding 

a drying effect in 

some areas.
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GENERAL TECHNICAL REPORT PNW-GTR-950

also affect the hydrologic balance. Furthermore, a shift in the percentage of precipitation falling as snow can drastically alter the annual hydrologic profile. 

Between the current decade and all future ones, the trend was toward greater 

precipitation in both January and July for both the A1B and A2 emissions scenarios 

(figs. A2-7 and A2-8). These figures depict only the starting and ending decades 

(2010s and 2060s) of this study. However, maps of precipitation projections for the 

2020s and 2040s are also available upon request.

Model Results: Freeze Dates, Thaw Dates, and Length 

of Growing Season

SNAP uses monthly temperature and precipitation projections and interpolation to 

estimate the dates at which the freezing point will be crossed in spring and fall. The 

intervening period is defined as summer-season length. Note that these dates do not 

necessarily correspond with other commonly used measures of “thaw,” “freeze-up,” 

and “growing season.” Some lag time is to be expected between mean temperatures 

and ice conditions on lakes or in soils. Different plant species begin their seasonal 

growth or leaf-out at different temperatures. Moreover, planting time for gardeners 

usually takes place when minimum daily temperatures, not mean daily temperatures, are above freezing. However, analyzing projected changes in these measures 

over time can serve as a useful proxy for other season-length metrics. 

Across the Chugach, date of thaw in spring is expected to become earlier, with 

the A1B scenario predicting a slightly less extreme shift than the A2 scenario (fig. 

A2-7). Of particular note is the shift of large areas of coastal and near-coastal land 

from early spring thaw to the “rarely freezes” category. This is likely to correspond 

with lack of winter snowpack and an altered hydrologic cycle. Primarily frozen 

areas—ice fields and glaciers—are expected to shrink significantly under both the 

A1B and the A2 scenario.

In inland areas, changes are projected to occur as a shift of 3 to 10 days, on 

average. For example, the A2 scenario shows spring thaw occurring in Soldotna and 

Kenai around April 4 in the current decade, but in late March by the 2060s. 

Figure A2-8 depicts similar changes for date of freeze.

Autumnal changes are, overall, slightly greater than those seen in spring, with 

the date at which the running mean temperature crosses the freezing point shifting 

noticeably later in just a single decade. For example, comparing the maps for the 

2010s and the 2020s for the A1B scenario shows the Soldotna area shifting from 

a freeze date around October 24 to a freeze date of about October 28. During this 

same decade, loss of areas of ice and snow is also clearly evident.

Of particular note 

is the shift of large 

areas of coastal and 

near-coastal land from 

early spring thaw to 

the “rarely freezes” 

category.
Figure A2-6—January precipitation projections for initial and ending decades of the study (2010s and 2060s) for the A1B and 

A2 scenarios.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Figure A2-9 combines the information in figures A2-7 and A2-8 to show the 

total projected length of the “warm season” (time during which mean temperatures 

are above freezing). Major changes in warm season length include incursion of the 

“rarely freezes” zone as far as 20 miles inland; an increase from about 200 days 

to about 230 days for Palmer, Anchorage, Wasilla, and Kenai; and an even more 

substantial increase for Seward, Valdez, and Cordova.
Figure A2-7—Date of thaw projection. Maps depict the date at which the running mean temperature crosses the freezing 

point in spring for the A1B emissions scenario for the 2010s, 2020s, 2040s, and 2060s (top four panels) and for the A2 

scenario for the 2010s and 2060s (bottom two panels).

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GENERAL TECHNICAL REPORT PNW-GTR-950
Figure A2-8—Date of freeze projections. Maps depict the date at which the running mean temperature crosses 0 °C for 

the A1B emissions scenario for the 2010s, 2020s, 2040s, and 2060s and for the A2 scenario for the 2010s and 2060s.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula
Figure A2-9—Length of growing season projections. Maps depict the number of days between the date at which the 

running mean temperature crosses 0 °C in spring and fall for the A1B emissions scenario for the 2010s, 2020s, 2040s, 

and 2060s and for the A2 scenario for the 2010s and 2060s.

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Figure A2-10—Snowline expressed as the percentage of December, January, and February precipitation that falls 

as snow.

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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Model Results: Snowline

SNAP downscaled GCM outputs do not directly model snowfall as a separate 

feature from overall precipitation, measured as rainfall equivalent. However, there 

are many possible ways to estimate snow cover. For the purposes of this project, the 

metric of greatest interest and clarity was snowline, as estimated by contour maps 

depicting the probability of snow versus rain during winter months. This work was 

based on algorithms derived by Legates and Bogart (2009). In other words, what 

proportion of precipitation can be expected to fall as snow versus rain, on a spatial 

basis? The 90-percent cutoff (fig. A2-10) is likely to be close to the cutoff at which 

snowpack occurs, although high variability is expected from year to year.
Figure A2-11—Projected snowline for the A2 emissions scenario for the current decade and a 50-year outlook.

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Summary and Conclusions

Overall, the Chugach National Forest area is expected to become warmer in the 

middle of this century, with earlier springs, later falls, a longer growing season, and 

shorter, less severe winters. Some increases in precipitation are likely, but overall 

snowfall will decrease because of higher temperatures. The snowline will move 

higher in elevation and farther from the coast. 

As can be seen, under the A1B scenario, a sharp change is expected in the 

snowline over each of the time steps examined. In the current decade, snowfall 

dominates all higher elevation areas. In the next 10 to 20 years, the modeled snowline shifts well inland from Valdez. By 2040, many areas are predicted to receive 

less than 30 percent of winter precipitation as snow, and, by the 2060s, snowline 

(as defined by the 90-percent contour) is predicted to shift back to only the highest 

peaks. Results for the A2 scenario (fig. A2-11) depict an even more extreme shift 

between the current decade and expected conditions in 50 years.

To assess the snowline during the coldest season, as opposed to the winter as 

a whole, we also examined the projected snowline for the month of January alone 

(fig. A2-12).
Figure A2-12—Projected proportion of January precipitation likely to occur as snow under the A1B scenario for the 2010s 

and 2060s.

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Results show that, for many areas that typically experience almost all January 

precipitation as snow, this pattern may shift in coming decades. By the 2060s, 

Anchorage, Kenai, Soldotna, Wasilla, and Palmer may have only intermittent snow 

cover, even in the coldest month of the year.

Connecting Climate with Landscape

Across the study region, modeled data point toward a significantly warmer environment, with increased precipitation but decreased snowfall. Glacial melt and loss of 

snowpack is likely, and as a result, annual hydrologic profiles are likely to change, 

with less of a spring surge, and greater runoff during winter months. The summerseason length will increase by days or even weeks, and some areas that regularly 

freeze now will no longer do so, or do so only rarely.

These changes are likely to have direct impacts on vegetation, including 

invasive species that may have previously been kept out of the area by cold winter 

conditions. Biome shift is likely, although trophic mismatches may occur, given discrepancies in the ability of different species to disperse and establish in new areas. 

By the 2060s, Anchorage, Kenai, Soldotna, 

Wasilla, and Palmer 

may have only 

intermittent snow 

cover, even in the 

coldest month of 

the year.
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GENERAL TECHNICAL REPORT PNW-GTR-950

Fire may play a larger role in the near future. Postfire, there would be a window 

of opportunity for succession by novel species, meaning that fire may facilitate 

vegetation shift, which would in turn be likely to affect wildlife. Many wildlife species are affected, either positively or negatively, by snow cover. Although it is hard 

to predict whether seasonal snowpack would be deeper, it is likely that the snow 

season would start later and end earlier. 

All the above changes are pertinent to human uses of the landscape. Impacts 

to vegetation and wildlife directly affect hunting and gathering. Changes in season 

length affect hunting seasons. Subsistence hunting may be affected by species shifts 

and changes in species abundance. Visitor experiences are also likely to be effected, 

with regard to species shifts and availability of snow, ice, and glaciers. Typical 

wildlife viewing may also change.

Further study and ground-truthing of modeled results is necessary to further 

elucidate and validate these predictions. Land managers should always take into 

account both natural fluctuations in weather patterns and model uncertainty. 

However, climate trends will almost certainly play a key role in any future 

scenario affecting the Chugach National Forest.

Literature Cited

Bieniek, P.A.; Walsh, J.E.; Thoman, R.L.; Bhatt, U.S. 2014. Using climate 

divisions to analyze variations and trends in Alaska temperature and 

precipitation. Journal of Climate. 27: 2800–2818.

Daly, C.; Halbleib, M.; Smith, J.I.; Gibson, W.P.; Doggett, M.K.; Taylor, 

G.H.; Curtis, J.; Pasteris, P.P. 2008. Physiographically sensitive mapping 

of climatological temperature and precipitation across the conterminous 

United States. International Journal of Climatology. 28(15): 2031–2064. 

doi:10.002/joc.1688.

Legates, D.R.; Bogart, T.A. 2009. Estimating the proportion of monthly 

precipitation that falls in solid form. Journal of Hydrometeorology. 10: 

1299–1306.

Walsh, J.E.; Overland, J.E.; Groisman, P.Y.; Rudolf, B. 2011. Ongoing 

climate change in the Arctic. AMBIO. 40: 6–16.
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Appendix 3: Estimated Number and Economic 

Value of Salmon Produced from Watersheds of 

the South-Central Alaska Study Area 

This appendix describes the method and calculations used to estimate the number 

and economic value of salmon that originate from the watersheds of the south-central Alaska climate vulnerability assessment area. The production estimate is also 

expressed as the percentage of the total salmon production for the Pacific Ocean. 

The sources of data used to make production and economic values estimates 

were commercial, sport, and personal-use fishery information collected by the 

Alaska Department of Fish and Game (ADFG). Catches of salmon, by species, were 

obtained for Cook Inlet, Kenai Peninsula, and Prince William Sound fisheries for 

the 5-year period from 2009 to 2013. These data are available in ADFG (2014a) and 

a range of annual reports (Begich and Pawluk 2011, Botz et al. 2012, Hochhalter et 

al. 2011, Shields and Dupuis 2012).

Number of Salmon

Among the five species of Pacific salmon in south-central Alaska, more pink 

salmon were caught than any other species (table A3-1). Most of these pink salmon 

were produced from hatcheries operating in watersheds of Prince William Sound. 

From 2009 to 2013, total salmon production for this area ranged from approximately 29 million fish in 2009 to 104 million fish in 2013 (table A3-1). 

Table A3-1—Millions of salmon caught, by species, in commercial, sport, and 

personal fisheries for the Cook Inlet, Kenai Peninsula, and Prince William 

Sound management areas from 2009 to 2013

Year Chinook Sockeye Coho Pink Chum Total

2009 0.04 4.96 0.83 20.25 3.38 29.46

2010 0.04 5.73 0.90 71.92 4.65 83.24

2011 0.05 10.07 0.92 33.78 2.07 46.89

2012 0.02 7.78 0.82 28.00 4.15 40.77

2013 0.03 5.72 1.35 93.14 4.20 104.44

5-year average 0.04 6.85 0.97 49.42 3.69 60.96

The total annual fish production for each species was estimated by dividing the 

number of fish that were caught by the proportion of each run that was caught. For 

example, if 0.40 of the salmon return was caught and the number of salmon caught 

was 20,000, then the total production (or run-size) would have been 20,000/0.40 = 

50,000 fish. 
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Fishery escapement and harvest rates used in this analysis were based on 

fishery catch and escapement data presented by ADFG (2013), Begich and Pawluk 

(2011), Botz et al. (2012), and Shields and Dupuis (2012). The highest lowest harvest 

was the 0.40 value estimated for Chinook salmon (table A3-2). Sockeye salmon 

were found to have had the highest harvest (lowest escapement). 

Table A3-2—Fishery harvest rates used to estimate total run-size for five 

species of salmon in south-central Alaska

Salmon species Harvest rate Comment

Chinook 0.40 Average of Kenai Peninsula and Copper River 

Sockeye 0.75 Average of Kenai Peninsula and Copper River 

Coho 0.57 Based on Copper River coho

Pink 0.61 Average of Prince William Sound estimates

Chum 0.50 Average of Prince William Sound estimates

Based on the production estimates for each species that were derived from the 

catch and escapement rate data, pink salmon were the dominant salmon species in 

the study area with a 5-year average run-size of 81 million fish (table A3-3). The 

pink salmon run, including a large number of hatchery-produced fish, outnumbered 

all other species combined by nearly 4 to 1. Across all species, the total number 

of salmon fluctuated considerably from 46.1 million total salmon in 2009 to 171.1 

million total salmon in 2013. The combined species average production for this 

time period was 99.3 million salmon.

Table A3-3—Millions of salmon produced from watersheds of south-central 

Alaska study area, by species, expressed as total run-size from 2009 to 2013

Year Chinook Sockeye Coho Pink Chum Total

2009 0.10 6.61 1.46 33.19 6.76 48.13

2010 0.10 7.64 1.58 117.91 9.30 136.52

2011 0.12 13.43 1.62 55.37 4.15 74.69

2012 0.05 10.37 1.44 45.90 8.29 66.06

2013 0.06 7.63 2.37 152.68 8.41 171.15

5-year average 0.09 9.14 1.69 81.01 7.38 99.31

To put this level of production in context, the annual production of wild and 

hatchery-origin salmon for the entire Pacific Ocean (North America, Russia, and 

Japan) was estimated by Ruggerone et al. (2010) to be 634 million fish. The 99.3 

million salmon production from south-central Alaska study area therefore represents about 15.6 percent of this total Pacific Ocean production (i.e., 99.3/634 = 

15.6 percent).

Pink salmon were 

the dominant salmon 

species in the study 

area with a 5-year 

average run-size of 

81 million fish.
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Table A3-4—Ex-vessel values (expressed in millions of dollars) of annual 

commercial catch of salmon from the Cook Inlet and Prince William Sound 

management areas, by species, from 2009 to 2013

Year Chinook Sockeye Coho Pink Chum Total

2009 1.24 39.69 4.24 15.66 13.95 74.78

2010 7.12 54.72 5.59 105.18 25.44 198.05

2011 2.84 89.52 3.37 46.57 13.51 155.81

2012 1.85 77.55 2.62 54.85 22.10 158.98

2013 1.16 70.17 7.40 108.48 18.11 205.32

5-year average 2.8 66.3 4.6 66.1 18.6 158.6

Source: ADFG (2014b).

Economic Impact of Salmon

Ex-vessel values for commercially caught salmon landed in the Cook Inlet and 

Prince William Sound management areas (ADFG 2014b) were used to develop an 

estimate of economic value of the commercial fisheries for south-central Alaska. 

Commercially caught sockeye and pink salmon had virtually the same total exvessel value of $66 million over the 5-year period from 2009 to 2013 (table A3-4). 

Despite the fact that the number of pink salmon caught was seven times greater, 

the larger size and higher price per pound for sockeye salmon resulted in the two 

species having nearly equal ex-vessel values. For all species combined, the 5-year 

average ex-vessel value was $158.6 million, with a range from $74.8 million in 2009 

to $205.3 million in 2013 (table A3-4).

These ex-vessel fishery values were expanded to an estimate of total economic 

impact using the ex-vessel value to total economic impact ratio from Northern 

Economics, Inc. (2009). Northern Economics, Inc. (2009) reported that an ex-vessel 

value of $1,550 million for Alaskan fisheries corresponded to a total economic 

impact of $5,800 million to the state’s economy, a 3.7-fold increase over the exvessel value. Based on this ratio, the 5-year average ex-vessel value of all salmon 

caught within the south-central Alaska study area of $158.6 million was expanded 

by a factor of 3.7 to yield an estimated total economic impact of $587 million. 

Economic impact, as used here, includes direct harvesting and processing impacts, 

as well as indirect and induced impacts.

Based on information provided by Northern Economics, Inc. (2009), each 

$73,867 added to the economy is associated with one additional job for the industry. 

Using this relationship, it was estimated that the economic impact of the commercial salmon fishery for the study area helped support 7,944 seafood-industry-related 

jobs. This estimation is based on extrapolations from existing statewide economic 

impact models, i.e., IMPLAN models derived by Northern Economics, Inc. (2009). 

The economic impact 

of the commercial 

salmon fishery in 

the study area was 

estimated to have 

supported 7,944 

seafood-industryrelated jobs.
328

GENERAL TECHNICAL REPORT PNW-GTR-950

Estimates may differ and could be higher if models or multipliers were derived 

specifically for salmon, the south-central region of Alaska, and data representing 

other years of harvest and ex-vessel prices.

Estimating the economic impact of sport fisheries for south-central Alaska 

is more difficult because recreational fishing visits and spending are a complex 

function of many factors. However, several pieces of information may help shed 

light on this question. The sport fishing industry, which is heavily dependent on 

salmon, trout, and char, contributes about $1,400 million to the economy of Alaska 

(Southwick Associates, Inc. 2008). Of that amount, Southwick and Associates 

(2008) estimated the total economic output of sport fishing by nonresidents of 

south-central Alaska to be $631 million. Further, they estimate that 6,365 jobs 

were supported by this economic impact. 

Literature Cited

Alaska Department of Fish and Game [ADFG]. 2013. Chinook salmon stock 

assessment and research plan, 2013. Special Publication No. 13-01. Anchorage, 

AK: Chinook Salmon Research Team. 56 p. 

Alaska Department of Fish and Game [ADFG]. 2014a. Commercial fishery 

harvest data for Cook Inlet and Prince William Sound. http://www.adfg.alaska.

gov/sf/FishCounts/index.cfm?ADFG=main.kenaiChinook. (15 October 2015).

Alaska Department of Fish and Game [ADFG]. 2014b. Ex-vessel values for 

salmon caught in the Cook Inlet and Prince William Sound commercial fisheries. 

http://www.adfg.alaska.gov/index.cfm?adfg=CommercialByFisherySalmon.

exvesselquery. (15 October 2015).

Alaska Department of Fish and Game [ADFG]. 2014c. Alaska sport fishing 

survey. [Database]. 1996–. Anchorage, AK. http://www.adfg.alaska.gov/sf/

sportfishingsurvey/. (4 November 2015).

Begich, R.N.; Pawluk, J.A. 2011. 2008–2010 recreational fisheries overview and 

historical information for North Kenai Peninsula: fisheries under consideration 

by the Alaska Board of Fisheries, February 2011. Fishery Management Report 

No. 10-51. Anchorage, AK: Alaska Department of Fish and Game, Division of 

Sport Fish, Research and Technical Services. 106 p.
329

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Botz, J.; Hollowell, G.; Sheridan, T.; Brenner, R.; Moffitt, S. 2013. 2011 Prince 

William Sound area finfish management report. Fishery Management Report No. 

13-11. Anchorage, AK: Alaska Department of Fish and Game, Division of Sport 

Fish, Research and Technical Services. 202 p.

Hochhalter, S.J.; Blain, B.J.; Failor, B.J. 2011. Recreational fisheries in the Prince 

William Sound Management Area 2008–2010. Fishery Management Report No. 

11-54. Anchorage, AK: Alaska Department of Fish and Game, Division of Sport 

Fish, Research and Technical Services. 51 p. 

Northern Economics, Inc. 2009. The seafood industry in Alaska’s economy. 

Prepared for the Marine Conservation Alliance, At-Sea Processors 

Association, and Pacific Seafood Processors Association. 6 p. http://www.

marineconservationalliance.org/wp-content/uploads/2011/02/SIAE_Feb2011a.

pdf. (December 27, 2016).

Ruggerone, G.T.; Peterman, R.M.; Dorner, B.; Myers, K.W. 2010. Magnitude and 

trends in abundance of hatchery and wild pink salmon, chum salmon, and sockeye 

salmon in the north Pacific Ocean. Marine and Coastal Fisheries: Dynamics, 

Management, and Ecosystem Science. 2: 306–328. 

Shields, P.; Dupuis, A. 2012. Upper Cook Inlet commercial fisheries annual 

management report, 2011. Fishery Management Report No. 12-25. Anchorage, 

AK: Alaska Department of Fish and Game, Division of Sport Fish, Research 

and Technical Services. 180 p.

Southwick Associates, Inc.; Romberg, W.J.; Bingham, A.E.; Jennings, G.B.; 

Clark, R.A. 2008. Economic impacts and contributions of sportfishing in Alaska, 

2007. Professional Pub. No. 08-01. Anchorage, AK: Alaska Department of Fish 

and Game, Division of Sport Fish, Research and Technical Services. 

289 p. 
330

GENERAL TECHNICAL REPORT PNW-GTR-950

Appendix 4: From Biomes to Species: Methods for 

Modeling Future Vegetation Distribution

Change in Land Cover Across the Chugach National 

Forest/Kenai Peninsula

We used the Random Forest algorithm to link climatic conditions with biomes/land 

cover and applied a transition threshold representing ecological legacy to mediate 

land cover conversion following the methods in Magness and Morton (n.d.). We 

obtained downscaled climate layers from the Scenario Network for Alaska Planning (chapter 2). Our models were built using 37 climate layers: average historical 

decadal temperature by month (n = 12); average historical decadal precipitation by 

month (n = 12); average historical decadal annual temperature (n = 1); average historical decadal annual precipitation (n = 1); average decadal projected day of freeze 

(n = 1); average historical projected day of thaw; and average projected decadal 

length of growing season (n = 1). We used 1960–1990 as the temporal baseline.

We used the Multi-Resolution Land Characteristics Consortium’s (MRLC) 

National Land Cover Database (NLCD) as the base land-cover classification (http://

www.mrlc.gov/nlcd01_data.php). NLCD delineates 13 land cover classes and 

several anthropogenic land use types in Alaska at a 30 m resolution using LandSat 

Thematic Mapper imagery and several ancillary data layers (see Vogelmann et al. 

1998 for methodology). We rescaled the 30 m NLCD data to 2-km pixels that correspond with the resolution of the climate layers. We classified each 2-km pixel based 

on the land cover with the largest maximum combined area. Anthropogenic classes 

(cultivated crops, high-intensity development, low-intensity development, medium 

development, developed open space, and pasture/hay) were not used in model 

development because they are driven by land use. Water pixels were also excluded 

from the analysis because these features are often driven more by hydrology and 

geomorphology than climate. Our model represents the Alaska-wide climate niche 

for each land cover type by a randomly selected 10 percent of the land cover pixels 

across the state of Alaska (n = 36,111) to use as training data for the model. The 

moss land cover type was excluded because only 93 pixels existed across Alaska 

at the 2-km scale, none of which were in the assessment area. We modeled the 

sedge/herbaceous cover type; however, it did not occur within the assessment area. 

Therefore, for this assessment, 10 land cover types were considered (see table 6-2). 

We applied our baseline land cover climate niche model to 2060–2069 climate 

conditions obtained from the Scenarios Network for Alaska and Arctic Planning 

(SNAP) climate projections (chapter 2). 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

The Random ForestTM output assigns each land cover type a proportional 

index of likelihood. The indices of likelihood for all land cover types sum to 1. We 

applied a threshold representing “ecological legacy” of 0.1 to mediate land cover 

conversion (Magness and Morton n.d.). When the index of likelihood for the current 

land cover type was >0.1, we did not allow the pixel to convert land cover. When 

the index of likelihood of the known land cover type was <0.1, the land cover with 

the highest index of likelihood was selected. 

We assessed model accuracy by comparing the percentage of misclassified 

pixels for each land cover type that occurred when the LandSat classification was 

compared to the 1960–1990 modeled land cover. We assessed misclassified pixels 

when the conversion threshold representing ecological fidelity was used, when only 

the maximum index of likelihood was used, and when land cover type was selected 

by random chance. We also calculated the proportion of the assessment area in each 

land cover type using the conversion threshold representing ecological fidelity and 

the maximum index of likelihood in 1910–1919 and 1960–1990 to understand which 

land cover types were over- and underforecast. 

To understand how model forecasts varied among GCMs and emission scenarios, we compared the number of acres forecast for each land cover type between the 

current LandSat and 2060–2069 for all GCMs and emission scenarios considered. 

We calculated the mean number across all eight climate projections, the standard 

deviation, minimum acres, and maximum acres. We mapped the number of models 

that forecast no land cover transition to index model uncertainty. We also mapped 

the number of land cover types forecast among all climate projections as indices of 

model agreement. We mapped the land cover forecast in 1900–1919 and 1960–1990 

to represent how model outputs compare to empirical data. We also compared the 

LandSat map to 2060–2069 by mapping the land cover that was forecast by the 

largest number of models. When two or more land cover types had the highest 

number of models, we randomly chose from those land cover types with the largest 

number of models.

Finally we mapped afforestation, deforestation, stable forest, and stable nonforest. We classified each land cover type as forest (deciduous forest, evergreen 

forest, mixed forest) and nonforest (perennial ice/snow, barren land, dwarf shrub, 

shrub/scrub, grassland/herbaceous, and woody wetland). We compared current 

conditions to each of the eight model outputs representing GCMs and emission 

scenarios to classify each pixel. We mapped the most forecast output and the 

number of models that agreed. 
332

GENERAL TECHNICAL REPORT PNW-GTR-950

Change in Spruce Distributions

To model potential spruce distribution shifts in response to climate change, we 

used an imputation model (detailed methods are described in Barrett et al. [2014]). 

The imputation method uses a mathematical model to relate presence/absence data 

to current climate, which is then used to predict future habitat potential based on 

projected future climate. We used an imputation procedure called most similar 

neighbors [MSN] (Moeur and Stage 1995). Presence/absence data for spruce came 

from a systematic grid of field plots across south-central and southeast Alaska, 

measured between 1995 and 2008 by the Forest Service Forest Inventory and 

Analysis program (Barrett and Christensen 2011). Of 2,059 forested plots sampled 

across this broad area, 88 percent had at least one spruce species present (table 

A4-1). From these inventory data, a model was developed to predict the probability 

of presence for the three species as a function of climate.

Table A4-1—Number of forested plots by spruce species 

occurrence (1 = present; 0 = absent)

Black spruce Sitka spruce White spruce No spruce

Number 

of plots

0 0 0 1 457

0 0 1 0 165

0 1 0 0 1,286

0 1 1 0 8

1 0 0 0 51

1 0 1 0 90

1 1 0 0 1

1 1 1 0 1

 Total number of forested plots 2,059

For information on climate at each plot, we used a spatial climate model called 

parameter-elevation regressions on independent slopes model (PRISM) (PRISM 

Climate Group 2002). This model predicts the minimum, mean, and maximum 

monthly temperature and precipitation of 30-year climate normals (1961–1990) 

across a GIS-based map at a 2 km resolution. We evaluated PRISM-derived climate 

data for each sample site and derived additional climate variables such as growing 

degree days, an evapotranspiration index, and length of growing season. For future 

climate, we used a five-model composite of the A1B scenario created by SNAP 

(chapter 2). Projections of potential future distributions of each spruce species were 

then derived from the relationship established between climate and current distribution and future climate projections.
333

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Changes in Habitat Suitability for Three Rare Plants

Current and future ecological niches were modeled using presence data for three 

of the four Alaska Region sensitive plant species (Goldstein et al. 2009) known to 

occur within the Kenai-Chugach area (Aphragmus eschscholtzianus Andrz. ex DC. 

[APES], Papaver alboroseum Hultén [PAAL], and Romanzoffia unalaschcensis

Cham. [ROUN]). Population locations were obtained from the Alaska Natural 

Heritage Program’s rare plant database (AKNHP 2012). 

The maximum entropy modeling program MaxEnt version 3.3.1 (Phillips and 

Dudik 2008, Phillips et al. 2006) was used to produce the species distribution 

models. MaxEnt calculates expected levels of species presence using presence-only 

data. The predictor variables used in the modeling included current and projected 

climate data created for Alaska by the Scenarios Network for Alaska and Arctic 

Planning (chapter 2). The climate projections for 2060 used intermediate (A1B) 

emission scenarios. Climate variables used to develop the models included mean 

annual temperature, mean annual precipitation, and growing-season length (number 

of frost-free days). Slope and elevation were also included in the models and were 

extracted from the National Elevation Dataset for Alaska (Gesch 2007, Gesch et al. 

2002) (approximately 60 m resolution).

Model output values ≥0.7 (on a 0 to 1 scale) were considered as indicative of the 

highly suitable habitats. Areas covered by glaciers1

 (23.6 percent) or open water 2

(3.6 percent) were excluded from consideration as suitable habitat. 

Invasive Species

Twenty-eight non-native plant species were selected that possessed modest-to-high 

invasiveness ranking scores (see Carlson et al. 2008, Nawrocki et al. 2011) and that 

represented a spectrum of current distributions in Alaska. Three species are currently not known in the state; nine species have fewer than 50 known infestations, 

and 16 have between 50 to more than 5,000 documented infestations in the state. 

Closely related species with high ecological similarity or taxonomic confusion 

(including cordgrasses, hawkweeds, hempnettles, sweetclovers, and knotweeds) 

were aggregated into species complexes. 

Global occurrence data were obtained from the Global Biodiversity Information Facility (GBIF 2015). Current climate data (2.5-minute spatial resolution) 

were obtained from the WORLDCLIM website for 19 environmental parameters 

(WORLDCLIM 2008). The selected future climate dataset was obtained for 2070 

1 Source: Randolph Glacier Inventory (http://www.glims.org/RGI).

2 Source: National Land Cover Database (http://www.mrlc.gov/ .
334

GENERAL TECHNICAL REPORT PNW-GTR-950

from the IPCC CCSM4 dataset, representative concentration pathway (RCP) 8.5, 

based on a relatively high greenhouse-gas concentration trajectory (IPCC 2013). 

The application Maximum Entropy (MaxEnt) was used to generate current and 

future habitat suitability models. Model performance was evaluated using standard 

quantitative approaches (Phillips and Dudik 2008, Phillips et al. 2006). Final model 

performance was evaluated using Area Under the Curve (AUC) scores, a standard 

quantitative approach (Phillips and Dudik 2008, Phillips et al. 2006). Receiving 

Operating Characteristic (ROC) curve AUC scores plot the fraction of true positive 

occurrences (suitable habitat is predicted where known occurrences are located) 

versus the fraction of false positives (suitable habitat is predicted where modelgenerated pseudo-absence points indicate that no data points occurred), such that 

a score of 0.5 is the same as random chance (Fielding and Bell 1997). AUC scores 

range from 0 to 1, with a score of 0.5 meaning that predictive ability of the model 

is the same as random chance, and a score of 1 indicating 100-percent predictive 

accuracy (Gormley et al. 2011, Mukherjee et al. 2011). Final habitat suitability maps 

for current climate and for 2070 were generated in ArcGISTM. Scenario maps can be 

used to compare potential current habitat to known populations to evaluate model 

performance. Potential current habitat maps can also be compared to predicted 

future habitat maps to assess possible range changes (increases, decreases, directional shifts). 

Literature Cited

Barrett, T.M.; Christensen, G.A., tech eds. 2011. Forests of southeast and southcentral Alaska, 2004–2008. Gen. Tech. Rep. PNW-GTR-835. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station. 

156 p.

Carlson, M.L.; Lapina, I.V.; Shephard, M. 2008. Invasiveness ranking system 

for non-native plants of Alaska. Tech. Paper R10-TP-143. Anchorage, AK: U.S. 

Department of Agriculture, Forest Service, Alaska Region. 218 p.

Fielding, A. H.; Bell, J.F. 1997. A review of methods for the assessment of 

prediction errors in conservation presence/absence models. Environmental 

Conservation. 1: 38–49.

GBIF. 2015. Global biodiversity information facility. [Database]. http://www.gbif.

org/. (27 May 2016).

Potential current 

habitat maps can 

be compared to 

predicted future 

habitat maps to 

assess possible range 

changes (increases, 

decreases, and 

directional shifts).
335

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Gesch, D.B. 2007. The national elevation dataset. In: Maune, D., ed., Digital 

elevation model technologies and applications: the DEM users manual, 2nd ed. 

Bethesda, MD: American Society for Photogrammetry and Remote Sensing: 

99–118.

Gesch, D.; Oimoen, M.; Greenlee, S.; Nelson, C.; Steuck, M.; Tyler, D. 2002. 

The national elevation dataset. Photogrammetric Engineering and Remote 

Sensing. 68: 5–11.

Goldstein, M.I.; Martin, D.; Stensvold, M.C. 2009. Forest Service Alaska Region 

sensitive species list: assessment and proposed revisions to the 2002 list. Juneau, 

AK: U.S. Department of Agriculture, Forest Service, Alaska Region. 47 p.

Gormley, A.M.; Forsyth, D.M.; Griffioen, P.; Lindeman, M.; Ramsey, D.S.L.; 

Scroggie, M.P.; Woodford L. 2011. Using presence-only and presence-absence 

data to estimate the current and potential distributions of established invasive 

species. Journal of Applied Ecology. 48: 25–34.

Intergovernmental Panel on Climate Change [IPCC]. 2013. Climate change 

2013: the physical science basis. Contribution of working group I to the 

fifth assessment report of the Intergovernmental Panel on Climate Change. 

Cambridge, United Kingdom, and New York: Cambridge University Press. 

129 p.

Magness, D.R.; Morton, J.M. [N.d.]. Using convergent signals to increase 

certainty of land cover conversion in a rapidly changing climate. Journal of 

Fish and Wildlife Management. 

Moeur, M.; Stage, A.R. 1995. Most similar neighbor: an improved sampling 

inference procedure for natural resource planning. Forest Science. 41(2): 337–359.

Mukherjee, A.; Christman, M.C.; Overholt, W.A.; Cuda, J.P. 2011. Prioritizing 

areas in the native range of Hygrophila for surveys to collect biological control 

agents. Biological Control. 56: 254–262.

Nawrocki, T.; Klein, H.; Carlson, M.; Flagstad, L.; Conn, J.; DeVelice, R.; 

Grant, A.; Graziano, G.; Million, B.; Rapp, W. 2011. Invasiveness ranking of 

50 non-native plant species for Alaska. Anchorage, AK: Alaska Natural Heritage 

Program, University of Alaska Anchorage. 253 p. http://aknhp.uaa.alaska.edu/

big-files/AKEPIC/Publications/2011/Invasiveness_Ranking_of_50_Non-Native_

Plant_Species_Alaska.pdf. (27 May 2016). 

Phillips, S.J.; Anderson, R.P.; Schapire, R.E. 2006. Maximum entropy modeling 

of species geographic distributions. Ecological Modeling. 190: 231–259.
336

GENERAL TECHNICAL REPORT PNW-GTR-950

Phillips, S.J.; Dudik, M. 2008. Modeling of species distributions with Maxent: 

new extensions and a comprehensive evaluation. Ecography. 31: 161–175.

Vogelmann, J.E.; Sohl, T.; Campbell, P.V.; Shaw, D.M. 1998. Regional land 

cover characterization using Landsat Thematic Mapper data and ancillary 

sources. Environmental Monintoring and Assessment. 51: 265–180.

WorldClim—Global climate data. 2008. http://www.worldclim.org/. 

(27 May 2016). 
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Appendix 5: Modeling and Projecting Development 

Status and Structure Value of Kenai Peninsula Property

To model the value of structural property on the Kenai Peninsula, we took a multistepped approach. The first step devised a set of equations to explain the timing and 

location of the first instance of a structure appearing on a parcel after 1960. The 

second step developed a set of equations to explain the value of the structure when 

it was first built and as it evolved over time. In the third step, the equations from the 

first two steps were projected 50 years into the future based on the assumption that 

the broad pattern of development will continue more or less as it has from 1960 to 

the present.

Step one. The basic approach for modeling the timing of development was to 

conduct a survival analysis for the probability that a parcel that was undeveloped 

in 1960 remained in an undeveloped state through a given year. Keep in mind that 

the objective was to model rates of development at a scale of several decades rather 

than at a particular point in time. If one assumes that the probability that a vacant 

parcel is developed is constant over time (a proportional hazard model), then the 

number of newly developed parcels would be bound to fall over time as the base—

the set of vacant parcels—declines. Historically, this has not occurred on the Kenai 

Peninsula. Consequently, we assumed a proportional hazard model but allowed the 

hazard rate to vary over time; in fact, we hypothesized that the percentage of vacant 

properties developed per unit time (hazard rate) would increase over the decades. 

A parametric proportional hazard model with variable hazard over time 

assumes a Weibull distribution, for which the hazard of development at time t, w(t), 

is given by the following function: 

w(t) = yρt ρ – 1 (1)

where the baseline hazard y = exβ, in which x represents a vector of explanatory 

factors determining variation among properties in the likelihood of development, 

such as proximity to roads and wetland percentage, and β is a set of coefficients to 

be estimated. Survival to time t in an undeveloped state, s(t), is given by:

s(t) = exp(-yt ρ

) (2)

The parameter ρ in equation 2 is called the shape parameter. If ρ < 1, the 

hazard decreases over time, and if ρ > 1, the hazard increases. If ρ = 1, the hazard 

is constant over time, and the Weibull model reduces to the exponential model. 

Separate Weibull survival equations were estimated for development of private 

parcels and development of parcels in public and Alaska Native ownership over 

the historical period. 

The objective was 

to model rates of 

development at a 

scale of several 

decades rather than 

at a particular point 

in time. 
338

GENERAL TECHNICAL REPORT PNW-GTR-950

Step two. If a property was developed, then a panel regression model explained 

the value of the structure and its possible evolution over time. The panel form was 

used because multiple observations appeared in the data for a parcel if additions or 

modifications to structures on the property occurred in different years. The survival analysis used only one observation per parcel—the observation corresponding 

to the year a structure was first built or 2014, if it was still vacant—and included all 

parcels. The equations for structure value, however, included only parcels that contained structures, but included all observations in the data for those parcels. Most of 

the factors that could explain if or when a parcel got developed also may explain the 

value of what was built there. In addition, the panel regressions for structure value 

included the year a structure first appeared on the property and the number of years 

since that time. These time variables captured changes in the type of structures 

being built over the years as well as possible changes in the price level that were not 

accurately measured in the property assessments.

One could undoubtedly obtain much more accurate estimates of the value of 

structures by including detailed characteristics of the structure, such as square 

footage, number of bathrooms, etc. However, the relevant question to be addressed 

is not the value of the structure given its characteristics, but rather what type of 

structure gets built in that particular place. Modeling the improvements simply in 

terms of value or cost is sufficient for the objectives of this study. 

The panel model assumed that the value of a structure existing on parcel i at 

time t, vi(t), that was first developed in year t1i was given by:

log vi

(t) = xitβ + γt1i + δ(t – t1i) + ui + εit (3)

where ui

 represents a random error that is specific to the property i, and εit is an 

independently distributed random error term. Separate loglinear random-effects 

panel regression equations were estimated for the value of structures on developed 

private parcels and the value of structures on developed parcels in public and 

Native ownership. 

Step three. The estimated equations for survival of a parcel in an undeveloped 

state and the value of structures on developed parcels formed the basis of long-term 

future projections of property at risk on the Kenai Peninsula. These projections 

assumed that the patterns of development that have become established on private 

and other lands in the region will continue over the next 50 years.

The projected probability that a parcel that was vacant today (time t0) will still 

be vacant at the beginning of 2065 (time T) is based on evaluating the Weibull 

survival function from t0 to T, assuming that the hazard rate continues to increase 

between t0 and T at the rate it did up to t0:
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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

s*(T) = exp(-yT ρ)/exp(-yt0

ρ

) = s(T)/s(t0) (4)

Spatially explicit scenarios for Kenai Peninsula property development were 

constructed by taking random draws for the state of development (structure built 

or not), with the probability that a parcel remained vacant calculated from equation 

4, with T set to 2065 and t0 set to 2015. Parcels already containing structures were 

assumed to contain structures in 2065 as well.

If a random draw produced a structure on a parcel in 2065 for a particular scenario, the value of structures on that parcel was established by projecting the value 

estimated from historical patterns for that kind of property to 2065. Additions, 

remodeling, and replacement of buildings on parcels already developed today were 

also based on established long-term trends. Specifically, if a parcel j was projected 

to be developed by time T (2065), the projected value of structures on that parcel 

was estimated as:

vj

*(T) = exp[xjβ + γt1j + δ(T – t1j) + uj] (5)

The estimated parcel-specific error term for parcel i, uj

, was included in the 

projected value if parcel i had a structure by 2014, but was assumed equal to zero 

for parcels without structures.

Results Explaining Historical Patterns of Land Development

Table A5-1 displays the complete results of the survival analysis for development of 

private land parcels. The factor with by far the largest effect on the likelihood that 

a structure gets built on private property was the parcel’s proximity to a road. The 

hazard rate for development for a parcel that had road frontage or lay within 400 m 

of a road was nearly three times that for a more remote parcel. Larger parcels were 

more likely to get developed, and those with a higher percentage of wetland were 

less likely to be developed. The baseline hazard rate for a structure being built was 

higher on parcels within the city limits of Kenai, Soldotna, and Homer relative to 

lands outside municipal boundaries. However, the baseline hazard rate was lower 

in Seward, perhaps because it is older than the other communities. The devastation 

Seward suffered from the 1964 earthquake also may have impeded development.

Areas with high fire risk were less likely to be developed, controlling for other 

factors. Extreme fire risk was associated with an even lower hazard of development. 

The estimate of the coefficient ρ in the Weibull regression shown in table A5-1 

is 1.63 (95-percent confidence interval 1.61 to 1.65). As hypothesized, the high 

estimated value for the Weibull shape parameter, ρ, means that the hazard rate 

for development of private lands has been strongly and significantly increasing 

over time (fig. A5-1). Keep in mind, however, that the parameters of the survival 

Areas with high fire 

risk were less likely 

to be developed, 

controlling for other 

factors.
Increasing hazard rate for Kenai Peninsula Borough 

property development — 1960 (0) to 2014 (54)

Probability structure built on vacant lot

0.025

0.020

0.015

0.010

0.005

0

0 20 40 60

Years observed

Figure A5-1—Baseline annual probability of development of a vacant private parcel estimated from the 

Weibull Hazard Function.

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GENERAL TECHNICAL REPORT PNW-GTR-950

Table A5-1— Survival equations for a parcel remaining in an undeveloped state on private lands

Hazard 

ratio

Standard 

error z P>|z|

95-percent

confidence interval

In Kenai 1.259 0.029 10.13 0 1.204 1.316

In Seward 0.844 0.043 -3.35 0.001 0.765 0.932

In Soldotna 1.333 0.036 10.55 0 1.264 1.406

In Homer 1.233 0.029 8.92 0 1.177 1.291

Road frontage 2.903 0.081 38.01 0 2.748 3.067

Within 400 m of road 2.930 0.084 37.48 0 2.770 3.100

400 m to 2 km from road 1.024 0.035 0.70 0.483 0.958 1.095

High spring fire risk 0.961 0.017 -2.19 0.029 0.928 0.996

Extreme spring fire risk 0.800 0.017 -10.55 0 0.768 0.834

Percentage of wetland 0.799 0.025 -7.15 0 0.752 0.850

Natural log of parcel acres 1.029 0.006 4.64 0 1.017 1.042

Natural log ρ 0.488 0.005 89.09 0 0.477 0.499

ρ 1.629 0.009 1.612 1.647

Number of parcels 51,413 Number of observations 51,413

Number of failures (structure built) 27,126

Time at risk (parcel-years) 2,127,992

Log likelihood -52425.1

LR chi2(11) 4386.6 Prob > chi2 0
341

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

equations were estimated assuming that the set of private parcels existing in 

2014 were present during the entire period since 1960, which is certainly not the 

case. Some of the lots were the result of subdivision of other parcels. Part of the 

explanation of the rapidly increasing hazard rate is that it adjusts for the ongoing 

subdivision of parcels, which is unobserved.

Table A5-2 displays the analogous results of the survival analysis for development of other lands. In this case, location within city limits of any of the larger 

towns had a large positive effect on the likelihood that a structure got built on the 

parcel. Road frontage still had a significant positive effect, and wetlands greatly 

reduced the likelihood of development, but other effects differed from those 

estimated for private lands. Parcels with high fire risk were much less likely to be 

developed, as were larger parcels. Municipal and state-owned parcels were less 

likely to be developed than borough, Native, and federal (the default) parcels. The 

estimate of the shape parameter ρ was still significantly positive—1.27 (95-percent 

confidence interval 1.18 to 1.37)—but the effect on increasing the hazard rate for 

development was much smaller than that estimated for private lands.

Table A5-2—Survival equations for a parcel remaining in an undeveloped state on other lands

Hazard 

ratio

Standard 

error z P>|z|

95-percent

confidence interval

In Kenai 2.006 0.329 4.24 0 1.454 2.766

In Seward 3.236 0.575 6.61 0 2.284 4.584

In Soldotna 2.911 0.521 5.96 0 2.049 4.135

In Homer 3.228 0.541 7.00 0 2.325 4.482

Road frontage 1.548 0.214 3.15 0.002 1.180 2.031

Within 400 m of road 1.166 0.163 1.10 0.273 0.886 1.533

400 m to 2 km from road 0.962 0.145 -0.26 0.797 0.716 1.293

High spring fire risk 0.344 0.061 -5.98 0 0.243 0.488

Extreme spring fire risk 0.913 0.218 -0.38 0.702 0.572 1.456

Percentage of wetland 0.325 0.051 -7.15 0 0.239 0.442

Municipal lands 0.586 0.112 -2.79 0.005 0.402 0.853

Borough lands 0.851 0.160 -0.86 0.39 0.588 1.230

State lands 0.632 0.114 -2.55 0.011 0.444 0.899

Alaska Native lands 1.115 0.201 0.60 0.546 0.783 1.588

Natural log of parcel acres 0.865 0.017 -7.17 0 0.832 0.900

Natural log ρ 0.242 0.039 6.21 0 0.166 0.318

ρ 1.274 0.050 1.180 1.375

Number of parcels 6,336 Number of observations 6,336

Number of failures (structure built) 615

Time at risk (parcel-years) 331,416

Log likelihood -2214.5

LR chi2(11) 617.9 Prob > chi2 0
342

GENERAL TECHNICAL REPORT PNW-GTR-950

Table A5-3 displays the results of the random effects panel regression for the 

value of structures built on private parcels. The results show that, on average, 

structures in towns were much more valuable than those built outside city limits, 

with those built in Kenai and Soldotna worth the most. The larger towns tended 

to have larger commercial buildings, as well as some multifamily residences. 

Structures on or near roads were more valuable than those built on remote parcels, 

which presumably tended to be recreational cabins and associated outbuildings. The 

results also showed that structures on larger parcels were worth more, controlling 

for other factors, and the value of structures built on lands with high spring fire risk 

or more wetlands was lower. As expected, structures and structure additions built 

more recently had higher values than older structures.

Table A5-3— Random effects regression equations for value of structures on private lands with 

structures in 2014

Coefficient

Standard 

error z P>|z| 95% confidence interval

In Kenai 1.077 0.033 33.01 0 1.013 1.140

In Seward 0.830 0.062 13.39 0 0.709 0.952

In Soldotna 1.090 0.039 28.00 0 1.014 1.166

In Homer 0.764 0.032 23.86 0 0.701 0.827

Road frontage 0.682 0.039 17.58 0 0.606 0.758

Within 400 m of road 0.885 0.040 22.35 0 0.807 0.962

400 m to 2 km from road 0.279 0.049 5.64 0 0.182 0.375

High spring fire risk -0.193 0.025 -7.63 0 -0.242 -0.143

Extreme spring fire risk -0.046 0.029 -1.57 0.12 -0.103 0.011

Percentage of wetland -0.207 0.044 -4.70 0 -0.294 -0.121

Natural log of parcel acres 0.129 0.009 14.36 0 0.112 0.147

Year developed—1960 0.0232 0.0006 36.45 0 0.022 0.024

Log years since developed 0.319 0.004 81.07 0 0.311 0.326

Constant 9.201 0.042 217.34 0 9.118 9.284

Between groups (parcels) standard error (u) 1.126

Residual standard error 1.028

ρ (between groups variance fraction) 0.545

Lagrangian multiplier test for Var(u) = 0 9438.9 Prob > chi2 0.000

Number of observations 55,191 Observations per group: min 1

Number of groups (parcels with structures) 28,127 max 36

R-sq: within 0.193 Between 0.100

 Overall 0.118

Random effects Wald chi2(13) 9491.2 Prob > chi2 0
343

Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Table A5-4 displays the results of the random effects panel regression estimated 

for structures built on public and Native lands. These structures are more diverse and 

therefore more difficult to predict, so the results show fewer significant effects. As 

found for private parcels, structures on lands within city limits of the larger towns 

were more valuable—in this case much more valuable—than those built outside 

city limits. Structures on or near roads were more valuable than those built on more 

remote parcels. Municipal structures were worth more than those on federal lands 

(the default), probably reflecting the fact that city-owned buildings would typically 

be office buildings or public utility structures, and therefore larger and more costly 

than residential structures. The results also showed that structures built on nonprivate parcels with a higher percentage of wetlands tended to have lower values, 

and structures with more recent additions were worth more than older structures.

Table A5-4—Random effects regression equations for value of structures on other lands with structures 

in 2014

Coefficient

Standard 

error z P>|z| 95% confidence interval

In Kenai 2.010 0.324 6.20 0 1.375 2.646

In Seward 2.635 0.365 7.23 0 1.921 3.350

In Soldotna 2.166 0.352 6.15 0 1.475 2.856

In Homer 1.886 0.336 5.62 0 1.229 2.544

Road frontage -0.332 0.290 -1.14 0.252 -0.899 0.236

Within 400 m of road 0.068 0.323 0.21 0.833 -0.566 0.702

400 m to 2 km from road -0.963 0.363 -2.65 0.008 -1.675 -0.250

High spring fire risk 0.334 0.391 0.85 0.393 -0.433 1.101

Extreme spring fire risk 0.193 0.494 0.39 0.696 -0.776 1.162

Percentage of wetland -0.768 0.361 -2.12 0.034 -1.476 -0.059

Natural log of parcel acres 0.064 0.046 1.41 0.158 -0.025 0.154

Year developed—1960 0.0082 0.0065 1.27 0.205 -0.004 0.021

Log years since developed 0.466 0.030 15.75 0 0.408 0.524

Municipal lands 0.745 0.378 1.97 0.049 0.003 1.486

Borough lands -0.557 0.356 -1.57 0.118 -1.254 0.140

State lands -0.568 0.386 -1.47 0.141 -1.324 0.188

Alaska Native lands 0.024 0.409 0.06 0.953 -0.777 0.826

Constant 9.980 0.430 23.20 0 9.137 10.824

Between groups (parcels) standard error (u) 1.854

Residual standard error 0.978

ρ (between groups variance fraction) 0.782

Lagrangian multiplier test for Var(u) = 0 279.35 Prob > chi2 0

Number of observations 1,103 Observations per group: min 1

Number of groups (parcels with structures) 630 max 17

R-sq: within 0.332 Between 0.247

 Overall 0.236

Random effects Wald chi2(17) 433.6 Prob > chi2 0
344

GENERAL TECHNICAL REPORT PNW-GTR-950

Projected Kenai Peninsula Property Development in 2065

The equations provided the basis for projecting future property vulnerability to 

wildfire. As discussed above, spatially explicit scenarios for Kenai Peninsula 

property development were constructed from the equations shown in tables A5-1 

through A5-4. For a parcel that was vacant through 2014, whether the parcel was 

still vacant or had a structure at the start of 2065 was determined by a random 

draw. The probability that the structure was still vacant in the random draw was a 

calculated by evaluating survival equations (4) at 2065 for parcels that were currently vacant. The shape parameter, ρ, was projected to increase the hazard from the 

2014 base at the historical rate, implying that the historical pattern of subdivision 

of private property continues. The development status of currently vacant private 

and other parcels was projected separately using the respective results displayed in 

tables A5-1 and A5-2. Parcels with structures present in 2014 were assumed to have 

structures in 2065.

A number of scenarios were constructed using different random draws from 

the projected survival functions for private property and other lands. As it turned 

out, the sample of properties is so large, and the estimated standard errors so small, 

that taking different sets of random draws made almost no difference in the results. 

The spatial distribution of developed and undeveloped properties was also similar, 

because what is predictable spatially—roads and wetlands—was also included in 

the survival likelihood. Because different property development scenarios produced 

essentially identical results, the results are reported below for a single representative scenario. The only real difference among scenarios amounted to the projected 

random location of a few relatively low-value structures on large tracts of public 

lands with low probability of development. 

Projected Values at Risk to Wildfire in 2065

Evaluating the survival equations to 2065 projects a 53-percent increase in the 

number of private parcels with structures. The value of structures on these parcels 

was estimated by evaluating the panel regression equations displayed in tables A5-3 

for structures on private lands, and in table A5-4 for structures on other lands. The 

equations projected that the total value of structures on private lands would increase 

by 66 percent over the next 50 years, and somewhat less, by about 60 percent, on 

other lands. The projected increase in value of structures is nearly identical for each 

wildfire risk category, yielding a symmetrical distribution of the enhanced value 

across categories. 

The equations projected that the total 

value of structures on 

private lands would 

increase by 66 percent 

over the next 50 years, 

and somewhat less, by 

about 60 percent, on 

other lands. 


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