U.S. Department of the Interior

U.S. Geological Survey

Professional Paper 1826

Baseline and Projected Future Carbon Storage and 

Greenhouse-Gas Fluxes in Ecosystems of Alaska
Front cover. Meandering Beaver Creek (a tributary of the Yukon River) in the autumn in 

central interior Alaska, with extensive coverage of birch (yellow) and spruce (dark green) 

trees and various deciduous shrub species (crimson), underlain by permafrost. Photograph 

by Mark Dornblaser, U.S. Geological Survey.

Inside covers. Expansive Yukon River Basin north of Fairbanks, Alaska, showing undulating 

landscapes of both uplands and wetlands covered by autumn foliage of various tree and 

shrub species, trees killed by past wildfires, and the trans-Alaska pipeline running across 

the center of the image. Photograph by Zhiliang Zhu, U.S. Geological Survey.

Back cover. Top, autumn season at Galbraith Lake (photograph by Hélène Genet, University 

of Alaska-Fairbanks). Bottom, shrub tundra landscape in North Slope of Alaska, showing the 

brown tundra vegetation underlain by permafrost in the arctic ecosystem, with mountains of 

the Brooks Range in the background (photograph by Zhiliang Zhu, U.S. Geological Survey).
Baseline and Projected Future Carbon 

Storage and Greenhouse-Gas Fluxes 

in Ecosystems of Alaska

Edited by Zhiliang Zhu and A. David McGuire

Professional Paper 1826

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Zhu, Zhiliang, and McGuire, A.D., eds., 2016, Baseline and projected future carbon storage and greenhouse-gas fluxes 

in ecosystems of Alaska: U.S. Geological Survey Professional Paper 1826, 196 p., http://dx.doi.org/10.3133/pp1826. 

ISSN 1044-9612 (print)

ISSN 2330-7102 (online)

ISBN 978-1-4113-4038-1 
iii

Contents

Executive Summary—Baseline and Projected Future Carbon Storage and GreenhouseGas Fluxes in Ecosystems of Alaska

By A. David McGuire, Bruce K. Wylie, David V. D’Amore, Xiaoping Zhou, 

T. Scott Rupp, Hélène Genet, Yujie He, Sarah Stackpoole, and Zhiliang Zhu...............1

Chapter 1. Introduction

 By A. David McGuire, T. Scott Rupp, Tom Kurkowski, and Sarah Stackpoole..............5

Chapter 2. Climate Scenarios, Land Cover, and Wildfire in Alaska

 By T. Scott Rupp, Paul Duffy, Matthew Leonawicz, Michael Lindgren, Amy Breen, 

Tom Kurkowski, Angelica Floyd, Alec Bennett, and Lena Krutikov ..............................17

Chapter 3. Soil Carbon and Permafrost Estimates and Susceptibility to Climate Change

in Alaska

 By Bruce K. Wylie, Neal J. Pastick, Kristopher D. Johnson, Norman Bliss, 

and Hélène Genet .................................................................................................................53 

Chapter 4. Watershed Carbon Budgets in the Southeastern Alaskan Coastal Forest Region

 By David V. D’Amore, Frances E. Biles, S. Mark Nay, and T. Scott Rupp ....................77

Chapter 5. Forest Inventory-Based Analysis and Projections of Forest Carbon Stocks

and Changes in Alaska Coastal Forests

 By Xiaoping Zhou, Svetlana A. Schroder, A. David McGuire, and Zhiliang Zhu.........95 

Chapter 6. Terrestrial Carbon Modeling: Baseline and Projections in Upland 

Ecosystems of Alaska

 By Hélène Genet, Yujie He, A. David McGuire, Qianlai Zhuang, Yujin Zhang,

Frances E. Biles, David V. D’Amore, Xiaoping Zhou, and Kristopher D. Johnson........105

Chapter 7. Terrestrial Carbon Modeling: Baseline and Projections in Lowland 

Ecosystems of Alaska

 By Yujie He, Hélène Genet, A. David McGuire, Qianlai Zhuang, Bruce K. Wylie,

and Yujin Zhang...................................................................................................................133

Chapter 8. Carbon Transport, Emission, and Burial From Inland Aquatic Ecosystems

in Alaska

 By Sarah Stackpoole, David Butman, David Clow, Kris Verdin, Ben Gaglioti,

and Robert Striegl...............................................................................................................159

Chapter 9. Alaska Carbon Balance

 By A. David McGuire, Hélène Genet, Yujie He, Sarah Stackpoole, David V. D’Amore,

T. Scott Rupp, Bruce K. Wylie, Xiaoping Zhou, and Zhiliang Zhu................................189
iv

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

cubic meter (m3) 35.31 cubic foot (ft3)

Flow rate

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

cubic meter per day (m3/d) 264.2 gallon per day (gal/d) 

cubic meter per day per square 

kilometer [(m3/d)/km2]

684.28 gallon per day per square mile 

[(gal/d)/mi2]

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

Pressure

atmosphere, standard (atm) 101.3 kilopascal (kPa)

Hydraulic conductivity

meter per day (m/d) 3.281 foot per day (ft/d) 

Transmissivity*

meter squared per day (m2/d) 10.76 foot squared per day (ft2/d) 

Application rate

kilograms per hectare per year 

[(kg/ha)/yr]

0.8921 pounds per acre per year

[(lb/acre)/yr]

*Transmissivity: The standard unit for transmissivity is cubic meter per day per square meter 

times meter of aquifer thickness [(m3/d)/m2]m. In this report, the mathematically reduced form, 

meter squared per day (m2/d), is used for convenience.
v

Datum and Supplemental Information

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as

°F= (1.8 ×°C)+32.

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25 °C).

Concentrations of chemical constituents in water are given in either milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

The resolution of pixels in spatial datasets follows the conventions used in the spatial data and 

modeling communities. The format is “n-meter resolution,” where n is a numerical value for the 

length. The usage translates into a pixel with a length of n on all sides that covers an area of 

n meters ×n meters.

How Megagrams, Gigagrams, Teragrams, and Petagram Relate to Metric Tons

1 megagram (Mg) = 1 million grams (106 g) = 1 metric ton (t)

1 gigagram (Gg) = 1 billion grams (109 g) = 1,000 metric tons

1 teragram (Tg) = 1 trillion grams (1012 g) = 1 million metric tons (Mt)

1 petagram (Pg) = 1 quadrillion grams (1015 g) = 1 billion metric tons (Gt)
vi

Abbreviations, Acronyms, and Chemical Symbols

°C degree Celsius

μatm microatmosphere

AICC Alaska Interagency Coordination Center

ALFRESCO Alaska Frame-Based Ecosystem Code

ALT active-layer thickness

BCR Bird Conservation Region

CALM Circumpolar Active Layer Monitoring

CAVM Circumpolar Arctic Vegetation Map

CGCM3.1 version 3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’

Coupled Global Climate Model

CH4 methane

cm centimeter

CMIP3 Coupled Model Intercomparison Project phase 3

CO carbon monoxide

CO2 carbon dioxide

CO2-eq carbon dioxide equivalent

CO2-eq/yr carbon dioxide equivalent per year

CRU Climatic Research Unit

DIC dissolved inorganic carbon

DOC dissolved organic carbon

DOS-TEM Dynamic Organic Soil version of the Terrestrial Ecosystem Model

ECHAM5 version 5 of the Max Planck Institute’s European Centre Hamburg Model

EDNA Elevation Derivatives for National Applications database

EISA Energy Independence and Security Act of 2007

EPA U.S. Environmental Protection Agency

FIA Forest Inventory and Analysis National Program

FVS Forest Vegetation Simulator

gC/m2 gram of carbon per square meter

gC/m2/yr gram of carbon per square meter per year

GCM general circulation model

gCO2-eq/m2/yr gram of carbon dioxide equivalent per square meter per year

GHG greenhouse gas
vii

Abbreviations, Acronyms, and Chemical Symbols—Continued

GIPL 1.3 Geophysical Institute Permafrost Laboratory version 1.3 transient model

GIS geographic information system

GPP gross primary productivity 

GWP global warming potential

HPU hydropedologic unit

HR heterotrophic respiration

HUC hydrologic unit code

IPCC Intergovernmental Panel on Climate Change

kgC/m2 kilogram of carbon per square meter

km kilometer

km2 square kilometer

km2/yr square kilometer per year

km3/yr cubic kilometer per year

LAI leaf area index

LANDFIRE Landscape Fire and Resource Management Planning Tools Project

LCC Landscape Conservation Cooperative

LOADEST USGS Load Estimator program

LTER Long Term Ecological Research

m meter

m/d meter per day

MDM-TEM Methane Dynamics Module of the Terrestrial Ecosystem Model

mg/L milligram per liter

MgC/yr megagram of carbon per year

MLR multiple linear regression

mm millimeter

mm/yr millimeter per year

MODIS Moderate Resolution Imaging Spectroradiometer

NALCMS North American Land Change Monitoring System

NASA National Aeronautics and Space Administration

NCSCDv2 Northern Circumpolar Soil Carbon Database version 2

NECB net ecosystem carbon balance

NEP net ecosystem production
viii

Abbreviations, Acronyms, and Chemical Symbols—Continued

NHD National Hydrography Dataset

NLCD National Land Cover Database

NOAA National Oceanic and Atmospheric Administration

NPP net primary productivity

NSP near-surface permafrost

NWI National Wetlands Inventory

NWIS National Water Information System

OLT organic-layer thickness

PCTR perhumid coastal temperate rainforest

PDO Pacific Decadal Oscillation

PgC petagram of carbon

POC particulate organic carbon

ppm part per million

PRISM Parameter-elevation Relationships on Independent Slopes Model

s.d. standard deviation 

SMU soil map unit

SNAP Scenarios Network for Alaska and Arctic Planning

SOC soil organic carbon

SRES Special Report on Emissions Scenarios

STATSGO state soil geographic database

TgC teragram of carbon

TgC/yr teragram of carbon per year

TgCO2-eq/yr teragram of carbon dioxide equivalent per year

TOC total organic carbon

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey
Executive Summary—Baseline and Projected Future 

Carbon Storage and Greenhouse-Gas Fluxes in 

Ecosystems of Alaska

By A. David McGuire,1 Bruce K. Wylie,2 David V. D’Amore,3 Xiaoping Zhou,4 T. Scott Rupp,5

Hélène Genet,5 Yujie He,6 Sarah Stackpoole,7 and Zhiliang Zhu8

1

U.S. Geological Survey, Fairbanks, Alaska.

2

U.S. Geological Survey, Sioux Falls, S. Dak.

3

U.S. Department of Agriculture Forest Service, Juneau, Alaska.

4

U.S. Department of Agriculture Forest Service, Portland, Oreg.

5

University of Alaska-Fairbanks, Fairbanks, Alaska.

6

Purdue University, West Lafayette, Ind.

7

U.S. Geological Survey, Denver, Colo.

8

U.S. Geological Survey, Reston, Va.

Alaska is approximately one-fifth the area of the 

conterminous United States and spans a broad range in 

climate from the maritime coastal regions of south-central 

and southeast Alaska to the boreal forest region in interior 

Alaska to arctic and maritime tundra regions of northern and 

western Alaska. The cold temperatures of Alaska have led to 

the storage of vast quantities of soil and vegetation carbon. 

Although forest ecosystems of southeast Alaska have been 

regularly included in national resource or greenhouse-gas 

inventory programs, other regions of Alaska have not been 

included in national-level resource or greenhouse-gas inventory programs because of the large size of Alaska, the lack of 

extensive transportation infrastructure, and the low density 

of field data to support such programs. Yet, high-latitude 

ecosystems are potentially more vulnerable to climate change 

than ecosystems in the temperate zone during the remainder 

of the 21st century because temperature is projected to 

increase more in boreal and arctic regions. In particular, 

these increases in temperature may expose the substantial 

stores of carbon in the region to loss from more wildfire and 

permafrost thaw, which could turn the ecosystems of Alaska 

into a net carbon source. Therefore, the assessment of Alaska 

ecosystem carbon stocks and fluxes as well as methane 

fluxes, as reported here, was conducted to better understand 

the baseline and projected carbon distributions and potential 

responses to a rapidly changing environment. The results 

of this assessment will inform national climate and carbon 

management policies. 

Major components of the assessment included carbon and 

methane fluxes in upland and lowland (wetland) ecosystems, 

carbon fluxes of inland aquatic ecosystems, synthesis of soil 

carbon stocks and permafrost distribution, effects of forest 

management, and effects of climate change and associated 

shifts in vegetation and wildfire regime over space and time. 

Methods varied depending on the components, as described in 

respective chapters of this report. For uplands and wetlands, 

the boundaries of four Landscape Conservation Cooperatives 

(LCCs) in Alaska were used to stratify and report the assessment. For inland aquatic ecosystems, the boundaries of six 

hydrologic regions were used for stratification and reporting. 

Major findings from the assessment are:

• Estimates of total soil organic carbon (SOC) storage in 

boreal and arctic regions in Alaska, derived from both 

field observations and model simulations, ranged from 

31 to 72 petagrams of carbon (PgC) from 1950 through 

2009. In ecosystems with permafrost, the mean activelayer thickness (ALT; the maximum annual thaw 

depth) ranged between 76 and 84 centimeters (cm) 

from surface-derived field data; model simulations 

indicated that mean ALT was 86 cm.

• A conceptual model of soil susceptibility to climate 

change indicated that the Arctic LCC and Western 

Alaska LCC lowland shrub tundra ecotypes are highly 

susceptible to climate change because of large frozen 

and unfrozen SOC stocks potentially available for 

loss by decomposition. 
2 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

• Although there is a high variability in fire regime 

across interior Alaska, fire frequency, severity, and area 

burned have increased in recent years, and the trend 

was projected to continue for the rest of the century 

across most of the regions and most of the climate 

scenarios, with the boreal region projected to see the 

highest increase in fire activities. Correspondingly, in 

the boreal region late successional vegetation, such as 

spruce forest, was projected to decline, whereas earlyto mid-successional vegetation, such as deciduous 

forest, was projected to increase. In tundra regions, 

shrub tundra was generally projected to increase 

and graminoid tundra to decrease.

• During the historical period of this assessment 

(1950–2009), upland ecosystems in Alaska were, on 

average, an overall carbon sink across the State of 

5.0 teragrams of carbon per year (TgC/yr). However, 

the boreal region of the State has been a carbon 

source, losing 5.1 TgC/yr as the result of increased 

fire activity in recent decades. The overall carbon 

sink was projected to increase to 14.7 to 34.6 TgC/yr 

during the projection period (2010–2099).

• Perhumid coastal rainforest watersheds in southeast 

Alaska were net carbon sinks of an average 142 grams 

of carbon per square meter per year (gC/m2/yr) from 

2006 through 2009. The non-fire-prone, cool, forested 

region is expected to remain a stable carbon sink, and 

potentially increase this sink strength in the future.

• During the historical period (1950–2009), wetland 

ecosystems in Alaska were, on average, an overall 

carbon source at 1.3 TgC/yr across the State. 

Net biogenic methane emissions increased from 

27.93 teragrams of carbon dioxide equivalent per 

year (TgCO2-eq/yr) in the first decade (1950–1959) 

to 30.93 TgCO2-eq/yr in the last decade (2000–2009). 

The combined global warming potential (GWP) of 

both carbon dioxide (CO2) and methane (CH4 ) was 

33 TgCO2-eq/yr over the historical period. Wetland 

ecosystems in Alaska were projected to be a net carbon 

sink ranging from 3.0 to 6.8 TgC/yr and an increased 

methane source ranging from 37 to 90 TgCO2-eq/yr 

by 2099, yielding a GWP of 17 to 64 TgCO2-eq/yr.

• Temperate forests in south-central and southeast 

coastal Alaska store 1,018 teragrams of carbon (TgC) 

in live and dead tree biomass. If managed with the 

current management plan (with forest harvesting) and 

assuming no climate change, the forest carbon could 

increase by 1 percent by the end of the century. Forest 

carbon could increase by 8 percent and 27 percent 

under the scenarios of climate change with and 

without management, respectively.

• The total net carbon flux (coastal export plus CO2

emissions from rivers and lakes minus burial in lake 

sediments) from inland waters of Alaska was approximately 41.2 TgC/yr (ranging from 30 to 60 TgC/yr 

in terms of 5th and 95th percentiles). Total carbon 

yield based on total land surface area was 27 gC/m2/yr 

(uncertainty of 20 to 40 gC/m2/yr).

• Putting it all together, we estimate that between 

1950 and 2009 the upland and wetland ecosystems 

of the State sequestered an average of 3.7 TgC/yr, 

which is almost 2 percent of the net primary productivity (NPP) of the upland and wetland ecosystems. 

We estimate that inland aquatic ecosystems of Alaska 

lost 41.2 TgC/yr, or about 17 percent of upland and 

wetland NPP. We estimate that the greenhouse gas 

(GHG) forcing potential of upland and wetland 

ecosystems of Alaska was 17.3 TgCO2-eq/yr 

during the historical period. 

• Carbon sequestration of upland and wetland 

ecosystems of Alaska in the projection period 

(2010–2099) would increase substantially to 

18.2 to 34.4 TgC/yr, primarily because of an 

increase in NPP of 12 to 30 percent associated 

with responses to rising atmospheric CO2, increased 

nitrogen cycling, and longer growing seasons. 

Although carbon emissions to the atmosphere from 

wildfire were projected to increase substantially 

for all of the projected climates, the increases 

in NPP would more than compensate for those 

losses. Our analysis indicates that upland and 

wetland ecosystems would be sinks for GHGs 

for all simulations during the projection period.

Limitations of the Assessment Report

The known limitations of the assessment report include 

the following: (1) So far as substantial progress was made 

in this assessment in terms of findings from data, remote 

sensing, and model simulations for Alaska ecosystems, the 

availability of field data is severely limited relative to the 

conterminous United States in all but the coastal forest region 

(North Pacific LCC). This limitation, and implications to 

uncertainties in the results, applies to analyses of permafrost 

and soil carbon in chapter 3, analyses of upland and wetland 

carbon and CH4 in chapters 6, 7, and 9, and analysis of 

aquatic CO2 in chapter 8. (2) The analysis of future forest 

carbon projections (chapter 5) in the temperate coastal 

Alaska region did not include all major management and 

climate change scenarios. (3) The estimates of terrestrial 

ecosystem carbon stocks and fluxes were based on a static 

land-cover distribution. Land-cover changes associated 

with wildfire disturbances that were reported in chapter 2 
Executive Summary 3

were not represented in the model simulations conducted 

in chapters 6 and 7. Instead, the effects of fire disturbances 

on carbon and CH4 were simulated internally by the 

biogeochemical modeling. (4) Similarly, CH4 emission was 

simulated based on one wetland distribution map. Given the 

high sensitivity of modeled CH4 emission to the inundation 

area, the current assessment did not quantify the uncertainty 

associated with wetland mapping. (5) The assessment 

included wildfires as the major disturbance regime in 

assessing its effects on carbon and CH4 dynamics in boreal 

and arctic regions of Alaska. However, effects of other major 

disturbances, such as forest insects in temperate and boreal 

regions, and thermokarst disturbances associated with ice-rich 

permafrost thaw in lowland boreal and arctic regions, were not 

included in the assessment. (6) The process-based models used 

in this study, although extensively evaluated in this assessment 

and in previous studies, have substantial conceptual and 

parameterization uncertainties. (7) The study of inland 

aquatic ecosystems was not integrated with that of upland 

and wetland ecosystems in a seamless analysis, which likely 

would compromise the estimates of heterotrophic respiration 

because losses of carbon to aquatic ecosystems from upstream 

ecosystems are not taken into account. 

With regard to future assessments, the technical needs 

for reduction of uncertainties present in this assessment will 

require enhancements in observation systems, research on 

landscape dynamics, process-based research, and modeling 

research. Key enhancements in observation systems include 

forest inventory measurements in interior Alaska, CO2 concentration measurements in large lakes, and CH4

 emissions from 

lakes and wetlands. Key enhancements in landscape dynamics 

include improved regional datasets on vegetation dynamics, 

lake dynamics, insect disturbance, and thermokarst disturbance. 

Key enhancements in process-based research include improved 

understanding of the transfer of carbon between terrestrial and 

inland aquatic ecosystems, of CH4 dynamics of inland aquatic 

ecosystems, and of controls over insect and thermokarst disturbance. Finally, key enhancements in modeling research include 

the development of models that can treat terrestrial-aquatic 

carbon linkages as an integrated system, improved modeling of 

wetland and lake CO2 and CH4 dynamics, and the prognostic 

modeling of insect and thermokarst disturbance and their impacts 

on carbon dynamics. Although there are substantial uncertainties 

in our analyses, the analyses themselves represent state-of-the-art 

science, and this assessment provides information for priorities 

in reducing uncertainties that should improve future assessments. 

Chapter 1. Introduction

By A. David McGuire,1 T. Scott Rupp,2 Tom Kurkowski,2 and Sarah Stackpoole3

1

U.S. Geological Survey, Fairbanks, Alaska.

2

University of Alaska-Fairbanks, Fairbanks, Alaska.

3

U.S. Geological Survey, Denver, Colo.

1.1. Requirements

This report is the result of an assessment focused on 

ecosystems of the State of Alaska and conducted by an interagency and interdisciplinary team composed of scientists from 

the U.S. Geological Survey (USGS), the U.S. Department of 

Agriculture (USDA) Forest Service, and the University of 

Alaska-Fairbanks. The reporting of the assessment results 

partially fulfills requirements set forth by the U.S. Congress 

through the Energy Independence and Security Act (EISA) of 

2007 for a national carbon sequestration and greenhouse-gas 

(GHG) flux assessment. The national assessment has been 

completed for the conterminous United States, with results 

provided in three separate regional reports (Zhu and others 

2011; Zhu and Reed, 2012, 2014). The main outcomes of this 

Alaska assessment include (1) estimates of the amount of 

carbon stored in ecosystems (such as forests and wetlands), 

(2) estimates of the capacity of ecosystems to sequester 

carbon, (3) estimates of the rate of GHG fluxes in and out of 

the ecosystems, and (4) evaluation of the effects of processes 

or driving forces that control ecosystem carbon balance 

and GHG fluxes. Climate change, ecosystem disturbances, 

wildfire, land use change, and land management represent 

the major driving forces, but their relative effects on an 

ecosystem’s potential for carbon sequestration vary regionally within Alaska. Information derived from the assessment 

is intended to inform mitigation and adaptation policies and 

land management decisions. This assessment also adds to our 

scientific understanding of the effects of environmental change 

on high-latitude ecosystem processes. 

The relative importance of driving forces that affect 

carbon storage and other ecosystem services vary regionally within Alaska (Wolken and others, 2011). For example, 

ongoing warming in arctic and boreal regions of Alaska, 

which influences ecosystem disturbances such as wildfire, 

insect outbreaks, and permafrost degradation, has the potential 

to substantially alter (1) the exchange of carbon dioxide (CO2) 

and methane (CH4 ) between ecosystems and the atmosphere 

and (2) the overall ecosystem carbon balance (Kurz and 

others, 2008; McGuire and others, 2009, 2010; Hayes and 

others, 2012; Yuan and others, 2012). The maritime region 

of southern and southeastern Alaska features dense forest 

cover and active forest management and other land uses such 

as recreation and urban centers (Wolken and others, 2011). 

Forest harvesting and changes in forest management policies 

have had profound effects on age, composition, carbon stock, 

and productivity of the temperate moist forests and forested 

wetlands in southeast Alaska (Leighty and others, 2006). Thus, 

the dynamics of ecosystem carbon balance and CO2

 and CH4

exchange of arctic, boreal, and maritime regions of Alaska in 

response to changes in major driving factors are the focus of 

this assessment. Arctic tundra, alpine tundra, boreal forests, 

maritime forests, surface waters (rivers and lakes), and arctic, 

boreal, and maritime wetlands are the main ecosystem types 

considered in this assessment.

To support the outcomes of the assessment for the entire 

State of Alaska, the assessors sought to address questions 

within regions of Alaska. These questions include (1) what are 

the magnitudes of carbon pools and fluxes of soil, biomass, 

and surface waters for different regions of Alaska?; (2) how 

are changes in fire regime, vegetation distribution, permafrost dynamics, and forest management influencing carbon 

balance in different regions of Alaska?; and (3) how might 

estimated sources and sinks of CO2 and CH4 of arctic, boreal, 

and maritime ecosystems change in response to projected 

changes in climate, fire regime, permafrost dynamics, and 

forest management? 

Unlike the rest of the United States, much of Alaska has 

not traditionally been included in various resource inventories, nor has the State previously been included in any major 

national carbon and greenhouse inventory reports. The lack 

of field data as input into assessment methods, the diverse land 

cover, and the rapid changes in driving factors were a challenge to the assessors and necessitated the use of methods and 

models (introduced below) that are different from those used 

for the conterminous United States.
6 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

1.2. Geography

1.2.1. Reporting Regions for the Inland Waters’ 

Component of the Assessment 

The reporting regions for the inland waters’ component 

of this assessment are based on the six main hydrologic units 

of Alaska: the Arctic Slope (called North Slope in this report), 

Northwest, Yukon, Southwest, South-Central, and Southeast 

(Seaber and others, 1987). These regions were chosen as the 

reporting units because, unlike the Alaska Landscape Conservation Cooperatives used in the terrestrial component of this report 

as described below, the hydrologic units have boundaries that 

coincide with natural drainage areas for rivers within the State, 

as hydrologic data from these units were important for developing estimates on inland water carbon fluxes. Descriptions and 

a map of the regional hydrologic units are presented in chapter 8. 

1.2.2. Reporting Regions for the Terrestrial 

Component of the Assessment

The five reporting regions for the terrestrial component of this assessment are based on the four large terrestrial 

Landscape Conservation Cooperatives (LCCs) in Alaska: 

(1) the Arctic LCC, (2) the Western Alaska LCC, (3) the 

Northwest Boreal LCC (split into northern and southern 

reporting regions), and (4) the North Pacific LCC (fig. 1.1). 

These regions were chosen so that the results of the terrestrial 

component of the assessment could inform regional consortia 

of natural resource agencies, which have been organized into 

these LCCs. The Northwest Boreal LCC was split into two 

reporting regions because the fire regime is quite different 

between the northern and southern parts of this LCC, which 

are separated by the Alaska Range. The boundaries of 

these LCCs are based on several sources discussed below, 

including six level II U.S. Environmental Protection Agency 

(EPA) ecoregions in Alaska (Gallant and others, 1995), 

which are derived from the ecoregions of Omernik (1987; 

http://archive.epa.gov/wed/ecoregions/web/html/na_eco.html), 

listed from north to south: the Alaska Tundra, Brooks Range 

Tundra, Alaska Boreal Interior, Taiga Cordillera, Boreal 

Cordillera, and Marine West Coast Forest Ecoregions 

(fig. 1.2). The following sections provide a brief description 

of each LCC, list the level III ecoregions, and provide links to 

additional details for each LCC region. A detailed description 

of each level III ecoregion, summarizing the climate, physiography, hydrology, and vegetation, can be found in Gallant 

and others (1995). This geographic characterization provides a 

foundation for understanding the differences in carbon sequestration and fluxes among LCCs and ecoregions in Alaska. 

The LCCs’ geographic areas were developed by a team 

of U.S. Fish and Wildlife Service and U.S. Geological Survey 

scientists and experts by integrating several data sources 

Arctic LCC

Western Alaska LCC

Northwest Boreal LCC

 North

 South

North Pacific LCC

Aleutian and Bering Sea

 Islands LCC (unreported)

LCC regions

EXPLANATION

Figure 1–1

N

0 200 400 KILOMETERS

0 200 400 MILES

Figure 1.1. Reporting regions considered in this assessment; the regions were based on the boundaries 

of the Arctic, Western Alaska, Northwest Boreal, and North Pacific Landscape Conservation Cooperatives 

(LCCs) in Alaska. The land area within the Aleutian and Bering Sea Islands LCC (identified as unreported) was 

not considered because the models used in this study are poorly suited to represent ecosystem dynamics 

in this unique region. The Northwest Boreal LCC was divided into northern and southern reporting regions 

owing to differences in fire regime on either side of the Alaska Range.
Chapter 1 7

(U.S. Fish and Wildlife Service, 2010). First, Bird Conservation Regions (BCRs) were incorporated; they are biologically 

based units used by long-term partners to facilitate conservation planning and design at landscape scales. To account 

for aquatic species’ needs, the Freshwater Ecoregions of the 

World, which is the same framework adopted by the National 

Fish Habitat Action Plan (NFHAP), was incorporated. To 

account for terrestrial species’ needs, Omernik’s level II 

and other existing ecological units were used (U.S. Fish and 

Wildlife Service, 2010). The resulting geographic framework 

identified large regions that crossed State and Federal administrative boundaries. In most geographic areas, the boundaries of key partnerships were left intact to preserve existing 

conservation and science capacities. 

1.2.2.1. Arctic Landscape Conservation 

Cooperative and Associated Ecoregions

The Arctic LCC (www.arcticlcc.org) region includes 

the Arctic Plains and Mountains Bird Conservation Region 

(BCR), which spans northern Alaska and Canada. The Arctic 

LCC encompasses three level III ecoregions: (1) the rugged 

slopes and valleys of the Brooks Range ecoregion, (2) the 

rolling hills and plateaus of the Arctic Foothills ecoregion, 

and (3) the broad, flat Arctic Coastal Plain ecoregion, which 

is characterized by extensive wetlands and numerous water 

bodies. The Arctic LCC has arctic climate conditions, with 

very low mean annual temperatures and very low annual 

precipitation. It is essentially a treeless region dominated by 

herbaceous vegetation, although shrub vegetation is found 

in better drained areas where soil moisture is not limiting. 

The region is underlain by continuous permafrost. Wildfire is 

constrained to mesic sites and, although common, represents 

a minimal amount of the area burned annually statewide.

1.2.2.2. Western Alaska Landscape Conservation 

Cooperative and Associated Ecoregions

The Western Alaska LCC (https://westernalaskalcc.org/

SitePages/Western Alaska LCC.aspx) includes the Western 

Alaska Bird Conservation Region as well as small portions 

of the Northwestern Pacific Rainforest and Northwest 

Interior Forest BCRs. The LCC spans over 1,200 kilometers 

(km) from north to south and includes a wide diversity of 

terrain. Landscapes include the permafrost-dominated tundra 

of the Seward Peninsula, complex delta systems of the Yukon 

and Kuskokwim Rivers, abundant volcanoes of the Alaska 

Peninsula, and transitional forests of permafrost-free Kodiak 

Island. The Western Alaska LCC includes portions of seven 

level III ecoregions: (1) the Subarctic Coastal Plains, (2) the 

Seward Peninsula, (3) the Ahklun and Kilbuck Mountains, 

(4) the Bristol Bay-Nushagak Lowlands, (5) the Alaska 

Peninsula Mountains, (6) the Interior Forested Lowlands 

and Uplands, and (7) the Interior Bottomlands ecoregions. 

Figure 1.2. The six level II ecoregions for the State of Alaska from Gallant and others (1995). 

Alaska Tundra

Brooks Range Tundra

Alaska Boreal Interior

Taiga Cordillera

Boreal Cordillera

Marine West Coast Forest

Level II ecoregions

EXPLANATION

0 200 400 KILOMETERS

0 200 400 MILES

Figure 1–2

N
8 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The climate is transitional between maritime and continental influences, and temperature and precipitation are 

variable throughout the region. Most of the region is treeless 

and supports wet to mesic graminoid herbaceous vegetation. 

Some forest stands are present in valley bottom regions. 

Permafrost distribution is discontinuous and variable. Wildfires are common in the more mesic portions of the region 

and range in size from less than 1 to more than 1,000 square 

kilometers (km2).

1.2.2.3. Northwest Boreal Landscape Conservation

Cooperative and Associated Ecoregions

The Northwest Boreal LCC (http://nwblcc.org/) boundary 

closely follows that of the Northwestern Interior Forest Bird 

Conservation Region. The LCC falls within the boreal forest 

biome and includes south-central and interior Alaska and parts 

of Canada: most of the Yukon Territory, the northern portion 

of British Columbia, and a small part of the Northwest Territories. The area includes over 1.3 million km2

 and encompasses 

large portions of the Yukon, Kuskokwim, Susitna, and Copper 

River Basins. The Northwest Boreal LCC includes nine 

level III ecoregions: (1) the Interior Forested Lowlands and 

Uplands, (2) the Interior Highlands, (3) the Interior Bottomlands, (4) the Yukon Flats, (5) the Ogilvie Mountains, (6) the 

Cook Inlet, (7) the Alaska Range, (8) the Copper Plateau, and 

(9) the Wrangell Mountains ecoregions.

The Northwest Boreal LCC domain encompasses not 

only a very large land area but also significant gradients in 

climate, hydrology, and disturbance dynamics (particularly 

with respect to wildfire and insect outbreaks). For this reason, 

the Northwest Boreal LCC was divided into northern and 

southern subregions that reflect the substantial differences in 

climate (continental versus maritime influenced) and disturbance (wildfire versus insects) found on opposite sides of the 

Alaska Range; the division was based on an existing model’s 

specific subregion calibration related to the “Unified Ecoregions of Alaska” (Nowacki and others, 2003). For assessment 

reporting purposes throughout the remainder of this report, 

results are reported for Northwest Boreal LCC North and 

Northwest Boreal LCC South.

The Northwest Boreal LCC North has a discontinuous 

permafrost distribution that is highly variable. The region 

is influenced by a strong continental climate with seasonal 

temperature extremes and low precipitation. The region is 

primarily forested. Wildfire is common, and fires range in size 

from less than 1 to more than 3,000 km2

. 

The Northwest Boreal LCC South also has a discontinuous permafrost distribution that is highly variable. The region 

includes significant high-elevation mountainous terrain. The 

region is influenced by a maritime climate with variable but 

relatively high precipitation. The region is primarily forested. 

Wildfire is common but represents a relatively small proportion of the area burned annually statewide.

1.2.2.4. North Pacific Landscape Conservation 

Cooperative and Associated Ecoregions in Alaska

The North Pacific LCC (http://northpacificlcc.org/) 

encompasses the Northwest Pacific Rainforest Bird Conservation Region. The LCC includes the entire range of the 

Pacific Coastal Temperate Rainforest extending over 3,500 km 

from Alaska through British Columbia in Canada and into 

three States in the conterminous United States: Washington, 

Oregon, and northern California. For this assessment, the 

focus is on only the portion of the LCC within Alaska. In 

Alaska, the North Pacific LCC includes three level III ecoregions: (1) the Alaska Peninsula Mountains, (2) the Pacific 

Coastal Mountains, and (3) the Coastal Western HemlockSitka Spruce Forests ecoregions. 

The North Pacific LCC is characterized by complex topography with steep and rugged mountains and significant glaciers 

and ice fields. Its climate has a maritime influence with moderate 

temperatures and high but variable annual precipitation ranging 

from 510 to 3,900 millimeters (mm). Wildfires are rare, usually 

human caused, and restricted to sizes less than 0.4 km2. Through 

2014, about 452,000 acres of timber have been harvested within 

the national forest lands of this region (U.S. Department of 

Agriculture Forest Service, 2014). The region is generally free 

of permafrost. Forests of western hemlock (Tsuga heterophylla) 

and Sitka spruce (Picea sitchensis) along with western redcedar 

(Thuja plicata Donn ex D. Don) and yellow-cedar (Callitropsis 

nootkatensis (D. Don) D.P. Little; naming after Hennon and 

others, 2016) are widespread.

1.3. A Brief Overview of Previous 

Studies of Carbon Dynamics in Alaska

The historical carbon dynamics of ecosystems in Alaska 

have been studied at local, subregional, and regional scales. 

Methods of analysis included the use of observations and 

process-based models. This section summarizes recent 

syntheses of data on historical carbon storage and dynamics 

by methodology. 

There have been several observational syntheses of 

data on soil carbon storage in Alaska at the statewide level. 

These include studies focused on understanding controls over 

carbon storage across the State, as well as studies focused 

on estimating the magnitude of carbon storage in the State. 

Johnson and others (2011) conducted a first-order assessment 

of the spatial distributions of soil carbon in Alaska from a 

soil carbon database compiled to better understand controls 

over soil carbon storage across the State. Temperature and 

landform type were the dominant controls on soil carbon 

distribution for selected ecoregions. Mean soil carbon pools 

(to a depth of 1 meter [m]) varied by threefold, sevenfold, 

and tenfold across ecoregion, landform, and ecosystem types, 

respectively. Climate interactions with landform type and soil 
Chapter 1 9

carbon were greatest in the uplands. For upland soil, there 

was a sixfold nonlinear increase in soil carbon with a decrease 

in latitude (that is, with an increasing temperature gradient), 

where soil carbon was lowest in the intermontane boreal 

ecoregion compared to the arctic tundra and coastal rainforest 

regions. Additional factors that appeared to be related to soil 

carbon distribution within ecoregions included stand age, 

aspect (direction faced by a slope), and permafrost presence or 

absence in black spruce (Picea mariana (Mill.) Britton, Sterns 

& Poggenb.) stands. Johnson and others (2011) estimated 

that soil carbon storage to a depth of 1 m in Alaska soils 

ranged from 14,000 grams of carbon per square meter (gC/m2) 

in intermontane boreal forests to 25,000 gC/m2 in coastal 

rainforests to 44,000 gC/m2 in arctic tundra. Bliss and Maursetter (2010) estimated soil organic carbon in Alaska totaling 

48 petagrams of carbon (PgC) and used much of the same 

information as Johnson and others (2011). Using a geostatistical approach, Mishra and Riley (2012) estimated that Alaska 

soils stored 77 PgC, of which 61 percent was in the active 

layer, 27 percent was in permafrost, and 12 percent was in 

nonpermafrost soils. The differences among these estimates 

indicate that substantial gaps exist in the soil carbon databases 

used to develop these estimates (see Johnson and others, 2011).

Analyses of vegetation carbon storage in Alaska have 

focused primarily on forests. Yarie and Billings (2002) estimated that aboveground carbon storage was approximately 

400 teragrams of carbon (TgC) (averaging about 2,400 gC/m2) 

in the boreal forests of the State; their work was based on 

several one-time inventories between 1963 and 1994. Leighty 

and others (2006) and Barrett (2014) estimated that aboveground carbon storage in trees was between 420 and 530 TgC 

in the Tongass National Forest of southeast Alaska, with 

another 120 TgC in belowground roots. Barrett and Christensen (2011) estimated that coastal forests of southeast 

and south-central Alaska stored approximately 650 TgC in 

aboveground trees. Statewide, these estimates indicate that 

aboveground carbon of forests in Alaska was likely greater 

than 1,000 TgC in 2009.

Some observational syntheses of the exchange of carbon 

with the atmosphere in Alaska have been based largely on 

scaling of chamber and eddy covariance measurements of CO2

and CH4 exchange. Manual chambers, automatic chambers, 

and eddy covariance towers use infrared gas analyzers that 

measure CO2 concentrations. For CH4, the field technology is 

less developed and has relied on gas sample collection in the 

field, with laboratory estimates of CH4 concentrations made 

by using gas chromatographs. Eddy covariance measurement 

systems have recently been developed that allow continuous 

direct CH4 concentration estimates in the field, and these are 

starting to be more commonly used to measure CH4 exchange. 

In an observational synthesis for arctic tundra of North 

America, which was dominated by measurements made in 

Alaska, McGuire and others (2012) suggested that arctic 

tundra in Alaska was an annual source of CO2 to the atmosphere between 1990 and 2009 of 10 ±20 gC/m2

 and that the 

release of CO2 in winter more than offset the uptake of CO2

during the summer. Although the synthesis suggested that 

arctic tundra in Alaska was a source of CO2 to the atmosphere, 

the uncertainties include the possibility that it could have been 

an annual sink of CO2 between 1990 and 2009. The synthesis 

also suggested that arctic tundra in Alaska was an annual source

of CH4 to the atmosphere of 5.4 ± 3.5 grams of carbon in methane

per square meter (gC-CH4/m2) between 1990 and 2009. 

In a recent synthesis of eddy covariance studies in 

Alaska, Ueyama and others (2013) evaluated factors influencing CO2

 exchange in eight Arctic tundra and five boreal 

ecosystems in the State and found that all of the boreal and 

seven of the eight Arctic tundra ecosystems acted as CO2 sinks 

during the growing season. The analysis revealed that there 

was a high sensitivity of the sink strength in tundra ecosystems 

to growing season length, whereas time since fire disturbance 

played a major role in the sink strength of boreal ecosystems. 

Thus, the analysis suggested that tundra ecosystems might 

increase sink strength during the growing season in response 

to warming, but that an increasing fire frequency would likely 

decrease sink strength in boreal ecosystems of Alaska.

Some syntheses of the exchange of carbon with the 

atmosphere within Alaska have been based on the analysis 

of forest inventory data. The repeated inventory of forests in 

Alaska has largely been focused on Tongass National Forest 

in southeast Alaska and Chugach National Forest in southcentral Alaska, although some one-time inventories have been 

conducted in other parts of Alaska. The most recent analyses 

by the Forest Inventory and Analysis (FIA) program of the 

USDA Forest Service indicates that forest carbon stocks in 

southeast and south-central Alaska increased by approximately 

6 ± 3 teragrams of carbon per year (TgC/yr) between 1990 

and 2013 (U.S. Department of Agriculture Forest Service, 

2015). On the basis of the analysis of one-time inventories 

conducted between 1963 and 1987 outside of maritime coastal 

Alaska, Yarie and Billings (2002) estimated that boreal forests 

in Alaska were an annual sink for 9.65 TgC in the last few 

decades of the last century. 

Previous research regarding carbon fluxes of rivers in 

Alaska has focused on the main stem Yukon River and its 

tributaries, large rivers that drain the North Slope, and small 

streams in the southeast. Yukon River total carbon exports 

have been estimated to be 7.8 TgC/yr, with 70 percent of the 

total carbon flux as dissolved inorganic carbon (Striegl and 

others, 2007). For the North Slope, estimates of organic carbon 

export from the Sagavanirktok, Kuparuk, and Colville Rivers 

were approximately 0.3 TgC/yr. The Yukon River Basin has 

an area of 831,000 square kilometers (km2

), and the three 

North Slope rivers sum to a total drainage area of 80,000 km2, 

producing yields of 9.4 grams of carbon per square meter 

per year (gC/m2/yr) and 2.5 gC/m2/yr, respectively. Rates of 

stream organic carbon exports for the southeastern regions 

of Alaska, mainly draining the coastal temperate rain forest, 

have been estimated as being between 10.5 and 30 gC/m2/yr 

(D’Amore and others, 2015).
10 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Riverine CO2 fluxes to the atmosphere have also been 

documented in the Yukon River Basin at 7.7 TgC/yr, or 

9.2 gC/m2/yr. These emission rates are roughly equivalent 

to total lateral carbon transport. Estimates of CO2 emissions 

for rivers in the Kuparuk River Basin ranged between 

0.02 gC/m2/yr for the time period of 2001 to 2013 (Cory and 

others, 2014) and 1.7 gC/m2/yr for 1994 to 1996 (Hobbie 

and Kling, 2014). Lake CO2 fluxes in the North Slope 

region of Alaska have been documented as being between 

0.4 and 1.1 gC/m2/yr (Cory and others, 2014; Hobbie and 

Kling, 2014). Many studies of glacial lakes, with objectives that 

included documenting long-term climate change (Anderson and 

others, 2001; Yu and others, 2008) and its effects on wildlife 

population dynamics (Barto, 2004; Rogers and others, 2013), 

are based on sediment analyses that provide lake carbon burial 

rates. From these studies and many others, estimates for carbon 

burial in glacial lakes throughout the State range between 

0.62 and 30 gC/m2/yr (for example, Anderson and others, 

2001; Yu and others, 2008; Rogers and others, 2013). Carbon 

burial estimates for thermokarst lakes in the interior and 

North Slope regions of Alaska are between 2 and 23 gC/m2/yr 

(Lynch and others, 2002; Mann and others, 2002). 

Comparisons among Terrestrial Biosphere Models 

(TBMs) indicate that the models do not agree about whether 

the North Slope of Alaska is a source or a sink for atmospheric CO2

. An analysis of model output from numerous 

TBMs indicates that net ecosystem exchange of CO2 with the 

atmosphere is –10 ±190 gC/m2/yr (the minus sign indicates a 

sink; Fisher and others, 2014). It is important to note that most 

of the TBMs in the model comparison represent tundra on the 

North Slope of Alaska as equivalent to temperate grassland, 

which is not physiologically representative of the wetland, 

graminoid, and shrub tundra that occurs on the North Slope. 

The analysis of TBM output results in inflated estimates of 

uncertainty about carbon dynamics in Alaska because these 

TBMs generally do not include processes such as permafrost 

dynamics and wildfire that are relevant to the region. 

An analysis of tundra carbon dynamics in the Arctic 

Ocean Drainage Basin suggested that arctic tundra has been 

a weakening sink for atmospheric CO2

 in recent decades 

because of the effects of climate in enhancing decomposition 

even though vegetation carbon of tundra was estimated to 

be increasing (McGuire and others, 2010; Hayes and others, 

2011, 2014). An analysis by Euskirchen and others (2009) 

suggested that vegetation carbon in tundra of the North Slope 

of Alaska will continue to increase in the 21st century because 

of longer growing seasons and increased soil nitrogen availability leading to more leaf area, but that soil carbon losses 

will greatly outpace the gains in vegetation carbon. 

An analysis of boreal forest carbon dynamics in the Arctic 

Ocean Drainage Basin, which is dominated by boreal forest, 

suggested that boreal forest was transitioning from being a sink 

to being a source because of the combination of both enhanced 

decomposition associated with permafrost thaw and an increase 

in wildfires throughout the region (McGuire and others, 2010; 

Hayes and others, 2011, 2014). A process-based model analysis 

of boreal forest carbon dynamics in the Yukon River Basin in 

Alaska and Canada is consistent with the panboreal analysis, 

as it indicated that soil carbon stocks would have increased by 

158 TgC between 1960 and 2006 if the basin had not undergone warming and changes in fire regime (Yuan and others, 

2012). Together, these analyses of the results of process-based 

models suggest that there is a complex interplay between the 

effects of climate and wildfire on vegetation and soil carbon 

dynamics, and that both climate and wildfire can influence 

these dynamics through effects on permafrost. 

Similar to the analysis of CO2 exchange, comparisons 

among TBMs indicate that the models do not agree about 

whether the North Slope of Alaska is a source or a sink for 

atmospheric CH4. An analysis of model output from several 

CH4 models indicated that emissions were estimated to be 

2.52 ±4.02 grams of methane per square meter per year 

(gCH4 /m2/yr) in the early 2000s (Fisher and others, 2014). 

Another analysis indicated that estimated emissions of CH4

between 1980 and 1996 were 4.01 gCH4 /m2/yr in tundra 

of the North Slope of Alaska, 2.00 gCH4 /m2/yr in interior 

Alaska, and 0.99 gCH4/m2/yr in southern Alaska (Zhuang 

and others, 2007). Zhuang and others (2007) estimated that 

combined emissions for Alaska between 1980 and 1996 were 

approximately 3 teragrams of methane per year (TgCH4 /yr), 

which is somewhat higher than the estimate of 2.1 teragrams 

of methane (TgCH4) for Alaska from May to September 2012 

that was based on data from an aircraft sampling campaign 

(Chang and others, 2014). The analysis by Zhuang and others 

(2007) further estimated that climate change would cause 

CH4 emissions to increase by 58 percent in northern Alaska, 

77 percent in interior Alaska, and 153 percent in southern 

Alaska by the end of the 21st century. Sensitivity analysis in 

the study indicated that soil temperature and depth to water 

table were the two most important drivers influencing changes 

in emissions. This regional analysis suggests that the response 

of methane emissions depends on interactions between 

changes in projected temperature and soil hydrology. 

In summary, analyses to date have indicated substantial 

variability in estimates of carbon pools and carbon fluxes 

in Alaska. Estimates of soil carbon storage varied from 

48 to 77 PgC. Estimates of total vegetation carbon at the 

statewide scale have not been formally assessed; however, 

estimates of vegetation carbon storage conducted for forests 

at the regional scale indicated that aboveground carbon is 

likely greater than 1,000 TgC at the statewide scale. Analyses 

of carbon fluxes in tundra and boreal forests in Alaska 

indicate substantial uncertainty as these ecosystems have 

been estimated to be both sources and sinks for atmospheric 

CO2 in different studies. There is general agreement that 

maritime forests of Alaska are a sink for atmospheric CO2

of less than 10 TgC/yr. Although analyses indicate that there 

are substantive fluxes of carbon from inland waters in some 

hydrologic basins of Alaska, these fluxes have not yet been 

estimated for all inland waters in Alaska. Finally, analyses 

of CH4 emissions for Alaska are quite variable and indicate 

substantial uncertainty. 
Chapter 1 11

1.4. Overall Methodology

The methodology developed for this assessment was 

designed to produce a scientific synthesis of carbon dynamics 

in the State that would be useful both to stakeholders in Alaska 

and to State, national, and international decision makers. This 

goal required the organization of input data for the State and 

technical components to make use of these data (fig. 1.3). 

The technical components include (1) the organization of 

input data for models and data syntheses; (2) modeling of 

processes in biogeography, fire regime, permafrost, and 

hydrologic dynamics; (3) syntheses of carbon dynamics via 

biogeochemical modeling for upland and wetland ecosystems, 

empirical syntheses of carbon cycling for ecosystems in 

south-central and southeast coastal Alaska, and syntheses of 

carbon fluxes for surface waters of Alaska; and (4) analysis, 

synthesis, report development, data distribution, and communication of results to stakeholders. The assessment is prepared 

for a historical period (1950–2009) and a future projection 

period (2010–2099).

Input data were organized for soil carbon; soil texture; 

permafrost distribution; active-layer thickness; vegetation 

carbon; historical forest harvest; future forest management; 

land-cover distribution; fire disturbance; wetland and surfacewater distribution; historical and future climate; upland and 

wetland biogeochemistry; and the transport, emission, and 

burial of aquatic carbon. The historical and future climate 

and fire disturbance datasets are described in chapter 2; data 

syntheses for soil carbon, permafrost, and soil texture are 

described in chapter 3; biogeochemical cycling, vegetation 

biomass, historical forest harvest, and future forest management scenarios for forests in south-central and southeast Alaska 

are described in chapters 4 and 5; upland extent and biogeochemistry are described in chapter 6; wetland extent and biogeochemistry are described in chapter 7; and the transport, emission, and burial of aquatic carbon are described in chapter 8.

The assessment uses the Alaska Frame-Based Ecosystem 

Code (ALFRESCO; Rupp and others, 2000, 2002) to simulate changes in fire regime and vegetation distribution from 

2010 through 2099. ALFRESCO was calibrated on the basis 

of historical data about fire occurrence for Alaska from 

1950 through 2009 (see chapter 2 for more details). The 

contemporary spatial distribution of permafrost was estimated by two different empirical approaches (see chapter 3). 

The empirical estimates were then used to validate permafrost simulation for the historical period (1950–2009) by the 

Dynamic Organic Soil version of the Terrestrial Ecosystem 

Model (DOS-TEM; Yi, Manies, and others, 2009; Yi, 

McGuire, and others, 2009; Yi and others, 2010; Yuan 

and others, 2012; Genet and others, 2013).

Figure 1.3. The general methodology used in the assessment of carbon storage and fluxes in Alaska. Input data were organized to 

provide information used to assess historical and future changes in vegetation, fire, permafrost, and hydrologic dynamics. The data 

on these changes were then used to assess carbon dynamics of inland aquatic ecosystems for the historical period (1950–2009) and 

carbon dynamics of upland and wetland ecosystems for the historical period and the future projection period (2010–2099). The results 

of these carbon assessments have been synthesized and are summarized in this report. The data from this assessment are being made 

available for distribution. 

Input data

Input data organized

Analysis components

Assessment components

Fire dynamics

Vegetation dynamics

Figure 1-3.

Hydrologic dynamics

Permafrost dynamics

Soil carbon

Soil texture

Permafrost distribution

Active-layer thickness

Vegetation carbon

Historical forest harvest

Future forest management

Land-cover distribution

Fire disturbance

Wetland distribution

Surface-water distribution

Historical climate

Future climate

Upland biogeochemistry

Wetland biogeochemistry

Lateral carbon transport

Surface-water carbon emissions

Burial of carbon in surface water

Upland and wetland

biogeochemical

modeling

Surface-water

carbon analysis

Analyses of carbon 

in southeast Alaska

Analysis,

synthesis,

report development,

data distribution,

communication
12 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The DOS-TEM model used input data on soil texture, 

land cover, historical climate, historical fire, historical 

forest harvest, and model projections of future climate, fire 

disturbance, and forest management to estimate changes 

in ecosystem pools and fluxes for the two time periods for 

upland and wetland ecosystems. In addition, these upland and 

wetland estimates were also separately evaluated as described 

in chapters 6 and 7. The Methane Dynamics Module of the 

Terrestrial Ecosystem Model (MDM-TEM; Zhuang and 

others, 2004, 2007) was used to estimate methane consumption in upland ecosystems (chapter 6) and both methane 

consumption and emissions in wetland ecosystems (chapter 7). 

An empirical model was used to estimate contemporary net 

ecosystem carbon balance of forest ecosystems in southeast 

Alaska (chapter 4). A Forest Vegetation Simulator (FVS) 

model was also used, together with FIA forest inventory data, 

for estimating contemporary and future forest carbon balance 

in relation to management actions in south-central and southeast coastal Alaska (chapter 5). 

A statewide map of lake area (U.S. Geological Survey, 

2012); modeled discharge, velocity and width values for 

streams (Kost and others, 2002); carbon concentration in 

surface waters; and carbon burial rates in lakes were assimilated into empirical models to estimate regional and statewide 

estimates of carbon transport, emission, and burial in aquatic 

ecosystems of Alaska (chapter 8).

1.5. Land-Cover Maps Used in 

the Assessment

To make comparisons between various aspects of this 

assessment, a common land-cover classification was required; 

this section describes the development of the common landcover classification used for the assessment from existing 

land-cover maps. Other datasets used in the assessment are 

described in various chapters, as indicated above. The definition of land-cover types in the common land-cover classification we developed for this assessment was primarily driven 

by the needs of ALFRESCO and DOS-TEM, which required 

highly aggregated land-cover types. Remote-sensing datasets 

defining land cover for Alaska and western Canada were used 

to develop more aggregated baseline vegetation input data as 

the spatial foundation for the assessment. These input datasets 

represented highly modified output originating from the North 

American Land Change Monitoring System 2005 (NALCMS 

2005) dataset (North American Land Change Monitoring 

System, 2010; for arctic and subarctic ecosystem types) and 

the National Land Cover Database (NLCD) 2001 (Homer 

and others, 2007; for coastal maritime ecosystem types). The 

NALCMS 2005 data were originally at a 250-m resolution and 

the NLCD at a 30-m resolution. The modified input data from 

both datasets were resampled to a consistent 1-km resolution 

to reduce the volume of data and meet modeling requirements. 

Several adjustments were made to the thematic classes to 

facilitate this assessment and to correspond with the capabilities of the methods used. To define land-cover types for the 

Arctic, Western Alaska, and Northwest Boreal LCCs, the 

NALCMS 2005 classes were adjusted to represent the general 

land-cover types of black spruce (Picea mariana (Mill.) 

Britton, Sterns & Poggenb.) forest, white spruce (Picea glauca

(Moench) Voss) forest, deciduous forest, shrub tundra, graminoid tundra, wetland tundra (including wet-sedge and tussock 

tundra), and heath tundra. Partitioning of coniferous forest into 

late successional spruce forest types was based on topographic 

position. Tundra land-cover types were partitioned primarily 

on the basis of ecoregion distribution and growing season 

temperature thresholds; the Circumpolar Arctic Vegetation 

Map (CAVM Team, 2003) was used to define the tree line. 

Heath tundra includes xeric ecosystems dominated by lichen 

crust and moss and was defined as high-elevation barren land 

not covered by snow or ice, as well as land included in the 

barren lichen-moss classes from the NALCMS 2005 classification. For this assessment, the NALCMS 2005 classes did 

not adequately represent the land-cover types of the maritime 

zone in the North Pacific LCC region of Alaska and in the 

vicinity of Kodiak Island. Therefore, NLCD 2001 classes were 

adjusted to represent land-cover types specific to the maritime zone: maritime upland forest, maritime wetland forest, 

maritime alder shrubland, and maritime fen. The correlation 

between the general NLCD classification and the maritime 

land-cover types was determined through spatial comparison 

with local vegetation maps. 

1.6. Organization of the Report

This report has an executive summary followed by nine 

chapters, which are briefly described below. Each chapter has 

its own references cited list. 

Chapter 1 (Introduction) describes the requirements of 

this assessment, the geography of Alaska, the previous understanding of carbon dynamics in Alaska, the overall methodology of the assessment, the strategy for the land-cover maps 

used in the assessment, and the organization of the report.

Chapter 2 (Climate Simulations, Land Cover, and Wildfire in Alaska) describes historical observations of climate and 

wildfire, the downscaling and development of climate data, 

and results from the application of ALFRESCO to simulate 

changes in vegetation distribution and wildfire from 2010 

through 2099. 
Chapter 1 13

Chapter 3 (Soil Carbon and Permafrost Estimates and 

Susceptibility to Climate Change in Alaska) reports on 

syntheses of data about soil carbon, permafrost (distribution 

and active-layer thickness), and soil texture for Alaska. A key 

feature of this chapter is the comparison of DOS-TEM outputs 

with these data syntheses.

Chapter 4 (Watershed Carbon Budgets in the Southeastern Alaskan Coastal Forest Region) reports results from 

an empirical model based on a synthesis of carbon cycling 

data from southeast Alaska. 

Chapter 5 (Forest Inventory-Based Analysis and Projections of Forest Carbon Stocks and Changes in Alaska Coastal 

Forests) reports results of outputs from the USDA Forest 

Service Forest Vegetation Simulator (FVS) for contemporary 

conditions and for future forest management scenarios. 

Chapter 6 (Terrestrial Carbon Modeling: Baseline and 

Projections in Upland Ecosystems of Alaska) reports on 

simulated changes in carbon pools and fluxes for the historical 

(1950–2009) and future projection (2010–2099) periods for 

upland terrestrial ecosystems of Alaska. 

Chapter 7 (Terrestrial Carbon Modeling: Baseline and 

Projections in Lowland Ecosystems of Alaska) reports on the 

simulated changes in carbon pools and fluxes for the historical 

(1950–2009) and future projection (2010–2099) periods for 

wetland terrestrial ecosystems of Alaska. 

Chapter 8 (Carbon Transport, Emission, and Burial from 

Inland Aquatic Ecosystems in Alaska) reports on contemporary estimates of the transport of carbon to coastal ecosystems, 

emissions of carbon from inland aquatic ecosystems to the 

atmosphere, and carbon burial in inland aquatic ecosystems 

based on syntheses of data for Alaska. 

Chapter 9 (Alaska Carbon Balance) reports on the 

integrated carbon balance of the entire State of Alaska for the 

historical (1950–2009) and future projection (2010–2099) 

periods based on a synthesis of estimates from the other 

chapters in this report. The carbon balance estimates at the 

regional scale can be found in chapters 6, 7, and 8. This 

chapter is intended as a summary for use by those interested 

in the role that the State of Alaska plays in national and 

international carbon budgets. 

1.7. Acknowledgments

For use of the North American Land Change Monitoring 

System 2005 (NALCMS 2005) dataset, we would like to 

acknowledge Natural Resources Canada, Instituto Nacional 

de Estadística y Geografía, the U.S. Geological Survey, and 

the Commission for Environmental Cooperation.

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0761(2007)017[0203:NEOCAC]2.0.CO;2.
Chapter 2. Climate Simulations, Land Cover, and Wildfire 

By T. Scott Rupp,1 Paul Duffy,2 Matthew Leonawicz,1 Michael Lindgren,1 Amy Breen,1 Tom Kurkowski,1

Angelica Floyd,1

 Alec Bennett,1 and Lena Krutikov1

1

University of Alaska-Fairbanks, Fairbanks, Alaska.

2

Neptune and Company, Inc., Fort Collins, Colo.

2.1. Highlights

• Climate models suggest a projected annual and 

seasonal increase in mean temperature throughout 

Alaska during the next 85 years. Warming has been 

projected to be greatest in the winter and spring 

and most pronounced in the northern and western 

regions of the State.

• Winter temperatures are projected to increase by as 

much as 8 degrees Celsius (°C) in the Arctic and 

Western Alaska Landscape Conservation Cooperatives 

(LCCs) by the end of this century.

• Wildfires burned an annual average of 3,791 square 

kilometers (km2) between 1950 and 2009 in Alaska. 

The inter-annual variability in the area that was 

burned was high—from as few as 10 km2 burned 

in 1961, 1964, and 1965 to as much as 27,071 km2

burned in 2004.

• Wildfire frequency and extent are projected to increase 

across most of the six future climate simulations used 

in this assessment and across the five LCC regions, 

with the Northwest Boreal LCC North projected to 

see the largest increase in fire activity.

• The areas of late successional boreal forest land 

cover are projected to decrease under almost all 

climate simulations, ranging from approximately 

8 to 44 percent; a concomitant increase in early 

successional deciduous forest land cover is 

also projected.

• Under most of the future climate simulations used in 

this assessment, the area of graminoid tundra land 

cover is projected to decrease whereas the area of 

shrub tundra land cover is projected to increase.

2.2. Introduction

As indicated in chapter 1, the ongoing warming trend 

of northern high-latitude regions, which influences vegetation 

distribution, ecosystem disturbances, and their interactions, 

has the potential to substantially alter the overall ecosystem 

carbon balance. Development of baseline and projected landcover and wildfire data as well as the driving climate projections

provide several of the primary spatial data foundations 

for this assessment. The simulations of future land-cover 

change and wildfire activity feed into other components of 

the assessment—primarily the simulation of carbon storage 

and greenhouse-gas (GHG) fluxes (see chapters 6 and 7).

Global temperature increases during the 20th century 

(Solomon and others, 2007) were amplified at high latitudes 

(Chapman and Walsh, 1993; Serreze and others, 2000; Overland and others, 2004; Serreze and Francis, 2006). In Alaska, 

warming since the 1950s appears to be unprecedented in at 

least the past 400 years (Overpeck and others, 1997; Barber 

and others, 2004; Kaufman and others, 2009). Mean annual 

air temperature in the boreal region of interior Alaska has 

increased by 1.3 degrees Celsius (°C) during the past 50 years, 

with the greatest warming occurring in winter (Hartmann and 

Wendler, 2005; Shulski and Wendler, 2007). Air temperature 

is projected to increase by an additional 3 to 7 °C by the end 

of this century (Walsh and others, 2008). 

Across Alaska, significant shifts in vegetation composition and production have been observed, including yellowcedar (Callitropsis nootkatensis (D. Don) D.P. Little) decline 

throughout the coastal temperate forest region (Hennon and 

others, 2006), decreased spruce growth in boreal Alaska 

(Barber and others, 2000; McGuire and others, 2010; Beck 

and others, 2011), woody vegetation encroachment into 

wetlands (Berg and others, 2009), and positive productivity 

throughout tundra regions with concurrent negative productivity throughout forested regions (for example, Goetz and 
18 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

others, 2005; Verbyla, 2008; Beck and others, 2011). Recent 

changes in major disturbance regimes in Alaska are linked to 

changes in climate. Wildfire, the dominant driver of ecosystem 

change in much of Alaska, is strongly linked to climate, 

where the average June temperature explains 78 percent of the 

inter-annual variability in total area burned (Duffy and others, 

2005). In the past decade, the average annual area burned has 

doubled compared with any decade of the previous 50 years 

(Kasischke and others, 2010). 

This chapter describes the methodology and results for the 

foundational modeling work required for this assessment. The 

following sections describe the major input datasets used for 

the entire assessment process, including climate, land cover, 

and wildfire; a description of the transient biogeographic model 

Alaska Frame-Based Ecosystem Code (ALFRESCO) and the 

methodology used for the simulation and analysis of climatevegetation-wildfire dynamics follows. The result sections 

first present the historical period (1950–2009), followed 

by the projection period (2010–2099). Both the historical 

and projected periods sections contain specific subsections 

covering climate, vegetation, and wildfire trends.

2.2.1. Climate

Alaska’s large climate gradient is influenced by latitude, 

elevation, and proximity to water bodies, including the influence of sea ice. This is reflected in the distribution of maritime, transitional, and continental climates across the State. 

The most recent U.S. National Climate Assessment—

Alaska Technical Regional Report (Markon and others, 2012), 

which included a comprehensive synthesis of the literature 

on Alaska’s climate records, indicated that average annual 

statewide temperatures have increased significantly—on the 

order of 2 °C over the past 50 years (Stafford and others, 2000; 

Shulski and Wendler, 2007). The warming is not uniform 

across the State and is not consistent across seasons. The 

greatest observed temperature increases have occurred over 

winter and spring—at two to three times the level of warming 

found in summer and fall. Regionally, the interior continental 

portions of the State have experienced the most warming, 

with some areas experiencing an increase of more than 4 °C, 

whereas coastal and maritime areas have experienced change 

on the order of 0.5 to 1 °C (Shulski and Wendler, 2007).

A significant portion of the observed warming in Alaska 

occurred as a sudden, step-like change in the mid-1970s 

coinciding with a major shift in the Pacific Decadal Oscillation 

(PDO) (Markon and others, 2012). The PDO index captures 

this shift as a transition from predominantly negative to 

predominantly positive values around 1976–77 (Mantua and 

others, 1997). The temperature increase in Alaska, however, 

mirrors trends across the arctic and subarctic (Hinzman and 

others, 2005; Solomon and others, 2007), suggesting that 

large-scale atmospheric circulation patterns (such as PDO) 

may have amplified or accelerated an underlying long-term 

warming trend.

These climate dynamics further highlighted the need to 

identify the most accurate general circulation models (GCMs) 

to develop realistic and consistent climate simulations to 

be used in this assessment. Walsh and others (2008) evaluated the Coupled Model Intercomparison Project phase 3 

(CMIP3; Meehl and others, 2007) models to identify the best 

performing GCMs for the Alaska region. The core statistic of 

the study was a root-mean-square-error (RMSE) evaluation 

of the differences between mean model output for each grid 

point and calendar month, and data from the European Centre 

for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 

ERA–40. ERA–40 is one of the most consistent and accurate 

gridded representations of these variables available. From this 

analysis, the best performing CMIP3 models were identified 

for further consideration. 

The climate data described here were aligned with the 

Intergovernmental Panel on Climate Change’s Special Report 

on Emissions Scenarios (IPCC-SRES; Nakićenović and 

Swart, 2000). This assessment used three emissions scenarios 

(A2, A1B, and B1, from high to low projected carbon dioxide 

emissions; table 2.1) to force the GCMs and capture the 

uncertainties within and across the models. Two of the best 

performing CMIP3 models were used in this assessment—

version 3.1-T47 of the Canadian Centre for Climate Modelling 

and Analysis’ Coupled Global Climate Model (CGCM3.1; 

McFarlane and others, 1992; Flato, 2005) and version 5 of 

the Max Planck Institute’s European Centre Hamburg Model 

(ECHAM5; Roeckner and others, 2003, 2004). The selected 

GCMs represented a range of projected climate change 

and were those to which the fire regime simulations were 

considered sensitive.

2.2.2. Land Cover

Land cover in Alaska includes substantial expanses 

of forest (both boreal and maritime) and tundra (including 

shrub-dominated and heath). There are six level II ecoregions 

covered by this assessment (see chapter 1). These six ecoregions correspond approximately to four primary Landscape 

Conservation Cooperatives (LCCs) in Alaska, which were 

used to summarize regional ecosystem characteristics.

Unlike the rest of the United States, Alaska does not have 

a consistent high-spatial-resolution remote sensing product 

that provides regular land-cover classification statewide, nor 

does it have consistent and widespread vegetation monitoring 

networks across the State. To simulate biological processes, 

including plant growth dynamics, tree line dynamics, 

vegetation composition and distribution, wildfire, and bio­geochemistry across the landscape, this assessment used the 

North American Land Change Monitoring System (NALCMS) 

2005 dataset (North American Land Change Monitoring 

System, 2010; for arctic and subarctic ecosystem types) and 

the National Land Cover Database (NLCD) 2001 (Homer and 

others, 2007; for coastal temperate forest ecosystem types). 

See chapter 1 for descriptions of the input land-cover data.
Chapter 2 19

2.2.3. Wildfire

Climate warming is also thought to be responsible for 

recent changes in fire regime in North American high-latitude 

regions (Gillett and others, 2004). In recent decades, annual 

area burned has increased in Alaska (Kasischke and Turetsky, 

2006; Kasischke and others, 2010) and Canada (Gillett and 

others, 2004) and is hypothesized to have increased in Eurasia 

(Hayes and others, 2011; Kharuk and others, 2013). Several 

studies indicate that this increase would be maintained at 

least during the first half of the 21st century (Balshi and 

others, 2009; Mann and others, 2012). Greater burned area 

as well as possible changes in fire severity have substantial 

implications for permafrost, as increased severity leads to 

greater consumption of the organic layer that may increase 

active-layer thickness (Dyrness and Norum, 1983; Yoshikawa 

and others, 2002; Burn and others, 2009; Genet and others, 

2013) and may potentially cause thermokarst disturbance in 

ice-rich soils (Jorgenson and others, 2001; Myers-Smith and 

others, 2008). Carbon cycling, albedo, and stand structure in 

the boreal forest are strongly influenced by the frequency and 

severity of wildfires (Randerson and others, 2006; Euskirchen 

and others, 2009; Johnstone and others, 2010; Turetsky and 

others, 2011), and burning is an important disturbance mechanism by which stored carbon is released to the atmosphere 

(Kasischke and others, 2000, 2005; Amiro and others, 2001).

A synthesis of contemporary fire trends in Alaska by 

Kasischke and others (2010) suggests a mixture of climatic and 

human controls on fire patterns. Between 2000 and 2009, an 

annual average of 7,670 square kilometers (km2

) was burned, 

50 percent more area burned than in any previous decade since 

the 1940s. Over the past 60 years, there was a decrease in 

the number of lightning-ignited fires, an increase in extreme 

lightning-ignited fire events, an increase in human-ignited 

fires, and a decrease in the number of extreme human-ignited 

fire events. The fraction of area burned from human-ignited 

fires fell from 26 percent for the 1950s and 1960s to 5 percent 

for the 1990s and 2000s as a consequence of the change in 

fire policy that gave the highest suppression priorities to fire 

events that occurred near human settlements. The amount of 

area burned during late-season fires has also increased over the 

past two decades. Deeper burning of surface organic layers in 

black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) 

forests occurred during late-growing-season fires and on more 

well-drained sites, with consequences for forest regeneration. 

These trends all point to the importance of accounting for the 

potential changes in the Alaska fire regime with respect to a 

credible assessment of carbon storage and fluxes. 

2.2.4. Other Drivers of Land-Cover Change

As discussed in the preceding sections, land-cover 

changes in Alaska are driven primarily by climate-induced 

environmental variability and disturbance (primarily 

wildfire-associated forest mortality and succession). There 

are, however, other drivers of land-cover change acting upon 

specific portions of the Alaskan landscape. For example, 

insects are affecting forests in the southern portions of the 

Alaskan boreal forest. In recent decades, warmer temperatures 

contributed to spruce beetle outbreaks, in part owing to 

a reduction of the beetle life cycle from 2 years to 1 year 

(Werner and others, 2006). This reduction in life cycle of 

an endemic species allowed populations to exceed critical 

thresholds that ultimately led to mortality across all forest age 

classes. This has led to white spruce (Picea glauca (Moench) 

Voss) mortality throughout the region of 12,000 km2

 between 

1990 and 2000 (Werner and others, 2006). In addition, earlier 

snowmelt and persistent early spring freezing have had 

important implications for Alaska cedar decline in the coastal 

temperate forest region of Alaska, where over 2,000 km2

of pristine forests have died in the past 100 years (Hennon 

and others, 2006). The loss of thermal cover and associated 

Table 2.1. Assumptions about the primary driving forces affecting land-use and land-cover change. 

[These assumptions were used to downscale the B1, A1B, and A2 and scenarios, in order of low to high projected CO2 emissions, of the Intergovernmental 

Panel for Climate Change’s Special Report on Emissions Scenarios (Nakićenović and Swart, 2000). Population and per capita income projections are from 

Strengers and others (2004)]

Driving forces A1B A2 B1

Population growth (global 

and United States)

Medium; globally, 8.7 billion 

by 2050, then declining; in 

the United States, 385 million 

by 2050

High; globally, 15.1 billion 

by 2100; in the United States, 

417 million by 2050

Medium; globally, 8.7 billion 

by 2050, then declining; in

the United States, 385 million 

by 2050

Economic growth Very high; U.S. per capita income 

$72,531 by 2050

Medium; U.S. per capita income 

$47,766 by 2050

High; U.S. per capita income 

$59,880 by 2050

Regional or global innovation Global Regional Global

Technological innovation Rapid Slow Rapid

Energy sector Balanced use Adaptation to local resources Smooth transition to renewable

Environmental protection Active management Local and regional focus Protection of biodiversity
20 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

dehardening of the species in late winter has resulted in 

fine-root damage in early spring.

Agricultural lands in Alaska occupy a small portion of 

the total land area of the State. Although roughly 90,000 km2

have been identified as having current agricultural potential, 

only 0.1 percent is currently being cropped (U.S. Department 

of Agriculture, National Agricultural Statistics Service, 2014). 

Likewise, human settlements contribute a minor fraction of 

the Alaskan land cover. State population is approximately 

736,000, which equates to approximately 2 persons per square 

kilometer. Although these other land-cover-change drivers 

impart little influence on the overall carbon assessment of 

Alaska, these drivers could play a larger role in altering 

the future carbon budget—especially with respect to forest 

insect- and disease-related mortality.

2.3. Input Data and Methods

2.3.1. Input Data Sources

The PRISM (Parameter-elevation Relationships on 

Independent Slopes Model; http://www.prism.oregonstate.edu) 

by Daly and others (2008) was used to provide gridded 

climate normals (monthly temperature and precipitation) for 

1961–1990 at a 2-kilometer (km) spatial resolution. These data 

were resampled to 1 km and used as the baseline climatology 

for the climate downscaling procedure.

We used the CRU TS v. 3.10.01 high-resolution 

(0.5 degree [°]×0.5°) gridded data (Harris and others, 2014) 

from the Climatic Research Unit (CRU; http://www.cru.uea.

ac.uk) at the University of East Anglia to provide historical 

climate data for the period 1950–2009. These data were 

downscaled and served as driving climate data input for the 

retrospective model simulations including model spin-up, 

which used the downscaled CRU data starting in 1900.

Analysis and synthesis of historical fire activity data 

(1950 –2009) were based on estimates from fire management 

records maintained by the Alaska Interagency Coordination 

Center (M. Henderson, Alaska Interagency Coordination 

Center, unpub. data, June 1, 2010). This database includes 

digitized fire perimeters, which were used to generate 

summary statistics across the assessment domain as well as 

the five LCC regions and for both annual and decadal time 

periods. The tables and figures presented in the historical 

fire activity subsection summarize these results.

2.3.2. Methods and Analysis

The baseline period for this assessment was defined as 

the period from 1950 through 2009. This historical period is 

bounded by the beginning of reliable wildfire observations 

and statistics (1950) and the end of contemporary downscaled 

historical climate observations (2009). The projection period 

was defined as the period from 2010 through 2099. The results 

in this chapter, both historical and projected, were summarized 

across the full assessment domain as well as for the five 

LCC regions described in chapter 1.

Historical (CRU TS v. 3.10.01) and projected (CMIP3) 

output variables of surface temperature and precipitation 

were downscaled via the delta method (Hay and others, 2000; 

Hayhoe, 2010) using PRISM 1961–1990 2-km-resolution 

climate normals as baseline climate (Daly and others, 

2008). The delta method was implemented by calculating 

climate anomalies applied as differences for temperature or 

quotients for precipitation between monthly future CMIP3 

data and calculated CRU climate normals for 1961–1990 

(see http://www.snap.uaf.edu for data and details). These 

coarse-resolution anomalies were then interpolated to PRISM 

spatial resolution via a spline technique and then added to 

(temperature) or multiplied by (precipitation) the PRISM 

climate normals. The downscaled climate data were then 

interpolated to a 1-km resolution for modeling purposes. Our 

modeling used the two GCMs of the best performing subset 

models for Alaska (Walsh and others, 2008), which bound 

the climate scenarios from most warming (ECHAM5) to least 

warming (CGCM3.1). Each model was downscaled for the 

three emissions scenarios (A1B, A2, B1; table 2.1).

Historical (CRU) and projected (CMIP3) output variables of surface downwelling shortwave radiation (RSDS; 

expressed as megajoules per square meter per day) and vapor 

pressure (VP; expressed as hectopascals) were calculated 

and downscaled for use by the biogeochemical Terrestrial 

Ecosystem Model (TEM). The historical data constitute 

the result of the delta-method downscaling procedure using 

the monthly time series (CRU TS v. 3.10.01; 1950–2009, 

0.5° spatial resolution) and global climatology (CRU CL 

v. 2.0; 1961–1990, 10-minute spatial resolution) data. The 

projected data constitute the same CMIP3 models used for 

temperature and precipitation. The data were bias corrected 

and downscaled to 1-km resolution.

For the surface downwelling shortwave radiation data, 

the baseline climatology for the top of the atmosphere solar 

radiation data (referred to as girr) was calculated using an 

equation from Allen and others (1998) at a monthly timestep, 

which is used to represent the historical period RSDS data at 

1-km spatial scale. The downscaled cloud cover time series 

was then used to calculate the surface solar radiation (referred 

to as nirr) from girr and downscaled cloud cover using the 

following equation:

nirr=monthly_ girr×

(0.251+(0.509(1.0 – cloud_cover/100))) (2.1)
Chapter 2 21

The CRU TS v. 3.10.01 data are distributed as vapor pressure 

and were converted to relative humidity using the conversion 

equation from the World Meteorological Organization’s 

Commission for Instruments and Methods of Observation 

(CIMO) guide (2008) using the following equations:

saturated_vapor_ pressure=6.112 ×

exp((22.46×temperature)/(272.62+temperature)) (2.2)

relative_humidity=(vapor_ pressure /

saturated_vapor_ pressure)×100 (2.3)

Proportional anomalies were then generated using the 

newly converted CRU TS v. 3.10.01 historical VP data where 

the climatological period used was 1961–1990. These CRU 

TS v. 3.10.01 proportional anomalies were then interpolated 

using a spline interpolation to a 10-minute resolution grid for 

downscaling with the CRU CL v. 2.0 relative humidity data. 

The final step was to convert the downscaled relative humidity 

data to vapor pressure using the below conversion equation:

saturated_vapor_ pressure=6.112×

exp((17.62×temperature)/(243.12+temperature)) (2.4)

vapor_ pressure=(relative_humidity×

saturated_vapor_ pressure)/100 (2.5)

Additional methodological details for all the downscaled 

climate variables can be found in the individual metadata 

files at the Scenarios Network for Alaska and Arctic Planning 

(SNAP) data download Web site (https://www.snap.uaf.edu/

tools/data-downloads).

We used the biogeographic simulation model ALFRESCO 

(Rupp and others, 2000, 2002, 2007; Johnstone and others, 

2011; Mann and others, 2012; Gustine and others, 2014; 

Amy Breen, University of Alaska-Fairbanks, written 

commun., January 15, 2015) to simulate changes in 

vegetation dynamics and fire regime for the projection period 

(2010 –2099) in response to the climate simulations used in 

this assessment. ALFRESCO is a spatially explicit, stochastic 

landscape succession model for arctic, subarctic, and boreal 

vegetation types that operates in the Alaska and Northwest 

Canada region at a 1-km resolution and an annual timestep. 

The model represents seven general vegetation types (shrub 

tundra, graminoid tundra, wetland tundra, white spruce forest, 

black spruce forest, early successional deciduous forest, and 

coastal temperate forest) in Alaska. Currently, the coastal 

temperate forest and the wetland tundra vegetation types 

are represented as static vegetation states.

The fire module of ALFRESCO uses a cellular automata 

approach with separate subroutines for cell ignition and spread 

to simulate annual fire season activity. Both ignition and fire 

spread (that is, flammability) are a function of growing-season 

climate (Duffy and others, 2005), vegetation type, and time 

since last fire. The ignition of any given cell is stochastic in 

nature and determined by comparing a randomly generated 

number against the flammability coefficient of that cell. The 

flammability coefficient allows for changes in flammability 

that take place through succession (that is, fuel build up). 

Following a wildfire in ALFRESCO, general successional 

trajectories for forested systems are as follows: burned spruce 

forest (white or black) transitions into early successional 

deciduous forest, and burned deciduous forest self-replaces. 

Vegetation transition times differed probabilistically between 

climax black and white spruce trajectories (Rupp and others, 

2002). Transitional times were modeled probabilistically to 

represent early successional (that is, recolonization) deciduous 

forest following wildfires in spruce and deciduous forest 

and to determine the amount of time, in the absence of fire, 

until the climax spruce stage dominates the site again. Selfreplacement of deciduous forest can occur when repeated 

burning and (or) climate conditions preclude transition to 

climax spruce. ALFRESCO incorporates the effects of fire 

severity on transition times using measurements of the area 

of the wildfire (that is, fire size), complex topography, and 

vegetation type on flat landscapes (Duffy and others, 2007; 

Johnstone and others, 2011).

Transitions in tundra are driven by succession or 

colonization and infilling. These processes are influenced by 

climate and fire history, which affect seedling establishment 

and growth conditions and proximity to seed source (Breen 

and others, written communication). For the transition from 

tundra to forest at the tree line, seed dispersal occurs within a 

1-km neighborhood. White spruce colonization and infilling 

are possible in both graminoid and shrub tundra with transition rates to spruce forest mediated by climate effects and 

basal area growth. Vegetation succession from graminoid 

to shrub tundra is modeled probabilistically, with a greater 

likelihood of transition to shrub tundra post-fire. In the case 

of wildfire activity, shrub tundra transitions to graminoid 

tundra and graminoid tundra self-replaces. Wetland tundra in 

the model does not currently burn and is represented as a static 

vegetation type.

The relationship between climate and fire was calibrated 

by comparing model output (such as, fire regime, stand age 

structure) to the corresponding historical data (Mann and 

others, 2012). Simulated vegetation and fire dynamics were 

analyzed and synthesized across the full ensemble of simulations (number of replicates [n]=200) and for all six climate 

simulations (combinations of the three emissions scenarios 

and the two GCMs) used in this assessment. Vegetation transitions, defined as at least one shift in vegetation type during the 

projection period (2010–2099), were calculated as a percent of 

total area for each of the five LCC regions. Tables and figures 

presented in the projected land-cover and wildfire subsections 

include synthesis of these results.
22 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

2.4. Results and Discussion

2.4.1. Baseline Land Cover 

Our baseline land-cover classes for the assessment 

domain, based on the NALCMS remote sensing product from 

2005, consisted of forest (51 percent), tundra (28 percent), 

and nonvegetated (21 percent) (table 2.2). Forest land cover 

was dominated by early successional deciduous forest 

(30 percent), followed by white spruce forest (9 percent), 

black spruce forest (6 percent), and coastal temperate forest 

(6 percent). Tundra land cover was dominated by shrub 

tundra (17 percent), followed by graminoid tundra (10 percent) 

and wetland tundra (1 percent). The nonvegetated regions 

were composed of rock, ice, snow, water, and coastlines and 

represent land-cover classes not modeled. 

The Arctic LCC is dominated by tundra land-cover types 

with a relatively even distribution between shrub and graminoid tundra, 108,226 square km2

 and 119,027 km2, respectively (table 2.3). There were minor components of deciduous 

forest (15,460 km2) and wetland tundra (10,621 km2) and very 

small components of black and white spruce forests. 

The Western Alaska LCC has a relatively even 

distribution of tundra land-cover types and deciduous forest 

(table 2.3). Deciduous forest (130,904 km2) is the dominant 

land-cover type. The LCC also has minor components of white 

and black spruce forests (9,424 km2 and 5,873 km2, respectively). Of the tundra land-cover types, shrub tundra dominates 

(119,517 km2) with a minor component of graminoid tundra 

(12,379 km2) and a very small component of wetland tundra. 

There is also a minor component of coastal temperate forest 

(9,268 km2) within the LCC and along the border with the far 

northwestern portion of the North Pacific LCC.

The Northwest Boreal LCC North and South are both 

dominated by boreal forest land-cover types (table 2.3). In the 

Northwest Boreal LCC South, early successional deciduous 

forest dominates (66,673 km2), followed by white spruce 

(18,706 km2) and then black spruce (11,812 km2). In the 

Northwest Boreal LCC North, there is a similar distribution 

of forest land-cover types; however, the areal extent is significantly greater than the Northwest Boreal LCC South. In the 

Northwest Boreal LCC North, early successional deciduous 

forest dominates (238,414 km2), followed by white spruce 

(101,688 km2) and then black spruce (77,183 km2). Both LCC 

regions also have minor components of tundra land-cover 

types; however, neither region contains wetland tundra. The 

Table 2.2. Land-cover types used in this assessment for the 

Alaska Frame-Based Ecosystem Code (ALFRESCO) model 

simulations, percent of area, and the source of the input data. 

[Percent of area calculated in 2005. Land-cover types ordered from late 

to early successional forest to tundra to other cover types. NALCMS 

2005, North American Land Change Monitoring System 2005 dataset; 

NLCD 2001, 2001 National Land Cover Database]

Land-cover type Area

(percent) Source

Black spruce forest 6 NALCMS 2005

White spruce forest 9 NALCMS 2005

Deciduous forest 30 NALCMS 2005

Shrub tundra 17 NALCMS 2005

Graminoid tundra 10 NALCMS 2005

Wetland tundra 1 NALCMS 2005

Heath tundra 0 NALCMS 2005

Coastal temperate forest 6 NLCD 2001

Nonvegetated 21 NALCMS 2005

Table 2.3. Land-cover types used in this assessment summarized by area for the five Landscape Conservation Cooperative 

(LCC) regions.

[Data may not add to totals shown because of independent rounding. km2, square kilometer]

LCC region

Area in 2005 (km2

)

Black 

spruce 

forest

White 

spruce 

forest

Deciduous

forest

Shrub 

tundra

Graminoid 

tundra

Wetland 

tundra

Heath

tundra

Coastal

temperate 

forest

Nonvegetated Total

Arctic LCC 248 648 15,460 108,226 119,027 10,621 0 0 54,363 308,593

Western Alaska 

LCC

5,873 9,424 130,904 119,517 12,379 515 0 9,268 90,341 378,221

Northwest 

Boreal LCC 

North 

77,183 101,688 238,414 11,942 16,961 0 9 0 10,387 456,584

Northwest 

Boreal LCC 

South 

11,812 18,706 66,673 12,961 3,238 0 0 2,542 70,371 186,303

North Pacific 

LCC

636 392 603 8 2 0 0 75,999 85,721 163,361
Chapter 2 23

Northwest Boreal LCC South contains a minor component 

of coastal temperate forest along the southern boundary and 

along the border with the North Pacific LCC.

The North Pacific LCC is dominated by coastal temperate 

forest (75,999 km2) as well as nonvegetated areas, mostly 

snow and ice (table 2.3). There are very small components of 

boreal forest land-cover types in the extreme northern portion 

of the LCC bordering the Northwest Boreal LCC South.

2.4.2. Baseline Wildfire

From fire management records, the number of wildfires 

between 1950 and 2009 across the full simulation domain of 

this assessment ranged from as high as 176 fires in 2005 to less 

than or equal to 1 in 1952, 1961, 1964, and 1965 (table 2.4). 

The minimum number of fires is difficult to estimate owing 

to detection issues associated with the remoteness and size 

of Alaska. These issues were likely more pronounced in the 

earlier period of the historical data. Consequently, the estimate 

of the minimum number of fires is highly uncertain. Similar 

issues exist with the estimation of area burned in years with 

little activity. Annual area burned averaged 3,791 km2—that is, 

0.25 percent of the total area of the Alaska simulation domain, 

which has a total area of 1.49 million km2. The maximum 

annual area burned was 27,071 km2 (that is, 1.3 percent of the 

total area), which occurred in 2004. There were three years 

(1961, 1964, and 1965) where less than 10 km2 were recorded 

as being burned. As reported by Kasischke and others (2002), 

the wildfire perimeter records for the 1960s were rated fair 

and included many missing data records. Historical data for 

each of the LCC regions is subsequently discussed in order of 

decreasing fire activity as measured by annual area burned.

The Northwest Boreal LCC North had most (approximately 85 percent of the statewide total) of the fire activity 

among the five LCC regions of the assessment (table 2.4, 

fig. 2.1). The number of wildfires between 1950 and 2009 

ranged from a high of 137 in 2005 to 0 in both 1961 and 1964. 

The annual area burned averaged 3,262 km2. The inter-annual 

variability in area burned was high, and area burned ranged 

from 0 km2

 in both 1961 and 1964 to 26,684 km2 in 2004. The 

range of annual area burned was between 0 and 5.8 percent 

of the Northwest Boreal LCC North, which has a total area 

of 456,584 km2.

The Western Alaska LCC had the second most 

(approximately 10 percent) fire activity among the five LCC 

regions of the assessment (table 2.4, fig. 2.1). In the Western 

Alaska LCC, the number of wildfires between 1950 and 2009 

averaged 6 per year—with a high of 37 in 1972 and 13 years 

recording no fires. Annual area burned averaged 394 km2, or 

0.1 percent of the Western Alaska LCC, which has a total area 

of 378,221 km2. The range of annual area burned was between 

0 and 2.2 percent of the Western Alaska LCC.

In the Arctic LCC, the number of wildfires between 1950 

and 2009 averaged 2 per year—with a high of 13 in 2005 and 

31 years recording no fires (table 2.4, fig. 2.1). Annual area 

Table 2.4. Baseline (1950–2009) wildfire statistics for each 

Landscape Conservation Cooperative (LCC) region and the full 

assessment domain (Alaska).

[Data from fire management records maintained by the Alaska Interagency 

Coordination Center. km2, square kilometer]

Metric Number of wildfires

per year

Annual area burned 

(km2

)

Arctic LCC

Mean 2.1 60.49

Standard deviation 3.2 178.16

Minimum 0 0

Median 0 0

Maximum 13 1,106.82

Year of maximum 2005 2007

Western Alaska LCC

Mean 5.82 393.81

Standard deviation 7.68 1,258.46

Minimum 0 0

Median 3.5 33.42

Maximum 37 8,313.5

Year of maximum 1972 1957

Northwest Boreal LCC North 

Mean 31.8 3,262.26

Standard deviation 33.07 5,178.67

Minimum 0 0

Median 20 1,296.82

Maximum 137 26,683.54

Year of maximum 2005 2004

Northwest Boreal LCC South 

Mean 2.15 54.49

Standard deviation 2.7 112.52

Minimum 0 0

Median 1 4.28

Maximum 15 615.4

Year of maximum 2009 2009

North Pacific LCC

Mean 0.6 2.21

Standard deviation 0.92 6.93

Minimum 0 0

Median 0 0

Maximum 4 36.31

Year of maximum 1991 1991

Alaska

Mean 41.3 3,791.5

Standard deviation 41.92 5,709.97

Minimum 0 0

Median 27 1,596.77

Maximum 176 27,071.72

Year of maximum 2005 2004
24 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Arctic LCC

Number of wildfires

Figure 2-1.

0

10

20

30

40

50

Area burned, in square kilometers

0

300

600

900

1,200

1,500

North Pacific LCC

0

3

6

9

12

15

18

10

0

20

30

40

50

Northwest Boreal LCC North

0

100

200

300

400

500

600

700

0

0

20,000

40,000

60,000

80,000

Northwest Boreal LCC South

0

10

20

30

40

50

60

300

600

900

1,200

1,500

Western Alaska LCC

Arctic LCC

North Pacific LCC

Northwest Boreal LCC North

Northwest Boreal LCC South

Western Alaska LCC

0

20

40

60

80

100

120

Decade

1950–

1959

1960–

1969

1970–

1979

1980–

1989

1990–

1999

2000–

2009

Decade

1950–

1959

1960–

1969

1970–

1979

1980–

1989

1990–

1999

2000–

2009

0

3,000

6,000

9,000

12,000

15,000

Figure 2.1. A decadal summary of number of wildfires and area burned for each Landscape Conservation Cooperative (LCC) 

region for the historical period (1950–2009). 
Chapter 2 25

burned averaged 60 km2, or 0.01 percent of the Arctic LCC, 

which has a total area of 308,593 km2. The range of annual area 

burned was between 0 and 0.35 percent of the Arctic LCC.

In the Northwest Boreal LCC South, the number of 

wildfires between 1950 and 2009 averaged 2 per year—with 

a high of 15 in 2009 and 15 years recording no fires (table 2.4, 

fig. 2.1). Annual area burned averaged 54 km2, or 0.03 percent 

of the Northwest Boreal LCC South, which has a total area of 

186,303 km2. The range of annual area burned was between 

0 and 0.33 percent of the Northwest Boreal LCC South.

Of the five assessment regions, the North Pacific LCC 

had the least amount of fire activity reflecting the LCC’s wet 

and cool climate (table 2.4, fig. 2.1). The number of wildfires 

between 1950 and 2009 averaged 1 per year—with a high of 

4 in 1991 and 37 years recording no fires. Annual area burned 

averaged 2 km2 and ranged from 0 to 36 km2. The range of 

annual area burned was between 0 and 0.02 percent of the 

North Pacific LCC, which has a total area of 163,361 km2. 

2.4.3. Climate Simulations

Visualizations of seasonal baseline (1950–2009) climate 

patterns for mean monthly temperature in degrees Celsius (°C) 

and total monthly precipitation in millimeters (mm), based 

on the downscaled CRU data (http://www.snap.uaf.edu), are 

presented in figure 2.2. The downscaled CRU historical period 

data exhibited strong variability across seasons and also indicated high spatial variability.

Figure 2.3 and table 2.5 present projected changes 

in mean monthly temperature (°C) and total precipitation 

(mm) by season, calculated using the difference in mean 

values from the last two decades of the historical period 

(1980–2009) to those of the projection period (2070–2099). 

Warming trends are projected by both GCMs and across all 

three emissions scenarios. Warming would be greatest in the 

northern and western regions of Alaska. There was greater 

projected variability among the scenarios than between GCMs. 

The A2 scenario projected the greatest warming and the 

B1 scenario the least warming. The ECHAM5 climate simulations project warmer temperatures than the CGCM3.1 simulations. Projected warming is not uniform seasonally and both 

GCMs project substantially greater warming during the winter 

(December, January, February) and fall (September, October, 

November). Growing season (defined here as April through 

September) is a particularly important annual time period as 

plant production and wildfire activity are constrained within 

these months. Growing season temperatures are projected to 

increase in all six climate simulations (fig. 2.4) across the full 

assessment period (1950–2099).

Precipitation increases are also projected by both GCMs 

and across all three emissions scenarios (table 2.5). Across 

the full assessment domain, differences between GCMs were 

Figure 2.2. Baseline (1950–2009) seasonal mean monthly temperature and total precipitation by season using 

downscaled Climate Research Unit data (http://www.snap.uaf.edu). Winter included December, January, and 

February; spring included March, April, and May; summer included June, July, and August; and fall included 

September, October, and November.

–31 –17 0 17 0 750 1,500

Winter

Figure 2-2.

Spring Summer Fall

Temperature Precipitation

Temperature, in degrees Celsius

EXPLANATION

Precipitation, in millimeters
26 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Scenario A1B

Scenario A2

Scenario B1

Temperature change Precipitation change

Winter

CGCM 3.1 ECHAM5 CGCM 3.1 ECHAM5 CGCM 3.1 ECHAM5

Spring Summer Fall Winter Spring Summer Fall

–2 0 2 4 7

Temperature change, in degrees Celsius

EXPLANATION EXPLANATION

Figure 2-3

–5 0 5

Precipitation change, in millimeters

Figure 2.3. Projected changes in mean monthly temperature and total precipitation by season, calculated using the difference in mean 

values between 1980–2009 and 2070–2099 using downscaled data for version 3.1-T47 of the Canadian Centre for Climate Modelling 

and Analysis’ Coupled Global Climate Model (CGCM3.1) and version 5 of the Max Planck Institute’s European Centre Hamburg Model 

(ECHAM5) general circulation models and for the three scenarios A1B, A2, and B1 from the Intergovernmental Panel on Climate 

Change’s Special Report on Emissions Scenarios (http://www.snap.uaf.edu). Winter included December, January, and February; spring 

included March, April, and May; summer included June, July, and August; and fall included September, October, and November.
Chapter 2 27

Table 2.5. Summary statistics of projected changes in mean monthly temperature and total precipitation by season, calculated using 

the difference in mean values from 1980–2009 and 2070–2099 for each Landscape Conservation Cooperative (LCC) region and the full 

assessment domain (Alaska).

[Values represent the integration of all 1-kilometer pixels within the full assessment domain and individual subregions using downscaled data for the version 

3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global Climate Model (CGCM3.1) and version 5 of the Max Planck Institute’s 

European Centre Hamburg Model (ECHAM5) general circulation models and for the three scenarios, B1, A1B, and A2, in order of low to high projected 

CO2 emissions, from the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios used in the assessment (http://www.snap.uaf.edu). 

Winter includes December, January, and February; spring includes March, April, and May; summer includes June, July, and August; and fall includes September, October, and November]

Season

Monthly temperature (degree Celsius) Total precipitation (millimeter)

CGCM3.1 ECHAM5 CGCM3.1 ECHAM5

A1B A2 B1 A1B A2 B1 A1B A2 B1 A1B A2 B1

Arctic LCC

Winter 6.36 8.09 3.55 8.03 8.19 4.93 8.42 9.55 6.81 9.94 7.08 5.25

Spring 2.55 2.46 1.26 3.78 3.96 2.53 5.30 4.82 2.45 4.33 3.72 3.99

Summer 1.25 1.79 0.23 2.76 2.94 1.08 6.75 14.73 8.04 3.73 3.10 4.03

Fall 4.92 5.77 2.58 7.21 7.51 5.56 9.59 7.39 5.96 18.39 20.08 16.53

Western Alaska LCC

Winter 6.24 7.52 3.76 8.23 8.37 5.79 12.06 15.47 6.50 19.67 14.53 9.09

Spring 1.87 2.13 1.19 4.21 4.34 3.51 9.68 8.71 2.45 6.34 4.91 5.36

Summer 1.97 2.47 0.87 3.00 2.72 1.51 4.89 12.39 5.32 0.21 1.11 1.91

Fall 3.20 3.83 1.61 5.38 5.65 4.20 5.51 11.89 1.66 5.80 10.67 6.43

Northwest Boreal LCC North

Winter 5.28 6.26 3.40 6.95 7.28 4.31 10.38 10.75 7.61 11.62 7.94 5.97

Spring 1.08 1.45 0.82 3.38 3.60 2.47 5.17 5.54 2.44 4.08 3.14 3.63

Summer 1.88 2.51 0.74 2.50 2.50 0.93 7.69 14.84 7.53 8.19 6.28 8.53

Fall 3.43 4.13 1.77 5.12 5.58 4.07 9.11 10.84 5.67 14.65 18.08 14.84

Northwest Boreal LCC South

Winter 4.67 5.38 2.90 5.56 6.05 3.24 32.39 31.99 23.01 41.83 33.07 21.17

Spring 0.60 1.37 0.70 2.78 3.06 2.07 17.92 20.22 13.85 17.32 15.45 17.24

Summer 1.99 2.79 0.93 2.21 2.06 0.72 20.94 25.35 16.08 19.50 22.23 20.33

Fall 3.25 3.93 1.85 4.17 4.59 3.26 22.20 33.97 12.52 29.26 35.55 27.46

North Pacific LCC

Winter 3.71 4.29 2.42 3.87 4.48 2.05 95.77 92.63 64.82 99.84 106.87 53.70

Spring 1.26 2.17 1.32 2.85 3.05 2.02 43.99 73.92 56.37 58.04 57.77 49.64

Summer 2.05 2.91 1.17 2.28 2.30 1.13 17.10 13.14 13.87 24.40 36.90 25.60

Fall 3.08 3.80 1.99 3.77 3.97 2.94 51.48 91.26 31.82 45.93 76.57 49.87

Alaska

Winter 5.50 6.64 3.36 7.00 7.30 4.44 22.06 22.89 15.06 26.29 22.88 13.49

Spring 1.54 1.90 1.04 3.54 3.74 2.65 11.98 15.15 9.49 11.98 10.94 10.64

Summer 1.80 2.43 0.73 2.62 2.57 1.10 9.44 15.35 8.82 8.37 9.52 9.20

Fall 3.62 4.33 1.93 5.36 5.70 4.19 14.37 21.68 8.31 18.30 24.92 18.31
28 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

4

6

8

10

1950 2000 2050 2100

Year

Mean temperature, in degrees Celsius Mean temperature, in degrees Celsius

Observed—CRU TS v. 3.10.01

EXPLANATION

Figure 2-4

A1B A2 B1

A. CGCM3.1

B. ECHAM5

5.0

7.5

10.0

12.5

Climate scenario

Figure 2.4. Projected growing season (April–September) mean temperature across the full assessment period (1950–2099).

A, climate simulations by version 3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global 

Climate Model (CGCM3.1) and B, climate simulations by version 5 of the Max Planck Institute’s European Centre Hamburg 

Model (ECHAM5). Historical observations are from the Climate Research Unit time series CRU TS v. 3.10.01. Climate scenarios 

A1B, A2, and B1 from the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios.
Chapter 2 29

smaller than the differences among scenarios. Portions of 

western Alaska are projected to increase the least and some 

areas would likely experience some seasonal decreases in 

spring and summer (fig. 2.3). Regional trends varied considerably among climate simulations, with the CGCM3.1 model 

projecting slightly larger increases for the A1B scenario 

than for the A2 scenario. In contrast, the ECHAM5 model 

projected slightly larger increases for the A2 scenario than 

for the A1B scenario. Of the simulations, that for scenario 

B1 with ECHAM5 produced the largest seasonal increases 

in precipitation in spring (March, April, May) and summer 

(June, July, August). 

2.4.4. Projected Land Cover

The projected changes in land cover varied substantially 

across the assessment domain, but varied little across GCM 

climate scenarios (table 2.6). Differences between the 

GCMs were minimal except for the Arctic LCC. Change 

was defined as the percentage of the domain that changed 

land-cover type at least once over the projection period 

(2010 –2099). Total projected land-cover change across the 

full domain ranged from a low of 56.5 percent in the B1 

scenario of the CGCM3.1 model to a high of 61.2 percent 

in the A1B scenario of the ECHAM5 model. The greatest 

amount of change occurred within the Northwest Boreal 

LCC North (approximately 97.5 percent in both models) 

and the least amount of change, not including the primarily 

static North Pacific LCC, was simulated in the Arctic LCC 

(ranging from 30 to 46 percent for the CGCM3.1 and 

ECHAM5 models, respectively) (fig. 2.5). 

Figure 2.5. Projected land-cover change footprint visualized 

by Landscape Conservation Cooperative (LCC) region. This 

visualization is for the version 3.1-T47 of the Canadian Centre 

for Climate Modelling and Analysis’ Coupled Global Climate 

Model (CGCM3.1) general circulation model, but the version 5 

of the Max Planck Institute European Centre Hamburg Model 

(ECHAM5) model produces the same results for the binned 

categories depicted, and the results are also consistent 

across climate change scenarios A1B, A2, and B1 from the 

Intergovernmental Panel on Climate Change’s Special Report 

on Emissions Scenarios. Finer differences between the two 

general circulation models and the three climate change 

scenarios are presented in table 2.6. Change was defined as 

the portion of the domain that changed cover type at least 

once over the projection period (2010–2099). 

Table 2.6. Projected land-cover-change footprint for the Landscape Conservation Cooperative (LCC) regions and the full assessment 

domain (Alaska).

[Change was defined as the percentage of the domain that changed land-cover type at least once over the projection period (2010–2099). This assessment used 

downscaled data for two general circulation models, version 3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global Climate 

Model (CGCM3.1) and version 5 of the Max Planck Institute’s European Centre Hamburg Model (ECHAM5), and three scenarios, B1, A1B, and A2, in order 

of low to high projected CO2 emissions, from the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios. km2, square kilometer]

LCC region Area

(km2

)

Land-cover change (percent)

CGCM3.1 ECHAM5

A1B A2 B1 A1B A2 B1

Arctic LCC 308,593 33.5 38.8 30.8 46.2 43.7 41.8

Western Alaska LCC 378,221 55.7 56.1 52.9 58.0 57.6 56.5

Northwest Boreal LCC North 456,584 97.5 97.5 97.4 97.5 97.5 97.5

Northwest Boreal LCC South 186,303 55.3 56.0 54.9 56.2 56.0 55.4

North Pacific LCC 163,361 0.9 0.9 0.9 0.9 0.9 0.9

Alaska 1,493,062 57.8 59.1 56.5 61.2 60.5 59.8

0 to 25

>25 to 50

>50 to 75

>75 to 100

Percent of LCC region area 

 expected to experience at

 least one shift in vegetation

 type between 2010 and 2099

EXPLANATION

Figure 2-5

0 200 400 KILOMETERS

0 200 400 MILES

N
30 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Projected land-cover changes across the full assessment 

domain between 2009 and 2099 occurred within both the 

forest and tundra types (table 2.7, fig. 2.6). No changes were 

projected in land cover for coastal temperate forest, heath 

tundra, or wetland tundra owing to the static nature in which 

the vegetation succession model treats these land-cover 

types. Model results for the full assessment domain indicated 

projected decreases in late successional white and black 

spruce forests and concomitant increase in early successional 

deciduous forest across GCMs and climate scenarios with the 

magnitudes of change being greatest for the ECHAM5 simulations. The exception was the simulation under scenario B1 with

CGCM3.1, which projected a small increase in black spruce 

forest (1.9 percent) and a moderate increase in white spruce 

forest (12.1 percent). Forest land-cover changes were greatest 

under the A2 scenario with the CGCM3.1 model, but were 

greatest under the A1B scenario with the ECHAM5 model. 

The CGCM3.1 simulations projected a consistent decreases 

in both graminoid and shrub tundra, whereas the ECHAM5 

simulations projected moderate decreases in graminoid tundra 

(20 –26 percent) but increases in shrub tundra (4–8 percent). 

Consistent decreases in white and black spruce forests 

and concomitant increases in deciduous forest were projected 

for all regions and scenarios under the ECHAM5 simulations (table 2.7). Projected decreases were greatest under 

the A1B scenario and smallest under the B1 scenario. For 

the CGCM3.1 simulations, the model results varied among 

regions and scenarios. The magnitude of change was greatest 

under the A2 scenario except for the Western Alaska LCC. 

Within the boreal-forest-dominated Northwest Boreal LCC North 

and South, the B1 scenario projected small increases in spruce 

forests whereas the A2 scenario projected moderate decreases. The 

A1B scenario produced opposite trends with small increases in 

spruce forests projected for the Northwest Boreal LCC South and 

small decreases projected for the Northwest Boreal LCC North. 

Although the Arctic and Western Alaska LCCs are both 

dominated by tundra, projected changes in tundra land cover 

exhibited opposite trends consistently across GCMs and 

scenarios. For the Arctic LCC, decreases in graminoid tundra 

and increases in shrub tundra were projected, with a greater 

magnitude of change under the ECHAM5 simulations. For 

the Western Alaska LCC, in contrast, increases in graminoid 

tundra and decreases in shrub tundra were projected, with a 

greater magnitude of change under the CGCM3.1 simulations.

 In the Arctic LCC, white spruce forest was projected to 

increase (18.2– 66.3 percent) whereas black spruce forest was 

projected to decrease (8.4 –36.1 percent) under the CGCM3.1 

simulations with the magnitude of change greatest under the 

B1 scenario for white spruce and under the A2 scenario for 

black spruce (table 2.7). In contrast, both white and black 

spruce forests were projected to decrease (24.9 – 63.6 percent) 

under the ECHAM5 simulations with the magnitude of the 

decreases greatest under the A2 scenario and smallest under 

the B1 scenario. For all simulations, graminoid tundra was 

projected to decrease (8.4 –23.6 percent), whereas shrub 

tundra was projected to increase (2.1–21.3 percent). Projected 

changes in tundra land cover were slightly greater under the 

A1B scenario than the A2 scenario and the least amount of 

change was projected under the B1 scenario.

In the Western Alaska LCC, white and black spruce 

forests were projected to decrease (12.0 – 45.6 percent) under 

all simulations, except for the CGCM3.1 simulation under 

the B1 scenario, which resulted in a 6.4-percent projected 

increase in white spruce forest (table 2.7). Graminoid tundra 

was projected to increase (0.4 –9.2 percent) across all simulations. In contrast, shrub tundra was projected to decrease 

(3.6 –11.3 percent) across all simulations. 

Figure 2.6. Projected land-cover change between 

2009 and 2099 across the full assessment domain under 

the three emissions scenarios A1B, A2, and B1 of the 

Intergovernmental Panel on Climate Change’s Special 

Report on Emissions Scenarios. A, changes projected 

by version 3.1-T47 of the Canadian Centre for Climate 

Modelling and Analysis’ Coupled Global Climate Model 

(CGCM3.1) climate simulations and B, changes projected 

by version 5 of the Max Planck Institute’s European Centre 

Hamburg Model (ECHAM5) climate simulations. 

Area percent change Area percent change

−32

−24

−16

−8

0

8

16

24

32

−56

−32

−40

−48

−24

−16

−8

0

16

8

24

32

40

48

56

Black spruce

forest

Deciduous

forest

Graminoid

tundra

Land-cover type

Shrub

tundra

White spruce

forest

A1B B1

EXPLANATION

A2

B. ECHAM5

Figure 2-6

A. CGCM3.1

Climate scenario
Chapter 2 31

Table 2.7. Projected change in land-cover type between the end of the historical period (2009) and the end of the projection 

period (2099) for each Landscape Conservation Cooperative (LCC) region and the full assessment domain (Alaska). 

[Heath tundra, wetland tundra, and coastal temperate forest are not presented due to the static nature of these cover types in the Alaska FrameBased Ecosystem Code (ALFRESCO) model. This assessment used downscaled data for two general circulation models, version 3.1-T47 of the 

Canadian Centre for Climate Modelling and Analysis’ Coupled Global Climate Model (CGCM3.1) and version 5 of the Max Planck Institute’s 

European Centre Hamburg Model (ECHAM5), and three scenarios, B1, A1B, and A2, in order of low to high projected CO2 emissions, from the 

Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios. NA, not applicable]

Land-cover type

Change in land-cover type (percent)

CGCM3.1 ECHAM5

A1B A2 B1 A1B A2 B1

Arctic LCC

Black spruce forest –19.7 –36.1 –8.4 –63.0 –63.6 –54.7

White spruce forest 49.8 18.2 66.3 –36.8 –46.1 –24.9

Deciduous forest 232.5 396.8 129.1 620.4 598.3 527.0

Shrub tundra 4.2 8.9 2.1 21.3 20.6 13.8

Graminoid tundra –10.2 –14.4 –8.4 –23.6 –22.1 –16.4

Western Alaska LCC

Black spruce forest –24.9 –29.4 –12.0 –45.6 –43.1 –33.6

White spruce forest –11.6 –16.1 6.4 –40.5 –40.3 –30.6

Deciduous forest 63.1 73.7 34.0 106.0 102.6 81.4

Shrub tundra –9.5 –9.8 –11.3 – 4.4 –3.6 –3.6

Graminoid tundra 8.2 9.2 6.6 3.9 3.7 0.4

Northwest Boreal LCC North

Black spruce forest –8.0 –25.9 3.0 – 43.9 –34.1 –13.6

White spruce forest –7.9 –24.3 3.7 – 41.9 –33.8 –12.2

Deciduous forest 24.5 69.2 –5.1 113.0 91.4 34.8

Shrub tundra 1.8 1.5 1.0 4.5 5.0 4.7

Graminoid tundra – 49.7 – 46.3 – 46.1 – 45.6 – 44.3 – 44.0

Northwest Boreal LCC South

Black spruce forest 1.3 –15.2 9.6 –30.2 –27.4 –14.9

White spruce forest 4.4 –10.9 13.9 –32.6 –30.5 –18.7

Deciduous forest 3.0 15.4 –3.9 29.1 27.5 17.4

Shrub tundra –17.5 –13.6 –25.1 3.9 1.9 3.2

Graminoid tundra –72.6 –69.9 –71.4 –67.9 –66.5 –67.9

North Pacific LCC

Black spruce forest –6.4 –9.5 –1.9 –20.0 –13.4 –4.3

White spruce forest –7.2 –10.0 –1.6 –19.4 –14.9 –3.6

Deciduous forest 90.0 127.3 20.9 248.2 174.5 53.2

Shrub tundra 0.0 0.0 0.0 0.0 0.0 0.0

Graminoid tundra NA NA NA NA NA NA

Alaska

Black spruce forest –8.3 –21.6 1.9 –37.8 –34.2 –21.1

White spruce forest –0.2 –13.6 12.1 –36.5 –34.4 –20.7

Deciduous forest 15.2 31.3 2.4 51.6 47.4 31.6

Shrub tundra –3.2 –1.0 –5.4 7.7 7.7 4.8

Graminoid tundra –15.2 –18.0 –13.6 –25.7 –24.4 –20.2
32 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

In the Northwest Boreal LCC North, white and black 

spruce forests were projected to decrease substantially 

(7.9 – 43.9 percent) under all simulations, except for the 

simulations under the B1 scenario, which resulted in minimal 

increases of approximately 3 percent (table 2.7). Graminoid 

tundra was also projected to decrease (44.0 – 49.7 percent) 

under all simulations. In contrast, shrub tundra was projected 

to increase (1.0–5.0 percent) under all simulations.

In the Northwest Boreal LCC South, small to moderate 

changes (that varied from decreases to increases) were 

projected in white and black spruce forests across the 

scenarios with CGCM3.1 (table 2.7). In contrast, decreases in 

white and black spruce forests were projected for all scenarios 

with ECHAM5. Graminoid and shrub tundra were projected to 

decrease (13.6 –72.6 percent) under the CGCM3.1 simulations. 

However, under the ECHAM5 simulations graminoid tundra 

was projected to decrease (66.5– 67.9 percent), whereas shrub 

tundra was projected to increase minimally (1.9 –3.9 percent).

Because ALFRESCO does not model changes in 

temperate forest types, projected land-cover changes were 

minimal in the North Pacific LCC where this land-cover 

type is dominant. For the small amount of spruce forest 

land-cover types found within this LCC, both white and black 

spruce forests were projected to decrease (1.6–20.0 percent) 

under all simulations.

Distributional trends across the full assessment domain 

revealed projected decreases in area of spruce forest land 

cover across all simulations (fig. 2.7). These decreases were 

greatest for the ECHAM5 simulations. An associated increase 

in early successional deciduous forest was projected under 

all simulations. Graminoid tundra was also projected to 

decrease in area across all simulations. In contrast, the area 

of shrub tundra was projected to be relatively stable under 

the CGCM3.1 simulations, whereas a small increase was 

projected under the ECHAM5 simulations.

Distributional trends among the LCC regions varied 

substantially (figs. 2.8 through 2.12). In the Arctic LCC, all 

simulations projected decreases in graminoid tundra and 

increases in shrub tundra (fig. 2.8). Under the CGCM3.1 simulations, a small increase in white spruce forest was projected. 

The Western Alaska LCC exhibited similar results 

to its tundra-dominated Arctic LCC counterpart for forest 

land-cover types, but opposite trends for the tundra land-cover 

types (fig. 2.9). In contrast to the Arctic LCC, the Western 

Alaska LCC simulations projected variable but decreasing 

shrub tundra trends across all simulations. Under the 

CGCM3.1 simulations, graminoid tundra was projected to 

increase slightly whereas under the ECHAM5 simulations, 

an increase at the beginning of the century was projected, 

followed by a decline ending in areal extents similar to 

beginning levels.

The simulations for the Northwest Boreal LCC North 

projected moderate to large changes in forest distribution 

(fig. 2.10). For the CGCM3.1 simulations, white and black 

spruce forest extent was projected to vary from moderate 

decreases under the A2 scenario to small increases under the 

B1 scenario. In contrast, the ECHAM5 simulations projected 

large decreases in white and black spruce forest extent across 

all scenarios, with concomitant increases in early successional 

deciduous forest. Graminoid tundra was projected to decrease 

in all simulations. Projected changes in shrub tundra extent 

were opposite for the GCMs, with projected decreases under 

CGCM3.1 but increases under ECHAM5.

The projections for the Northwest Boreal LCC South 

mirrored the trends exhibited in the Northwest Boreal LCC 

North, except that under the CGCM3.1 simulations shrub 

tundra was projected to increase across all scenarios—in 

contrast to the CGCM3.1 simulations for the Northwest 

Boreal LCC North (fig. 2.11). In all simulations, the projected 

areal extent of change was substantially smaller than for the 

Northwest Boreal LCC North.

The simulations for the North Pacific LCC projected 

only minor changes owing to the static nature of the major 

ecosystem type (coastal temperate forest) in ALFRESCO. 

Along the extreme northern portions of this LCC, minor 

decreases in spruce forests were projected with concomitant 

increases in deciduous forest (fig. 2.12). These trends were 

consistent across all simulations.

2.4.5. Projected Wildfire

2.4.5.1. Retrospective Simulations

Calibration simulations were performed to tune the 

modeled relationship between climate and fire. Calibrations 

iteratively adjusted the quantitative linkage between climate 

and fire by comparing model output (such as, fire numbers 

and extent, stand age structure) to the corresponding historical 

data. Several metrics were used to calibrate the performance 

of ALFRESCO simulations (Rupp and others, 2000, 2002, 

2006, 2007). These metrics included (1) the frequency-area 

distribution of the fire sizes, (2) the inter-annual variability 

from 1950 through 2009, and (3) the mean area burned from 

1950 through 2009. Calibration results are not presented here 

but followed the methods outlined in Mann and others (2012). 

Once a sufficient correspondence between the historical 

data and the simulation output was obtained across multiple 

metrics, the calibration optimization was halted.

ALFRESCO simulations of the historical period of record 

provided reasonable depictions of the fire regime in Alaska. 

Specifically, the frequency-area distribution, inter-annual 

variability in area burned, and spatial distribution of fires 

were consistent with those from the observed record. To 

account for stochastic components of the fire regime (such as, 

ignitions, duration of vegetation dominance through succession, and so forth), multiple replicates (n=200) of fire activity 

and subsequent succession were simulated. Projected model 

results were then assembled and distributional properties 

across replicates were analyzed. The model performed 

relatively well in simulating historical wildfire activity driven 

by historical climate data (table 2.8). The simulated number 

of wildfires averaged 60 per year, ranging from 48 to 78. 
Chapter 2 33

120

140

160

180

Black spruce forest

300

350

400

450

Deciduous forest

110

120

130

140

150

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

260

265

270

275

280

Shrub tundra

2020 2040 2060 2080 2100

140

120

160

180

200

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

140

150

160

170

180

190

280

300

320

340

360

380

400

120

125

130

135

140

145

2020 2040 2060

Year Year

Year

Year Year

Year

2080

250

255

260

2020 2040 2060 2080

170

180

190

200

210

220

2020 2040 2060 2080

2100 2100

2100

B. Full assessment domain—ECHAM5

A. Full assessment domain—CGCM3.1

Figure 2-7

Area, in thousands of square kilometers Area, in thousands of square kilometers

A1B

A2

B1

EXPLANATION

Climate scenario

Figure 2.7. Projected change in land-cover type from 2009 through 2099 across the full assessment domain. 

A, changes projected by version 3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global 

Climate Model (CGCM3.1) climate simulations and B, changes projected by version 5 of the Max Planck Institute’s 

European Centre Hamburg Model (ECHAM5) climate simulations under the three emissions scenarios A1B, A2, and B1 

of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios. Heath tundra, wetland 

tundra, and coastal temperate forest land-cover types are not presented owing to the static nature of these land-cover 

types in the Alaska Frame-Based Ecosystem Code (ALFRESCO) model.
34 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

4.0

4.5

5.0

5.5

6.0

2

4

6

8

100

105

110

115

112

114

116

118

120

9

8

10

11

12

13

14

2

3

4

5

6

2

4

6

8

10

12

90

95

100

105

110

115

110

115

120

125

130

135

5

6

7

8

Black spruce forest Deciduous forest

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

Shrub tundra

2020 2040 2060 2080 2100

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

2020 2040 2060 2080 2020 2040 2060 2080

2020 2040 2060 2080

2100 2100

2100

Figure 2-8

Area, in thousands of square kilometers Area, in thousands of square kilometers

B. Arctic LCC—ECHAM5

A. Arctic LCC—CGCM3.1

A1B

A2

B1

EXPLANATION

Climate scenario

Figure 2.8. Projected change in land-cover type from 2009 through 2099 across the Arctic Landscape Conservation 

Cooperative (LCC). Details regarding the simulations of land-cover change shown can be found in figure 2.7.
Chapter 2 35

35

40

45

50

50

40

60

70

80

13.0

13.5

14.0

14.5

15.0

15.5

106

108

110

112

114

116

118

45

50

55

30

35

40

45

50

50

60

70

80

90

14

15

16

17

18

113

114

115

116

117

118

30

35

40

45

50

B. Western Alaska LCC—ECHAM5

A. Western Alaska LCC—CGCM3.1

Black spruce forest Deciduous forest

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

Shrub tundra

2020 2040 2060 2080 2100

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

2020 2040 2060 2080

2020 2040 2060 2080 2020 2040 2060 2080

2100

2100 2100

Figure 2-12

A1B

A2

B1

Area, in thousands of square kilometers Area, in thousands of square kilometers

EXPLANATION

Climate scenario

Figure 2.9. Projected change in land-cover type from 2009 through 2099 across the Western Alaska Landscape 

Conservation Cooperative (LCC). Details regarding the simulations of land-cover change shown can be found in 

figure 2.7.
36 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

80

85

90

95

100

105

210

220

230

240

250

260

4

6

8

10

12

12

13

14

15

85

90

95

100

105

110

65

70

75

80

85

90

95

220

240

260

280

300

4

6

8

10

12

14.5

15.0

15.5

16.0

70

80

90

100

B. Northwest Boreal LCC North—ECHAM5

A. Northwest Boreal LCC North—CGCM3.1

Black spruce forest Deciduous forest

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

Shrub tundra

2020 2040 2060 2080 2100

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

2020 2040 2060 2080

2020 2040 2060 2080 2020 2040 2060 2080

2100

2100 2100

Figure 2-10

Area, in thousands of square kilometers Area, in thousands of square kilometers

A1B

A2

B1

EXPLANATION

Climate scenario

Figure 2.10. Projected change in land-cover type from 2009 through 2099 across the Northwest Boreal Landscape 

Conservation Cooperative (LCC) North. Details regarding the simulations of land-cover change shown can be found in 

figure 2.7.
Chapter 2 37

24

26

28

30

32

34

25

30

35

40

45

1.4

1.6

1.8

2.0

2.2

2.4

2.6

13.45

13.50

13.55

13.60

13.65

13.70

13.75

30

32

34

36

38

40

20

25

30

25

30

35

40

45

50

55

1.6

1.8

2.0

2.2

2.4

2.6

13.4

13.5

13.6

13.7

13.8

13.9

14.0

14.1

25

30

35

B. Northwest Boreal LCC South—ECHAM5

A. Northwest Boreal LCC South—CGCM3.1

Black spruce forest Deciduous forest

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

Shrub tundra

2020 2040 2060 2080 2100

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

2020 2040 2060 2080

2020 2040 2060 2080 2020 2040 2060 2080

2100

2100 2100

Figure 2-11

Area, in thousands of square kilometers Area, in thousands of square kilometers

A1B

A2

B1

EXPLANATION

Climate scenario

Figure 2.11. Projected change in land-cover type from 2009 through 2099 across the Northwest Boreal Landscape 

Conservation Cooperative (LCC) South. Details regarding the simulations of land-cover change shown can be found in 

figure 2.7.
38 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

0.76

0.78

0.80

0.82

0.84

0.10

0.15

0.20

0.25

−1.0

−0.5

0.0

0.5

1.0

0.004

0.005

0.006

0.007

0.008

0.52

0.53

0.54

0.55

0.56

0.57

0.58

0.70

0.75

0.80

0.85

0.10

0.15

0.20

0.25

0.30

0.35

−1.0

−0.5

0.0

0.5

1.0

0.004

0.005

0.006

0.007

0.008

0.48

0.50

0.52

0.54

0.56

0.58

B. North Pacific LCC—ECHAM5

A. North Pacific LCC—CGCM3.1

Black spruce forest Deciduous forest

Graminoid tundra

Black spruce forest Deciduous forest

Graminoid tundra

2020 2040 2060 2080 2100

Shrub tundra

2020 2040 2060 2080 2100

White spruce forest

Shrub tundra

White spruce forest

2020 2040 2060 2080 2100

2020 2040 2060 2080 2020 2040 2060 2080

2020 2040 2060 2080

2100 2100

2100

Figure 2-9

Area, in thousands of square kilometers Area, in thousands of square kilometers

A1B

A2

B1

EXPLANATION

Climate scenario

No graminoid tundra

No graminoid tundra

Figure 2.12. Projected change in land-cover type from 2009 through 2099 across the North Pacific Landscape Conservation 

Cooperative (LCC). Details regarding the simulations of land-cover change shown can be found in figure 2.7.
Chapter 2 39

Table 2.8. Observed and simulated fire data from Alaska Frame-Based Ecosystem Code (ALFRESCO) model 

for each Landscape Conservation Cooperative (LCC) region and the full assessment domain (Alaska) during the 

baseline period (1950–2009).

[Summary statistics are based on 200 model replicates. km2, square kilometer; —, not applicable]

Metric

Observed Simulated

Number of wildfires 

per year

Annual area burned 

(km2

)

Number of wildfires 

per year

Annual area burned 

(km2)

Arctic LCC

Mean 2.1 60.49 0.9 86

Standard deviation 3.2 178.16 0.3 210

Minimum 0 0 0 2

Median 0 0 1 10

Maximum 13 1,106.82 2 1,130

Year of maximum 2005 2007 — —

Western Alaska LCC

Mean 5.82 393.81 8 762

Standard deviation 7.68 1,258.46 2 1,168

Minimum 0 0 5 23

Median 3.5 33.42 8 255

Maximum 37 8,313.50 12 6,411

Year of maximum 1972 1957 — —

Northwest Boreal LCC North

Mean 31.8 3,262.26 44 2,802

Standard deviation 33.07 5,178.67 5 3,089

Minimum 0 0 35 335

Median 20 1,296.82 43 1,612

Maximum 137 26,683.54 57 13,983

Year of maximum 2005 2004 — —

Northwest Boreal LCC South

Mean 2.15 54.49 10 331

Standard deviation 2.7 112.52 2 441

Minimum 0 0 6 31

Median 1 4.28 9 187

Maximum 15 615.4 14 2,059

Year of maximum 2009 2009 — —

North Pacific LCC

Mean 0.6 2.21 0.1 4

Standard deviation 0.92 6.93 0.2 2

Minimum 0 0 0 1

Median 0 0 0 3

Maximum 4 36.31 1 11

Year of maximum 1991 1991 — —

Alaska 

Mean 41.3 3,791.50 60 3,789

Standard deviation 41.92 5,709.97 7 4,343

Minimum 0 0 48 433

Median 27 1,596.77 59 2,028

Maximum 176 27,071.72 78 18,391

Year of maximum 2005 2004 — —
40 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The average number of simulated fires is slightly higher 

than the historical average; however, the estimate of the 

average for the historical data is likely biased low owing 

to under-reporting in the first decade of the observational 

record. Historical annual area burned averaged 3,791 km2 

(table 2.4) —that is, 0.25 percent of the total area of the 

Alaska simulation domain, which has a total area of 

1.49 million km2. Simulation results for that same period 

had an average annual area burned of 3,789 km2. The 

inter-annual variability of the simulated fire activity was 

smaller than that of the historical data. This is largely due 

to deficiencies in the model’s ability to depict inter-annual 

variability in the ignitions because of a lack of reliable 

historical data regarding ignitions.

Table 2.9. Summary of fire activity for the full assessment domain simulated for the last decades of the historical period (2000–2009)

and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. The six simulations were 

combinations of the two general circulation models, version 3.1-T47 of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global 

Climate Model (CGCM 3.1; McFarlane and others, 1992; Flato, 2005) and version 5 of the Max Planck Institute’s European Centre Hamburg Model 

(ECHAM5; Roeckner and others, 2003, 2004), and three climate change scenarios, B1, A1B, and A2, in order of low to high projected CO2 emissions, 

of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović and Swart, 2000). km2, square kilometer]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent) Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 60 3,398 60 2,887 0.8 –15.0

A1B 95th 84 17,971 84 19,259 0.9 7.2

A2 Median 59 3,155 62 5,324 4.2 68.8

A2 95th 84 17,880 88 32,569 5.2 82.2

B1 Median 59 3,089 66 3,636 12.7 17.7

B1 95th 84 17,667 88 16,038 4.8 –9.2

ECHAM5

A1B Median 59 3,135 55 4,904 –6.8 56.4

A1B 95th 85 18,079 83 25,677 –2.5 42.0

A2 Median 59 3,060 51 3,412 –13.6 11.5

A2 95th 84 17,579 79 23,435 –5.9 33.3

B1 Median 59 3,159 53 2,576 –10.2 –18.5

B1 95th 85 17,428 75 11,429 –11.4 –34.4

2.4.5.2. Future Simulations

The simulation results of fire activity for the last 

decade of the projection period (2090–2099) were compared 

with simulated results from the most recent full decade 

(2000–2009), referred to as the historical reference period. 

Results are summarized using percentiles from the distribution 

of output (such as, number of wildfires, area burned) across 

200 model replicates for each simulation. Greater differences 

were found between simulated output from GCMs across all 

emissions scenarios than among emissions scenarios within 

a given GCM. Across the full domain of this assessment and 

across all simulations, the median (across 200 model replicates) projected number of wildfires for 2090–2099 ranged 

from 51 to 88 (table 2.9, fig. 2.13). The projected annual area 
Chapter 2 41

0

20

40

60

80

Number of wildlfires per year Number of wildlfires per year

A. Full assessment domain—CGCM3.1

B. Full assessment domain—ECHAM5

0

5

10

15

20

25

30

Climate

scenario

A1B

A2

B1

95th and 5th

percentiles Median

Figure 2-13

0

20

40

60

80

0

20

40

60

80

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers

EXPLANATION

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2060–2069

2070–2079

2080–2089

2090–2099

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2060–2069

2070–2079

2080–2089

2090–2099

burned across 200 model replicates averaged 12,591 km2, or 

0.85 percent of the assessment domain, which has a total area 

of 1.49 million km2. The median (across 200 model replicates) 

annual area burned ranged from 2,477 km2 (0.16 percent of 

total area) to 33,039 km2 (2.2 percent of total area). 

Under the CGCM3.1 simulations, the 50th (median) and 

95th percentiles for the number of wildfires were projected 

to increase across all scenarios (table 2.9, fig. 2.13A). The 

increases for both percentiles were least pronounced and 

somewhat negligible under the A1B scenario. Projections 

of future area burned varied in magnitude and direction 

of change (relative to present) across scenarios. The 

50th percentile of area burned for the B1 and A2 scenarios 

was projected to increase by 17.7 percent and 68.8 percent, 

respectively. In contrast, the 50th percentile of area burned 

for the A1B scenario was projected to decrease by 15 percent. 

The 95th percentile of area burned was projected to increase 

for the A1B and A2 scenarios by 7.2 percent and 82.2 percent, 

respectively. In contrast, the 95th percentile of area burned for 

the B1 scenario was projected to decrease by 9.2 percent.

Under the ECHAM5 simulations, the 50th and 95th percentiles for the number of wildfires were projected to decrease 

across all scenarios (table 2.9, fig. 2.13B), with the greatest 

projected decrease under the A2 scenario and the smallest 

projected decrease under the B1 scenario. The area burned was 

projected to increase for the A1B and A2 scenarios; in contrast, 

the area burned was projected to decrease for the B1 scenario. 

The greatest change from present was projected under the 

A1B scenario, with 56-percent and 42-percent increases for 

the 50th and 95th percentiles of area burned, respectively.

Figure 2.13. Simulated fire activity showing decadal summaries of annual projected number of wildfires and area 

burned for each decade from 2000 through 2099 across the full assessment domain for the A, version 3.1-T47 of 

the Canadian Centre for Climate Modelling and Analysis’ Coupled Global Climate Model (CGCM3.1) simulation and 

B, version 5 of the Max Planck Institute’s European Centre Hamburg Model (ECHAM5) simulation. The 50th (median), 

5th, and 95th percentiles were computed across 200 simulations for climate change scenarios A1B, A2, and B1 of the 

Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios. 
42 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The Northwest Boreal LCC North had the most 

(approximately 85 percent of the statewide total) historical fire 

activity among the five LCC regions of the assessment and 

was also projected to have the most future fire activity. Under 

the CGCM3.1 simulations, the 50th and 95th percentiles for 

the number of wildfires were projected to increase for all 

scenarios (table 2.10, fig. 2.14A). The projected magnitude and 

direction of change in area burned varied across scenarios. The 

50th percentile was projected to increase (0.9–47.6 percent) 

for all scenarios. In contrast, the 95th percentile was projected 

Table 2.10. Summary of fire activity for the Northwest Boreal Landscape Conservation Cooperative North 

simulated for the last decades of the historical period (2000–2009) and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. Details 

regarding the models and scenarios shown can be found in table 2.9. km2, square kilometer]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent)

Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 42 2,274 45 2,295 5.9 0.9

A1B 95th 62 10,342 64 10,199 3.5 –1.4

A2 Median 42 2,194 44 3,239 7.2 47.6

A2 95th 63 10,459 66 16,626 5.3 59.0

B1 Median 42 2,216 48 2,622 15.5 18.3

B1 95th 62 10,511 67 7,855 7.3 –25.3

ECHAM5

A1B Median 42 2,200 40 3,174 –3.6 44.3

A1B 95th 63 10,426 62 12,217 –0.6 17.2

A2 Median 42 2,186 37 2,176 –10.8 –0.5

A2 95th 63 10,422 61 12,642 –3.1 21.3

B1 Median 42 2,230 38 1,798 –7.2 –19.4

B1 95th 63 10,264 57 8,090 –9.2 –21.2

to decrease under the A1B and B1 scenarios and increase 

under the A2 scenario. Under the ECHAM5 simulations, 

the 50th and 95th percentiles for the number of wildfires 

were projected to decrease across all scenarios (table 2.10, 

fig. 2.14B). General increases in the distribution of area 

burned were projected under the A1B and A2 scenarios, 

whereas decreases were projected under the B1 scenario. 

The simulation under the A1B scenario projected the largest 

change, with 44-percent and 17-percent increases projected 

for the 50th and 95th percentiles of area burned, respectively.

Figure 2.14. Simulated fire 

activity showing decadal 

summaries of annual projected 

number of wildfires and area 

burned for each decade from 

2000 through 2099 across the 

Northwest Boreal Landscape 

Conservation Cooperative (LCC) 

North. Details regarding the 

simulations of fire activity shown 

can be found in figure 2.13.

0

10

20

30

40

50

60

70

0

5

10

15

0

10

20

30

40

50

60

0

5

10

15

20

25

30

35

A. Northwest Boreal LCC North—CGCM3.1

B. Northwest Boreal LCC North—ECHAM5

Number of wildlfires per year Number of wildlfires per year

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers

Climate

scenario

A1B

A2

B1

95th and 5th

percentiles Median

EXPLANATION

Figure 2-14

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

2060–2069

2070–2079

2080–2089

2090–2099

Decade

2060–2069

2070–2079

2080–2089

2090–2099
Chapter 2 43

Table 2.11. Summary of fire activity for the Western Alaska Landscape Conservation Cooperative simulated for 

the last decades of the historical period (2000–2009) and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. Details 

regarding the models and scenarios shown can be found in table 2.9. km2, square kilometer]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent)

Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 9 714 7 344 –22.2 –51.8

A1B 95th 17 7,735 16 9,185 –7.9 18.8

A2 Median 8 332 8 929 6.2 179.8

A2 95th 17 7,315 16 10,729 –6.0 46.7

B1 Median 8 336 8 802 6.2 138.7

B1 95th 17 7,533 17 8,400 –2.6 11.5

ECHAM5

A1B Median 8 330 7 1,329 –17.6 302.7

A1B 95th 17 7,529 15 9,979 –14.4 32.5

A2 Median 8 334 7 824 –17.6 146.7

A2 95th 17 7,407 15 10,651 –12.1 43.8

B1 Median 8 332 6 360 –23.5 8.4

B1 95th 17 7,251 14 5,842 –18.1 –19.4

Figure 2.15. Simulated fire 

activity showing decadal 

summaries of annual projected 

number of wildfires and area 

burned for each decade from 

2000 through 2099 across the 

Western Alaska Landscape 

Conservation Cooperative (LCC). 

Details regarding the simulations 

of fire activity shown can be

found in figure 2.13.

The Western Alaska LCC had the second most historical 

fire activity among the five LCC regions and was also 

projected to have the second most future fire activity. Under 

the CGCM3.1 simulations, projected median changes in 

number of wildfires varied in magnitude and direction. Under 

the B1 and A2 scenarios, moderate increases were projected 

in the 50th percentile, whereas the greatest change among 

the scenarios was projected under the A1B scenario, with a 

decrease of 22.2 percent (table 2.11, fig. 2.15A). The distribution in area burned was projected to increase for all scenarios, 

except for the 50th percentile under the A1B scenario, which 

was projected to decrease. Under the B1 and A2 scenarios, 

large increases (139 percent and 180 percent, respectively) 

were projected in the 50th percentile. Under the ECHAM5 

simulations, the 50th and 95th percentiles for the number 

of wildfires were projected to decrease across all scenarios 

(table 2.11, fig. 2.15B). The distribution of area burned was 

projected to increase under the A1B and A2 scenarios, whereas 

the 95th percentile under the B1 scenario was projected to 

decrease. The 50th percentile was projected to increase under 

each of the scenarios with the largest increases under the A2 

and A1B scenarios (146 percent and 302 percent, respectively). 

0

5

10

15

0

5

10

15

0

5

10

15

0

10

20

30

A. Western Alaska LCC—CGCM3.1

B. Western Alaska LCC—ECHAM5

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

2060–2069

2070–2079

2080–2089

2090–2099

Decade

2060–2069

2070–2079

2080–2089

2090–2099

Climate

scenario

A1B

A2

B1

95th and 5th

percentiles Median

EXPLANATION

Figure 2-15 Number of wildfires per year Number of wildfires per year

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers
44 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

In the Arctic LCC, no change was projected in the 

50th percentile of the number of wildfires across all scenarios 

with CGCM3.1 (table 2.12, fig. 2.16A). The 95th percentile 

under the A1B and A2 scenarios was projected to increase. 

The projected change in area burned varied in magnitude and 

direction across scenarios. The 50th percentile was projected 

to increase under scenarios B1 and A2, whereas no change was 

projected under scenario A1B. The largest change was projected 

under the A2 scenario with an increase of 1,300 percent. 

The 95th percentile was projected to decrease under the B1 

and A1B scenarios; however, the 95th percentile under the 

A2 scenario was projected to increase. Under the ECHAM5 

simulations, the 50th percentile for the number of wildfires 

was not projected to change across all scenarios (table 2.12, 

fig. 2.16B). The 95th percentile under the B1 and A2 scenarios 

was projected to decrease, whereas that under the A1B scenario 

was projected to increase. The 50th percentile of the distribution of area burned was projected to increase across all of the 

scenarios, with the greatest (2,050-percent) increase projected 

under the A1B scenario. The 95th percentile was projected to 

decrease under the B1 and A2 scenarios, whereas an increase 

in the 95th percentile was projected under the A1B scenario.

Table 2.12. Summary of fire activity for the Arctic Landscape Conservation Cooperative simulated for the last 

decades of the historical period (2000–2009) and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. Details 

regarding the models and scenarios shown can be found in table 2.9. km2, square kilometer]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent)

Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 1 22 1 22 0 0

A1B 95th 3 5,553 4 4,128 18.3 –25.7

A2 Median 1 10 1 140 0 1,300

A2 95th 3 5,919 4 8,362 33.3 41.3

B1 Median 1 10 1 16 0 60

B1 95th 3 6,230 3 3,323 0 –46.7

ECHAM5

A1B Median 1 10 1 215 0 2,050

A1B 95th 3 6,058 4 7,525 18.3 24.2

A2 Median 1 10 1 56 0 460

A2 95th 3 6,042 3 5,473 –15 –9.4

B1 Median 1 10 1 40 0 300

B1 95th 4 6,232 3 1,421 –28.2 –77.2

0

1

2

3

4

0

2

4

6

8

0

1

2

3

4

0

5

10

15

A. Arctic LCC—CGCM3.1

B. Arctic LCC—ECHAM5

Number of wildfires per year Number of wildfires per year

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers

Climatescenario

A1B

A2

B1

95th and 5th

percentiles Median

EXPLANATION

Figure 2-16

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

2060–2069

2070–2079

2080–2089

2090–2099

Decade

2060–2069

2070–2079

2080–2089

2090–2099

Figure 2.16. Simulated fire 

activity showing decadal 

summaries of annual projected 

number of wildfires and area 

burned for each decade from 

2000 through 2099 across the 

Arctic Landscape Conservation 

Cooperative (LCC). Details 

regarding the simulations 

of fire activity shown can be 

found in figure 2.13.
Chapter 2 45

Table 2.13. Summary of fire activity for the Northwest Boreal Landscape Conservation Cooperative South 

simulated for the last decades of the historical period (2000–2009) and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. Details 

regarding the models and scenarios shown can be found in table 2.9. km2, square kilometer]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent)

Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 10 264 10 167 –5 –36.7

A1B 95th 20 2,337 18 1,355 –7.9 –42.0

A2 Median 10 208 10 430 10.5 106.7

A2 95th 20 2,355 22 12,523 10.2 431.8

B1 Median 10 204 11 258 15.8 26.5

B1 95th 20 2,328 20 2,064 2.3 –11.3

ECHAM5

A1B Median 10 203 9 308 –5.3 51.7

A1B 95th 20 2,362 19 8,689 –2.3 267.9

A2 Median 10 200 8 203 –15.8 1.5

A2 95th 20 2,289 18 4,810 –10.2 110.1

B1 Median 10 212 8 143 –10.5 –32.5

B1 95th 20 2,283 17 1,298 –15.3 –43.1

In the Northwest Boreal LCC South, increases in the 

50th and 95th percentiles for the number of wildfires were 

projected under the B1 and A2 scenarios with CGCM3.1, 

whereas decreases were projected under the A1B scenario 

(table 2.13, fig. 2.17A). The projected change in area burned 

varied in magnitude and direction across scenarios. The 

50th percentile was projected to increase under the B1 and 

A2 scenarios. A decrease in the 95th percentile was projected 

under the B1 scenario, whereas an increase was projected 

under the A2 scenario. The magnitude of change was largest 

under the A2 scenario with the 50th percentile projected to 

increase by 107 percent and the 95th percentile projected to 

increase by 432 percent. Under the ECHAM5 simulations, 

the 50th and 95th percentiles for the number of wildfires 

were projected to decrease across all scenarios (table 2.13, 

fig. 2.17B). The 50th and 95th percentiles of the distribution 

of area burned were projected to increase under the A1B 

and A2 scenarios, whereas decreases were projected under 

the B1 scenario. The largest increase in the 50th percentile 

(51 percent) was projected under the A1B scenario, and the 

largest increase in the 95th percentile (110 percent) was 

projected under the A2 scenario.

0

5

10

15

20

0

2

4

6

8

10

12

0

5

10

15

20

0

2

4

6

8

10

A. Northwest Boreal LCC South—CGCM3.1

B. Northwest Boreal LCC South—ECHAM5

Climate

scenario

A1B

A2

B1

95th and 5th

percentiles Median

EXPLANATION

Figure 2-17

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

2060–2069

2070–2079

2080–2089

2090–2099

Decade

2060–2069

2070–2079

2080–2089

2090–2099

Number of wildfires per year Number of wildfires per year

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers

Figure 2.17. Simulated fire 

activity showing decadal 

summaries of annual projected 

number of wildfires and area 

burned for each decade from 

2000 through 2099 across the 

Northwest Boreal Landscape 

Conservation Cooperative (LCC) 

South. Details regarding the 

simulations of fire activity shown 

can be found in figure 2.13.
46 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Of the five assessment regions, the North Pacific LCC 

had the least amount of fire activity reflecting the LCC’s wet 

and cool climate. Future simulations showed little indication 

of any meaningful future fire activity in this region (table 2.14, 

fig. 2.18A, B).

Table 2.14. Summary of fire activity for the North Pacific Landscape Conservation Cooperative simulated for the 

last decades of the historical period (2000–2009) and the projection period (2090–2099).

[The 50th (median) and 95th percentiles were computed across 200 model replicates for each future climate simulation. Details 

regarding the models and scenarios shown can be found in table 2.9. km2, square kilometer; —, not applicable]

Climate 

scenario

Metric

(percentile)

Historical period Projection period Change in

number of

wildfires

(percent)

Change in

area burned 

(percent)

Number of 

wildfires

Area burned 

(km2

)

Number of 

wildfires

Area burned 

(km2)

CGCM3.1

A1B Median 0 2 0 2 — —

A1B 95th 2 27 2 28 –22.5 3.7

A2 Median 0 2 0 5 — —

A2 95th 2 25 3 160 64.52 540

B1 Median 0 2 0 2 — —

B1 95th 2 25 2 25 0 0

ECHAM5

A1B Median 0 2 0 6 — —

A1B 95th 2 25 3 274 93.55 996

A2 Median 0 2 0 4 — —

A2 95th 2 24 3 121 64.52 404.17

B1 Median 0 2 0 2 — —

B1 95th 2 23 2 44 29.03 91.3

0

0.5

1.0

1.5

2.0

2.5

0

0.5

1.0

1.5

2.0

2.5

3.0

0

0.05

0.10

0.15

0

0.05

0.10

0.15

0.20

0.25

A. North Pacific LCC—CGCM3.1

B. North Pacific LCC—ECHAM5

Climate

scenario

A1B

A2

B1

95th and 5th

percentiles Median

EXPLANATION

Figure 2-18

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

Decade

2000–2009

2010–2019

2020–2029

2030–2039

2040–2049

2050–2059

2060–2069

2070–2079

2080–2089

2090–2099

Decade

2060–2069

2070–2079

2080–2089

2090–2099

Number of wildfires per year Number of wildfires per year

Annual area burned,

in thousands of square kilometers

Annual area burned,

in thousands of square kilometers

Figure 2.18. Simulated fire 

activity showing decadal 

summaries of annual projected 

number of wildfires and area 

burned for each decade from 

2000 through 2099 across the

North Pacific Landscape 

Conservation Cooperative (LCC). 

Details regarding the simulations 

of fire activity shown can be 

found in figure 2.13.
Chapter 2 47

2.4.6. Discussion and Conclusions

Climate effects, both direct and indirect, are projected 

to influence the vegetation-disturbance dynamics of Alaska 

through the 21st century. Most of the vegetation change 

simulated by ALFRESCO would result from the indirect 

effects of climate as mediated by the fire regime. On the basis 

of the characterization of fire-climate relationships from the 

past six decades, fire regimes are forecast to change for most 

of the simulations considered. The Northwest Boreal LCC 

North would exhibit the greatest amount of change in simulated fire activity relative to historical observations. One of the 

dominant results following from this projected change in fire 

regimes is a projected decrease in the area occupied by late 

successional spruce forests. This decrease would be consistent 

across all the climate simulations for Alaska as a whole and in 

most of the five LCC regions.

One of the defining characteristics of the fire regime in 

Alaska is significant variability across both space and time. 

The simulated fire activity from both GCMs produced large 

inter-annual variability in the fire activity in a manner consistent with the historical fire data in Alaska. A steady increase 

in both the number of wildfires and median area burned 

after 2050 was projected for CGCM3.1 simulations under 

all scenarios. The variability of the distribution of simulated 

area burned also was projected to increase, as depicted by 

the increase in the 95th percentiles across the second half of 

the 21st century. In contrast, under the ECHAM5 simulations 

across all scenarios a small decrease in the number of wildfires 

and an increase in the median area burned after 2050 were 

projected as well as a decrease in the variability of the distribution of simulated area burned, shown in the 95th percentile.

The differences between the CGCM3.1 and ECHAM5 

simulation output for the period 2010–2030 is driven largely 

by changes in the climate forcing of the fire activity. This is 

a consequence of the fact that the initial spatial distribution 

of differentially flammable vegetation is relatively consistent 

among simulations since it is determined by the patterns of 

historical fire. As the changes in future climate affect the landscape configuration of vegetation types through fire-initiated 

secondary succession, there are potential feedbacks to the fire 

regime. This is likely the reason that there are differences in 

the trends of the number of wildfires between the GCMs. That 

is, differences in the spatial configuration of differentially 

flammable vegetation types emerge uniquely for the GCMs 

as a function of the climate forcing effects on the fire regime. 

Ultimately, the configuration of the landscape dictates how the 

fire activity emerges for the climate signal in each year.

Simulated land-cover types in tundra-dominated portions 

of the assessment domain exhibited moderate levels of change. 

Graminoid tundra was projected to decrease under most of 

the future climate simulations, whereas shrub tundra was 

projected to increase. The magnitude and direction of change 

would vary considerably across the LCC regions, though 

consistently within each region. That is, in the Arctic LCC, 

graminoid tundra was projected to decrease and shrub tundra 

was projected to increase across the simulations, whereas 

opposite trends were projected for the Western Alaska LCC. 

These patterns reflect positive influences of a warming 

climate, in the case of shrub tundra increases in the Arctic 

LCC, as well as the indirect effects through wildfire activity, in 

the case of shrub tundra decreases in the Western Alaska LCC.

2.4.6.1. Uncertainty

The use of models to simulate future fire activity provides 

a means to assemble the state of the science and assess the 

likely responses to future scenarios. Generally speaking, 

models are developed with the intent to characterize the 

functional relationships between ecological responses and 

environmental drivers. One metric of model performance 

is to meaningfully explain the observed variability in an 

ecological response at varying resolutions of space and time. 

Best performing models are able to parsimoniously explain 

variability in a manner that is robust to the multiple sources 

of uncertainty that are constrained through assumptions.

The projections presented here were generated through 

the development of several layers of modeling, each with its 

own corresponding uncertainty. The process starts with data. 

One can consider data to be an observation of some underlying 

true latent process. In most settings, observed data are considered to be truth. That is, the difference between the observed 

value and the true state of the underlying latent process is zero. 

This is the implementation of the time-honored uncertainty 

reduction technique of assumption making. In some cases this 

is appropriate, in others it may make more sense to employ 

a hierarchical modeling approach to separate the uncertainty 

associated with the input data from that of the process model 

(Calder and others, 2003; Cressie and Wikle, 2011). When 

constructing simulation models, whether statistical, processbased, or otherwise, there is uncertainty associated with the 

functional form of the model. Finally, there is uncertainty 

associated with the parameters that are estimated in the 

modeling process.

In the work presented here, we explicitly considered 

uncertainty in the simulations of future fire activity that is 

associated with the GCMs and emissions scenarios. This was 

done by running simulations for different combinations of 

GCMs and scenarios. Bounding results were presented in the 

sense that the ECHAM5 model generally corresponds to the 

most active future fire regime and the CGCM3.1 model to the 

least. In using this approach, we conditioned on the functional 

relationship depicted in the ALFRESCO model and assumed 

this was the best model through the application of calibration 

to the historical data. That is, uncertainty in the depiction of 

the functional linkage between future climate and fire was 

constrained. One potential source of uncertainty associated 

with the results is the potential for the functional linkage 

between climate and fire to change in the future.
48 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The historical data were also assumed, for the sake of 

these analyses, to be unbiased representations of the true 

underlying latent process of fire across the boreal forest. This 

assumption was used when quantifying the linkage between 

climate and area burned based on the historical data. There 

is good reason to think this is not the case in Alaska and, 

understandably, the reliability of the historical fire data is 

more questionable for the earlier part of the record (Kasischke 

and others, 2002, 2006). To some extent, issues related to the 

uncertainty associated with the data are potentially reduced 

owing to the increased detectability associated with large fire 

events. That is, it is more likely to miss a smaller fire in the 

record than a larger one. Since over half of the area burned in 

Alaska comes from approximately the largest 2.5 percent of 

the fires (Kasischke and Turetsky, 2006), these issues associated with uncertainty in the data are likely negligible relative 

to the uncertainty associated with the actual unfolding of the 

future climate scenario.

When considering the full assessment domain, the 

uncertainty associated with the projected results was greater 

between GCMs than it was between the emissions scenarios 

for a given GCM, which tend to be generally consistent. This 

assertion holds less credence when considering the LCC 

regions. To some extent this may be driven by the simple fact 

that there was greater variability among replicates for the 

consideration of smaller landscape areas.

2.4.6.2. Sensitivity 

When considering the greatest potential for change in 

the future scenarios, it is of interest to examine not only the 

measure of central tendency for the future scenarios but also 

higher statistical moments as well. Across the entire State, 

there were divergent results among GCMs with respect to the 

change in number of wildfires. The 50th and 95th percentiles 

of the wildfire distributions were projected to increase 

for all scenarios in the CGCM3.1 model, whereas the 

corresponding simulated data were projected to decrease in 

the ECHAM5 model. Projected ignitions were placed in a 

spatially random manner so an increase would correspond to 

increases in the temperature and (or) prevalence of flammable 

vegetation across the landscape. The increase in number of 

wildfires suggests the potential for a subsequent increase in 

the heterogeneity of the landscape as a consequence. Across 

the entire State, the simulation output projected an increase 

in the area burned with more burning associated with the 

CGCM3.1 model. Interestingly, along with the increase in 

area burned was an opposite response in the change through 

time between the 95th percentiles of the CGCM3.1 and the 

ECHAM5 models. The CGCM3.1 model projected an increase 

in number of wildfires and an increase in the variability 

of area burned, whereas the ECHAM5 model projected a 

relatively stable number of wildfires and a steady decrease in 

the variability of area burned through time. In this context, the 

CGCM3.1 model projects a much more variable future with 

respect to landscape-scale disturbance.

When considering the LCC regions, several stand out 

as being most sensitive in terms of future changes in the fire 

regime. The North Pacific LCC had the lowest historical fire 

activity so any future increase would be a relatively large shift, 

although the absolute amount of fire activity may be negligible 

in the context of statewide area burned. The variability of the 

number of wildfires and the variability associated with the 

distribution of the simulated area burned were projected to 

increase consistently over the projection period in the North 

Pacific LCC. In this sense, the simulated results are consistent 

among all simulations, projecting that there would be a 

relatively large increase in fire activity. This is an example of 

a region where there appears to be potential for a threshold to 

be crossed (that is, for greater influence of fire activity) in the 

next century.

The simulation results also indicate that the Northwest 

Boreal LCC South may experience increased wildfires and 

area burned in the future. The ECHAM5 model projected a 

relatively constant number of wildfires but an increase in the 

variability of the area burned under the A2 and A1B scenarios. 

The CGCM3.1 model projected an increase in number of 

wildfires, but only under the A2 scenario was a corresponding 

increase in the variability of the area burned projected.

The simulation results for the Western Alaska LCC 

were relatively divergent between the two GCMs. For the 

CGCM3.1 model, the number of wildfires forecast was relatively constant; however, a forecast increase in the variability 

of area burned was consistent across the scenarios considered. 

Conversely, for the ECHAM5 model, the number of wildfires 

was projected to decrease with a corresponding decrease in 

the variability associated with the distribution of area burned. 

Thus, the sensitivity of the Western Alaska LCC to changes in 

future climate over the 21st century is dependent on the model 

used to simulate the future climate, with the CGCM3.1 model 

being more sensitive and the ECHAM5 model relatively stable 

and therefore less sensitive to climate change. 
Chapter 2 49

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Chapter 3. Soil Carbon and Permafrost Estimates and 

Susceptibility to Climate Change in Alaska

By Bruce K. Wylie,1 Neal J. Pastick,2 Kristopher D. Johnson,3 Norman Bliss,4 and Hélène Genet5

1

U.S. Geological Survey, Sioux Falls, S. Dak.

2

Stinger Ghaffarian Technologies, Inc., Sioux Falls, S. Dak., and University of Minnesota, St. Paul, Minn.

3

U.S. Department of Agriculture Forest Service, Newtown Square, Pa.

4

ASRC Federal InuTeq, Sioux Falls, S. Dak.

5

University of Alaska-Fairbanks, Fairbanks, Alaska.

3.1. Highlights

• Several soil carbon and permafrost data products in 

Alaska, either produced for this assessment or available from the literature, were evaluated to synthesize 

observation-based estimates of distributions of soil 

organic carbon (SOC), permafrost, and other variables. The synthesis was also compared to simulated 

estimates by an ecosystem carbon dynamic model 

called DOS-TEM (Dynamic Organic Soil version 

of the Terrestrial Ecosystem Model).

• The total SOC storage in boreal and arctic regions 

in Alaska ranged from 31 to 72 petagrams of carbon 

(PgC) among different mapped products, and SOC 

simulated by DOS-TEM was well within this range 

at 46 PgC (with a standard deviation [s.d.] of 22 PgC).

• Near-surface (within 1 meter [m]) permafrost (NSP) 

was estimated to underlie 36 to 67 percent of Alaska 

among different map products used in the evaluation, 

and NSP simulated by DOS-TEM was within 

this range at 44 percent. Furthermore, DOS-TEM 

simulations of NSP fell within the range of map 

product estimates for 87 percent of ecotypes, and 

outlier ecotypes constitute approximately 16 percent 

of Alaska.

• Average active-layer thickness (ALT) ranged from 

76 to 84 centimeters (cm) from surface among 

different mapped products, and ALT simulated 

by DOS-TEM was slightly outside of this range 

at 86±8 cm, but within the range of map product 

uncertainty. The ALT derived from the state soil 

geographic database (STATSGO) was generally 

higher than other estimates, possibly owing to 

how ALT is described in soil pedon datasets and 

measured in the field.

• Organic soils were estimated to underlie 

8 to 30 percent of Alaska among different 

map products, and organic soil simulations by 

DOS-TEM were within this range at 18 percent.

• A simple conceptual model of soil susceptibility 

to climate change indicated that Arctic Landscape 

Conservation Cooperative (LCC) lowland shrub 

tundra and Western Alaska LCC lowland shrub 

tundra ecotypes are highly susceptible to climate 

change because of large and potentially liable 

frozen and unfrozen SOC stocks. 

• Intermediate susceptible ecotypes were Arctic 

LCC upland shrub and graminoid tundra with 

susceptibility being driven by potential changes 

to continuous NSP extent and the lack of thick 

insulating layers of organic soils.
54 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

3.2. Conceptual Discussion of Roles 

and State of the Knowledge About 

Soil Carbon and Permafrost in 

Boreal and Arctic Ecosystems 

There are large accumulations of soil organic carbon 

in arctic and boreal forest ecosystems. Hugelius and others 

(2014) indicated that the northern permafrost regions of the 

world contain approximately 1,300 petagrams of organic 

carbon (PgC), of which 800 PgC (61 percent) occurs in perennially frozen soils and deposits. For comparison, the amount of 

carbon as carbon dioxide (CO2) currently in the atmosphere is 

approximately 800 PgC, of which 240 PgC represents the net 

accumulation in the atmosphere from fossil-fuel and land-use 

emissions between 1750 and 2011 (Stocker and others, 2013). 

Thus, the carbon in permafrost-affected ground is nearly 

double the carbon in the atmosphere. The thaw and decay of 

permafrost and permafrost carbon will be irreversible with the 

onset of warming trend of the 2014 and 2100 timeframes, and 

the release of this carbon (the permafrost carbon feedback) 

is not currently accounted for in the climate models used by 

the Intergovernmental Panel on Climate Change (IPCC) Fifth 

Assessment Report (Stocker and others, 2013). Accounting for 

this additional carbon will require larger reductions in fossilfuel emissions to reach a target atmospheric CO2

 concentration 

and therefore a target limit on global temperature increase 

(Schaefer and others, 2011).

To estimate the quantity of soil organic carbon in Alaska, 

Johnson and others (2011) compiled data for many soil profile 

measurements and evaluated the distributions of soil carbon by 

climate regions, landforms, ecoregions, and ecosystem types.

Bliss and Maursetter (2010) estimated a total of 48 PgC of 

soil organic carbon for Alaska and Mishra and Riley (2012) 

estimated 77 PgC, with these estimates influenced by assumptions and data availability for deeper soils (for example, 

between 1 and 3 meters [m]). Although these soil carbon 

inventory studies largely used the same soil carbon measurements, with slight variations and additions between assessments, each study made use of different upscaling approaches.

The status of the permafrost system can be measured 

using deep boreholes with accurate measurements of the 

change of temperature with depth through the Global 

Terrestrial Network for Permafrost (GTN–P; Clow, 2014). 

The thickness of the active layer (that is, maximum annual 

thaw depth) is another measure for the status of the permafrost 

system. With warmer temperatures, the thickness of the active 

layer is expected to increase. Although point-based measurements are useful for understanding local permafrost dynamics, 

these measurements are typically sparse and there is an 

increasing need to monitor and map permafrost properties at 

larger scales (National Research Council, 2014). As part of an 

international network of Circumpolar Active Layer Monitoring 

(CALM) sites, Hinkel and Nelson (2003) concluded that in 

Alaska, active-layer thickness is correlated with inter-annual 

variability in summer temperature and that local variations 

in active-layer thickness and near-surface soil moisture are 

influenced by vegetation, substrate properties, snow cover 

dynamics, and terrain. More recently, empirical models have 

also been used to relate permafrost and soil characteristics 

to environmental factors for regional-scale mapping. For 

instance, remotely sensed or derived datasets have been 

combined with digital elevation models and field data to map 

permafrost properties over large areas of Alaska (Pastick and 

others, 2013; Pastick, Jorgenson, and others, 2014).

Time-series analyses of aerial photos and remote sensing 

imagery are also useful for understanding the rate and extent 

of change associated with permafrost degradation. Using aerial 

photo analyses, Jorgenson and others (2001) found that in the 

Tanana Flats in central Alaska, permafrost degradation has 

been widespread and rapid, causing large shifts in ecosystems 

from birch forests to fens and bogs. With warming, areas that 

are ice-rich experience a collapse of the surface topography 

(thermokarst) on the order of 1 to 1.5 m. In arctic Alaska, 

Jorgenson and others (2006) found that recent degradation has 

mainly affected massive wedges of ice that previously had 

been stable for thousands of years. Thermokarst potentially 

can affect 10 to 30 percent of arctic lowland landscapes and 

severely alter tundra ecosystems even with modest climate 

warming (Jorgenson and others, 2006). Additionally, approximately 40 percent of subarctic Alaska may also be susceptible 

to permafrost degradation and thermokarst (Jorgenson and 

others, 2008).

A variety of methods are used to understand the potential 

rate of carbon release with warming, including chronosequences (Johnston and others, 2014), flux studies on wetland 

gradients (McConnell and others, 2013), incubation of soil 

samples (Wickland and Neff, 2008; Mu and others, 2014; 

Treat and others, 2014), and manipulations that artificially 

warm the soil (Natali and others, 2011, 2012). The insulating 

effect of moss may depend on the water content (O’Donnell 

and others, 2009). Microtopography and slope may influence 

groundwater and surface water flow and thus the formation of 

taliks, thermokarst ponds, and pond drainage (Yoshikawa and 

Hinzman, 2003; Osterkamp and others, 2009; Wellman and 

others, 2013).

Schaefer and others (2014) synthesized results from 

14 studies projecting the magnitude of the permafrost carbon 

feedback across the pan-Arctic to the year 2100, and found 

an ensemble average of 120±85 PgC and a median of 

100 PgC. There is considerable uncertainty from the variety 

of methods and assumptions, but this amount of carbon 

would be equivalent to 5.7 percent of projected anthropogenic 

emissions through 2100 (IPCC scenario RCP 8.5; Riahi and 

others, 2011), and would increase global temperatures by 

0.29±0.21 degrees Celsius (°C), or 7.8±5.7 percent. Projections indicate that 60 percent of the permafrost emissions 

will occur after 2100, so releases of greenhouse gases from 

thawing permafrost will continue for centuries. Schuur and 

others (2015) suggest a similar median magnitude for the 

permafrost carbon feedback with evidence for a gradual and 
Chapter 3 55

prolonged release of greenhouse-gas emissions in a warming 

climate, and present a research strategy for reducing the 

uncertainties. Schaefer and others (2011) suggest that the 

Arctic as a whole may change from a carbon sink to a carbon 

source after the mid-2020s.

Structural and functional changes in boreal forest 

ecosystems associated with warming (for example, reduced 

growth of dominant tree species, plant disease and insect 

outbreaks, warming and thawing of permafrost, and increased 

wildfire extent) that are unprecedented in the past 6,000 years 

are expected over the next few decades (Chapin and others, 

2010; Euskirchen and others, 2010). A shift from coniferous to 

deciduous vegetation in Alaska began about 1990 (Mann and 

others, 2012), and there has also been a documented increase 

in the frequency, intensity, and extent of fire disturbance 

associated with warming and drying trends (Kasischke and 

others, 2010).

Fire directly releases carbon from ecosystems, and 

resulting changes in heat absorption and organic-layer 

thickness influence the degradation and (or) reformation 

of permafrost. Large fires have become more frequent in 

recent years in interior Alaska, with 17 percent of the land 

area burning in a decade (Barrett and others, 2011). Barrett 

and others (2011) estimated that 39 percent (approximately 

4,000 square kilometers [km2

]) of all burned black spruce 

(Picea mariana (Mill.) Britton, Sterns & Poggenb.) stands in 

2004 had less than 10 centimeters (cm) of residual organic 

layer, which may lead to a post-fire loss of permafrost and 

high-quality seedbeds better suited for the establishment of 

deciduous species. With a severe fire, the trees and moss 

will be killed, leaving a blackened soil surface that absorbs 

sunlight directly onto the soil, transferring heat into the soil 

and thawing permafrost (Genet and others, 2013). Harden and 

others (2006) found that for every centimeter of soil organiclayer thickness, the temperature at 5-cm depth was about 

0.5 °C cooler in summer months. Turetsky and others (2011) 

indicate that some black spruce stands have become a net 

source of carbon to the atmosphere, and Chambers and Chapin 

(2002) show there can be an initial reduction of minimum 

albedo following fire from 0.09 to 0.06, followed by a rapid 

increase to 0.135 as the vegetation increased. Increases in fire 

disturbance will augment the amount of carbon released to the 

atmosphere and thus contribute to climate warming, and shifts 

in species composition also have the potential to influence 

the regional climate by changing surface albedo and rates of 

evapotranspiration and conductance (Amiro and others, 2006).

From the conceptual description above, it is shown that 

to properly represent the spatial and temporal variability of 

soil carbon stocks in high-latitude ecosystems, process-based 

ecosystem models need to (1) correctly initialize soil carbon 

stocks and permafrost distribution as a function of drainage 

conditions, soil properties (for example, texture, organic-layer 

thickness), and vegetation composition and (2) represent the 

effects of climate and disturbance regimes on permafrost 

dynamics and the consequences on soil carbon stocks over 

time. One way to evaluate how well process-based ecosystem 

models simulate regional ecological processes and estimate 

the spatial and temporal variability of soil properties is 

by comparing modeled outputs to a set of data products 

developed using other methods and observations, as is 

described below.

3.3. Objectives of the Study and 

General Methods 

As part of this assessment, one objective of this chapter 

is a synthesis of available current and new data products of 

permafrost distribution and soil carbon in Alaska. Several 

new soil property products were created as part of this study 

to help improve and refine soil property estimates, to increase 

the number of product versions beyond the existing spatial 

products, and to better quantify and assess landscape-scale map 

uncertainties. The new soil property products incorporated new 

field data, higher resolution inputs layers, and (or) different 

mapping algorithms relative to previous studies. Statisticalempirical techniques were used in the development of new 

products to quantify near-surface soil properties throughout 

Alaska. The convergence of multiple products derived from 

observations, both in terms of spatial patterns and quantification of uncertainties, was the basis for a confidence measure 

used for the evaluation of a process-based model (that is, 

DOS-TEM). Multiproduct comparisons reduce the consequences of both false-positive and false-negative results that 

may occur when only one reference product is used. 

The second objective of this chapter is to evaluate how 

well processes related to the spatial distribution of soil properties were represented in the biogeochemical process-based 

model used in this assessment by observing the differences 

between the model and the mapped products. The Dynamic 

Organic Soil version of the Terrestrial Ecosystem Model 

(DOS-TEM) is a large-scale ecosystem model designed 

to study interactions among carbon and nitrogen cycling, 

vegetation composition, and the effects of climate change and 

disturbances on soil physical properties, including permafrost 

and active-layer dynamics. Over the last two decades, 

DOS-TEM has been developed to simulate biogeochemical 

cycles and vegetation dynamics in high latitudes, including 

development of an environmental module to reproduce the 

thermal and hydrological regimes of the organic and mineral 

layers in permafrost soils (Zhuang and others, 2003; Yi and 

others, 2009). The DOS-TEM model is a widely used model 

for high-latitude boreal and arctic systems and one of the 

primary models used in this Alaska assessment for current 

and future fluxes and stock of carbon. The synthesis provided 

in the present chapter was designed to provide a baseline to 

evaluate how DOS-TEM reproduced the spatial variability of 

historical soil carbon and permafrost distribution in Alaska 

compared to other empirical and process-based models. 

DOS-TEM used a soil texture data source independent of 

datasets (see chapter 6) discussed in this chapter because 
56 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

of an issue with timing. The state soil geographic database 

(STATSGO) soil texture maps were not available at the time 

DOS-TEM simulations began. Chapters 6 and 7 present how 

DOS-TEM represents the temporal variability of soil carbon 

stocks and permafrost.

The final objective of this chapter was to develop a 

simple conceptual model and map of soil relative susceptibility to climate change based on expert knowledge, new soil 

and ecotype maps, and the literature. Here we define relative 

susceptibility as the degree to which particular “ecotypes” 

(that is, combinations of ecoregion [Landscape Conservation 

Cooperative, LCC region], upland or lowland, and land-cover 

type) and soils are open, liable, or sensitive to climate stimuli 

(Smit and others, 1999). The results of this analysis will 

be informative for land use and land management decision 

making and provide actionable science information for 

sustainable resource management practices.

3.4. Methods and Analysis of Soil Products

Statistical and geospatial methods were used to produce 

and examine spatially explicit estimates of near-surface soil 

properties (that is, soil organic carbon, SOC; permafrost 

distribution; active-layer thickness, ALT; and organic-layer 

thickness, OLT) throughout Alaska, although parameters 

related to permafrost and soil carbon were not assessed in 

the North Pacific LCC because (1) permafrost occurrence 

in this region is typically rare, (2) a portion of the spatial 

datasets did not cover this region, and (3) carbon simulation 

results were not available at the time of these analyses and 

were being done separately for this report (see chapter 4). 

The newly generated products were then compared with other 

existing assessments. Accuracy assessments were conducted 

for a portion of these map products, but accuracies can’t be 

directly compared because of model and mapping differences. 

Because the various products were generated at different 

resolutions and with different mapping methods, comparisons 

were conducted at the ecotype level. DOS-TEM outputs were 

compared to the means, ranges, and uncertainties of the other 

spatial products within ecotype units. Masked areas or areas 

with no data were excluded from ALT and OLT comparisons. 

Ecotypes are broad strata that can have significant soil carbon 

and permafrost variability related to adjacency to water, soil 

texture, and effect of fire. Uncertainty was characterized 

in three ways: (1) as the mean absolute difference (MAD) 

from the ecotype mean, (2) as the data range of the spatial 

product means for each ecotype, and (3) as the difference 

between product ecotype means and respective DOS-TEM 

ecotype means. We employ convergence of evidence in our 

analysis approach, but acknowledge that the true value can 

potentially be one of the outliers. The ecotype product means 

(excluding DOS-TEM) were used to develop six ecologically 

based sensitivity criteria. A simple susceptibility index was 

developed related to an inter-criteria score (sum of 0 [false] or 

1 [true] across the six criteria) and the potential area affected, 

as is further described in section 3.4.4. Environmental factors 

controlling the distribution of near-surface soil properties and 

distributions are also briefly highlighted and discussed. 

It is also important to note that all empirically derived 

products rely heavily on the same soil pedon dataset (with 

varying additions of other field data and albeit different 

extrapolation methods) but these observations are not a 

systematic or random sample of Alaska’s ecotypes. Thus, 

some ecotypes are poorly represented whereas others are 

fairly well represented, which creates a source of uncertainty 

between ecotypes that is difficult to quantify (Johnson and 

others, 2011). For example, the Western Alaska LCC has the 

lowest representation of soil carbon observations, having a 

sample density of 2, 8, and 15 times lower than the Northwest 

Boreal LCC North, Arctic LCC, and Northwest Boreal LCC 

South, respectively. Thus, soil carbon estimates of the Western 

Alaska LCC should qualitatively be considered the most 

uncertain compared to other regions. 

3.4.1. Soil Organic Carbon

For the analysis of multiple products conducted for this 

assessment, the available SOC products included two new 

SOC maps and several existing maps (table 3.1). 

The JOHNSON SOC product (fig. 3.1A ) developed by 

Johnson and others (2011) and modified for the present report 

was created using 724 soil pedon observations. Soil pedon 

data sources included data from the U.S. Geological Survey 

(USGS) and the National Resources Conservation Service 

(NRCS) and a variety of data available from the Bonanza 

Creek Long Term Ecological Research (LTER) Web site. The 

SOC was estimated for the surface organic layer (OL), the 

mineral soil to a 1-m depth below the OL (MIN1m), and the 

total organic plus mineral soils (OL+MIN1m). These three soil 

carbon pools are directly comparable to DOS-TEM outputs. 

Additionally, the OL and MIN1m pools were partitioned into 

frozen and unfrozen components based on horizon designation 

(that is, the presence or absence of the “f” suffix). Frozen and 

unfrozen soil carbon were used to analyze the vulnerability of 

SOC loss from thawing permafrost and associated decomposition. Cryoturbated organic horizons were included as mineral 

soil. The means of the components (and their totals) were 

scaled up to land-cover types using the methods described in 

Hugelius and others (2012). A total of 33 land-cover types 

were derived for the soil carbon analysis from a landform map 

(Johnson and others, 2011), National Land Cover Database 

(NLCD) vegetation cover (Homer and others, 2004), and additional information on white spruce (Picea glauca (Moench) 

Voss) distribution from LANDFIRE (2008). These carbon 

statistics were crosswalked to the ecotypes in this study using 

similar land-cover types with large areas in each ecotype. 

The STATSGO SOC data layer was developed from the 

1:500,000-scale state soil geographic database (STATSGO) 

for Alaska (Soil Survey Staff, 2012), using expert knowledge 
Chapter 3 57

Table 3.1. Summary of spatial products and process-based model evaluated for soil organic carbon (SOC) in this assessment. 

[STATSGO, state soil geographic database; NCSCDv2, Northern Circumpolar Soil Carbon Database version 2; CAVM, Circumpolar Arctic Vegetation 

Map; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model; OL, surface organic layer; MIN1m, mineral soil to 1-meter depth 

below the bottom of the surface organic layer; OL+MIN1m, the sum of OL and MIN1m]

Product name Extent Resolution Method Depth Reference

JOHNSON Alaska 1 kilometer Means extrapolated

to raster

OL, MIN1m,

OL+MIN1m

Johnson and others (2011) 

and this report

STATSGO Alaska 1 kilometer Means extrapolated

to polygons

OL, MIN1m,

OL+MIN1m 

This report

MISHRA Alaska 60 meter Geostatistical “whole profile” Mishra and Riley (2012)

NCSCDv2 Boreal and arctic 

Alaska

0.012° Means extrapolated

to polygons

0 to 152 centimeters Hugelius and others 

(2013)

CAVM Arctic Alaska 1 kilometer Means extrapolated

to polygons

OL, 0 to 1 meter Ping and others (2008)

DOS-TEM Alaska 1 kilometer Process-based model OL, MIN1m,

OL+MIN1m

This report

Figure 3.1. Total profile soil organic carbon (SOC) for those products used in the assessment that covered all of Alaska. 

A, SOC from Johnson and others (2011). B, SOC derived from the state soil geographic database (STATSGO). C, SOC from 

Mishra and Riley (2012). D, SOC simulated by the Dynamic Organic Soil version of the Terrestrial Ecosystem Model (DOS-TEM).

Soil organic carbon,

 in kilograms of carbon

 per square meter

EXPLANATION

A. JOHNSON B. STATSGO

C. MISHRA D. DOS-TEM

0

25

50

75

100

150

300

N

Figure 3-1

0 200 400 KILOMETERS

0 200 400 MILES
58 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

to extrapolate NRCS soil pedon observations and rasterized 

to 1-kilometer (km) resolution. The SOC was computed for 

each horizon from the representative organic matter attribute 

(om_r) by converting organic matter to organic carbon with 

a 0.58 factor and accounting for rock fragments. A map 

unit average soil carbon in kilograms of carbon per square 

meter (kgC/m2) was computed for each analysis depth zone 

(for example, OL or OL+MIN1m) by accumulating across 

horizons and components with the mass of soil and the 

component percentage (comppct_r) as weighting factors 

with methods following Bliss and others (2014) (fig. 3.1B ).

Several other existing products were available for 

comparisons of SOC density or SOC totals. The MISHRA 

SOC product (fig. 3.1C) represents “whole profile from the 

surface to the C horizon” SOC predicted from topographic, 

land-cover type, geologic, and climate data and 472 soil 

profiles for all of Alaska at a 60-m resolution (Mishra and 

Riley, 2012). In contrast to the other products of this study, it 

is the only one to use a geostatistical approach (geographically 

weighted regression). The Northern Circumpolar Soil Carbon 

Database version 2 (NCSCDv2) SOC product assigns mean 

SOC values to a compilation of soil polygon maps for the 

whole circumpolar arctic but does not include southeastern 

Alaska (Hugelius and others, 2013). The data are reported by 

meter depth (that is, 0–100 cm, 100–200 cm, and 200–300 cm) 

but for comparability to the other products of this study the 

first two depths were converted to represent SOC to usually 

the 152-cm depth, or the C horizon. The CAVM SOC product 

is similar to other SOC products mentioned in this study in 

that mean SOC values were assigned to land-cover types for 

upscaling (Ping and others, 2008). However, land-cover types 

were determined from a separate map, the Circumpolar Arctic 

Vegetation Map (CAVM), and only cover the Arctic LCC and 

the Aleutian and Bering Sea Islands LCC ecoregions. 

The DOS-TEM SOC product (fig. 3.1D) provides 

estimates of SOC in the organic layer and the mineral soil 

(down to 1 m below the OL) separately. DOS-TEM simulates 

the dynamic changes of the SOC pools as a result of the 

incorporation of the litterfall to the soil, the carbon loss from 

decomposition by microbes, and combustion during wildfire. 

Therefore, soil carbon pools in DOS-TEM depend mainly on 

climate history, drainage conditions, vegetation productivity, 

and the fire regime (frequency and intensity). DOS-TEM 

estimates of SOC are provided at a 1-km resolution for the 

entire State. Readers are referred to Yi and others (2010) for a 

description of the processes that drive soil carbon dynamics in 

DOS-TEM. The DOS-TEM output at spin-up (models are run 

repetitively to get equilibrium conditions) were selected for 

the comparisons shown in this section.

3.4.2. Permafrost and Active-Layer Thickness

For the multiple product analysis of this report, the 

available permafrost products included three new permafrost 

maps and one existing map (table 3.2).

The “LANDCARBON” (products produced in this 

assessment) permafrost and associated ALT map products 

were developed using machine learning algorithms (that is, 

regression and classification trees) that spatially and statistically extended late-season field observations (sample size 

[n] ~17,000), collected from 1990 to 2013, for the mapping 

of near-surface permafrost (NSP, within the upper 1 m of the 

soil column) throughout Alaska (Pastick and others, 2015). 

This approach made use of remotely sensed, climatic, and 

biophysical geospatial data to produce moderate-resolution 

(30-m pixel) maps of the presence or absence of NSP, probability of NSP, and ALT (seasonal maximum depth of the 

permafrost) (figs. 3.2A, 3.3, and 3.4A). Readers are referred 

to Pastick and others (2015) for a detailed discussion of those 

data and methods used to derive and assess the map products. 

Climate variables (that is, mean annual air temperature, length 

of growing season, annual and winter precipitation) were 

identified to be the most important environmental factors 

controlling landscape-level distributions of permafrost. 

Whereas climate was identified to have first-order controls on 

landscape NSP distributions, permafrost-climate-ecological 

interactions are scale dependent. Remotely sensed or mapped 

data were also an influential predictor in the model and 

suggested that certain surface features (that is, vegetation, 

topography) are also good indicators of NSP properties. 

Accuracy assessments consisted of independent test datasets 

and f-fold cross-validations (Martin and others, 2011). 

Cross-validation and independent test accuracies indicated 

that the NSP map had an overall accuracy of 85 percent 

(95-percent confidence interval [CI]: 84.7, 85.8). Independent 

tests showed that the ALT map had a mean average error 

(MAE), mean bias error (MBE), relative mean average error 

(rMAE), relative mean bias error (rMBE), and Pearson’s 

correlation coefficient of 15 cm, 0 cm, 27 percent, 0 percent, 

and 0.61, respectively. 

The STATSGO permafrost estimates of the presence 

or absence of NSP were computed by evaluating the texture 

designation for the horizon at 100-cm depth. If the horizon 

was designated as “frozen” (“PF” in the texture code), then 

the component was flagged as having NSP. If the sum of 

the component percentages flagged in this way was at least 

50 percent, then the map unit was coded as having NSP 

“present” and if it was less than 50 percent, then permafrost 

was considered “absent” (fig. 3.2B). The ALT for a component 

was the depth of the soil at the top of a permanently frozen 

soil horizon. If permafrost was not present in the component 

or the permafrost was deeper than 100 cm, then the ALT 

for the component was set to 101 cm for comparability of 

methods with other datasets being evaluated in this study. A 

weighted average of ALT was computed with the component 

percentage as the weighting factor and assigned to the map 

units, as shown in figure 3.4B.

The GIPL 1.3 permafrost products were made using 

the University of Alaska-Fairbanks, Geophysical Institute 

Permafrost Laboratory (GIPL; 2011) version 1.3 transient 

model. The GIPL 1.3 spatial transient model simulates 
Chapter 3 59

Figure 3.2. Estimated presence or absence of near-surface (within 1 meter) permafrost for Alaska. A, Permafrost from LANDCARBON 

(Pastick and others, 2015). B, Permafrost derived from the state soil geographic database (STATSGO). C, Permafrost simulated by the

Geophysical Institute Permafrost Laboratory version 1.3 transient model (GIPL 1.3). D, Permafrost simulated by the Dynamic Organic 

Soil version of the Terrestrial Ecosystem Model (DOS-TEM). Map estimates are not reported for the North Pacific LCC. 

Table 3.2. Summary of spatial products and process-based model evaluated for permafrost in this assessment. 

[LANDCARBON, data product developed for this assessment; STATSGO, state soil geographic database; GIPL 1.3, Geophysical Institute Permafrost Laboratory version 1.3 transient model; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model]

Product name Extent Resolution Method

Near-surface 

permafrost 

depth threshold

Time period Reference

LANDCARBON Alaska 30 meter Machine 

learning

1 meter 1990–2013 Pastick and others (2015) 

and this report

STATSGO Alaska 1 kilometer Means 

extrapolated 

to polygons

1 meter Not specified 

(expert judgment in 2012)

This report

GIPL 1.3 Alaska 2 kilometer Process-based 

model

1 meter 2000–2009 Marchenko and 

others (2008)

DOS-TEM Alaska (excluding the North 

Pacific Landscape Conservation Cooperative)

1 kilometer Process-based 

model

1 meter 1950–1960 This report

Near-surface permafrost

Figure 3-2

EXPLANATION

Absent

Present

No data

N

A. LANDCARBON B. STATSGO

C. GIPL 1.3 D. DOS-TEM

0 200 400 KILOMETERS

0 200 400 MILES
60 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 3-3

0 50 100 KILOMETERS

0 50 100 MILES

A

A

C

C

B

B

N

0 100 200 300 400 KILOMETERS

0 100 200 300 400 MILES

0 50 100 KILOMETERS

0 50 100 MILES

0 50 100 KILOMETERS

0 50 100 MILES

Masked

area

Probability of near-surface permafrost, in percent

EXPLANATION

0 10 20 30 40 50 60 70 80 90 100

Figure 3.3. Probabilistic estimation of near-surface (within 1 meter) permafrost occurrence for the State of Alaska at 30-meter 

spatial resolution derived from LANDCARBON. Estimates were made using machine learning algorithms, field observations, 

remotely sensed or mapped imagery, and climatic data (Pastick and others, 2015). 
Chapter 3 61

Figure 3-4

N

5

30

40

50

60

70

80

90

100

0 200 400 KILOMETERS

0 200 400 MILES

Active-layer thickness,

 in centimeters

EXPLANATION

Masked

No data

A. LANDCARBON B. STATSGO

C. GIPL 1.3 D. DOS-TEM

depth of seasonal freezing and thaw by numerically solving 

one-dimensional nonlinear heat equations with phase change 

(Marchenko and others, 2008). Note that the spatial resolution 

(2-km pixels) of the GIPL 1.3 product is different than 

products developed in this study. Furthermore, predictions 

of seasonal frost or thaw depths greater than 1 m made 

by GIPL 1.3, for the years 2000 to 2009, were recoded to 

101 cm for direct comparison with estimates of NSP and 

ALT produced for this study (figs. 3.2C, 3.4C).

The DOS-TEM permafrost products were produced from 

the historical simulations for Alaska at a 1-km resolution. In this 

process-based model, NSP and ALT were assessed based on 

soil temperature and soil moisture simulations over a 5-m-deep 

soil column. NSP is considered present when soil temperature 

at 1 m from the surface remained frozen for two consecutive 

years. Soil temperature and soil moisture in DOS-TEM are 

driven by climate, soil texture, and drainage conditions. The 

insulating properties of the snow cover, moss, and organic 

layers are also reproduced in the model. Soil moisture is also 

affected by water uptake from vegetation and runoff. NSP 

and ALT distributions were estimated by averaging annual 

estimates from 1950 to 1960, which is temporally inconsistent 

from other products but the only TEM outputs available for 

comparison at the time of analyses (figs. 3.2D, 3.4D).

Figure 3.4. Active-layer thickness (ALT) estimates for Alaska. A, ALT derived from LANDCARBON (Pastick and others, 

2015). B, ALT derived from the state soil geographic database (STATSGO). C, ALT simulated by the Geophysical Institute 

Permafrost Laboratory version 1.3 transient model (GIPL 1.3). D, ALT simulated by the Dynamic Organic Soil version of 

the Terrestrial Ecosystem Model (DOS-TEM). Map estimates are not reported for the North Pacific LCC. Areas where ALT 

was estimated to be greater than 1 meter were given a consistent value (dark red) because of differences in investigation 

depths and for direct comparison. ALT greater than 1 meter is dependent on site, soil, climate, and fire history. 
62 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

3.4.3. Organic-Layer Thickness

For the multiple product analysis of this report, the 

available organic soil products included three new maps and 

one existing map (table 3.3).

The LANDCARBON OLT products were developed for 

this assessment using decision tree classifications to spatially 

extend field observations of soil organic-layer thickness 

(OLT; excluding buried O horizons) throughout Alaska. This 

approach made use of approximately 3,500 field observations 

and topographical, climatic, and remotely sensed geospatial 

data to map the presence or absence of organic soils (that is, 

OLT ≥ 40 cm=organic soil present; OLT< 40 cm=organic 

soil absent) (fig. 3.5A ). The 40-cm depth interval was chosen 

because it coincides with Soil Taxonomy (Soil Survey Staff, 

1999) nomenclature for organic soils (that is, Histosol or 

Histel) that are commonly associated with peatlands. Field 

observations were primarily collected by the National 

Resource Conservation Service, ABR, Inc., and the USGS 

(Pastick, Rigge, and others, 2014). Topography (that is, slope, 

soil-wetness proxies), length of growing season, and remotely 

sensed or mapped data (that is, land cover, vegetation indices) 

were identified to be the most important factors in estimating 

organic soil distributions in Alaska. Cross-validation accuracy 

assessments indicated that the map of the presence or 

absence of organic soil had an overall accuracy of 71 percent 

(95-percent CI: 69.5, 73.2). Readers are referred to Pastick, 

Rigge, and others (2014) for a thorough discussion on digital 

mapping of organic soils in Alaska.

The STATSGO OLT product was developed by evaluating the textural component of the horizons from the surface 

to 152-cm depth. For the STATSGO OLT, each horizon is 

labeled as organic or not organic, and the thickness of organic 

horizons is summed to create a component-level variable. 

This is weighted by the component percentage to give an 

average OLT at the map unit level. If an organic layer is 

not present, the thickness will be recorded as zero, and this 

will be included in the weighted average. The thicknesses 

Table 3.3. Summary of spatial products and process-based model evaluated for soil organic-layer thickness in this assessment. 

[LANDCARBON, data product developed for this assessment; STATSGO, state soil geographic database; NCSCDv2, Northern Circumpolar Soil Carbon 

Database version 2; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model; —, not applicable]

Product name Extent Resolution Method Number of

observations Reference

LANDCARBON Alaska 30 meter Machine learning ~3,500 This report

STATSGO Alaska 1 kilometer Means extrapolated 

to polygons

— This report

NCSCDv2 Boreal and arctic Alaska Polygons Means extrapolated 

to polygons

— Hugelius and others 

(2013)

DOS-TEM Alaska (excluding the North 

Pacific Landscape Conservation Cooperative) 

1 kilometer Process-based model — This report

of the organic layers in the 0- to 152-cm-depth zone were 

summed for each component, and a weighted average (with 

component percentage as the weight) was computed. If the 

average was more than 40 cm, then the map unit was labeled 

as “OLT≥40 cm” in figure 3.5B.

The NCSCDv2 OLT product assigns soil type frequencies 

to a compilation of soil polygon maps for the whole circumpolar region, but does not include a large portion of 

southeastern Alaska (Hugelius and others, 2013). Although 

available at approximately 1-km resolution, the polygon 

representation of soils is considerably coarser than that 

represented by other products in this study. The data used 

for comparison are frequency of Histel occurrence estimates, 

which are comparable to organic soil products developed 

for this study.

The DOS-TEM OLT product (fig. 3.5C) provides 

estimates of OLT as a function of SOC content in the organic 

layer. The dynamic organic soil module of DOS-TEM simulates post-fire re-accumulation of the organic layer as dead 

organic soil horizons accumulate above the mineral horizons 

with post-fire vegetation succession. DOS-TEM estimates of 

organic soils are provided at a 1-km resolution for the entire 

State, excluding the North Pacific LCC, and represent averaged model estimates from 1950 to 1960.

3.4.4. Soil Susceptibility to Climate Change 

The permafrost, soil carbon, and organic soils summaries 

presented in this study can yield useful information about the 

potential effects of climate change, particularly when multiple 

ecotype attributes are combined. For this purpose, ecotype 

means and measures of uncertainty from the multiple sources 

of relative frequency of NSP, relative frequency of organic 

soils, and mean ALT were combined in a database with 

ecotype estimates of frozen carbon and total organic carbon 

in the depth range of 0 to 1 m. This synthesis effort was 

constrained to the soil parameters presented in this chapter 

to focus on relative susceptibility. Important susceptibility 
Chapter 3 63

drivers, such as fire return interval and temperature, were not 

included and are included in other chapters. Six ecologically 

driven criteria were constructed with each criterion having its 

separate binary response (0=false and 1=true). Generally, the 

first condition in these criteria sought to capture one-third of 

the ecotypes and subsequent condition constraints with other 

variables sought to identify the top two to six ecotypes. The 

six criteria consisted of:

1. Thicker soil organic layers provide protecting insulation 

for NSP (Johnson and others, 2013). To identify areas 

which may be prone to permafrost degradation, we 

selected ecotypes with a higher chance of having thin 

organic soils (relative frequency of organic soils less 

than 20 percent) and with permafrost close to the surface 

(relative frequency of NSP greater than 50 percent).

2. Criterion 2 is a subset of criterion 1. In the sensitive 

areas where criterion 1 is focused (NSP and thin organic 

soils), high uncertainty and wide variations in ecotype 

ALT would affect current and future hydrology and 

greenhouse-gas emissions significantly. Given the 

significance of future permafrost degradation related 

to greenhouse gases (Schaefer and others, 2011) and 

changing permafrost’s potential effects on hydrologic 

flows (Walvoord and others, 2012), ecotypes with 

high uncertainties of ALT could experience greaterthan-expected permafrost degradation and increased 

ecosystem respiration. We refined criterion 1 to NSP 

with thin organic soils, with an additional requirement 

of a relatively high uncertainty of ALT (inter-product 

ecotype ALT MAD greater than 6 cm). 

3. Moderate to high OLTs could be particularly susceptible 

to wildfire (Kasischke and Johnstone, 2005). The fire 

susceptibility criterion was defined here as an organic 

soil relative frequency greater than 20 percent and 

not to include land-cover types with low flammability 

(deciduous forests and wetlands). 

4. Thermokarst land surfaces often occur in regions with 

near-surface, ice-rich permafrost and moderate to thick 

soil organic layers. This criterion consisted of ecotypes 

with relative frequency of organic soils greater than 

20 percent, with the relative frequency of NSP greater 

than 50 percent, and with ALT less than 76 cm.

5. Areas with high carbon stocks in the organic layer and in 

the mineral layers in the top 1 to 1.5 m represent potential hot spots for massive carbon loss owing to warming, 

fires, and accelerated soil respiration and greenhouse-gas 

emissions (Schuur and others, 2008). This criterion was 

simply ecotypes with inter-product total profile soil 

carbon ecotype means greater than 40 kgC/m2.

Figure 3.5. Organic-layer thickness (OLT) estimates for Alaska.

A, OLT derived from LANDCARBON (Pastick and others, 2015).

B, OLT derived from the state soil geographic database (STATSGO).

C, OLT simulated by the Dynamic Organic Soil version of the 

Terrestrial Ecosystem Model (DOS-TEM). DOS-TEM model 

estimates are not reported for the North Pacific LCC.

Figure 3-5

N

<40

≥40

No data

Organic-layer thickness,

 in centimeters

EXPLANATION

B. STATSGO

A. LANDCARBON

C. DOS-TEM

0 200 400 KILOMETERS

0 200 400 MILES
64 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

6. Criterion 6 is a subset of criterion 5. Permafrost can 

contain significant organic carbon. If this permafrost 

carbon is close to the soil surface, it may be susceptible 

to thaw and be available for decomposition (Schuur 

and others, 2008). As permafrost thaws, the active layer 

thickens. This criterion consisted of ecotypes where 

the frozen carbon was greater than 10 kgC/m2 and the 

total carbon in the top 1 to 1.5 m of soil was more than 

40 kgC/m2.

These six diverse criteria were focused on known or 

expected dynamics of permafrost, carbon, and the organic 

layer in boreal and arctic systems. To summarize overall 

factors, a susceptibility score, or ranking, was computed by 

summing all of the binary criteria variables and using area 

as a tie-breaker.

3.5. Results and Discussion

3.5.1. Comparison of Soil Organic Carbon Estimates 

Generally, SOC densities of the three soil product 

properties (total profile, surface organic layer, and 1-m mineral 

layer; fig. 3.6) had the largest range of estimated values as well 

as high uncertainty in the Arctic LCC. Additional field data 

collection in these regions could reduce uncertainty in these 

important arctic systems. STATSGO SOC estimates appeared 

to be consistently low and MISHRA estimates tended to be 

consistently high in nearly all the ecotypes, but particularly so 

in the Arctic LCC. DOS-TEM estimates generally agreed with 

mean organic carbon estimates for each ecotype, falling within 

the range of estimates for 78 percent of the major ecotypes. 

However, in some forests of the Northwest Boreal LCC North, 

DOS-TEM SOC estimates were either near or below the lower 

data range of the SOC ecotype ranges. DOS-TEM estimates 

were only substantially above the range of other SOC estimates 

in the Northwest Boreal LCC South upland heath tundra. 

The SOC density of the surface organic layer had high 

magnitudes and narrow data ranges (less uncertainty) in the 

deciduous forest, black spruce forest, and white spruce forest 

in the Northwest Boreal LCC North lowlands. DOS-TEM estimates of surface organic-layer carbon were substantially lower 

in these ecotypes and also in the Western Alaska LCC lowland 

shrub tundra. Thirty percent of all the ecotypes had DOS-TEM 

estimates within the soil organic-layer prediction range. The 

mean surface organic-layer carbon among all ecosystem types 

accounted for about 30 percent of the mean total soil carbon.

Only two spatial estimates (STATSGO and JOHNSON) of 

carbon in the mineral soil layer down to a depth of 1 m below 

the surface organic layer were available for comparison with 

estimates from DOS-TEM. The highest values for both spatial 

estimates occurred in the Arctic LCC. Disagreement between 

the two estimates and DOS-TEM estimates indicated possible 

overestimation by DOS-TEM in the Northwest Boreal LCC 

South upland heath tundra and the Western Alaska LCC. 

In terms of total soil carbon storage (in PgC) by LCC 

region, DOS-TEM SOC estimates were close to the mean and 

within the range of four products in the Arctic and Western 

Alaska LCCs (table 3.4). However, DOS-TEM estimates 

were 40 percent lower than the mean of all the products in 

the Northwest Boreal LCC North and 94 percent higher than 

the mean in the Northwest Boreal LCC South. These general 

differences were true even when compared to the JOHNSON 

SOC product with exactly comparable SOC by depth. Despite 

these differences in distribution of SOC pools by LCC region, 

when all the LCC regions were summed, DOS-TEM estimates 

were only 2 percent higher than the mean.

Table 3.4. Comparisons of soil organic carbon storage for each of the Landscape Conservation Cooperative (LCC) regions in 

Alaska (except the North Pacific LCC) estimated using spatial products and DOS-TEM outputs.

[PgC, petagram of carbon; STATSGO, state soil geographic database; NCSCDv2, Northern Circumpolar Soil Carbon Database version 2; CAVM, 

Circumpolar Arctic Vegetation Map; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model; OL, surface organic layer; 

MIN1m, mineral soil to 1-meter depth below the bottom of the surface organic layer; OL+MIN1m, the sum of OL and MIN1m; —, not applicable]

Product name Depth

Soil organic carbon (PgC)

Arctic

LCC

Western

Alaska LCC

Northwest Boreal 

LCC North

Northwest Boreal 

LCC South

Total

or mean

JOHNSON OL+MIN1m 13.6 11.3 14.4 4.4 43.7

STATSGO OL+MIN1m 6.2 9.8 12.5 2.7 31.2

MISHRA “whole profile” 20.7 22.8 22.6 6.1 72.1

NCSCDv2 0–152 centimeters 11.6 8.6 10.9 2.1 33.2

Mean — 13.0 13.1 15.1 3.8 45.2

DOS-TEM OL+MIN1m 12.4 17.3 9.0 7.4 46.1

MAD1 — 0.6 4.2 6.1 3.6 0.9

  % Diff 2 — 4.8 31.8 40.4 93.5 2.1

1

Mean Absolute Difference (MAD)= |DOS-TEM–Mean|

2% Diff =(MAD/Mean)×100
Chapter 3 65

Figure 3.6. Soil organic carbon (SOC) characterization for the largest (in terms of SOC, in petagrams of carbon, PgC) five or six ecotype 

classes in each Landscape Conservation Cooperative (LCC) region. Total baseline SOC estimates simulated by the Dynamic Organic 

Soil version of the Terrestrial Ecosystem Model (DOS-TEM). Total profile SOC density is the sum of the surface organic layer and the 

0- to 1-meter mineral layer for all estimates. “Mean” refers to the mean of all spatial products: JOHNSON, SOC from Johnson and others 

(2011); STATSGO, SOC from the state soil geographic database; MISHRA, SOC from Mishra and Riley (2012); NCSCDv2, SOC from the 

Northern Circumpolar Soil Carbon Database version 2; and CAVM, SOC from the Circumpolar Arctic Vegetation Map. Estimates are 

not reported for the North Pacific LCC.

Lowland graminoid tundra

Lowland shrub tundra

Upland graminoid tundra

Upland shrub tundra

Upland heath tundra

Lowland wet-sedge tundra

0 2 4 0 30 60 90 0 5 10 15 20 25 30 0 10 20 30 40 50

Total SOC, in

petagrams of carbon

Ecotype Total baseline

soil carbon estimates Total profile Surface organic layer 0- to 1-meter

mineral layer LCC region

Soil organic carbon (SOC) density, in kilograms of carbon per square meter

6 8 10

JOHNSON

STATSGO

CAVM

Mean

MISHRA

NCSCDv2

EXPLANATION

Figure 3-6

Spatial products

DOS-TEM

Arctic LCC 

Western

Alaska

LCC

Northwest

Boreal LCC

North

Northwest

Boreal LCC

South

Upland deciduous forest

Lowland deciduous forest

Lowland black spruce forest

Lowland white spruce forest

Upland black spruce forest

Upland white spruce forest

Upland heath tundra

Upland deciduous forest

Lowland deciduous forest

Upland shrub tundra

Lowland white spruce forest

Lowland black spruce forest

Lowland shrub tundra

Upland deciduous forest

Lowland deciduous forest

Lowland wet-sedge tundra

Upland shrub tundra
66 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The lower estimate of SOC in the organic layer provided 

by DOS-TEM compared with the other models in the Northwest Boreal LCC North may be attributed to the effect of fire 

simulated in the model. The version of DOS-TEM used for the 

present assessment has been designed to reproduce the effect 

of fire on nitrogen and carbon cycles in the soil and vegetation 

and on the soil environment, including the re-accumulation 

of the organic layer after a fire (Yi and others, 2009, 2010). 

Furthermore, a fire severity model has also been implemented 

in order to reproduce the spatial and temporal variability of 

fire severity on the organic layer (Genet and others, 2013). 

During the model spin-up, the fire regime was simulated by a 

constant fire return interval computed from the historical fire 

records for Alaska. Because the fire return interval is shortest 

in the Northwest Boreal LCC North, more organic layer is 

burned and less SOC accumulates. 

The higher SOC in the mineral layers observed in 

DOS-TEM compared with the JOHNSON SOC and STATSGO 

SOC estimates in the Western Alaska LCC might be related to 

the underestimation of the effect of the warmer climate on soil 

decomposition processes in this region. However, given that 

there are only two products available for comparison and the 

low sample density in the Western Alaska LCC, there is less 

confidence that the mineral SOC product is a reliable reference.

3.5.2. Comparison of Permafrost Estimates

NSP estimates (fig. 3.2) were summarized as relative 

frequency in percent by LCC region and DOS-TEM landcover type in tables 3.5 and 3.6. According to empirically 

or numerically derived NSP products, NSP was estimated 

to underlie a large portion of Alaska with a wide range of 

estimates (36 to 67 percent). The mean NSP frequency of 

DOS-TEM outputs (44 percent) falls within this range. On 

average, NSP distributions followed north-to-south temperature gradients as expected, with the largest NSP frequencies 

in the Arctic LCC (73 percent), followed by the Northwest 

Boreal LCC North (55 percent), and Western Alaska LCC and 

Northwest Boreal LCC South (30 percent each). DOS-TEM 

outputs were consistently within the range of other model 

outputs and were (on average) within 3 percent of the mean 

of other products (table 3.5). 

A mean estimate (excluding DOS-TEM) of NSP frequencies varied nearly seventyfold between DOS-TEM land-cover 

types (table 3.6). Graminoid tundra had the highest mean 

product frequency of being underlain by NSP (73 percent), 

followed by shrub tundra (60 percent), black spruce forest 

(53 percent), white spruce forest (52 percent), deciduous forest 

(40 percent), wet-sedge tundra (34 percent), nonvegetated areas 

(31 percent), heath tundra (29 percent), and maritime classes 

(1 percent). Vegetation succession can serve as a positive 

feedback to permafrost systems, where increasing components 

of soil organics and moisture can provide insulation to permafrost, consistent with higher NSP frequencies in white and 

black spruce forests (late successional stages) compared with 

deciduous forests (early successional stages). Furthermore, 

DOS-TEM outputs were significantly correlated (p-value 

<0.05) with mean product NSP frequencies when stratified 

by LCC region (coefficient of determination, R2=0.86; n=5) 

or land-cover type (R2=0.69; n=9). Empirically derived 

NSP maps (that is, STATSGO and LANDCARBON) were 

significantly correlated when comparing NSP estimates 

stratified by LCC region (R2 =0.82; n=5) and DOS-TEM 

land-cover type (R2=0.63; n=9). DOS-TEM outputs best 

correlated with the GIPL 1.3 map when examining mean 

NSP estimates stratified by LCC region (R2=0.98; n=5) and 

DOS-TEM land-cover type (R2=0.72; n=9). Large differences 

between model outputs are most evident for areas estimated 

to have little to no vegetation. Mean product NSP frequencies 

indicate that lowland areas are more frequently underlain by 

NSP than uplands in Alaska (57 percent versus 36 percent), 

consistent with trends in DOS-TEM outputs (49 percent versus 

41 percent). Large differences between mean NSP frequencies 

in uplands suggest that estimates of NSP distributions are 

relatively more uncertain for upland than lowland ecosystems, 

coinciding with large differences in NSP estimates for areas 

with little to no vegetation. A portion of these differences 

can be attributed to how models handled glaciated areas.

Finer scale comparisons were also made where NSP 

estimates were stratified by ecotype in each LCC region 

(excluding the North Pacific LCC; fig. 3.7). Empirically 

derived NSP maps were in global accordance with one another 

and with DOS-TEM outputs when mean frequency estimates 

are stratified by ecotype. For instance, the empirically derived 

NSP maps (that is, STATSGO and LANDCARBON) were 

significantly (p-value <0.05) correlated when estimates were 

stratified by ecotype in each LCC region (R2=0.83; n=23). 

Additionally, DOS-TEM outputs were significantly correlated 

(R2=0.67; n=23) with mean product NSP frequencies 

stratified by ecotype. Furthermore, in 87 percent of the major 

ecotype classes the DOS-TEM output is within the range of 

other products, whereas three of the DOS-TEM outputs were 

outside the range. The three low DOS-TEM outputs were 

within upland and lowland shrub tundra in the Western Alaska 

LCC and lowland deciduous forests in the Northwest Boreal 

LCC North. These significant differences could have a large 

effect on future model simulations because these ecotypes 

account for a substantial portion of Western Alaska LCC 

(33 percent) and Northwest Boreal LCC North (25 percent). 

The lower DOS-TEM estimate of NSP distribution compared 

with the other models in interior Alaska (Northwest Boreal 

LCC North) might be related to the lower estimate of OLT 

related to the effect of fire activity and high fire return interval 

in the region (see section 2.4.2.). With a thinner organic layer, 

the soil is less insulated from large variability of air temperature, especially high summer temperature. As a consequence, 

the annual mean of soil temperature increases, thawing 

permafrost and increasing ALT.
Chapter 3 67

Table 3.5. Comparisons of the frequency of near-surface (within 1 meter) permafrost, mean active-layer 

thickness, and frequency of organic soils for Landscape Conservation Cooperative (LCC) regions in Alaska 

(except the North Pacific LCC) estimated using spatial products and DOS-TEM outputs.

[Organic soils are defined here as those soils with a surface organic epipedon that is greater than or equal to 40 centimeters. LANDCARBON, data product developed for this assessment; STATSGO, state soil geographic database; GIPL 1.3, Geophysical Institute 

Permafrost Laboratory version 1.3 transient model; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model; 

NCSCDv2, Northern Circumpolar Soil Carbon Database version 2]

Product name

Landscape Conservation Cooperative region

Arctic

LCC

Western

 Alaska LCC

Northwest Boreal 

LCC North

Northwest Boreal 

LCC South Mean

Frequency of near-surface permafrost (percent)

LANDCARBON 66 21 44 22 38

STATSGO 56 28 49 11 36

GIPL 1.3 97 41 72 57 67

Mean 73 30 55 30 47

DOS-TEM 73 22 45 38 44

MAD1 0 8 10 8 3

% Diff2 0 26 19 25 5

Mean active-layer thickness (centimeter)

LANDCARBON 59.51 88.94 81.14 91.43 80.26

STATSGO 79.71 85.08 76.68 95.60 84.27

GIPL 1.3 60.88 85.55 78.49 78.34 75.81

Mean 66.70 86.52 78.77 88.45 80.11

DOS-TEM 72.80 95.86 88.63 87.46 86.19

MAD1 6.10 9.34 9.86 0.99 6.08

% Diff2 9.14 10.79 12.51 1.12 7.58

Frequency of organic soils (percent)

LANDCARBON 6 31 35 19 24

STATSGO 27 25 37 9 30

NCSCDv2 4 13 7 2 8

Mean 12 23 26 10 21

DOS-TEM 3 24 26 38 18

MAD1 10 1 1 29 3

% Diff2 77 4 2 298 15

1

Mean Absolute Difference (MAD)= |DOS-TEM–Mean|

2% Diff =(MAD/Mean)×100
68 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 3.6. Comparisons of the frequency of near-surface (within 1 meter) permafrost, mean active-layer thickness, and frequency of 

organic soils by DOS-TEM land-cover type estimated using spatial products and DOS-TEM outputs.

[Areas estimated to have active-layer thicknesses greater than 1 meter were given a value of 101 centimeters for direct comparison and to account for differences in investigation depths. Organic soils are defined here as those soils with a surface organic epipedon that is greater than or equal to 40 centimeters. 

LANDCARBON, data product developed for this assessment; STATSGO, state soil geographic database; GIPL 1.3, Geophysical Institute Permafrost Laboratory version 1.3 transient model; DOS-TEM, Dynamic Organic Soil version of the Terrestrial Ecosystem Model; NCSCDv2, Northern Circumpolar Soil 

Carbon Database version 2]

Product name

Land-cover type

Black spruce 

forest

White spruce 

forest

Deciduous 

forest

Shrub 

tundra

Graminoid 

tundra

Wet-sedge 

tundra

Heath 

tundra

Nonvegetated

Frequency of near-surface permafrost (percent)

LANDCARBON 40 38 30 54 70 27 7 44

STATSGO 52 48 35 55 57 27 1 8

GIPL 1.3 66 70 55 71 93 49 77 40

Mean 53 52 40 60 73 34 29 31

DOS-TEM 49 52 36 47 62 59 40 20

MAD1 4 0 4 13 11 24 11 11

% Diff2 7 1 9 22 15 71 39 35

Mean active-layer thickness (centimeter)

LANDCARBON 84.1 85.0 88.6 67.1 60.3 44.3 101.0 92.0

STATSGO 76.5 78.2 83.5 79.3 78.9 81.5 97.9 96.1

GIPL 1.3 80.2 77.9 83.3 72.5 59.7 39.6 101.0 85.8

Mean 80.3 80.3 85.1 72.9 66.3 55.1 100.0 91.3

DOS-TEM 87.9 85.9 90.4 84.2 76.0 74.9 90.9 91.2

MAD1 7.7 5.6 5.2 11.3 9.7 19.8 9.0 0.1

% Diff2 9.5 6.9 6.2 15.5 14.7 35.9 9.0 0.1

Frequency of organic soils (percent)

LANDCARBON 34 32 36 16 10 48 1 7

STATSGO 40 36 27 22 38 39 1 17

NCSCDv2 8 8 5 6 6 28 0 8

Mean 27 25 23 15 18 38 1 11

DOS-TEM 25 22 43 7 1 4 0 8

MAD1 2 3 21 7 17 35 0 2

% Diff2 8 13 91 49 95 90 62 21

1

Mean Absolute Difference (MAD)= |DOS-TEM–Mean|

2% Diff =(MAD/Mean)×100
Chapter 3 69

Figure 3.7. Permafrost and organic soil characterization for the largest (in terms of soil organic carbon, in petagrams 

of carbon) five or six ecotype classes in each Landscape Conservation Cooperative (LCC) region. Areas estimated to 

have active-layer thicknesses greater than 1 meter were given a value of 101 centimeters for direct comparison and 

to account for differences in investigation depths. Organic soils are defined here as those soils with a surface organic 

epipedon that is greater than or equal to 40 centimeters. “Mean” refers to the mean of all relevant estimates except the 

Dynamic Organic Soil version of the Terrestrial Ecosystem Model (DOS-TEM): LANDCARBON, permafrost and organic 

soil data from Pastick and others (2015); STATSGO, permafrost and organic soil data from the state soil geographic 

database; GIPL 1.3, permafrost data from the Geophysical Institute Permafrost Laboratory version 1.3 transient model; 

and NCSCDv2, organic soil data from the Northern Circumpolar Soil Carbon Database version 2.

Mean estimated

active-layer thickness

Relative frequency

of organic soils

Relative frequency

of near-surface

permafrost estimates

10 30 50 70 90

Percent Centimeters

0 20 40 60 80 100

Percent

0 20 40 60 80 100

Figure 3-7

LANDCARBON

STATSGO

GIPL 1.3

NCSCDv2

Mean

EXPLANATION

Spatial products

DOS-TEM

Lowland graminoid tundra

Lowland shrub tundra

Upland graminoid tundra

Upland shrub tundra

Upland heath tundra

Lowland wet-sedge tundra

LCC region Ecotype

Arctic LCC 

Western

Alaska LCC

Northwest

Boreal LCC

North

Northwest

Boreal LCC

South

Upland deciduous forest

Lowland deciduous forest

Lowland black spruce forest

Lowland white spruce forest

Upland black spruce forest

Upland white spruce forest

Upland heath tundra

Upland deciduous forest

Lowland deciduous forest

Upland shrub tundra

Lowland white spruce forest

Lowland black spruce forest

Lowland shrub tundra

Upland deciduous forest

Lowland deciduous forest

Lowland wet-sedge tundra

Upland shrub tundra
70 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Spatial predictions of ALT (fig. 3.4) were also summarized by LCC region and DOS-TEM land-cover type in 

tables 3.5 and 3.6. Areas where the ALT was estimated to 

be greater than 1 m were given a consistent value (101 cm) 

because of differences in investigation depths and for a direct 

comparison of ALT products. Empirically or numerically 

derived ALT estimates had a mean ALT of 80 cm in Alaska 

(excluding the North Pacific LCC), with a range of 76 to 84 cm

between estimates (table 3.5). The mean ALT estimate of the 

DOS-TEM output (86 cm) was slightly outside the range of 

other products, but within the range of uncertainty between 

other products (that is, MAD from product averages was 

6 cm). The mean product ALT was thinnest in the Arctic LCC 

(67 cm), followed by the Northwest Boreal LCC North 

(79 cm), Western Alaska LCC (87 cm), and Northwest 

Boreal LCC South (88 cm). The mean ALT estimates of the 

DOS-TEM model were generally higher than other estimates. 

Furthermore, there was no significant correlation between 

DOS-TEM and mean product ALT estimates when stratified 

by LCC region. It is important to note, however, that there 

were significant correlations between mean ALT estimates 

of DOS-TEM, GIPL 1.3, and LANDCARBON when stratified by LCC region (R2>0.75; n=5). The lack of correlation 

between the mean product and DOS-TEM estimates was due 

to higher estimates of ALT in the STATSGO product for the 

Arctic LCC, which drives up the mean of other products. 

Higher Arctic LCC ALT estimates in the STATSGO product 

may stem from the fact that a frozen soil horizon was recorded 

in the database only if ice lenses were visible, not using 

a temperature criterion as in the definition of permafrost. 

Furthermore, there was no temporal constraint on those 

observations used during the creation of the STATSGOderived ALT map.

Mean product ALT estimates varied approximately 

twofold between DOS-TEM land-cover types with no 

significant correlation to mean product NSP frequency 

estimates (table 3.6). Wet-sedge tundra had the lowest mean 

product ALT (55 cm), followed by graminoid tundra (66 cm), 

shrub tundra (73 cm), black and white spruce forests (80 cm 

each), deciduous forest (85 cm), nonvegetated areas (91 cm), 

heath tundra (100 cm), and maritime vegetation (>100 cm). 

DOS-TEM ALT outputs were significantly correlated with the 

mean ALT of other products when stratified by land-cover type 

(R2=0.87; n=9), despite 67 percent of the DOS-TEM outputs 

falling outside of the range of other ALT products. The largest 

differences between DOS-TEM output and the mean of other 

product ALT outputs were for tundra vegetation (that is, shrub, 

graminoid, wet-sedge, and heath tundra), which accounts for 

approximately 32 percent of Alaska, and where there is the 

largest range in other ALT estimates.

ALT estimates were also stratified and averaged by 

ecotype in each LCC region (excluding the North Pacific 

LCC; fig. 3.7). Empirically or numerically derived ALT maps 

were generally in global accordance with one another and with 

DOS-TEM outputs. For instance, the ALT maps produced by 

Pastick and others (2015) (LANDCARBON) and Marchenko 

and others (2008) (GIPL 1.3) were significantly correlated 

when ALT estimates are averaged by ecotype (R2=0.67; 

n=23). The DOS-TEM and GIPL 1.3 products had the highest 

correlation (R2=0.78; n=23) when comparing mean ALT 

estimates of each major ecotype, but DOS-TEM outputs were 

also significantly correlated (R2=0.73; n=23) with estimates 

made by Pastick and others (2015) (LANDCARBON). 

DOS-TEM outputs were significantly correlated with the 

mean ALT of other outputs (R2=0.73; n=23). Furthermore, 

in 48 percent of the ecotype classes, the DOS-TEM output 

was within the range of other products, whereas 12 of the 

DOS-TEM outputs were outside the range. It is important to 

note, however, that DOS-TEM ALT outputs were generally 

close to the intra-product means. For example, the rMAE 

or MAE of DOS-TEM outputs compared to the mean of 

all products was 8 percent or 7 cm, respectively. More than 

three-fourths of the high DOS-TEM outputs were for the 

Northwest Boreal LCC North and Western Alaska LCC, where 

permafrost is particularly vulnerable to climate warming (Shur 

and Jorgenson, 2007) and the ranges in mean ALT estimates 

are lowest. A spatially exhaustive comparison of continuous 

estimations of ALT could not be made because of differences 

in investigation depths.

3.5.3. Comparison of Soil Organic-Layer Estimates

Organic soil estimates were summarized by LCC 

region and DOS-TEM land-cover type in tables 3.5 and 3.6. 

According to observation-based OLT products, organic 

soils were estimated to underlie a fairly small portion of 

Alaska (excluding the North Pacific LCC) with a narrow 

range (8 to 30 percent) in frequency of occurrence. The 

mean organic soil frequency from DOS-TEM outputs 

(18 percent) falls within this range. On average, organic 

soils most frequently occurred in Northwest Boreal LCC 

North (26 percent), followed by the Western Alaska LCC 

(23 percent), the Arctic LCC (12 percent), and the Northwest 

Boreal LCC South (10 percent). At the LCC region level, 

DOS-TEM outputs were within the range of other products 

60 percent of the time, with the largest difference occurring 

in the Northwest Boreal LCC South (table 3.5). 

Wet-sedge tundra was estimated to be most frequently 

underlain by organic soils (38 percent; table 3.6), followed 

by black spruce forest (27 percent), white spruce forest 

(25 percent), deciduous forest (23 percent), graminoid tundra 

(18 percent), shrub tundra (15 percent), nonvegetated areas 

(11 percent), and heath tundra (1 percent). For a comparison, 

organic soil frequencies, as estimated in the LANDCARBON 

OLT product, varied considerably when stratified by NLCD 

class (wetlands and moss equal 97 to 100 percent; deciduous 

forest and dwarf shrub equal 0 to 8 percent) (Homer and 

others, 2007) but this land-cover dataset was also used for 

model development. DOS-TEM outputs were not significantly 

(p-value >0.18) correlated with the individual organic soil 
Chapter 3 71

products or the mean of those products when estimates 

were averaged by land-cover type. However, 63 percent of 

DOS-TEM outputs were within the range of other product 

estimates, with only three outputs outside of the range of 

other products. Moreover, two of the three outlier DOS-TEM 

outputs were only slightly outside the range of other products, 

with the largest difference occurring in areas mapped as 

wet-sedge tundra, which represents a small fraction (4 percent) 

of the land cover of Alaska. Empirical model estimates (that 

is, STATSGO, LANDCARBON) were significantly correlated 

(R2=0.53; n=9) when stratified by land-cover type. 

Empirically derived organic soil maps are in global 

accordance with one another, when mean frequency estimates 

are stratified by ecotype in each LCC region, but less so in

correspondence with DOS-TEM outputs (fig. 3.7). In 

57 percent of the ecotype classes, the DOS-TEM output 

is within the range of other products, whereas 10 of the 

DOS-TEM outputs were outside the range. Moreover, 

DOS-TEM outputs were not significantly correlated 

(p-value=0.63; n=23) with the mean of other estimates 

when stratified by ecotype in each LCC region. Mean product 

organic soil frequencies indicate that lowlands are more 

frequently underlain by organic soils than uplands in Alaska 

(32 percent versus 8 percent), a pattern not seen in DOS-TEM 

outputs (19 percent versus 22 percent). The Western Alaska 

LCC and Northwest Boreal LCC South had the largest 

differences in organic soil estimates. Large differences in 

the range of model outputs suggest that the simulation of 

thick soil organic layers is difficult, partially owing to a large 

and complex set of biophysical factors (for example, soil 

moisture, disturbances) influencing organic soil distributions, 

and that DOS-TEM outputs of organic soils are currently 

highly uncertain. 

3.5.4. Soil Susceptibility to Climate Change

The conceptual model of ecotype and soil susceptibility 

to climate change resulted in susceptibility scores ranging 

from a high of 4 to a low of 0. The map produced from this 

model can be seen in figure 3.8.

Figure 3.8. Overall soil susceptibility of major ecotypes in Alaska. Susceptibility was higher in ecotypes with greater respiration 

to the atmosphere, burning, and near-surface (within 1 meter) permafrost thaw. 

Ecotype

Marginal or no susceptibility 

EXPLANATION

Overall susceptibility High Low

Figure 3-8. Is a reference needed?

Arctic LCC lowland shrub tundra

Western Alaska LCC lowland shrub tundra

Arctic LCC lowland graminoid tundra

Arctic LCC lowland wet-sedge tundra

Arctic LCC upland shrub tundra

Arctic LCC upland graminoid tundra

Northwest Boreal LCC North lowland deciduous forest

Northwest Boreal LCC North lowland white spruce forest

Northwest Boreal LCC North lowland black spruce forest

Northwest Boreal LCC North upland white spruce forest

Western Alaska LCC lowland wet-sedge tundra

Northwest Boreal LCC North upland black spruce forest

Northwest Boreal LCC South lowland black spruce forest

N

Arctic LCC

Western

Alaska LCC

Northwest Boreal LCC North

Northwest Boreal

LCC South

North Pacific LCC

0 200 400 KILOMETERS

0 200 400 MILES
72 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The tundra ecotypes, Arctic LCC lowland graminoid, 

shrub, and wet-sedge tundra and Western Alaska LCC lowland 

shrub tundra, had the highest susceptibility scores. These four 

ecotypes had the highest total profile organic carbon product 

mean values, ranging from 44.5 to 66.9 kgC/m2, and generally 

high frozen organic carbon and NSP (in three out of the four 

ecotypes). Arctic LCC upland shrub and graminoid tundra 

ecotypes had intermediate susceptibility scores, which were 

driven by susceptibility of NSP and high uncertainty. The 

persistence of the Arctic LCC within these top susceptibility 

scores is compounded by also having the highest carbon stocks 

and high uncertainty of SOC densities (fig. 3.6). The Arctic 

LCC graminoid and shrub tundra ecotypes had the highest 

ecotype frequencies of NSP. Western Alaska LCC lowland 

shrub tundra makes up 33 percent of the Western Alaska LCC 

and has the second highest susceptibility score. DOS-TEM 

outputs in this ecotype tended to underestimate organic-layer 

carbon, NSP, and organic soil relative frequencies and to 

overestimate ALT in this system with known permafrost 

vulnerabilities (Shur and Jorgenson, 2007). Extended ground 

surveys linked with mapping efforts in these susceptible 

ecotypes could reduce uncertainty and provide more accurate 

baseline datasets for assessing future changes and effects. 

Ecotypes with low susceptibility scores (Northwest 

Boreal LCC North lowland black spruce, deciduous, and 

white spruce forests; Northwest Boreal LCC North upland 

black and white spruce forests; Northwest Boreal LCC South 

lowland black spruce forest; and Western Alaska LCC lowland 

wet-sedge tundra) further underscore the susceptibility of the 

Western Alaska LCC and Northwest Boreal LCC systems 

over the other remaining Alaska ecotypes, which had the 

lowest susceptibility scores. Low susceptibility scores of 

some Northwest Boreal LCC North ecotypes might have been 

higher if fire return interval or temperature were included in 

selected criteria. Given expected increases in fire severity 

and frequency (Barrett and others, 2011), this LCC region is 

important for understanding and forecasting future ecosystem 

responses. Organic-layer carbon estimates in Northwest 

Boreal LCC North lowland forest ecotypes had high magnitudes (fig. 3.6). DOS-TEM outputs for the Northwest Boreal 

LCC North tended to underestimate total profile carbon, soil 

organic-layer carbon, and mineral soil carbon (fig. 3.6) and 

to overestimate ALT (fig. 3.7). 

Ecotypes with the lowest susceptibility scores were 

Arctic LCC upland heath tundra; Northwest Boreal LCC 

North upland deciduous forest; Northwest Boreal LCC South 

lowland deciduous and white spruce forests; Northwest 

Boreal LCC South upland deciduous forest and heath and 

shrub tundra; Western Alaska LCC lowland deciduous forest; 

and Western Alaska LCC upland deciduous forest and shrub 

tundra. Remarkably the separation of northern and southern 

components of the Northwest Boreal LCC was captured in 

susceptibility scores based solely on soil properties with no 

fire probability or climate inputs.

Despite the discrepancies between DOS-TEM outputs 

and those of other products highlighted above, in general 

DOS-TEM did a reasonable job capturing the spatial variations 

of important high-latitude soil attributes. The DOS-TEM total 

profile carbon, soil organic-layer carbon, ALT, and organic soil 

relative frequency were within the other product data range 

for 78 percent, 30 percent, 48 percent, and 57 percent of the 

ecotypes, respectively. 

3.6. Conclusions

Soil carbon, permafrost, and organic soils are important 

components of high-latitude ecosystems. This chapter provides 

fundamental information as to the current state of knowledge 

of the distribution of these soil properties. This information 

will be of use for future field campaigns aimed at quantifying 

the resilience and vulnerability of high-latitude ecosystems to 

changes in climate and environment. 

The synoptic comparison of estimates from multiple soil 

products with those of a process-based model (DOS-TEM) 

was useful to (1) identify areas of high soil attribute uncertainty, (2) identify areas of disagreement with the DOS-TEM 

estimates and mapped current conditions, (3) provide interproduct mean values which may be useful as baseline data for 

assessing and understanding future changes, and (4) identify 

areas of potential susceptibility to changing climates through 

inter-soil attribute combinations. These uncertainties could 

help prioritize field collections and studies on ecotypes with 

high uncertainties and high soil carbon stocks (particularly, 

the Arctic LCC). Discrepancies between DOS-TEM and 

other product estimates in Northwest Boreal LCC North SOC 

and ALT and upland versus lowland OLT will be useful for 

informing the uncertainty of future ecosystem responses to a 

changing climate made by DOS-TEM. The high soil susceptibility scores for the Arctic and Western Alaska LCCs may 

reinforce DOS-TEM future change projections.

This soil product spatial synthesis was constrained to 

the soil parameters presented in this chapter and was done 

at a relatively coarse resolution, at ecotype or coarser levels 

(LCC region or land-cover type). These coarser evaluation 

units helped to mitigate possible geolocation errors and to not 

penalize coarser polygon approaches. However, significant 

variations of permafrost and soil carbon related to adjacency 

to water and soil texture changes certainly occur within the 

broad ecotypes used in this study.

Refined and higher resolution products for Alaska can 

potentially be used to better insure that the current status of 

Alaska systems are captured in process-based models. This would 

improve confidence of future predictions made by these models.
Chapter 3 73

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Stocker, T.F., Qin, Dahe, Plattner, G.-K., Tignor, M.M.B., 

Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu,

Bex, Vincent, and Midgley, P.M., eds., 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, Cambridge University Press, 1535 p. 

[Also available at http://www.ipcc.ch/report/ar5/wg1/.]

Treat, C.C., Wollheim, W.M., Varner, R.K., Grandy, A.S., 

Talbot, J., and Frolking, S., 2014, Temperature and peat 

type control CO2 and CH4 production in Alaskan permafrost 

peats: Global Change Biology, v. 20, no. 8, p. 2674–2686, 

http://dx.doi.org/10.1111/gcb.12572.
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Manies, K.L., Hoy, Elizabeth, and Kasischke, E.S., 2011, 

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j.1600-0889.2003.00060.x.
Chapter 4. Watershed Carbon Budgets in the 

Southeastern Alaskan Coastal Forest Region

By David V. D’Amore,1 Frances E. Biles,1 S. Mark Nay,2 and T. Scott Rupp3

1

U.S. Department of Agriculture Forest Service, Juneau, Alaska. 

2

U.S. Department of Agriculture Forest Service, Corvallis, Oreg.

3

University of Alaska-Fairbanks, Fairbanks, Alaska.

4.1. Highlights

• The perhumid coastal temperate rainforest (PCTR) 

of the North Pacific coast has some of the densest 

terrestrial carbon stocks in the world. Net carbon 

balance in this region has a distinct lateral loss vector 

through both dissolved organic and inorganic carbon. 

• Estimates of net ecosystem carbon balance averaged 

142 grams of carbon per square meter per year 

(gC/m2/yr) and ranged from 117 to 177 gC/m2/yr 

among three watersheds in the region, illustrating the 

net terrestrial carbon gain in coastal forests. These 

estimates are consistent with carbon gains associated 

with mature temperate forests worldwide and illustrate 

the potential for carbon sequestration across a range 

of landscape types in the PCTR.

4.2. Introduction

Forests worldwide provide a substantial carbon sink 

of approximately 1.1 ±0.8 petagrams of carbon per year 

(PgC/yr) (Pan and others, 2011). The northeast Pacific coastal 

margin has extensive and densely forested stands across the 

coastal temperate rainforest biome. The perhumid subregion 

(PCTR) of this biome includes the coastal forests of southeast Alaska (fig. 4.1). In addition to the extensive forests in 

the PCTR, sparsely forested and nonforested peatlands with 

deep (~3–5 meter [m]) organic soils are abundant. The spatial 

heterogeneity of ecosystems across the PCTR and the variability in the carbon pools associated with these ecosystems 

creates complex conditions for estimating carbon balance. 

To develop carbon balance models across the diverse PCTR 

region, spatially explicit estimates of carbon flux are needed 

for scaling plot measurements across the varying terrain and 

ecosystem types (Schimel, 1995).

The PCTR of southeast Alaska has functioned as a 

carbon sink during the postglacial Holocene as this region is 

cold and wet and does not have a fire regime. There is little 

urban development in the region, and the primary sources 

of disturbance are small-scale wind disturbances leading to 

gap-phase-type forest structures, and forest harvest, which 

has been limited to a relatively small portion of the landscape. 

Mean carbon densities in the region exceed 30 kilograms of 

carbon per square meter (kgC/m2

) (Heath and others, 2011). 

About 66 percent of this carbon is belowground, where carbon 

densities in wetlands are as high as 50 to 90 kgC/m2 (Leighty 

and others, 2006). Carbon stocks in the soils and forests of 

the Tongass National Forest have been estimated to contain 

2.8±0.5 petagrams of carbon (PgC) (Leighty and others, 

2006). The consideration of the exchange of carbon in both the 

dense belowground stock and the aboveground biomass pool 

is essential to accurately estimate carbon balance. In addition, the belowground stock in the PCTR is unfrozen, and the 

aboveground stock is not subject to fire, making the stability 

of carbon stocks in the PCTR an important feature of carbon 

accounting for North America.

Our approach in this chapter is to compare the common 

carbon balance model used to calculate net ecosystem production (NEP) with the calculation of net ecosystem carbon 

balance (NECB; Chapin and others, 2006). In areas where 

major disturbances (such as fire and forest harvest) are absent, 

as in much of the Alaska PCTR, it has been argued that NEP 

can be used interchangeably with NECB (Turner and others, 

2011). Unlike NECB, however, the NEP model does not 

account for lateral exports of dissolved carbon in the carbon 

balance. Dissolved carbon, both in organic and inorganic 

forms, constitutes a major export vector from both forested 

and nonforested systems in the PCTR (D’Amore and others, 

2015). Using ground-based measurements, we can construct 

estimates of NECB to compare with NEP estimates to illustrate the importance of accounting for dissolved carbon fluxes 

in carbon balance models for the Alaska PCTR.
78 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 4.1. North American Pacific coastal temperate rainforest extent. The “panhandle” or southeast 

Alaska region is noted along with the Tongass National Forest. Adapted from Wolf and others (1995).

ALASKA

BRITISH 

COLUMBIA

ALBERTA

YUKON

NORTHWEST

TERRITORIES

WASHINGTON

OREGON

CALIFORNIA

CANADA

UNITED STATES

PACIFIC OCEAN

170° W

160° W

150° W

140° W

130° W

120°W

70°N

60°N

50°N

40°N

Figure 4-1

Seasonal

Subpolar

Perhumid

Seasonal-Coast Redwood Mix

0 250

0 250 500 MILES

500 KILOMETERS

Southeast Alaska

Tongass National Forest

Coastal temperate rainforest 

 zones of North America

EXPLANATION
Chapter 4 79

The varying distribution of abundant water across the 

Alaska PCTR creates a mosaic of ecosystem types ranging from 

well-drained, densely forested uplands to forested wetlands 

and nonforested peatlands (Neiland, 1971). To measure carbon 

flows and evaluate terrestrial-aquatic linkages associated with 

these three ecosystem types, we created a sampling scheme 

based on a hydropedologic framework (D’Amore and others, 

2012). Measurements taken across this framework can then be 

generalized across the overall landscape. The field measurements can also be used to calibrate carbon budget estimates 

based on remote sensing or process-based models. The carbon 

cycle science research program in the Alaska PCTR developed 

by the U.S. Department of Agriculture Forest Service Pacific 

Northwest Research Station (PNW) has established a series 

of plots distributed across component landscape units in three 

watersheds to estimate major carbon fluxes (fig. 4.2). Data 

collected from these plots along with remotely sensed estimates of net primary productivity (NPP) were used to determine the NECB for the three PCTR watersheds.

This assessment is primarily a landscape analysis for 

use in national and regional reporting and evaluations. The 

process-based Dynamic Organic Soil version of the Terrestrial Ecosystem Model (DOS-TEM) was the main tool used 

for deriving carbon stock and flux in all regions of Alaska 

for the assessment. Measurements from the PNW plots were 

used to calibrate DOS-TEM for the coastal forests region 

(chapters 6 and 7). In this chapter, we present an alternate 

approach to estimating carbon stock and flux for coastal 

forests using the plot-based measurements combined with 

values of NPP from remote sensing. 

4.3. Input Data and Methods

4.3.1. Site Descriptions

The research was conducted within three watersheds 

in the Alaska PCTR. The watersheds are located in Juneau, 

Alaska, with mean annual precipitation of 1,580 millimeters 

(mm) and mean annual average temperatures ranging from 

2 to 9 degrees Celsius (°C). We chose watersheds in three 

different ecological subsections that represent three distinct 

landscapes characterized by different lithology and dominant 

forms of landscape evolution (Nowacki and others, 2001; 

fig. 4.2). Peterson Creek watershed is a wetland-dominated 

watershed (53 percent of watershed area) in the Stephens 

Passage Glaciomarine Terraces ecological subsection that is 

composed primarily of slowly permeable glaciomarine sediments (Miller, 1973) along with bedrock outcrops that occur 

on moderate to low slopes. Disturbance in the Peterson Creek 

watershed is limited to hiking trails in addition to a small area 

(~4 kilometers [km]) with roads and light rural-residential 

development near the watershed outlet. In contrast, the 

McGinnis Creek watershed is composed primarily of recently 

deglaciated areas within the Boundary Ranges Icefields 

ecological subsection and has low wetland coverage (less than 

5 percent of watershed area). Hiking trails are the only type 

of anthropogenic disturbance present in the McGinnis Creek 

watershed. Fish Creek watershed consists of intrusive volcanic 

and sedimentary rocks in the Stephens Passage Volcanics 

ecological subsection and has a mix of physiographic features 

that include alpine, productive temperate rainforest, and 

wetlands. The Fish Creek watershed contains hiking trails, a 

road, and a small (~ 0.01 square kilometer [km2

]) rock pit, but 

these features cover only a minor portion of the watershed. 

In addition, a ski area occupies part of the upper Fish Creek 

watershed, but this area is not included in the final carbon 

budget estimates owing to the lack of soil map data for the 

upper watershed (see section 4.3.8).

Within each of the three watersheds we identified three 

sub-catchments representing discrete hydropedologic units 

that characterize key ecosystem processes occurring within 

the vegetated portion of the PCTR landscape: maritime 

fen, maritime wetland forest, and maritime upland forest 

(D’Amore and others, 2012; fig. 4.2). A hydropedologic 

unit (HPU) is “a grouping of variations in soil morphology 

that directly relate influence of water table regime, flow 

paths, and soil saturation to soil development” (Gannon and 

others, 2014). The forms of the HPUs influence soil function 

including biogeochemistry, runoff production, and vegetation 

diversity and structure, which all, in turn, affect carbon cycling. 

Each HPU type (maritime fen, maritime wetland forest, 

and maritime upland forest) represents an experimental 

unit, replicated in each watershed for a total of nine sites 

in the experimental design (fig. 4.2). Freshwater discharge, 

dissolved carbon, and soil respiration measurements were 

collected at the nine sub-catchments at various times from 

2006 through 2009. 

4.3.2. Soil Respiration Rates

Soil respiration measurements were taken every 

2 to 4 weeks during the spring, summer, and fall over a 4-year 

(yr) period from 2006 through 2009. Measurements were 

taken on collars constructed of 21-centimeter (cm)-insidediameter PVC pipe that were permanently installed at the 

ground surface. The soil respiration collars were deployed in 

three seven-collar clusters arranged in a 2-m spacing pattern 

within each HPU for a total of 21 collars per HPU per watershed. Each HPU sampled had a total of 63 collars. Respiration 

data were collected within 1 to 2 days over the entire study 

area, for a total of 189 possible measurements at each temporal 

sampling. All soil respiration measurements were accompanied by a soil temperature measurement (at 10-cm depth) 

at each collar. Soil respiration measurements were achieved 

using a dynamic-closed chamber procedure without drawdown 
80 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 4.2. Locations of the study sites for three component hydropedologic units (maritime fen, maritime 

wetland forest, and maritime upland forest) within three watersheds (Peterson Creek, McGinnis Creek, 

and Fish Creek) on different ecological subsections (Stephens Passage Glaciomarine Terraces, Boundary 

Ranges Icefields, and Stephens Passage Volcanics) near Juneau, Alaska. 

!!

!!

!!! !

! !

! !

!

0 1 2 3 4 5 KILOMETERS

0 1 2 3 4 5 MILES

0 500

METERS

0 500

METERS

0 500

METERS

McGinnis Creek, Boundary Ranges Icefields

200

600

800

500

400

300

200

100

300

400

500

600

200

700

Fish Creek, Stephens Passage Volcanics

Peterson Creek, Stephens Passage 

Glaciomarine Terraces

ALASKA

PACIFIC OCEAN

Map

area CANADA

 UNITED

STATES

100

200

100

300

200

400

EXPLANATION

Figure 5.2

Research watershed boundary

Maritime fen

Hydropedologic unit

Maritime wetland forest

Land-surface elevation—

 Contour interval 50 meters.

 Datum is NGVD 29

Study site

Maritime upland forest

Glacier

300

N

Contours generated from the USGS 2-arc-second National Elevation Dataset, 2008, from seamless.usgs.gov (replaced by The National Map) 

Watershed boundaries modified from USDA-NRCS, USGS, and EPA 12-Digit Watershed Boundary Data, 2012 

Water features from USDA Forest Service, Tongass National Forest, 2012

Glaciers from Randolph Glacier Inventory, version 5.0
Chapter 4 81

(Nay and others, 1994). The collars were designed to receive 

a portable closed-chamber field respirometer that used a 

LiCor 820 infrared gas analyzer (LiCor Inc., Lincoln, Nebr.). 

We measured soil respiration on coarse woody debris in 

contact with the soil. Decomposition of aboveground (or 

aerial) coarse woody debris (that is, snags and elevated logs) 

was not accounted for, but we make the assumption that this 

component is small owing to the age of the forest system 

(Janisch and others, 2005). 

Annual fluxes were estimated by combining temperaturedependent models with soil temperature measurements 

(D’Amore, 2011). Soil respiration measurements were 

not made during winter; however, owing to the low soil 

temperatures and snow cover during that period, we believe 

that the total unaccounted for carbon dioxide (CO2) flux 

is small based on our observations of CO2 flux and soil 

temperature. We partitioned autotrophic and heterotrophic 

respiration differently between the maritime wetland forest 

and maritime upland forest HPUs. Wetland units were 

estimated to have 40-percent heterotrophic contribution based 

on peatland measurements in boreal Alaska (McConnell 

and others, 2013). The maritime upland forests were 

estimated to have 60-percent contribution from heterotrophic 

respiration based on measurements in forested systems 

in British Columbia (Lalonde and Prescott, 2007). The 

contribution of root respiration to total soil respiration 

across both maritime wetland forest and maritime fen, and 

maritime upland forest systems was similar to a 50-percent 

partitioning based on the overall mean reported in a review 

of the contribution of root respiration to soil respiration 

(Hanson and others, 2000). Local measurements indicate 

that values for root respiration range from 40 to 70 percent 

(David D’Amore, U.S. Department of Agriculture Forest 

Service, unpub. data, September 30, 2014).

4.3.3. Dissolved Organic Carbon Export

Stream water dissolved organic carbon (DOC) flux was 

calculated from continuous discharge measurements combined 

with intermittent stream water samples of DOC concentration 

(D’Amore and others, 2015). Concentration-discharge 

relationships and estimation of DOC flux were calculated 

using the U.S. Geological Survey Load Estimator program, 

LOADEST (Runkel and others, 2004). Estimates of annual 

flux were calculated by interpolation from the concentrationdischarge relationship to account for days when stream water 

DOC concentrations were not measured. 

4.3.4. Estimate of Particulate Organic Carbon

Particulate organic carbon (POC) was not measured 

in the study. We estimated POC for each HPU from our 

measured DOC values based on the proportion of POC:DOC 

from similar regions (Hope and others, 1994).

4.3.5. Estimate of Dissolved Inorganic Carbon

Dissolved inorganic carbon (DIC) estimates were derived 

from stream water concentrations measured in outlet streams 

from each HPU. DIC fluxes were derived from the relationship 

between stream water DIC concentration and flux in similar 

systems (Worrall and others, 2005). A relationship between 

annual stream water DIC concentration and annual DIC flux 

was constructed from published values of bicarbonate (HCO3

– ) 

and partial pressure of CO2 (pCO2 ) in stream water (Worrall 

and others, 2005). The total annual export of DIC was 

estimated based on measured stream water HCO3

–

 and p CO2

values from the HPU streams.

4.3.6. Estimate of Net Primary Productivity

Net primary productivity (NPP) estimates at a 1-km 

resolution were obtained from the Moderate Resolution 

Imaging Spectroradiometer (MODIS) satellite MOD17 

project (Zhao and others, 2005; ftp://ftp.ntsg.umt.edu/pub/

MODIS/NTSG_Products/MOD17/GeoTIFF/MOD17A3/

GeoTIFF_30arcsec/). Refer to the MOD17 User’s Guide 

(Heinsch and others, 2003) for a detailed description of the 

MOD17 algorithm. Raster datasets of total annual NPP were 

downloaded for 2006 through 2009, overlapping the time 

period of our field sampling. Using ArcGIS software (Esri, 

2012), we created a single output raster containing the average 

annual total NPP for the 2006–2009 period across the three 

study watersheds using the following general formula:

Integer (Mean (2006 Total NPP, 2007 Total NPP, 

2008 Total NPP, 2009 Total NPP)+0.5) (4.1)

Average annual NPP data were missing for 2.5 percent 

(2.91 km2) of the McGinnis Creek watershed (fig. 4.3, white 

pixels). These areas are composed of a mix of bare rock, snow, 

alpine vegetation, alder-covered slopes, and high-elevation forest. 

NPP values for these “no data” cells were estimated by taking the 

average NPP of surrounding pixels having similar land cover.

We concluded that these remote sensing values were 

a reasonable estimation of the average total NPP based on 

comparisons with similar coniferous forests in the Pacific 

Northwest. However, the estimated NPP values for maritime 

fen were extremely high compared to published values for 

similar fen ecosystem NPP estimates based on ground surveys 

(Asada and Warner, 2005; Wania and others, 2009). We attributed this to the inclusion of forested areas mixed within the 

maritime fens in the 1-km MODIS pixels. In order to avoid a 

greatly biased estimate of maritime fen productivity, we used a 

fixed input value of 177 grams of carbon per square meter per 

year (gC/m2/yr) calculated from the average reported NPP for 

peatlands in two studies (Asada and Warner, 2005; Wania and 

others, 2009).
82 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Net primary productivity,

 in grams of carbon per 

 square meter per year

EXPLANATION

Figure 4-3

No data

500

0

Hydropedologic unit sub-

 catchment boundary

Research watershed 

 boundary

McGinnis

Creek

Peterson

Creek

Fish Creek

N

0 1 2 3 4 5 KILOMETERS

Hillshade generated from U.S. Geological Survey 2-arc-second 0 1 2 3 4 5 MILES

National Elevation Dataset, December 4, 2008, from 

seamless.usgs.gov (site replaced by The National Map)

Figure 4.3. Distribution of 2006–2009 average total net primary productivity across the three research 

watersheds and hydropedologic sub-catchments in southeast Alaska. Net primary productivity data from the 

Moderate Resolution Imaging Spectroradiometer (MODIS) satellite MOD17 project (Zhao and others, 2005). 

See figure 4.2 for location. 
Chapter 4 83

4.3.7. Carbon Budget Model

We used both the NEP (equation 4.2) and NECB 

(equation 4.3) accounting methods to determine the carbon 

flux from HPU sub-catchments and the larger watersheds. 

The NECB method includes the exchange of CO2 from the 

atmosphere to the terrestrial ecosystem, losses owing to 

lateral transfers of dissolved carbon, and losses from major 

ecosystem disturbances such as fire and forest harvest. NEP 

and NECB are similar in that heterotrophic respiration is 

subtracted from NPP estimates in both cases; however, NECB 

accounts for the additional loss of carbon (exports) from the 

ecosystem. In this study, the only additional export of carbon 

in the watersheds was the lateral export of DOC, POC, and 

DIC as there was no forest harvest removal or other large 

disturbances occurring in the study area. 

NEP=NPP–HR (4.2)

NECB=NEP–DOC–POC–DIC (4.3)

where

NEP is net ecosystem productivity,

NECB is net ecosystem carbon balance,

NPP is net primary productivity,

HR is heterotrophic soil respiration,

DOC is dissolved organic carbon,

POC is particulate organic carbon, and

DIC is dissolved inorganic carbon.

Fluxes of methane (CH4 ), carbon monoxide (CO), and 

volatile organic compounds (VOCs) are also considered in the 

calculation of the NECB (Chapin and others, 2006); however, 

we did not have adequate site-specific measurements to 

include these additional components into the overall NECB 

budget. In the Alaska PCTR, annual fluxes of CH4, CO2, and 

VOCs and their overall effect on NECB are assumed to be 

small (chapter 7; Zhuang and others, 2007; U.S. Environmental 

Protection Agency, 2014). So although we are likely overestimating the NECB by excluding CH4

, CO2, and VOC fluxes 

from the budget, we do not believe that omitting these three 

elements will appreciably change our results or conclusions.

4.3.8. Alaska Assessment Vegetation Components

NECB and NEP were calculated for each watershed 

using two methods of spatial assignment for carbon flux. 

The first approach defined HPUs according to mapped soil 

drainage classes (DCs). The soil DC was created as a weighted 

average of the soil map unit (SMU) component DCs in the 

Tongass National Forest soil geographic information system 

(GIS) dataset (U.S. Department of Agriculture Forest Service, 

unpub. data, 2014). The soil DC categories and associated 

values are as follows: very poorly drained (1), poorly drained 

(2), somewhat poorly drained (3), moderately well drained 

(4), and well drained (5). The DCs were divided into three 

bins that corresponded to the drainage characteristics of 

the HPUs:

Drainage Class

(DC)

Hydropedologic Unit

(HPU)

0<DC< 1.5 Maritime fen

1.5≤DC< 3.5 Maritime wetland forest 

≥3.5 Maritime upland forest 

SMUs classified as alpine soils, which existed in both the 

Fish Creek and McGinnis Creek watersheds, did not follow the 

assignment rules in the above table. Alpine SMUs had average 

DC values ranging from 2.63 to 4.45, which would have placed 

alpine areas into the wetland or upland forest HPUs, leading to 

inflated estimates of alpine productivity. Instead, alpine SMUs 

were assigned to the lower productivity maritime fen HPU. 

NECB and NEP flux densities were then calculated for each 

HPU in the three experimental watersheds using remotely 

sensed inputs (NPP) and field measurement outputs (DIC, 

DOC, HR, POC). Next, annual NECB and NEP fluxes were 

computed by extrapolating the HPU flux density estimates 

across each HPU’s total area (fig. 4.4). 

The second approach used the vegetation map developed for this assessment to delineate HPUs (fig. 4.5). This 

vegetation map is a 1-km-resolution map derived from several 

input variables as described in chapters 1 and 2. Maritime 

fen, maritime wetland forest, and maritime upland forest 

already exist as vegetation classes in the vegetation map; 

therefore, these classes were used to represent the HPUs. The 

subsequent calculation of NECB and NEP flux density and 

total flux followed the same method as above for soil DCs. 

We excluded the portion of the Fish Creek watershed where 

SMU mapping was absent from the vegetation map carbon 

balance calculations.

The two approaches for defining HPUs (SMU DCs 

versus vegetation map classes) enabled us to compare NECB 

results from the more finely resolved HPU map derived from 

the SMU DCs with the NECB results based on aggregating 

HPUs by the more generalized vegetation map. SMU data 

were not available for nearly half of the Fish Creek watershed 

(fig. 4.4). Therefore, in order to compare carbon balance estimates between the SMU-DC-based and vegetation-map-based 

HPUs, we limited the area over which the carbon balance was 

calculated in the Fish Creek watershed to only the portion 

where SMUs were present. In the McGinnis Creek watershed, 

the vegetation map identified a large portion of the area as 

rock and ice, which decreased the area of the watershed that 

contained any carbon. This altered the comparison of the total 

carbon in the watershed calculated from the vegetation map 

HPU versus the SMU-DC HPU.
84 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

McGinnis Creek

Peterson

Creek

Fish

Creek

Figure 4-4

0 1 2 3 4 5 KILOMETERS

0 1 2 3 4 5 MILES

N

EXPLANATION

Maritime fen

Hydropedologic unit (HPU)

No soil (rock, ice, water, etc.)

Not mapped

Maritime wetland forest

Maritime upland forest

HPU sub-catchment boundary

Research watershed boundary

Water features from USDA Forest Service, Tongass National Forest, 2012

Watershed boundaries modified from USDA-NRCS, USGS, and 

EPA 12-Digit Watershed Boundary Data, 2012 

Figure 4.4. Distribution of hydropedologic units derived from soil map unit drainage classes. See figure 4.2 for 

location.
Chapter 4 85

Figure 4.5. Distribution of hydropedologic units derived from the vegetation map developed for this assessment. 

See figure 4.2 for location.

McGinnis Creek

Peterson

Creek

Fish

Creek

N

Figure 4-5

0 1 2 3 4 5 KILOMETERS

0 1 2 3 4 5 MILES

EXPLANATION

Maritime fen

No vegetation (ice/barren/

 water/developed)

Not included in carbon balance

Maritime wetland forest

Maritime upland forest

Heath tundra (alpine)

Other

HPU sub-catchment boundary

Research watershed boundary

Hydropedologic unit (HPU)

Water features from USDA Forest Service, Tongass National Forest, 2012

Watershed boundaries modified from USDA-NRCS, USGS, and 

EPA 12-Digit Watershed Boundary Data, 2012 

4.3.9. Geographic Information System Analysis

We used ArcGIS to combine the average annual total 

NPP raster for 2006–2009 with the SMU, vegetation map, 

and watershed boundary datasets. Before the datasets were 

combined, the NPP raster was projected to Alaska Albers 

(North American Datum of 1983) using nearest neighbor 

resampling and snapped to the vegetation map raster. The 

Alaska Albers projection was used for all GIS data. Both the 

NPP and vegetation map rasters were converted to vector 

polygon datasets and combined with the SMU and watershed 

boundary polygons. The combined dataset was exported to a 

spreadsheet for the carbon balance accounting.
86 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

4.4. Results

4.4.1. Watershed Net Ecosystem Carbon Budgets

Each of the watersheds had a net gain in carbon stock 

calculated from both the NEP and NECB carbon balance 

models (table 4.1). The calculation method had a notable 

effect on the amount calculated for each watershed overall. 

The NEP values were 18 percent, 32 percent, and 51 

percent higher than the NECB values in the Peterson Creek, 

McGinnis Creek, and Fish Creek watersheds, respectively. 

The lowest unit-area carbon accretion values were in the 

youngest landscape of the postglacial McGinnis Creek watershed. The highest values were calculated in Peterson Creek, 

whereas Fish Creek values were intermediate between the 

other two watersheds.

Net carbon balance tended to increase with stand age, 

until it appeared to reach a plateau at 200 years (fig. 4.6). 

Owing to the limited number of data points used in the 

assessment, it was not possible to make definitive statements 

regarding these trends, but the overall carbon balance was 

consistent with the expected phases of stand development 

in southeast Alaskan coniferous forests. The increase was 

greatest during the period of stem exclusion (~100–200 yr). 

The plateau in net balance occurred in the range of understory re-initiation (~200–400 yr). There was an outlier in 

the trend from the Fish Creek maritime upland forest where 

the net carbon balance calculated as NECB was much lower 

than the general trend in the rest of the data. The discrepancy in this value was due to the high rate of DIC loss in 

this HPU. The remaining carbon loss values were similar 

to the other HPUs.

4.4.2. Comparison of Carbon Budgets from 

Drainage Class Versus Vegetation Map 

Hydropedologic Units

All watersheds had a net gain in carbon calculated 

by either the finer resolution DC HPUs or the less highly 

resolved vegetation-map-based HPUs. The area-weighted 

average NECB calculated for the three watersheds using the 

DC HPUs was 148 gC/m2/yr, with an NEP of 194 gC/m2/yr 

(table 4.1), very similar to the average vegetation map NECB 

of 142 gC/m2/yr, with a NEP of 193 gC/m2/yr (table 4.2). The 

watershed totals for NEP and NECB were also of roughly 

similar magnitude in both models. Therefore, the details 

regarding the implications of the magnitude of the carbon 

balance and patterns among landscape units and watersheds 

are discussed relative to values in table 4.1.

4.4.3. Relationship of Carbon Budget Estimates 

to Measures of Net Primary Productivity and 

Net Ecosystem Production in Other Systems

Examining the relationship of NPP with NEP and 

NECB provides a means for evaluating our carbon budget 

estimates (Pregitzer and Euskirchen, 2004). The modeled 

ratios of NPP:NEP and NPP:NECB were consistent with the 

regression model for these measures in ecosystems worldwide (fig. 4.7). In addition, the model estimates ranged along 

the mean ratio of NPP to NEP and NECB along a similar 

slope as the world biome model. The shift in the relationship 

of NPP to NEP and NECB was evident in each comparison 

among the models. The reduction in NEP using NECB was 

consistent across the watershed partitioning. 

A major influence on the relationships illustrated 

in figure 4.7 was the value for NPP calculated for each 

SMU-DC HPU and vegetation map HPU. The NPP 

estimates varied among the HPUs coincident with the 

range of ecosystem types located within the watershed 

(tables 4.1, 4.2). In table 4.1, the highest mean NPP 

was in the Fish Creek watershed with 305 gC/m2/yr, 

closely followed by the Peterson Creek watershed with 

350

300

250

200

150

100

50

0

0 100 200

Stand age, in years

Net carbon balance, in grams of carbon

per square meter per year

300 400

EXPLANATION

Figure 4-6

Net ecosystem production

Lateral carbon export

Net ecosystem carbon balance

Figure 4.6. Average stand age compared to net carbon 

balance as both net ecosystem production (NEP) and 

net ecosystem carbon balance (NECB). The difference in 

these two values is the amount of lateral carbon export 

added to the NEP to calculate NECB.
Chapter 4 87

268 gC/m2/yr. These estimates were influenced primarily by 

the amount of mature forest in the watersheds. The Peterson 

Creek watershed is completely located on uplifted marine 

sediment, which has promoted the development of wetland 

(maritime fen and maritime wetland forest) plant communities. 

The lowest mean watershed NPP was in the McGinnis Creek 

watershed with 211 gC/m2/yr. This value was likely due to 

extensive areas with early successional alpine vegetation, bare 

rock, and ice. The mean values for all watersheds were below 

400 gC/m2/yr, which is at the low range of values for similar 

forests of the Pacific Northwest where NPP values are often 

greater than 500 gC/m2/yr (Hudiburg and others, 2009).

4.4.4. Soil Carbon Export Pathways

Heterotrophic respiration was the largest export pathway 

among the various export components of the NECB (equation 4.3) 

and accounted for 57 percent, 60 percent, and 65 percent of 

the carbon export in the Fish Creek, McGinnis Creek, and 

Peterson Creek watersheds, respectively. Estimated heterotrophic respiration rates were 56 gC/m2

/yr, 59 gC/m2/yr, and 

96 gC/m2/yr in the McGinnis Creek, Peterson Creek, and 

Fish Creek watersheds, respectively (table 4.1). 

The export of DOC from the Peterson Creek, McGinnis 

Creek, and Fish Creek watersheds was 19 gC/m2/yr, 20 gC/m2/yr,

D. Vegetation map model

50 100 150 200 250 300 350 400 450

0

50

100

150

200

250

300

350

50 100 150 200 250 300 350 400 450

0

50

100

150

200

250

300

350

0

50

100

150

200

250

300

350

Hydropedologic unit

EXPLANATION

Figure 4-7

Watershed

B. Soil map unit model

C. Vegetation map model

A. Soil map unit model

0

50

100

150

200

250

300

350

Net primary productivity, in grams of carbon per square meter per year

Net ecosystem production, in grams of carbon per square meter per year

Net ecosystem carbon balance, in grams of carbon per square meter per year

Figure 4.7. Comparison of ratios of net primary productivity (NPP) to both net ecosystem production (NEP) and net 

ecosystem carbon balance (NECB) calculated in each hydropedologic unit and watershed in both the finer scale SMU 

drainage class model and the coarser vegetation map model. The regression line is derived from values for worldwide 

forests (Pregitzer and Euskirchen, 2004) and used as a comparison to the values calculated in the present study. The 

graphs are for A, soil map unit drainage class NPP:NEP; B, soil map unit drainage class NPP:NECB; C, vegetation map 

NPP:NEP; and D, vegetation map NPP:NECB. 
Table 4.1. Carbon balance, inputs, and outputs in three watersheds in the Alaskan coastal forest region modeled from hydropedologic units defined drainage classes. [Flux: NPP, net primary productivity from remote sensing (MODIS); Rs, soil respiration; HR, heterotrophic respiration based on proportional estimate of autotrophic to heterotrwater dissolved organic carbon; DIC, stream water dissolved inorganic carbon; POC, estimated particulate organic carbon; NEP, calculated net ecosystem production from NPP ecosystem carbon balance from NPP, HR, DOC, DIC, and POC. Units: yr, year; km2, square kilometer; gC/m2/yr; gram of carbon per square meter per year; MgC/yr, megagram o

Watershed (ecological subsection) Hydropedologic unit Stand age (yr) Area (km2) Flux density (gC/m2/yr) 

NPP Rs HR DOC DIC POC NEP NECB 

Peterson Creek (Stephens Passage Glaciomarine Terraces) Maritime fen n.d. 12.24 177 85 34 20 7 5 143 111 

Maritime wetland forest 203 8.17 364 159 63 21 10 5 301 265 

Maritime upland forest 154 3.33 369 238 143 9 12 2 226 203 

Watershed n.d. 23.74 268 132 59 19 8 5 209 177 

McGinnis Creek (Boundary Ranges Icefields) Maritime fen n.d. 11.81 177 72 29 24 7 6 148 111 

Maritime wetland forest 114 .60 261 181 72 50 7 13 189 119 

Maritime upland forest 109 4.49 295 208 125 6 29 2 170 133 

Watershed n.d. 16.90 211 112 56 20 13 5 155 117 

Fish Creek (Stephens Passage Volcanics) Maritime fen n.d. 4.91 177 79 32 30 23 8 145 85 

Maritime wetland forest 379 5.72 344 189 75 19 9 5 268 235 

Maritime upland forest 234 8.24 355 249 150 17 82 4 205 102 

Watershed n.d. 18.87 305 187 96 21 45 5 209 138 

Study area n.d. 59.51 264 144 70 20 21 5 194 148 
Table 4.2. Carbon balance, inputs, and outputs in three watersheds in the Alaskan coastal forest region modeled from hydropedologic units defined by [Flux: NPP, net primary productivity from remote sensing (MODIS); Rs, soil respiration; HR, heterotrophic respiration based on proportional estimate of autotrophic to heterotrwater dissolved organic carbon; DIC, stream water dissolved inorganic carbon; POC, estimated particulate organic carbon; NEP, calculated net ecosystem production from NPP ecosystem carbon balance from NPP, HR, DOC, DIC, and POC. Units: yr, year; km2, square kilometer; gC/m2/yr, gram of carbon per square meter per year; MgC/yr, megagram of 

Watershed 

(ecological

subsection)

Hydropedologic 

unit

Stand 

age

(yr)

Area

(km2)

Flux density (gC/m2/yr) 

NPP Rs HR DOC DIC POC NEP NECB 

Peterson Creek

(Stephens Passage Glaciomarine Terraces)

Maritime fen n.d. 5.01 177 79 32 25 12 6 145 102 

Maritime wetland 

forest

203 3.25 341 179 72 30 9 8 270 224 

Maritime upland 

forest

154 16.42 361 237 142 11 41 3 219 165 

Watershed n.d. 24.68 321 197 110 16 31 4 211 160 

McGinnis Creek

(Boundary RangesIcefields)

Maritime fen n.d. 2.36 177 79 32 25 12 6 145 102 

Maritime wetland 

forest

n.d. 0 NA NA NA NA NA NA NA NA 

Maritime upland 

forest

109 6.58 294 237 142 11 41 3 152 97 

Watershed n.d. 8.94 263 195 113 14 34 4 150 98 

Fish Creek

(StephensPassageVolcanics)

Maritime fen n.d. 3.00 177 79 32 25 12 6 145 102 

Maritime wetland 

forest

379 .02 355 179 72 30 9 8 284 237 

Maritime upland 

forest

234 15.85 342 237 142 11 41 3 199 145 

Watershed n.d. 18.87 315 212 125 13 37 3 191 138 

Study area n.d. 52.49 309 202 116 15 33 4 193 142 
90 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

and 21 gC/m2/yr (table 4.1). The DOC estimates were much 

more closely aligned than heterotrophic respiration export 

among the watersheds. The export of DOC accounted for 

12 percent, 20 percent, and 21 percent of the total carbon 

export from the Fish Creek, Peterson Creek, and McGinnis 

Creek watersheds, respectively. 

The export of DIC from the Peterson Creek, McGinnis

Creek, and Fish Creek watersheds was 8 gC/m2/yr, 13 gC/m2/yr,

and 45 gC/m2/yr (table 4.1). This disparity was driven by 

high values in the maritime upland forest at Fish Creek. The 

high calculation of DIC flux from the maritime upland forest 

had considerable leverage on the total carbon balance for the 

Fish Creek watershed. 

The export of POC from each of the watersheds was 

5 gC/m2/yr (table 4.1). These values vary with the amount of 

DOC owing to the construction of the model for the prediction of 

POC driven by DOC. These estimates of POC export accounted 

for about 4 percent of the total carbon export in the McGinnis 

Creek, Fish Creek, and Peterson Creek watersheds. The export 

of POC was the smallest of all the soluble carbon components.

4.5. Discussion

4.5.1. Watershed Carbon Budgets

The visual evidence of abundant woody debris and deep 

organic soils was consistent with the hypothesis that the 

PCTR forest and maritime fen ecosystems have been a net 

sink for carbon during the Holocene. The actual size of the 

sink was uncertain given the variability in NPP and exported 

amounts. The modeled carbon budgets indicate that all three 

study watersheds were net sinks during the period of observation (2006 –2009), which agrees with the finding that the 

North American continent was a sink for carbon during the 

2000 –2010 decade (King and others, 2015). Our results are 

also consistent with other studies that have estimated northern 

high-latitude forests as carbon sinks (Houghton, 2003). For 

example, mature forest stands (80–200 yr) in the Pacific Northwest had estimated carbon sinks of 286 gC/m2

/yr, whereas 

older forests (>200 yr) had rates of 165 gC/m2/yr (Turner 

and others, 2000). Given that these estimates did not account 

for lateral carbon losses in the budget, the calculated carbon 

values using our modeling approach seem to be reasonable 

carbon budget approximations for the observed watersheds.

In addition to the net carbon balance estimates, the calculated ratio of NPP to NECB in the PCTR was very similar 

to the average ratio compared to other forested ecosystems 

(fig. 4.7). The overall watershed estimates and the components 

(hydropedologic units) within the watershed matrix are in 

good agreement with the trend. In particular, the maritime fen 

and maritime wetland forest communities are well represented 

among the units and contribute considerably to the carbon 

balance among the watersheds. The relationship illustrated by 

the model fit in figure 4.7 indicates that the trajectory of NECB 

is positive, or a net sink for carbon, in the PCTR ecosystem.

Average annual temperature decreased (–0.056 °C per 

decade; National Oceanic and Atmospheric Administration, 

National Centers for Environmental Information, 2015) and 

precipitation increased over the 1981–2010 normal period for 

Juneau, and average annual temperatures during the 2006–

2009 study period were –0.445 to –1.06 °C cooler than the 

1981–2010 average (5.56 °C; National Oceanic and Atmospheric Administration, National Centers for Environmental 

Information, 2015); however, the Alaska PCTR is predicted to 

warm by 4 to 6 °C by the end of the century and precipitation 

is predicted to increase (Melillo and others, 2014). Changes in 

temperature and precipitation lead to variability in the carbon 

balance by influencing a host of interconnected processes, 

such as respiration, photosynthesis, and the occurrence of 

natural disturbance events (Reichstein and others, 2013). 

For example, average annual soil respiration increased with 

average annual temperature (National Oceanic and Atmospheric Administration, National Centers for Environmental 

Information, 2015) at our three study watersheds, suggesting 

that warmer temperatures in the Alaska PCTR may lead 

to increases in HR, the largest soil carbon export pathway. 

Increased HR with increased temperature has been reported in 

other studies, but the resulting effect on NPP and NECB also 

depended on other factors, such as the amount and timing of 

precipitation and presence or absence of disturbance (Arnone 

and others, 2008; Hirata and others, 2014). Previous carbon 

balance modeling in Alaska has focused on the arctic and 

northern interior regions. Models including the Alaska PCTR 

are difficult to validate because of the lack of data (chapter 7). 

How climate variability will influence the carbon balance in 

the Alaska PCTR is highly uncertain. There is a clear need for 

more field experiments to fill the existing data gap in Alaska’s 

highly productive coastal forests.

4.5.2. Landscape Variability and Carbon Balance

The watersheds in the study have varying geologic and 

geomorphic histories that influence the carbon balance through 

both the uptake of atmospheric carbon and the loss of carbon 

via the multiple export pathways. The variability in the low 

and high net balance estimates can be explained by the relative 

postglacial development of the watersheds. The hydropedologic units used in the landscape partitioning reflect the dominant underlying influence on carbon cycling in the watersheds. 

The watersheds provide surrogates for many PCTR 

watershed types, including early seral/postglacial, windthrowderived forests, and mature or late seral forest communities. The estimates in the two older watersheds reflect the 

underlying geomorphic history but are also driven by stand 

age and vegetation type. Peterson Creek has evidence of a 

large, regional windthrow event, which has been associated 

with stands originating in the decade beginning around 1880 

(Harris, 1989; Nowacki and Kramer, 1998). The average 

stand age in Peterson Creek is consistent with this period 

of disturbance in the maritime upland forest. The maritime 

wetland forest is somewhat older, but still in the mature stand 
Chapter 4 91

development phase. The average age of the Fish Creek stands 

is older, and the aspect of the watershed protects it from the 

prevailing storm winds from the southeast and southwest. 

4.5.3. Potential Application of the Vegetation 

Map for Regional Carbon Modeling

Our approach using two types of landscape stratification 

reveals the potential for variability when estimating a carbon 

balance over smaller areas (such as, subwatershed-scale). It is 

reasonable to expect discrepancies in carbon balance estimates 

between the two stratification methods owing to the different 

resolution and origin of the input data (SMU polygons versus 

the 1-km vegetation map pixels). However, the consistency 

across larger areas in the final study results indicates that 

the alternative scaling approach models are comparable. 

The consistent array of the carbon balance estimates among 

varying landscape units and watersheds along with similar 

regression relationships for NPP:NEP and NPP:NECB 

(fig. 4.7) illustrate that these carbon balance estimates can 

be reliably extrapolated to the broader region. 

4.5.4. Evaluation of Regional Net Primary 

Productivity Estimates

The NPP estimate from MOD17 has several challenges 

that make accurate estimates problematic in the coastal 

temperate rainforest. One of the more significant obstacles to 

accurate measurements is the common occurrence of cloud 

cover, which contaminates some of the satellite source data 

used for deriving NPP. Cloud-contaminated input data are estimated in the MOD17 product by linear interpolation between 

the previous and next cloud-free periods (Heinsch and others, 

2003). In the three experimental watersheds, the percentage of 

days during the growing season that NPP values were estimated because of cloud cover ranged from 63 to 93 percent 

(average 76 percent). The coarse resolution of the MOD17 

input climate and vegetation datasets and the biophysical 

assumptions used in the MOD17 algorithm also contribute to 

uncertainties in the NPP values (Heinsch and others, 2006).

Studies evaluating the accuracy and utility of MODIS 

NPP estimates at various scales are limited owing to a lack of 

empirical validation data and the difficulty of comparing plotlevel measurements to a 1-km MODIS grid cell (Turner and 

others, 2006), and no such assessments have been made for 

the Alaska coastal forest region. MODIS appears to capture 

the general variability in the magnitude of NPP in comparison 

to local data, but has also been shown to overestimate NPP 

at low-productivity sites and underestimate NPP at highproductivity sites in North America (Turner and others, 2006) 

and to overestimate NPP in some forests in the northeastern 

United States (Tang and others, 2010). Nevertheless, at the 

regional scale MODIS NPP can be useful for broad estimates, 

and regional estimates can also be improved by incorporating 

ancillary datasets (Heinsch and others, 2006; Pan and others, 

2006). Despite the limitations inherent in the MOD17 product, 

our estimates of NECB, even at the watershed scale, compared 

favorably with other reported values in the literature. This 

general agreement with other findings provides us some 

assurance that the MODIS data will be useful for providing 

NPP values for NECB accounting across the expanse of the 

PCTR. Future work on NEP estimates in the region should 

include other potential sources of data for NPP, such as other 

remote sensing products or plot-based estimates.

4.5.5. Net Ecosystem Carbon Balance as a 

Land Management Tool for the Perhumid 

Coastal Temperate Rainforest

The carbon-dense region of the PCTR has become a 

focus for carbon cycling research owing to the large accumulations of carbon in old-growth forests and the potential for 

accumulating carbon in young-growth forests. Mature forests 

can continue to sequester carbon (Harmon and others, 1990), 

and Luyssaert and others (2008) have proposed that old forests 

retain their sink strength longer than previously thought. 

Carbon sequestration in this region may provide a long-term 

reservoir and continuing sink in Alaska and is therefore a key 

piece of the carbon budget for Alaska and North America. 

Clearly it is important to use the NECB model for PCTR 

watersheds, as it illustrates the importance of accounting for 

lateral carbon losses. It is notable that heterotrophic respiration 

accounted for only 57 to 65 percent of the carbon export from 

the watersheds. This is in contrast to most temperate ecosystems where heterotrophic respiration typically accounts for 

70 to 90 percent of the carbon export. More accurate regional 

carbon balance models will need to consider the lateral loss of 

carbon. Forest inventory measurements do not account for soil 

carbon or the overall balance in forested ecosystems. This may 

lead to disparate estimates and conclusions regarding the total 

carbon balance. 

4.6. Conclusions

Watershed NECB estimates for the period of observation 

(2006–2009) indicate that the Alaska PCTR is a net carbon 

sink and that carbon is accreting at rates consistent with values 

for mature (100–300 yr) conifer forests in the Pacific Northwest. Stratifying the landscape into hydropedologic units for 

the purpose of estimating carbon balance inputs and outputs 

representative of the dominant landscape processes in a region 

provides a means to estimate watershed carbon balance and 

address terrestrial ecosystem heterogeneity. The approach of 

extrapolating the hydropedologic unit estimates across larger 

areas can provide preliminary estimates of ecosystem carbon 

balance, and continued field measurements can improve 

the accuracy of the predictions within each unit and across 

varying site conditions.
92 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

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10.1890/1051-0761(2007)017[0203:NEOCAC]2.0.CO;2.
Chapter 5. Forest Inventory-Based Analysis and 

Projections of Forest Carbon Stocks and Changes 

in Alaskan Coastal Forests

By Xiaoping Zhou1, Svetlana A. Schroder2, A. David McGuire3, and Zhiliang Zhu4

1

U.S. Department of Agriculture Forest Service, Portland, Oreg.

2

University of Washington, Seattle, Wash.

3

U.S. Geological Survey, Fairbanks, Alaska.

4

U.S. Geological Survey, Reston, Va.

5.1. Highlights

• Baseline (average of 2004–2013) estimates derived 

from U.S. Department of Agriculture Forest Service 

forest inventory show that forests in south-central 

and southeast coastal Alaska contain 1,018 teragrams 

of carbon (TgC) in both live and dead tree biomass. 

Over 80 percent of the forest carbon in coastal Alaska 

is in the Chugach and Tongass National Forests.

• Projected to 2099 using a forest simulation model, 

forest carbon stock would increase by 1 percent, 

8 percent, and 27 percent under the scenarios of 

current forest management (including harvesting) 

with current climate, climate change with forest 

management, and climate change without forest 

management, respectively. To conduct the simulations of climate change, the A1B scenario from the 

Intergovernmental Panel on Climate Change’s Special 

Report on Emissions Scenarios was used to drive the 

version 3.1-T47 of the Canadian Centre for Climate 

Modelling and Analysis’ Coupled Global Climate 

Model general circulation model.

• Managed with present forest harvest and present 

climate, the forest carbon would increase by 10 TgC 

by the end of the century compared with the baseline 

(2004 –2013). Forest carbon stock would increase 

by 86 TgC under the climate change with harvesting 

scenario and by 276 TgC under the climate change 

without harvesting scenario. 

5.2. Introduction

Alaska represents over 15 percent of the total U.S. land 

area and over 15 percent of U.S. forest land (Oswalt and 

others, 2014). Coastal Alaska includes two inventory 

regions by the U.S. Forest Service: southeast Alaska, and 

south-central Alaska and Kodiak Island (fig. 5.1, Barrett and 

Christensen, 2011). The two regions are part of the North 

Pacific Landscape Conservation Cooperative (LCC), except 

for Kodiak Island, which is in the Western Alaska LCC. 

Coastal Alaska contains two main forest types: boreal forest 

and temperate maritime forest. In this region of Alaska, 

approximately 88 percent of the forest land is publicly 

owned. The substantial amounts of forest land in reserved 

status and of old-growth forests make coastal Alaska forests 

different from those in the other coastal regions. The forests in 

coastal Alaska store about 1,018 teragrams of carbon (TgC). 

Smith and others (2013) estimated the average total live tree 

carbon (aboveground and belowground) to be approximately 

12.5 kilograms of carbon per square meter (kgC/m2) in 

coastal Alaska compared with an average of 6.9 kgC/m2 for 

all U.S. forest land. In addition, they estimated the average 

carbon stored in dead trees (standing dead and down dead 

wood) to be 2.8 kgC/m2

 in coastal Alaska compared with 

1.1 kgC/m2 on all U.S. forest land. 

The analysis presented in this chapter supports the 

ecosystem carbon assessment (Zhu and others, 2010) in 

Alaska, as required by the Energy Independence and Security 

Act of 2007 (EISA). It uses detailed field-plot data measured 

by the U.S. Department of Agriculture (USDA) Forest Service 
96 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

as baseline to project the future forest ecosystem carbon under

different management activities and climate change assumptions.

Such a study will assess the forest carbon from different angles 

for this heavily forested region. The major objectives of this 

study are to estimate how much carbon is stored in the coastal 

forests of Alaska by forest carbon pools except the soil organic 

carbon, and to assess the responses of carbon storage in this 

region to potential management activities and climate change. 

5.3. Data and Methods 

There are over 62,000 square kilometers (km2) of forest 

land in coastal Alaska, of which about 89 percent is publicly 

owned. The USDA Forest Service, Forest Inventory and 

Analysis (FIA) Program provides information needed to assess 

the condition of America’s forests. FIA collects tree-level field 

data and provides the public forest inventory data in a standard 

accessible format for those interested in further analysis. The 

inventories were conducted on a periodic basis before 1999. 

With the passage of the 1998 Farm Bill, FIA was required to 

collect data annually on plots within each State (O’Connell 

and others, 2014). The USDA Forest Service regional research 

stations are responsible for measuring the plots and publishing 

summary reports for each of the States. Note that FIA plots are 

measured on a moving panel system and it takes about 5 years 

to measure all the plots in the eastern States and 10 years in 

the western States.

FIA ground plots are designed to cover a 1-acre 

(0.00405-km2

) sample area. The recent annual inventories use 

a national standard, fixed-radius plot layout for sample tree 

selection and measurement (O’Connell and others, 2014). 

The variables reported in the FIA database are very detailed, 

including plot variables, condition variables, and tree-level 

variables. The plot variables provide information relevant to 

the entire 1-acre field plot, such as plot location; the condition variables provide information on landscape attributes 

that define the condition, such as the reserved status, owner 

group, forest type, and others; and the tree-level variables 

provide information for each tree 1 inch (2.54 centimeters) in 

diameter and larger found on the plot, including tree species, 

diameter at breast height, and height (Barrett and Christensen, 

2011). The Pacific Northwest Research Station (PNW) is 

responsible for collecting and compiling the forest inventory 

of Alaska, California, Oregon, and Washington. The first full 

10-year cycle of annual inventory for coastal Alaska, which 

was completed by PNW–FIA in 2013 (2004–2013), was used 

in this study for analyzing current forest conditions and as a 

baseline for making future projections.

A total of 2,163 plots were used in this study (table 5.1). 

The current carbon pools were analyzed using tree-level data 

from the Alaska coastal forest inventory database (Barrett 

and Christensen, 2011) and were reported by land ownership, 

forest type, and stand age group. 

The USDA Forest Service Forest Vegetation Simulator 

(FVS) is a widely used modeling tool for predicting forest 

stand dynamics in U.S. forests (Dixon, 2002). It has been 

used to summarize current stand conditions, predict future 

stand conditions under various management alternatives, 

and update inventory statistics. Basic modules for growth, 

mortality, regeneration, and volume are built into each variant 

of the FVS, whereas other linkable extensions are available for 

modeling specific changes, such as fire and fuels, insects and 

Figure 5.1. Coastal Alaska 

study region, including 

southeast and south-central 

Alaska and Kodiak Island.

Figure 5–1

Kodiak

Island

South-central

Alaska

Southeast

Alaska

N

0 200 400 KILOMETERS

0 200 400 MILES
Chapter 5 97

diseases, and climate-induced effects. In this study, tree-level 

FIA data were used as inputs into the FVS to produce vegetation projections and corresponding carbon volumes. Because 

the forest inventory data were collected in different years, the 

plots collected before 2012 were grown to that point using the 

FVS model. The FVS simulates vegetation in cycle-by-cycle 

lists, where a cycle is a period of time for which increments 

of tree characteristics are predicted. 

FVS variants account for the local peculiarities of 

vegetation and fuel types in different forests throughout the 

United States. For this study, we used the current Alaska 

variant of the FVS (Keyser, 2008), which includes models 

designed specifically for the southeast Alaska coastal forest 

types found in the study region. 

The height and diameter growth rates were adjusted using 

a previously published validation procedure (Robinson and 

others, 2005; Leites and others, 2009). To accomplish this, 

we compiled the FIA data and ran a simulation. The results 

of the simulation run were compared with the forest inventory 

data, and we adjusted the growth rates to minimize modeldata discrepancies. The comparison showed that the FVS 

overestimated diameter and height growth, which is consistent 

with the results reported by Peterson and others (2014). 

The subsequent growth-rate adjustment reduced the bias in 

diameter growth to 4.7 percent and height growth to 3 percent. 

The carbon modeling simulation was run using the FVS 

Alaska variant with two modifications. First, we did not use 

the built-in regeneration model. Instead, we simulated natural 

regeneration based on the basal area in each plot by species. 

Using this algorithm, we were able to include regrowth 

more evenly compared with adding it every 50 years as the 

built-in regeneration model simulates. Second, we adjusted 

mortality in the model by using data on historical mortality 

rates (Haynes, 2003) and updating decay classes to match the 

classes for different species in Alaska (Keyser, 2008). Decomposition rates were not modified from those in the Alaska 

variant of the FVS.

For each of the scenarios we ran 25 simulations for each 

ecological region to assess uncertainty. We ran the model in 

a stochastic mode and seeded a random number into each 

simulation run by using the “RANNSEED” keyword in the 

FVS. This procedure was conducted to achieve random effects 

via distribution of the errors associated with prediction of 

the logarithm of basal area increments (Dixon, 2002). In the 

Alaska variant of the FVS, the inclusion of random effects 

alters the equations for calculating diameter and crown ratio 

and thus provides a basis for quantifying uncertainty. There 

is a random component in the estimate of the height growth 

as well (Keyser, 2008). For each simulation the output tables 

(that is, the carbon pools and tree lists) were summarized 

in 5-year intervals and exported to a separate database for 

further analysis. The data were then processed and analyzed 

to describe future projections for different scenarios. 

5.4. Management and Climate Scenarios 

Three scenarios were developed and analyzed in this 

assessment: (1) current management with no climate change, 

(2) climate change with management, and (3) climate change 

with no management. These scenarios are described below. 

The major forest activity associated with management in 

the study region is forest harvest. Currently, the forest plans 

for both the Tongass (2008 forest plan) and Chugach (2002 

forest plan) National Forests are being revised and amended 

(visit http://www.fs.usda.gov/tongass/ and http://www.fs.usda.

gov/chugach/ for more information and updates). The planned 

amendment for the Tongass National Forest would make 

changes to young-growth management, as well as changes 

to make renewable energy development more permissive. 

However, no concrete projections of future harvesting rates 

have yet been made. Therefore, for this study, the most recent 

5-year average of harvest volume available was applied to the 

whole projection for the management scenario (Alexander, 

2012; Zhou, 2013) (fig. 5.2). The management scenario also 

assumed that the harvest would only take place on timberlands 

(that is, non-reserved, accessible areas with productivity of at 

least 1.4 cubic meters per hectare per year where merchantable 

volume is at least 175 cubic meters per hectare). The model 

also assumes harvesting would not take place on slopes greater 

than 35 percent owing to the logistical challenges and higher 

costs associated with working in these areas. Wildfire was 

evaluated for the coastal region based on the fire incident data 

from the Alaska Interagency Coordination Center (AICC), 

but was not modeled because of its low occurrence rate. The 

effects of insects and disease were modeled using the average 

mortality rate from the FIA historical database. 

Table 5.1. Number of plots and trees by inventory year as 

measured by the U.S. Department of Agriculture Forest Service 

Forest Inventory and Analysis Program in the study region.

Inventory year Number of plots Number of trees

2004 214 7,055

2005 270 9,754

2006 214 7,795

2007 217 7,234

2008 211 8,126

2009 203 7,295

2010 224 8,385

2011 202 6,984

2012 207 7,340

2013 201 6,989

Total 2,163 76,957
98 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

For the climate change simulations used in this study, 

we chose to use one of the climate scenarios from the 

Intergovernmental Panel on Climate Change’s (IPCC’s) 

Special Report on Emissions Scenarios (Nakićenović and 

Swart, 2000). Version 3.1-T47 of the Canadian Centre for 

Climate Modelling and Analysis’ Coupled Global Climate 

Model (CGCM3.1; Flato, 2005) general circulation model 

data for IPCC scenario A1B was coupled with the FVS 

to simulate climate change effects on forest carbon. It is 

important to note that the climate extension (Climate-FVS) 

is not readily available for the Alaska variant, but we were 

able to obtain a custom version of this extension from the 

USDA Forest Service’s Forest Management Service Center 

(Crookston, 2014). The FVS simulations were performed 

using the climate data and species viability scores for that 

climate; species viability scores were used to adjust the 

growth of different species in each plot given changed 

climate conditions. The species viability scores used in this 

study were calculated by West Virginia University’s Forest 

Resources Management, School of Natural Resources.

The FVS output allows tracking of changes in various 

carbon pools. It was used to calculate total carbon storage for 

the study area by weighting each plot with the area it represents in the FIA sample using the statistical analysis software 

(SAS Institute Inc., 2008).

Forest carbon pools analyzed in this study include live 

tree biomass (aboveground and belowground), understory 

vegetation, dead wood (standing dead and down dead wood), 

forest floor (litter carbon), and soil organic carbon (SOC). The 

definition of each pool is listed in table 5.2 (Smith and others, 

2013; O’Connell and others, 2014). The SOC pool, which 

is available from FIA data, was not included in projected 

results because the FVS does not simulate the SOC in the 

carbon module.

Table 5.2. Forest carbon pools analyzed in this study.

[cm, centimeter; m, meter]

Carbon pool category Definition

Aboveground and belowground live tree Aboveground and belowground portions of the live trees with diameters greater than 

2.54 cm at 1.37 m above the forest floor.

Standing dead wood Standing dead trees, including coarse roots.

Down dead wood Woody material greater than 7.6 cm in diameter on the ground and stumps and their 

roots greater than 7.6 cm in diameter.

Understory vegetation Aboveground and belowground portions of seedlings and woody shrubs.

Forest floor or litter Organic material on the floor of the forest, including fine woody debris, humus, and 

fine roots in the organic forest floor layer above mineral soil.

Soil organic carbon Fine organic material below the soil surface to a depth of 1 m (does not include roots).

800

700

600

500

400

300

200

100

0

Harvest volume, in millions of board feet

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

2009 2010 2011 2012 2015 2020 2025 2030

Historical

Projection

Harvest

EXPLANATION

Figure 5-2 Figure 5.2. Coastal Alaska historical annual harvest and projected harvest. 
Chapter 5 99

5.5. Results and Discussion

5.5.1. Current Status of Forest Carbon Pools

The baseline forest carbon was derived from the USDA 

Forest Service EVALIDator program (Miles, 2015). The total 

forest carbon without soil organic carbon (SOC) in coastal 

Alaska was estimated to be 1,018 TgC (2004–2013), of which 

about 557 TgC was live biomass carbon (aboveground and 

belowground), 175 TgC was dead wood carbon (standing dead 

and down dead wood), and 275 TgC was forest floor carbon. 

The estimates of forest carbon without SOC are reported 

below by land ownership, forest type, and stand age class. 

5.5.1.1. Current Forest Carbon Pools by 

Land Ownership Group

The two largest land owners, the Chugach and Tongass 

National Forests, represent 71 percent of the forest land and 

contain 80 percent of the forest carbon in coastal Alaska 

(fig. 5.3). The Tongass National Forest, which is the largest 

national forest in the country, has 64 percent of the total 

coastal forest land and over 74 percent of the total Alaska 

coastal forest carbon. Nearly 18 percent of the forest land 

in coastal Alaska is in other public ownership (other Federal 

or State and local government), and over 11 percent of forest 

land is in private ownership.

5.5.1.2. Current Forest Carbon Stock by 

Forest Type

The major forest types in coastal Alaska are western 

hemlock (Tsuga heterophylla (Raf.) Sarg.), mountain hemlock 

(Tsuga mertensiana (Bong.) Carrière), Sitka spruce (Picea 

sitchensis (Bong.) Carrière), yellow-cedar (Callitropsis 

nootkatensis (D. Don) D.P. Little), and western red cedar 

(Thuja plicata Donn ex D. Don). These five forest types 

account for over 90 percent of the coastal forest carbon, 

of which over 38 percent is stored in the western hemlock 

forest type (table 5.3).

5.5.1.3. Current Forest Carbon Pools by 

Stand Age Class

Over 44 percent of coastal Alaska forest land is more 

than 200 years old, and 58 percent of forest carbon is stored in 

this age class. The age class of 200 to 300 years old occupies 

approximately 20,000 km2 of forest land and stores the 

greatest proportion of the forest carbon (fig. 5.4). 

5.5.2. Projected Changes in Forest Carbon 

(Not Including Soil Organic Carbon) 

The analysis of the FVS simulations conducted for the 

scenario of current management without considering climate 

change (fig. 5.5) indicated that forest carbon (live tree, dead 

wood, forest floor, and understory carbon) in coastal Alaska 

would decrease by 3.9 teragrams of carbon per year (TgC/yr) 

until the 2050s and then would increase by 1.8 TgC/yr 

throughout the remainder of the century. The carbon in live 

Figure 5.3. Coastal Alaska current forest carbon stock 

and carbon density by land ownership group (not including 

soil organic carbon). Data source: USDA Forest Service 

Forest Inventory and Analysis Program.

900

800

700

600

500

400

300

200

100

0

20

18

16

14

12

10

8

6

4

2

0

Total forest carbon, in teragrams of carbon

Carbon density, in kilograms of

carbon per square meter 

National

forest

Other

Federal

Land ownership group

State and local Private

government

Total forest carbon Carbon density

EXPLANATION

Figure 5-3

Table 5.3. Coastal Alaska current forest carbon distribution by forest type (not including soil organic carbon).

[Data source: USDA Forest Service Forest Inventory and Analysis Program. kgC/m2, kilogram of carbon per square meter]

Carbon

Forest type

Western

hemlock

Mountain 

hemlock

Sitka 

spruce

YellowcedarWestern 

red cedar

Other

softwood

Other

hardwood

Distribution (percent) 37.8 17.9 16.2 13.2 8.6 4.5 2.8

Density (kgC/m2

) 23.63 13.97 20.34 14.32 20.92 5.91 6.36
100 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 5-4

500

400

300

200

100

0

25

20

15

10

5

Total forest carbon, in teragrams of carbon

0

Carbon density, in kilograms of

carbon per square meter

≤20 >20–40 >40–60 >60–80 >80–100 >100–120 >120–140 >140–160 >160–180 >180–200 >200–300 >300–400 >400

Stand age class, in years

Total forest carbon

Carbon density

EXPLANATION

Figure 5.4. Coastal Alaska baseline (2003–2014) forest carbon and carbon density by stand age class (not 

including soil organic carbon). Data source: USDA Forest Service Forest Inventory and Analysis Program.

Figure 5.5. Results of projected forest carbon stock (not including soil organic carbon) in coastal Alaska under the scenario 

of forest management with no climate change. Data source: USDA Forest Service Forest Inventory and Analysis Program.

1,200

1,000

800

600

400

200

Forest carbon stock, in teragrams of carbon

0

Years

2004–2013

2015–2020

2020–2025

2025–2030

2030–2035

2035–2040

2040–2045

2045–2050

2050–2055

2055–2060

2060–2065

2065–2070

2070–2075

2075–2080

2080–2085

2085–2090

2090–2095

2095–2100

Figure 5-5

Carbon distribution

Forest floor 

Dead wood

Understory

Live tree

EXPLANATION

Historical inventory

trees (aboveground and belowground) would decrease by 

1.2 TgC/yr until the early 2040s and then would increase by 

2.6 TgC/yr through 2099. Although the projected harvesting 

rate was set to the same level for every 5-year cycle, the model 

does not distribute it evenly, and the intensity of the harvest of 

live trees at the beginning would contribute to the decrease of 

live tree carbon. In general, the live trees are the major carbon 

pool for the region. Carbon in the forest floor would decrease 

from 2010 to 2050 at the rate of 0.8 TgC/yr before stabilizing 

until 2070 after which it slightly increases through 2099. Whereas 

the total forest carbon (not including SOC) would increase by 

1 percent, live tree carbon was projected to increase by almost 

27 percent by the end of this century under this scenario. 

The analysis of the FVS simulations conducted for the 

scenario of climate change with management (fig. 5.6) indicated that forest carbon stock would increase by 8.5 percent

in total forest carbon with a 38-percent increase in live tree 

carbon storage by the end of the century. The timing of the 

trajectory was similar to the first scenario. Total forest carbon 

stock would decrease until the mid-2040s at 3.6 TgC/yr 

followed by an increase throughout the remainder of the 

century of 2.5 TgC/yr. Similarly, the live tree carbon would 

decrease by 0.4 TgC/yr until the early 2040s and then increase 

by 3.2 TgC/yr. Changes in understory carbon, which is only 

about 0.5 percent of the total forest carbon, is not influential in 

the calculation. Carbon in the forest floor would decrease until 

2060 at 0.6 TgC/yr then slightly increase or remain constant. 

Finally, the analysis of the FVS simulations conducted for 

the scenario of climate change with no management (fig. 5.7) 

indicated that forest carbon would decrease by 2.7 TgC/yr for 

the first decade and then increase by 3.2 TgC/yr throughout the 

remainder of the century. The live tree carbon was projected to 

slowly increase through the whole projection period at the rate of 

4.0 TgC/yr. The carbon stored in forest floor was relatively stable. 

At the end of century, the total forest carbon would increase by 

nearly 27 percent and the live tree carbon by about 68 percent. 

Because the management scenarios assume a relatively 

low rate of harvesting, the estimated amount of carbon 
Chapter 5 101

Figure 5.6. Results of projected forest carbon stock (not including soil organic carbon) in coastal Alaska under the scenario 

of climate change with forest management. Data source: USDA Forest Service Forest Inventory and Analysis Program. 

To conduct the simulations of climate change, the A1B scenario from the Intergovernmental Panel on Climate Change’s Special

Report on Emissions Scenarios (Nakićenović and Swart, 2000) was used to drive the version 3.1-T47 of the Canadian Centre for 

Climate Modelling and Analysis’ Coupled Global Climate Model (CGCM3.1; Flato, 2005) general circulation model. 

Figure 5.7. Results of projected forest carbon stock (not including soil organic carbon) in coastal Alaska under the 

scenario of climate change with no forest management. Data source: USDA Forest Service Forest Inventory and 

Analysis Program. Details regarding the simulations of climate change can be found in figure 5.6. 

rate derived from the historical database could also be lower 

compared to the current rate observed in coastal Alaska. Because 

actual decomposition rates for coastal Alaska are not currently 

available, we instead used rates derived for the Pacific Northwest 

region, thus our models may have overestimated this parameter. 

Although 25 simulations were run to assess uncertainty 

for each scenario, little variability was shown in each of the 

above described scenarios. 

1,200

1,000

800

600

400

200

0

Forest carbon stock, in teragrams of carbon

Carbon distribution

Forest floor 

Dead wood

Understory

Live tree

EXPLANATION

Historical inventory

Years

2004–2013

2015–2020

2020–2025

2025–2030

2030–2035

2035–2040

2040–2045

2045–2050

2050–2055

2055–2060

2060–2065

2065–2070

2070–2075

2075–2080

2080–2085

2085–2090

2090–2095

2095–2100

Figure 5-6

1,400

1,200

1,000

800

600

400

200

0

Forest carbon stock, in teragrams of carbon

Carbon distribution

Forest floor 

Dead wood

Understory

Live tree

EXPLANATION

Historical inventory

Years

2004–2013

2015–2020

2020–2025

2025–2030

2030–2035

2035–2040

2040–2045

2045–2050

2050–2055

2055–2060

2060–2065

2065–2070

2070–2075

2075–2080

2080–2085

2085–2090

2090–2095

2095–2100

Figure 5-7

removed ranged from 0.1 to 0.3 TgC/yr under the climate 

change scenario and from 0.1 to 0.2 TgC/yr at the present 

climate in coastal Alaska. 

All the scenarios described above showed a significant 

decrease in the dead wood carbon pool for the first 50- to 60-year 

period. Such a decrease, especially at the beginning of the 

projection period, can be explained by the harvest and the 

relatively fast decomposition in the region. The tree mortality 
102 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

5.6. Conclusions

Overall, the results show that the balance of forest carbon 

in the study region in Alaska is sensitive to management 

actions and climate change. Managed with present forest 

harvest and present climate, the forest carbon would increase 

by 10 TgC by the end of the century compared to the baseline 

(2004–2013). Forest carbon stock would increase by 86 TgC 

under the climate change with management and by 276 TgC 

under the climate change with no management (fig. 5.8). 

The results suggest relative effects of forest management 

and climate change on forests of the Alaska coastal region. 

Forest management by itself would have a negative effect 

on the balance of forest carbon, whereas the climate change 

simulation under scenario A1B with CGCM3.1 would lead to 

increased carbon stock. Since the majority of the forest lands 

are publicly owned in coastal Alaska, forest policies of State 

and Federal agencies focused on conservation and carbon 

sequestration could substantially increase carbon storage in 

the region by the end of the century. It is important to note 

that there are several uncertainties in this study. First, the FVS 

is designed as a growth and yield model—the carbon tool in 

the current FVS calculates major forest carbon estimates but 

may not include all ecosystem processes in reporting carbon 

emission and sequestration, such as soil organic carbon and 

management-related emissions. Second, the scenario for forest 

harvest that we used is just one of many possible scenarios. It 

essentially represents a “business as usual” scenario. Future 

forest harvest could be substantially affected by amendments 

to the forest plan for the Tongass National Forest as well as the 

global market for wood products. Third, there is substantial 

uncertainty about future projections on climate change, 

which may affect the species viability and the growth rate 

for coastal Alaska.

Figure 5.8. Projected coastal Alaska forest carbon stock (not including soil organic carbon) under three 

scenarios: management with no climate change, climate change with management, and climate change 

with no management. Data source: USDA Forest Service Forest Inventory and Analysis Program. To conduct 

the simulations of climate change, the A1B scenario from the Intergovernmental Panel on Climate Change’s 

Special Report on Emissions Scenarios (Nakićenović and Swart, 2000) was used to drive the version 3.1-T47 

of the Canadian Centre for Climate Modelling and Analysis’ Coupled Global Climate Model (CGCM3.1; Flato, 

2005) general circulation model. 

1,400

1,200

1,000

800

600

400

200

0

Forest carbon stock, in teragrams of carbon

Years

2004–2013

2015–2020

2020–2025

2025–2030

2030–2035

2035–2040

2040–2045

2045–2050

2050–2055

2055–2060

2060–2065

2065–2070

2070–2075

2075–2080

2080–2085

2085–2090

2090–2095

2095–2100

Figure 5-8

Management with no climate change

Management with climate change

No management with climate change

EXPLANATION
Chapter 5 103

5.7. References Cited

Alexander, S.J., 2012, Timber supply and demand, Alaska 

National Interest Lands Conservation Act, Section 706(a) 

report to Congress: U.S. Department of Agriculture, Forest 

Service, Alaska Region, Report Number 27. 

Barrett, T.M., and Christensen, G.A., tech. eds., 2011, Forests 

of southeast and south-central Alaska, 2004–2008; Fiveyear forest inventory and analysis report: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research 

Station, General Technical Report PNW–GTR–835, 156 p. 

[Also available at http://www.fs.fed.us/pnw/pubs/

pnw_gtr835.pdf.]

Crookston, N.L., 2014, Climate-FVS Version 2; Content, 

users guide, applications, and behavior: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research 

Station, General Technical Report RMRS–GTR–319, 38 p. 

[Also available at http://www.fs.fed.us/fmsc/ftp/fvs/docs/

climateFVS/ClimateFVS_UsersGuide.pdf.]

Dixon, G.E., 2002, Essential FVS—A user’s guide to the 

Forest Vegetation Simulator (revised November 2015): 

U.S. Department of Agriculture, Forest Service, Forest 

Management Service Center, 226 p. [Also available at 

http://www.fs.fed.us/fmsc/ftp/fvs/docs/gtr/EssentialFVS.pdf.]

Flato, G.M., 2005, The third generation coupled global climate 

model (CGCM3) (and included links to the description 

of the AGCM3 atmospheric model): Canadian Centre for 

Climate Modelling and Analysis, available at http://ec.gc.ca/

ccmac-cccma/default.asp?lang=En&n=1299529F-1. 

Haynes, R.W., tech. coord., 2003, An analysis of the timber 

situation in the United States; 1952 to 2050: U.S. Department

of Agriculture, Forest Service, Pacific Northwest Research 

Station, General Technical Report PNW–GTR–560, 254 p. 

[Also available at http://www.fs.fed.us/pnw/pubs/gtr560/.]

Keyser, C.E., comp., 2008, Southeast Alaska and coastal British Columbia (AK) variant overview—Forest Vegetation 

Simulator (revised September 3, 2014): U.S. Department 

of Agriculture, Forest Service, Forest Management Service 

Center, 38 p. [Also available at http://www.fs.fed.us/fmsc/

ftp/fvs/docs/overviews/FVSak_Overview.pdf.]

Leites, L.P., Robinson, A.P., and Crookston, N.L., 2009, 

Accuracy and equivalence testing of crown ratio models 

and assessment of their impact on diameter growth and 

basal area increment predictions of two variants of the 

Forest Vegetation Simulator: Canadian Journal of Forest

Research, v. 39, no. 3, p. 655–665, http://dx.doi.org/

10.1139/X08-205. 

Miles, P.D., 2015, Forest Inventory EVALIDator web application (ver. 1.6.0.01): U.S. Department of Agriculture, Forest 

Service, Northern Research Station, accessed April 7, 2015, 

at http://apps.fs.fed.us/Evalidator/evalidator.jsp.

Nakićenović, Nebojša, and Swart, Rob, eds., 2000, Special 

report on emissions scenarios—A special report of Working Group III of the Intergovernmental Panel on Climate 

Change: Cambridge, United Kingdom, Cambridge University Press, 599 p., http://www.ipcc.ch/ipccreports/sres/

emission/index.php?idp=0.

O’Connell, B.M., LaPoint, E.B., Turner, J.A., Ridley, Ted, 

Pugh, S.A., Wilson, A.M., Waddell, K.L., and Conkling, B.L.,

2014, The Forest Inventory and Analysis database; Database 

description and user guide version 6.0 for phase 2: 

U.S. Department of Agriculture Forest Service, 624 p. 

[Also available at http://www.fia.fs.fed.us/library/databasedocumentation/historic/ver6/FIADB_user%20guide_6-0_

p2_5-6-2014.pdf.]

Oswalt, S.N., Smith, W.B., Miles, P.D., and Pugh, S.A., 

2014, Forest resources of the United States, 2012; A 

technical document supporting the Forest Service 2015 

update of the RPA Assessment: U.S. Department of 

Agriculture, Forest Service, Washington Office, General 

Technical Report GTR–WO–91, 218 p. [Also available 

at http://www.srs.fs.usda.gov/pubs/47322.]

Peterson, R.L., Liang, Jingjing, and Barrett, T.M., 2014, 

Modeling population dynamics and woody biomass in 

Alaska coastal forest: Forest Science, v. 60, no. 2, 

p. 391–401, http://dx.doi.org/10.5849/forsci.12-540. 

Robinson, A.P., Duursma, R.A., and Marshall, J.D., 2005, 

A regression-based equivalence test for model validation; 

Shifting the burden of proof: Tree Physiology, v. 25, no. 7, 

p. 903–913, http://dx.doi.org/10.1093/treephys/25.7.903.

SAS Institute Inc., 2008, Introduction, chap. 2 of SAS/STAT® 

9.2 user’s guide: Cary, N.C., SAS Institute Inc., p. 19–26. 

Smith, J.E., Heath, L.S., and Hoover, C.M., 2013, Carbon 

factors and models for forest carbon estimates for the 

2005–2011 National Greenhouse Gas Inventories of the 

United States: Forest Ecology and Management, v. 307, 

p. 7–19, http://dx.doi.org/10.1016/j.foreco.2013.06.061. 

Zhou, Xiaoping, 2013, Production, prices, employment, and 

trade in Northwest forest industries, all quarters 2012: 

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

Northwest Research Station, Resource Bulletin PNW–RB–265,

163 p. [Also available at http://www.fs.fed.us/pnw/pubs/

pnw_rb265.pdf.]
104 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Zhu, Zhiliang, ed., Bergamaschi, Brian, Bernknopf, Richard, 

Clow, David, Dye, Dennis, Faulkner, Stephen, 

Forney, William, Gleason, Robert, Hawbaker, Todd, 

Liu, Jinxun, Liu, Shuguang, Prisley, Stephen, Reed, Bradley,

Reeves, Matthew, Rollins, Matthew, Sleeter, Benjamin, 

Sohl, Terry, Stackpoole, Sarah, Stehman, Stephen,

Striegl, Robert, Wein, Anne, and Zhu, Zhiliang, 2010, 

A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the 

United States under present conditions and future scenarios: 

U.S. Geological Survey Scientific Investigations Report 

2010–5233, 190 p. [Also available at http://pubs.usgs.gov/

sir/2010/5233/.] [Supersedes U.S. Geological Survey 

Open-File Report 2010–1144.]
Chapter 6. Terrestrial Carbon Modeling: Baseline 

and Projections in Upland Ecosystems 

By Hélène Genet,1 Yujie He,2 A. David McGuire,3 Qianlai Zhuang,2 Yujin Zhang,1, Frances E. Biles,4

David V. D’Amore,4

 Xiaoping Zhou,5 and Kristopher D. Johnson6

1

University of Alaska-Fairbanks, Fairbanks, Alaska.

2

Purdue University, West Lafayette, Ind.

3

U.S. Geological Survey, Fairbanks, Alaska.

4

U.S. Department of Agriculture Forest Service, Juneau, Alaska.

5

U.S. Department of Agriculture Forest Service, Portland, Oreg.

6

U.S. Department of Agriculture Forest Service, Newtown Square, Pa.

6.1. Highlights

• Ecosystem carbon balance of the Alaska assessment 

domain (as outlined in chapter 1) was examined 

using a process-based model framework for two 

time periods: a historical period (1950 –2009), 

for which historical climate and disturbance 

observations were used, and a projection period 

(2010 –2099), for which projected climate and 

disturbance data were used. 

• During the historical period, upland ecosystems 

in Alaska were a net carbon sink of an average 

of 5.01 teragrams of carbon per year (TgC/yr). 

All Landscape Conservation Cooperative (LCC) 

regions in Alaska were net carbon sinks, except 

for the Northwest Boreal LCC North. This 

carbon sink was mostly due to an increase in 

vegetation productivity associated with 

recent warming. 

• For the Northwest Boreal LCC North, the carbon 

source averaged –5.12 TgC/yr and was associated 

with large carbon losses from wildfire, specifically 

during large fire years in 1957, 1969, 1977, 

1990 –1991, 2004, and 2005.

• Carbon loss from forest harvest exports in the 

North Pacific LCC represented 1.6 percent of 

the statewide gross carbon losses between 

1950 and 2009.

• During the projection period (2010–2099), all LCC 

regions of Alaska were projected to be carbon sinks, 

storing between 14.72 and 30.15 TgC/yr statewide. 

• Methane consumption in upland ecosystems was 

projected to be low relative to gross primary productivity (GPP), representing on average 0.0011 percent 

of the projected GPP by 2099. 

• Disturbances, mainly wildfires, would be a strong 

determinant of the future spatial and temporal 

variability of carbon dynamics, particularly in 

the Northwest Boreal LCC North.

6.2. Introduction

Arctic and boreal permafrost soils hold about 

1,700 petagrams of organic carbon (Zimov and others, 

2006; Schuur and others, 2008; Tarnocai and others, 2009), 

more than twice the carbon in atmospheric carbon dioxide 

(CO2). Thus, changes in the carbon balance of permafrost 

ecosystems in response to climate warming could profoundly 

alter the composition of the atmosphere to affect the climate 

system (Schaefer and others, 2011; Schuur and others, 2013). 

As permafrost warms, organic matter that has been frozen 

for hundreds to thousands of years is exposed to microbial 

decomposition, mineralization, and release to the atmosphere 

as CO2

 and methane (CH4) greenhouse gases that may offset 

carbon gain from potential increases in vegetation productivity 

in response to climate warming. 
106 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Boreal and arctic regions are thought to have been a 

strong carbon sink during the 20th century (McGuire and 

others, 2009; Pan and others, 2011). More recent analyses 

that included consideration of the last decade of intensive fire 

activity throughout the boreal zone and CH4 emissions in the 

region indicate that the carbon sink is weakening and that the 

region is acting as a net source of greenhouse gases when the 

global warming potential of CH4 is considered (McGuire and 

others, 2010; Hayes and others, 2011). A recent analysis of 

historical carbon exchange in arctic tundra (1990–2006), using 

observations, regional and global applications of processbased models, and atmospheric inversion models, suggests 

that large uncertainties existed that could not be distinguished 

from neutral balance (McGuire and others, 2012). One of the 

sources of this uncertainty is related to the weak ability of 

process-based models to represent the temporal variability 

of carbon dynamics across the landscape (Fisher and others, 

2014). In Alaska, the spatial and temporal variability of 

carbon dynamics depend primarily on drainage conditions and 

disturbance regimes (Schuur and others, 2009; Tarnocai and 

others, 2009; Grosse and others, 2011). Indeed, uplands and 

wetlands are dominated by different soil carbon processes; 

vegetation productivity; and nature, frequency, and severity 

of disturbance regimes.

In this report, we review the main drivers of carbon 

dynamics separately for uplands (this chapter) and wetlands 

(chapter 7). Uplands in Alaska are characterized by moderately to well drained ecosystems composed of forest and 

alpine ecosystems in the boreal and maritime regions and 

the tundra ecosystem in the arctic region. Because of good 

drainage conditions, soil biogeochemical dynamics in uplands 

are dominated by aerobic processes (Schuur and others, 2009). 

Carbon and nutrient turnover is faster and the vegetation is 

generally more productive in uplands than in wetlands. In past 

syntheses of regional carbon dynamics, the role of aerated 

soils as a sink for atmospheric CH4 has been neglected. 

However, it has been documented that CH4 consumption 

exceeds CH4 production in moist tundra soils of Alaska 

(Whalen and Reeburgh, 1990). Therefore, for a comprehensive 

assessment of carbon dynamics in northern high latitudes, 

it is important to consider CH4

 uptake in uplands. Wildfire 

and forest harvest are two important disturbance regimes 

in uplands in Alaska. Whereas wildfire occurs mostly in 

boreal forest and to a lesser extent in arctic tundra (Mack and 

others, 2011; Turetsky and others, 2011), forest harvest is 

concentrated in southern coastal Alaska. Annual area burned 

has increased in Alaska (Kasischke and Turetsky 2006; 

Kasischke and others, 2010) and Canada (Gillett and others, 

2004) during the second half of the 20th century. In Alaska, 

the decade beginning in 2000 experienced the highest burned 

area (76,700 square kilometers per year [km2

/yr]) during 

the modern record period (baseline at 39,970 km2/yr from 

1920 to 2009). In Canada, average burned area increased 

continuously from the 1940s (81,650 km2/yr) through the 

1990s (317,070 km2/yr) before sharply decreasing in the 2000s 

(165,430 km2

/yr). Several studies indicate that this increase is 

predicted to be maintained at least during the first half of the 

21st century (Balshi and others, 2009; Mann and others, 2012; 

chapter 2). In addition to an increase in carbon emissions from 

burning, greater fire frequency and severity have substantial 

implications for permafrost, as increased severity leads to 

greater consumption of the insulating organic layer, which 

may accelerate permafrost thaw and associated deep carbon 

decomposition (Dyrness and Norum, 1983; Yoshikawa and 

others, 2002; Burn and others, 2009). Finally, commercial 

harvesting of maritime upland forest (that is, western hemlock 

[Tsuga heterophylla (Raf.) Sarg.] and Sitka spruce [Picea 

sitchensis (Bong.) Carrière]) in southeast and south-central 

Alaska has been developing since the late 19th century 

(Rakestraw, 1981; chapter 5). Although harvesting reduces 

aboveground carbon stocks by exporting wood out of the 

ecosystem, it might promote vegetation productivity by 

increasing areas of secondary growth (Cole and others, 2010).

In this chapter, we assess historical and projected carbon 

dynamics of upland ecosystems in Alaska by using a modeling 

framework that combines process-based biogeochemical and 

biogeographic-disturbance models at a spatial resolution of 

1 kilometer (km). We evaluated the long-term consequences 

of a projected warming and disturbance regime on the regional 

carbon balance in uplands in Alaska from 2009 to 2099 using 

six climate simulations from two general circulation models 

(GCMs) for three atmospheric CO2 emissions scenarios. 

6.3. Material and Methods 

6.3.1. Model Framework 

Changes in soil and vegetation carbon stocks and fluxes 

in response to climate change and disturbances were analyzed 

using a modeling framework that combines a wildfire 

disturbance model, the Alaska Frame-Based Ecosystem 

Code (ALFRESCO; Rupp and others, 2000, 2002, 2007; 

Johnstone and others, 2011; Mann and others, 2012; Gustine 

and others, 2014; Amy Breen, University of Alaska-Fairbanks, 

written commun., 2015), and two process-based ecosystem 

models that simulate (1) carbon and nitrogen pools and CO2

dynamics using the Dynamic Organic Soil version of the 

Terrestrial Ecosystem Model (DOS-TEM; Raich and others, 

1991; McGuire and others, 1992) and (2) CH4 dynamics 

using the Methane Dynamics Module of the Terrestrial 

Ecosystem Model (MDM-TEM; Zhuang and others, 2004). 

These three models have been coupled in an asynchronous 

way, in which the time series of fire occurrence simulated by 

ALFRESCO is used to drive DOS-TEM, which simulates 

the effects of wildfire, warming, and forest harvest on carbon 

pools and aerobic carbon processes. Monthly net primary 

productivity (NPP) and leaf area index (LAI) simulated by 

DOS-TEM are used to drive MDM-TEM, which simulates 

anaerobic (methanogenesis) and aerobic (methane oxidation) 

carbon processes (fig. 6.1). Description of the ALFRESCO 

model is provided in chapter 2, section 2.3.2. As MDM-TEM 
Chapter 6 107

simulations are of particular importance for simulating 

wetland carbon dynamics, the MDM-TEM model is described 

in chapter 7, section 7.3.1. Here we focus on descriptions of 

DOS-TEM for upland carbon modeling.

6.3.2. Dynamic Organic Soil Version of the 

Terrestrial Ecosystem Model (DOS-TEM) 

Description 

DOS-TEM belongs to the Terrestrial Ecosystem Model 

(TEM) family of process-based ecosystem models that has 

been designed to simulate carbon and nitrogen pools in 

vegetation and soil, and carbon and nitrogen fluxes among 

vegetation, soil, and the atmosphere (Raich and others, 1991; 

McGuire and others, 1992). DOS-TEM is composed of four 

modules: an environmental module, an ecological module, a 

disturbance module, and a dynamic organic soil module. 

The environmental module computes dynamics of 

biophysical processes in the soil and the atmosphere, driven 

by climate and soil texture input data, leaf area index from 

the ecological module, and soil structure from the dynamic 

organic soil module. Soil temperature and moisture conditions 

are calculated for multiple layers within various soil horizons, 

including moss, fibric and humic organic layers, and mineral 

horizons. A stable snow/soil thermal model integrated into 

the environmental module uses the Two-Directional Stefan 

Algorithm (TDSA; Woo and others, 2004). The TDSA can 

satisfactorily simulate the positions of the freeze-thaw front 

and active-layer thickness in a land surface model when 

proper surface forcing is provided (Yi and others, 2006). The 

environmental module provides information regarding the 

atmospheric and soil environment to the ecological module 

and the disturbance module.

The ecological module simulates carbon and nitrogen 

dynamics among the atmosphere, the vegetation, and the 

soil. Carbon and nitrogen dynamics are driven by climate 

input data, information on soil and atmospheric environment 

from the environmental module, information on soil structure 

provided by the dynamic organic soil module, and information 

on timing and severity of wildfire or forest harvest occurrences 

provided by the disturbance module. 

The dynamic organic soil module calculates the thickness 

of the fibric and humic organic layers after soil carbon pools 

are altered by ecological processes (litterfall, decomposition, 

and burial) and fire disturbance. The estimation of organic 

Figure 6.1. Modeling framework for this assessment. Red text and arrows represent input drivers. Black text and arrows 

represent flows of information within and among models. Blue boxes represent the four modules composing the Dynamic 

Organic Soil version of the Terrestrial Ecosystem Model (DOS-TEM). The orange box represents the disturbance model Alaska 

Frame-Based Ecosystem Code (ALFRESCO) and the green box represents the methane dynamics model Methane Dynamics 

Module of the Terrestrial Ecosystem Model (MDM-TEM).

Climate, topography

Climate,

atmospheric

carbon dioxide

Fire occurrence

Environmental module

Atmosphere and

soil environment

LAI

 Net primary 

 productivity, 

leaf area index (LAI)

Litterfall

Figure 6-1

Organic-layer

thickness and

soil structure

Fire severity

Logging rate

Disturbance module ALFRESCO

(disturbance model)

MDM-TEM

(methane dynamics model)

DOS-TEM

(biogeochemical model 

[excluding methane])

Dynamic organic soil module Ecological module
108 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

horizon thickness is computed from soil carbon content using 

relationships that link soil organic carbon content and soil 

organic thickness (Yi, McGuire, and others, 2009). These 

relationships have been developed for fibric, humic, and 

mineral horizons for every vegetation type, based on data from 

the soil carbon network database for Alaska (Johnson and 

others, 2011). Once the thickness of each organic soil horizon 

is estimated, the dynamic organic soil module calculates the 

number of layers in each organic horizon and the thickness 

of each layer to maintain stability and efficiency of soil 

temperature and moisture calculations along the soil column, 

as a function of the soil characteristics of each layer. 

Finally, the disturbance module simulates how forest 

harvest and wildfire affect carbon and nitrogen pools of the 

vegetation and the soil. For wildfire, the module computes 

combustion emissions to the atmosphere, the fate of uncombusted carbon and nitrogen pools, and the flux of nitrogen 

from the atmosphere to the soil via biological nitrogen fixation 

in the years following a fire. The amount of soil carbon 

combusted during a wildfire is determined using input data on 

topography, drainage, and vegetation, as well as soil (moisture 

and temperature) and atmospheric (evapotranspiration) data 

from the environmental module (Genet and others, 2013).

Previous regional applications of DOS-TEM in northern 

high latitudes have investigated how biogeochemical 

dynamics of terrestrial ecosystems in these regions are affected 

at seasonal to century scales by processes like soil thermal 

activities (Zhuang and others, 2001, 2002, 2003), snow cover 

(Euskirchen and others, 2006, 2007), and fire (Balshi and 

others, 2007; Yuan and others, 2012). DOS-TEM has been 

developed primarily to represent the effects of disturbances, 

wildfire especially, on carbon stocks in vegetation and soil 

organic horizons and on the soil environment in permafrost 

regions (Yi, Manies, and others, 2009; Yi, McGuire, and 

others, 2009; Yi and others, 2010). Recent model developments have focused on the spatial and temporal heterogeneity 

of fire severity and carbon loss associated with the influence of 

drainage conditions, vegetation composition, topography, and 

weather conditions across the landscape (Genet and others, 

2013). In this study, we developed an additional capability for 

DOS-TEM to consider the effects of forest harvest disturbance 

on carbon balance, which is described below.

6.3.3. Dynamic Organic Soil Version of the 

Terrestrial Ecosystem Model (DOS-TEM) 

Development—Modeling the Effect of 

Forest Harvest on Carbon Dynamics

For this assessment, we further developed the disturbance 

module of DOS-TEM to represent the effects of forest harvest 

on carbon and nitrogen dynamics. The harvesting of timber 

in southern coastal Alaska took place primarily between the 

mid-1950s and mid-1990s (fig. 6.2) when two pulp mills 

opened in Sitka and Ketchikan to process large volumes of 

low-grade timber, mainly from the Tongass National Forest 

where the U.S. Department of Agriculture (USDA) Forest 

Service began offering 50-year timber sale contracts (Colt and 

others, 2007). After 1990, the USDA Forest Service reduced 

the volume of timber offered for sale annually, and in 1997, 

the agency imposed harvest constraints that resulted in large 

increases in the cost of harvesting timber on national forest 

lands and a decrease of the annual volume harvested. 

Forest harvest by clear-cutting was widespread in 

southeastern Alaska since the early 1950s (Alaback, 1982; 

Cole and others, 2010). We developed a harvesting module 

with an assumption that 95 percent of the aboveground 

vegetation biomass would be harvested (Deal and others, 

2002). Among the residual biomass, 4 percent was considered 

dead and 1 percent alive to allow post-harvest recruitment. 

As a consequence, 99 percent of the belowground vegetation 

biomass (root biomass) was considered dead and transferred 

to the soil organic matter pool. Exported out of the ecosystem, 

the carbon in timber will mainly be stored in permanent 

constructions or furniture.

6.3.4. Model Parameterization and Validation 

6.3.4.1. Dynamic Organic Soil Version of the 

Terrestrial Ecosystem Model (DOS-TEM)

Rate-limiting parameters of the model were calibrated 

for 11 main land-cover types in Alaska—4 types of tundra 

(graminoid, shrub, heath, and wet-sedge tundra), 3 types of 

boreal forest (black spruce [Picea mariana (Mill.) Britton, 

Sterns & Poggenb.], white spruce [Picea glauca (Moench) 

Voss], and deciduous forest), and 4 types of maritime communities (upland forest, wetland forest, fen, and alder shrubland). 

(See chapter 2, section 2.4.1 for further description of these 

land-cover types.) In boreal regions, similar vegetation 

composition can occur in very different drainage conditions 

leading to high variability in carbon and nitrogen turnover 

(Schuur and others, 2009; Wickland and others, 2010) and 

vulnerability to disturbance (Turetsky and others, 2011); therefore, the three types of boreal forest were calibrated separately 

for uplands and for wetlands. In this chapter, we focus on the 

calibration of upland ecosystems: graminoid tundra, shrub 

tundra, heath tundra, boreal upland black spruce forest, boreal 

upland white spruce forest, boreal upland deciduous forest, 

and maritime upland forest. (See chapter 7, section 7.3.2.3 

for calibration of wetlands and lowland boreal forests.) 

We calibrated the rate-limiting parameters of DOS-TEM 

using target values of carbon and nitrogen pools and fluxes 

representative of mature ecosystems. These parameters were 

“tuned” until the model reached target values of the main 

carbon and nitrogen pools and fluxes (Clein and others, 2002). 

The calibration of these parameters is an effective means of 

dealing with temporal scaling issues in ecosystem models 

(Rastetter and others, 1992). For boreal forest communities, 

an existing set of target values for vegetation and soil 

carbon and nitrogen pools and fluxes was assembled using 
Chapter 6 109

data collected in the Bonanza Creek Long Term Ecological 

Research (LTER) program (Yuan and others, 2012). For the 

tundra communities, we used data collected at the Toolik 

Field Station (Shaver and Chapin, 1991; Van Wijk and others, 

2003; Sullivan and others, 2007; Euskirchen and others, 2012; 

Gough and others, 2012; Sistla and others, 2013). Finally, 

for the maritime upland forest, we used data summarized in 

chapter 4, collected from a long-term carbon flux study in 

the North American Carbon Program (D’Amore and others, 

2012). The target values for maritime alder shrubland were 

assessed from Binkley (1982). Target values of vegetation 

biomass, soil carbon pools, net primary productivity, and 

gross primary productivity for each upland land-cover type 

are described in table 6.1. 

6.3.4.2. Methane Dynamics Module of the 

Terrestrial Ecosystem Model (MDM-TEM)

The upland simulation of MDM-TEM was parameterized 

using CH4 measurements and key soil and climate factors 

made at three upland field sites—boreal forest at Bonanza 

Creek (B-F), tundra at the North Slope of Alaska (Tundra-NS), 

and moist tundra on Unalaska Island (Tundra-UI) (table 6.2). 

Because daily time series of CH4 consumption were not 

available, we parameterized the MDM-TEM for upland 

ecosystems such that the difference between the simulated and 

observed maximum daily CH4 consumption rate is minimized 

at these sites. Specifically, we altered the parameters of 

the methane module until the simulation CH4 consumption 

Figure 6.2. Forest harvest history in southeast and south-central Alaska: A, spatial distribution of 

harvests from 1850 through 2012 and B, time series of annual area harvested from 1850 through 2012. Area harvested, in square kilometers

Year

Map area

ALASKA CANADA

0 100 200 KILOMETERS

N

0 100 200 MILES

Periods of forest harvest

EXPLANATION

Figure 6-2

≤1950

1951 to 1970

1971 to 1990

1991 to 2012

A

B

20 

0 

40 

60 

80 

100 

120 

140 

160 

180 

200 

1850 1870 1890 1910 1930 1950 1970 1990 1860 1880 1900 1920 1940 1960 1980 2000 2010 2015 
110 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

by soil reached the maximum consumption rates of 

31.6 milligrams of carbon dioxide equivalent per square 

meter per day (mgCO2-eq/m2/d), 39.9 mgCO2-eq/m2/d, and 

56.5 mgCO2-eq/m2/d at the B-F, Tundra-NS, and Tundra-UI 

sites, respectively (Zhuang and others, 2004). 

6.3.4.3. Model Validation and Verification

We validated the model by testing the ability of the model 

to extrapolate carbon dynamics across space and time. We 

compared model simulations with observations collected outside 

the spatial and temporal range of the data used for model parameterization and calibration. When independent observations were 

not available, we tested the ability of the model to reproduce 

the same data used for parameterization and calibration. 

DOS-TEM parameterization has been validated using soil 

and vegetation biomass data derived from field observations 

independent of the data used for model parameterization. The 

National Soil Carbon Network database for Alaska was used 

to validate DOS-TEM estimates of soil carbon stocks (Johnson 

and others, 2011). In order to compare similar estimates from 

the model and observations, only deep profiles were selected 

from the database—that is, profiles with a description of the 

entire organic layer and the 90- to 110-centimeter (cm)-thick 

mineral layer below the organic layer.

Estimates of vegetation carbon stocks for tundra landcover types were compared with observations recorded in 

the data catalog of the Arctic LTER at Toolik Field Station 

(http://toolik.alaska.edu; Shaver and Chapin, 1986). For boreal 

forest land-cover types, vegetation carbon stocks simulated by 

Table 6.1. Target values for carbon pool and flux variables used to calibrate the Dynamic Organic Soil version of the Terrestrial 

Ecosystem Model (DOS-TEM) for major upland land-cover types in Alaska.

[gC/m2/yr, gram of carbon per square meter per year; gC/m2, gram of carbon per square meter]

Upland land-cover type

Net primary 

productivity

(gC/m2

/yr) 

Gross primary 

productivity

(gC/m2/yr)

Carbon pool (gC/m2)

Vegetation Soil fibric Soil humic Soil mineral1

Boreal upland black spruce forest 186 372 6,405 1,199 4,432 19,821

Boreal upland white spruce forest 305 610 9,000 1,156 4,254 11,005

Boreal upland deciduous forest 510 1,020 8,546 996 3,597 11,005

Shrub tundra 136 272 1,808 2,340 5,853 37,022

Graminoid tundra 112 224 561 3,079 7,703 43,403

Heath tundra 23 46 249 1,065 1,071 32,640

Maritime upland forest 375 750 809 825 2,912 23,232

Maritime alder shrubland 300 600 24,290 2,557 4,136 15,564

1

Soil mineral carbon pools are estimated from the bottom of the organic layer down to 1 meter into the mineral soil.

Table 6.2. Description of sites used in the model parameterization and validation process.

[n.d., no data]

Site name Location Elevation 

(meters) Land cover Observed data

Boreal forest at Bonanza Creek 

(B-F)

148°15' W. 64°41' N. 133 Black spruce (Picea mariana), 

feather moss (Hylocomium 

splendens)

Methane fluxes from late May 

through September 1990

Tundra at North Slope of Alaska 

(Tundra-NS)

149°36' W. 68°38' N. 760 Sedge (Carex spp.), moss 

tussock tundra dominated 

by Eriophorum vaginatum

Static chamber measured 

methane uptake

Moist tundra on Unalaska Island 

(Tundra-UI)

167°00' W. 53°00' N. n.d. Wet tundra dominated by sedges 

(Carex spp.)

Static chamber measured 

methane uptake

Tundra at Fairbanks, Alaska 

(Tundra-F; validation site)

147°51' W. 64°52' N. 158.5 Tussock tundra dominated by 

Eriophorum vaginatum

Three sites of methane 

emissions observed using 

chamber techniques from 

1987 to 1990
Chapter 6 111

DOS-TEM were compared with estimates from forest inventories conducted by the Cooperative Alaska Forest Inventory 

(Malone and others, 2009). The forest inventory only provided 

estimates of aboveground biomass. Aboveground biomass was 

converted to total biomass by using a ratio of aboveground 

versus total biomass of 0.8 in forest (Ruess and others, 1996) 

and 0.6 in tundra land-cover types (Gough and Hobbie, 2003). 

Carbon content of the biomass was estimated at 50 percent. 

Finally, for the land-cover types of southern coastal 

Alaska (that is, the North Pacific Landscape Conservation 

Cooperative (LCC) maritime upland and wetland forests 

and maritime fen), model validation was not possible as no 

additional independent data were available in this region. For 

these land-cover types, we compared the model simulations 

with observed data on the same sites that were used for 

model parameterization. (See chapter 4, section 4.3.1 for 

site descriptions).

For MDM-TEM, the model was validated at a tundra 

site (Tundra-F) at Fairbanks, Alaska, which was not used 

during the parameterization process (table 6.2). The simulated 

daily CH4 fluxes were compared to the observations. The 

Tundra-NS parameterization was used for the Tundra-F 

site simulations.

6.3.5. Model Application and Analysis

6.3.5.1. Forcing Data

The distribution of uplands in Alaska was assessed from 

topographic information. Uplands in Alaska are estimated to 

cover 1,237,775 square kilometers (km2), which represents 

about 84 percent of the total Alaska lands (see chapter 7, 

section 7.4.1). Simulations were conducted across Alaska 

at a 1-km resolution from 1950 through 2099. DOS-TEM 

is driven by monthly mean air temperature, total precipitation, net incoming shortwave radiation, and vapor pressure. 

To evaluate the effects of historical and projected climate 

warming, a series of six climate simulations was conducted. 

The simulations combined (1) historical climate variability 

from 1901 through 2009 using Climatic Research Unit 

(CRU TS v. 3.10.01; Harris and others, 2014) data and 

(2) climate variability from 2010 through 2099 projected 

by version 3.1-T47 of the Coupled Global Climate Model 

(CGCM3.1, www.cccma.ec.gc.ca/data/cgcm3/; McFarlane 

and others, 1992; Flato, 2005) developed by the Canadian 

Centre for Climate Modelling and Analysis and version 5 

of the European Centre Hamburg Model (ECHAM5, 

www.mpimet.mpg.de/en/wissenschaft/modelle/echam/; 

Roeckner and others 2003, 2004) developed by the 

Max Planck Institute. The climate projections were aligned 

with the Intergovernmental Panel on Climate Change’s 

Special Report on Emissions Scenarios (IPCC-SRES; 

Nakićenović and Swart, 2000). The assessment used three 

low-, mid- and high-range CO2 emissions scenarios (B1, 

A1B, and A2; see further details in chapter 2, section 2.2.1.). 

The climate data were bias corrected and downscaled using 

the delta method (Hay and others, 2000; Hayhoe, 2010) by 

the Scenarios Network for Alaska and Arctic Planning (SNAP, 

www.snap.uaf.edu/) from 0.5-degree original resolution data 

to 1-km resolution. The fire occurrence dataset combined 

(1) historical records from 1950 through 2009 obtained from 

the Alaska Interagency Coordination Center (AICC) large 

fire scar database (http://fire.ak.blm.gov/; see Kasischke and 

others, 2002) and (2) projected scenarios from ALFRESCO 

(see chapter 2, section 2.4.5.). These scenarios represent the 

changes in fire frequency in response to climate change and 

changes in vegetation composition over time. Topographic 

information used to compute fire severity was computed from 

the National Elevation Dataset of the U.S. Geological Survey 

at 60-meter (m) resolution (NED, http://ned.usgs.gov/). 

The topographic descriptors included slope, aspect, and 

log-transformed flow accumulation. Finally, soil texture 

information originated from the Global Gridded Surfaces 

of Selected Soil Characteristics dataset (Global Soil Data 

Task Group, 2000).

Historical records of area harvested from 1950 through 

2009 in southeast and south-central Alaska were compiled 

combining geographic information system (GIS) data 

from four different sources: (1) The USDA Forest Service, 

Tongass National Forest; (2) The Nature Conservancy’s past 

harvest repository; (3) three layers obtained from the State of 

Alaska—one covering Cape Yakataga to Icy Bay harvests, one 

for southeast Alaska, and one for Haines State Forest harvests; 

and (4) screen digitizing from high-resolution orthophotos of 

some harvests not included in the previously listed sources. 

In addition, the first three sources were edited using highresolution orthophotos to improve some of the boundary 

delineations. Second-growth stands owing to forest harvest 

account for about 3.8 percent of southeast Alaska. We were 

unable to obtain reliable forest harvest data for areas west of 

Cape Yakataga and Icy Bay (that is, west of approximately 

long 142.55° W.). We used the harvest layer for two purposes: 

(1) to determine where forest harvest has taken place and 

(2) to identify second-growth areas on the landscape. 

6.3.5.2. Analysis of Changes in Carbon Stocks 

and Climate-Related Uncertainty

Vegetation carbon stock estimates were derived from the 

sum of the aboveground and belowground living biomass. 

Soil carbon stocks were composed of carbon stored in the 

dead woody debris fallen to the ground, moss and litter, 

organic layers, and mineral layers. Historical changes in soil 

and vegetation carbon stocks were evaluated by quantifying 

annual differences of decadal averages between the first 

decade (1950–1959) and the last decade (2000–2009) of 

the historical period. Projected changes in soil and vegetation carbon stocks were evaluated by quantifying annual 

differences of decadal averages between the last decade of 

the historical period (2000–2009) and the last decade of the 

projection period (2090–2099). 
112 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The net ecosystem carbon balance (NECB) is the 

difference between total carbon inputs and total carbon outputs 

to the ecosystem (Chapin and others, 2006). NECB is the 

sum of all carbon fluxes coming in and out of the ecosystems, 

through gaseous and nongaseous, dissolved and nondissolved 

exchanges with the atmosphere and the hydrologic network. 

This chapter and chapter 7 report the carbon exchange 

between the terrestrial ecosystem and the atmosphere. 

Chapter 8 will consider carbon exchanges in inland aquatic 

ecosystems (gaseous and nongaseous). In terrestrial ecosystems, NECB is the result of net primary productivity (NPP) 

and net biogenic methane flux (BioCH4) minus heterotrophic 

respiration (HR), fire emissions (Fire), and forest harvest 

exports (Harvest).

NECB=NPP+BioCH4 –HR–Fire–Harvest (6.1)

NPP results from carbon assimilation from vegetation 

photosynthesis minus the respiration of the primary producers 

(autotrophic respiration). In uplands, the activity of soil 

methanotrophs offset the activity of methanogens. For this 

reason, BioCH4 is a positive flux in uplands. HR results from 

the decomposition of unfrozen soil organic carbon. Fire 

emissions encompass CO2, CH4, and carbon monoxide (CO) 

emissions. Forest harvest quantifies the amount of vegetation 

carbon that is exported out of the terrestrial ecosystem in the 

form of timber. For the analysis of the inter-annual variations 

in sections 6.4.1.2 and 6.4.2.1, carbon fluxes were expressed in 

grams of carbon per square meter per year (gC/m2/yr). For the 

regional assessments in sections 6.4.1.4 and 6.4.2.3, carbon 

fluxes were summed across the regions and expressed in teragrams of carbon per year (TgC/yr). Positive NECB indicates 

a gain of carbon to the ecosystem from the atmosphere, and 

negative NECB indicates a loss of carbon from the ecosystem 

to the atmosphere.

The uncertainty of carbon dynamics projected through 

the 21st century associated with climate forcing was estimated 

spatially by computing the range of change in NECB among 

the six climate simulations. For every 1-km grid cell and every 

climate scenario, the annual change in NECB was computed 

as the difference in the mean decadal NECB centered on 2095 

and 2005 divided by the length of this period: 

 ΔNECB (–) NECB NECB

90

[2090–2099] [2000–2009]

= (6.2)

The uncertainty was computed as the difference between 

the maximum and minimum ΔNECB among the six climate 

simulations.

Global warming potential (GWP) across time and the 

landscape was estimated taking into consideration that CH4

has 25 times the GWP of CO2 over a 100-year timeframe 

(Forster and others, 2007). GWP values were reported 

in CO2 equivalent after first converting C-CH4 fluxes to 

CH4 equivalent by multiplying the fluxes by 16/12, the ratio of 

the molecular weight of CH4 to the weight of carbon in CH4, 

and then converting CH4 equivalent fluxes to CO2 equivalent 

by multiplying by 25. All C-CO2 fluxes were converted to 

CO2 equivalent by multiplying them by 44/12, the ratio of 

the molecular weight of CO2 to the weight of carbon in CO2. 

CH4 production from fire emissions (Fire(CH4 )) was considered 

in addition to soil CH4 uptake and emissions by applying 

emission factors among CO2, CH4, and CO on DOS-TEM 

simulations of fire emissions (French and others, 2002). The 

carbon in CO was considered CO2 because it converts to CO2

in the atmosphere within a year (Weinstock, 1969).

GWP= –44/12×(NPP–HR–Harvest–Fire(CO2+CO) )

+ 25×16/12×(Fire(CH4) –BioCH4 ) (6.3)

Positive GWP indicates net CO2 loss from the ecosystem 

to the atmosphere, and negative GWP indicates net CO2 gain 

to the ecosystem from the atmosphere.

Analysis of the time series was conducted using linear 

regression and the Fisher test for test of significance on the 

time series. For the analysis of the inter-annual variations in 

sections 6.4.1.2 and 6.4.2.1, carbon fluxes were expressed in 

gC/m2/yr with associated standard deviation (s.d.). For the 

regional assessments in sections 6.4.1.4 and 6.4.2.3, carbon 

fluxes were summed across the regions and expressed in 

TgC/yr. The assumptions of normality and homoscedasticity 

were verified by examining residual plots. The relative 

effects of temperature, precipitation, total area burned, and 

atmospheric CO2 concentration on the carbon fluxes were 

tested using multiple regression analysis. The effects were 

considered significant when the p-value is lower than 0.05. 
Chapter 6 113

6.4. Results and Discussion

6.4.1. Historical Assessment of Carbon 

Dynamics (1950–2009)

6.4.1.1. Model Validation and Verification

For the historical period of the simulations (1950–2009), 

soil and vegetation carbon stocks were validated when 

possible by comparing modeled and observed estimates at 

sites independent from the sites used for model parameterization. When independent data (that is, data collected outside of 

the sites used for model parameterization) were not available, a verification of modeled versus observed stocks was 

conducted on the same sites used for model parameterization. 

Globally, no significant differences were observed 

between modeled and observed contemporary vegetation 

carbon stocks (table 6.3; p-value, p=0.340) and soil carbon 

stocks (table 6.4; p=0.085). In general, DOS-TEM simulations successfully reproduced differences between land-cover 

types. Arctic or alpine tundra and shrubland presented the 

lowest vegetation carbon stocks (table 6.3). Boreal landcover types had intermediate vegetation carbon stocks and 

maritime upland forest presented the largest vegetation 

carbon stocks, with 19.3 kilograms of carbon per square 

meter (kgC/m2) observed.

In contrast, arctic and alpine tundra and shrublands 

presented larger soil carbon stocks than boreal forests and 

maritime upland forest (table 6.4).

Table 6.3. Comparison of observed and modeled vegetation carbon stocks for the main upland land-cover types in Alaska.

[kgC/m2, kilogram of carbon per square meter; NA, not applicable]

Land-cover type

Number of

sites used for 

model testing

Vegetation carbon stocks (kgC/m2

)

Mean Standard deviation

Observed Modeled Observed Modeled

Black spruce forest 45 2.47 1.99 0.85 0.38

White spruce forest 20 4.40 4.29 0.74 0.32

Deciduous forest 24 6.85 6.56 0.46 0.85

Shrub tundra 4 1.81 2.26 0.12 0.50

Tussock tundra 3 0.56 0.44 0.26 0.21

Wet-sedge tundra 2 0.46 0.83 0.17 0.32

Heath tundra1 1 0.25 0.32 NA NA

Maritime upland forest1 3 19.26 22.10 3.79 4.56

Maritime alder shrubland1 1 0.81 0.96 NA NA

1

Comparisons between observed and modeled vegetation carbon stocks have been conducted for parameterization (that is, verification).

Table 6.4. Comparison of observed and modeled soil carbon stocks for the main upland land-cover types in Alaska.

[kgC/m2, kilogram of carbon per square meter; NA, not applicable] 

Land-cover type

Number

of sites used for 

model testing

Soil carbon stocks (kgC/m2

)

Mean Standard deviation

Observed Modeled Observed Modeled

Black spruce forest 40 29.85 46.84 11.15 55.64

White spruce forest 32 23.05 25.18 9.61 54.08

Deciduous forest 65 23.87 22.10 12.96 29.83

Shrub tundra 66 36.73 44.14 19.11 77.93

Tussock tundra 11 62.53 65.44 20.83 49.59

Wet-sedge tundra 23 42.01 50.73 30.49 31.46

Heath tundra 5 34.78 32.71 20.41 38.38

Maritime upland forest1 1 15.05 23.89 NA NA

Maritime alder shrubland1 1 26.97 23.55 NA NA

1

Comparisons between observed and modeled soil carbon stocks have been conducted for parameterization (that is, verification).
114 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

6.4.1.2. Times Series for Upland Alaska

From 1950 to 2000, the long-term trend of vegetation 

carbon stocks in uplands increased slightly owing to 

an increase in NPP (fig. 6.3A). The increase of NPP 

(0.185 gC/m2/yr, s.d. 0.254 gC/m2/yr computed for the entire 

study area) across the entire historical period was significant 

(fig. 6.3C; Fisher value, F=17.15; p<0.001). However, large 

fire years in 1957, 1969, 1977, 1990 and 1991 (fig. 6.3D) 

caused sudden decreases of vegetation carbon stocks (by 37 

grams of carbon per square meter [gC/m2], 28 gC/m2, 3 gC/m2, 

and 22 gC/m2, respectively) that slowed carbon accumulation 

over the period. The intense fire years of 2004 and 2005 

caused the largest loss of vegetation carbon stocks of the 

historical period—by 80 gC/m2 over the two consecutive 

years—shifting the vegetation net change over the historical 

period from a net carbon gain by 2000 to a net carbon loss 

by 2009. For the entire historical period, 2.09 gC/m2

/yr of 

vegetation carbon stocks was exported out of the ecosystem 

by forest harvest activities (fig. 6.3D). By 2009, the vegetation 

across upland Alaska lost 35.9 gC/m2 from 1950. 

Soil carbon stocks in upland Alaska remained relatively 

stable from 1950 to the late 1970s (fig. 6.3B). The large fire 

years 1957, 1969, and 1977 induced a loss of soil carbon stocks 

of 35 gC/m2, 21 gC/m2, and 17 gC/m2, respectively. From the 

early 1980s through 2009, soil carbon stocks increased mostly 

because of increases in litterfall associated with increases in 

NPP and the increase in dead woody debris produced during 

wildfire, which more than offset the increase of carbon loss 

from heterotrophic respiration (fig. 6.3E ) and carbon emissions 

from wildfire (fig. 6.3D). CH4 uptake by the methanotrophs 

in upland Alaska is quite low (fig. 6.3F ), ranging from 

3.43 to 6.35 milligrams of carbon dioxide equivalent per square 

meter per year. The MDM-TEM simulation estimates Alaskan 

uplands to be a net sink of CH4. Overall, soils across upland 

Alaska accumulated carbon throughout the historical period, 

increasing by 159 gC/m2 from 1950 through 2009.

The mean NECB throughout the historical period was 

estimated at 1.66 gC/m2/yr (s.d. 3.82 gC/m2/yr computed 

amongst all five LCC regions; fig. 6.3G ). Carbon gain to the 

ecosystem was mainly composed of net primary productivity, 

CH4 uptake being negligible. Carbon loss from the ecosystem 

was composed of heterotrophic respiration (93.6 percent), 

wildfire emissions (4.7 percent), and forest harvest exports 

(1.6 percent). Despite the larger CH4 emissions from fire 

compared to CH4 uptake by methanotrophs, upland Alaska 

was on average a carbon sink through the historical period of 

–13.3 grams of carbon dioxide equivalent per square meter 

per year (gCO2-eq/m2/yr) (s.d. 33.8 gCO2-eq/m2/yr computed 

amongst all five LCC regions; fig. 6.3H).

6.4.1.3. Environmental Drivers of the Temporal 

Variability of Net Ecosystem Carbon Balance in 

Upland Alaska

During the historical period, NPP was influenced 

primarily by mean annual temperature (table 6.5). Heterotrophic respiration increased during dry and warm years 

and large fire years. The positive relationship between 

heterotrophic respiration and large fire years might be related 

to (1) permafrost thaw in burned soils and (2) large inputs 

of carbon to the soil from the dead belowground vegetation biomass. Not surprisingly, fire emissions were driven 

Table 6.5. Results of multiple linear regressions testing the main drivers of carbon dioxide and methane fluxes in upland ecosystems 

among the total annual precipitation, mean annual temperature, annual area burned, and mean annual atmospheric carbon dioxide 

(CO2

) concentration for the entire study area during the historical period (1950–2009).

[F, Fisher value; P, probability value. Trend: +, positive; –, negative; n.s., trend not significant. Units: mm, milllimeter; °C, degree Celsius; km2, square kilometer; ppm, part per million]

Driver of 

carbon dioxide 

and methane 

fluxes

Net primary

productivity

Heterotrophic

respiration Fire emissions Methane uptake Net ecosystemcarbon balance

F P Trend F P Trend F P Trend F P Trend F P Trend

Total annual 

precipitation 

(mm)

3.37 0.07 n.s. 8.74 <0.01 – 0.02 0.89 n.s. 0.01 0.93 n.s. 0.94 0.34 n.s.

Mean annual 

temperature 

(°C) 

14.29 0.00 + 5.78 0.02 + 1.18 0.28 n.s. 31.96 <0.01 + 0.33 0.57 n.s.

Annual area 

burned

(km2

)

3.04 0.09 n.s. 10.29 <0.01 + 252.4 <0.01 + 19.59 <0.01 + 154.85 <0.01 –

Mean annual 

atmospheric 

CO2

 concentration (ppm)

1.83 0.07 n.s. 3.72 0.06 n.s. 1.29 0.26 n.s. 0.26 0.61 n.s. 7.43 0.01 +
Chapter 6 115

Figure 6.3. Time series of relative changes in carbon stocks and fluxes through the historical period (1950–2009). A, vegetation 

carbon stocks. B, soil carbon stocks. C, net primary productivity carbon flux. D, carbon loss from fire emissions and forest harvest

exports. E, soil heterotrophic respiration. F, biogenic methane uptake and pyrogenic methane emissions. G, net ecosystem 

carbon balance. H, global warming potential. Mean and standard deviations for the entire study area are indicated in each panel.

Vegetation carbon stocks, in

grams of carbon per square meter

Soil carbon stocks, in

grams of carbon per square meter

Net primary productiviy, in grams

of carbon per square meter per year

Carbon loss from fire and harvest, in grams

of carbon per square meter per year

Biogenic methane uptake, in grams of carbon

dioxide equivalent per square meter per year

Pyrogenic methane emissions, in grams of carbon

dioxide equivalent per square meter per year

Heterotrophic respiration, in grams

of carbon per square meter per year

Net ecosystem carbon balance, in grams

of carbon per square meter per year

Global warming potential, in grams of carbondioxide equivalent per square meter per year

A

C

E

G

Figure 6-3

B

D

F

H

Mean= –2.45 gC/m2

S.D.=1.47 gC/m2

Mean= 41.77 gC/m2

S.D.=14.13 gC/m2

Fire: Mean= 5.97 gC/m2/yr

 S.D.=8.20 gC/m2

/yr

Harvest: Mean= 2.09 gC/m2

/yr

 S.D.=0.85 gC/m2

/yr

Mean= 129.44 gC/m2

/yr

S.D.=28.09 gC/m2

/yr

Harvest

Fire

Mean= 1.66 gC/m2

/yr

S.D.=3.82 gC/m2

/yr

Mean= –13.32 gCO2

-eq/m2/yr

S.D.=33.78 gCO2

-eq/m2/yr

Emission mean= 0.4911 gCO2-eq/m2/yr

Emission S.D.= 0.7958 gCO2

-eq/m2/yr

Uptake mean= 0.0045 gCO2

-eq/m2/yr

Uptake S.D.= 0.0006 gCO2

-eq/m2/yr

(Gain)

(Source)

(Sink)

(Loss)

Mean= 119.63 gC/m2

/yr

S.D.=24.76 gC/m2

/yr

1940 1950 1960 1970

Year

1980 1990 2000 2010 1940 1950 1960 1970

Year

1980 1990 2000 2010

30

20

10

0

–10

–20

–30

–40

–50

–60

–70

150

140

130

120

110

300

200

100

0

–100

50

40

30

20

10

0

200

150

100

50

0

50

–100

170

160

150

140

130

120

110

100

4

3

2

1

0

30

20

10

0

–10

–20

–30

–40

–50

–60

–70

–80

Emissions

Uptake

0.0064

0.0062

0.0060

0.0058

0.0056

0.0054

0.0052

0.0050

0.0048

0.0046

0.0044

0.0042

0.0040

0.0038

0.0036

0.0034
116 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

primarily by large fire years. CH4 uptake was positively 

correlated to air temperature and large fire years, perhaps 

because of the influence of fire on soil temperature. Finally, 

the primary drivers of the temporal variability of NECB were 

the fire activity and the atmospheric CO2 concentration. The 

lack of effect of mean annual temperature on NECB was 

related to the fact that temperature had a positive effect on 

NPP and CH4 uptake, which was offset by its positive effect 

on heterotrophic respiration. 

6.4.1.4. Spatial Distribution of Net Ecosystem 

Carbon Balance Across Upland Alaska

The largest upland vegetation carbon stocks for the 

historical period are located in the Northwest Boreal LCC 

North and North Pacific LCC, whereas the largest soil carbon 

stocks are located in the Arctic and Western Alaska LCCs 

(table 6.6). Vegetation carbon storage decreased during the 

historical period in the regions that represent the largest 

vegetation carbon stocks: the Northwest Boreal LCC North 

and North Pacific LCC. The decrease of vegetation carbon 

stocks in the Northwest Boreal LCC North is related to 

carbon loss from fire. Carbon exports associated with forest 

harvest disturbance induced a decrease of vegetation carbon 

stocks in the North Pacific LCC (tables 6.6 and 6.7; chapter 5, 

section 5.5). The largest NPP and HR values were found in 

the two largest ecoregions (Northwest Boreal LCC North and 

Western Alaska LCC). The Northwest Boreal LCC North and 

Western Alaska LCC were the two major contributors to the 

regional total upland CH4 uptake, together contributing more 

than 70 percent of the total regional uptake, followed by the 

Northwest Boreal LCC South and Arctic LCC, which each 

contributed around 10 percent of the total. Whereas loss of 

vegetation carbon stocks in the North Pacific LCC was offset 

Table 6.6. Average vegetation and soil carbon stocks from the last decade (2000–2009) of the historical period and mean annual 

change in vegetation and soil carbon stocks between the first (1950–1959) and the last (2000–2009) decades of the historical period in 

each Landscape Conservation Cooperative region.

[Data may not add to totals shown because of independent rounding. TgC, teragram of carbon; km2, square kilometer]

Landscape Conservation

Cooperative (LCC) region

Upland

total area

(km2

) 

Upland

cover

(percent)

Vegetation carbon stocks (TgC) Soil carbon stocks (TgC)

Average Mean annual 

change Average Mean annual change

Arctic LCC 261,481 86 344 0.77 10,864 2.41

Western Alaska LCC 327,327 88 1,054 0.66 17,790 3.13

Northwest Boreal LCC North 335,491 73 1,272 –1.75 6,686 –3.37

Northwest Boreal LCC South 163,388 88 505 0.15 6,975 0.41

North Pacific LCC 150,087 97 1,119 – 0.10 4,799 2.69

Total 1,237,774 84 4,293 –0.26 47,113 5.27

Table 6.7. Average vegetation and soil carbon fluxes in upland ecosystems per Landscape Conservation Cooperative region from 

2000 through 2009.

[Data may not add to totals or compute to net ecosystem carbon balance shown because of independent rounding. CO, carbon monoxide; CO2, carbon dioxide; 

TgC/yr, teragram of carbon per year; TgCO2-eq/yr, teragram of carbon dioxide equivalent per year; NA, not applicable] 

Landscape

Conservation 

Cooperative

(LCC) region

Fire

emissions 

(CO+CO2

) 

(TgC/yr)

Pyrogenic 

methane 

emissions 

(TgCO2

-eq/yr)

Net primary 

productivity

(TgC/yr)

Harvesting

(TgC/yr)

Methane 

uptake

(TgCO2

-eq/yr)

Heterotrophic 

respiration

(TgC/yr)

Net ecosystem 

carbon balance

(TgC/yr)

Global

warming

potential

(TgCO2

-eq/yr)

Arctic LCC 2.47 0.26 32.14 NA 7.58×10– 4 26.48 3.18 –11.44

Western Alaska LCC 1.34 0.13 60.29 NA 1.74×10–3 55.15 3.79 –13.78

Northwest Boreal 

LCC North

22.23 2.00 71.57 NA 2.89×10–3 54.40 –5.12 20.57

Northwest Boreal 

LCC South

2.75 0.29 22.70 NA 7.61×10–4 19.38 0.57 –1.82

North Pacific LCC 0.14 0.01 25.27 2.91 9.31×10–5 19.62 2.59 –9.49

Total 28.94 2.69 211.97 2.91 6.25×10–3 175.03 5.01 (gain) –15.96 (sink)
Chapter 6 117

by the increase in soil carbon stocks, resulting in a positive 

NECB (table 6.7), carbon loss from wildfire caused negative NECB in the Northwest Boreal LCC North, the largest 

ecoregion of Alaska. Statewide for the historical period, 

upland ecosystems were a carbon sink, gaining on average 

5 TgC/yr. The Arctic and the North Pacific LCCs and southern 

portions of the Western Alaska LCC were hotspots of high 

carbon gain (blue shades in fig. 6.4A). Areas that lost carbon 

in Northwest Boreal LCC North mainly correspond to large 

historical fire scars (fig. 6.4B).

6.4.2. Assessment of Future Potential Carbon 

Dynamics (2010–2099) 

6.4.2.1. Times Series for Upland Alaska

The carbon accumulation rate in vegetation was projected 

to increase over the 21st century for all climate simulations. 

The carbon accumulation rate was higher for the ECHAM5 

simulations than for the CGCM3.1 simulations and higher 

for the highest CO2 emissions scenarios (A2, followed by 

A1B and B1) (fig. 6.5A). From 2010 through 2099, the mean 

annual increase of vegetation carbon stocks would range 

from 310 gC/m2/yr (under scenario B1 with CGCM3.1) to 

579 gC/m2/yr (under scenario A2 with ECHAM5). Carbon 

accumulation in the soil was quantitatively more important 

for the CGCM3.1 simulations than for the ECHAM5 simulations and higher for the higher CO2

 emissions scenarios 

(fig. 6.5B). From 2010 through 2099, the mean annual 

increase in soil carbon stocks would range from 296 gC/m2/yr 

(under scenario A2 with ECHAM5) to 1,041 gC/m2/yr 

(under scenario A2 with CGCM3.1). For all climate simulations, NPP and CH4

 uptake were projected to increase over 

the 21st century (figs. 6.5C, 6.5F), whereas heterotrophic 

respiration would not (fig. 6.5E). The projected increase 

in CH4 uptake in the upland ecosystems is likely attributed 

to increasing microbial substrate availability as a result of 

increased vegetation productivity (van den Pol-van Dasselarr 

and others, 1998). Projected warming may also contribute to 

the enhanced metabolic activity of methanotroph microbes 

(Yonemura and others, 2000). The difference in magnitude 

of CH4 uptake among emissions scenarios is generally 

greater than that between the GCMs. The A2 scenario has the 

highest projected increase in uptake, whereas the projected 

CH4 uptake under scenario B1 does not differ significantly 

from the historical period, owing to the scenario’s low 

anthropogenic CO2 emissions. CH4 emissions from wildfire 

would offset CH4 uptake by methanotrophs in all climate 

scenarios (figs. 6.5F, 6.5G). The mean fire emissions for 

scenarios B1, A1B, and A2 were projected to be 9.7 gC/m2/yr, 

6.7 gC/m2/yr, and 15.6 gC/m2/yr for CGCM3.1 compared with 

15.5 gC/m2/yr, 17.0 gC/m2/yr, and 18.2 gC/m2/yr, respectively, 

for ECHAM5 simulations. CH4 emissions represented 

0.25 percent of the total projected carbon emissions from 

wildfire. The larger fire emissions associated with the 

ECHAM5 climate simulations compared with the CGCM3.1 

simulations (fig. 6.5D) were mostly responsible for lower soil 

carbon accumulation with the ECHAM5 climate simulations 

compared with the CGCM3.1 simulations (fig. 6.5B).

The projected larger carbon accumulation in the 

ecosystem for the highest CO2 emissions scenario was mostly 

related to the projected increase in ecosystem productivity in 

response to the fertilization effect of rising atmospheric CO2

Figure 6.4. Spatial distribution of A, annual carbon loss 

and gain across upland Alaska during the historical period 

(1950–2009) and B, historical fire scars from 2000 through 

2009 among the five Landscape Conservation Cooperative 

regions. See figure 7.2 for the distribution of uplands and 

wetlands in Alaska.

Figure 6-4

Net ecosystem carbon balance, in grams

 of carbon per square meter per year

EXPLANATION

<–90

>90

No data

0

A

B

Fire scar, 2000–2009

EXPLANATION

Boundary of Landscape

 Conservation Cooperative

 region

N

0 200 400 KILOMETERS

0 200 400 MILES

Boundary of Landscape 

 Conservation Cooperative

 region
118 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska Vegetation carbon stocks, in grams of carbon per square meter Soil carbon stocks, in grams of carbon per square meter

A

B

Figure 6-5

Year

2000 2020 2040 2060 2080 2100

600

500

400

300

200

100

0

–100

1,200

1,000

800

600

400

200

0

–200

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION

Figure 6.5 (pages 118–122). Time series of relative changes in carbon stocks and fluxes for the projection period 

(2010–2099) for the six climate simulations: A, vegetation carbon stocks; B, soil carbon stocks; C, net primary productivity; 

D, carbon loss from fire emissions; E, soil heterotrophic respiration; F, biogenic methane uptake; G, pyrogenic methane 

emissions; H, net ecosystem carbon balance; and I, global warming potential. Thick black lines represent annual 

averages amongst all six simulations. The six climate simulations are combinations of two general circulation models, 

version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) developed by the Canadian Centre for Climate Modelling 

and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the Max Planck Institute, 

and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios 

(Nakićenović and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2

 emissions.
Chapter 6 119

Net primary productivity, in grams of carbon per square meter per year Fire emissions, in grams of carbon per square meter per year

CD

Figure 6-5—Continued

Year

2000 2020 2040 2060 2080 2100

180

170

160

150

140

130

120

110

100

90

200

180

160

140

120

100

80

60

40

200

–20

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION Figure 6.5.—Continued
120 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska Biogenic methane uptake, in grams of carbon dioxide equivalent per square meter per year Heterotrophic respiration, in grams of carbon per square meter per year

E

F

Figure 6-5—Continued

Year

2000 2020 2040 2060 2080 2100

160

150

140

130

120

110

100

90

80

70

0.014

0.012

0.010

0.008

0.006

0.004

0.002

0

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION Figure 6.5.—Continued
Chapter 6 121

Pyrogenic methane emissions, in grams of

carbon dioxide equivalent per square meter per year

G

H

Figure 6-5—Continued

Year

2000 2020 2040 2060 2080 2100

Net ecosystem carbon balance, in grams of

carbon per square meter per year

(Gain)

(Loss)

16

14

12

10

8

6

4

2

0

–2

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION Figure 6.5.—Continued
122 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

I

Figure 6-5—Continued

Year

2000 2020 2040 2060 2080 2100

Global warming potential, in grams of

carbon dioxide equivalent per square meter per year

(Source)

(Sink)

700

600

500

400

300

200

100

0

–100

–200

–300

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION Figure 6.5.—Continued

concentration. NECB would increase during the 21st century 

for all scenarios (fig. 6.5H ). However, this increase was 

only marginally significant because of the large inter-annual 

variability associated with large fire years (fig. 6.5D). On 

average, annual carbon gain in upland ecosystems in Alaska 

between 2010 and 2099 was projected to be 12.4 gC/m2

/yr, 

17.1 gC/m2/yr, and 17.0 gC/m2/yr for scenarios B1, A1B, 

and A2, respectively, with CGCM3.1 and 9.4 gC/m2/yr, 

9.3 gC/m2/yr, and 12.9 gC/m2/yr for scenarios B1, A1B, and 

A2, respectively, with ECHAM5. Despite the large projected 

CH4 emissions from wildfires, uplands in Alaska would be a 

CO2 sink of –44.82 gCO2-eq/m2/yr, –61.12 gCO2-eq/m2/yr, 

and –62.2 gCO2-eq/m2/yr for scenarios B1, A1B, and A2, 

respectively, with CGCM3.1 and –33.22 gCO2-eq/m2/yr, 

–32.82 gCO2

-eq/m2/yr, and –45.8 gCO2-eq/m2/yr for 

scenarios B1, A1B, and A2, respectively, with ECHAM5 

for the same period.

6.4.2.2. Environmental Drivers of the Temporal 

Variability of Net Ecosystem Carbon Balance in 

Upland Alaska

Mean annual temperature was projected to positively 

control NPP and CH4 uptake (table 6.8). Compared with 

similar analysis on the historical period, the present analysis 

across climate and CO2 simulations projected a positive 

effect of atmospheric CO2 concentration on NPP and CH4

uptake, and as a result, NECB. Finally, annual area burned 

would influence heterotrophic respiration, fire emissions, 

and CH4 uptake. As for the historical period, the positive 

effect of annual area burned on heterotrophic respiration and 

CH4 uptake might be related to the effect of wildfire on soil 

temperature. The positive effect of area burned on heterotrophic respiration and fire emissions would cause a negative 

relationship between area burned and NECB.
Chapter 6 123

Table 6.8. Results of multiple linear regressions testing the main drivers of carbon dioxide and methane fluxes in upland ecosystems 

among the total annual precipitation, mean annual temperature, annual area burned, and mean annual atmospheric carbon dioxide 

(CO2

) concentration for the entire study area during the projection period (2010 –2099).

[F, Fisher value; P, probability value. Trend: +, positive; –, negative; n.s., trend not significant. Units: mm, milllimeter; °C, degree Celsius; km2, square kilometer; ppm, part per million]

Driver of 

carbon dioxide 

and methane 

fluxes

Net primary

productivity

Heterotrophic

respiration Fire emissions Methane uptake Net ecosystemcarbon balance

F P Trend F P Trend F P Trend F P Trend F P Trend

Total annual 

precipitation 

(mm)

0.02 0.88 n.s. 1.69 0.19 n.s. 0.09 0.76 n.s. 3.89 0.05 n.s. 0.56 0.34 n.s.

Mean annual 

temperature 

(°C)

24.7 <0.01 + 2.61 0.11 n.s. 1.68 0.19 n.s. 89.4 <0.01 + 1.98 0.57 n.s.

Annual area 

burned (km2

)

1.68 0.19 n.s. 96.6 <0.01 + 1,144 <0.01 + 78.2 <0.01 + 997 <0.01 –

Mean annual 

atmospheric

CO2

 concentration 

(ppm)

20.63 <0.01 + 0.72 0.39 n.s. 0.62 0.43 n.s. 85.2 <0.01 + 12.6 <0.01 +

6.4.2.3. Spatial Distribution of Net Ecosystem 

Carbon Balance Across Upland Alaska

Vegetation carbon stocks in uplands were projected to 

increase through the 21st century for the six climate simulations and all LCC regions of Alaska, except for the Northwest 

Boreal LCC South for the A1B and B1 scenarios with 

CGCM3.1. Statewide projected annual change in vegetation 

carbon stocks would range from 5.1 to 10.5 TgC/yr. As for the 

historical period, the largest vegetation carbon accumulation 

was projected for the North Pacific LCC and the Northwest 

Boreal LCC North (table 6.9). Soil carbon stocks would 

increase between 2000–2009 and 2090–2099 for all climate 

scenarios and LCC regions, except for ECHAM5 simulations 

in the Western Alaska LCC. In this region, precipitation 

was projected to increase the least, and some areas would 

likely experience some seasonal decreases in spring and 

summer (chapter 2, fig. 2.3). Drought stresses associated 

with these scenarios might decrease vegetation productivity 

in the Western Alaska LCC and also increase heterotrophic 

respiration and fire occurrence by decreasing soil moisture 

(table 6.10). Statewide, projected annual change in soil carbon 

stocks between 2000–2009 and 2090–2099 would range 

from 6.5 to 23.0 TgC/yr. 

As a result, by the late 2090s, NECB would be positive 

for all LCC regions and climate simulations, ranging from 

0.1 to 9.84 TgC/yr (table 6.10). Compared with the historical 

period, NECB would increase for each climate simulation, 

except for the ECHAM5 simulations in the Western Alaska 

LCC. Across Alaska, the increase in NPP associated with 

increasing air temperature would offset the carbon loss from 

increased wildfire and heterotrophic respiration.

The increase of NECB during the 21st century was 

higher for the model representing the lowest warming trend 

(CGCM3.1) compared with the model with the highest 

warming trend (ECHAM5). The negative relationship between 

change in NECB and warming was related to the effect of 

warming on wildfire regime that would offset the increase of 

vegetation productivity (table 6.11). 

The spatial variability of the change in NECB over the 

21st century was projected to be largest in the Northwest 

Boreal LCC North. As for the magnitude of NECB, this might 

be related to the active fire regime in the region (fig. 6.6A). 

The variability of the projected change in NECB among 

the six climate simulations was the highest in the Western 

Alaska LCC (fig. 6.6B). The largest uncertainty in this 

region might not only be related to the uncertainty related to 

climate and disturbance forcings (uncertainty illustrated in 

chapter 2, section 2.4.6.1), but also to (1) the weakness of 

the parameterization and (or) (2) the lack of representation of 

processes that are at play specifically in this region. The lack 

of observations in the Western Alaska LCC compared with the 

other ecoregions greatly limits our current understanding of 

the drivers of carbon dynamics in the region (fig. 6.6C ).
124 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 6.9. Average vegetation and soil carbon stocks for the last decade (2090–2099) of the projection period and mean 

annual change in vegetation and soil carbon stocks between the last decades of the historical period (2000–2009) and 

the projection period (2090–2099) per Landscape Conservation Cooperative region for each of the six climate simulations.

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model 

(CGCM3.1) developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model 

(ECHAM5) developed by the Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special 

Report on Emissions Scenarios (Nakićenović and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. Data may 

not add to totals shown because of independent rounding. TgC, teragram of carbon]

Climate 

scenario

Landscape Conservation

Cooperative (LCC) region

Vegetation carbon stocks (TgC) Soil carbon stocks (TgC)

Average Mean annual

change Average Mean annualchange

CGCM3.1

A1B Arctic LCC 430 0.94 11,424 6.1

Western Alaska LCC 1,219 1.61 20,591 8.2

Northwest Boreal LCC North 1,496 2.50 6,942 2.7

Northwest Boreal LCC South 494 –0.16 7,942 2.0

North Pacific LCC 1,340 2.30 9,902 3.9

Total 4,979 7.19 56,801 23.0

A2 Arctic LCC 432 0.98 11,227 4.0

Western Alaska LCC 1,238 2.04 18,176 4.3

Northwest Boreal LCC North 1,500 2.53 7,197 5.7

Northwest Boreal LCC South 520 0.17 7,186 2.3

North Pacific LCC 1,370 2.79 4,979 2.0

Total 5,060 8.52 48,765 18.4

B1 Arctic LCC 410 0.82 10,929 6.9

Western Alaska LCC 1,137 0.88 19,236 4.6

Northwest Boreal LCC North 1,423 1.69 6,796 1.7

Northwest Boreal LCC South 497 –0.09 7,229 1.8

North Pacific LCC 1,284 1.81 5,115 3.6

Total 4,751 5.11 49,305 18.6

ECHAM5

A1B Arctic LCC 473 1.41 11,106 3.0

Western Alaska LCC 1,313 2.63 19411 –2.5

Northwest Boreal LCC North 1,585 3.50 7,023 3.8

Northwest Boreal LCC South 518 0.11 7,857 1.4

North Pacific LCC 1,391 2.85 9,628 0.8

Total 5,281 10.50 55,024 6.5

A2 Arctic LCC 473 1.45 11,190 3.8

Western Alaska LCC 1,293 2.60 18,201 –0.6

Northwest Boreal LCC North 1,600 3.65 7,079 4.3

Northwest Boreal LCC South 526 0.24 6,974 1.0

North Pacific LCC 1,343 2.47 4,856 0.8

Total 5,235 10.41 48,301 9.2

B1 Arctic LCC 426 0.88 11,165 3.5

Western Alaska LCC 1,226 1.87 17,815 –1.7

Northwest Boreal LCC North 1,564 3.25 6,942 2.6

Northwest Boreal LCC South 506 0.00 7,131 0.8

North Pacific LCC 1,301 2.00 4,931 1.6

Total 5,024 8.01 47,984 6.7
Chapter 6 125

Table 6.10. Average annual vegetation and soil carbon fluxes for the last decade of the projection period (2090–2099) and mean 

annual change in net ecosystem carbon balance between the last decades of the historical period (2000–2009) and the projection 

period (2090–2099) per Landscape Conservation Cooperative region for each of the six climate simulations.

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) 

developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed 

by the Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios 

(Nakićenović and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. Data may not add to totals or compute to net ecosystem 

carbon balance shown because of independent rounding. TgC/yr, teragram of carbon per year; TgCO2-eq/yr, teragram of carbon dioxide equivalent per year]

Climate

scenario

Landscape Conservation

Cooperative (LCC) region

Net primary 

productivity 

(TgC/yr)

Heterotrophic

respiration 

(TgC/yr)

Fire

emissions 

(CO+CO2

) 

(TgC/yr)

Pyrogenic 

methane 

emissions 

(TgCO2-eq/yr)

Methane

uptake

(TgCO2-eq/yr)

Net

ecosystem 

carbon

balance

(TgC/yr)

Global 

warming 

potential

(TgCO2-eq/yr)

Mean annual 

change in net 

ecosystem 

carbon

balance

(TgC/yr)

CGCM3.1

A1B Arctic LCC 40.7 33.6 0.1 0.01 –7.32×10–4 7.02 –25.8 0.044

Western Alaska LCC 74.0 53.7 10.5 1.06 –2.19×10–3 9.84 –35.1 0.061

Northwest Boreal LCC North 78.0 64.1 8.8 0.81 –3.99×10–4 5.17 –18.2 0.115

Northwest Boreal LCC South 24.0 20.0 2.2 0.21 –5.32×10–4 1.88 –6.7 0.013

North Pacific LCC 32.9 26.6 0.0 0.00 –1.50×10–4 6.24 –22.9 0.041

Total 249.7 198.0 21.5 2.09 –3.99×10–3 30.15 –108.7 0.274

A2 Arctic LCC 45.7 21.9 18.7 1.93 –9.31×10–4 5.02 –16.7 0.020

Western Alaska LCC 78.1 23.9 47.8 4.89 –3.23×10–3 6.34 –18.9 0.028

Northwest Boreal LCC North 76.8 55.9 12.7 1.21 –5.32×10–3 8.21 –29.0 0.148

Northwest Boreal LCC South 25.4 15.3 7.5 0.76 –1.30×10–3 2.51 –8.5 0.022

North Pacific LCC 36.3 25.4 6.0 0.64 –1.80×10–4 4.80 –17.0 0.025

Total 262.3 142.4 92.7 9.44 –1.10×10–2 26.87 –90.1 0.243

B1 Arctic LCC 41.0 33.3 0.1 0.01 –5.65×10–4 7.69 –28.2 0.057

Western Alaska LCC 69.9 52.4 12.0 1.19 –2.23×10–3 5.48 –19.0 0.020

Northwest Boreal LCC North 73.5 62.0 8.2 0.76 –3.23×10–3 3.39 –11.8 0.096

Northwest Boreal LCC South 23.0 16.0 5.3 0.54 –7.98×10–4 1.74 –5.9 0.013

North Pacific LCC 29.8 24.3 0.0 0.00 –7.65×10–5 5.46 –20.0 0.031

Total 237.2 187.9 25.5 2.49 –6.98×10–3 23.75 –84.9 0.216

ECHAM5

A1B Arctic LCC 51.3 24.1 22.7 2.33 –1.13×10–3 4.42 –14.1 0.013

Western Alaska LCC 83.3 26.3 56.8 5.82 –3.16×10–3 0.10 4.9 –0.050

Northwest Boreal LCC North 79.6 59.8 12.5 1.18 –5.65×10–3 7.29 –25.7 0.138

Northwest Boreal LCC South 25.9 15.0 9.4 0.94 –1.33×10–3 1.55 –4.8 0.010

North Pacific LCC 38.1 24.6 9.8 1.04 –2.09×10–4 3.68 –12.6 0.013

Total 278.3 149.7 111.2 11.34 –1.16×10–2 17.04 –52.4 0.124

A2 Arctic LCC 51.2 23.9 22 2.27 –1.13×10–3 5.21 –17.1 0.023

Western Alaska LCC 84.1 35.3 46.6 4.78 –5.32×10–3 1.97 –3.0 –0.022

Northwest Boreal LCC North 81.2 61.4 11.9 1.11 –6.32×10–3 7.91 –28.0 0.144

Northwest Boreal LCC South 26.2 14.5 10.4 1.05 –1.83×10–3 1.25 –3.6 0.008

North Pacific LCC 33.8 26.2 4.3 0.45 –2.69×10–4 3.24 –11.5 0.006

Total 276.5 161.3 95.3 9.66 –1.50×10–2 19.57 –63.1 0.159

B1 Arctic LCC 42.4 30.5 7.6 0.78 –7.65×10–4 4.33 –15.2 0.012

Western Alaska LCC 70.0 53.5 16.2 1.66 –2.63×10–3 0.21 0.7 –0.041

Northwest Boreal LCC North 74.1 63.5 4.8 0.45 –3.66×10–3 5.81 –20.9 0.121

Northwest Boreal LCC South 22.9 18.5 3.6 0.36 –8.31×10–4 0.77 –2.5 0.002

North Pacific LCC 31.5 24.1 3.8 0.40 –7.98×10–5 3.60 –12.9 0.011

Total 240.9 190.1 36.0 3.66 –7.98×10–3 14.72 –50.7 0.105
126 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 6.11. Change in decadal averages of mean annual net ecosystem carbon balance between the last 

decades of the historical period (2000–2009) and projection period (2090–2099) compared with corresponding 

changes in mean annual temperature, total annual precipitation, mean annual atmospheric carbon dioxide, 

(CO2) concentration, and total area burned for each of the six climate change simulations.

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate 

Model (CGCM3.1) developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre 

Hamburg Model (ECHAM5) developed by the Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on 

Climate Change’s Special Report on Emissions Scenarios (Nakićenović and Swart, 2000), B1, A1B, and A2, in order of low to high 

projected CO2 emissions. gC/m2/yr, gram of carbon per square meter per year; °C/yr, degree Celsius per year; mm/yr, millimeter per 

year; ppm/yr, part per million per year; km2, square kilometer]

Climate

scenario

Change in

mean annual

net ecosystem

carbon balance

(gC/m2

/yr)

Change in

mean annual

temperature

(°C/yr)

Change in

total annual

precipitation

(mm/yr)

Change in

mean annual 

atmospheric CO2

concentration

(ppm/yr)

Change in

total area

burned

(km2

) 

CGCM3.1

A1B 30.17 0.031 1.27 3.72 386,165

A2 25.23 0.046 2.19 4.92 448,945

B1 21.71 0.018 0.83 1.88 353,393

ECHAM5

A1B 14.16 0.059 1.89 3.72 623,016

A2 17.01 0.061 1.62 4.92 607,247

B1 11.56 0.038 1.09 1.88 504,987

6.5. Conclusion: Carbon Dynamics in 

Upland Alaska

We have examined carbon dynamics in upland ecosystems 

of Alaska for the two time periods using a modeling framework coupling biogeographic-disturbance and biogeochemical 

models. Through the historical period 1950–2009, we used 

historical climate and disturbance records to simulate annual 

carbon dynamics through upland Alaska. The assessment was 

conducted at a 1-km spatial resolution, which is unprecedented 

for Alaska and allows for integrating the effect of mediumscale diversity in vegetation composition and physiography 

on regional carbon dynamics. We also projected the potential 

changes in carbon dynamics through 2099 using a set of 

climate simulations that best represent the range of warming 

scenarios for the region. This set of climate simulations 

allowed us to quantify the uncertainty of future carbon 

balance in upland Alaska associated with the variability of 

climate projections.

During the historical period, upland ecosystems in 

Alaska were gaining 5 TgC/yr of carbon to the ecosystem 

(NECB); all LCC regions were net carbon sinks, except for 

the Northwest Boreal LCC North where large carbon losses 

from wildfire (specifically during large fire years in 1956, 

1969, 1977, and in the 1990s and 2000s) in addition to carbon 

loss from heterotrophic respiration offset carbon gain from 

net primary productivity. Pyrogenic CH4 emissions during the 

historical period were not enough to offset the carbon gain 

at the State level. Global warming potentials were therefore 

negative in Alaskan upland ecosystems, with a net carbon 

sink of –16 TgCO2

-eq/yr on average. The historical carbon 

simulations were validated by comparing modeled vegetation 

and soil carbon stocks in arctic and boreal ecosystems 

with independent field observations. Proper validation was 

not possible for the maritime forests because of the lack 

of independent, site-specific observations available in the 

region. Although climate and disturbance history are quite 

well constrained by field observations at the regional level, 

uncertainty remains on land-cover distribution and dynamics 
Chapter 6 127

Figure 6-6

A

B

C

N

0 200 400 KILOMETERS

0 200 400 MILES

Observation site for soil

 and vegetation carbon

 stocks and fluxes

Soil

Soil and vegetation

Vegetation

Boundary of Landscape

 Conservation Cooperative

 region

Uncertainty of projected change in net

 ecosystem carbon balance, in grams

 of carbon per square meter per year

EXPLANATION

<50

>450

No data

200

Mean projected change in net ecosystem

 carbon balance, in grams of carbon per 

 square meter per year

EXPLANATION

≤–80

>–80 to –60

>–60 to –40

>–40 to –20

>–20 to 0

>0 to 20

>20 to 40

>40 to 60

>60 to 80

>80

No data

Boundary of Landscape 

 Conservation Cooperative

 region

Boundary of Landscape 

 Conservation Cooperative

 region

Figure 6.6. Spatial distribution of A, average mean change in net 

ecosystem carbon balance for all climate simulations between the

last decades of the historical period (2000–2009) and the projection

period (2090–2099) and B, corresponding uncertainty. C, Distribution

of existing observation sites for soil and vegetation carbon stocks 

and fluxes. 

in response to warming in the region. In the present 

assessment, vegetation was considered static through time. 

Future assessments may explore how the current vegetation 

distribution and the effect of fire and permafrost thaw on land 

cover will affect regional land carbon dynamics. Similarly, 

soil texture has a large effect on soil hydrologic fluxes that 

affect soil carbon and permafrost dynamics (see chapter 3), 

and large uncertainty remains on the spatial distribution of 

soil texture in Alaska (Liu and others, 2013).

During the projection period (2010 –2099), all LCC 

regions of Alaska were projected to be carbon sinks. On 

average, upland ecosystems would store 22.0 TgC/yr, 

associated with the negative global warming potential of 

–75.0 TgCO2-eq/yr (carbon sink) despite the projected 

increase in pyrogenic CH4 emissions. The uncertainty of 

projected NECB associated with climate forcing would 

range from 14.7 to 30.2 TgC/yr statewide. Compared with 

the historical period, carbon storage in upland ecosystems 

was projected to increase between 0.105 and 0.274 TgC/yr 

by the end of the century. As shown in chapter 2, projected 

disturbance regimes associated with future climate changes 

are highly variable. The current assessment is based on 

a single disturbance scenario for each climate simulation 

that reproduces the best historical fire records. However, 

future assessments may explore the additional uncertainty 

of future carbon dynamics associated with multiple 

disturbance regimes. 
128 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

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Chapter 7. Terrestrial Carbon Modeling: Baseline 

and Projections in Lowland Ecosystems of Alaska

By Yujie He,1 Hélène Genet,2 A. David McGuire,3 Qianlai Zhuang,1 Bruce K. Wylie,4 and Yujin Zhang2

1

Purdue University, West Lafayette, Ind.

2

University of Alaska-Fairbanks, Fairbanks, Alaska.

3

U.S. Geological Survey, Fairbanks, Alaska.

4

U.S. Geological Survey, Sioux Falls, S. Dak.

7.1. Highlights

• The total area of wetland in Alaska was estimated at 

177,069 square kilometers (km2), which represents 

nearly 12 percent of the total land surface area 

(including uplands and inland waters) of the State. 

• During the historical period (1950–2009), wetland 

ecosystems in Alaska lost carbon at a rate of 1.3 teragrams of carbon per year (TgC/yr). The loss was the 

result of the net carbon source in the Northwest Boreal 

Landscape Conservation Cooperative (LCC) North, 

which overrode gains in all other LCC regions in 

Alaska to yield a net carbon loss statewide. 

• Historical biogenic and pyrogenic methane (CH4) 

emissions from Alaska wetland ecosystems were 

estimated to average about 27.93 teragrams of carbon 

dioxide equivalent per year (TgCO2-eq/yr) and 

0.466 TgCO2-eq/yr during the 1950s, respectively. 

Biogenic and pyrogenic CH4 emissions significantly 

increased from 1950 through 2009 by 0.977 grams 

of carbon dioxide equivalent per square meter per 

year (gCO2-eq/m2/yr) and 0.037 gCO2-eq/m2/yr, 

respectively. The global warming potential (GWP) 

of wetlands over the historical period indicates a 

significant source of greenhouse gas forcing of 

33 TgCO2

-eq/yr.

• By the end of the 21st century, wetland ecosystems 

of all LCC regions of Alaska were projected to gain 

carbon, storing between 3.0 and 5.3 TgC/yr statewide 

by the 2090s, depending on the climate change 

simulations used in the assessment. 

• Future mean annual CH4 emissions are estimated to 

range from 37 to 90 TgCO2-eq/yr by 2090–2099, 

depending on the climate change simulations used 

in the assessment, representing an increase of 

15 to 182 percent compared with those of 2000–2009. 

The large warming potential of CH4 emissions would 

be enough to offset the cooling effect of carbon gain 

statewide. The average annual GWP of wetlands 

over the projection period indicates a potential 

significant source of greenhouse gas forcing of 

17 to 64 TgCO2-eq/yr, despite the net carbon storage 

to wetland ecosystems.

• Biogenic CH4 emissions during the historical and 

future periods were found to be positively correlated with the atmospheric carbon dioxide (CO2) 

concentrations; future CH4 emissions were also 

projected to be significantly influenced by mean 

annual temperature.

7.2. Introduction

Wetlands accumulate peat owing to positive net ecosystem 

carbon balance, where net primary productivity and associated 

litterfall exceeds soil carbon loss from decomposition and 

methane production (Frolking and Roulet, 2007). Methane 

(CH4) is currently the second most important anthropogenic 

greenhouse gas, for which wetlands are the single largest 

natural source. CH4 emissions from high-latitude wetlands are 

an important component of the global climate system (Fisher 

and others, 2014). There is major concern about potential 

feedbacks between the climate system and CH4 emissions 

from wetlands, as climate, atmospheric carbon dioxide (CO2 ) 

concentrations, and deposition of sulfate and nitrogen are all 

known to affect CH4 emissions positively or negatively (Forster 

and others, 2007). There is compelling evidence that CH4 emissions from wetlands have been strongly responsive to climate 
134 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

in the past (He and others, 2014) and will likely continue to be 

responsive to anthropogenically driven climate change in the 

future. The high sensitivity of CH4 emissions to soil temperature and moisture conditions and its subsequent effect on the 

climate system is an important issue to assess in northern high 

latitudes, because this region contains nearly half of the world’s 

wetlands (Lehner and Döll, 2004) and because high latitudes 

have been and are forecast to continue experiencing more rapid 

warming than elsewhere (Stocker and others, 2013). Another 

concern is the potential release of previously frozen, labile soil 

carbon from thawing permafrost in the form of CO2 and (or) 

CH4 through mineralization owing to climate warming over 

the next century (Schuur and others, 2008; Koven and others, 

2011; Schaefer and others, 2011).

In addition to high vulnerability for carbon loss from 

CH4 production, wetlands, especially in boreal regions, are 

susceptible to carbon loss from wildfires. Wetlands generally 

burn less frequently than uplands because poorly drained 

conditions are responsible for low flammability, which minimizes fire activity in wetlands compared with better drained 

uplands. However, extended periods of dry weather along 

with increased occurrence of late-season burning (Kasischke 

and Turetsky, 2006) and changes in drainage conditions have 

the potential to trigger deep organic soil burning in wetlands 

(Turetsky and others, 2011), making these ecosystems 

potentially more vulnerable to fire and carbon loss. Wetland 

distribution also has local effects on wildlife habitat and 

subsistence resources (Grand and others, 1997). For instance, 

wetlands are refuges to a number of waterbird species that 

migrate from across the world to breed in the wetlands of 

Alaska (Martin and others, 2009). Wetlands are also an 

important habitat for moose, which represent an important 

source of food for local populations (Martin and others, 2009). 

Because of their importance in local and regional carbon 

dynamics and biodiversity, accurate distribution of wetlands 

across Alaska is of great importance. However, mapping 

wetlands in Alaska using remote sensing is challenging, and 

specific wetland classes, such as bogs, are particularly difficult 

to discriminate because woody overstory vegetation can block 

understory wetland vegetation and surface water. To assess 

carbon dynamics in the wetlands of Alaska, we developed a 

wetland distribution map that separated bogs and fens using 

the Alaska National Wetlands Inventory as a reference dataset.

In this chapter, we present a modeling synthesis of 

changes in carbon stocks and CH4

 emissions and other carbon 

fluxes among the soil, the vegetation, and the atmosphere 

over the historical period (1950–2009) and projection period 

(2010–2099) for Alaska. The modeling framework we used 

in this assessment couples a wildfire disturbance model with 

two process-based ecosystem models to estimate current and 

projected carbon stocks and CO2 and CH4 fluxes for wetlands 

in Alaska. Projections were made for two climate models 

that simulated future climates for each of three different CO2

emissions scenarios to estimate uncertainties in future climate 

forcing. We used the wetland distribution map to quantify CH4

emissions over the historical and future time period in Alaska. 

7.3. Methods

7.3.1. Methane Dynamics Module of the Terrestrial

Ecosystem Model (MDM-TEM) Description

Changes in soil and vegetation carbon stocks and 

CO2 fluxes in response to climate change and disturbances 

were simulated using a modeling framework that couples 

the output of a wildfire disturbance model, the Alaska 

Frame-Based Ecosystem Code (ALFRESCO; Rupp and 

others, 2000, 2002, 2007; see chapter 2), to a process-based 

ecosystem model that simulates carbon and nitrogen pools 

and CO2 dynamics, the Dynamic Organic Soil version of the 

Terrestrial Ecosystem Model (DOS-TEM; Yi, Manies, and 

others, 2009; Yi, McGuire, and others, 2009; Yi and others, 

2010; Genet and others, 2013; see chapter 6). Changes in 

biogenic CH4 fluxes were simulated by coupling the output of 

DOS-TEM with the Methane Dynamics Module (MDM) of 

the Terrestrial Ecosystem Model (MDM-TEM; Zhuang and 

others, 2004). The ALFRESCO and DOS-TEM aspects of 

the model framework used in this assessment are described in 

chapters 2 and 6. Here we will provide a detailed description of 

MDM-TEM. MDM-TEM simulates biogenic CH4 dynamics at 

daily timesteps for the Terrestrial Ecosystem Model (TEM) and 

explicitly considers the process of CH4 production (methanogenesis) as well as CH4

 oxidation (methanotrophy) and the 

transport of the gas from the soil to the atmosphere (Zhuang 

and others, 2004). The MDM has been coupled to several 

existing TEM modules, including the core carbon and nitrogen 

dynamics module (Zhuang and others, 2003), the soil thermal 

module that incorporates permafrost dynamics (Zhuang and 

others, 2001), and a hydrological module that simulates water 

movements across an atmosphere-vegetation-soil continuum 

(Zhuang and others, 2002, 2004). Specifically, the soil 

component of the hydrological module considers moisture 

dynamics explicitly in moss, organic soil, and mineral soil 

layers (Zhuang and others, 2002, 2004), and is designed to 

consider fluctuations in water table depth. 

In the MDM-TEM, the fluxes of CH4

 between soils and 

the atmosphere depend on the relative rates of CH4

 production 

and oxidation within the soil profile and the transport of CH4

across the surface of soils. The soil in the model is separated 

into an upper unsaturated zone and a lower saturated zone 

according to the water table depth. The net emissions (or 

uptake) of CH4 between the soil and the atmosphere are the 

balance between CH4 production and oxidation. If the rate of 

production is larger than the rate of oxidation within the soil 

profile, CH4 will be emitted to the atmosphere through diffusion. In wetland ecosystems, two other pathways in addition 

to diffusion are important for CH4

 transport to the atmosphere. 

One is plant-aided transport, where CH4 can move through 

aerenchyma tissues (that is, “hollow tubes”) that run from the 

roots through the stems to the leaves of some plants. Another 

is ebullition, where a high concentration of CH4 causes the 

formation of CH4 bubbles that can move through the overlying 

water or soils and escape into the atmosphere. 
Chapter 7 135

CH4 production is modeled as an anaerobic process that 

occurs in the saturated zone of the soil profile and is influenced 

by (1) substrate availability, which is a function of net primary 

productivity of the overlying vegetation from DOS-TEM 

wetland/lowland simulations (see more detail below); (2) soil 

temperature, which uses a Q10 function (Q10 denotes the 

change in biogeochemical process rate per 10 °C change in 

temperature) with a reference temperature and Q10 coefficients 

that vary across ecosystems; (3) soil pH, where the optimum is 

set to 7.5; and (4) the availability of electron acceptors related 

to the effects of redox potential. CH4 oxidation is modeled as 

an aerobic process that occurs in the unsaturated zone of the 

soil profile and is influenced by (1) soil temperature and redox 

potential, (2) substrate availability via a Michaelis-Menten 

function, and (3) soil moisture, which diminishes oxidation 

above the optimum soil moisture for oxidation. 

7.3.2. Model Parameterization and Validation

7.3.2.1. Wetland Classification

Wetlands are ecosystems that are waterlogged seasonally 

or year-round. Wetland ecosystems are characterized by poor 

drainage conditions and a thick organic layer (see table 7.1). 

Wetlands in tundra regions of the Arctic and Western Alaska 

Landscape Conservation Cooperatives (LCCs) are composed 

primarily of graminoid tundra and wet-sedge tundra. In the 

Arctic LCC, tundra wetland regions consisted of 84.7 percent 

graminoid tundra and 15.3 percent wet-sedge tundra. In the 

Western Alaska LCC, tundra wetland regions consisted of 

27.6 percent graminoid tundra and 72.4 percent wet-sedge 

tundra. In the Northwest Boreal LCC, wetlands consisted of 

97 percent lowland permafrost plateau forest (46 percent evergreen forest and 51 percent deciduous forest) and 3 percent 

treeless ecosystems (that is, bogs and fens). Bogs and fens are 

especially important to assessing CH4 dynamics owing to their 

high emissions. Because treeless wetlands were not extensive 

in the region, we used the parameterization for graminoid 

tundra for the simulation of these land-cover types. In the 

North Pacific LCC, wetlands consisted of 86 percent maritime 

fen and 14 percent maritime wetland forest (dominated by 

Sitka spruce [Picea sitchensis (Bong.) Carrière] and black 

cottonwood [Populus trichocarpa Torr. & A. Gray ex Hook.]). 

7.3.2.2. Methane Dynamics Module of the 

Terrestrial Ecosystem Model (MDM-TEM)

MDM-TEM is parameterized for three different types 

of wetland (table 7.2) based on specific vegetation and 

hydrological characteristics. Therefore, the seven wetland 

land-cover types for the vegetation map were identified 

with three MDM-TEM parameterizations. Specifically, 

(1) lowland black spruce (Picea mariana (Mill.) Britton, 

Sterns & Poggenb.), white spruce (Picea glauca (Moench) 

Voss), and deciduous forests and maritime wetland forest 

were identified with the boreal forest wetland category 

from MDM-TEM; (2) graminoid tundra was identified with 

the alpine tundra wetland category from MDM-TEM; and 

(3) wet-sedge tundra and maritime fen were identified with 

the moist tundra wetland category from MDM-TEM. The 

MDM-TEM was parameterized using CH4 measurements and 

soil and climate factors from three wetland field sites in arctic 

tundra and Canadian wetland (first three sites in table 7.2). 

The MDM-TEM was parameterized by minimizing the 

differences between observed fluxes and simulated fluxes at 

the Toolik-D, Toolik-W (Arctic LCC, Alaska), and SSA-FEN 

(Saskatchewan, Canada) field sites. For each site, the model 

was initialized by a set of parameter values determined by 

a literature review. Each individual parameter was bounded 

by the ranges of values from the literature review and then 

Table 7.1. Target values for carbon pool and flux variables used to calibrate the Dynamic Organic Soil version of the 

Terrestrial Ecosystem Model (DOS-TEM) for major wetland land-cover types in Alaska.

[Soil mineral carbon pools are estimated from the bottom of the organic layer down to 1 meter into the mineral soil. gC/m2/yr, gram of 

carbon per square meter per year; gC/m2, gram of carbon per square meter; —, not applicable]

Wetland land-cover type

Net primary 

productivity

(gC/m2

/yr) 

Carbon pool (gC/m2

)

Vegetation Soil fibric Soil humic Soil mineral 

Boreal lowland black spruce forest 103 2,105 2,432 10,757 19,821

Boreal lowland white spruce forest 259 4,180 1,875 8,311 11,005

Boreal lowland deciduous forest 299 6,673 1,243 5,523 11,005

Graminoid 112 561 3,079 7,703 43,403

Wet-sedge tundra 54 458 3,358 8,401 44,252

Maritime forested wetland 893 16,344 1,666 28,666 10,380

Maritime fen 113 960 2,666 59,115 —
136 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

adjusted so that the root mean square error (RMSE) between 

the daily simulated and observed CH4 fluxes was minimized. 

This procedure was conducted sequentially for all parameters 

until the minimized RMSE for the Toolik-D, Toolik-W, 

and SSA-FEN sites were 665 milligrams of carbon dioxide 

equivalent per square meter per day (mgCO2-eq/m2/d), 

1,729 mgCO2-eq/m2/d, and 1,396 mgCO2-eq/m2/d, respectively. 

7.3.2.3. Dynamic Organic Soil Version of the 

Terrestrial Ecosystem Model (DOS-TEM)

Wetland land-cover types considered in DOS-TEM 

include wet-sedge and graminoid tundra; black spruce, 

white spruce, and deciduous lowland boreal forests; and 

maritime wetland forest and maritime fen. We calibrated the 

rate-limiting parameters of DOS-TEM using target values 

of carbon and nitrogen pools and fluxes representative of 

mature ecosystems. These parameters are “tuned” until the 

model reaches target values of the main carbon and nitrogen 

pools and fluxes (Clein and others, 2002). The calibration of 

these parameters is an effective way of dealing with temporal 

scaling issues in ecosystem models (Rastetter and others, 

1992). For boreal forest communities, an existing set of target 

values for vegetation and soil carbon and nitrogen pools and 

fluxes were assembled using data collected in the Bonanza 

Creek Long Term Ecological Research (LTER) program 

(Yuan and others, 2012). For tundra communities, we used 

data collected at the Toolik Field Station (Shaver and Chapin, 

1991; Van Wijk and others, 2003; Sullivan and others, 2007; 

Euskirchen and others, 2012; Gough and others, 2012; Sistla 

and others, 2013). For the maritime and boreal-lowlandforest communities, we used data summarized in chapter 4, 

collected within three watersheds located near Juneau, 

Alaska, with mean annual precipitation of 1,580 millimeters 

(mm) and mean monthly average temperatures ranging from 

2 to 9 degrees Celsius (°C) (see chapter 4, section 4.3.1 for 

details). Target values of vegetation biomass, soil carbon 

pools, and net primary productivity for each wetland landcover type are described in table 7.1.

7.3.2.4. Model Validation

Model validation consists of testing the ability of a model 

to extrapolate carbon dynamics across space and time. It 

consists of comparing model simulations with observations 

collected at sites and times independent of the data used for 

model parameterization and calibration. When independent 

observations are not available, model verification consists 

of testing the ability of the model to reproduce the data used 

for calibration. 

For MDM-TEM, the wetland parameterization was 

validated at the NSA-FEN site in Canada using the parameterization from SSA-FEN site (table 7.2). A geometric mean 

regression between the simulated monthly mean and observed 

net emissions was significant (p<0.01; n=10months; where 

p denotes p-value, n denotes number of observations) with 

coefficient of determination, R2=0.90; slope=24.3±2.3 grams 

of carbon dioxide equivalent per square meter per month 

(gCO2-eq/m2/mo); and intercept= 11.6± 7.7 gCO2-eq/m2/mo. 

DOS-TEM parameterization has been validated using soil 

and vegetation biomass data derived from field observations 

Table 7.2. Description of sites used in the model parameterization and validation process.

[MDM-TEM, Methane Dynamics Module of the Terrestrial Ecosystem Model; BOREAS, Boreal Ecosystem-Atmosphere Study]

Site name Location Elevation 

(meters) Land cover Wetland type in MDM-TEM Observed data

Tundra at Toolik 

Field Station 

(Toolik-D)

149°36' W. 

68°38' N.

760 Tussock tundra Alpine tundra 

wetland

Soil temperatures at depths of 10, 20, and 

50 centimeters, methane fluxes from 

1992 and 1993

Tundra at Toolik 

Field Station 

(Toolik-W)

149°36' W.

68°38' N.

760 Wet tussock tundra Moist tundra 

wetland

Soil temperatures at depths of 3, 5, 7, 9, 

and 11 centimeters, methane fluxes from 

1994 and 1995

Fen at southern 

study area of 

BOREAS 

(SSA-FEN)

105°57' W.

53°57' N.

524.7 Complex fen with 

buckbean, sedges, 

birch, and willow

Boreal forest 

wetland

Soil temperatures at depths of 10 and 

20 centimeters, daily evapotranspiration 

and eddy covariance measurements of 

methane fluxes for May to October of 

1994 and 1995

Fen at northern 

study area of 

BOREAS 

(NSA-FEN; 

validation site)

98°25' W.

55°55' N.

218 Fen complex 

including sedge, 

moss, moat, 

and shrubs

Boreal forest 

wetland 

Soil temperatures at depths of 5, 10, 20, 50, 

and 100 centimeters; water-table depth 

(1994) and chamber measurements of 

methane fluxes of May through September 1994 and June through October 1996
Chapter 7 137

independent of the data used for model parameterization. The 

National Soil Carbon Network database for Alaska was used 

to validate DOS-TEM estimates of soil carbon stocks (Johnson 

and others, 2011). In order to compare similar estimates from 

model and observation, only deep profiles were selected from 

the database—that is, profiles with a description of the entire 

organic layer and the 90- to 110-centimeter (cm)-thick mineral 

layer beneath the organic layer.

Estimates of vegetation carbon stocks for tundra wetlands 

were compared with observations recorded in the data catalog of

the Arctic LTER at Toolik Field Station (http://toolik.alaska.edu; 

Shaver and Chapin, 1986). For the boreal forest wetlands, 

vegetation carbon stocks simulated by DOS-TEM were 

compared with estimates from forest inventories conducted 

by the Cooperative Alaska Forest Inventory (Malone and 

others, 2009). The forest inventory only provided estimates of 

aboveground biomass. Aboveground biomass was converted 

to total biomass by using a ratio of aboveground versus total 

biomass of 0.8 in forest and 0.6 in tundra ecosystems. Carbon 

content of the biomass was estimated at 50 percent. 

Finally, for maritime fen and maritime wetland forests, 

model validation was not possible as no additional independent data were available in this region. For these wetland 

ecosystems, we compared the model simulations with observed 

data at the same sites that were used for model parameterization. (See chapter 4, section 4.3.1 for site descriptions).

7.4. Model Application and Analysis

Spatially explicit data for climate, land cover, and soil 

texture were used to drive DOS-TEM and MDM-TEM. 

In addition, MDM-TEM used DOS-TEM estimates of 

monthly net primary productivity (NPP) and leaf area index 

(LAI) to simulate CH4 dynamics. Because MDM-TEM 

runs at a daily timestep, the monthly forcing data were 

interpolated to daily timesteps within MDM-TEM (Zhuang 

and others, 2004). Chapter 6, section 6.3.4.2 provides 

descriptions of these data sources. To evaluate the effects 

of historical and projected climate warming, we conducted 

a series of six climate simulations combining (1) historical 

climate variability from 1901 through 2009 using Climatic 

Research Unit (CRU TS v. 3.10.01; Harris and others, 2014; 

www.cru.uea.ac.uk/) data and (2) climate variability from 

2010 through 2099 projected by two general circulation 

models (GCMs): version 3.1-T47 of the Coupled Global 

Climate Model (CGCM3.1, www.cccma.ec.gc.ca/data/cgcm3/; 

McFarlane and others, 1992; Flato, 2005) developed by the 

Canadian Centre for Climate Modelling and Analysis and 

version 5 of the European Centre Hamburg Model (ECHAM5, 

www.mpimet.mpg.de/en/wissenschaft/modelle/echam/; 

Roeckner and others, 2003, 2004) developed by the 

Max Planck Institute. The climate projections were aligned 

with the Intergovernmental Panel on Climate Change’s 

Special Report on Emissions Scenarios (IPCC-SRES; 

Nakićenović and Swart, 2000). The assessment used three 

low-, mid- and high-range CO2 emissions scenarios (B1, 

A1B, and A2; see further details in chapter 2, section 2.2.1.). 

A new wetland distribution map of Alaska was developed 

for this study (see section 7.4.1 below) based on the National 

Wetlands Inventory (fig. 7.1) and used for both DOS-TEM 

and MDM-TEM to represent wetland ecosystem distribution 

(fig. 7.2). The heterogeneity and small clumped nature of 

wetlands was not possible to reproduce at a 1-kilometer (km) 

resolution. Therefore, the original wetland map was developed 

at a 30-meter (m) resolution, and percent cover of wetland was 

computed for each 1-km pixel. 

7.4.1. Development of an Alaska Wetland 

Distribution Map

In this effort, we developed a new wetland distribution 

map that separated bogs and fens (fig. 7.2). The Alaska 

National Wetlands Inventory (NWI; http://www.fws.gov/

wetlands/Data/State-Downloads.html) was used as a reference dataset (fig. 7.1), which helped with the development 

of a representative mapping model to estimate bog and fen 

distribution. Model development was conducted using a 

machine-learning, data-driven, nonparametric classification 

approach driven by Web-enabled Landsat Data spectral and 

derived indices and ancillary spatially explicit data (table 7.3). 

Bogs are generally flooded seasonally during spring melt. From 

the comparison of the NWI classification with field observations collected in the boreal and arctic regions of Alaska, we 

assumed bogs were identified as saturated scrub shrub in the 

NWI database because of the presence of dwarf shrubs and 

mosses. The NWI palustrine codes SS4B (scrub-shrub, needleleaved, saturated), SS1E (scrub-shrub, broad-leaved, seasonally flooded/saturated), and SS7B (scrub-shrub, deciduous,

saturated) were therefore used to define bogs. Fens are generally

flooded throughout the growing season. We therefore assumed 

they were identified as persistent emergent wetlands—that 

is, the NWI palustrine codes EM1F (emergent, broad-leaved, 

semipermanently flooded) and EM1E (emergent, broadleaved, seasonally flooded/saturated).

The wetland distribution map was developed based on a 

random selection of 18,024 pixels. A database for these pixels 

was built based on spatial inputs and NWI classes. Attributes 

from each of the potential input layers (table 7.3) were 

extracted for each pixel. Out of this set of pixels, 1,030 pixels 

were randomly selected and withheld for testing purposes. 

As an additional model sensitivity test, a twentyfold crossvalidation was conducted on the model development dataset. 

Winnowing was used to select a subset of relevant 

input spatial variables (Kivinen and others, 1997). A tenfold 

boosted regression tree (Sutton, 2005), which used the subset 

of winnowed variables, was developed (table 7.4). Overall 

accuracies were 75 percent for the independent test and 
138 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

N

0 100 200 300 KILOMETERS

0 100 200 300 MILES

Land cover

EXPLANATION

Figure 7-1

Fen

Bog

Other

Not mapped 

Figure 7.1. National Wetland Inventory data distribution and bog and fen distributions used for the development of 

an Alaska-wide mapping algorithm.
Chapter 7 139

N

0 100 200 KILOMETERS

0 100 200 MILES

A. Bog B. Fen

Bog or fen, in percent

EXPLANATION

Figure 7-2

≤10

>10 to 20

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90

Figure 7.2. Wetland ecosystems as defined by the vegetation distribution 

map developed for this assessment. Wetland distribution expressed as the 

areal percent within each 1-kilometer pixel. A, bog wetlands. B, fen wetlands.

67 percent for the cross-validation (table 7.5). Fens tended 

to be classed more reliably than bogs, likely because woody 

overstory vegetation masked bog characteristics. Nonparametric techniques are sensitive to class frequency distributions 

so the number of pixels per class (bog, fen, other) is representative of the population being mapped. 

The original 30-m maps of probable bog and fen 

distribution were converted to two maps of percent bog and 

percent fen at a 1-km resolution to match the resolution of the 

simulations (fig. 7.2). 

7.4.2. Scaling Simulation Results for the 

Alternative Map of Wetland Distribution

Simulations of wetland distribution were conducted for 

the wetland ecosystems throughout Alaska at 1-km resolution. The estimates for a particular 1-km grid cell were then 

area-weighted by the wetland fraction for that 1-km grid cell 

provided by the wetland distribution map. The area-weighted 

estimates were then aggregated to the scale of LCC regions or 

to the scale of Alaska for purposes of analysis and reporting. 
140 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 7.5. Accuracy assessments for the mapping model used 

to estimate bog and fen distribution compared with Alaska 

National Wetlands Inventory (NWI) reference classes for fens 

and bogs based on cross-validation of the pixels from the Alaska 

wetland distribution map developed for this assessment, not 

including those withheld for the independent test. 

[NA, not applicable]

NWI

reference

Mapping model

Bog Fen Other Sum Percent 

agreement

Bog 1,385 60 1,807 3,252 43

Fen 118 2,077 1,705 3,900 53

Other 985 956 7,901 9,842 80

Sum 2,488 3,093 11,413 16,994 NA

Percent 56 67 69 NA 67

Table 7.3. Potential spatial input variables and those winnowed and subsequently mapped using a regression tree model.

[X, variable part of winnowed subset; —, variable not part of winnowed subset; ?, unknown; NA, not applicable] 

Potential spatial input variables

Winnowed 

subset of 

variables

Variable usage by 

regression tree 

model (percent)

Reference

Web-enabled Landsat Data band 1 X 32 Roy and others (2010)

Web-enabled Landsat Data band 2 X 71 Roy and others (2010)

Web-enabled Landsat Data band 3 X 95 Roy and others (2010)

Web-enabled Landsat Data band 4 X 91 Roy and others (2010)

Web-enabled Landsat Data band 5 X ? Roy and others (2010)

Web-enabled Landsat Data band 6 X 49 Roy and others (2010)

Web-enabled Landsat Data band 7 X 100 Roy and others (2010)

EVI (enhanced vegetation index) — — Ji and others (2014)

gNDVI (green normalized difference vegetation index) X 68 Ji and others (2014)

NDVI (normalized difference vegetation index) — — Ji and others (2014)

SAVI (soil-adjusted vegetation index) X 85 Ji and others (2014)

NDII (normalized difference infrared index) — — Ji and others (2011)

NDII7 (normalized difference infrared index using band 7) X 57 Ji and others (2011)

NDWI (normalized difference water index) X 47 Ji and others (2011)

NDWI7 (normalized difference water index using band 7) X 53 Ji and others (2011)

Sum of bands 4, 5, and 6 X 80 NA

Elevation X 100 Gesch and others (2002)

Slope, in degrees X 97 NA

Compound terrain index X 47 Lu (2008)

Radar-based wetlands X 32 Whitcomb and others (2009)

National Land Cover Database X 87 Homer and others (2004)

China 2000 X 47 Liao and others (2014)

Soil texture X 71 Jorgenson, Yoshikawa, 

and others (2008a)

Table 7.4. Accuracy assessments for the mapping model 

used to estimate bog and fen distribution compared with Alaska 

National Wetlands Inventory (NWI) reference classes for fens 

and bogs based on an independent test of 1,030 randomly 

selected pixels from the Alaska wetland distribution map 

developed for this assessment. 

[NA, not applicable]

NWI

reference

Mapping model

Bog Fen Other Sum Percent 

agreement

Bog 52 3 64 119 44

Fen 7 188 124 319 59

Other 42 18 532 592 90

Sum 101 209 720 1,030 NA

Percent 52 90 74 NA 75
Chapter 7 141

7.4.3. Net Ecosystem Carbon Balance and 

Ecosystem Carbon Source/Sink Potential

Vegetation carbon stock estimates consisted of the sum 

of the aboveground and belowground living biomass. Soil 

carbon stocks consisted of the sum of carbon stored in the 

dead woody debris fallen to the ground, moss and litter, 

organic soil layers, and mineral soil layers. Historical changes 

in soil and vegetation carbon stocks were evaluated by 

quantifying annual differences of decadal averages between 

the first decade (1950–1959) and the last decade (2000 –2009) 

of the historical period. Projected changes in soil and vegetation carbon stocks were evaluated by quantifying annual 

differences of decadal averages between the last decade of 

the historical period (2000–2009) and the last decade of the 

projection period (2090–2099). 

The net ecosystem carbon balance (NECB) is the 

difference between total carbon inputs and total carbon outputs 

to the ecosystem (Chapin and others, 2006). NECB is the 

sum of all carbon fluxes coming in and out of the ecosystems, 

through gaseous and nongaseous, dissolved and nondissolved 

exchanges with the atmosphere and the hydrologic network. 

In terrestrial wetland ecosystems, NECB is the result of net 

primary productivity (NPP, net CO2

 uptake by the vegetation) 

minus heterotrophic respiration (HR), biogenic methane 

exchange (BioCH4 ), and fire emissions (Fire). No export of 

carbon from forest harvest activities (Harvest) was expected 

in wetlands. No methane consumption was expected in 

wetlands for the anaerobic conditions are not favorable to 

methanotrophs activities. Furthermore, logging activities do 

not take place in lowlands because of limited accessibility and 

low productivity of forested lowland ecosystems. 

NECB=NPP–HR–Fire–Harvest –BioCH4 (7.1)

NPP results from carbon assimilation from vegetation 

photosynthesis minus the respiration of the primary producers 

(autotrophic respiration). BioCH4 results from the activity of 

methanogens and methanotrophs under anaerobic conditions. 

HR results from the decomposition of unfrozen soil organic 

carbon. Fire emissions encompass CO2

, CH4, and carbon 

monoxide (CO) emissions. For the analysis of the inter-annual 

variations in sections 7.5.1.2 and 7.5.2.1, carbon fluxes were 

expressed in grams of carbon per square meter per year 

(gC/m2/yr). For the regional assessments in sections 7.5.1.4 

and 7.5.2.3, carbon fluxes were summed across the regions 

and expressed in teragrams of carbon per year (TgC/yr). 

Positive NECB indicates a gain of carbon to the ecosystem 

from the atmosphere, and negative NECB indicates a loss of 

carbon from the ecosystem to the atmosphere.

The uncertainty of carbon dynamics projected through 

the 21st century associated with climate forcing was estimated 

spatially by computing the range of change in NECB among 

the six climate simulations. For every 1-km grid cell and every 

climate simulation, the annual change in NECB was computed 

as the difference in the mean decadal NECB centered on 2095 

and 2005 divided by the length of this period: 

 ΔNECB (–) NECB NECB

90

[2090–2099] [2000–2009]

= (7.2)

The uncertainty was computed as the difference between 

the maximum and minimum ΔNECB among the six climate 

simulations.

Global warming potential (GWP) across time and the 

landscape was estimated taking into account that CH4 has 

25 times the GWP of CO2 over a 100-year timeframe (Forster 

and others, 2007). GWP was reported in CO2

 equivalent by 

multiplying C-CH4 fluxes by 33.33 (see chapter 6, section 

6.3.5.2 for details). All C-CO2 fluxes were converted to 

CO2 equivalent by multiplying them by 3.66. CH4 production 

from fire emissions (Fire(CH4)) was considered in addition to 

biogenic CH4 emissions by applying emission factors to CO2, 

CH4, and CO on DOS-TEM simulations of fire emissions 

(French and others, 2002). The carbon in CO was considered 

CO2 because it converts to CO2 in the atmosphere within a 

year (Weinstock, 1969).

GWP= –44/12×(NPP–HR–Harvest–Fire(CO2+CO) ) 

+25×16/12×(Fire(CH4)+ BioCH4 ) (7.3)

Positive GWP indicates a net loss of CO2 from the 

ecosystem to the atmosphere, and negative GWP indicates a 

net gain of CO2 to the ecosystem from the atmosphere.

Analysis of the time series was conducted using linear 

regression and the Fisher test for test of significance on the 

time series. For the analysis of the inter-annual variations in 

sections 7.5.1.2 and 7.5.2.1, carbon fluxes were expressed in 

gC/m2

/yr with associated standard deviation (s.d.). For the 

regional assessments in sections 7.5.1.4 and 7.5.2.3., carbon 

fluxes were summed across the regions and expressed in 

TgC/yr. The assumptions of normality and homoscedasticity 

were verified by examining residual plots. The relative 

effects of temperature, precipitation, total area burned, and 

atmospheric CO2 concentration on the carbon fluxes were 

tested using multiple regression analysis. The effects were 

considered significant when the p-value is lower than 0.05. 
142 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

7.5. Results and Discussion

7.5.1. Historical Assessment of Carbon 

Dynamics (1950–2009)

7.5.1.1. Model Validation and Verification

For the historical period of the simulations (1950–2009), 

soil and vegetation carbon stocks were validated when 

possible by comparing modeled and observed estimates at 

sites independent from the sites used for model parameterization. When independent data (that is, data collected outside of 

the sites used for model parameterization) were not available, a verification of modeled versus observed stocks was 

conducted on the same sites used for model parameterization. 

Globally, no significant differences were observed 

between modeled and observed contemporary vegetation 

carbon stocks (table 7.6; p=0.340) and soil carbon stocks 

(table 7.7; p=0.182). In general, DOS-TEM simulations 

successfully reproduced differences between land-cover types. 

Graminoid and wet-sedge tundra and maritime fen presented 

the lowest vegetation carbon stocks (table 7.3). Boreal lowland 

forests (that is, deciduous, white spruce, and black spruce 

lowland forests) had intermediate vegetation carbon stocks, 

and maritime wetland forest presented the largest vegetation 

carbon stocks, with 13.6 kilograms of carbon per square meter 

(kgC/m2) observed.

In contrast, arctic and alpine tundra wetlands and 

maritime wetlands contained larger soil carbon stocks than 

boreal forest ecosystems (table 7.7).

Table 7.6. Comparison of observed and modeled vegetation carbon stocks for the main wetland land-cover types 

in Alaska.

[kgC/m2, kilogram of carbon per square meter; NA, not applicable]

Wetland land-cover type

Number of

sites used

for model

testing

Vegetation carbon stocks (kgC/m2

)

Mean Standard deviation

Observed Modeled Observed Modeled

Black spruce forest 45 2.47 1.99 0.85 0.38

White spruce forest 20 4.40 4.29 0.74 0.32

Deciduous forest 24 6.85 6.56 0.46 0.85

Graminoid tundra 3 0.56 0.44 0.26 0.21

Wet-sedge tundra 2 0.46 0.83 0.17 0.32

Maritime wetland forest1 3 13.62 13.11 1.16 3.26

Maritime fen1 1 0.96 1.67 NA NA

1

Comparisons between observed and modeled vegetation carbon stocks have been conducted for parameterization (that is, verification). 

Table 7.7. Comparison of observed and modeled soil carbon stocks for the main wetland land-cover types in Alaska. 

[kgC/m2, kilogram of carbon per square meter; NA, not applicable]

Wetland land-cover type Number

of sites

Soil carbon stocks (kgC/m2

)

Mean Standard deviation

Observed Modeled Observed Modeled

Black spruce forest 22 29.85 46.84 11.15 55.64

White spruce forest 14 23.05 25.18 9.61 54.08

Deciduous forest 8 23.87 22.10 12.96 29.83

Tussock tundra 11 62.53 65.44 20.83 49.59

Wet-sedge tundra 23 42.01 50.73 30.49 31.46

Maritime wetland forest1 1 40.71 32.83 NA NA

Maritime fen1 1 61.78 75.87 NA NA

1

Comparisons between observed and modeled vegetation carbon stocks have been conducted for parameterization (that is, verification).
Chapter 7 143

7.5.1.2. Time Series Biogenic Methane 

Emissions, Net Ecosystem Carbon Balance,

and Global Warming Potential for 

Wetland Alaska

For the wetland distribution map, the MDM-TEM 

simulation estimated net biogenic CH4 emissions of 

wetlands in Alaska over the historical period (1950 –2009) to 

be 157 grams of carbon dioxide equivalent per square meter 

per year (gCO2-eq/m2/yr) (s.d. of about 33 gCO2-eq/m2/yr), 

ranging from 97 to 267 gCO2-eq/m2/yr (fig. 7.3A). Biogenic 

CH4 emissions increased significantly during the historical 

period, at a rate of about 0.977 gCO2-eq/m2/yr (Fisher 

value, F = 20.56, p < 0.01). Pyrogenic CH4 emissions were 

estimated to be 1.50 gCO2-eq/m2/yr, which represented 

about 1 percent of the biogenic emissions. Pyrogenic CH4

emissions significantly increased over the historical period 

(F = 2.13, p = 0.0383) at a rate of 0.037 gCO -eq/m2

2 /yr. 

NECB was estimated at –12.6 gC/m2/yr (s.d. 7.2 gC/m2/yr), 

ranging from –313.3 to 50.5 gC/m2/yr. NECB did not change 

significantly over the historical period (F = 0.22, p = 0.63, 

fig. 7.3B). The GWP over the historical period indicated 

that wetlands were a significant source of greenhouse gas 

forcing of 187.7 gCO2-eq/m2/yr (s.d. 112.4 gCO2-eq/m2/yr), 

ranging from 57.4 to 1,399 gCO2-eq/m2/yr. GWP did not 

change significantly over the historical period (F = 0.87, 

p = 0.35, fig. 7.3C ) and was significantly different from zero 

(t = – 6.00; p < 0.01).

7.5.1.3. Environmental Drivers of the 

Historical Temporal Variability of Biogenic 

Methane Emissions, Net Ecosystem Carbon 

Balance, and Global Warming Potential of 

Wetland Alaska

Total CH4 emissions (biogenic and pyrogenic) 

during the historical period were positively correlated 

with the annual area of wetlands burned associated with 

peaks of pyrogenic methane emissions. CH4

 emissions 

increased with increasing atmospheric CO2 concentration 

(table 7.8). The positive relationship between atmospheric 

CO2 concentration and CH4 emissions is likely related 

to the effect of increasing atmospheric CO2 on NPP (see 

chapter 6, section 6.4.2.2). The negative effect of annual 

area burned on NECB is related to the effect of wildfire 

on CO2 emissions from combustion of soil and vegetation 

carbon. Although GWP is positively correlated with annual 

area burned, the relationship likely depends on the correlation of GWP and CH4

 emissions.

Figure 7.3. Time series of annual A, net biogenic 

and pyrogenic methane emissions; B, net ecosystem 

carbon balance; and C, global warming potential 

during the historical period (1950–2009) for wetland 

ecosystems of Alaska defined by the wetland 

distribution map. The mean and standard deviation 

for the study area are indicated in each panel.

Net biogenic methane emissions, in grams of

carbon dioxide equivalent per square meter per year

Net pyrogenic methane emissions, in grams of

carbon dioxide equivalent per square meter per year

Net ecosystem carbon balance, in

grams of carbon per square meter per year

(Gain)

(Loss)

Global warming potential, in grams of

carbon dioxide equivalent per square meter per year

(Source)

(Sink)

1940 1950 1960 1970

Year

1980 1990 2000 2010

A

B

C

Biogenic

 Mean=157.05 gCO2

-eq/m2/yr

 S.D.=33.88 gCO2

-eq/m2/yr 

Pyrogenic

 Mean= 1.50 gCO2

-eq/m2/yr 

 S.D.=2.45 gCO2

-eq/m2/yr 

Mean= –12.61 gC/m2

/yr

S.D.=7.17 gC/m2

/yr

Mean= 187.72 gCO2

-eq/m2/yr

S.D.=112.43 gCO2

-eq/m2/yr

Biogenic

Pyrogenic

100

0

–100

–200

300

400

300

200

100

0

14

13

12

11

10

9

8

7

6

5

4

3

2

1

0

1,400

1,200

1,000

800

600

400

200

0

–200

Figure 7-3
144 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

7.5.1.4. Spatial Distribution of Carbon Stocks, 

Biogenic Methane Emissions, Net Ecosystem 

Carbon Balance, and Global Warming Potential 

Across Wetland Alaska

The largest carbon stocks of wetlands in Alaska were 

located in the Northwest Boreal LCC North (table 7.9), with 

storage of 427 teragrams of carbon (TgC) in the vegetation 

and 1,965 TgC in the soil. The Northwest Boreal LCC 

North also contains the largest proportion of wetlands 

in Alaska and is the only LCC region in which carbon 

stocks in the vegetation and the soil decreased during the 

historical period. The largest increase of carbon stocks in 

the vegetation and the soil was observed in the Arctic LCC, 

with a gain of 0.12 TgC/yr and 0.49 TgC/yr in the vegetation and the soil, respectively.

During the first decade of the 21st century (2000–

2009), the largest biogenic CH4

 emissions were observed 

in the Northwest Boreal LCC North (table 7.10, fig. 7.4A). 

The largest NPP also was observed in the Northwest Boreal 

LCC North but the carbon gain from vegetation growth was 

offset by carbon loss from heterotrophic respiration and fire 

emissions, resulting in the wetlands in the region being a net 

carbon source of –2.21 TgC/yr during the historical period 

(fig. 7.4B), equivalent to 27 teragrams of carbon dioxide 

equivalent per year (TgCO2-eq/yr) (fig. 7.4C). The other 

LCC regions were carbon sinks during the historical period: 

the largest sink was located in the Arctic LCC with storage 

of 0.62 TgC/yr. However, these smaller sinks were not large 

enough to compensate for the carbon loss from the Northwest Boreal LCC North. Statewide, wetland ecosystems in 

Alaska were a carbon source during the historical period, 

losing about 1.34 TgC/yr. The carbon losses were about 

equally distributed among soil and vegetation (table 7.9). 

Although total CH4 emissions represented only 2.6 percent 

of NPP, the trend of GWP is dominated by CH4 emissions. 

As a result, mean annual GWP between 2000 and 2009 

was 33 TgCO2-eq/yr in wetlands of Alaska.

Figure 7-4

A

B

C

N

0 200 400 KILOMETERS

0 200 400 MILES

Biogenic methane emissions, in grams of carbon

 dioxide equivalent per square meter per year

EXPLANATION

No data

Boundary of Landscape

 Conservation Cooperative

 region

Boundary of Landscape

 Conservation Cooperative

 region

Boundary of Landscape

 Conservation Cooperative

 region

≤–665

>–665 to –499

>–499 to –332

>–332 to –166

>–166 to 0

>0 to 166

>166 to 332

>332 to 499

>499 to 665

>665

Net ecosystem carbon balance, in grams 

 of carbon per square meter per year

EXPLANATION

No data

≤–40

>–40 to –30

>–30 to –20

>–20 to –10

>–10 to 0

>0 to 10

>10 to 20

>20 to 30

>30 to 40

>40

Global warming potential, in grams of carbon 

 dioxide equivalent per square meter per year

EXPLANATION

No data

≤–300

>–300 to –200

>–200 to –100

>–100 to 0

>0 to 100

>100 to 200

>200 to 300

>300 to 400

>400

Figure 7.4. Spatial distribution of mean annual A, biogenic 

methane emissions; B, net ecosystem carbon balance; and 

C, global warming potential for the historical period (1950–2009) 

for wetland ecosystems of Alaska. 
Chapter 7 145

Table 7.8. Results of multiple linear regressions testing the main drivers of carbon dioxide and total methane (biogenic and 

pyrogenic) fluxes in wetland ecosystems among total annual precipitation, mean annual temperature, annual area burned, and mean 

annual atmospheric carbon dioxide concentration during the historical period (1950–2009).

[F, Fisher value; P, probability value. Trend: +, positive; –, negative; n.s., trend not significant. Units: mm, milllimeter; °C, degree Celsius; km2, square kilometer; ppm, part per million. CO2

, carbon dioxide]

Drivers of carbon dioxide and 

methane fluxes

Total methane emissions Net ecosystem carbon balance Global warming potential

F P Trend F P Trend F P Trend

Total annual precipitation (mm) 0.01 0.92 n.s. 0.55 0.46 n.s. 0.41 0.52 n.s.

Mean annual temperature (°C) 1.75 0.19 n.s. 0.44 0.51 n.s. 0.02 0.88 n.s.

Annual area burned (km2

) 29.3 <0.01 + 217.77 <0.01 – 221.36 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

5.13 <0.01 + 1.07 0.31 n.s. 0.55 0.46 n.s.

Table 7.9. Average vegetation and soil carbon stocks for the last decade of the historical period (2000–2009) and mean annual 

change in vegetation and soil carbon stocks between the first (1950–1959) and last (2000–2009) decades of the historical period in each 

Landscape Conservation Cooperative region for wetland ecosystems of Alaska defined by the vegetation distribution map developed 

for this assessment.

[Data may not add to totals shown because of independent rounding. km2, square kilometer; TgC, teragram of carbon]

Landscape Conservation

Cooperative (LCC) region

Wetland

total area

(km2

) 

Wetland

cover

(percent)

Vegetation carbon stocks (TgC) Soil carbon stocks (TgC)

Average Mean annual 

change Average Mean annual change

Arctic LCC 29,818 9.8 44 0.12 1,281 0.49

Western Alaska LCC 14,582 3.9 57 0.04 788 0.06

Northwest Boreal LCC North 112,077 24.5 427 –0.87 1,965 –1.33

Northwest Boreal LCC South 18,627 10.0 83 0.05 865 0.03

North Pacific LCC 1,965 1.3 19 0.00 107 0.06

Total 177,069 12.0 630 –0.65 5,006 –0.68

Table 7.10. Average vegetation and soil carbon fluxes in wetland ecosystems per Landscape Conservation Cooperative region from 

2000 through 2009.

[Data may not add to totals or compute to net ecosystem carbon balance shown because of independent rounding. CO2, carbon dioxide; CO, carbon monoxide; 

CH4, methane; TgC/yr, teragram of carbon per year; TgCO2-eq/yr, teragram of carbon dioxide equivalent per year]

Landscape

Conservation 

Cooperative

(LCC) region

Pyrogenic 

CO2+CO

emissions

(TgC/yr)

Pyrogenic CH4

emissions

(TgCO2

-eq/yr)

Net primary 

productivity

(TgC/yr)

Biogenic CH4

emissions

(TgCO2

-eq/yr)

Heterotrophic 

respiration

(TgC/yr)

Net ecosystem 

carbon balance

(TgC/yr)

Global warming 

potential

(TgCO2-eq/yr)

Arctic LCC 1.08 0.112 4.68 2.49 2.91 0.62 0.06

Western Alaska LCC 0.08 0.008 2.99 1.67 2.76 0.10 1.12

Northwest Boreal 

LCC North

8.23 0.740 24.95 20.45 18.29 –2.21 27.01

Northwest Boreal 

LCC South

1.00 0.105 4.11 5.74 2.85 0.08 4.90

North Pacific LCC 0.01 0.001 0.56 0.50 0.47 0.07 0.20

Total 10.40 0.966 37.28 30.85 27.27 –1.34 33.30
146 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

7.5.2. Future Assessment of Carbon Dynamics 

(2010–2099) 

7.5.2.1. Times Series for Wetland Alaska 

Estimates of future biogenic CH4 emissions exhibited 

substantial inter-annual variability and substantial differences 

between climate models for a given emissions scenario and 

among different emissions scenarios for a given climate 

model. The greatest mean annual biogenic CH4 emissions 

among climate models and emissions scenarios would 

be about 229 gCO2-eq/m2/yr under scenario A1B with 

ECHAM5. The lowest mean annual biogenic CH4 emissions 

were projected under scenario B1 with both climate models 

(159 gCO2-eq/m2/yr and 176 gCO2-eq/m2/yr with CGCM3.1 

and ECHAM5, respectively; fig. 7.5A). Biogenic CH4 emissions 

would increase significantly during the 21st century under 

scenarios B1, A1B, and A2 at a rate of 0.680 gCO2-eq/m2/yr, 

1.406 gCO2

-eq/m2/yr, and 3.073 gCO2-eq/m2/yr, respectively, 

with CGCM3.1 and 0.923 gCO2-eq/m2/yr, 1.003 gCO2-eq/m2/yr, 

and 2.183 gCO2-eq/m2/yr, respectively, with ECHAM5. 

Projected mean annual pyrogenic CH4 emissions were 

larger for the climate simulations from the ECHAM5 climate 

model than from the CGCM3.1 climate model (fig. 7.5B). 

Among all climate simulations, mean annual pyrogenic 

CH4 emissions would range from 0.967 gCO2-eq/m2/yr 

(s.d. 1.3 gCO2-eq/m2/yr) under scenario A1B with CGCM3.1 

to 1.467 gCO2-eq/m2/yr (s.d. 2.7 gCO2-eq/m2/yr) under 

scenario A2 with ECHAM5. Pyrogenic CH4 emissions are 

not projected to significantly increase over time, except for 

the climate simulations with the largest warming and increase 

in atmospheric CO2 concentration (that is, scenario A2 with 

CGCM3.1 and ECHAM5), for which pyrogenic CH4 emissions would increase at a rate of 0.00243 gCO2

-eq/m2/yr and 

0.0193 gCO2-eq/m2/yr, respectively. Combining historical 

simulation results, CH4 emissions significantly increased from 

1950 through 2009 by 0.967 gCO2-eq/m2/yr. 

For each climate model, projected mean annual NECB 

was the lowest for the lowest CO2-emissions scenario B1 

(13.8 gC/m2

/yr and 23.5 gC/m2/yr with CGCM3.1 and 

ECHAM5, respectively) and the highest for the highest 

CO2-emissions scenario A2 (33.6 gC/m2/yr and 31.7 gC/m2/yr 

with CGCM3.1 and ECHAM5, respectively). In contrast 

with CH4 emissions, NECB would not change significantly 

over time (fig. 7.5C). As a result, GWP was projected 

to increase during the second half of the 21st century 

(fig. 7.5D). This increase was significant for CGCM3.1 

climate simulations under A2 and B1 emissions scenarios 

(rate of 2.59 gCO2-eq/m2/yr and 1.09 gCO2-eq/m2/yr, respectively) and the ECHAM5 climate simulation under the A1B 

scenario (rate of 2.15 gCO2-eq/m2/yr).

7.5.2.2. Environmental Drivers of the Future 

Temporal Variability of Net Ecosystem Carbon 

Balance and Global Warming Potential in 

Wetland Alaska

Over the projection period, increasing annual area 

burned and rising atmospheric CO2 concentrations were 

projected to be associated with an increase in CH4 emissions in wetlands during the 21st century for all six climate 

simulations. Increasing air temperature also would have a 

significant positive effect on CH4 emissions under scenarios 

A1B and A2 with CGCM3.1 (table 7.11). Increasing annual 

area burned would have a negative effect on NECB for 

all climate simulations. Increasing air temperature and 

atmospheric CO2 concentration also would have a positive 

effect on NECB for scenarios B1 and A1B, respectively, 

with ECHAM5. Increases in GWP would be associated with 

increases in annual area burned owing to climate warming 

(see chapter 2, section 2.4.5.2.). For all simulations, fire 

regime would therefore be the main driver of the carbon 

balance in wetlands during the 21st century. However, the 

present assessment does not take into account the effect environmental changes associated with thermokarst formation 

have on the carbon balance. Permafrost in wetlands of Alaska 

is often ice rich (Jorgenson, Shur, and others, 2008). With 

increasing temperature and permafrost thaw, the soil of icerich wetlands can collapse as a result of ice melting to water 

and draining out of the ecosystem (Jorgenson, Yoshikawa, 

and others, 2008b). These collapses are associated with 

drastic changes in hydrology—transitioning from moist 

permafrost plateau to saturated drainage conditions—and 

important changes in the vegetation composition (from 

permafrost plateau forest to bog or fen, for instance) 

(Jorgenson and Osterkamp, 2005). Thermokarsts can be 

triggered by climate and fire (Myers-Smith and others, 

2008). The transition from moist to saturated conditions may 

considerably affect the local carbon balance, increasing not 

only CH4 production (Turetsky and others, 2008) but also 

soil carbon storage (O’Donnell and others, 2012). However, 

the extent of thermokarst across Alaska is still unknown. 

Therefore, it is difficult to assess the effect of thermokarst 

disturbance on the regional carbon balance of Alaska.
Chapter 7 147

Table 7.11. Results of multiple linear regressions testing the main drivers of carbon dioxide and methane fluxes in wetland 

ecosystems among total annual precipitation, mean annual temperature, annual area burned, and mean annual atmospheric carbon 

dioxide concentration for each future climate simulation for the projection period (2010–2099).

[The six future climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) 

developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the 

Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović 

and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. F, Fisher value; P, probability value. Trend: +, positive; –, negative; 

n.s., trend not significant. Units: mm, milllimeter; °C, degree Celsius; km2, square kilometer; ppm, part per million. CO2, carbon dioxide]

Climate 

scenario Parameter

Total methane emissions Net ecosystem carbon balance Global warming potential

F P Trend F P Trend F P Trend

CGCM3.1

A1B Total annual precipitation (mm) 0.01 0.92 n.s. 0.1 0.75 n.s. 0.1 0.75 n.s.

Mean annual temperature (°C) 5.79 0.02 + 3 0.09 n.s. 0.21 0.65 n.s.

Annual area burned (km2

) 7.04 0.01 + 78.6 <0.01 – 79.63 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

4.42 0.04 + 0.03 0.87 n.s. 0.61 0.44 n.s.

A2 Total annual precipitation (mm) 0.01 0.92 n.s. 0.1 0.75 n.s. 0.1 0.75 n.s.

Mean annual temperature (°C) 5.79 0.02 + 3 0.09 n.s. 0.21 0.65 n.s.

Annual area burned (km2

) 7.04 0.01 + 78.6 <0.01 – 79.63 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

4.42 0.04 + 0.03 0.87 n.s. 0.61 0.44 n.s.

B1 Total annual precipitation (mm) 0 0.98 n.s. 0.58 0.45 n.s. 0.46 0.50 n.s.

Mean annual temperature (°C) 1.49 0.23 n.s. 0.52 0.47 n.s. 0.05 0.82 n.s.

Annual area burned (km2

) 17.87 <0.01 + 109.52 <0.01 – 119.76 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

4.89 0.03 + 0.17 0.68 n.s. 0.15 0.70 n.s.

ECHAM5

A1B Total annual precipitation (mm) 1.78 0.19 n.s. 2.2 0.14 n.s. 3.48 0.07 n.s.

Mean annual temperature (°C) 0.14 0.71 n.s. 1.64 0.20 n.s. 1.04 0.31 n.s.

Annual area burned (km2

) 19.61 <0.01 + 169.32 <0.01 – 117.55 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

11.67 <0.01 + 5.86 0.02 + 1.45 0.23 n.s.

A2 Total annual precipitation (mm) 0.47 0.49 n.s. 2.48 0.12 n.s. 2.16 0.15 n.s.

Mean annual temperature (°C) 9.27 <0.01 + 0.07 0.80 n.s. 2.39 0.13 n.s.

Annual area burned (km2

) 14.22 <0.01 + 136.49 <0.01 – 106.3 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

0.53 0.47 n.s. 1.27 0.26 n.s. 0.26 0.61 n.s.

 B1 Total annual precipitation (mm) 0.97 0.33 n.s. 0.05 0.83 n.s. 0.61 0.44 n.s.

Mean annual temperature (°C) 0.88 0.35 n.s. 5.03 0.03 + 1.76 0.19 n.s.

Annual area burned (km2

) 19.03 <0.01 + 150.02 <0.01 – 168.94 <0.01 +

Mean annual atmospheric CO2

concentration (ppm)

4.33 0.04 + 0.52 0.47 n.s. 3.49 0.07 n.s.
148 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 7-5

Year

2000 2020 2040 2060 2080 2100

18

16

14

12

10

8

6

4

2

0

–2

Net biogenic methane emissions, in grams of

carbon dioxide equivalent per square meter per year Net pyrogenic methane emissions, in grams of carbon dioxide equivalent per square meter per year

B

A

1,000

900

800

700

600

500

400

300

200

100

0

–100

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION

Figure 7.5. (pages 148 and 149). Time series of projected annual A, net biogenic methane emissions; 

B, net pyrogenic methane emissions; C, net ecosystem carbon balance; and D, global warming potential

for the projection period (2010–2099) for wetland ecosystems of Alaska defined by the wetland distribution

map for the two general circulation models, version 3.1-T47 of the Coupled Global Climate Model 

(CGCM3.1) developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the 

European Centre Hamburg Model (ECHAM5) developed by the Max Planck Institute, under three climate 

scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios 

(Nakićenović and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2

 emissions.
Chapter 7 149

Figure 7-5—Continued

Year

2000 2020 2040 2060 2080 2100

100

0

–100

–200

–300

1,200

1,000

800

600

400

200

0

–200

–400

Net ecosystem carbon balance, in grams of

carbon per square meter per year

Global warming potential, in grams of

carbon dioxide equivalent per square meter per year

D

C

(Gain)

(Loss)

(Source)

(Sink)

Climate

scenario

Mean

A1B

A2

B1

CGCM3.1 ECHAM5

EXPLANATION Figure 7.5.—Continued
150 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

7.5.2.3. Spatial Distribution of Changes in 

Carbon Stocks, Biogenic Methane Emissions, 

Net Ecosystem Carbon Balance, and Global 

Warming Potential Across Wetland Alaska

For each LCC region, the climate scenario associated 

with the largest increase in atmospheric CO2 (that is, emissions scenario A2; table 7.12) induced the largest increase in 

vegetation and soil carbon stocks by 2099. Vegetation carbon 

stocks for the ECHAM5 climate simulations were generally 

projected to be higher than the vegetation carbon stocks for the 

CGCM3.1 climate simulations. In contrast, soil carbon stocks 

for the ECHAM5 climate simulations were generally projected 

to be lower than for the CGCM3.1 climate simulations. These 

differences were related to the fact that the ECHAM5 simulations presented larger warming trends and larger fire activity 

than the climate simulations from CGCM3.1 (see chapter 2 for 

detailed comparison). Warmer temperatures from the ECHAM5 

climate simulations induced larger vegetation productivity 

and biomass compared with the results from the CGCM3.1 

simulations. However, the larger litterfall associated with 

larger vegetation productivity was offset by carbon loss 

from the higher heterotrophic respiration and larger carbon 

emissions from wildfire, leading to lower soil carbon stocks 

projected for the ECHAM5 climate simulations compared 

with the CGCM3.1 climate simulations. Vegetation and soil 

carbon stocks were projected to increase from 2010 to 2099 

for all regions and all simulations, except for the B1 scenario 

with CGCM3.1 in the Northwest Boreal LCC South (vegetation carbon stocks only) and the Western Alaska LCC. 

Vegetation productivity was generally projected to be 

higher for the ECHAM5 climate simulations than for the 

CGCM3.1 climate simulations, except for the North Pacific 

LCC (table 7.13). Similarly, NPP was projected to increase 

in response to the scenarios projecting a greater increase in 

atmospheric CO2

 (scenarios A1B and A2 versus scenario B1). 

Statewide, mean annual NPP was projected to range from 

41.0 to 46.7 TgC/yr between 2090 and 2099.

Among all time periods, the North Pacific LCC remained 

the minimal contributor to CH4 emissions owing to the small 

wetland fraction in that region. Similar to the regional total 

CH4 emission, the inter-scenario difference in emission 

magnitude is generally greater than that between the two 

GCMs within a scenario (table 7.13), indicating the dominating effects of climate controls on regional CH4

 dynamics. 

By the end of the 21st century, biogenic and pyrogenic CH4

emissions were projected to range from 36 to 90 TgCO2-eq/yr 

with biogenic CH4

 emissions representing 98 to 99 percent of 

the total emissions.

Projected fire emissions tended to increase in response to 

warming in the LCC regions. These projected increases were 

often accompanied by lower projected heterotrophic respiration (compared with simulations with lower warming trends). 

For instance, in the Northwest Boreal LCC North, whereas 

fire emissions were projected to increase by 95 percent on 

average in the A2 simulations compared with the B1 simulations, heterotrophic respiration was projected to decrease by 

5 percent (table 7.13). This decrease in heterotrophic respiration is likely caused by the loss of organic horizon carbon 

from wildfire.

Carbon stocks in wetland ecosystems of all LCC regions 

were projected to increase within all six future climate simulations. The projected increase in carbon storage for Alaska 

ranged from 3.01 to 5.28 TgC/yr between the CGCM3.1 

climate simulations under scenarios B1 and A2, respectively. 

NECB was generally projected to be highest for the A2 

climate simulations, which had the greatest projected increases 

in atmospheric CO2 and warming. Because the projected fire 

activity for 2090–2099 was lower than during 2000–2009, 

the NECB of the LCC regions was generally projected to be 

higher compared with 2000–2009 (table 7.10), except for 

the Western Alaska LCC under the B1 scenario. However, 

GWP estimates for the future climates indicate that all LCC 

regions would become sources of greenhouse gas radiative 

forcing by the end of the 21st century, except for CGCM3.1 

simulations under scenarios A1B and B1 for the Arctic and 

North Pacific LCCs and the ECHAM5 simulation under 

scenario A2 for the Arctic LCC. 

The spatial distribution of changes in biogenic CH4

emissions indicated that there are large areas of projected 

increases in CH4 emissions across Alaska from 2000 –2009 

to 2090 –2099 (fig. 7.6), especially in central Alaska 

(Northwest Boreal LCC). Projected increases in central 

Alaska ranged from 6.7 gCO2-eq/m2/yr for the B1 climates 

to 16.7 gCO2-eq/m2/yr for the A2 climates, primarily owing 

to relatively large vegetation biomass, leaf area index, and 

productivity, associated with higher CH4

 production.

The average projected increase of NECB from 

2000–2009 to 2090–2099 was largest in the Northwest Boreal 

LCC North (fig. 7.7A). The largest variation across climate 

simulations in changes of the projected NECB was in the 

Western Alaska LCC and the Northwest Boreal LCC South 

(fig. 7.7B). These regions could therefore be major sources 

of uncertainty as to how NECB responds to future climate 

and disturbance regimes.
Chapter 7 151

Table 7.12. Average vegetation and soil carbon stocks for the last decade of the projection period (2090–2099) and mean annual 

change in vegetation and soil carbon stocks between the last decades of the historical period (2000–2009) and the projection 

period (2090–2099) per Landscape Conservation Cooperative region for each of the six climate simulations. 

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) 

developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the 

Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović 

and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. Data may not add to totals shown because of independent rounding. 

TgC, teragram of carbon] 

Climate 

scenario

Landscape Conservation

Cooperative (LCC) region

Vegetation carbon stocks (TgC) Soil carbon stocks (TgC)

Average Mean annual

change Average Mean annualchange

CGCM3.1

A1B Arctic LCC 59 0.16 1,395 1.23

Western Alaska LCC 61 0.04 861 0.28

Northwest Boreal LCC North 546 1.32 2,039 0.70

Northwest Boreal LCC South 83 0.00 823 0.33

North Pacific LCC 23 0.04 129 0.09

Total 771 1.57 5,248 2.64

A2 Arctic LCC 58 0.15 1,365 0.94

Western Alaska LCC 63 0.07 816 0.30

Northwest Boreal LCC North 551 1.38 2,121 1.73

Northwest Boreal LCC South 87 0.04 912 0.52

North Pacific LCC 24 0.05 114 0.08

Total 783 1.70 5,328 3.58

B1 Arctic LCC 55 0.14 1,323 1.25

Western Alaska LCC 56 – 0.00 836 –0.16

Northwest Boreal LCC North 512 0.95 2,003 0.46

Northwest Boreal LCC South 83 – 0.01 888 0.24

North Pacific LCC 23 0.03 116 0.10

Total 728 1.11 5,165 1.90

ECHAM5 

A1B Arctic LCC 62 0.19 1,354 0.80

Western Alaska LCC 66 0.10 787 0.03

Northwest Boreal LCC North 578 1.69 2,074 1.18

Northwest Boreal LCC South 86 0.04 807 0.27

North Pacific LCC 24 0.05 124 0.04

Total 815 2.07 5,146 2.32

A2 Arctic LCC 62 0.20 1,359 0.90

Western Alaska LCC 66 0.10 817 0.10

Northwest Boreal LCC North 579 1.69 2,093 1.38

Northwest Boreal LCC South 92 0.09 847 0.17

North Pacific LCC 23 0.04 111 0.04

Total 822 2.13 5,226 2.58

B1 Arctic LCC 56 0.13 1,357 0.86

Western Alaska LCC 64 0.08 796 – 0.02

Northwest Boreal LCC North 563 1.49 2,049 0.71

Northwest Boreal LCC South 85 0.01 878 0.13

North Pacific LCC 23 0.04 114 0.07

Total 791 1.75 5,193 1.75
152 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 7.13. Average annual vegetation and soil carbon fluxes for the last decade of the projection period (2090–2099) per Landscape 

Conservation Cooperative region for each of the six future climate simulations.

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) 

developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the 

Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović 

and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. Data may not add to totals or compute to net ecosystem carbon balance 

shown because of independent rounding. CO2, carbon dioxide; CO, carbon monoxide; CH4, methane; TgC/yr, teragram of carbon per year; TgCO2-eq/yr, 

teragram of carbon dioxide equivalent per year]

Climate 

scenario

Landscape

Conservation

Cooperative

(LCC) region

Pyrogenic

CO2+CO

emissions

(TgC/yr)

Pyrogenic CH4

emissions

(TgCO2

-eq/yr)

Net primary 

productivity

(TgC/yr)

Biogenic CH4

emissions

(TgCO2

-eq/yr)

Heterotrophic 

respiration

(TgC/yr)

Net ecosystem 

carbon

balance

(TgC/yr)

Global

warming

potential

(TgCO2

-eq/yr)

CGCM3.1

A1B Arctic LCC 0.00 0.00 6.16 2.63 4.68 1.40 –2.77

Western Alaska LCC 0.50 0.05 3.48 1.98 2.59 0.32 0.63

Northwest Boreal LCC 

North

2.65 0.24 28.20 24.36 22.79 2.02 14.55

Northwest Boreal LCC 

South

0.45 0.04 4.36 6.66 3.38 0.33 4.76

North Pacific LCC 0.01 0.00 0.68 0.49 0.52 0.13 – 0.04

Total 3.61 0.34 42.87 36.13 33.96 4.20 17.13

A2 Arctic LCC 2.86 0.30 6.86 4.29 2.77 1.09 0.10

Western Alaska LCC 2.38 0.24 3.68 3.07 0.82 0.38 1.57

Northwest Boreal LCC 

North

3.70 0.35 28.35 44.94 20.17 3.12 28.98

Northwest Boreal LCC 

South

1.57 0.16 4.61 11.45 2.12 0.57 8.27

North Pacific LCC 0.17 0.02 0.79 0.69 0.47 0.13 0.17

Total 10.68 1.06 44.29 64.44 26.36 5.28 39.09

B1 Arctic LCC 0.00 0.00 6.18 2.57 4.71 1.39 –2.82

Western Alaska LCC 1.33 0.13 3.30 1.85 2.07 – 0.16 2.36

Northwest Boreal LCC 

North

2.58 0.24 26.64 24.97 21.89 1.40 17.34

Northwest Boreal LCC 

South

0.96 0.10 4.21 7.15 2.80 0.24 5.59

North Pacific LCC 0.00 0.00 0.66 0.52 0.51 0.13 – 0.01

Total 4.87 0.47 40.99 37.06 31.98 3.01 22.46

ECHAM5

A1B Arctic LCC 3.54 0.37 7.50 6.23 2.77 0.99 2.25

Western Alaska LCC 2.16 0.22 3.82 3.95 1.40 0.13 3.23

Northwest Boreal LCC 

North

3.81 0.35 29.02 61.48 20.49 2.87 44.65

Northwest Boreal LCC 

South

1.84 0.18 4.68 15.72 2.05 0.31 13.05

North Pacific LCC 0.20 0.02 0.78 1.28 0.44 0.09 0.83

Total 11.55 1.15 45.80 88.66 27.15 4.39 64.02

A2 Arctic LCC 3.59 0.37 7.51 3.32 2.72 1.10 – 0.72

Western Alaska LCC 2.20 0.22 3.92 3.84 1.40 0.20 2.91

Northwest Boreal LCC 

North

3.56 0.33 29.57 35.24 21.87 3.07 20.48

Northwest Boreal LCC 

South

1.76 0.18 4.99 10.82 2.63 0.27 8.82

North Pacific LCC 0.10 0.01 0.74 0.86 0.53 0.08 0.49

Total 11.22 1.11 46.73 54.09 29.15 4.71 31.99

B1 Arctic LCC 1.22 0.13 6.30 4.19 3.96 0.99 0.21

Western Alaska LCC 0.78 0.08 3.28 2.64 2.36 0.06 2.22

Northwest Boreal LCC 

North

1.44 0.13 26.98 28.40 22.49 2.20 17.41

Northwest Boreal LCC 

South

0.76 0.08 4.12 7.56 2.99 0.14 6.28

North Pacific LCC 0.09 0.01 0.69 0.55 0.47 0.10 0.11

Total 4.29 0.42 41.36 43.34 32.26 3.49 26.23
N

CGCM3.1 ECHAM5

Climate scenario A1B

Figure 7-6

Climate scenario A2 Climate sce

Annual change in net methane emissions be in grams of carbon dioxide equivalent per 

EXPLANATIO

– 17 – 7 0 3 

0 200 400 KILOMETERS

0 200 400 MILES

Figure 7.6. Spatial distribution of annual change in net methane emissions between the last decades of the historical period (2000–2009) and the projection period (2090–2099) for wetland ecosystems of Alaska for the two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the Max Planck Institute, under three climate scenariosof the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović

and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions.
154 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Figure 7-7

A

B

N

Standard deviation of change in net 

 ecosystem carbon balance, in grams

 of carbon per square meter per year

EXPLANATION

0

>45

No data

20

Mean change in net ecosystem

 carbon balance, in grams of carbon 

 per square meter per year

EXPLANATION

≤–40

>–40 to –30

>–30 to –20

>–20 to –10

>–10 to 0

>0 to 10

>10 to 20

>20 to 30

>30 to 40

>40

No data

Boundary of Landscape 

 Conservation Cooperative

 region

Boundary of Landscape 

 Conservation Cooperative

 region 

0 200 400 KILOMETERS

0 200 400 MILES

7.6. Conclusions

Alaska’s wetland ecosystems were estimated to have 

produced net CH4 emissions averaging 1 TgC/yr from 

1950 through 2009. Biogenic CH4 accounted for most 

(>90 percent) of the total emissions. Estimates of NECB 

during the historical period indicated that all LCC regions in 

Alaska were net carbon sinks except the Northwest Boreal 

LCC North. Carbon loss in the Northwest Boreal LCC 

North offset carbon gain from the other regions. As a result, 

1.3 TgC/yr was lost during the historical period statewide—

in addition to the 1 TgC/yr lost as CH4, 0.3 TgC/yr was lost 

because of changes in NPP and wildfire emission—and the 

carbon loss was about equally distributed in the soil and 

the vegetation. The GWP of wetlands over the historical 

period indicated that wetlands were a significant source of 

greenhouse gas forcing at 33 TgCO2-eq/yr.

The projected total CH4

 emissions across three 

anthropogenic CO2 emissions scenarios and two GCMs were 

estimated to range from 36 to 90 TgCO2-eq/yr by the 2090s, 

which represented an increase of 15 to 182 percent from 

the historical period. Comparatively, projected NPP ranged 

from 41.0 to 46.7 TgC/yr, which represented an increase 

of 10 to 25 percent from the historical period. Projected 

HR ranged from 26.4 to 34.0 TgC/yr, which represented a 

change of –3 to 25 percent from the historical period. Overall, 

by the end of the 21st century, carbon stocks in wetland 

ecosystems of all LCC regions of Alaska were projected to 

continue or start to grow. On average, wetland ecosystems 

would store 4.2 TgC/yr in 2090–2099, ranging from 

3.0 to 5.3 TgC/yr statewide depending on climate simulation. 

Despite the uncertainty around the absolute value of projected 

NECB related to climate forcing, the trend of NECB is 

consistent among all six climate simulations, predicting 

that wetlands in Alaska will be a net carbon sink by 2099. 

However, mainly because of the relatively large 

increase in CH4

 emissions during the projected period, 

GWP is projected to remain positive by the 2090s, ranging 

from 17.1 to 64.0 TgCO2-eq/yr, which represented a 

change of – 49 to 92 percent compared with the historical 

period. Atmospheric CO2

 concentration and mean annual 

temperature were identified to be the primary environmental 

controls of the projected increase in biogenic CH4 emissions. 

The increase in vegetation productivity and subsequent 

increase in substrate for CH4 production was the likely 

cause of the projected increased CH4 production in response 

to climate change. This projected increase was enough to 

offset projected carbon storage in wetlands of Alaska during 

the 21st century. Furthermore, little is known about the 

environmental controls of thermokarst disturbance and its 

effect on local and regional carbon balance. For this reason, 

thermokarst disturbance was not included in the present 

assessment. However, field evidence suggests that changes 

in drainage conditions associated with thermokarst formation may increase CH4

 production and potentially increase 

the release of greenhouse gas in wetlands of Alaska. 

Figure 7.7. Spatial distribution of A, mean change in net 

ecosystem carbon balance between the last decades of the 

historical period (2000–2009) and the projection period (2090–2099) 

among the six climate simulations and B, corresponding standard 

deviation. The six climate simulations used in this report are 

combinations of two general circulation models, version 3.1-T47 

of the Coupled Global Climate Model (CGCM3.1) developed by the 

Canadian Centre for Climate Modelling and Analysis and version 5 

of the European Centre Hamburg Model (ECHAM5) developed by 

the Max Planck Institute, under three climate scenarios of the 

Intergovernmental Panel on Climate Change’s Special Report 

on Emissions Scenarios (Nakićenović and Swart, 2000), B1, A1B, 

and A2, in order of low to high projected CO2

 emissions.
Chapter 7 155

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Chapter 8. Carbon Burial, Transport, and Emission 

from Inland Aquatic Ecosystems in Alaska

By Sarah Stackpoole,1 David Butman,1,2 David Clow,1 Kris Verdin,1 Ben Gaglioti,1,3 and Robert Striegl1

1

U.S. Geological Survey, Denver, Colo.

2

University of Washington, Seattle, Wash.

3

University of Alaska-Fairbanks, Fairbanks, Alaska.

8.1. Highlights

• The total estimated surface area of inland waters in 

Alaska was approximately 60,000 square kilometers 

(km2), which represents nearly 3.5 percent of the 

total land surface area. 

• The total net estimated carbon flux (coastal export 

plus carbon dioxide [CO2] emissions from rivers 

and lakes minus burial in lake sediments) from 

inland waters of Alaska was 41.2 teragrams of 

carbon per year (TgC/yr) (5th and 95th percentiles 

of 30.4 TgC/yr and 59.7 TgC/yr). Total carbon yield 

based on total land surface area was 27.5 grams 

of carbon per square meter per year (gC/m2/yr) 

(5th and 95th percentiles of 20.1 gC/m2/yr and 

39.5 gC/m2/yr).

• Riverine systems of Alaska functioned as carbon 

sources to coastal ecosystems and the atmosphere. 

Dissolved inorganic carbon and total organic 

carbon exports to coastal areas were 12.2 gC/m2/yr 

(5th and 95th percentiles of 10.8 gC/m2/yr and 

16.6 gC/m2/yr) and CO2 emissions to the atmosphere were 11.0 gC/m2

/yr (5th and 95th percentiles 

of 6.0 gC/m2

/yr and 17.4 gC/m2/yr). 

• Lacustrine systems acted as both sources and sinks 

of carbon with 5.5 gC/m2/yr (5th and 95th percentiles of 4.0 gC/m2

/yr and 7.4 gC/m2/yr) emitted as 

CO2 to the atmosphere and –1.2 gC/m2/yr (5th and 

95th percentiles of –0.7 gC/m2/yr and –1.9 gC/m2/yr) 

of organic carbon buried in lake sediments. 

Negative values represent sequestration. 

• There was considerable variability in the estimated 

carbon fluxes of inland waters among the six hydrologic regions in Alaska. This was due to the differences 

in the size and abundance of water bodies, topography, 

climate, land cover, permafrost, and glacier extent 

associated with each region.

8.2. Introduction

Section 712 of the Energy Independence and Security Act 

(EISA) of 2007 required an assessment of carbon fluxes related 

to freshwater aquatic ecosystems, including rivers and lakes, 

which collectively are categorized as inland waters. Carbon 

fluxes associated with aquatic ecosystems (this chapter) are 

assessed separately from those of the terrestrial ecosystems 

(chapters 6 and 7) in this report because of limited empirical 

aquatic data and a lack of a large-scale, spatially explicit 

carbon model that integrates terrestrial and aquatic fluxes. 

Inland aquatic ecosystems are critical components of 

the carbon cycle. Rivers and lakes serve as sites for biogeochemical carbon reactions that result in an exchange of carbon 

dioxide (CO2) among aquatic and terrestrial environments and 

the atmosphere, and rivers act as conduits that deliver carbon 

to the coast (Kling and others, 1992; Striegl and others, 2012; 

Tank, Raymond, and others, 2012; Crawford and others, 2013; 

McClelland and others, 2014; Sepulveda-Jauregui and others, 

2014). Carbon sequestration in lake sediments may offset 

fluxes to oceans and the atmosphere (Naidu and others, 1999; 

Walter Anthony and others, 2014). Collectively, the amount 

of carbon moving through, into, and out of aquatic ecosystems 

makes them a significant component of carbon budgets at 

local, regional, and global scales (Cole and others, 2007; 
160 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Tranvik and others, 2009; Aufdenkampe and others, 2011; 

Butman and Raymond, 2011; Crawford and others, 2013; 

Raymond and others, 2013).

The objective of this chapter is to provide baseline estimates

of aquatic carbon fluxes from inland waters for the six main 

hydrologic regions of Alaska: the Arctic Slope (called North 

Slope in this report), Northwest, Yukon, Southwest, SouthCentral, and Southeast (Seaber and others, 1987) (fig. 8.1). 

These regions were chosen as the reporting units because, 

unlike the Alaska Landscape Conservation Cooperatives 

used in previous chapters of this report, the boundaries of the 

hydrologic regions coincide with natural drainage areas for 

rivers within the State. By leveraging existing hydrologic and 

carbon chemistry datasets, we estimated the following aquatic 

carbon fluxes: (1) lateral transport of dissolved inorganic 

carbon (DIC) and total organic carbon (TOC, composed of 

dissolved and particulate phases) from riverine systems to 

the coast, (2) gaseous carbon emissions of CO2 from riverine 

systems, (3) gaseous carbon emissions of CO2 from lacustrine 

systems, and (4) carbon burial in sediments of lakes. Aquatic 

flux values presented in this chapter were normalized to total 

land surface area to produce yield estimates, and comparisons 

of regional variability among yields are presented. Given the 

vulnerability of carbon stored in the soils and permafrost of 

Alaska to a warming climate and the strong linkages between 

terrestrial and aquatic ecosystems, it is important to provide 

accurate estimates of current carbon fluxes in aquatic environments, so that future climate change effects can be determined. 

Figure 8.1. The six major hydrologic regions of Alaska: the Arctic Slope, called North Slope in this report, Northwest, 

Yukon, Southwest, South-Central, and Southeast from Seaber and others (1987). 

North Slope

Northwest

Yukon

South-Central

Southwest

Figure 8-1 

Base modified from GADM Database of Global Administrative Areas

and U.S. Geological Survey Hydrologic Unit Map digital files

Alaska Albers projection, North American Datum of 1983

Southeast

ARCTIC OCEAN

Beaufort Sea

PACIFIC OCEAN

Chukchi Sea

Bering Sea

Bristol Bay

Gulf of Alaska

N

CANADA

RUSSIA

0 200 400 KILOMETERS

0 200 400 MILES
Chapter 8 161

8.3. Methods and Data

8.3.1. Physiography of Alaska Related to 

Inland Waters

The regional hydrology of Alaska is influenced by the 

State’s varied physiography. The North Slope hydrologic 

region is characterized largely by an arctic coastal plain, which 

is poorly drained and dominated by dune-trough (Jorgenson 

and Shur, 2007) and thermokarst (Frohn and others, 2005) 

lakes. This region is underlain mostly by continuous permafrost (Jorgenson and others, 2008; Schuur and others, 2008). 

The Colville, Kuparuk, and Sagavanirktok Rivers, the largest 

rivers in the region, drain from the northern slope of the 

Brooks Range into the Beaufort Sea (fig. 8.2). The mountains 

in the Northwest hydrologic region have small pockets of 

glaciers and lakes at higher elevations (Wahrhaftig, 1965). 

Scattered lakes occur in lower elevation areas embedded 

in multiple sequences representing the late-Pleistocene 

(Hamilton, 1982) and Holocene glacial advances (Ellis and 

Calkin, 1984). The Kobuk and Noatak Rivers, the major rivers 

of the Northwest region, drain into the Chukchi Sea. 

The intermontane plateau, commonly known as interior 

Alaska, occurs between the Brooks Range and the coastal 

mountain ranges of the Gulf of Alaska, and constitutes the 

Yukon hydrologic region. This region is underlain by discontinuous permafrost (Jorgenson and others, 2008; Schuur and 

Figure 8.2. Coastal receiving waters and mountain ranges in the State of Alaska. Letters indicate major rivers: 

A, Unuk; B, Stikine; C, Taku; D, Alsek; E, Copper; F, Susitna; G, Nushagak; H, Kuskokwim; I, Yukon; J, Kobuk; K, Noatak; 

L, Colville; M, Kuparuk; and N, Sagavanirktok. 

Figure 8-2 

ARCTIC OCEAN

CANADA

RUSSIA

Beaufort Sea

PACIFIC OCEAN

Chukchi Sea

Bering Sea

Bristol Bay

Gulf of Alaska

N

Brooks

Alaska Range

Range

N L

M

K

J

I H F

E

G

D

C

B A

Base modified from GADM Database of Global Administrative Areas,

U.S. Geological Survey Hydrologic Unit Map digital files, and National hydrography dataset

Alaska Albers projection, North American Datum of 1983

Elevation is 200-meter resolution from National Atlas of the United States

Lake Iliamna

0 200 400 KILOMETERS

0 200 400 MILES
162 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

others, 2008) and, outside of the Brooks and Alaska Ranges, 

most of this region has never been glaciated. The Yukon 

River (3,340-kilometer [km] long) flows through this region 

to the Bering Sea and is the largest free-flowing river in the 

world. In the lowland areas near the Yukon River, there are 

areas with high density of lake coverage (Williams, 1962; 

Patton and Miller, 1970; Patton, 1973). The southwestern 

part of the intermontane plateau (the Southwest hydrologic 

region), is underlain by discontinuous, sporadic, and isolated 

permafrost (Jorgenson and others, 2008). Lowland areas 

contain many morainal lakes. Many large lakes occur in this 

region, and Lake Iliamna is the largest of these with a surface 

area of 2,600 square kilometers (km2). The major rivers are 

the Nushagak River, which drains into Bristol Bay, and the 

Kuskokwim River, which drains to Kuskokwim Bay on the 

Bering Sea. 

In the South-Central hydrologic region, the coastal 

mountain ranges contribute meltwater that is the dominant 

flow to the Susitna and Copper Rivers, which empty into the 

Cook Inlet and the Gulf of Alaska, respectively. The area is 

underlain by discontinuous and sporadic permafrost, and the 

mountainous areas contain glaciated regions, where many 

lakes are found in the ice-carved bedrock basins (Wahrhaftig, 

1965). In the Southeast hydrologic region, short meltwater 

streams contribute a large volume of flow to the Gulf of 

Alaska. Larger transboundary rivers, including the Stikine, 

Taku, and Unuk Rivers, originate in the coastal mountain 

forests of Canada and contribute to the large volumes of flow 

in this region. The high-elevation mountains in this region 

have few lakes, but at lower elevations, rock-basin, cirque, 

and proglacial fjord lakes are abundant. 

8.3.2. Estimating River Discharge, Watershed 

Area, and Lake Area

Estimates of mean annual discharge for the entire State 

of Alaska were made by applying previously developed 

regional regression equations (Parks and Madison, 1985), 

whereas the underlying framework, which provided the 

stream network and connectivity, was developed using the 

Elevation Derivatives for National Applications (EDNA) 

database (Verdin, 2000; Kost and others, 2002). The regional 

regression equations take the form of equation 8.1:

Q=(10a)×(DAb)×(Pc) (8.1)

where 

Q is the mean annual streamflow, in 

cubic meters per second (m3/s), 

DA is the drainage area, in km2, 

P is the precipitation, in meters (m), and 

a, b, and c are regression parameters that vary 

by hydrologic regions.

Equation 8.1 was evaluated in a raster environment for 

every location (pixel) within the State of Alaska. Inputs to 

equation 8.1 for mean annual streamflow were derived from 

two main data sources: (1) Drainage area was derived from 

the EDNA layers, with necessary adjustments to account for 

drainage from Canada. The area was represented in the flow 

accumulation values associated with each pixel, converted to 

square kilometers. (2) The precipitation data used to evaluate 

equation 8.1 were historical monthly precipitation data that 

were obtained from the Scenarios Network for Alaska and 

Arctic Planning (SNAP, 2014). The original climate data were 

derived from the University of East Anglia Climatic Research 

Unit gridded high-resolution (0.5°×0.5°) global climate dataset 

(1901 to 2009) (CRU TS v. 3.10.01; New and others, 1999, 

2000; Harris and others, 2014). The SNAP team extracted data 

for the State of Alaska and western Canada from the global 

dataset, and these files were bias corrected and downscaled 

via the delta method using the 1961–1990 Parameter-elevation 

Relationships on Independent Slopes Model (PRISM; PRISM 

Climate Group, 2004). These data were averaged and processed 

into pixel-specific basin averages using the continuous parameterization technique of Verdin and Worstell (2008). The discharge

estimates were developed for the entire Alaska land mass and 

transferred onto the EDNA-derived stream network.

Alaska is not a self-contained drainage system, as portions 

of some watersheds lie within Canada. The EDNA database 

does not extend into Canada; therefore, the Canadian portion 

of the drainage basin and the associated flow estimates were 

developed using the National Aeronautics and Space Administration Shuttle Radar Topographic Mission data (NASA-SRTM; 

Jarvis and others, 2008). Discharge derived from these basins 

was calculated and propagated into the Canadian flow network 

through the EDNA-derived drainage network in Alaska. This 

report accounts for the discharge coming from Canada for 

basins with a greater-than-10-km2 drainage threshold.

The National Hydrography Dataset (NHD, at a scale 

of 1:63,360; U.S. Geological Survey, 2014a) was used to 

estimate the number and area of lakes in Alaska. The NHD 

data were compiled to meet the National Map Accuracy 

Standards. Within the NHD water body geodatabase attributes, 

vectors classified as reservoirs, lakes, or ponds were extracted 

to represent surface water extents in each of the study areas. 

Discharge, watershed area, and lake area were summarized by 4-digit hydrologic regions using the U.S. Geological 

Survey (USGS) Hydrologic Unit Codes (HUCs; Seaber 

and others, 1987). There are six 4-digit hydrologic regions 

covering the State of Alaska (fig. 8.1).

8.3.3. Coastal Export of Carbon From 

Riverine Systems

Alkalinity, temperature, and pH measurements for rivers 

in Alaska were obtained from the USGS’s National Water 

Information Service (NWIS; U.S. Geological Survey, 2014b), 
Chapter 8 163

the U.S. Environmental Protection Agency’s Storage and 

Retrieval System (STORET; U.S. Environmental Protection Agency, 2014), and the Pebble Partnership’s baseline 

water-quality datasets for the Bristol Bay and Cook Inlet 

drainages (Pebble Partnership, 2011a,b). We converted the 

various alkalinity measurements (alkalinity, bicarbonate, 

carbonate, and acid neutralizing capacity) to “alkalinity as 

calcium carbonate.” Organic acids are a major contributor 

to noncarbonate alkalinity at low pH levels (Driscoll and 

others, 1989). We did not have enough organic carbon data to 

pair with our alkalinity data to directly estimate organic acid 

contribution to alkalinity, but we did remove alkalinity values 

that had an associated pH of less than 5.6 (Driscoll and others, 

1989). Only sites with paired daily alkalinity, temperature, 

and pH data for 1970 to 2013 were used in this assessment. 

These 7,563 data points were representative of 1,301 individual siteson rivers throughout Alaska (fig. 8.3A). 

Estimated dissolved DIC, composed of CO2, bicarbonate, 

and carbonate, was computed using the speciation model of 

PHREEQC (Parkhurst and Appelo, 1999; Charlton and others, 

2014). Required input variables were water temperature, pH, 

and alkalinity concentrations. We removed any DIC estimates 

that fell outside of the interval (Q1–IQR, Q3+IQR), where 

IQR is the interquartile range and Q1 and Q3 are the first and 

third quartiles, respectively. This resulted in the removal of 

18 data points, so that our final DIC concentration dataset had 

7,545 data points from 1,296 sites. 

Dissolved organic carbon (DOC) and total organic 

carbon (TOC) values for rivers in Alaska were obtained 

from NWIS (U.S. Geological Survey, 2014b), the Pebble 

Partnership (2011a,b), and from various existing datasets in 

the Southwest (Daniel E. Schindler and Gordon Holtgrieve, 

University of Washington, unpub. data, October 23, 2013), 

Northwest (Larouche and others, 2012), and North Slope 

regions (Josh Koch, USGS, Anchorage, Alaska, unpub. data, 

November 5, 2013). We compiled a total of 1,679 DOC 

samples and 623 TOC samples. To use the more extensive 

DOC concentration dataset in our TOC flux estimates, we built 

a simple-linear-regression (SLR) model between paired TOC 

and DOC collected at the same site on the same day. We used 

the results of the SLR (R2=0.88, y=0.25+1.00481x, where 

R2 is the coefficient of determination, x is the DOC concentration, and y is the TOC concentration) to predict TOC from 

the extensive DOC dataset. This process resulted in a TOC 

dataset with 2,574 samples from 519 sites. We removed any 

samples that fell outside of the interquartile range as described 

above. This resulted in the removal of 14 data points, so that 

our final TOC concentration dataset had 2,560 data points 

from 515  sites.

Carbon fluxes for coastal watersheds in Alaska were 

estimated in three different ways, depending on the amount 

of data available for each particular watershed. For coastal 

watersheds that contained USGS gaging stations with robust 

DIC and TOC concentration datasets and 3 years or more 

of daily discharge, carbon fluxes were estimated using the 

USGS Load Estimator program (LOADEST; Runkel and 

others, 2004). LOADEST is a multiple-regression adjusted 

maximum likelihood estimation model that uses measured 

DIC or TOC concentration values to calibrate a regression 

between constituent load, streamflow, seasonality, and time. 

The model requires at least 13 paired concentration and daily 

discharge values. Daily streamflow values were downloaded 

from NWIS. The LOADEST model uses the Akaike (1981) 

Information Criterion (AIC) to select the best combination 

of coefficients at each streamgage station from the full model, 

which is based on the equation:

ln Flux=a0+a1lnQ+a2lnQ2+a3sin(2πdtime)

+a4cos(2πdtime)+a5dtime+a6dtime2+ε (8.2)

where

ln Flux is the natural log of the constituent flux, 

in kilograms per day (kg/d),

Q is the discharge, in m3/s,

dtime is time, in decimal years,

a0, a1,...a6 are regression coefficients, and

ε is an independent and normally 

distributed error.

The input data were log-transformed to avoid bias and 

centered to avoid multicollinearity. The model produces a flux 

estimate (in kg/d) with standard error values, which were used 

to create a distribution of LOADEST modeled fluxes for these 

watersheds. 

For coastal watersheds that had 3 years or more of daily 

discharge but did not have sufficient concentration records for 

use in the LOADEST model, we multiplied carbon concentration by discharge to obtain a flux using the following equation:

Flux =C×Q (8.3)

where

Flux is the constituent flux, in kg/d,

C is carbon concentration of surface water

at the site, in milligrams per liter

(mg/L), and

Q is discharge, in m3/s.

A specified range of discharge and concentration values were 

used in equation 8.3 for each watershed. The distribution 

of empirical streamflow for each watershed was based on 

the standard deviation (s.d.) of recorded flows for that site. 

The distribution of carbon concentrations was based on all 

available carbon concentrations within that regional HUC. 

If the coastal watershed lay within the boundaries of the 

Southeast region, then the concentration distribution for 

that watershed was drawn from all concentration values 

within that region. In the Southeast region, 226 DIC values 

and 45 TOC measurements were used to create the concentration distributions.
164 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

0 200 400 KILOMETERS

0 200 400 MILES

N

A. Riverine lateral carbon fluxes to coastal waters

B. Riverine carbon dioxide emissions

Figure 8-3

Carbon yield, in grams of carbon 

 per square meter per year

Carbon sample used for analysis

Total organic carbon

Dissolved inorganic carbon

Partial pressure of carbon dioxide,

 in microatmospheres

0 to 400

>400 to 1,000

>1,000 to 2,000

>2,000 to 3,000

>3,000 to 4,000

>4000 to 10,000

>10,000

EXPLANATION

0 to 4

>4 to 8.7

>8.7 to 9.0

>9.0 to 10.5

>10.5 to 16.9

>16.9 to 36.7

Carbon yield, in grams of carbon 

 per square meter per year

0 to 4

>4 to 5.2

>5.2 to 8.0

>8.0 to 11.2

>11.2 to 21.2

>21.2 to 36.0

Figure 8.3. Estimated relative magnitude of carbon yields. A, Coastal carbon transport by rivers. B, Carbon dioxide (CO2) 

emissions from rivers. C, CO2

 emissions from lakes. D, Carbon burial rates in lakes. B and C also indicate the estimated relative 

magnitude of the partial pressure of CO2

 (pCO2) concentrations at the sampling locations.
Chapter 8 165

Figure 8-3—Continued

Carbon yield, in grams of carbon 

 per square meter per year

0 to 1.7

>1.7 to 3.8

>3.8 to 5.6

>5.6 to 9.3

>9.3 to 14.3

>14.3 to 21.6

>21.6 to 30.2

EXPLANATION

0 to 0.8

>0.8 to 2.1

>2.1 to 3.1

>3.1 to 3.2

>3.2 to 3.4

>3.4 to 19.8

Carbon yield, in grams of carbon 

 per square meter per year

0 to 0.34

>0.34 to 0.46

>0.46 to 0.48

>0.48 to 0.87

>0.87 to 1.61

>1.61 to 3.04

N

C. Lacustrine carbon dioxide emissions

D. Lacustrine organic carbon burial

Carbon percentage in sediments

 by dry weight

Partial pressure of carbon dioxide,

 in microatmospheres

0 to 400

>400 to 1,000

>1,000 to 2,000

>2,000 to 3,000

>3,000 to 4,000

>4000 to 10,000

>10,000

0 200 400 KILOMETERS

0 200 400 MILES

Figure 8.3. Estimated relative magnitude of carbon yields. A, Coastal carbon transport by rivers. B, Carbon dioxide (CO2) 

emissions from rivers. C, CO2

 emissions from lakes. D, Carbon burial rates in lakes. B and C also indicate the estimated relative 

magnitude of the partial pressure of CO2

 (pCO2) concentrations at the sampling locations.—Continued
166 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

If empirical carbon concentration and streamflow data 

were not available for a watershed, the distribution of carbon 

concentrations was established as described in the previous 

paragraph. To generate a distribution of discharge values 

for each watershed, we developed prediction intervals of 

discharge estimated using EDNA (see section 8.3.2) using the 

predict function in R (R Development Core Team, 2008). The 

predict function was based on the output from a simple linear 

model that describes estimated EDNA discharge values by 

empirical NWIS discharge values. 

We used the distribution of LOADEST fluxes, concentrations, and discharge values as described in the previous 

three paragraphs in a Monte Carlo approach with 10,000 iterations to estimate total flux estimates for the approximately 

6,000 coastal watersheds draining Alaska. The DIC and TOC 

fluxes were summed for each watershed, and the fluxes from 

all the watersheds within a region were summed, so that 

each of the six regions had 10,000 possible total carbon flux 

estimates. The median flux with 5th and 95th percentiles from 

that range of values are presented in table 8.1. Yields were 

calculated by dividing the total carbon flux by HUC area. 

The percent of the total area of the State of Alaska that was 

represented by LOADEST modeled fluxes (equation 8.2) 

was 75 percent for DIC and 65 percent for TOC, and the 

carbon fluxes for the remainder of the land surface area 

were estimated using equation 8.3. 

The contribution of the Canadian drainage area to the 

total Alaskan coastal carbon flux estimates is also presented 

in table 8.1. The Yukon River, along with several smaller 

rivers in the Southeast and South-Central regions that drain 

into the Gulf of Alaska, has headwaters in Canada. We used 

equation 8.2 to estimate Yukon River carbon sourced in 

Canada with discharge and DIC and TOC concentration data 

(1990–2005) from a streamgage just inside the border of

Alaska, the Yukon River at Eagle (NWIS station ID=15356000).

We used equation 8.3 to estimate carbon fluxes from Canada 

for rivers in the Southeast and South-Central regions, 

including the Alsek, Stikine, and Taku Rivers, with EDNAderived discharge values and carbon concentration values 

from Environment and Climate Change Canada (2014).

Table 8.1. Estimated coastal carbon exports and yields from riverine systems in Alaska.

[Sites are locations for which dissolved inorganic carbon (DIC) or total organic carbon (TOC) concentration data were available. Values are estimates of the 

median with 5th and 95th percentile estimates in parentheses. Total exports and total yields were calculated by summing dissolved inorganic carbon (DIC) 

and total organic carbon (TOC). km2, square kilometer; DIC, dissolved inorganic carbon; TOC, total organic carbon; km3/yr, cubic kilometer per year; TgC/yr, 

teragram of carbon per year; gC/m2/yr, gram of carbon per square meter per year]

Hydrologic 

region

Area of

hydrologic 

region 

(km2

)

Number of sites with

concentration data

Estimated total

discharge

(km3

/yr)

Estimated total

carbon export

(TgC/yr)

Estimated total 

carbon yield

(gC/m2/yr)

Estimated 

carbon export 

as DIC 

DIC TOC (percent)

Southeast 104,000 226 45 356 (297, 569) 3.8 (3.4, 5.3)a 36.7 (32.4, 50.7) 67.2

South-Central 207,000 454 185 220 (156, 514) 3.5 (3.1, 5.0)b 17.0 (15.1, 24.0) 73.5

Southwest 291,000 169 130 174 (142, 260) 2.7 (2.2, 3.4) 9.2 (7.7, 11.6) 78.4

Yukon 526,000 264 73 144 (35, 685) 5.5 (5.4, 5.7)c 10.5 (10.2, 10.8) 67.1

Northwest 178,000 76 31 69 (52, 176) 1.5 (1.3, 3.4) 8.7 (7.2, 19.0) 76.7

North Slope 204,000 107 51 53 (44, 132) 1.3 (0.9, 2.3) 6.1 (4.3, 11.5) 62.3

 Total or mean 1,510,000 1,296 515 1,015 (725, 2,237) 18.3 (16.3, 25.0) 12.2 (10.8, 16.6) 69.8

a

Includes estimated sum lateral flux from transboundary Canadian rivers into the Southeast region of 0.13 (0.1, 0.17) TgC/yr. 

b

Includes estimated sum lateral flux from transboundary Canadian rivers into the South-Central region of 2.7 (2.1, 3.4) TgC/yr.

c

Includes estimated sum lateral flux from transboundary Canadian rivers into the Yukon region of 1.7 (1.6, 1.8) TgC/yr.
Chapter 8 167

8.3.4. Carbon Dioxide Flux From Riverine Systems

Three variables were required to estimate the CO2 gas 

fluxes from aquatic systems: (1) the concentration of dissolved 

CO2, (2) the gas transfer velocity, and (3) the surface area 

of the water body. The CO2 emission from rivers (and lakes, 

see section 8.3.5) across Alaska was modeled according to 

established methods (Butman and Raymond, 2011; Zhu and 

Reed, 2012, 2014) and as outlined in equation 8.4:

CO2 flux=(CO2-water–CO2-air)×kCO2×SA (8.4)

where 

CO2 flux is the total net emission of CO2 from 

rivers of Alaska, in teragrams of 

carbon per year (TgC/yr), 

CO2-water is the CO2 concentration of the water, 

in moles per liter (mol/L), 

CO2-air is the CO2 concentration in the

atmosphere, in mol/L,

kCO2 is the river or lake gas transfer velocity 

of CO2 across the air-water interface, 

in meters per day (m/d), and 

SA is the river or lake surface area, in 

square meters (m2).

The total flux was estimated by summing all of the 

mean annual fluxes for a stream order (Strahler, 1952) within 

a hydrologic region.

The median dissolved CO2 concentrations (CO2-water) were 

estimated from stream and river alkalinity data available from 

the same three data sources listed in section 8.3.3. All alkalinity measurements were converted to dissolved CO2

 using 

the CO2SYS program (Van Heuven and others, 2011). Daily 

measurements of pH paired with temperature and alkalinity 

measurements from the late 1960s through 2013 were used 

to estimate dissolved CO2. Similar to the methods described 

in section 8.3.3, alkalinity data with an associated pH of less 

than 5.6 were removed to reduce the effect of organic acids on 

alkalinity (Driscoll and others, 1989). A total of 9,466 daily 

chemical measurements from 1,469 sampling locations 

was used (fig. 8.3B). For equation 8.4, current atmospheric 

partial pressure of carbon dioxide (pCO2) was assumed to 

be 390 microatmospheres (μatm) for all of the hydrologic 

regions across Alaska. 

The gas transfer velocity (kCO2) was modeled based on 

a meta-analysis of measurements of gas exchange and the gas 

transfer velocity made by direct tracer injections across smallto mid-sized river systems in the United States (Melching and 

Flores, 1999; Raymond and others, 2012). The variation in gas 

transfer velocities within rivers was a function of turbulence 

at the air-water interface (Zappa and others, 2007). Physical 

parameters of stream slope and water velocity were used to 

predict gas transfer velocity:

kCO2–river= S×V×2,841.6+2.03 (8.5)

where

kCO2–river is the gas transfer velocity of CO2 (in m/d) 

normalized to the Schmidt number 

(a dimensionless ratio that approximates 

the relationship between the viscosity and 

gas diffusivity across a boundary layer) 

for CO2 at ambient water temperature 

and standard atmospheric pressure 

(Wanninkhof, 1992; Raymond and 

others, 2012),

S is the average slope of a stream reach, and 

V is the average velocity of water, in m/d. 

A total of 563 independent gas tracer injection measurements was included in the development of this equation. 

A total of 32,672 discharge measurements was used 

to derive hydraulic geometry coefficients specific to each 

hydrologic region. Hydraulic geometry of stream reaches 

showed remarkable consistency to approximate channel 

width within and across watersheds (Leopold and Maddock, 

1953; Park, 1977). All scaling relationships to estimate width 

derived by the calculation of hydraulic geometry coefficients 

were statistically significant ( p-value < 0.001) with coefficients ranging from 0.46 in the Southeast region to 0.57 in 

the Southwest region. R2 values for the nonlinear regression 

between width and stream discharge ranged from 0.90 to 0.96. 

Modeled average annual discharge (see section 8.3.2) was 

used to estimate average channel width and velocity using the 

hydraulic geometry coefficients specific to each hydrologic 

region. Both stream velocity and slope estimates were 

normalized by total length and aggregated to hydrologic 

regions by stream order. Average slope and velocity were 

then calculated by stream order to estimate the gas transfer 

velocity of CO2 according to equation 8.5. 

River surface area (SA) was calculated based on the same 

hydraulic geometry coefficients discussed above for width 

within each hydrologic region. Average discharge was used to 

then calculate an average width for each stream order within a 

hydrologic region. The total stream length was then calculated 

for each stream order within a hydrologic region. Stream and 

river surface area was then calculated as the product of the 

average width and total length of streams by stream order. 
168 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Error propagation and uncertainty analyses were 

performed for each component of equation 8.4. To estimate 

error, we utilized a bootstrapping technique as outlined in 

Efron and Tibshirani (1994) and Butman and Raymond 

(2011). Bootstrap with replacement was run for 1,000 iterations to calculate 95 percent confidence intervals for the pCO2

values for each stream order within a hydrologic region. 

Similarly, bootstrap with replacement was used to estimate 

confidence intervals associated with the hydraulic geometry 

coefficients derived from the measurements of stream width 

and velocity, which were subsequently used to estimate both 

the river surface area and gas transfer velocity. Overall bias 

associated with estimates of pCO2 remained low and had a 

negligible effect on the error associated with the use of the 

median value for each stream order. Similarly, the effect of 

bootstrapping the hydraulic geometry coefficients produced 

minimal bias. 

A Monte Carlo simulation was performed for each 

stream order estimate of the total flux (in TgC/yr) from river 

surfaces (equation 8.4). The confidences derived from the 

bootstrapping procedure were used to bound the Monte Carlo 

simulation for each parameter of equation 8.4. The total flux 

calculation was replicated 1,000 times. Gas transfer velocities 

were not permitted to exceed 30 m/d. This selection criterion 

only affected first through third stream orders in the Southeast 

and South-Central regions. This constraint was placed because 

of the lack of measurements above that value presented in 

Raymond and others (2012), and there is a lack of evidence 

in the literature that small streams can exceed this threshold.

All estimates for the total carbon flux within each 

hydrologic unit were presented with the 5th and 95th percentiles derived from the Monte Carlo simulation. This approach 

is considered conservative as it allowed for the same probability for all combinations of each parameter in the total flux 

equation to be selected for each stream order and may have 

overestimated the error associated with the river CO2 emission. In general, this conservative approach biased the range 

of estimates high owing to a slight skew in the distribution of 

pCO2 values within a stream order and hydrologic region. But 

by using median values and bootstrapped 5th and 95th percentiles, this skew is minimized. All estimates derived from the 

Monte Carlo simulation were adjusted to account for monthly 

temperatures below freezing under the assumption that river 

CO2 emission did not occur when monthly temperatures 

averaged below 0 degrees Celsius (°C). Average monthly 

temperatures were derived from the WorldClim 1-km monthly 

averages spanning from 1950 to 2000 (Hijmans and others, 

2004). Each stream segment was attributed with a proportion 

of the year as frozen. This adjustment reduced the efflux for 

the Southeast region by 32 percent, South-Central region by 

50 percent, Southwest region by 54 percent, Yukon region by 

59 percent, Northwest region by 62 percent, and North Slope 

region by 69 percent. 

8.3.5. Carbon Dioxide Flux From Lacustrine Systems

Water-chemistry data used to estimate lake CO2 emissions

from Alaska were obtained from NWIS (U.S. Geological 

Survey, 2014b), STORET (U.S. Environmental Protection Agency, 2014), the Pebble Partnership (2011a,b), the 

U.S. National Park Service Shallow Lake Monitoring Program 

(Larsen and Kristenson, 2012), and the dataset from studies 

of lakes at Yukon Flats, Alaska (Halm and Guldager, 2013; 

Halm and Griffith, 2014). A total of 891 locations and 

1,329 measurements with daily lake chemistry data suitable 

for CO2 emission estimates collected between 1949 and 2011 

(fig. 8.3C ) were used in this study.

The estimated CO2 emission from lakes was also calculated

using the general equation 8.4. Dissolved CO2 values were 

also calculated using the CO2SYS program (Van Heuven and 

others, 2011). We did not have enough organic carbon data to 

pair with our alkalinity data to directly measure organic acid 

contribution to alkalinity. In general, noncarbonate alkalinity 

will introduce a bias resulting in higher alkalinity generating 

a higher calculated pCO2

; however, a recent estimate of 

bias for the evasion of CO2 from lakes in the conterminous 

United States suggests that this will alter flux measurements 

by no more than 2 to 5 percent (McDonald and others, 2013). 

All lakes within the high-resolution NHD were attributed 

with average summer wind speed (U10), median pCO2, and the 

annual ice-free fraction needed to calculate the vertical efflux 

of carbon. This method differs from those of McDonald and 

others (2013) in that, for Alaska, a single flux is calculated for 

each specific lake and then aggregated into regional estimates. 

McDonald others (2013) summed lake area only by region, 

then used average ecoregional wind speed and carbon concentrations to calculate fluxes. 

The gas transfer velocity for lakes (kCO2–lake ) was 

calculated as a function of wind speed (Cole and Caraco, 

1998; Cole and others, 2007):

kCO2–lake=2.07+0.215×U10

1.7 (8.6)

where 

kCO2–lake is a gas transfer coefficient normalized 

to ambient water temperature and 

atmospheric pressure (STP), in m/d, and

U10 is the surface wind speed above the lake 

surface, in m/d. 

The estimated mean summer (June to September) wind 

speeds for each hydrologic region were determined from the 

National Aeronautics and Space Administration (NASA; 2014) 

surface meteorology and solar energy data. Each lake within 

the NHD was assigned a CO2-specific transfer coefficient for 

lake surface efflux.

Many of the parameters involved in these calculations 

violated normality assumptions; therefore, nonparametric 
Chapter 8 169

confidence intervals (95 percent) were determined on 

10,000 ordinary bootstrap replicates. Bootstrapped 

nonparametric confidence intervals for median pCO2 values 

were calculated in R using the bias corrected and accelerated 

methods of Efron and Tibshirani (1994) in each distribution 

of values by hydrologic region. Median values were chosen 

owing to the highly skewed distributions of concentration 

data. Bootstrapped confidence intervals for water temperature 

were similarly calculated. In general, water temperature was 

normally distributed about the mean value allowing normal 

confidence intervals to be used. Each lake within a region was 

assumed to have a range of potential dissolved CO2 within 

the calculated nonparametric confidence intervals. The total 

flux by region was calculated holding the average wind speed 

for a lake constant. In the case of the total flux from Alaska, 

the 5th and 95th percentiles were assumed to be additive 

(uncertainty was not propagated) because potential errors in 

the regional estimates were likely to be systematic. Similar 

to the methods for river CO2 emission, each lake within a 

region was attributed with a scalar representing the proportion 

of the year below freezing, when flux was assumed to equal 

zero. This scalar was applied to each lake and had the same 

reducing effect as for river CO2 emission. This approach was 

conservative because CO2 produced and stored under ice is 

commonly released rapidly when the ice melts (Anderson and 

others, 1999). Final estimates represent total fluxes for each 

hydrologic region, with 5th and 95th percentiles. 

8.3.6. Carbon Burial in Lacustrine Systems

Carbon burial in lakes, which is a function of sedimentation rates, bulk density of the sediment, sediment carbon 

concentrations, and lake area (Mulholland and Elwood, 1982; 

Dean and Gorham, 1998), was estimated as:

Cburial = SAWB× SedRt ×Cconc×10 –12 (8.7)

where

Cburial is the carbon burial rate, in TgC/yr,

SAWB is the surface area of the water body, 

in m2,

SedRt is the sedimentation rate, in grams 

of carbon per square meter per year 

(gC/m2/yr), 

Cconc is the concentration of carbon in sediments 

(percent carbon by dry weight of sediment 

divided by 100), and 

10 –12 is a conversion factor to convert from grams 

to teragrams. 

Water body surface areas were derived from the highresolution NHD and included lakes, ponds, and reservoirs. 

We did not differentiate between lakes and reservoirs in this 

report. Data on sedimentation rates and lake sediment carbon 

concentrations were derived from previously collected and 

dated sediment cores from 70 Alaskan lakes; these data were 

obtained from published and unpublished sources (table 8.2; 

fig. 8.3D). The cores were dated using 210Pb and 137Cs isotope 

techniques, as well as 14C dating of terrestrial plants deposited 

in near-surface layers. Sedimentation results pertain to the 

most recent 100 years of sediment accumulation, which 

typically corresponds to the top 5 to 15 centimeters (cm) of 

sediment, depending on sedimentation rates. For 14C-dated 

cores, sedimentation rates were interpolated based on linear 

sedimentation rates from the most recent 14C date to the top of 

the core, which we assumed represents the year of collection. 

Data from the cores were used to build geostatistical 

models for sedimentation rates and sediment carbon concentrations using stepwise multiple linear regression (MLR); 

these predictive equations were applied statewide. Potential 

explanatory variables included lake area and lake elevation 

from the NHD, mean annual precipitation and temperature 

from SNAP, vegetation characteristics from the National 

Land Cover Database (NLCD) 2006 (Fry and others, 2011), 

permafrost and bog extent from chapters 3 and 7 of this report, 

and soil organic carbon in the top 152 cm of soil from the state 

soil geographic database (STATSGO; U.S. Department of 

Agriculture, Natural Resource Conservation Service, 2014). 

Each of these parameters was calculated for the 12-digit HUC 

that intersected each water body. In the MLR, the independent 

variable that explained the most variance in the dependent 

variable entered the model first. The variances explained by 

the remaining independent variables were recalculated, and the 

variable that explained the next greatest amount of variance 

entered the model next. This iterative process was repeated 

until the minimum AIC was obtained (Akaike, 1981). Multicollinearity among explanatory variables was evaluated using 

the variance inflation factor (1/(1–R2); Hair and others, 2006) 

with a threshold for exclusion of 2.0. Uncertainty in model 

predications was evaluated by using a Monte Carlo approach 

in which the coefficients for model parameters were allowed to 

vary randomly within their 5th and 95th percentiles; this procedure was repeated for 1,000 iterations, yielding 1,000 estimates 

of sedimentation rate and sediment carbon concentration for 

each water body in the Alaska NHD. Results are summarized 

for each of the six hydrologic regions in Alaska.

8.3.7. Limitations and Uncertainties

Methods used to calculate river dissolved carbon fluxes 

to the coast had some limitations. For 25 to 35 percent of 

the land surface area, carbon flux was calculated simply as 

flow multiplied by concentration (equation 8.3), which does 

not allow for variability in seasonal discharge of carbon 

concentrations (Runkel and others, 2004). Additionally, the 

concentration data used in the coastal flux estimates had 

relatively good spatial coverage, and watersheds with DIC 

or TOC concentrations represented over 50 percent of the 
170 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

Table 8.2. Site information and data sources used to develop multiple-linear-regression models for estimation of carbon burial in 

Alaskan water bodies.—Continued

[Data included sediment carbon concentrations, sediment accumulation rates, and dry bulk density. Coordinates are in decimal degrees, NAD 83; HUC; 

hydrologic unit code; 1901, Southeast hydrologic region; 1902; South-Central hydrologic region; 1903, Southwest hydrologic region; 1904, Yukon hydrologic region; 1905, Northwest hydrologic region; 1906, North Slope hydrologic region]

4-digit 

HUC Site name Latitude Longitude References and data sources

1901 Chilkat Lake 59.33 –135.90 Barto (2004)

1902 Long Lake 62.55 –143.40 N. Bigelow, University of Alaska-Fairbanks, unpub. data (2014) 

1902 Hickerson Lake 59.93 –152.92 Cohn (2009)

1902 Frazer Lake 57.26 –154.14 B. Finney, Idaho State University, unpub. data (2014)

1902 Coghill Lake 61.09 –147.82 B. Finney, Idaho State University, unpub. data (2014)

1902 Phalarope Lake 57.18 –154.41 Jones, Peteet, and others (2009)

1902 Rock Lake 60.65 –150.64 Lynch and others (2002)

1902 Arrow Lake 60.75 –150.49 Lynch and others (2002)

1902 Portage Lake 60.72 –150.53 Lynch and others (2002); Hinzman and others (2005)

1902 Greyling Lake 61.38 –145.74 McKay and Kaufman (2009)

1902 Hallet Lake 61.49 –146.24 McKay and Kaufman (2009)

1902 Skilak Lake 60.42 –150.37 Naidu and others (1999)

1902 Packer Lake 60.47 –151.92 Rogers and others (2013)

1902 Swampbuggy Lake 63.05 –147.42 Rohr (2001)

1902 Nutella Lake 63.20 –147.63 Rohr (2001)

1902 Canyon Lake 62.70 –145.57 Shimer (2009)

1902 Kepler Lake 61.55 –149.21 Yu and others (2008)

1902 Hundred Mile Lake 61.81 –147.85 Yu and others (2008)

1903 Little Swift Lake 60.22 –159.77 Axford and Kaufman (2004)

1903 Ongoke Lake 59.27 –159.43 Chipman and others (2009)

1903 Kontrashabuna Lake 60.19 –154.03 Cohn (2009)

1903 Portage Lake 60.50 –153.86 Cohn (2009)

1903 Lower Ugashik Lake 57.66 –156.73 B. Finney, Idaho State University, unpub. data (2014)

1903 Lone Spruce Pond 60.01 –159.15 Kaufman and others (2012)

1903 Aleknagik Lake 59.36 –158.89 Rogers and others (2013)

1903 Amanka Lake 59.10 –159.22 Rogers and others (2013)

1903 Bear Lake 56.02 –160.25 Rogers and others (2013)

1903 Beverly Lake 59.68 –158.77 Rogers and others (2013)

1903 Gechiak Lake 59.39 –160.37 Rogers and others (2013)

1903 Goodnews Lake 59.49 –160.56 Rogers and others (2013)

1903 Grant Lake 59.81 –158.53 Rogers and others (2013)

1903 High Lake 59.94 –159.50 Rogers and others (2013)

1903 Kagati Lake 59.87 –160.06 Rogers and others (2013)

1903 Little Togiak Lake 59.58 –159.15 Rogers and others (2013)

1903 Nagugun Lake 59.69 –160.39 Rogers and others (2013)

1903 Naknek Lake 58.69 –156.28 Rogers and others (2013)

1903 Nerka Lake 59.58 –158.83 Rogers and others (2013)

1903 Nunavaugaluk Lake 59.20 –158.91 Rogers and others (2013)

1903 Tazmina Lake 60.05 –154.15 Rogers and others (2013)

1903 Togiak Lake 59.61 –159.62 Rogers and others (2013)

1903 Ualik Lake 59.09 –159.44 Rogers and others (2013)

1903 Upper Togiak Lake 59.83 –159.48 Rogers and others (2013)
Chapter 8 171

Table 8.2. Site information and data sources used to develop multiple-linear-regression models for estimation of carbon burial in 

Alaskan water bodies.—Continued

[Data included sediment carbon concentrations, sediment accumulation rates, and dry bulk density. Coordinates are in decimal degrees, NAD 83; HUC; 

hydrologic unit code; 1901, Southeast hydrologic region; 1902; South-Central hydrologic region; 1903, Southwest hydrologic region; 1904, Yukon hydrologic region; 1905, Northwest hydrologic region; 1906, North Slope hydrologic region]

4-digit 

HUC Site name Latitude Longitude References and data sources

1904 Birch Lake 64.31 –146.66 Abbott and others (2000)

1904 Tangled Up Lake 67.67 –149.72 Anderson and others (2001)

1904 Track Lake 66.86 –145.17 D. Anderson, U.S. Geological Survey, unpub. data (2014)

1904 Greenpepper Lake 66.09 –146.73 D. Anderson, U.S. Geological Survey, unpub. data (2014)

1904 Twelve-Mile Lake 66.45 –145.55 D. Anderson, U.S. Geological Survey, unpub. data (2014)

1904 Six Mile Lake 64.87 –141.12 N. Bigelow, University of Alaska-Fairbanks, unpub. data (2014)

1904 Little Harding Lake 64.41 –146.90 Brady (2013)

1904 Jan Lake 63.56 –143.92 Carlson and Finney (2004)

1904 Keche Lake 68.02 –146.93 Chipman and others (2012)

1904 Takahula Lake 67.35 –153.67 Clegg and Hu (2010)

1904 Harding Lake 64.42 –146.85 Finkenbinder and others (2014)

1904 Dune Lake 64.42 –149.90 Finney and others (2012)

1904 Oops Lake 65.44 –147.63 Finney, University of Idaho, unpub. data (2014)

1904 Ace Lake 64.86 –147.94 B. Gaglioti, University of Alaska-Fairbanks, unpub. data (2014)

1904 Deuce Lake 64.86 –147.94 Lynch and others (2002)

1905 Burial Lake 68.43 –159.17 Abbott and others (2010)

1905 Lake Wolverine 67.09 –158.92 Mann and others (2002)

1906 Meli Lake 68.68 –149.08 Anderson and others (2001)

1906 Blue Lake 68.09 –150.47 Bird and others (2009)

1906 Lake Helen 70.36 –153.68 B. Gaglioti, University of Alaska-Fairbanks, unpub. data (2014)

1906 Nikivlik Lake 68.59 –156.25 B. Gaglioti, University of Alaska-Fairbanks, unpub. data (2014)

1906 Dimple Lake 68.94 –150.50 Johnson and others (2011)

1906 NE-14 68.67 –149.63 Johnson and others (2011)

1906 Perch Lake 68.95 –150.20 Johnson and others (2011)

1906 Toolik Lake 68.63 –149.61 Johnson and others (2011)

total regional area. There were some gaps, however, and 

the watersheds that contained DIC concentration data only 

represented 16 percent of the total area of the Southeast 

region, and the watersheds that contained TOC concentration 

data only represented 8 percent of total area of the Southeast 

region and 35 percent of the Southwest region. 

Summertime concentrations were better represented in 

the concentration datasets than those collected in the winter. 

The Southeast, South-Central, Southwest, and Yukon regions 

had DIC concentrations collected through the year. However, 

nearly 80 percent of the DIC values and 65 percent of TOC 

values for the North Slope region were collected in June 

through August, with no TOC values for January, February, 

or March. Nearly 60 percent of DIC and TOC samples in the 

Northwest region were collected from June through August, 

which may have biased distributions of concentrations to 

represent more carbon transport via surface-water rather than 

groundwater flow.

In this assessment, the gas transfer velocity for CO2

efflux calculation for rivers was not permitted to exceed 

30 m/d based on equation 8.5. It is important to note that the 

model to estimate gas transfer velocity of CO2 outlined in 

Raymond and others (2012) and used for this assessment was 

developed from a dataset that did not include any measurements from steep-slope or high-altitude locations and, as such, 

the results of this model in highly diverse landscapes should 

be interpreted with appropriate caution. Validation data to 

support evasion rates of this magnitude do not currently exist; 

however, recent research measuring oxygen transfer rates 

suggests that gas transfer velocities in the upper reaches of 

the Colorado River can range from 9 m/d in the large main 

channels up to 338 m/d in rapids (Hall and others, 2012). 
172 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

The estimates of stream and river surface area for each 

hydrologic region ranged from 0.3 to 0.8 percent of the total 

area and are consistent with other published values (Aufdenkampe and others, 2011; Downing and others, 2012); however, 

remote sensing techniques could be used to further constrain 

the hydraulic geometry coefficients that are appropriate at 

the basin scale (Striegl and others, 2012). Specifically, there 

is a need to constrain the surface areas of first-order stream 

systems in headwater areas that may be poorly characterized 

within the EDNA dataset. The underlying 30-m digital 

elevation model used to define stream reaches is coarse, did 

not capture strong seasonal variation in flow, and had some 

inaccurate streamflow lines, particularly in low-elevation 

areas (Holmes and others, 2000). 

The locations of USGS streamgages, which were used 

to calculate the hydraulic geometry coefficients, introduced a 

bias because they were placed in a location that was best suited 

for accurate discharge measurements (Leopold and Maddock, 

1953; Park, 1977). Therefore, the locations most likely do not 

represent the entire range of variability in the relationships 

among stream depth, width, and velocity that exists for river 

channels across the State of Alaska. A wider range of variability of these stream geomorphology factors likely exists in 

Alaska because of the low levels of human development and 

lack of stream channelization or other flow restrictions. 

Using the available data, we were not able to accurately 

model the effect of seasonality on estimated mean CO2 emission from lacustrine or riverine systems (Rouse and others, 

1997). In dimictic lakes (lakes that experience ice cover and 

mix completely in the spring and fall), CO2 concentrations 

build up under ice cover and in the hypolimnetic bottom 

waters as a result of heterotrophic respiration and are degassed 

rapidly during mixing following ice melt (Michmerhuizen 

and others, 1996; Riera and others, 1999). In addition, boreal 

and arctic lakes are commonly shallow, resulting in a higher 

susceptibility to climate and seasonal shifts in temperature, 

reducing ice cover and increasing levels of light penetration 

to sediments, which could, in turn, influence the seasonality 

of dissolved CO2 through biological metabolism and thermal 

stratification (Rouse and others, 1997; Smol and others, 

2005). These aspects of the seasonal pCO2

 dynamics were 

not included in the estimates.

Changes in permafrost are likely to have effects on 

regional-scale hydrology (Osterkamp and others, 2000; 

Walvoord and others, 2012), particularly related to lake 

surface area. Lake surface area was a key component in the 

modeling of CO2 emissions and carbon burial in lakes in this 

assessment. Some studies in regions with degrading permafrost 

have reported significant decreases in lake surface area extent 

(Riordan and others, 2006; Roach and others, 2013), whereas 

others report no substantial change in lake surface area for 

most lakes in a study region (Jones, Arp, and others, 2009; 

Rover and others, 2012). Regardless of the study, it is clear 

that accurate assessment of changes in lake surface area relies 

on proper treatment of both inter- and intra-annual time scales 

(Rover and others, 2012; Chen and others, 2013). However, 

a remote sensing data archive that can address both inter- and 

intra-annual variability in lake surface areas in continuous and 

discontinuous permafrost regions for the entire State of Alaska 

is currently not available. Given that the aquatic carbon flux 

results in this chapter represent long-term annual averages and 

that roughly half of the lake surface area lies in nonpermafrost 

regions of Alaska, the effect of changes in lake surface area 

on these regional results is expected to be minimal. 

There were several important sources of uncertainty in 

the carbon burial estimates including (1) loss of sediment 

carbon to postdepositional mineralization at 100 years, 

(2) effects of within-basin variability of sedimentation rates, 

and (3) extrapolation of sedimentation rate and sediment 

carbon concentration data from a relatively small sample 

(n = 70) of lakes to the larger population of lakes in Alaska 

(approximately 1 million). Loss of carbon in sediments 

because of mineralization occurs primarily in the top layers of 

sediments, where oxygen concentrations are highest (Sobek 

and others, 2009). This loss of carbon was partly compensated 

for by using an average carbon concentration for the sediment 

deposited during the previous 100 years, which typically was 

the top 5 to 15 cm of sediments. Additional research is needed 

to better account for variations in carbon burial efficiency in 

Alaskan lakes. Sediment focusing refers to the propensity 

for fine-grained sediment to accumulate preferentially in the 

deepest part of the lake (Davis and Ford, 1982). Alternatively, 

in calm-water lakes, where sediment redistribution is rare, 

littoral areas can have higher sedimentation rates because 

of highly productive macrophyte beds, high allochthonous 

subsidies nearshore, and (or) thermokarsting lake margins. 

The sediment focusing factor (the ratio of sedimentation at a 

specific location in a lake to average sedimentation in the lake) 

for lakes in Alaska varies widely and appears to be at least 

partly a function of lake morphometry, with steep-sided lakes 

having high sediment focusing factors. Focusing factors also 

can change over time, as young, steep-sided lakes fill in (Blais 

and Kalff, 1995). A study of eight high-elevation lakes in the 

Rocky Mountain and Glacier National Parks indicated that 

sediment focusing factors averaged 1.6±0.5 (mean±s.d.; 

1 anomalous sample excluded) (Mast and others, 2010). 

Most of these lakes are in glacial cirques with high relief and 

have steeply sloping lake bottoms; thus, they may represent 

the upper end of the range of sediment focusing effects. If 

postdepositional sediment carbon mineralization and sediment 

focusing are important in Alaskan lakes, the estimated carbon 

burial rates are likely overestimated. Error in regression model 

predictions represents another source of uncertainty in carbon 

burial calculations. Although the MLR models for sedimentation rates and sediment carbon concentrations (key parameters 

in the carbon burial calculations) explained 70 to 75 percent 

of the variance in observed values, at least 25 to 30 percent of 

the variance remains unexplained. An additional concern is 

that the suite of lakes that was used to derive the MLR models 

was not selected randomly from the entire lake population, 

potentially biasing the model regressions; this deficiency could 

be at least partly addressed through additional sampling. 
Chapter 8 173

8.4. Results

8.4.1. Coastal Carbon Export From Riverine Systems

The total coastal carbon export for the State of Alaska 

was 18.3 TgC/yr (5th and 95th percentiles of 16.3 TgC/yr 

and 25.0 TgC/yr; table 8.1) with 70 percent of the export as 

inorganic carbon. The highest total carbon export was from 

the Yukon region, at 5.5 TgC/yr. Most of the flux is from the 

Yukon River, which had the single highest flux for any river in 

the State. The coastal flux from the Yukon River flows north 

through the Bering Strait to the Arctic Ocean. 

The Southeast and South-Central regions exported 

similar magnitudes of carbon to the coast, 3.8 TgC/yr 

(5th and 95th percentiles of 3.4 TgC/yr and 5.3 TgC/yr), 

and 3.5 TgC/yr (5th and 95th percentiles of 3.1 TgC/yr and 

5.0 TgC/yr), respectively (table 8.1). The Southwest region 

had a lower flux of 2.7 TgC/yr (5th and 95th percentiles of 

2.2 TgC/yr and 3.4 TgC/yr). The Northwest and North Slope 

regions had the lowest annual carbon fluxes of 1.5 TgC/yr 

(5th and 95th percentiles of 1.3 TgC/yr and 3.4 TgC/yr) 

and 1.3 TgC/yr (5th and 95th percentiles of 0.9 TgC/yr and 

2.3 TgC/yr), respectively. 

To illustrate the connectivity between the aquatic fluxes 

and contributing terrestrial drainage area, we divided carbon 

fluxes by watershed area to produce a carbon yield. The 

estimated total carbon yields were by far the highest in the 

Southeast region at 36.7 gC/m2/yr (5th and 95th percentiles 

of 32.4 gC/m2/yr and 50.7 gC/m2/yr; table 8.1). The 

second highest yields were in the South-Central region at 

17.0 gC/m2/yr (5th and 95th percentiles of 15.1 gC/m2/yr 

and 24.0 gC/m2/yr). The lowest carbon yields were in the 

North Slope region with 6.1 gC/m2/yr (5th and 95th percentiles 

of 4.3 gC/m2/yr and 11.5 gC/m2/yr). 

There was substantial variability in the mean runoff 

among the regions, with the greatest total runoff in the

Southeast region at 3.4 meters per year (m/yr) (5th and 95th 

percentiles of 2.9 m/yr and 5.5 m/yr) and the smallest total 

runoff in the North Slope region at 0.3 m/yr (5th and 95th 

percentiles of 0.2 m/yr and 0.6 m/yr). The mean DIC concentration in the Yukon region of 18.7 mg/L (s.d. of 9.9 mg/L) 

was nearly three times higher than the estimated mean DIC 

concentration in the Southeast region, which was 6.6 mg/L 

(s.d. of 5.2 mg/L). The range of TOC concentrations were 

narrower, with the highest estimated mean concentration in 

the North Slope region at 8.5 mg/L (s.d. of 6.1 mg/L) and the 

lowest in the Southwest region at 2.4 mg/L (s.d. of 1.9 mg/L). 

8.4.2. Carbon Dioxide Flux From Riverine Systems

Overall, median pCO2 values ranged from 1,000 to 

5,900 μatm across all stream orders and hydrologic regions, 

representing a gradient spanning 3 to 15 times the mean 

atmospheric CO2 concentration. This suggests that streams 

and rivers in the State remain continuously supersaturated 

compared with atmospheric CO2 concentrations. The highest 

median pCO2 value by region was in the Northwest region 

at 3,100 μatm, whereas the lowest median pCO2 value of 

1,700 μatm was in the Southeast region. Measurements 

of dissolved CO2 are defined by the pH, alkalinity, and 

temperature at the time of measurement, and the small sample 

size within the Northwest region affected these concentration 

distributions. Average median pCO2 for the State of Alaska 

was 2,200 μatm, or 5.6 times the approximate atmospheric 

CO2 concentration of 390 μatm.

Estimated stream and river surface area was 8,000 km2

(5th and 95th percentiles of 7,600 km2 and 8,300 km2), representing about 0.5 percent of the total area of Alaska (table 8.3). 

The highest total stream surface area of 4,200 km2 (5th and 

95th percentiles of 4,100 km2 and 4,400 km2) was in the 

Yukon region, whereas the smallest total stream surface area 

Table 8.3. Estimated river and stream surface area and vertical fluxes and yields of carbon dioxide from riverine 

systems in Alaska.

[Sites are those used in the calculation of estimated partial pressure of carbon dioxide (p CO2 ). Values are estimates of the total median 

river and stream surface areas, fluxes, and yields (fluxes normalized to watershed areas), and those presented in parentheses represent 

associated errors at the 5th and 95th percentiles. Total yields were calculated by dividing the estimated total flux by the regional area. 

Units: km2, square kilometer; TgC/yr, teragram of carbon per year; gC/m2/yr, gram of carbon per square meter per year]

Hydrologic

region

Number

of sites

River and stream 

surface area

(km2

)

Estimated total

carbon flux

(TgC/yr)

Estimated total

carbon yield

(gC/m2/yr)

Southeast 242 486 (461, 512) 3.9 (2.2, 5.8) 37.3 (21.2, 55.6)

South-Central 511 1,003 (947, 1,061) 4.5 (2.5, 6.8) 21.7 (12, 32.9)

Southwest 166 1,235 (1,164, 1,307) 3.1 (1.6, 5.0) 10.5 (5.6, 17.3)

Yukon 333 4,217 (4,063, 4,379) 3.7 (2.0, 6.3) 7.1 (3.7, 12)

Northwest 81 507 (481, 533) 0.8 (0.4, 1.3) 4.2 (2.2, 7.1)

North Slope 136 524 (496, 553) 0.6 (0.3, 1.1) 3.1 (1.7, 5.3)

 Total or mean 1,469 7,972 (7,612, 8,345) 16.6 (9.0, 26.3) 11.0 (6.0, 17.4)
174 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

was in the Southeast region at 490 km2 (5th and 95th percentiles 

of 460 km2 and 510 km2). Stream and river surface area as a 

percentage of the landscape was largest in the Yukon region 

at 0.80 percent (5th and 95th percentiles of 0.77 percent and 

0.83 percent), but only 0.26 percent (5th and 95th percentiles of 

0.24 percent and 0.27 percent) across the North Slope region. 

Total efflux of CO2 from rivers in Alaska was 16.6 TgC/yr

(5th and 95th percentiles of 9.0 TgC/yr and 26.3 TgC/yr) 

with the highest fluxes from the Yukon and South-Central 

regions at 3.7 TgC/yr (5th and 95th percentiles of 2.0 TgC/yr 

and 6.3 TgC/yr) and 4.5 TgC/yr (5th and 95th percentiles 

of 2.5 TgC/yr and 6.8 TgC/yr), respectively (table 8.3). The 

smallest efflux was estimated for the North Slope region 

at 0.6 TgC/yr (5th and 95th percentiles of 0.3 TgC/yr and 

1.1 TgC/yr). Normalizing greenhouse-gas emissions to 

CO2 equivalent using global warming potentials produced 

a total river efflux of 58.7 teragrams of carbon dioxide 

equivalent per year (TgCO2-eq/yr)for the State of Alaska. 

Carbon yield for the State of Alaska was 11.0 gC/m2/yr 

(5th and 95th percentiles of 6.0 gC/m2/yr and 17.4 gC/m2/yr). 

The distribution of yield estimates across regions differed 

from the distribution of total flux estimates. Highest yield 

was estimated for the Southeast region at 37.3 gC/m2/yr 

(5th and 95th percentiles of 21.2 gC/m2/yr and 55.6 gC/m2/yr), 

whereas the smallest yield was estimated for the North 

Slope region at 3.1 gC/m2/yr (5th and 95th percentiles of 

1.7 gC/m2/yr and 5.3 gC/m2/yr). In general, the range of 

yield estimates among the six regions of Alaska decreased 

with increasing latitude, where colder regions tended to have 

smaller yields primarily because of a larger proportion of 

defined ice cover throughout the year. 

8.4.3. Carbon Dioxide Flux from Lacustrine 

Systems

Across the State, median lake pCO2 concentrations 

ranged from 973 μatm, or 2 times atmospheric concentration, 

across the Yukon region to a high of 4,189 μatm, or nearly 

10 times atmospheric concentration, in the Southwest region. 

These numbers represent high estimates for the dissolved CO2

concentrations; however, limited measurements (n =1,329) 

may be contributing a bias to these calculations. 

Overall, 1,019,224 individual lakes and reservoirs 

derived from the NHD dataset were used for this study, 

totaling 52,300 km2 representing 4 percent of the State of Alaska 

(table 8.4). The proportion of lake area varied across each of the 

six hydrologic regions from a low of 2 percent in the Southeast 

region up to 7 percent in the Southwest region. This very high 

value of 7 percent is the result of a few very large lakes found 

within the Southwest region and is dominated by Lake Iliamna, 

which has a surface area of approximately 2,600 km2

.

 

Gas transfer velocities (kCO2–lake ) calculated using 

equation 8.6 averaged across regions did not vary significantly 

because of similarity in average wind speeds (U10). Lowest 

kCO2–lake values were estimated for the Southeast region at 

0.6 m/d, (s.d. of 0.04 m/d) ranging to a high of 0.8 m/d

(s.d. of 0.1 m/d) for the Northwest region. 

Total lake CO2 emission was estimated to be 8.2 TgC/yr 

(5th and 95th percentiles of 6.1 TgC/yr and 11.2 TgC/yr; 

table 8.4). The highest lake efflux was estimated for the 

Southwest region at 5.8 TgC/yr (5th and 95th percentiles of 

4.3 TgC/yr and 7.6 TgC/yr). The flux from this region was 

an order of magnitude larger than the remaining regions 

Table 8.4. Estimated lake surface area and vertical fluxes and yields of carbon dioxide from lacustrine 

systems in Alaska.

[Sites are those used in the calculation of the partial pressure of carbon dioxide (pCO2). Values are estimates of the total 

median lake surface areas, fluxes, and yields (fluxes normalized to watershed areas), and those presented in parentheses 

represent associated errors at the 5th and 95th percentiles. Total yields were calculated by dividing the estimated total flux 

by the regional area. Units: km2, square kilometer; TgC/yr, teragram of carbon per year; gC/m2/yr, gram of carbon per square 

meter per year]

Hydrologic

region

Number

of sites

Lake surface

area

(km2

)

Estimated total

carbon flux

(TgC/yr)

Estimated total

carbon yield

(gC/m2/yr)

Southeast 44 1,587 0.3 (0.2, 0.4) 3.2 (2.2, 3.8)

South-Central 148 3,841 0.7 (0.6, 0.9) 3.4 (2.8, 4.4)

Southwest 214 20,946 5.8 (4.3, 7.6) 19.8 (14.9, 26.1)

Yukon 305 10,947 0.4 (0.2, 0.6) 0.8 (0.4, 1.2)

Northwest 46 4,069 0.4 (0.3, 0.8) 2.1 (1.5, 4.3)

North Slope 133 10,886 0.6 (0.5, 0.9) 3.1 (2.2, 4.5)

 Total or mean 890 52,276 8.2 (6.1, 11.2) 5.5 (4.0, 7.4)
Chapter 8 175

across Alaska. Lowest lake fluxes were estimated for the 

Southeast region at 0.3 TgC/yr (5th and 95th percentiles of 

0.2 TgC/yr and 0.4 TgC/yr). Excluding the estimated flux for 

the Southwest, lake emission ranged from 0.3 to 0.7 TgC/yr 

across all other hydrologic regions. Normalizing greenhousegas emissions to CO2

 equivalent produced a total lake CO2 

emission of 30.1 TgCO2-eq/yr (5th and 95th percentiles of 

22.4 TgCO2-eq/yr and 41.1 TgCO2-eq/yr).

Average lake yield for the State of Alaska was 5.5 gC/m2/yr

(5th and 95th percentiles of 4.0 gC/m2/yr and 7.4 gC/m2/yr). 

Highest yield was estimated for the Southwest region at 

19.8 gC/m2/yr (5th and 95th percentiles of 14.9 gC/m2/yr and 

26.1 gC/m2/yr), whereas the smallest yield was estimated for 

the Yukon region at 0.8 gC/m2/yr (5th and 95th percentiles of 

0.4 gC/m2/yr and 1.2 gC/m2/yr). Overall, the variation in both 

the total efflux and yield estimates among the regions was 

driven primarily by regional differences in the median lake 

CO2 concentrations and total lake area. 

8.4.4. Carbon Burial in Lacustrine Systems

Observed water body sedimentation rates at the cored 

lakes ranged from 0.1 to 5.3 millimeters per year (mm/yr) and 

had an exponential or log-normal distribution. The estimated 

median sedimentation rate for lakes of Alaska was 1.5 mm/yr, 

with the lowest rates in the Northwest and South-Central 

regions (0.4 mm/yr and 0.6 mm/yr); intermediate rates in the 

North Slope, Yukon, and Southeast regions (1.2 to 1.8 mm/yr); 

and highest rates in the Southwest region (2.3 mm/yr). 

Observed sediment carbon concentrations at the cored 

lakes ranged from 0.6 to 30.7 percent (by dry weight) and 

were exponentially or log-normally distributed. The estimated 

median sediment carbon concentration in lakes of Alaska was 

18.2 percent and were lowest in the Southeast, Southwest, 

and North Slope regions (16.6 to 17.7 percent); intermediate 

in the Northwest and South-Central regions (18.0 percent and 

18.1 percent); and highest in the Yukon region (19.1 percent). 

The median estimated total flux of carbon owing to burial 

in lakes of Alaska was –1.88 TgC/yr (5th and 95th percentiles 

of –1.03 TgC/yr and –2.82 TgC/yr), and values varied 

substantially among hydrologic regions (table 8.5). Estimated 

flux from carbon burial was smallest in the Southeast, Northwest, and South-Central regions (– 0.05 to – 0.10 TgC/yr); 

intermediate in the North Slope and Yukon regions 

(– 0.33 TgC/yr and – 0.46 TgC/yr, respectively); and 

greatest in the Southwest region (– 0.88 TgC/yr). When 

normalized to the area of each hydrologic region (yield), 

the median estimated flux from carbon burial in lakes 

was –1.2 gC/m2/yr (5th and 95th percentile estimates of 

– 0.7 gC/m2/yr and –1.9 gC/m2/yr). Carbon burial yields were 

lowest in the Northwest, Southeast, and South-Central regions 

(– 0.3 to – 0.5 gC/m2/yr); intermediate in the Yukon region 

(– 0.9 gC/m2/yr); and highest in the North Slope and Southwest 

regions (–1.6 gC/m2/yr and –3.0 gC/m2/yr, respectively).

The estimated carbon burial rate normalized to water 

body area was –31 gC/m2/yr for Alaska, but the rates varied 

substantially among hydrologic regions. The Northwest and 

South-Central regions had the lowest estimated carbon burial 

rates (–8 gC/m2/yr and –11 gC/m2/yr); the North Slope and 

Yukon regions had intermediate rates (–23 gC/m2/yr and 

–30 gC/m2/yr); and the Southeast and Southwest regions had 

the highest rates (–36 gC/m2/yr and –44 gC/m2/yr; table 8.5). 

Table 8.5. Estimated carbon burial in lacustrine sediments in Alaska.

[Sites are those where carbon burial was calculated from sediment cores. Values are estimates of the total median fluxes, yields (fluxes 

normalized to watershed areas), and burial rates, and those presented in parentheses represent associated errors at the 5th and 95th percentiles. Carbon yields were calculated by dividing the estimated total flux divided by the hydrologic region area. Negative values represent 

sequestration. Units: TgC/yr, teragram of carbon per year; gC/m2/yr, gram of carbon per square meter per year]

Hydrologic

region

Number

of sites

Estimated total

carbon flux

(TgC/yr)

Estimated total carbon 

yield normalized to 

watershed area

(gC/m2

/yr)

Estimated carbon

burial rate normalized

to water body area

(gC/m2

/yr)

Southeast 1 – 0.05 (– 0.03, – 0.07) –0.5 (– 0.3, – 0.6) –36 (–22, –50)

South-Central 17 – 0.10 (– 0.07, – 0.13) –0.5 (– 0.3, – 0.6) –11 (–2, –22)

Southwest 24 – 0.88 (– 0.54, –1.24) –3.0 (–1.9, – 4.3) – 44 (–27, –63)

Yukon 15 – 0.46 (– 0.26, – 0.64) –0.9 (– 0.5, –1.2) –30 (–16, – 44)

Northwest 2 – 0.06 (– 0.02, – 0.12) –0.3 (– 0.1, – 0.7) –8 (–2, –23)

North Slope 9 – 0.33 (– 0.10, – 0.62) –1.6 (– 0.5, –3.0) –23 (–2, –47)

 Total or mean 68 –1.88 (–1.03, –2.82) –1.2 (–0.7, –1.9) –31 (–16, –49)
176 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

8.5. Discussion

The magnitude of carbon transported laterally, emitted, 

and stored in inland waters is controlled largely by variability 

in climate, hydrology, and exchanges of both inorganic and 

organic carbon among the terrestrial landscape, aquatic environments, and the atmosphere. The source of aquatic carbon 

is the combination of instream production and delivery of 

terrestrially derived carbon via surface-water or groundwater 

flow paths. Rising temperatures have already affected the large 

pools of carbon stored in the soils and permafrost (Striegl and 

others, 2007; Schuur and others, 2008; Frey and McClelland, 

2009; Tarnocai and others, 2009) and have reduced the size 

of glaciers in Alaska (Arendt and others, 2002; Berthier and 

others, 2010). Therefore, it is important to provide accurate 

estimates of current carbon fluxes in aquatic environments, 

so that future effects of climate change can be determined. 

8.5.1. Coastal Export and Carbon Dioxide Flux 

From Riverine Systems

The coastal carbon export from the Southeast and 

South-Central regions of Alaska accounted for approximately 

40 percent of the State’s total lateral flux at 7.3 TgC/yr. The 

lateral carbon yields from the Southeast region were the 

highest in the State at 36.7 gC/m2/yr. The South-Central 

region had total carbon export nearly equivalent to that of 

the Southeast region; however, given its larger total area, the 

yields were lower (17.0 gC/m2/yr). The mean annual precipitation for these regions, particularly along the coast, can be 

between 2 and 3 m, and in mountainous headwaters mean 

annual precipitation can be a great as 8 m (Powell and Molnia, 

1989). Heavy precipitation produces an abundance of small, 

swift-moving streams. For the Southeast region, the estimated 

magnitude of total discharge was 356 cubic kilometers per 

year (km3/yr), which translates to a runoff of 3.4 m/yr. Total 

discharge for the South-Central region was 220 km3/yr, which 

translates to a water yield of 1.1 m/yr (5th and 95th percentiles 

of 0.8 m/yr and 2.5 m/yr). 

Forested and wetland soils in the Southeast and SouthCentral regions of Alaska deliver organic carbon to streams 

draining to the coast (Fellman and others, 2008; D’Amore and 

others, 2010), and mean organic carbon concentrations for 

these regions were between 3 and 4 mg/L. The organic carbon 

fluxes estimated within this report are comparable with other 

estimates for this region (D’Amore and others, 2015). With 

substantial ice cover in these two regions, glacial runoff is a 

significant source of organic carbon (Hood and others, 2009; 

Schroth and others, 2011) and inorganic carbon, as considerable rock weathering occurs under ice sheets (Ludwig and 

others, 1999). Mean inorganic carbon concentrations for rivers 

and streams were 6.6 mg/L and 11 mg/L for the Southeast and 

South-Central regions, respectively. 

Estimated riverine CO2

 efflux from the Southeast and 

South-Central regions was over 50 percent of the total vertical 

CO2 emission for the State of Alaska at 8.4 TgC/yr, yet these 

regions only account for 19 percent of the total water area. 

These two regions had very high yield estimates—from 

21.7 gC/m2/yr for the South-Central region to 37.3 gC/m2/yr 

for the Southeast region. Average gas transfer velocities in 

this region were the highest in the State at approximately 

24 m/d. Although this is within published estimates of stream 

kCO2 (Hall and others, 2012; Huotari and others, 2013), it 

remains unclear if riverine CO2 emissions can be maintained 

with transfer rates this high. To date, there are no comparable 

vertical CO2 emissions from riverine systems for these 

two regions.

The coastal carbon export combined with CO2 emissions 

produced a river carbon flux of 5.8 TgC/yr from the Southwest 

region. This region had a high percent of total carbon flux as 

DIC (78.4 percent). This is likely due to the sedimentary rocks 

rich in carbonates that underlie this region (Dürr and others, 

2005), and the Kuskwokwim River has significantly affected 

the carbonate saturation indices of the Bering Sea (Mathis and 

others, 2011). Currently, there are no other studies that can be 

used as a comparison for the estimates from this region.

Carbon cycling and storage at high latitudes have been 

key issues in carbon cycle science. Therefore, estimates of 

aquatic carbon fluxes exist for major watersheds draining to 

the Arctic Ocean, including the Yukon River and several of 

the main rivers draining the North Slope of Alaska (Colville, 

Kuparuk, and Sagavanirktok Rivers). The results presented in 

this chapter support many of these existing flux estimates. 

The highest lateral carbon fluxes for Alaska, estimated 

at 5.5 TgC/yr, were in the Yukon River Basin, likely owing to 

high discharge (125 km3

/yr), DIC concentration (18.6 mg/L, 

s.d. of 10.0 mg/L), and TOC concentration (4.8 mg/L, s.d. 

of 10.0 mg/L). This estimate is about 35 percent lower than 

previous Yukon River carbon flux estimates of 7.2 TgC/yr 

(Striegl and others, 2007), in part because of the difference 

in the period of record between the two studies. This report 

used data from 1975 to 2013 to calculate fluxes for the Yukon 

River at Pilot Station (NWIS station ID =15565447), but 

Striegl and others (2007) only used data between 2001 and 

2005. The water chemistry data for these two different studies 

were comparable. The median alkalinity as calcium carbonate 

for the Yukon River at Pilot Station was 76.2 mg/L in this 

assessment compared with 74.5 mg/L of Striegl and others 

(2007). The median alkalinity as DOC was 5.6 mg/L in this 

assessment compared with 5.5 mg/L of Striegl and others 

(2007). However the median discharge of 125 km3

/yr in this 

report was much lower than 146 km3/yr for the time period 

used in Striegl and others (2007). The 17-percent difference in 

discharge is likely the main cause for different flux estimates 

between the two studies. 

In this assessment, riverine CO2 emission from the Yukon 

region was estimated to be 3.7 TgC/yr (5th and 95th percentiles of 2.0 TgC/yr and 6.3 TgC/yr), which is lower compared 
Chapter 8 177

with 7.7 TgC/yr (5th and 95th percentiles of 6.7 TgC/yr 

and 9.2 TgC/yr) of Striegl and others (2012; herein called 

Striegl and others). In this report, the yield estimate for the 

Yukon region was 7.1 gC/m2/yr (5th and 95th percentiles 

of 3.7 gC/m2/yr and 12.0 gC/m2/yr), which is lower than 

10.5 g/C/m2/yr (5th and 95th percentiles of 9.0 gC/m2/yr 

and 13.0 gC/m2/yr) of Striegl and others, although their 

value falls within our error estimate. 

Multiple factors contributed to the difference in total river 

evasion estimates between the two studies. Striegl and others 

included the total efflux for the Canadian proportion of the 

Yukon River Basin, whereas this study presents fluxes only 

for the portion of the basin within the Alaskan State boundary. 

The area of the Yukon River Basin within the State of Alaska 

estimated in this report was approximately 530,000 km2, 

which represents 60 percent of the total Yukon River Basin. 

Additionally, Striegl and others used high-resolution remote 

sensing methods to determine stream and river surface 

area for all but the smaller tributaries and estimated a total 

river surface area of 10,000 km2, which is 1.2 percent of 

the total land area in the Yukon River Basin. In this report, 

the water surface area was lower, about 0.8 percent of the 

land area, because EDNA did not adequately represent 

low-order streams. 

Another methodological difference between the two 

studies was that this report used modeled estimates for 

kCO2–river that ranged from 2.5 to 28.0 m/d from first through 

tenth stream orders for the Yukon River according to 

methods outlined in Zhu and Reed (2014), but the maximum 

kCO2–river used by Striegl and others approached 6.9 m/d with 

a minimum of 0.3 m/d along the main stem of the upper 

Yukon River Basin. In addition, Striegl and others estimated 

a total of 200 days, or 54 percent of the year, where no efflux 

occurred based on ice cover conditions; this was applied 

uniformly across the basin to the total flux estimate. In this 

report, ice cover was estimated spatially using mean monthly 

temperature to produce a range of 145 to 152 days that are 

ice free depending upon topographic location. Thus, this 

report presents higher flux rates for shorter periods of time 

and for less surface area compared with Striegl and others. 

Because of the difference in methods between the two studies, 

identification of CO2

 emission hotspots also diverged. The 

methods used to derive the CO2 emission estimates from rivers 

presented in this report indicated that the dominant fluxes 

occurred either in the smaller tributaries or the highest order 

main stem of the river network determined by kCO2–river or by 

area, respectively. In contrast, Striegl and others suggest that 

the majority of the efflux occurs within the tributaries only. 

Despite these methodological differences, if we increase our 

flux by the increased proportion of surface area suggested by 

Striegl and others, we obtain a yield of 9.6 gC/m2/yr, which is 

very similar to their 10.5 gC/m2/yr. 

Despite relatively high DIC (14.7 mg/L) and TOC 

concentrations (8.5 mg/L), the North Slope region had a 

low coastal carbon export rate of 1.3 TgC/yr, likely owing 

to relatively low discharge of 53 km3

/yr. This region had 

the highest TOC concentrations and the highest regional 

value of total carbon flux as TOC (38 percent). The summed 

organic carbon flux estimate for the Sagavanirktok, Kuparuk, 

and Colville Rivers in the North Slope region presented 

in this report was 0.41 TgC/yr (5th and 95th percentiles 

of 0.05 TgC/yr and 0.8 TgC/yr), which encompassed the 

McClelland and others (2014) estimate of 0.30 TgC/yr (s.d. 

of 0.03 TgC/yr) for the same rivers. Overall, the Northwest 

region is one of the most data-poor regions in the State, and 

this report presents the first estimates for river coastal carbon 

flux from this region of 1.5 TgC/yr. 

The riverine CO2 effluxes for the North Slope and Northwest regions were estimated to be 0.6 TgC/yr and 0.8 TgC/yr 

with yields of 3.1 gC/m2/yr and 4.2 gC/m2/yr, respectively. 

These regions represent only 10 percent of the total statewide 

riverine efflux, and the flux was driven primarily by a short 

ice-free period, averaging a total of 113 to 138 days, and 

by small surface areas of streams and rivers, representing 

only 0.26 percent of the land surface area in the North Slope 

region and 0.29 percent in the Northwest region. These 

yield estimates are larger than those derived from within the 

Kuparuk River watershed as part of the Toolik Lake LTER 

(Kling and others, 1992, 2000). At 3.8 gC/m2/yr, this report’s 

value is double the estimate for the Kuparuk River network 

of 1.7 gC/m2/yr derived for 1994 through 1996 (Hobbie and 

Kling, 2014) and significantly higher than a more recent 

estimate for the same watershed of 0.02 gC/m2/yr (Cory 

and others, 2014). Cory and others (2014) estimated the 

proportion of the landscape as stream and river surface to be 

about 0.23 percent, which is similar to this report’s estimate, 

although it remains unclear how they estimated lateral inputs 

of DIC contributing to pCO2 in their study, possibly biasing 

their total flux to be low. 

8.5.2. Carbon Dioxide Flux From and Carbon 

Burial in Lacustrine Systems

The Southeast and South-Central regions showed nearly 

identical lake vertical flux estimates at 3.2 gC/m2/yr and 

3.4 gC/m2/yr, respectively. To our knowledge, these are the 

first estimates for lake efflux in coastal regions that include 

temperate rainforest ecosystems. The calculated pCO2 values 

were nearly identical across both the Southeast and SouthCentral regions at about 2,600 μatm. Input of organic carbon 

from forested and wetland systems may affect lake pCO2

estimates, as median alkalinity estimates for the Southeast 

region were very low at around 2 mg/L, whereas median pH 

was 6.7 (David D’Amore, U.S. Department of Agriculture, 

Forest Service, written commun., November 3, 2014). A 

complication for modeling the efflux of CO2 from lake 

ecosystems within these regions is the lack of data from 

central lake locations. Nearly all identified sites in the Southeast region of Alaska are from samples taken at either inlet or 
178 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

outlet of a lake. These concentrations are likely to be different 

than the carbon concentrations at central points within the 

lake, where the greatest turbulence and greatest gas emission 

rates likely occur.

A very high lake CO2 emission of 5.8 TgC/yr was 

calculated for the Southwest region. This value is larger than 

all other regions in Alaska combined. The median estimated 

pCO2 value for this region was 4,200 μatm, with values 

ranging between 19 and 71,000 μatm. There are currently 

no published estimates of direct measurements of lake CO2

concentrations within this region for comparison. Direct 

measurements refer to in situ monitoring of CO2 with a type 

of infrared sensor, as opposed to the indirect measurement 

used in this report by estimating CO2 concentrations from 

alkalinity, pH, and temperature measurements. Unpublished 

direct dissolved CO2 measurements for small lakes near 

Lake Iliamna had a maximum pCO2 of 2,800 μatm and 

were undersaturated (<390 μatm) for half of the period that 

the lake was covered with ice (G. Holtgrieve, University of 

Washington-Seattle, written commun.). In addition to the 

high concentration of dissolved CO2 identified within the 

Southwest region, this region also has significantly higher lake 

surface area at 21,000 km2, nearly double the estimates for the 

Yukon and North Slope regions. Obtaining direct dissolved 

CO2 concentration measurements from multiple points on 

large lakes is critical to reducing the uncertainty for the high 

estimated lake carbon emissions in this region. 

Current research on boreal and arctic lakes consistently 

show annual dissolved CO2 concentrations to be in excess 

of atmospheric CO2 (Huttunen and others, 2003; Sobek and 

others, 2005; Jonsson and others, 2008). In lakes across the 

boreal and arctic regions of Alaska, including the North Slope, 

Northwest, and Yukon regions, median pCO2 values ranged 

from about 970 μatm in the Yukon to about 1,900 μatm in the 

Northwest. Much of the CO2 supersaturation is hypothesized 

to originate from the mineralization of both allochthonous 

and autochthonous carbon sources (Roehm and others, 2009; 

Solomon and others, 2013). However, although DOC correlates with dissolved CO2

 values, significant regional variation 

exists so that a model that can be applied across the circumboreal region is still not available (Roehm and others, 2009). 

For this study, the North Slope lake efflux was 

estimated to be 0.6 TgC/yr (5th and 95th percentiles of 

0.5 TgC/yr and 0.9 TgC/yr), resulting in a flux of 3.1 gC/m2/yr 

(5th and 95th percentiles of 2.2 gC/m2/yr and 4.5 gC/m2/yr). 

Previous lake studies at the Toolik Lake LTER estimated 

fluxes of 1.1 gC/m2/yr (Hobbie and Kling, 2014) and 

0.4 gC/m2/yr (Cory and others, 2014). These flux measurements, made at a single site, were estimated with data from 

two different time periods—1994 through 1996 for the Hobbie 

and Kling study and 2001 through 2013 for the Cory and 

others study. These differences indicate that there is significant 

temporal variation in carbon fluxes within the Arctic. The lake 

CO2 emissions estimated for the North Slope region in this 

study may be high owing to high initial DIC concentrations 

derived from the calculation of pCO2 from ancillary data and 

because the timing of samples taken for this analysis may not 

be representative of the influence that seasonal hydrology and 

in situ primary production impart on carbon concentrations 

(Huttunen and others, 2003). 

For lakes in the Yukon Flats area, the lake CO2 emissions 

were estimated with direct dissolved CO2 concentrations 

(Halm and Guldager, 2013; Halm and Griffith, 2014). The 

Yukon region’s average pCO2 values were the lowest across all 

regions, and the median value of 970 μatm was considerably 

lower than the next closest median pCO2 value of 1,400 μatm, 

which was calculated for the North Slope region. The Yukon 

Flats data represent only single measurements, usually made 

during the summer, and significant seasonal fluctuations in 

these values are likely. Additional direct seasonal measurements of CO2

 concentrations for lakes throughout Alaska are 

necessary to determine if (1) low pCO2 values are representative of lakes in other regions and (2) low concentrations 

persist even under seasonal fluctuations of in-lake biological 

processes, including photosynthesis and respiration. 

The average sedimentation rate observed in Alaskan 

lakes (1.5 mm/yr) was similar to those from a study of lakes 

in eastern North America, which reported that historical 

sedimentation rates had a log-normal distribution, with a 

median of 2.2 mm/yr (Webb and Webb, 1988). A few studies 

have reported sedimentation rates for Alaskan lakes: Cornwell 

(1985) reported a sediment accumulation rate of 0.16 mm/yr 

for Toolik Lake, in the foothills of the North Slope region, and 

noted that low rainfall, a short runoff period, and stabilization 

of soils by thick organic matter layers and nearly continuous 

tundra vegetation contribute to low sedimentation rates in the 

lake. Higher sedimentation rates (2.5 mm/yr) were reported for 

Black Lake, on the Alaskan Peninsula, which receives more 

precipitation and is covered in dense forest, rather than tundra 

vegetation (Post, 2011). 

Sediment carbon concentrations for Alaskan lakes were 

similar to those documented for 3 oligotrophic, humic-rich 

lakes in Sweden (21 to 31 percent; Sobek and others, 2009, 

2014), 16 lakes in western Ontario (20 percent, s.d. of 

7 percent), and 23 lakes in Wisconsin (20 percent, s.d. of 

10 percent) (Brunskill and others, 1971). In contrast, sediment 

carbon concentrations were much lower in seven lakes in 

western Greenland (1.8 to 8.9 percent); the study area was 

cold and dry, with a mean annual temperature of –6 °C and 

mean annual precipitation of 150 mm/yr (Sobek and others, 

2014); sparse vegetation and poorly developed soils may have 

limited contributions of organic matter to the lakes from the 

surrounding terrestrial environment.

Variations in carbon burial rate in Alaska reflect differences in sedimentation rate, sediment carbon concentration, 

and the extent of water bodies (see equation 8.7). Based on 

results from the stepwise MLR modeling, sedimentation rates 

were strongly related to land-cover type, with shrubland, 
Chapter 8 179

mixed forest, and herbaceous wetland exerting a positive 

influence and permafrost exerting a negative influence 

(table 8.6). The positive influence of shrubland on sedimentation rates may reflect the susceptibility of this land-cover type 

to erosion, whereas the positive influence of mixed forest and 

herbaceous wetlands may be related to high productivity of 

vegetation in these areas. In addition, shrub distribution in 

tundra terrain is often confined to recently eroded gullies and 

water-track habitats, which may indicate recent erosion (Tape 

and others, 2011). The negative influence of permafrost on 

sedimentation rates probably is due to low erosion rates of 

frozen soil, except in areas with thermokarst where erosion 

can be important (Osterkamp and others, 2000). Differences 

in estimated sedimentation rates among regions reflect the 

relative abundance of land-cover types and permafrost. Low 

estimated sedimentation rates in the Northwest region, for 

example, may be attributed to the importance of permafrost, 

which underlies over 70 percent of the landscape in this region 

(chapter 3 of this report; Pastick, Jorgenson, and others, 2014). 

Relatively high sedimentation rates were estimated for the 

Southwest region, which has abundant shrubland and herbaceous wetlands and relatively little permafrost. 

The stepwise MLR modeling indicated that sediment 

carbon concentrations were strongly related to log water 

body surface area, mean basin elevation, and percent of basin 

covered by bogs (table 8.6). Sediment carbon concentrations 

were inversely related to water body surface area, reflecting 

relatively high productivity rates in the littoral zone of lakes 

compared to deeper water zones; small lakes typically are 

shallower than large lakes, and littoral zones constitute a 

greater percentage of total lake area (Wetzel, 2001). Mean 

basin elevation also was a negative influence on sediment 

carbon concentration, probably because of decreases in 

primary productivity and associated soil organic carbon 

with elevation (Jobbágy and Jackson, 2000; Ping and others, 

2008). Bogs were a positive influence on sediment carbon, 

as expected given the high organic carbon content of bog 

soils that have the potential for providing carbon subsidies 

to nearby lakes (Jobbágy and Jackson, 2000; Pastick, Rigge, 

and others, 2014).

The carbon burial rate estimated for Alaskan lakes in 

this study normalized to lake area (–31 gC/m2/yr) is higher 

than long-term (Holocene) rates for boreal lakes in northern 

Quebec (–3.8 gC/m2/yr; Ferland and others, 2012) and 

Finland (–0.2 to –8.5 gC/m2/yr; Kortelainen and others, 2004); 

however, our estimates pertain only to the past 100 years. The 

carbon burial rates estimated herein for Alaska approach those 

in thermokarst lakes in Siberia (–47±10 gC/m2/yr), which 

are relatively high because of thermokarst erosion, which 

contributes organic matter and nutrients to lakes, and cold 

temperatures, which limit decomposition (Walter Anthony and 

others, 2014). At the global scale, Dean and Gorham (1998) 

noted an inverse relation between carbon burial rates and lake 

area and estimated an average carbon accumulation rate of 

–5 gC/m2/yr for large lakes and –72 gC/m2/yr for small lakes; 

an inverse relation between carbon burial rates and lake size 

was observed in the present study as well.

Variations in estimated total carbon burial fluxes 

(in TgC/yr) among hydrologic regions primarily reflect 

differences in sedimentation rates and the areal extent of 

water bodies; these parameters showed much more variability 

among regions than sediment carbon concentrations, as 

indicated by the coefficient of variation (CV) for each parameter. The CV for sedimentation rates and water body area were 

0.51 and 0.14, whereas the CV for sediment carbon concentrations was only 0.04. The Southwest region had the highest 

carbon burial rates among the regions of Alaska (table 8.5); 

this region also had greatest water body surface area 

(table 8.4) and the highest sedimentation rates. In contrast, 

carbon burial in lakes of the South-Central and Northwest 

regions was much smaller (table 8.5), reflecting lower water 

body surface areas (table 8.4) and sedimentation rates.

Table 8.6. Estimates, standard error of estimates, and p-values for independent variables used in multiple-linear-regression models 

for sedimentation rates and sediment carbon concentrations in Alaskan water bodies.

[km2, square kilometer; m, meter]

Independent

variable

Sedimentation rate model Sediment carbon concentration model

Intercept

Mixed

forest

(percent)

Shrubland 

(percent)

Herbaceous 

wetland 

(percent)

Permafrost 

(percent) Intercept

Log lake

 area

(km2

)

Mean

elevation

(m)

Bogs

(percent)

Estimate – 0.0117 0.0161 0.0017 0.0126 – 0.0013 10.838 –3.2805 – 0.0093 0.3781

Standard 

error of 

estimate

– 0.0117 0.0161 0.0017 0.0126 – 0.0013 1.0039 0.5014 0.0016 0.0518

p-value 0.63 <0.0001 <0.0001 <0.0001 0.0008 <0.0001 <0.0001 <0.0001 <0.0001
180 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

8.6. Summary and Conclusions 

The total statewide carbon flux from inland waters 

(coastal export plus CO2 emissions from rivers and lakes 

minus burial in sediments) was 41.2 TgC/yr, and total carbon 

yields were 27.5 gC/m2/yr. The greenhouse-gas-emission 

estimates reported in this chapter may be low because 

methane (CH4) fluxes were not included owing to a lack of 

lacustrine and riverine CH4 concentration data across Alaska. 

Recent studies have documented that both CO2 and CH4

may represent a consistent carbon loss pathway from aquatic 

ecosystems to the atmosphere (Walter and others, 2007; 

Crawford and others, 2013; Sepulveda-Jauregui and others, 

2014). Using the lake CH4 estimates for northern latitudes 

(Wik and others, 2016) and applying them to the lake surface 

area of Alaska indicates that lake CH4 emissions may equal 

0.5 TgC/yr. Current modeling efforts strive to integrate 

CH4 concentration samples from a variety of lake types, 

including deep glacial lakes as well as thermokarst water 

bodies in Alaska, as well as low- and high-order streams 

and rivers in order to provide an accurate estimate of current 

lake and river CH4 emissions in Alaska. 

 There was considerable variability in the estimated 

inland waters’ carbon fluxes among the six hydrologic regions 

in Alaska. The Southeast region, with high precipitation rates 

and an abundance of short, steep streams, had the highest 

total yield (flux divided by hydrologic area) in the State at 

76.7 gC/m2/yr. The South-Central region had the second 

highest total yield of 41.6 gC/m2/yr. The Southwest region 

had the highest lake surface area (nearly 21,000 km2) 

and the highest lake CO2 emission rates (5.8 TgC/yr), 

which contributed to the relatively high overall yield 

of 36.5 gC/m2/yr. The Yukon region had a total yield of 

17.5 gC/m2/yr. Both the Northwest and the North Slope 

regions, with the lowest temperatures and precipitation 

among the six regions, had the smallest overall combined 

yields of 14.7 gC/m2/yr and 10.7 gC/m2/yr, respectively. 

This chapter focused on presenting results of current 

carbon fluxes from inland aquatic systems, but projected 

climate changes are likely to influence the magnitude of 

aquatic carbon fluxes in future decades. Predicted changes 

to the hydrologic cycle include increased streamflow in 

high-latitude regions (Hinzman and others, 2005; Brabets 

and Walvoord, 2009) and increased runoff owing to increased 

rates of glacier melt (Arendt and others, 2002). The influence of groundwater on regional hydrology is also likely to 

change because of permafrost thaw (Walvoord and others, 

2012). Increased forest fires (Kasischke and others, 2010), 

permafrost thaw (Walvoord and Striegl, 2007; Tank, Frey, 

and others, 2012), and changes in vegetation composition 

(Chapin and others, 1995) are all likely to affect the amount and 

composition of carbon delivered to aquatic systems. Coupling 

both hydrologic and biogeochemical reactions over space 

and time will be key to accurately predicting changes in the 

magnitude of aquatic carbon fluxes in the future. 

The results presented in this chapter indicate that 

the magnitude of inland waters’ aquatic carbon fluxes is 

significant at 41.2 TgC/yr. Since the models that produced the 

soil CO2 flux estimates in the assessment were not coupled 

with the models that produced the riverine inorganic carbon 

flux or the vertical CO2 emissions, the appropriate amount 

of CO2 that is emitted from the aquatic ecosystems was not 

subtracted from the heterotrophic respiration term, which may 

lead to double counting. Additionally, if the DOC leaching 

from soil organic matter pools was not adequately represented 

within the terrestrial modeling environment, the modeled 

soil pools could be amassing organic carbon that has actually 

been transported, processed, or stored in aquatic ecosystems. 

Development of a model framework that couples the terrestrial 

and aquatic carbon processing and transport is key to understanding whether terrestrial landscapes represent a dominant 

source of carbon to freshwater systems. Chapter 9 of this 

report will present a complete picture of terrestrial and aquatic 

ecosystems and will illustrate a comparison that indicates that 

the linkage between these two environments is important to 

understand fully the role they play in greenhouse-gas storage 

and cycling.

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Chapter 9. Alaska Carbon Balance 

By A. David McGuire,1 Hélène Genet,2 Yujie He,3 Sarah Stackpoole,4 David D’Amore,5

T. Scott Rupp,2

 Bruce K. Wylie,6 Xiaoping Zhou,7 and Zhiliang Zhu8

1

U.S. Geological Survey, Fairbanks, Alaska.

2

University of Alaska-Fairbanks, Fairbanks, Alaska.

3

Purdue University, West Lafayette, Ind.

4

U.S. Geological Survey, Denver, Colo.

5

U.S. Department of Agriculture Forest Service, Juneau, Alaska.

6

U.S. Geological Survey, Sioux Falls, S. Dak.

7

U.S. Department of Agriculture Forest Service, Portland, Oreg.

8

U.S. Geological Survey, Reston, Va.

9.1. Highlights

• Ecosystem carbon balance of the Alaska assessment 

domain (as outlined in chapter 1) was estimated for 

two time periods, a historical period (1950 –2009) 

and a projection period (2010 –2099) by synthesizing 

results for upland (chapter 6), wetland (chapter 7), 

and inland aquatic (chapter 8) ecosystems.

• The total area of Alaska considered in this assessment 

was 1,475,089 square kilometers (km2) (97.9 percent 

of the State), which is composed of 84 percent uplands, 

12 percent wetlands, and 4 percent inland waters. 

• Between 1950 and 2009, the upland and wetland 

ecosystems of the State were estimated to have 

sequestered an average of 3.7 teragrams of carbon per 

year (TgC/yr) (–141.4 to 72.0 TgC/yr inter-annual 

variability), which was 1.5 percent of net primary 

productivity (NPP) in upland and wetland ecosystems. 

However, this sequestration is spatially variable 

with the Northwest Boreal Landscape Conservation 

Cooperative (LCC) North losing carbon because of 

fire disturbance and other regions gaining carbon.

• The combined carbon loss through various pathways 

of the inland aquatic ecosystems of Alaska was 

estimated to be 41.2 TgC/yr (5th and 95th percentiles 

of 30.4 TgC/yr and 59.7 TgC/yr), or about 17 percent 

of upland and wetland NPP. 

• The greenhouse gas forcing potential of upland 

and wetland ecosystems of Alaska was estimated 

to be 17.3 teragrams of carbon dioxide equivalent 

per year during the historical period, and for 

the State as a whole was likely substantially 

larger because of methane (CH4 ) emissions from 

lake ecosystems.

• During the projection period (2010 –2099), carbon 

sequestration of upland and wetland ecosystems 

of Alaska were projected to increase substantially 

(18.2 to 34.4 TgC/yr) primarily because of an 

increase in NPP of 12 to 30 percent associated 

with responses to rising atmospheric carbon 

dioxide (CO2), increased nitrogen cycling, and 

longer growing seasons. Although carbon 

emissions to the atmosphere from wildfire were 

projected to increase substantially for all of the 

projected climates, the increases in NPP would 

more than compensate for those losses.

• Upland and wetland ecosystems were projected 

to be sinks for greenhouse gases (GHGs) for all 

but one of the simulations during the projection 

period. However, as in the case of the analysis 

of the historical period, there was an uncertainty 

as to whether the State would be a net source for 

GHGs if emissions of CH4 from lakes in Alaska 

were considered.
190 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

9.2. Introduction

Alaska occupies an area that is approximately one-fifth 

that of the conterminous United States. Ongoing warming in 

Alaska has the potential to substantially alter the exchange of 

carbon dioxide (CO2) and methane (CH4) between ecosystems 

and the atmosphere and the overall ecosystem carbon balance 

of the State (Striegl and others, 2007; Zhuang and others, 

2007; Wolken and others, 2011; Yuan and others, 2012). Thus, 

the response of carbon balance to changes in climate and 

CO2 concentrations in Alaska has implications for policies 

concerning the management of carbon in the United States. 

However, much of Alaska has not previously been included 

in any major national carbon and greenhouse-gas inventory 

reports. Thus, the historical baseline carbon balance is poorly 

understood at a statewide level, and the potential for climate 

change to affect carbon dynamics in Alaska has not been 

formally assessed. 

The main outcomes of this assessment for Alaska include 

(1) estimates of the amount of carbon stored in ecosystems 

(such as forests and wetlands), (2) estimates of the capacity 

of ecosystems to sequester carbon, (3) estimates of the rate of 

greenhouse-gas (GHG) fluxes in and out of the ecosystems, 

and (4) evaluation of the effects of processes or driving forces 

that control ecosystem carbon balance and GHG fluxes. To 

support the outcomes of the assessment for the entire State 

of Alaska, the assessors sought to address questions within 

regions of Alaska. These questions include (1) what are the 

magnitudes of carbon pools and fluxes of soil, biomass, and 

surface waters for different regions of Alaska?; (2) how are 

changes in fire regime, vegetation distribution, permafrost 

dynamics, and forest management influencing carbon 

balance in different regions of Alaska?; and (3) how might 

estimated sources and sinks of CO2 and CH4 of arctic, boreal, 

and maritime ecosystems change in response to projected 

changes in climate, fire regime, permafrost dynamics, and 

forest management? Chapters 2 through 8 of this report 

addressed various aspects of these questions. This chapter 

focuses on synthesizing results across uplands, wetlands, 

and inland aquatic ecosystems to summarize information 

from this assessment at the statewide level on changes in 

carbon stocks, carbon fluxes, and greenhouse gas forcing 

for (1) historical/baseline (1950–2009) and (2) projection 

(2010 –2099) periods. 

9.3. Methods 

9.3.1. Synthesis Estimates of Changes in 

Carbon Stocks 

Changes in carbon stocks were estimated for upland 

and wetland ecosystems, but were not estimated for inland 

aquatic ecosystems because of a lack of data on carbon stocks 

in inland aquatic ecosystems. For the historical period, mean 

annual changes in vegetation and soil carbon stocks were 

calculated separately for uplands and wetlands by subtracting 

the area-weighted mean, in grams of carbon per square meter 

(gC/m2

), at the end of December 1949 from the area-weighted 

mean at the end of December 2009 and then dividing by 

60 years. The area-weighted means were obtained from the 

simulations conducted by the Dynamic Organic Soil version 

of the Terrestrial Ecosystem Model (DOS-TEM; Genet and 

others, 2013) for uplands (chapter 6) and wetlands (chapter 7). 

To convert to units of teragrams of carbon per year (TgC/yr), 

the mean change in carbon stocks for uplands and wetlands 

was multiplied by the area in square meters (m2) occupied by 

uplands (1.237774×1012 m2) and wetlands (0.177069×1012 m2). 

A similar procedure was followed for the projection period, 

except that the area-weighted mean for December 2009 was 

subtracted from the area-weighted mean for December 2099 

and then divided by 90 years. 

9.3.2. Synthesis Estimates of Carbon Fluxes 

For uplands and wetlands, we report synthesis estimates 

of net primary productivity (NPP), heterotrophic respiration 

(HR), fire emissions (Fire), biogenic methane exchange 

(BioCH4 ), and forest harvest (Harvest). Biogenic methane 

exchange was dominated by uptake of CH4 from the atmosphere in uplands and by emissions of CH4

 to the atmosphere 

in wetlands. For the historical period, each mean annual 

carbon flux was separately calculated for uplands and wetlands 

by averaging the area-weighted mean flux in grams of carbon 

per square meter per year (gC/m2/yr) from 1950 through 

2009. The area-weighted means were obtained from the 

simulations conducted by DOS-TEM for uplands (chapter 6) 

and wetlands (chapter 7). The mean flux was then multiplied 

by the respective area of uplands and wetlands (see above) to 

convert to units of TgC/yr. A similar procedure was followed 

for the projection period, except that the area-weighted mean 

flux was averaged from 2010 through 2099. We calculated 

net ecosystem carbon balance (NECB; see Chapin and others, 

2006) for upland and wetland ecosystems as follows:

NECB=NPP–HR–Fire–Harvest –BioCH4 (9.1)

for which the acronyms for the fluxes are defined as above.

For inland aquatic ecosystems, we report synthesis 

estimates of the export of carbon from rivers to the coastal 

ocean, the emission of CO2 from rivers, the emission of CO2

from lakes, and the burial of carbon in lakes. Estimates were 

obtained from chapter 8 in TgC/yr for the historical period. 

9.3.3. Synthesis Estimates of Greenhouse 

Gas Forcing

We used the global warming potential (GWP) concept to 

estimate the greenhouse gas forcing potential across terrestrial 

upland and wetland ecosystems of Alaska. In our calculations 

of GWP, we assumed that CH4

 has 25 times the GWP of 
Chapter 9 191

CO2 over a 100-year timeframe (Forster and others, 2007). 

GWP was reported in CO2 equivalent: (1) C-CH4 fluxes were 

converted to CH4 equivalent by multiplying the fluxes by 

16/12, the ratio of the molecular weight of CH4 to the weight 

of carbon in CH4, and the CH4 equivalent fluxes were then 

converted to CO2 equivalent multiplying them by 25, and 

(2) all C-CO2 fluxes were converted to CO2 equivalent by 

multiplying them by 44/12, the ratio of the molecular weight 

of CO2 to the weight of carbon in CO2. Positive GWP indicates 

net CO2 emissions from the ecosystem to the atmosphere, 

and negative GWP indicates net removal of greenhouse gases 

by ecosystems. 

9.4. Results and Discussion

9.4.1. Synthesis of Carbon Dynamics in the 

Historical Period (1950–2009)

Average soil carbon storage in Alaskan terrestrial ecosystems for the last decade of the historical period (2000–2009) 

was estimated to be 52.1 petagrams of carbon (PgC), with 

47.1 PgC stored in upland ecosystems. Vegetation carbon 

storage in Alaskan terrestrial ecosystems over the same period 

was estimated to be 5.0 PgC with 4.3 PgC stored in upland 

ecosystems. The storage in upland ecosystems is greater 

because they occupy about 84 percent of the area (1.24 million 

square kilometers [km2]) in Alaska compared with wetland 

coverage of 12 percent (0.18 million km2); inland aquatic 

ecosystems occupy 4 percent (0.06 million km2) of Alaska. 

Between 1950 and 2009, upland and wetland terrestrial 

ecosystems of Alaska were estimated to have sequestered 

3.7 TgC/yr (–141.4 to 72.0 TgC/yr inter-annual variability), 

which is 1.5 percent of annual NPP (table 9.1, fig. 9.1). This 

was largely because soil carbon sequestration (4.6 TgC/yr) 

more than offset losses of vegetation carbon (–0.9 TgC/yr). 

Upland ecosystems of Alaska were primarily responsible for 

the gain in soil carbon (5.3 TgC/yr) as wetland ecosystems 

were estimated to have lost soil carbon (–0.7 TgC/yr). 

Vegetation carbon was estimated to have decreased in upland 

ecosystems at –0.3 TgC/yr and in wetland ecosystems at 

–0.7 TgC/yr. The magnitude of NPP and HR in uplands was 

approximately six times greater than that in wetlands, and the 

loss of carbon in wildfire was three times greater in uplands 

than in wetlands. Modeled forest harvest was entirely concentrated in uplands, and modeled biogenic methane emissions 

were entirely concentrated in wetlands. Fire was the primary 

reason for the loss of vegetation carbon in the historical 

period, and most of the loss occurred in recent decades and in 

the Northwest Boreal Landscape Conservation Cooperative 

(LCC) North (chapters 6 and 7). Although the Northwest 

Boreal LCC North lost soil carbon because of fire, upland 

ecosystems of other LCC regions gained soil carbon during 

the historical period. 

Terrestrial upland and wetland ecosystems of Alaska 

were estimated to have been a carbon source of 17.3 teragrams 

of carbon dioxide equivalent per year (TgCO2

-eq/yr) with 

respect to greenhouse gas forcing of the climate system 

during the historical period as net CO2 uptake from uplands 

(NPP–HR–Fire=–16.0 TgCO2-eq/yr as a sink) was lower 

in magnitude than the global warming potential of wetlands 

(33.3 TgCO2-eq/yr), which is dominated by biogenic methane 

emissions. It is important to note that harvested carbon was 

transferred from live vegetation to an inert carbon pool, and it 

did not contribute to our estimate of HR. If we had considered 

the decomposition of harvested carbon in our analysis, it 

would have resulted in terrestrial uplands and wetlands of 

Alaska being a larger net source of greenhouse gases between 

1950 and 2009 than we have estimated.

Inland aquatic ecosystems were estimated to have lost 

41.2 TgC/yr (5th and 95th percentiles of 30.4 TgC/yr and 

59.7 TgC/yr) through export to the coast and CO2 emissions from rivers and lakes, minus burial in lake sediments 

(table 9.2; fig. 9.1, chapter 8), which is about 17 percent of 

NPP in terrestrial ecosystems. This report does not include 

estimates of stock changes in aquatic ecosystems and, because 

terrestrial and aquatic models were not integrated, terrestrial 

loading of carbon to aquatic ecosystems was not quantified. 

However, the sum of lateral export of carbon to Alaska coasts 

and carbon emissions across water surfaces is significant, and 

Table 9.1. Sixty-year carbon balance of upland and wetland ecosystems in Alaska during the historical period (1950–2009).

[Soil carbon includes carbon in fibric, humic, and mineral soil horizons as well as carbon in coarse woody debris. Net ecosystem carbon balance is calculated 

as either change in vegetation carbon plus change in soil carbon or as net primary productivity minus heterotrophic respiration minus fire emissions minus 

biogenic methane emissions minus forest harvest. Positive values of net ecosystem carbon balance indicate increases in pools or fluxes of carbon into the ecosystem. Data may not add to totals or compute to net ecosystem carbon balance shown because of independent rounding. TgC/yr, teragram of carbon per year; 

TgCO2-eq/yr, teragram of carbon dioxide equivalent per year]

Terrestrial

component

Change in

vegetation 

carbon

Change in

soil carbon

Net primary 

productivity 

Heterotrophic 

respiration

Fire

emissions

Biogenic 

methane

emissions

Forest

harvest

Net 

ecosystem 

carbon

balance

Global

warming

potential

(TgCO2

-eq/yr) (TgC/yr)

Upland – 0.3 5.3 212.0 175.0 29.0 0.0 2.9 5.0 –16.0

Wetland – 0.7 – 0.7 37.3 27.3 10.4 0.9 0.0 –1.3 33.3

Total –0.9 4.6 249.2 202.3 39.4 0.9 2.9 3.7 17.3
192 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

these results suggest that, when the processing and removal 

of carbon through inland waters is properly taken into account, 

the calculated capacity of soil and vegetation to store carbon 

and the heterotrophic respiration estimates for uplands and 

wetlands (table 9.1) may be reduced. 

The methodology applied in this assessment does not 

allow us to combine the estimated carbon balance of upland 

and wetland ecosystems with the estimated carbon balance 

of inland aquatic ecosystems over the historical period. 

Net

primary

productivity

249.2

Litterfall

88.5

Figure 9-1

Fire

emissions

39.4

Heterotrophic

respiration

205.2 Change in

vegetation carbon

–0.9

Change in soil carbon and

carbon from coarse woody debris

4.6

Inland surface waters

Methane

emissions

0.9

Carbon

dioxide

emissions

24.8

Burial

1.9

Coastal

export

18.3

Figure 9.1. The carbon balance of the historical period (1950–2009) of this assessment estimated for the terrestrial (upland 

and wetland) component (left) and the inland aquatic component (right), in teragrams of carbon per year (TgC/yr). The 

arrows indicate the direction of carbon flows between the pools (including the atmosphere [not shown]) considered in this 

assessment. The litterfall flux of carbon from terrestrial vegetation to soil (88.5 TgC/yr) is provided in this figure to provide 

information relevant to the mass balance of terrestrial soil carbon. The linkage between fluxes of terrestrial vegetation and 

soil and that of the inland aquatic environment, as indicated by the dashed line, was not investigated in this assessment.

Table 9.2. Sixty-year carbon balance of inland aquatic ecosystems in Alaska during the historical period (1950–2009).

[Net ecosystem carbon balance for inland aquatic ecosystems is calculated as coastal carbon export from riverine systems plus carbon dioxide emissions

from riverine systems plus carbon dioxide emissions from lacustrine systems plus methane emissions from lacustrine systems minus carbon burial in 

lacustrine systems. Because methane emissions from lacustrine systems were not estimated in this assessment, net ecosystem carbon balance was also 

not estimated. Net ecosystem carbon balance for aquatic systems can also be measured as change in carbon storage of inland aquatic ecosystems plus 

input of carbon into inland aquatic ecosystems from terrestrial (upland and wetland ecosystems), but neither of these was estimated in this assessment. 

TgC/yr, teragram of carbon per year; NE, not estimated]

Coastal carbon

export from

riverine systems 

(TgC/yr)

Carbon dioxide

emissions from

 riverine systems 

(TgC/yr)

Carbon dioxide

emissions from

lacustrine systems

(TgC/yr)

Methane

emissions from

lacustrine systems

(TgC/yr)

Carbon burial 

in lacustrine 

systems

(TgC/yr)

Net ecosystem 

carbon balance 

(TgC/yr)

18.3 16.6 8.2 NE 1.9 NE

Thus, it is not clear whether Alaskan ecosystems have acted 

to sequester carbon in the historical period or whether they 

have lost carbon. The key methodological uncertainties 

concern both the heterotrophic respiration flux and the flux 

of carbon from terrestrial to inland aquatic ecosystems. The 

heterotrophic respiration estimate (205.2 TgC/yr, table 9.1) is 

likely an overestimate because the DOS-TEM model does not 

represent losses to inland aquatic ecosystems. If the estimated 

heterotrophic respiration flux were reduced by an amount to 
Chapter 9 193

carbon storage in terrestrial ecosystems would substantially 

increase across all six climate simulations—combinations 

of three climate scenarios (B1, A1B, and A2, in order of low 

to high projected CO2 emissions) of the Intergovernmental 

Panel on Climate Change’s Special Report on Emissions 

Scenarios (Nakićenović and Swart, 2000) used to force two 

general circulation models, version 3.1-T47 of the Canadian 

Centre for Climate Modelling and Analysis’ Coupled Global 

Climate Model (CGCM3.1) and version 5 of the Max Planck 

Institute’s European Centre Hamburg Model (ECHAM5). 

The range of carbon storage values, from 18.2 TgC/yr under 

scenario B1 with ECHAM5 to 34.4 TgC/yr under scenario 

A1B with CGCM3.1 (NECB in table 9.3), represents an 

approximate fivefold to ninefold increase over NECB for the 

historical period (3.7 TgC/yr, table 9.1). It should be noted that 

our simulations with DOS-TEM reported here did not model 

future forest harvest in southeast Alaska. The business-asusual forest harvest we considered in chapter 5 for southeast 

Alaska would likely translate to an approximately 3-TgC/yr 

decrease in NECB (see chapter 6), and therefore would have 

little effect on the projected estimates of NECB we report 

here. The projected increase in carbon storage is primarily 

driven by increases in NPP of between 12 and 30 percent 

associated with increases in atmospheric CO2 concentration, 

increases in nitrogen cycling, and longer growing seasons. 

Projected fire emissions across the climate simulations 

varied from a 36-percent decrease to a 212-percent increase. 

Projected HR across the climate simulations varied from an 

18-percent decrease to a 13-percent increase primarily because 

increased fire in the Northwest Boreal LCC North would 

cause a substantial decrease in HR in that region associated 

with the substantial loss of soil carbon in fire. In other LCC 

regions, HR would increase in the future because of warmer 

soils, which would lead to higher rates of nitrogen cycling 

that increase NPP and lead to greater soil carbon stocks. 

Our simulations indicated that terrestrial uplands 

and wetlands would act as sinks for greenhouse gases 

from 2010 through 2099 with GWP ranging from 

–24.5 to –91.6 TgCO2-eq/yr, except for one simulation 

(11.6 TgCO2

-eq/yr, under scenario A1B with ECHAM5) 

for which biogenic CH4 emissions and fire emissions were 

greater than for other simulations (table 9.3). Although 

we project that biogenic CH4 emissions from wetlands 

will increase between 17 and 187 percent depending on 

the climate simulation, the increases do not offset the net 

increase in CO2 uptake by upland and wetland ecosystems 

of Alaska for five of the six climate simulations in this 

assessment. This contrasts with our analysis for the historical 

period, which indicated that uplands and wetlands of Alaska 

were sources of greenhouse gas forcing. Because we did not 

assess the future dynamics of CH4 emissions from lakes, 

we do not know if Alaska would be a net sink or source for 

greenhouse gases in the future. 

balance the carbon budget of inland aquatic ecosystems, then 

the carbon balance for Alaska during the historical period 

would be equivalent to the total NECB of terrestrial uplands 

and wetlands (carbon sequestration of 3.7 TgC/yr, table 9.1). 

Clearly, it is important to treat the carbon dynamics of upland, 

wetland, and aquatic ecosystems as an integrated system to 

better estimate the net carbon balance of Alaska. The State as 

a whole was likely a much stronger source for greenhouse gas 

forcing to the climate system than estimated from uplands and 

wetlands alone because of CH4

 emissions from lake ecosystems, which we did not estimate in this assessment. 

The estimates of soil and vegetation carbon storage by 

DOS-TEM were validated with data independent from those 

used in model development (chapters 6 and 7). The evaluation 

of the soil carbon estimates of DOS-TEM generally indicated 

good agreement with other available products for Alaska 

(chapter 3). There were no available products of vegetation 

carbon storage at the statewide level with which to evaluate 

the vegetation carbon estimates of DOS-TEM.

The large-scale flux estimates of the historical period 

are difficult to evaluate with independent analyses, because 

these analyses are restricted in spatial and temporal scope. 

For example, the synthesis of eddy covariance data in 

Alaska by Ueyama and others (2013) found that all five of 

the boreal and seven of the eight arctic tundra ecosystems 

analyzed acted as CO2 sinks during the growing season. 

Our results for the historical period of mature undisturbed 

ecosystems of Alaska are certainly consistent with this result, 

but the study of Ueyama and others (2013) doesn’t provide a 

quantitative means of evaluating the DOS-TEM simulations 

at the State scale and across the 60 years of the historical 

period. Our estimate of CH4 emissions from wetlands for the 

historical period of 0.9 TgC/yr (1.2 teragrams of methane 

per year [TgCH4/yr]) is substantially less than the estimate 

of 1.6 TgC/yr (2.1 TgCH4/yr) for Alaska from May to 

September 2012 based on data from an aircraft sampling 

campaign (Chang and others, 2014). The difference in 

magnitude between the two estimates may, in part, be from 

CH4 emissions of lakes, which we did not estimate in this 

assessment (table 9.2). Although the observational data on 

carbon dynamics in Alaska do not yet provide enough information for fully evaluating the exchange of greenhouse gases 

estimated by process-based models, the observational information is useful for some first-order evaluation of the magnitude 

and seasonality simulated by process-based models.

9.4.2. Assessment of Future Potential Carbon 

Dynamics (2010–2099) 

Our assessment of future estimated carbon dynamics of 

Alaska (2010–2099) focused primarily on terrestrial upland 

and wetland ecosystems. The simulations indicated that 
194 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska

9.5. Conclusions

Our synthesis of carbon dynamics in Alaska indicates that 

between 1950 and 2009 the upland and wetland ecosystems 

of the State have sequestered 3.7 TgC/yr, which is almost 

2 percent of upland and wetland NPP. However, this sequestration was spatially variable, with the Northwest Boreal 

LCC North losing carbon because of fire disturbance and 

other regions gaining carbon. We estimate that inland aquatic 

ecosystems of Alaska lost 41.2 TgC/yr, or about 17 percent 

of upland and wetland NPP, through various pathways. We 

estimate that the greenhouse gas forcing potential of upland 

and wetland ecosystems of Alaska was a source during the 

historical period, and we infer that the State as a whole was 

likely an even greater source for greenhouse gas forcing 

to the climate system because of CH4

 emissions from lake 

ecosystems, which we did not estimate in this assessment. 

In contrast to the historical period, our synthesis of carbon 

dynamics in the projection period (2010–2099) indicates that 

carbon sequestration of upland and wetland ecosystems of 

Alaska would increase substantially (18.2 to 34.4 TgC/yr) 

primarily because of an increase in NPP of 12 to 30 percent 

associated with responses to rising atmospheric CO2, increased 

nitrogen cycling, and longer growing seasons. Although 

carbon emissions to the atmosphere from wildfire were 

projected to increase substantially for all climate simulations, 

the increases in NPP would more than compensate for 

those losses. Our analysis indicates that upland and wetland 

ecosystems would be sinks for greenhouse gases for all 

scenarios during the projection period. Because we did not 

assess the future dynamics of CH4 emissions from lakes, 

we do not know if Alaska would be a net sink or source for 

greenhouse gases in the future.

The results of our synthesis have implications for 

carbon management strategies that might be implemented 

as part of national policies aimed at controlling the rate 

and overall magnitude of climate change. These results 

suggest that Alaska could be a sink for greenhouse gases 

under some climate scenarios, but under others it could be a 

source, depending on the response of CH4

 emissions of lakes. 

However, it is important to recognize that CH4 emissions 

from lakes have not been considered in this assessment, and 

it is likely that Alaska would be a source of greenhouse gases 

under all climate simulations if these emissions were considered in the assessment. Models have recently been developed 

for simulating CH4 emissions of arctic lakes (Tan and others, 

2015), and these models may be useful for estimating regional 

CH4 emissions of lakes in Alaska in future assessments to 

more fully inform policy decisions concerning the mitigation 

of greenhouse-gas emissions in the United States. 

Table 9.3. Projected carbon balance and global warming potential of terrestrial upland and wetland ecosystems in Alaska for the 

projection period (2010–2099).

[The six climate simulations are combinations of two general circulation models, version 3.1-T47 of the Coupled Global Climate Model (CGCM3.1) 

developed by the Canadian Centre for Climate Modelling and Analysis and version 5 of the European Centre Hamburg Model (ECHAM5) developed by the 

Max Planck Institute, and three climate scenarios of the Intergovernmental Panel on Climate Change’s Special Report on Emissions Scenarios (Nakićenović 

and Swart, 2000), B1, A1B, and A2, in order of low to high projected CO2 emissions. Soil carbon includes carbon in fibric, humic, and mineral soil horizons 

as well as carbon in coarse woody debris. Net ecosystem carbon balance is calculated as either change in vegetation carbon plus change in soil carbon or as 

net primary productivity minus heterotrophic respiration minus fire emissions minus biogenic methane emissions. Positive net ecosystem carbon balance 

indicates an increases in pools or fluxes of carbon into the ecosystem. Positive global warming potential indicates a net flux of greenhouse gas from the 

ecosystem to the atmosphere. Data may not compute to net ecosystem carbon balance shown because of independent rounding. TgC/yr, teragram of carbon 

per year; TgCO2-eq/yr, teragram of carbon dioxide equivalent per year]

Climate 

scenario

Change in

vegetation 

carbon

(TgC/yr)

Change in

soil carbon 

(TgC/yr) 

Net primary 

productivity 

(TgC/yr) 

Heterotrophic 

respiration

(TgC/yr)

Fire

emissions 

(TgC/yr)

Biogenic

methane

emissions 

(TgCO2

-eq/yr)

Net ecosystem

carbon balance

(TgC/yr)

Global 

warming

potential 

(TgCO2

-eq/yr)

CGCM3.1

A1B 8.8 25.6 292.6 232.0 25.1 36.1 34.4 –91.6

A2 10.2 21.9 306.6 168.7 103.4 64.4 32.2 –51.0

B1 6.2 20.5 278.2 219.9 30.4 37.1 26.8 – 62.4

ECHAM5

A1B 12.6 8.9 324.1 176.9 122.7 88.7 21.4 11.6

A2 12.5 11.7 323.3 190.5 106.4 54.1 24.3 –31.2

B1 9.8 8.5 282.3 222.4 40.3 43.3 18.2 –24.5
Chapter 9 195

It is important to recognize that there are many uncertainties in the results reported here. At the top of the list is the fact 

that the analyses of inland aquatic ecosystems were not integrated with those of upland and wetland ecosystems, which 

likely compromises the estimates of heterotrophic respiration 

because losses of carbon to aquatic ecosystems are not taken 

into account. Also, CH4 emissions of lakes were not quantified 

in either the historical or projection time periods, and whether 

or not Alaska is a sink or source of greenhouse gases depends 

substantially on the magnitude of CH4 emissions from lakes. 

The effects of insect disturbance were not considered in 

this study because of a lack of information on the effects of 

insects on carbon dynamics, the lack of a regional dataset on 

historical insect disturbance, and the lack of a model capable 

of making estimates of future insect disturbance. Our analyses 

in this study also did not consider the effect of thermokarst 

disturbance associated with the thawing of ice-rich permafrost, 

which often results in the subsidence and the development 

of wetlands. Finally, the process-based models used in this 

study, although extensively evaluated in this assessment and 

in previous studies, also have substantial conceptual and 

parameterization uncertainties. These uncertainties have been 

discussed in chapters 6, 7, and 8. Reduction in these uncertainties will require enhancements in observation systems, 

research on landscape dynamics, process-based research, and 

modeling research. Key enhancements in observation systems 

would include forest inventory measurements in interior 

Alaska, CO2

 concentration measurements in large lakes, and 

measurements of CH4 emissions from lakes and wetlands. 

Key enhancements in research on landscape dynamics 

include improved regional datasets on vegetation dynamics, 

lake dynamics, and insect and thermokarst disturbance. 

Key enhancements in process-based research would include 

improved understanding of the transfer of carbon between 

terrestrial and inland aquatic ecosystems, of CH4 dynamics 

of inland aquatic ecosystems, and of controls over insect 

and thermokarst disturbance. Finally, key enhancements in 

modeling research would include the development of models 

that can treat terrestrial-aquatic carbon linkages as an integrated system, improved modeling of wetland and lake CO2

and CH4 dynamics, and the prognostic modeling of insect and 

thermokarst disturbance and their effects on carbon dynamics. 

Although there are substantial uncertainties in our analyses, 

the analyses themselves represent state-of-the-art science, and 

this assessment provides information for priorities in reducing 

uncertainties that should improve future assessments. 

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1051-0761(2007)017[0203:NEOCAC]2.0.CO;2.
Manuscript approved April 4, 2016

Publishing support provided by the U.S. Geological Survey Science Publishing Network

Editing by Natalie Juda, Reston Publishing Service Center

Illustrations and layout by Caryl J. Wipperfurth, Raleigh Publishing Service Center

For more information concerning the research in this report, contact the

Land Change Science Program

U.S. Geological Survey

12201 Sunrise Valley Drive

MS 519A National Center

Reston, VA 20192

http://www.usgs.gov/climate_landuse/lcs/
Zhu and McGuire, eds.—Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of Alaska—Professional Paper 1826

ISSN 1044-9612 (print)

ISSN 2330-7102 (online)

http://dx.doi.org/10.3133/pp1826