Baseline and Projected Future Carbon Storage and 

Greenhouse-Gas Fluxes in Ecosystems of the 

Eastern United States

Professional Paper 1804

U.S. Department of the Interior

U.S. Geological Survey
Front cover: Photograph of kayaks on Lake Drummond in Great Dismal Swamp National 

Wildlife Refuge, a marshy region of southeastern Virginia and northeastern North Carolina. 

Photograph by Chris Lowie, U.S. Fish and Wildlife Service.

Back cover: Photograph of a marshland in the Everglades National Park, which is the largest 

subtropical wetland in the United States and home to many endangered wildlife species. 

Photograph by Thomas Loveland, U.S. Geological Survey.

Inside cover: Photograph of an even-aged young pine plantation of about 10 years of age in 

the Southern Coastal Plain of Georgia. Plantations in this area reach maturity 20 to 25 years 

after planting and are part of the very active and cyclic forestry land use of the southeastern 

United States. Photograph by Thomas Loveland, U.S. Geological Survey.
Baseline and Projected Future Carbon 

Storage and Greenhouse-Gas Fluxes in 

Ecosystems of the Eastern United States

Edited by Zhiliang Zhu and Bradley C. Reed

Professional Paper 1804

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, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Zhu, Zhiliang, and Reed, B.C., eds., 2014, Baseline and projected future carbon storage and greenhouse-gas 

fluxes in ecosystems of the eastern United States: U.S. Geological Survey Professional Paper 1804, 204 p., 

http://dx.doi.org/10.3133/pp1804.

ISSN 1044-9612 (print)

ISSN 2330-7102 (online)

ISBN 978-1-4113-3794-7
iii

Contents

Executive Summary

By Zhiliang Zhu, Benjamin M. Sleeter, Ryan R. Reker, Todd J. Hawbaker, 

Sarah Stackpoole, Brian A. Bergamaschi, and Shuguang Liu ........................................1

Chapter 1 Scope, Methodology, and Current Knowledge

By Zhiliang Zhu, Benjamin M. Sleeter, Terry L. Sohl, Todd J. Hawbaker, 

Shuguang Liu, Sarah Stackpoole, Brian A. Bergamaschi, and 

Ashwan D. Reddy ....................................................................................................................7

Chapter 2 Ecoregion and Scenario Framework

By Benjamin M. Sleeter, Glenn E. Griffith, Tamara S. Wilson, Rachel R. Sleeter,

Christopher E. Soulard, Kristi L. Sayler, Ryan R. Reker, Michelle A. Bouchard, 

and Terry L. Sohl ....................................................................................................................17

Chapter 3 Mapping and Modeling of Land Use and Land Cover in the Eastern 

United States From 1992 Through 2050

By Ryan R. Reker, Kristi L. Sayler, Aaron M. Friesz, Terry L. Sohl, 

Michelle A. Bouchard, Benjamin M. Sleeter, Rachel R. Sleeter, 

Tamara S. Wilson, Glenn E. Griffith, and Michelle L. Knuppe ........................................27

Chapter 4 Wildland Fire Occurrence and Emissions in the Eastern United States 

From 2001 Through 2050

By Todd Hawbaker and Zhiliang Zhu .................................................................................55

Chapter 5 Carbon Sequestration, Transport, and Emission From Inland Aquatic 

Ecosystems in the Eastern United States

By Sarah Stackpoole, David Butman, David Clow, Cory McDonald, 

Edward Stets, and Robert Striegl .......................................................................................71

Chapter 6 Terrestrial Fluxes of Nutrients and Sediment to Coastal Waters and 

Their Effects on Coastal Carbon Storage in the Eastern United States

By Brian A. Bergamaschi, Richard A. Smith, Michael J. Sauer,

Jhih-Shyang Shih, and Lei Ji...............................................................................................85

Chapter 7 Baseline and Projected Future Carbon Storage, Carbon Sequestration, 

and Greenhouse-Gas Fluxes in Terrestrial Ecosystems of the Eastern 

United States

By Shuguang Liu, Jinxun Liu, Yiping Wu, Claudia J. Young, 

Jeremy M. Werner, Devendra Dahal, Jennifer Oeding, and 

Gail L. Schmidt .....................................................................................................................115

Selected References.................................................................................................................................157

Appendix 1. The Erosion Deposition Carbon Model............................................................................180

Appendix 2. The Land Greenhouse-Gas Accounting Tool..................................................................182

Appendix 3. Coefficients of Forest Partial Cutting and Biomass Mortality......................................184

Appendix 4. Optimized Maximum Monthly Gross Primary Production ............................................189

Appendix 5. Comparison of Sampling-Based and Per-Pixel Model Runs........................................190

Appendix 6. Emission Factors of Nitrous Oxide and Methane in the Eastern United States .......193
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.

Datums 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.

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.

Abbreviations

°C degrees Celsius

μatm microatmospheres
v

AMLE adjusted maximum likelihood estimation

ANC acid-neutralizing capacity

BGC biogeochemical

CCCma Canadian Centre for Climate Modelling and Analysis

CGCM 3.1 The Third Generation Coupled Global Climate Model

CH4 methane

cm centimeters

CMIP3 Coupled Model Intercomparison Project phase 3

CO carbon monoxide

CO

2 carbon dioxide

CO

2

-eq carbon dioxide equivalent

CO

2

-eq/yr carbon dioxide equivalent per year

CO

2

-water dissolved carbon dioxide concentrations

CO

3

2– carbonate

CSIRO Mk3.0 Commonwealth Scientific and Industrial Research Organisation Mark 3.0 model

DB 0.33 bar H

2

O oven-dry weight of the less than 2-millimeter soil material per unit volume of 

soil at a water tension of 0.33 bar (as used in the SSURGO database)

DIC dissolved inorganic carbon

DOC dissolved organic carbon

DOI U.S. Department of the Interior

EDCM Erosion Deposition Carbon Model

EISA Energy Independence and Security Act of 2007

EPA U.S. Environmental Protection Agency

ETM+ Landsat Enhanced Thematic Mapper Plus

ERC energy release component

FCCS Fuel Characteristic Classification System

FIA Forest Inventory and Analysis National Program

FIDO Forest Inventory Database Online

FIPS Federal Information Processing Standard

FLM Fuel Loading Model

FOFEM First Order Fire Effects Model

FORE–SCE forecasting scenarios of future land cover

gC/m

2

/yr grams of carbon per square meter per year

g/cm

3 grams per cubic meter

GCM general circulation model

GEMS General Ensemble Biogeochemical Modeling System

GFED Global Fire Emissions Database

Gg/yr billion grams per year

GHG greenhouse gas

GIS geographic information system

GWP global warming potential

HCO

3

– bicarbonate

H

2

CO

3 carbonic acid

ha hectares

HR heterotrophic respiration

HWD harvested wood

HUC hydrologic unit code

IPCC Intergovernmental Panel on Climate Change

kgC/m

2 kilograms of carbon per square meter

kgC/m

2

/yr kilograms of carbon per square meter per year

kg/km

2

/yr kilograms per square kilometer per year

km kilometer

km

2 square kilometers
vi

km

2

/yr square kilometers per year

LANDFIRE Landscape Fire and Resource Management Planning Tools Project

LGAT Land Greenhouse Gas Accounting Tool

LOADEST USGS Load Estimator program

LULC land-use and land-cover m meters

m/d meters per day

MERIS medium resolution imaging spectrometer

mg/L milligrams per liter

MgC/ha megagrams of carbon per hectare

MIROC 3.2-medresModel for Interdisciplinary Research on Climate version 3.2 medium resolution

Mkm

2 million square kilometers

MLR multiple-linear-regression

mm millimeters

mm/yr millimeters per year

mmol/m

2

/yr millimoles per square meter per year

MODIS moderate resolution imaging spectroradiometer

MTBS Monitoring Trends in Burn Severity Project

MTT minimum travel time [algorithm] N2O nitrous oxide

NASA National Aeronautics and Space Administration

NECB net ecosystem carbon balance

NEE net ecosystem exchange

NEP net ecosystem production

NHD National Hydrography Dataset

NLA national lakes assessment

NLCD National Land Cover Database

NOAA National Oceanic and Atmospheric Administration

NPP net primary production

NWIS National Water Information System

PgC petagrams of carbon

ppm parts per million

RESSED Reservoir Sedimentation Database

RPA Resource Planning Act

SeaDAS SeaWiFS Data Analysis System

SOC soil organic carbon

SPARROW Spatially Referenced Regression on Watershed Attributes

SRES Special Report on Emission Scenarios

Tg teragrams

Tg/yr teragrams per year

TgC teragrams of carbon

TgC/yr teragrams of carbon per year

TgCO

2

-eq teragrams of carbon dioxide equivalent

TgCO

2

-eq/yr teragrams of carbon dioxide equivalent per year

TM Landsat Thematic Mapper

TN total nitrogen

TOC total organic carbon

TP total phosphorus

TSS total suspended sediment

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

VCT vegetation change tracker

WCRP World Climate Research Programme
Baseline and Projected Future Carbon Storage and 

Greenhouse-Gas Fluxes in Ecosystems of the

Eastern United States—Executive Summary

By Zhiliang Zhu,1 Benjamin M. Sleeter,1 Ryan R. Reker,2 Todd J. Hawbaker,1 Sarah Stackpoole,1

Brian A. Bergamaschi,1

 and Shuguang Liu1

1

U.S. Geological Survey

2

ASRC Federal InuTeq

Introduction

This is the third in a series of reports produced by the 

U.S. Geological Survey (USGS) to fulfill the requirements 

of section 712 of the Energy Independence and Security Act 

(EISA) of 2007 and to conduct a comprehensive national 

assessment of storage and flux (flow) of carbon and the fluxes 

of other greenhouse gases (GHGs, including carbon dioxide 

(CO2), methane (CH4 ), and nitrous oxide (N2O)). These 

carbon and GHG variables were examined for major terrestrial 

ecosystems (forests, grasslands/shrublands, agricultural lands, 

and wetlands) and aquatic ecosystems (rivers, streams, lakes, 

estuaries, and coastal waters) in the Eastern United States 

in two time periods: baseline (from 2001 through 2005) and 

future (projections from the end of the baseline through 2050). 

The Great Lakes were not included in this assessment due 

to a lack of input data. Chapter 1 of this report provides an 

overview of the general methodology. Topics of this report 

include the storage and fluxes of carbon estimated based 

on data included in maps of classes of land-use and landcover (LULC) change, climate change, land management, 

and wildland fire. Changes in storage and fluxes of carbon 

estimated based on potential changes in natural vegetation and 

future potential vegetation change due to climate change were 

not included in this report.

This regional assessment was conducted to achieve two 

primary objectives: (1) to contribute to the EISA mandate for 

a national assessment of carbon sequestration and GHG fluxes, 

and (2) to help improve the understanding of carbon cycling 

at a regional scale by focusing on different ecosystems and 

their relations with natural and anthropogenic controlling 

processes (such as climate change, LULC, land management, 

and wildland fires). To meet the objectives, the assessment was 

designed to provide answers to such questions as “How much 

carbon was stored in ecosystems of the Eastern United States 

and how would storage of carbon change over space and 

time?” and “How might the stored carbon and carbon fluxes be 

affected by the natural and anthropogenic processes that affect 

the region?”

The assessment covered an area of 3.05 million 

square kilometers in the Eastern United States, which is 

divided into in seven level II ecoregions (as defined by the 

U.S. Environmental Protection Agency, 2013): the Mixed 

Wood Shield, Atlantic Highlands, Mixed Wood Plains, 

Central USA Plains, Southeastern USA Plains, Ozark, 

Ouachita-Appalachian Forests, and Mississippi Alluvial and 

Southeast USA Coastal Plains ecoregions. Further description of the ecoregional framework is provided in detail in 

chapters 1 and 2. The assessment was based on measured and 

remotely sensed data collected by the USGS and many other 

agencies and organizations, combined with statistical methods 

and simulation models. Specific input data used in different 

components of the assessment methodology are described in 

chapters 3 through 7. The major findings are discussed in this 

executive summary.

Baseline and Projected Future Land-Use

and Land-Cover Change

In 2005, the total area of the Eastern United States 

(3.05 million square kilometers) included the following 

ecosystems: forests (47.9 percent), agricultural lands 

(31.2 percent), wetlands (8.9 percent), open water (lakes, 

reservoirs, rivers, and streams; 5.4 percent), grasslands/

shrublands (1.7 percent), and other land types (5 percent). 

Between 1992 and 2005, changes in land use (such as 

croplands) and land cover (such as wetlands) in the Eastern 

United States affected 8.9 percent of that land area (compared 

with 2.9 percent in the Western United States) and were 

driven primarily by demands for forest products and 

urban development.
2 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

The projected changes (to 2050) in both LULC and 

climate were used in this assessment to support the projection 

of future potential storage and fluxes of carbon in relation 

to ecological and economic processes. The projections in 

LULC were highly variable across the seven ecoregions of 

the Eastern United States, as were the assumptions that were 

made. For the Eastern United States as a whole, the total 

projected change ranged from 17.2 to 22.9 percent between 

different scenarios, including changes between different 

LULC categories such as forests, agriculture, and developed 

lands. The most active ecoregion in terms of projected LULC 

change was the Southeastern USA Plains ecoregion, averaging 

31 percent of change between the different LULC types over 

the projection years.

Baseline Carbon Storage and 

Greenhouse Gas Fluxes

Main findings from this assessment for baseline years are 

described in paragraphs below and are summarized in table E1. 

The average total storage of carbon in the terrestrial ecosystems 

of the Eastern United States in 2005 was estimated to be 26,962 

teragrams of carbon (TgC), ranging between a low of 25,069 and 

a high of 28,497 TgC. The total carbon stock was distributed in 

forests (68.1 percent), wetlands (15.1 percent), agricultural lands 

(14.7 percent), grasslands/shrublands (0.9 percent), and other 

lands (1,1 percent). Geographically, the estimated total carbon 

stocks ranged from 1,499.5 TgC in the Central USA Plains 

ecoregion to 4,791 TgC in the Ozark, Ouachita-Appalachian 

Forests ecoregion, and the per-unit-of-area carbon stocks ranged 

from 6.3 kilograms of carbon per square meter (kgC/m2) in the 

Central USA Plains ecoregion to 16.1 kgC/m2 in the Atlantic 

Highlands ecoregion. On average, the wetlands maintained the 

largest stock of carbon per unit of area among all the ecosystems 

in the Eastern United States with 15 kgC/m2, followed by forests 

(12.6 kgC/m2), grasslands/shrublands (4.7 kgC/m2), agricultural 

lands (4.2 kgC/m2), and other land types (1 kgC/m2). Overall, live 

biomass accounted for 42 percent of the total carbon stock, and 

soil organic carbon (SOC; assessed in the top 20-centimeter-thick 

layer only) accounted for 43 percent of the total carbon stock; 

woody debris and other surface carbon pools represented the 

remaining 15 percent.

The net flux of carbon was calculated as the change of 

carbon stock between two points in time. A negative number 

indicates carbon uptake, carbon sequestration, or a carbon 

sink; a positive number indicates a carbon emission or a 

carbon source. From 2001 to 2005, an average annual net flux 

of −279.4 TgC/yr, ranging from a low of −405.5 TgC/yr to 

a high of −112.5 TgC/yr, was estimated for all the terrestrial 

ecosystems in the Eastern United States. Most of the net 

carbon flux in the study area was in forests (81 percent, 

−154.8 grams of carbon per square meter per year (gC/m2

/yr)), 

followed by wetlands (12.8 percent, −132.2 gC/m2/yr), agricultural lands (4.1 percent, −12.1 gC/m2

/yr), and grasslands/

shrublands (0.8 percent, −40.9 gC/m2/yr). Of the total carbon 

sink, live biomass accounted for 67.6 percent, SOC accounted 

for 23.4 percent, and dead biomass accounted for 9 percent.

For comparison with the estimated net carbon flux in 

this study, a recent study found that the net carbon flux from 

LULC change and forestry was –246.8 TgC in 2011 for the 

area of the conterminous United States (U.S. Environmental 

Protection Agency, 2013b). In a separate assessment for the 

Western United States (Liu and others, 2012), using the same 

method as described in this report, the per-unit-area net carbon 

flux for forests was estimated to be –72.1 gC/m2/yr. Net forest 

carbon flux was −93 gC/m2/yr for the United States as a whole 

(Pan and others, 2011).

Between 2001 and 2008 in the Eastern United States, 

875 to 4,842 square kilometers per year was burned, releasing 

1.8 to 20.5 teragrams of CO2-equivalent per year (TgCO2-eq/yr)

(0.5 to 5.6 TgC/yr), mostly along the Atlantic coast in the 

Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregion, which includes the Everglades and Texas-Louisiana 

Coastal Plains ecoregions. The average annual GHG emission 

from the fires was 5.9 TgCO2-eq/yr (1.6 TgC/yr).

Removal of carbon from forest ecosystems in the Eastern 

United States was estimated for forest harvesting (clearcutting 

and partial cutting, with forests remaining as forests) and 

forests converting to other uses (including agriculture, 

developed, and other land uses). Carbon removal from forest 

harvesting was estimated to be an average of 41 TgC/yr from 

2001 through 2005, whereas from land use conversions, the 

average was 7.3 TgC/yr.

Using data collected between the 1970s and the present, 

rivers and streams throughout the Eastern United States were 

estimated to transport 36.5 TgC/yr (or an average of 6.4 gC/m2/yr 

in yield per unit of area), ranging from 28.1 to 44.8 TgC/yr (or 

an average of 5 to 7.9 gC/m2/yr) of dissolved inorganic and total 

organic carbon from upstream sources to estuaries and the coastal 

oceans. The emissions of carbon dioxide to the atmosphere from 

inland waters of the Eastern United States were 51.3 TgC/yr (or 

an average of 5.2 gC/m2/yr in yield per unit of area), ranging from 

34.3 to 71.4 TgC/yr (or an average of 3.8 to 6.3 gC/m2/yr in yield 

per unit of area); 81 percent of the total emissions was from rivers 

and streams, and 19 percent was from lakes and reservoirs.

The rate of carbon burial (sequestration) in the sediments 

in the lakes and reservoirs was estimated to be –9.2 TgC/yr 

(or an average of −3.1 gC/m2

/yr in yield per unit of area), 

ranging from – 4.6 to –13.8 TgC/yr (or an average of 

–1.6 to –4.7 gC/m2/yr in yield per unit of area). The estimates 

of carbon fluxes in aquatic ecosystems were highly variable 

because of differences in precipitation, topography, lithology, 

and other controlling processes (Pacala and others, 2001).

Carbon is also stored in coastal waters in the Eastern 

United States through burial in coastal sediments and through 

transport of carbon from the surface into the deep ocean. 

The contribution of terrestrial processes to the total annual 

rate of carbon storage in coastal waters was estimated to be 

–7.8 TgC/yr, ranging from –1.8 to –22.4 TgC/yr, similar in 

the ranges of values of estimated burial of carbon in lakes and 
Executive Summary 3

reservoirs. However, coastal sediments and deep ocean waters 

represent an important long-term reservoir for carbon storage, 

amounting to storage of more than 15,000 TgC over millennial time scales under modeled conditions, or approximately 

60 percent of the total terrestrial carbon pool.

In addition to the baseline net terrestrial carbon flux

estimates (−279.4 TgC/tr, ranging from −405.5 to 

−112.5 TgC/yr, or −1,024.6 TgCO2-eq/yr, ranging from 

–1,487 to – 412.9 TgCO2-eq/yr), the baseline flux rates of 

CH4 and N2O were estimated to be relatively low and highly 

variable among ecosystems and ecoregions. Overall, the 

estimated flux rate of CH4 during the baseline years was 

193 TgC/yr, ranging from 191.7 to 195 TgCO2

-eq/yr. The 

estimated flux rate of N2O was 174.7 TgCO2-eq/yr, ranging 

from 173 to 176.6 TgCO2-eq/yr.

Projected Future Carbon Storage and 

Greenhouse Gas Fluxes

In order to project the future potential carbon storage 

amounts and flux rates, combinations of LULC scenarios 

and climate projections, developed on an annual basis for 

2006 through 2050, were used for the assessment along 

with multiple biogeochemical models. The results of these 

combinations led to a range of estimates for storage and flux 

of carbon under a range of projected future conditions. The 

results of projected future potential stocks and flux of carbon 

were highly variable among multiple model runs, ecoregions, 

and ecosystems

The total amount of carbon that potentially could be 

stored in the terrestrial ecosystems of the Eastern United States 

in 2050 was projected to be 37,082 TgC, ranging from 

25,512 to 46,002 TgC, which is an increase of 10,121 TgC or 

37.5 percent in total carbon stock from the stocks stored in the 

baseline period. Seventy percent of the increase is in forest 

ecosystems of the study area.

The potential mean annual net carbon flux between 2006 

and 2050 was projected to be –224.9 TgC/yr, ranging from 

–403.7 to 1.4 TgC/yr. When compared with the –279.4 TgC/yr 

net carbon flux estimates for the baseline period, ranging 

from −405.5 to −112.5 TgC/yr, the projected rates of future 

carbon sequestration in the Eastern United States represented 

a potential decrease of 54.5 TgC/yr, ranging from 1.8 to 

113.9 TgC/yr. The projected decrease was largely driven by a 

potential decrease in the rate of sequestration in forest lands; 

the rates of carbon sequestration of agricultural lands and 

wetlands were projected to increase.

Under future projections of climate change, the area 

burned by wildland fires was projected to increase by 

17 to 51 percent, and the GHG combustion emissions from 

wildland fires were projected to increase by 1 to 41 percent, 

relative to baseline conditions. Under extreme climate 

conditions, wildland fire emissions were projected to increase 

by 43 to 122 percent relative to baseline conditions. Carbon 

stored in wetlands of the Atlantic coast may be especially 

vulnerable to wildland-fire emissions because most of the 

Table E1. Baseline estimates by ecosystems in the Eastern United States.

[gC/m2/yr, grams of carbon per square meter per year; kgC/m2, kilograms of carbon per square meter; TgC, teragrams of carbon; TgC/yr, teragrams of carbon 

per year; TgCO2-eq/yr, teragrams of carbon dioxide-equivalent per year]

Variable Ecosystem Mean (range if available)

Total carbon stock All terrestrial ecosystems 26,961.8 (25,068.8 to 28,497.0) TgC

Per unit area carbon stock Forests 12.6 kgC/m2

Wetlands 15.0 kgC/m2

Agricultural lands 4.2 kgC/m2

Average annual net carbon flux All terrestrial ecosystems –279.4 (– 405.5 to –112.5) TgC/yr

Per unit area net carbon flux Forests –154.8 gC/m2/yr

Wetlands –132.2 gC/m2

/yr

Agricultural lands –12.1 gC/m2

/yr

Grasslands/shrublands –40.9 gC/m2

/yr

Wildland fire emissions Forests, wetlands 1.6 (0.5 to 5.6) TgC/yr

Carbon removal from harvest Forests 41.0 TgC/yr

Carbon removal from forest conversions Forests converting to nonforest land use 7.3 TgC/yr

Riverine transport Inland waters 36.5 (28.1 to 44.8) TgC/yr

Emissions (efflux) Inland waters 51.3 (34.3 to 71.4) TgC/yr

Carbon storage rate Lakes and reservoirs –9.2 (–4.6 to –13.8) TgC/yr

Carbon storage rate Coastal waters –7.8 (–1.8 to –22.4) TgC/yr

Coastal storage Coastal waters 15,000 TgC

Total methane emissions All terrestrial ecosystems 193.0 (191.7 to 195) TgCO2-eq/yr

Total nitrous oxide emissions All terrestrial ecosystems 174.7 (173.0 to 176.6) TgCO2-eq/yr
4 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

fire activities were projected to be concentrated in the coastal 

regions where years of land use have altered some characteristics of hydrologic and fire regimes, such as exposed peat 

soils, and increased fire frequency.

Rates of carbon storage in coastal and estuarine waters 

were projected to increase by 19 to 25 percent between 2005 

and 2050, largely a result of projected changes in nutrient and 

sediment supply to coastal waters. The environmental scenario 

showed the lowest projected change, whereas the economic 

scenario where energy development was not based on any one 

particular source (renewable technologies played an equal part 

in the energy mix as fossil fuels) had the greatest projected 

change. Nevertheless, the annual rate of coastal carbon 

storage represents less than 5 percent of those projected for 

the terrestrial system, so the increased rates projected for 

coastal waters will do little to offset the decreases seen in the 

terrestrial estimates.

Limitations of the Assessment Report

The known limitations of the assessment report include 

the following:

• LULC change was used as the geographic foundation 

of the assessment, but specific effects of LULC change 

on stocks and flux of carbon were not fully quantified 

and analyzed in this report.

• Future climate projections were used to project future 

ecosystem storage and fluxes of carbon, but the 

specific effects of the future climate projections on 

ecosystem carbon balance were not fully quantified 

and analyzed in this report.

• Wildland-fire combustion emissions were estimated, 

but the long-term effects of wildland fires on the 

production of carbon in ecosystems were not 

analyzed separately in this report.

• Other ecosystem disturbances, such as insect-caused 

forest mortalities or windstorms were not included 

in the assessment.

• Fluxes of CH4 and N2O were projected to future 

years based on a set of LULC scenarios without 

considering the effects of climate projections.

• The baseline carbon fluxes were estimated for the 

first time for aquatic ecosystems, but the existing carbon storage in the sediments of the aquatic 

ecosystems was not estimated.

• The SOC pool estimated and mapped in this 

assessment only represented available data for 

soil profile up to 20 centimeters deep. These data 

should not be compared with other SOC results 

produced at profiles deeper than 20 centimeters.

• Uncertainties from model runs of different 

components of the assessment were quantified

using simple statistical methods to account for

 the spread of the estimates.

Other sources of uncertainties were described in the 

report but were not quantified. As a result, the total uncertainty 

of the assessment is unknown. In addition, there were limitations resulting from the methodology used for the assessment; 

specifically, the mapping and modeling of major components 

(such as LULC and wildland fires) of the assessment were not 

coupled in a completely integrative modeling system.

Selected References

Liu, Shuguang, Liu, Jinxun, Young, C.J., Werner, J.M., 

Wu, Yiping, Li, Zhengpeng, Dahal, Devendra, 

Oeding, Jennifer, Schmidt, G.L., Sohl, T.L., Hawbaker, T.J.,

and Sleeter, B.M., 2012, Baseline carbon storage, carbon 

sequestration, and greenhouse-gas fluxes in terrestrial 

ecosystems of the western United States, chap. 5 of

Zhu, Zhiliang, and Reed, B.C., eds., Baseline and projected future carbon storage and greenhouse-gas fluxes in 

ecosystems of the western United States: U.S. Geological 

Survey Professional Paper 1797, p. 45– 63. (Also available 

at http://pubs.usgs/gov/pp/1797/.)

Pacala, Stephen, Birdsey, R.A., Bridgham, S.D., Conant, R.T., 

Davis, Kenneth, Hales, Burke, Houghton, R.A., Jenkins, J.C.,

Johnston, Mark, Marland, Gregg, Paustian, Keith, 

Caspersen, John, Socolow, Robert, and Tol, R.S.J., 2007, 

The North American carbon budget past and present, 

chap. 3 of King, A.W., Dilling, Lisa, Zimmerman, G.P., 

Fairman, D.M., Houghton, R.A., Marland, Gregg, 

Rose, A.Z., and Wilbanks, T.J., eds., The first state of the 

carbon cycle report (SOCCR)—The North American 

carbon budget and implications for the global carbon cycle, 

U.S. Climate Change Committee Synthesis and Assessment 

Product 2.2: Asheville, North Carolina, National Oceanic 

and Atmospheric Administration, p. 29–36, accessed 

November 7, 2013, at http://downloads.globalchange.gov/

sap/sap2-2/sap2-2-final-all.pdf.

Pan, Yude, Birdsey, R.A., Fang, Jingyun, Houghton, Richard,

Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, Anatoly,

Lewis, S.L., Canadell, J.G., Ciais, Philippe, Jackson, R.B., 

Pacala, S.W., McGuire, A.D., Piao, Shilong, Rautiainen, Aapo,

Sitch, Stephen, and Hayes, Daniel, 2011, A large and 

persistent carbon sink in the world’s forests: Science, 

v. 333, no. 6045, p. 988–993, accessed October 31, 2013, 

at http://dx.doi.org/10.1126/science.1201609.

U.S. Congress, 2007, Energy independence and security act 

of 2007—Public Law 110–140: U.S. Congress, 311 p., 

accessed November 7, 2013, at http://frwebgate.access.gpo.gov/

cgi-bin/getdoc.cgi?dbname=110_cong_public_laws&docid=f:

publ140.110.pdf.
Executive Summary 5

accessed November 13, 2013, at http://www.epa.gov/wed/

pages/ecoregions/na_eco.htm.

U.S. Environmental Protection Agency, 2013b, Inventory of 

U.S. greenhouse gas emissions and sinks —1990–2011: 

U.S. Environmental Protection Agency EPA 430–R–

13– 001, [variously paged], accessed April 29, 2013,

at http://www.epa.gov/climatechange/Downloads/

ghgemissions/US-GHG-Inventory-2013-Main-Text.pdf.

Zhu, Zhiliang, and Reed, B.C., eds., 2012, Baseline and projected future carbon storage and greenhouse-gas fluxes in 

ecosystems of the western United States: U.S. Geological 

Survey Professional Paper 1797, 192 p. (Also available at 

http://pubs.usgs.gov/pp/1797/.)

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.)

Zhu, Zhiliang, ed., Bouchard, Michelle, Butman, David, 

Hawbaker, Todd, Li, Zhengpeng, Liu, Jinxun, 

Liu, Shuguang, McDonald, Cory, Reker, Ryan, 

Sayler, Kristi, Sleeter, Benjamin, Sohl, Terry, 

Stackpoole, Sarah, Wein, Anne, and Zhu, Zhiliang, 2011, 

Baseline and projected future carbon storage and greenhouse-gas fluxes in the Great Plains region of the United 

States: U.S. Geological Survey Professional Paper 1787, 

28 p. (Also available at http://pubs.usgs.gov/pp/1787/.)

Chapter 1. Scope, Methodology, and Current Knowledge

By Zhiliang Zhu, Benjamin M. Sleeter, Terry L. Sohl, Todd J. Hawbaker, Shuguang Liu, Sarah Stackpoole, 

Brian A. Bergamaschi, and Ashwan D. Reddy

1.1. Scope and General Methodology

This is the third in a series of reports produced by the 

U.S. Geological Survey (USGS) for a national assessment 

of carbon sequestration and greenhouse-gas (GHG) fluxes in 

ecosystems. The first two reports covered ecosystems of the 

Great Plains (Zhu and others, 2011) and Western (Zhu and 

Reed, 2012) regions of the United States. This report covers 

ecosystems in the Eastern United States, an area of about 

3.05 million square kilometers (Mkm2; fig. 1–1) that extends 

from the Great Lakes, the Mississippi flood plains, and the 

Appalachian Mountains to the plains of the coasts of the 

Atlantic Ocean and the Gulf of Mexico.

The Eastern United States is a region largely dominated 

by forests. Conifer and deciduous forests span the Northeast 

and the areas near and around the Great Lakes, and plantationstyle forestry is prevalent in the southern coastal region. In the 

Southeast, forest land use is dynamic and involves management 

and short harvest cycles. In other regions of the Eastern United 

States, the pattern of forest lands decreasing in area because of 

urbanization but gaining in area from abandoned agricultural 

lands is prevalent. There is a substantial amount of agriculture in 

the Eastern United States; at the same time, the region is highly 

urbanized in areas along the eastern seaboard and around the 

southern coast of the Great Lakes. Some of the major population 

centers within the United States are in the East, and these centers 

are proving to be a key driver of land-use change from forest and 

agriculture. Wetlands have a large presence along the Atlantic and 

the gulf coasts as well as in the Great Lakes region, representing 

the majority of wetland areas in the conterminous United States.

This assessment of carbon stocks and sequestration and 

GHG fluxes was part of a national assessment required by the 

Energy Independence and Security Act of 2007 (EISA; U.S. 

Congress, 2007), which mandated that the U.S. Department 

of the Interior provide estimates of (1) the amount of carbon 

stored in ecosystems, (2) the capacity of ecosystems to 

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

the ecosystems, and (4) the effects of the natural and anthropogenic processes that control ecosystem carbon balances and 

GHG fluxes. The GHGs considered in this assessment were 

carbon dioxide (CO2 ), carbon monoxide (CO, from wildland 

fires only), dissolved inorganic carbon (DIC), methane (CH4 ), 

and nitrous oxide (N2O).

This regional assessment was conducted to (1) contribute 

to the EISA mandate for a national assessment of carbon 

sequestration and GHG fluxes and (2) help improve the 

understanding of carbon cycling at a regional scale by focusing 

on different ecosystems and their relations with natural and 

anthropogenic controlling processes (such as climate change, 

land use, land management, and wildland fires). To meet these 

objectives, the assessment was designed to provide answers 

to questions including the following: How much carbon was 

stored in ecosystems of the Eastern United States and how may 

it change over space and time? How might the stored carbon 

and carbon fluxes be affected by the natural and anthropogenic processes that control their storage and release in the 

ecosystems of the Eastern United States? Results and analyses 

provided in this report, as well as the previous two reports 

covering the Great Plains (Zhu and others, 2011) and Western 

(Zhu and Reed, 2012) regions of the United States, pertain to 

the first question and parts of the second question (because 

not all processes were exhaustively analyzed and presented). 

Results pertaining to the natural vegetation change under future 

climate change are not within the purview of this report.

The major ecosystems evaluated in this study are classified as terrestrial (forests, wetlands, grasslands/shrublands, 

and agricultural lands) or aquatic (rivers, lakes, estuaries, and 

coastal waters). The thematic definitions of the ecosystems 

and their spatial boundaries are outlined in table 1–1 and in 

Zhu and others (2010). The definitions are largely based on 

the National Land Cover Database (NLCD; Vogelmann and 

others, 2001; Homer and others, 2007), which was the primary 

source of initial land-use and land-cover (LULC) data for this 

assessment. The LULC data, derived from Landsat remote 

sensing data, were used to define the spatial boundaries of 

the ecosystems that were assessed in this study. Because the 

remote-sensing data allowed for complete coverage of the 

Eastern United States, the resulting spatially and temporally 

explicit data products provided a convenient and comprehensive basis for deriving carbon and GHG estimates.

Within the NLCD database, land-use (for example, 

mechanically disturbed classes (that is, forest clearcuts)) 

and land-cover (for example, forests) classes were mapped 

using data acquired from Landsat satellites. Because of the 

input data and the remote sensing methods used, areas in 

urban centers with significant tree cover, as determined by 

the NLCD, were treated as forest cover in this study. LULC 

change refers to changes between the LULC classes, including 

land conversions (such as from forest to developed lands) and 

cover change as the result of forest clearcuts.
8 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

100° 95° 90° 85° 80° 75° 70° 65°

50°

45°

40°

35°

30°

25°

0 100 200 300 400 MILES

0 100 200 300 400 KILOMETERS

Base from 2001 National Land Cover Database

(Homer and others, 2007)

CANADA

MEXICO

BAHAMAS

NORTH

DAKOTA

SOUTH

DAKOTA

NEBRASKA

MINNESOTA

IOWA

KANSAS

OKLAHOMA

TEXAS

MISSOURI

MARYLAND

VIRGINIA

PENNSYLVANIA

MAINE

KENTUCKY

INDIANA

MICHIGAN

WISCONSIN

ILLINOIS

ARKANSAS

OHIO

TENNESSEE

ALABAMA

LOUISIANA

MISSISSIPPI

GEORGIA

FLORIDA

SOUTH

CAROLINA

NORTH

CAROLINA

WEST

VIRGINIA

DELAWARE

NEW JERSEY

MASSACHUSETTS

RHODE ISLAND

CONNECTICUT

VERMONT

NEW

HAMPSHIRE

NEW YORK

SWA

SEP

SEP

MACP

MACP

BRM

RV

RV

NCA

NAPU

NP

NEH

LPH

ACPB

ACPB

NECZ

NEH

NEH

NCA

NEH

EDP

EGLHL

WAP

CA

IP

IRL

CCBP

ECBP

SMNIDP

HELP

HELP

NLF

DA

NCHF

NCHF

SEWTP

NLF

NMW

OH

MAP

MVLP

BM

AV

OM

SCP

WGCP

ECTP

SCOP

SFCP

P

Figure 1.1. (pages ? and ?).

ATLANTIC

OCEAN

Figure 1–1. Map showing the spatial extent of the assessment of carbon storage and fluxes in the Eastern United States. The 

region consists of seven level II ecoregions (modified from U.S. Environmental Protection Agency, 2013a). The total area of the 

Eastern United States is about 3.05 million square kilometers (table 2–2). The land-use and land-cover classes shown on the map 

represent conditions that existed in 2005, the year that was chosen as the baseline for this assessment.
Chapter 1 9

Mixed Wood

Shield

Index map of Level II ecoregions

in the Eastern United States

Ozark, QuachitaAppalachian Forests

Southeastern

USA Plains

Mixed Wood Plains

Atlantic

Highlands

Mixed Wood Shield

NLF Northern Lakes and Forests

NMW Northern Minnesota Wetlands

Ozark, Quachita-Appalachian Forests

AV Arkansas Valley

BRM Blue Ridge Mountains

BM Boston Mountains

CA Central Appalachians

OH Ozark Highlands

OM Ouachita Mountains

RV Ridge and Valley

SWA Southwestern Appalachians

WAP Western Allegheny Plateau

Southeastern USA Plains

ECTP East Central Texas Plains

IP Interior Plateau

IRL Interior River Lowland

MVLP Mississippi Valley Loess Plains

NP Northern Piedmont

P Piedmont

SCP South Central Plains

SEP Southeastern Plains

Atlantic Highlands

NAPU Northern Appalachian Plateau 

 and Uplands

NCA North Central Appalachians

NEH Northeastern Highlands

Central USA Plains

CCBP Central Corn Belt Plains

ECBP Eastern Corn Belt Plains

HELP Huron/Erie Lake Plains

SEWTP Southeastern Wisconsin Till Plains

Mississippi Alluvial and Southeast 

 USA Coastal Plains

ACPB Atlantic Coastal Pine Barrens

MACP Middle Atlantic Coastal Plain

MAP Mississippi Alluvial Plain

SCOP Southern Coastal Plain

SFCP Southern Florida Coastal Plain

 [Everglades]

WGCP Western Gulf Coastal Plain

 [Texas-Louisiana Coastal PLain]

Mixed Wood Plains

DA Driftless Area

EGLHL Eastern Great Lakes and 

 Hudson Lowlands

EP Erie Drift Plain

LPH Laurentian Plains and Hills

NCHF North Central Hardwood Forests

NECZ Northeastern Coastal Zone

SMNIDP Southern Michigan/Northern 

 Indiana Drift Plains

Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Water

Developed

Mechanically disturbed 

 national forest

Mechanically disturbed 

 other public land

Mechanically disturbed 

 private land

Mining

Barren

Deciduous forest

Evergreen forest

Mixed forest

Grassland

Shrubland

Agriculture

Hay/pasture

Herbaceous wetland

Woody wetland

Ice/snow

Level III

Level II

Ecoregion boundary

Land cover

EXPLANATION

Figure 1–1.—Continued
10 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 1–1. Land use and land cover in the Eastern United States.

[For the National Land Cover Dataset, see Vogelmann and others (2001). Vegetation Change Tracker is a product of Landscape Fire and Resource Management Planning Tools Project (LANDFIRE; Rollins, 2009); see Huang and others (2010)]

LULC class Ecosystem

Area,

in percent

of total

Source

National Land Cover Dataset

Barren Other lands 0.1 Bare rock, sand, and clay

Cultivated crop Agricultural lands 13.9 Row crop; small grains; fallow

Deciduous forest Forests 23.7 Deciduous forest

Evergreen forest Forests 16.4 Evergreen forest

Grassland Grasslands/shrublands 0.6 Grassland and herbaceous

Hay/pasture Agricultural lands 17.9 Pasture and hay

Herbaceous wetland Wetlands 0.4 Emergent herbaceous wetlands

Ice/snow Other lands 0.0 Perennial ice and snow

Mining Other lands 0.1 Quarries, strip mines, and gravel pits

Mixed forest Forests 12.6 Mixed forest

Open water Other lands 2.0 Open water

Shrubland Grasslands/shrublands 0.5 Shrubland

Urban/developed Developed 3.5 Low- and high-intensity residential; commercial, 

industry, and transportation; urban and 

recreational grasses

Woody wetland Wetlands 5.6 Woody wetlands

Landscape Fire and Resource Management Planning Tools Project

Mechanically disturbed, national forest Forests 0.1 Vegetation Change Tracker

Mechanically disturbed, other public land Forests 0.0 Vegetation Change Tracker

Mechanically disturbed, private land Forests 2.6 Vegetation Change Tracker

Within the LULC classes, land-management activities 

were defined as those actions that were aligned with the 

LULC classes and modified the way land was used, but did 

not change the LULC classes. Forest clearcuts were mapped 

as a LULC change class but were treated as a management 

activity when deriving and analyzing carbon estimates. Input 

data describing land-management or ecosystem disturbances 

were developed in addition to the classifications in the NLCD; 

these additional data are described in chapters 3 and 4. As a 

result, forest clearcuts and thinning were land-management 

activities included in the assessment, as were several agricultural management activities described in chapter 7. Ecosystem 

disturbances are defined as those natural disturbances that 

altered the production of carbon or other functions in an 

ecosystem. For this assessment, wildland fire was the only 

natural disturbance that was considered; natural disturbances 

such as persistent drought and insect-driven forest defoliation 

(such as gypsy moth) or mortality (such as spruce budworm 

epidemics) were not included.

The assessment was conducted based on a methodology 

framework that (1) collected and used existing data, including 

various biophysical data derived from remote sensing, 

inventories of biological resources and soil properties, climate 

histories and future projections, and measurements made 

by a national network of streamgages; (2) linked land-use, 

land-management, and climate data with statistical and 

process-based methods and models to generate spatially and 

temporally explicit carbon storage and GHG flux estimates; 

and (3) applied a set of future LULC and climate change 

scenarios to the assessment to project a range of estimates for 

carbon stock and sequestration and GHG flux in ecosystems. 

The major components of the assessment methodology and 

their roles and relations are listed in table 1–2 and shown 

in figure 1–2, with corresponding chapters of this report 

indicated in the boxes along with the methodological details 

described in the chapters.

The methodology framework shown in figure 1–2 is 

spatially and temporally explicit. The spatial and temporal 

framework is described in chapter 2. Based on this framework, 

the ecosystems were mapped or modeled, and all pixels 

from the mapping effort were partitioned into LULC and 

LULC-change classes, using the landscape LULC model 

described in chapter 3. The resulting digital maps, described 

in chapters 3 through 7, have a nominal spatial resolution of 

250 meters. Because the assessment was conducted at national 

and broad regional scales, the resulting information and data 

products should be applied only at the regional scale or broader.

In this report, the term “region” is often used in a general 

sense, depending on the context, whereas the term “ecoregion” 

refers to the U.S. Environmental Protection Agency (EPA) 

ecoregion mapping hierarchy (U.S. Environmental Protection 

Agency, 2013a). In total, seven level II EPA ecoregions 
Chapter 1 11

Table 1–2. Main features of the assessment methodology of carbon storage and fluxes in the Eastern United States.

[EPA, U.S. Environmental Protection Agency; FIA, Forest Inventory and Analysis National Program (USDA); FORE–SCE, forecasting scenarios of future 

land cover; LANDFIRE, Landscape Fire and Resource Management Planning Tools Program (Rollins, 2009); LULC, land use and land cover; IPCC, Intergovernmental Panel on Climate Change; MTBS, Monitoring Trends in Burn Severity Project; NASA, National Aeronautics and Space Administration; NHD, 

National Hydrography Dataset; NLCD, National Land Cover Dataset (Vogelmann and others, 2001); NOAA, National Oceanic and Atmospheric Administration; NWIS, National Water Information System; RESSED, Reservoir Sedimentation Database; SRES, Special Report on Emissions Scenarios; USDA, U.S. 

Department of Agriculture; USGS, U.S. Geological Survey; VCT, vegetation change tracker (a product of LANDFIRE; Huang and others, 2010)]

Methodology feature Input data Chapters

Maps of baseline and projected future LULC classes, forest age, 

and ecosystems at 250-meter resolution using a set of LULC 

change scenarios and FORE–SCE landscape model

NLCD, VCT, FIA data, and LULC change history from USGS 

Land Cover Trends project, the IPCC SRES

scenarios, and references from literature

2, 3

Baseline and projected future wildfire areas, severity and 

emissions using an integrated fire prediction and behavior 

model. Derived data produced at 250-meter resolution

Fire data from MTBS, LANDFIRE fuel data, downscaled 

weather and future climate data

4

Baseline carbon transport, emissions, and burial by inland aquatic 

ecosystems derived using empirical methods

(tabular data only)

Surface water areas derived using the USGS NHD, and measured variables from the USGS NWIS, the USGS RESSED, 

and the EPA national lakes assessment

5

Baseline and projected future fluxes of nutrients and

sediments to estuarine and coastal waters, and resulting

terrestrial component of coastal carbon storage. Products 

are digital maps at 250-meter spatial resolution

LULC data from chapters 2 and 3; historic water-quality 

data from USGS NWIS; USGS SPARROW model output; 

coastal bathymetric and estuary parameter data from NOAA; 

remote sensing data for chlorophyll and suspended sediment 

from NASA

6

Baseline and projected future carbon stock and flux using three 

models: a spreadsheet model, CENTURY, and EDCM. Simulations were run at 1 percent systematic sample (full-resolution 

maps subsequently produced at 250-meter resolution). A total 

of 3 simulations were run at annual steps for baseline and 21 

for future projections

Biomass and land-management data derived from resource

inventories (FIA agricultural data), soil organic carbon from 

national soil databases, future climate projections,

and the baseline and future projected LULC, forest age, 

ecosystems, and fire data from chapters 2, 3, and 4

7

were used for the assessment: (1) Mixed Wood Shield, 

(2) Atlantic Shield, (3) Mixed Wood Plains, (4) Central USA 

Plains, (5) Southeastern USA Plains, (6) Ozark, OuachitaAppalachian Forests, and (7) Mississippi Alluvial and 

Southeast USA Coastal Plains (which includes the Everglades 

and the Texas-Louisiana Coastal Plain ecoregions for the 

purposes of this assessment; Zhu and others, 2010, fig. 3–1).

All the models used in this assessment were parameterized, and the results were calibrated and validated on the 

basis of these ecoregions. The major terrestrial and aquatic 

ecosystems are analyzed within these ecoregions. The use of 

the ecoregions and the NLCD LULC classes chosen for the 

ecosystems in this assessment suggest that the reported results 

are meaningful within the defined ecoregion and ecosystem 

definitions and may not be directly comparable with other 

national- or regional-level estimates because of the different 

boundary definitions. Further discussion of the ecoregions and 

ecosystems may be found in Zhu and others (2010).

The temporal foundation of the assessment was twofold 

and included a baseline and a range of future projections, 

as described in chapter 2. Baseline is defined as the average 

contemporary annual conditions to be assessed. Different 

components of the assessment have different ranges of 

baseline years stemming from considerations for data 

availabilities: LULC are assessed from 1992 through 2005; 

wildland fires, from 2001 through 2008; terrestrial carbon and 

GHG fluxes, from 2001 through 2005; and aquatic carbon 

fluxes, from the 1920s through 2011. A range of future LULC 

scenarios projecting from the time after the end of the baseline 

period through 2050 were developed by incorporating rationales and criteria of a set of GHG emission trajectories used 

in the Intergovernmental Panel on Climate Change (IPCC) 

Special Report on Emission Scenarios (SRES; Nakićenović 

and others, 2000), ensuring consistency of globally available 

climate and land-use data with the LULC scenarios developed 

for this assessment at the national and regional scales.

Based on the spatial and temporal frameworks, changes 

in LULC and forest age were mapped (and modeled) in annual 

steps using a spatially explicit landscape model (chap. 3). 

Fire areas, severity, and combustion emissions also were 

estimated and mapped, using a coupled statistical and landscape simulation model (chap. 4). The derived LULC, forest 

age, and fire data, along with other available data (including 

national soil databases, biomass growth curves, available 

land-management activity data (such as partial forest cutting 

in forests and the use of fertilization in agriculture), and future 

climate projections), were incorporated into a modeling task 

using three process-based ecosystem models (described in 

chap. 7) to estimate the current and projected future carbon 

stock, carbon flux, and GHG flux in four terrestrial ecosystems 

(forests, grasslands/shrublands, agricultural lands, and 

wetlands). Three general carbon pools were represented (live 

biomass, soil organic matter, and dead biomass (including 

litter and woody debris)). Ecosystem processes represented 
12 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

LULC scenarios 

(chapter 2)

Regional LULC change scenarios 

aligned with IPCC-SRES scenarios

A1B, A2, and B1, projected to 2050

Climate projections

(various chapters)

Global circulation models 

aligned with IPCC-SRES scenarios 

A1B, A2, and B1, projected to 2050

LULC modeling 

(chapter 3)

Including baseline and

projected future annual LULC

and forest age maps

Fire modeling 

(chapter 4)

Including baseline and projected 

future fire areas, severity, and 

GHG combustion emissions

Aquatic carbon estimation

(chapters 5 and 6)

Including carbon fluxes and burials

in inland and coastal waters

Terrestrial carbon 

and GHG modeling 

(chapter 7)

Including baseline

and projected future

carbon stocks, carbon

fluxes, and GHG fluxes

Estimates and

data products

(various chapters)

Including spatially

and temporally

explicit estimates

of baseline and

projected future

carbon stocks,

carbon fluxes,

other GHG fluxes,

and analyses

Input data 

(various chapters)

Including biomass,

GHG emission

factors, data 

derived from 

remote sensing, 

soils, and landmanagement

variables

Input data 

(chapters 5 and 6)

LULC results, national streamgage network, 

sedimentation data, lake chemistry data

Figure 1–2.

Figure 1–2. Flow diagram showing the general framework of the methodology used in the assessment of carbon storage 

and fluxes in the Eastern United States. The heading in each box represents a major component of the assessment. 

The chapter numbers indicate where in this report those components are discussed. Arrows show relations between 

components. GHG, greenhouse gas; IPCC–SRES, Intergovernmental Panel of Climate Change Special Report on Emissions 

Scenarios (Nakićenović and others, 2000); LULC, land use and land cover.

in the modeling task included transitions between the LULC 

and ecosystem classes, net primary production (NPP), 

heterotrophic respiration (HR), harvested wood (HWD; 

only accounting for carbon removed from landscapes from 

clearcuts and partial cuts), long-term effects of wildland 

fire, and available land-management activities (detailed in 

chap. 7). Estimates of carbon transport through riverine 

systems, emissions and burial by all inland water bodies, and 

flux and burial in estuaries and coastal waters were calculated 

separately and were based on a series of methods and models 

detailed in chapters 5 and 6. The results represent a baseline 

and a range of potential carbon sequestration rates by the 

ecosystems under a range of projected climate and land-use 

conditions, which is a requirement of the EISA.

Flux refers to emissions of GHG (such as carbon) to the 

atmosphere and to uptake by ecosystems. In presenting the 

results of terrestrial carbon flux assessment, net flux and net 

ecosystem carbon balance (NECB) are used interchangeably 

in this report to refer to the net rate of the change in carbon 

storage in ecosystems. For a given ecosystem and GHG, 

net flux and NECB are the same as net ecosystem exchange 

(NEE). However, the use of net carbon flux or NECB is 

conventional in large-area, land-based studies, such as this 

assessment. The terms used in calculating NECB and (or) 

net flux include net ecosystem production (NEP, which is 

the difference between the NPP and the HR), combustion 

and long-term emissions from wildland fires, and biomass 

harvesting. When reporting losses or gains in carbon 

storage, a negative number indicates carbon uptake, carbon 

sequestration, or a carbon sink; a positive number indicates 

a carbon emission or a carbon source. These conventions are 

used throughout this report unless noted. The usages follow 

standard conventions found in the literature on this topic 

(for example, Chapin and others, 2006; U.S. Environmental 

Protection Agency, 2012) and are consistent with the terms 

used in the assessment reports for the Great Plains (Zhu and 

others, 2011) and Western (Zhu and Reed, 2012) regions of 

the United States. In addition to assessing carbon storage 

and fluxes in ecosystems, the fluxes or emissions of other 

GHGs (such as CO2, CH4 , CO, DIC, and N2

O) were also 

assessed. Fluxes or emissions of the other GHGs are reported 

as CO2-equivalent values and flux estimates were calculated 

based on their respective global warming potential factors. The 

units of measurement used in this report also follow previous 

usages. The total amount of carbon stored in the three major 

carbon pools (live biomass, soil organic matter, and dead 

biomass) for a given region is reported in teragrams of carbon. 

When reporting carbon stock per unit of area, the values are 

given in kilograms of carbon. When reporting the carbon flux 

per unit of area, the values are given in grams of carbon.
Chapter 1 13

Whenever possible, the estimates are provided as a range of 

values, in addition to mean values, in order to represent the spread 

of variability in assessment results. The ranges of values were 

presented depending on chapters or components of the report. 

For variables of wildland fires (chap. 4), estimates were presented 

for the 5th and 95th percentile values, as well as median, mean, 

minimum, and maximum values, using Monte Carlo simulations. 

For the inland water component (chap. 5), the range of values 

was derived from the 5th and 95th confidence intervals of 

Monte Carlo simulations. Range values from the coastal water 

chapter reflected 10th and 90th confidence intervals using Monte 

Carlo simulations. Range values of terrestrial carbon and GHG 

estimates (chap. 7) refer to the minimum and maximum of annual 

mean values by model runs in the baseline and by model runs in 

LULC scenarios and future climate projections.

The spread of variability in the assessment results, as 

represented by the range values, covers a major portion of 

uncertainties in the resulting estimates. Uncertainties in this 

assessment were related to two general sources: the use of 

various input data and methods and models (in the baseline 

and future projections) and the use of LULC scenarios 

(future projections only). Monte Carlo simulations were used 

to quantify uncertainties and produce a range of estimated 

results related to the input data and the methods in the fire and 

aquatic assessments. For terrestrial carbon and GHG modeling 

(chap. 7), the Monte Carlo method was not used to estimate the 

uncertainties in the final results due to constraints of computation intensity and time. However, because model structure 

and parameterization are often a major source of uncertainty, 

multiple ecosystem models (as described in chap. 5) were 

used to produce ranges of terrestrial carbon results. The LULC 

scenarios were a primary foundation of projecting future potential carbon estimates, including stock and fluxes. By design, 

multiple scenarios were used to allow analysis of the effects of a 

range of potential choices and to bound the overall uncertainties 

in the projected results (Zhu and others, 2010). As a result, the 

ranges of results presented in this assessment for terrestrial 

carbon and GHG estimates represent a major part of the overall 

uncertainties. The methodology, design rationales, and technical 

specifications are detailed in Zhu and others (2010).

1.2. National and Regional Studies of 

Carbon Sequestration and GreenhouseGas Flux

The Eastern United States has seen the most active 

LULC change swings in the country, including pressure to 

expand urban or suburban infrastructure (development pressure), cropland expansion and contraction, increased timber 

harvesting activities in recent decades, and a continued recovery 

from deforestation of the 19th century (chaps. 2 and 3; Claggett 

and others, 2004; Chen and others, 2006; Auch and others, 

2012). LULC change, particularly the rate of land conversions 

to developed lands, and land-management activities, particularly 

forest harvesting (clearcutting) of plantation forestry, in the 

Eastern United States in recent decades have been rapid and 

more active than in any other region in the Nation (Sleeter 

and others, 2013). The rapid LULC changes have significant 

hydrological and biogeochemical consequences, including 

carbon fluxes and sequestration (Houghton and others, 1999; 

Milesi and others, 2003; Birdsey and others, 2006; Chen 

and others, 2006; Woodbury and others, 2006; Houghton, 

2010; Michalak and others, 2011; Williams and others, 2012; 

Robinson and others, 2013).

Existing estimates of carbon storage and sequestration 

and GHG fluxes varied widely by ecosystem and region in 

the Eastern United States. Forests occupy a significant land 

base in the east, representing about one-half the total area in 

the region and are the most important carbon sink. The forest 

inventories that were conducted by the U.S. Department of 

Agriculture Forest Service indicated that, in 2010, forest lands 

in the Eastern United States had a combined 24.2 petagrams 

of carbon (PgC) in forest ecosystems (table 1–3). The carbon 

inventory estimate of 24.2 PgC stock for the forests in the 

Eastern United States (Brad Smith, U.S. Department of 

Agriculture, Forest Service, unpub. data, 2010) was higher 

than that of the forests in the Western United States by more 

than 50 percent, whereas the per unit of area carbon stock was 

also higher compared with that in the forests of the Western 

United States (15.7 kilograms of carbon per square meter 

(kgC/m2) in the east compared with 14.5 kgC/m2 in the west). 

Among the seven ecoregions in the Eastern United States, 

the Southeastern USA Plains contained more stored carbon 

than any other ecoregion. The most recent Resource Planning 

Table 1–3. Total carbon stock from all major pools in the forest 

ecosystems in the seven ecoregions used in the assessment of 

carbon storage and fluxes in the Eastern United States.

[Data were derived from the Forest Inventory Database Online (FIDO; 

U.S. Department of Agriculture, Forest Service, 2012b) and were analyzed 

by Brad Smith (U.S. Department of Agriculture, Forest Service, unpub. 

data, 2010). Ecoregions are from Zhu and others (2010) as modified from 

U.S. Environmental Protection Agency (1999). kg/m2

, kilograms per square 

meter; TgC, teragrams of carbon]

Ecoregion Carbon, in 

TgC

Carbon per 

unit of area, 

in kg/m2

Mixed Wood Shield 3,556.2 23.5

Atlantic Shield 2,815.8 18.6

Mixed Wood Plains 2,845.6 18.5

Central USA Plains 426.2 16.9

Southeastern USA Plains 7,230.5 12.9

Ozark, Quachita-Appalachian Forests 4,646.0 14.0

Mississippi Alluvial and Southeast Coastal 

Plains1

2,660.8 15.5

Eastern United States, total 24,181.2 15.7

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.
14 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Act (RPA) assessment produced by the Forest Service for 

the conterminous United States and coastal Alaska shows a 

steady increase of the total carbon stock from 44,643 TgC in 

1990 to 48,437 TgC in 2010, and the net annual change varied 

from 185.7 teragrams of carbon per year (TgC/yr) in 1990 to 

248.6 TgC/yr in 2005 (U.S. Department of Agriculture, Forest 

Service, 2012).

Studies confirm that ecosystems in the United States 

are a carbon sink (Houghton and others, 1999; Pacala and 

others, 2001; Pan and others, 2011). There are differences 

in definitions, boundaries of reporting classes (ecosystems), 

components, and methods used in these studies. However, 

results from these studies were useful as references to this 

assessment. For example, the most recent national GHG 

inventory report by the EPA covering the conterminous 

United States suggested a net sink of –246.8 teragrams of 

carbon (TgC) in 2011 by forests, grasslands/shrublands, agricultural lands, and settled areas, which represented an offset of 

about 15.4 percent of the total fossil fuel CO2 emissions in the 

United States (U.S. Environmental Protection Agency, 2012). 

Most of the –226.7 TgC/yr (–75.6 grams of carbon per square 

meter per year (gC/m2/yr)), as a mean value, of carbon sequestration between 2007 and 2011 came from forest lands. Data 

from the Forest Inventory and Analysis Program of the Forest 

Service were the common basis for the EPA report as well 

as other similar publications. In describing the methodology 

and analysis using the Forest Service data, Heath and others 

(2011a,b) estimated that the combined net carbon flux of 

forest ecosystems and urban trees in the conterminous United 

States, including pools of soil organic carbon and HWD 

products, was about –223.5 TgC/yr (–89.8 gC/m2/yr) in 2002, 

–244.1 TgC/yr (–97.5 gC/m2/yr) in 2005, and –241.8 TgC/yr 

(–95.4 gC/m2/yr) in 2008.

A net forest carbon flux of –180 TgC/yr in the conterminous United States between 1992 and 2001 was derived 

(Zheng and others, 2011) by relating forest cover change 

from the NLCD data with carbon density estimates from 

the Forest Inventory Analysis (FIA). Carbon removed from 

forests as a result of wildfire (9.9 TgC/yr) and HWD products 

(142 TgC/yr) was included in deriving the net forest carbon 

flux. However, it is not clear from Zheng and others (2011) 

whether including forest harvesting from the FIA inventory 

and NLCD forest cover change data resulted in double 

counting of carbon removed from the landscape. Using a 

relationship between forest age derived from forest inventory 

and carbon recovery derived from remote sensing, Williams 

and others (2012) arrived at a much lower total net forest 

carbon flux (– 47 TgC/yr), together with carbon removed from 

forest harvest (107 TgC/yr) and wildland fire (10 TgC/yr) for 

the conterminous United States in 2005.

Three recent studies provided estimates for regional 

forest carbon flux and balances for areas that approximately 

correspond to the area of study for this assessment. For two 

Forest Service regions (the Southern and Eastern regions), 

Heath and others (2011a,b) reported forests of all ownerships 

had a net sequestration rate (excluding soil organic carbon) 

ranging between –132.2 and –42.1 TgC/yr, with a mean value 

of –88.2 TgC/yr, or about two-thirds the total estimate in the 

conterminous United States. On a per-unit-area basis using 

areas of NLCD forest classes in the two regions, the mean 

carbon sink was about –57.2 gC/m2/yr (excluding woody 

wetlands). Turner and others (1995) analyzed regional forest 

carbon change from the FIA forest survey data collected 

in the 1990s and estimated regional sink strengths to be 

–190 gC/m2/yr for the Northeast region, –290 gC/m2/yr for the 

Southeast region, and –250 gC/m2/yr for the South-Central 

region. For comparison, Williams and others (2012) provided 

forest carbon flux estimates for four of several regions in the 

conterminous United States (Northeast, Southeast, SouthCentral, and northern States in the Great Lakes region). The 

total gross forest carbon flux for 2005 was –114 TgC/yr for 

the four regions before subtracting carbon removal terms, 

including forest harvesting and wildland fire emission.

In addition to estimates of forest carbon fluxes in recent 

years, long-term trends and future projections have also been 

reported. Between 1600 and 1800, forests were in a state of 

carbon balance. The 19th century saw significant land-userelated carbon emissions due to land clearing, followed by 

regrowth of forests and resulting carbon sequestration in the 

20th century (Birdsey and others, 2006). Projected to future 

years based on the IPCC emission scenarios (Nakićenović and 

others, 2000), forest lands in various regions in the conterminous United States could become a net carbon source (U.S. 

Department of Agriculture, Forest Service, 2012) or weakened 

carbon sink (Hurtt and others, 2002; Birdsey and others, 

2006; Liu and others, 2012b). As a comparison, the projected 

future forest carbon sink in the Western United States ranged 

between –10.8 and –100.2 gC/m2/yr, depending on land-use 

scenarios, future climate projections, and biogeochemical 

models used (Liu and others, 2012b).

Carbon sequestration is a function of the biogeochemical 

exchange between the atmosphere and biosphere, including 

soils, and is strongly influenced by key controlling processes 

such as land use, land-management activities, ecosystem 

disturbances, and climate (Bachelet and others, 2003; Law 

and others, 2004; Running, 2008). In the Southeastern United 

States, where the LULC change is the most active in the recent 

decades, estimation of carbon stocks and fluxes and effects 

of primary driving processes are of particular importance. 

Tian and others (2010, 2012) estimated from several related 

studies covering 13 Southeastern States extending from Texas 

to Virginia that the total terrestrial ecosystem carbon stock 

was about 30.2 PgC, with 64 percent as soil organic carbon 

(SOC). Between 1951 and 2007, there has been a trend of 

increase in carbon stored in the ecosystems, totaling –2.0 PgC 

over the 57 years, or a mean uptake of –35 TgC/yr. Among 

different land and vegetation cover types, the net primary 

production was 679 gC/m2/yr by broadleaf deciduous forests, 

715 gC/m2/yr by needleleaf evergreen forests, 676 gC/m2/yr 

by herbaceous and woody wetlands, and 520 gC/m2/yr by 

agricultural lands. Using LULC change data derived from a 

sample of Landsat imagery and a biogeochemical model, Liu 
Chapter 1 15

and others (2004) estimated that, between 1973 and 2000, 

the Southern region averaged a carbon sink of 89 gC/m2/yr. 

Albani and others (2006) analyzed the effects of LULC change 

and timber harvesting for all terrestrial ecosystems in the 

Eastern United States and estimated that the net carbon flux 

ranged from 0.21 petagram of carbon per year (PgC/yr) in the 

1980s to 0.25 PgC/yr in the 1990s and 0.26 PgC/yr in 2008; 

they also projected that the net carbon flux in future years 

would be about 0.25 PgC/yr.

The specific effects of various controlling processes are 

difficult to estimate. Land-use change in the Southern United 

States is often characterized by conversions from forests to 

agricultural and developed areas and by forestry practices of 

economically determined rotation ages and planting improved 

stock, which have led to dynamic cover loss and gain patterns. 

Woodbury and others (2006) attributed changes in carbon balance 

to land-use conversions, and estimated that, between 1990 and 

2004, carbon gains as the result of afforestation outweighed 

carbon losses due to deforestation by 49 to 88 TgC. Over a 

50-year period, converting marginal pasture lands to southern 

pine plantations could result in improved sequestration from 

an average of 8.3 megagrams of carbon per hectare (MgC/ha; 

0.83 kgC/m2) by marginal pastures to 58 MgC/ha (5.8 kgC/m2) 

by southern pine plantations (Lee and Dodson, 1996). Tian and 

others (2012) noted that the strength of the carbon sink in the 

Southeastern States may be attributed to fertilization by increased 

CO2 in the atmosphere and nitrogen deposition in the past five to 

six decades, although such effects could be easily overestimated if 

relying on simulation modeling alone (Albani and others, 2006).

Climate is also a major controlling factor in the direction 

and size of carbon sources or sinks. For example, Chen and 

others (2012) estimated that the terrestrial ecosystems in the 

Southeast (the 13 Southeastern States from Texas to Virginia) 

could be a carbon source (emitting 72.5 TgC/yr) during the 

driest 10 years in the recent record or a sink (–81.45 TgC/yr) 

during the wettest 10 years in the recent record. Keenan 

and others (2012) noted that changes in climate (including 

lengthening of the growing season) may be responsible for 

increases in forest growth rates and carbon uptake in eastern 

North America and specifically the observed increases in 

NEE in the Harvard Forest in Petersham, Massachusetts, 

from 1992 to 2009.

The effects of wildland fires on carbon sequestration in 

ecosystems include (1) the immediate release of GHGs from 

combustion emissions and (2) the long-term combined effects 

of decomposing biomass, which releases carbon into the 

atmosphere, and regenerating vegetation, which increases the 

uptake of carbon (Law and others, 2004; Hurteau and Brooks, 

2011). Hawbaker and Zhu (2012) estimated that the immediate 

release of GHGs from wildland fires in the Western United 

States in recent years offset about 10 percent of the total 

ecosystem carbon sequestration. Although fires of ecosystems 

in the Eastern United States are less frequent and intense than 

those of the Western United States and are not expected to 

affect carbon balance on a comparable scale (chap. 4 of this 

report), fires in the Eastern United States have had unique 

and significant effect on individual ecosystems and in specific 

locations, such as the carbon-rich pocosin peatland soils along 

the Atlantic coastal regions. Emissions from a fire that burned 

Pocosin Lakes in North Carolina in 1985, for example, were 

estimated to range between 1.01 and 3.76 TgC, depending 

on depth of peat consumed (Poulter and others, 2006). To 

illustrate the importance of peat fires in the Atlantic coastal 

ecosystems, this one fire emitted as much as 30 percent of the 

average annual fire emissions of the ecosystems in the Western 

United States (Hawbaker and Zhu, 2012).

Development pressure in the Southeastern States has a 

significant effect on the overall carbon balances, but estimates 

of the effects of development pressure have been variable. 

Milesi and others (2003), using remote sensing data, estimated 

that development in the Southeastern States was responsible 

for a reduction of 3.04 TgC/yr in NPP by terrestrial 

ecosystems. However, Zhang and others (2012) estimated the 

loss of carbon from southeastern ecosystems to be 3.4 TgC/yr 

between 1945 and 2007 and suggested that whether developed 

areas were a carbon source or sink is dependent on whether 

the lands were converted to developed areas from forests 

or agricultural lands. Forests in established developed areas 

remain a sizeable carbon sink and are responsible for about 

8 to 10 percent of the total forest carbon sequestration in the 

conterminous United States (Nowak and Crane, 2002; U.S. 

Environmental Protection Agency, 2012).

The Eastern United States is a major agricultural region. 

Agricultural lands account for about 32 percent of the total 

area in the region, mostly concentrated in the Corn Belt of 

the Ohio River Valley and the Mississippi Alluvia Plains 

as well as interspersed throughout the eastern plains. In a 

synthesis study for tillage practices in the conterminous 

United States, West and Marland (2002) estimated that the 

average rates of carbon sequestration in agricultural soils 

were about 0 gC/m2/yr in conventionally tilled lands and 

–37 gC/m2/yr in no-till lands. West and Marland (2002) 

further noted that, when taking soil carbon sequestration 

and farm operation emissions into calculations, the average 

net carbon flux would be 16.8 gC/m2/yr for conventional 

tillage and –20 gC/m2/yr for no-till practices. However, when 

considering only SOC balance in farmlands in the Midwest, 

Christopher and Mishra (2009) found that tillage effects 

in a whole profile were mixed and there was no significant 

difference in SOC between no-till and conventional till 

farming; the two practices yielded an average SOC stock 

of 4.8 kgC/m2.

A majority of the freshwater and saltwater wetlands with 

herbaceous and woody vegetation cover in the conterminous 

United States are located in the East. As mapped by the NLCD 

(Vogelmann and others, 2001; Homer and others, 2007), 

the wetlands in the conterminous United States cover about 

312,571 square kilometers (km2), or 4 percent of the land 

mass of the conterminous United States. In the Eastern United 

States, wetlands total 272,442 km2, representing 8.9 percent 

of the total area in the region and 87 percent of all wetland 

areas in the conterminous United States. These wetlands 
16 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

include peat and mineral soil types. Large wetland areas are 

distributed in the Great Lakes region, particularly in northern 

Minnesota and Wisconsin, and in the coastal plains of the 

Atlantic coast and the Gulf of Mexico.

Bridgham and others (2006) synthesized data from 

published sources to estimate the total rates of carbon stock 

and sequestration in the wetlands of the conterminous United 

States to be 19,600 TgC and –17.3 TgC/yr, respectively. Using 

the total wetland area estimate of 431,000 km2, these estimates 

translate to 45.5 kgC/m2 for the per-unit-of-area of carbon 

stock and –40.1 gC/m2/yr for the per-unit-of-area sequestration 

rate in the conterminous United States. Freshwater peatlands 

and mineral soil wetlands store most of wetland carbon in the 

conterminous United States; however, tidal marshes have the 

highest per-unit-of-area sequestration rate at –220 gC/m2/yr, 

followed by peatlands (–71 gC/m2/yr) and mineral soil 

wetlands (–17 gC/m2/yr). Sequestration in estuaries is mostly 

accomplished as the result of sedimentation (Bridgham and 

others, 2006). For comparison, Armentano and Menges (1986) 

estimated a range of soil carbon accumulation rates that 

ranged from –48 gC/m2/yr in peatlands in the northern part of 

the region to –2.25 gC/m2/yr in peatlands in Florida. Chmura 

and others (2003) reported carbon accumulation in the coastal 

tidal marshlands of –136.5 gC/m2/yr and –296.6 gC/m2/yr for 

the Atlantic and the gulf coast areas, respectively. Methane 

emissions from ecosystems in the conterminous United States 

were estimated to be about 50.4 teragrams of CO2-equivalent 

per year (Bridgham and others, 2006) and were mostly 

emitted from freshwater mineral-rich soil in wetlands because 

of the low salinity content of this soil type (Poffenbarger 

and others, 2011).

The emission, transport, and sequestration of carbon by 

aquatic ecosystems should be considered when estimating 

carbon balances in ecosystems (Chapin and others, 2006; Cole 

and others, 2007; Tranvik and others, 2009). National-scale 

studies suggest that lateral transport of carbon fixed within 

the conterminous United States and exported to coastal areas 

can represent about 10 percent of the total carbon sequestered 

in forests (trees and soils), croplands, and shrublands (Pacala 

and others, 2001; Butman and Raymond, 2011; Stackpoole 

and others, 2012). In addition to the role of aquatic ecosystems 

in transporting and sequestering carbon, recent global studies 

have indicated that inland waters can also be sources of 

carbon emitted into the atmosphere (Cole and others, 2007; 

Tranvik and others, 2009). In a carbon flux and sequestration 

assessment for the Western United States (Stackpoole and 

others, 2012), the mean values of emission of CO2

 from lateral 

fluxes, water surfaces, and sequestration (burial) in lakes and 

reservoirs were estimated to be 7.2 TgC/yr, 28.2 TgC/yr, and 

–2.4 TgC/yr, respectively.

The above-referenced studies produced estimates of 

net carbon flux that were spatially and temporally variable; 

however, these estimates also provide a set of reference points 

against which this assessment may be compared. Estimates 

for the agricultural lands, forests, and wetlands ecosystems 

in the Eastern United States from these recent studies are 

summarized in table 1–4.

Table 1–4. Mean net carbon flux per unit of area from a selected sample of studies for the four major terrestrial ecosystems used in 

the assessment of carbon storage and fluxes in the Eastern United States. 

[The four major terrestrial ecosystems are forests, agricultural lands, grasslands/shrublands, and wetlands. C, carbon; gC/m2/yr, grams of carbon per square 

meter per year; kgC/m2/yr, kilograms of carbon per square meter per year; SOC, soil organic carbon]

Ecosystem Geography Estimates, units, and

type of estimate Source Timeframe of the sourced work

Agricultural 

lands

Conterminous United States 1.5 gC/m2

/yr, net C flux U.S. Environmental Protection 

Agency (2012b)

2007–2011

Conterminous United States 0 gC/m2

/yr, net C flux by

conventional tillage

West and Marland (2002) 1990s

Conterminous United States –37 gC/m2

/yr, net C flux by 

no-till farming

West and Marland (2002) 1990s

Midwestern United States 4.8 kgC/m2

, mean SOC stock Christopher and Mishra (2009) 2006

Forests Conterminous United States –84.6 gC/m2

/yr, net C flux Heath and others (2011a,b) 2002, 2005, 2008

Major Eastern United States –82.8 gC/m2

/yr, net C flux Williams and others (2012) 2005

Eastern United States –243 gC/m2

/yr, net C flux Turner and others (1995) Early 1990s

Peat wetlands Northern United States –37.8 gC/m2

/yr Yu (2012) 1998–2009

Tidal marshlands Atlantic and gulf coast areas –136.5 gC/m2

/yr and 

–296.6 gC/m2

/yr

Chmura and others (2003) 1990s

Wetlands Conterminous United States – 40.1 gC/m2

/yr Bridgham and others (2006) Various, previous decades
Chapter 2. Ecoregion and Scenario Framework

By Benjamin M. Sleeter,1 Glenn E. Griffith,1 Tamara S. Wilson,1 Rachel R. Sleeter,1 Christopher E. Soulard,1

Kristi L. Sayler,1

 Ryan R. Reker,2 Michelle A. Bouchard,2 and Terry L. Sohl1

1

U.S. Geological Survey

2

ASRC Federal InuTeq

2.1. Introduction

The current and projected changes in LULC are key 

components for this assessment of carbon and GHG stocks 

and fluxes (Zhu and others, 2010). Mapping of the baseline 

(1992–2005) LULC conditions (chap. 3) provided a spatial 

foundation for the wall-to-wall assessment of carbon stocks 

and GHG fluxes in various ecosystems (chap. 1). The development of a range of potential future LULC projections together 

with corresponding climate-change projections allowed 

for an evaluation of potential future carbon sequestration 

capacities and vulnerabilities as influenced by these projected 

drivers. This chapter provides an overview of methods used 

to construct the alternative LULC scenarios and descriptions 

of the distinguishing characteristics of each of the ecoregions 

used for the assessment.

2.2. Definition of Terms

The framework for projected changes in LULC and land 

management for three future scenarios from 2005 through 

2050 are presented in this chapter. LULC classes used for this 

assessment are described in detail in chapter 3. To maintain 

consistency with the overall assessment, we present results of 

the land use scenario downscaling process consistent with the 

broad ecosystem types used to assess regional-scale carbon 

dynamics. These ecosystems include forests, grasslands/shrublands, agricultural lands, and wetlands. Urban development 

was modeled as a separate LULC class but was collapsed into 

the “other” category. Urban areas with significant tree cover 

were considered in carbon modeling, as noted in chapter 1.

Changes in land use (conversions) are associated with 

the conversion of lands from one use type to another. Typical 

conversions include changes between forest and agricultural 

lands (afforestation and deforestation) and conversions to 

development (for example, urbanization). Land management 

practices, such as timber harvesting, are captured as changes 

in land cover where it is assumed that logged areas no longer 

meet the definition of a forest cover classification. Areas of 

forest logging are accounted for through the use of a temporary 

mechanical disturbance category, where the specific areas 

remain until the next temporal period (5 years for scenarios) 

before being reassigned to the forest class. As a result, forest 

land use is considered a land use class, consisting of areas 

ranging from mature forest to areas where logging has recently 

occurred and trees may not be present or have only recently 

been replanted. In the following sections, we present projections 

of changes in forest cover, forest use, and forest management 

(mechanical disturbance of forest land or logging). For some 

ecoregions and scenarios, we also present the gross conversions 

associated with deforestation and afforestation where those 

changes were important to regional-scale land use dynamics.

2.3. Spatial Domain Used for

the Assessment

This assessment is organized by seven level II ecological 

regions (ecoregions) that cover the Eastern United States; the 

ecoregions were adapted from the ecoregion frameworks of 

the EPA (U.S. Environmental Protection Agency, 2013a) and 

for the purposes of this assessment are as follows: (1) Mixed 

Wood Shield, (2) Atlantic Highlands, (3) Mixed Wood 

Plains, (4) Central USA Plains (5) Southeastern USA Plains, 

(6) Ozark, Ouachita-Appalachian Forests, and (7) Mississippi 

Alluvial and Southeast USA Coastal Plains (includes the 

Everglades and the Texas-Louisiana Coastal Plain level II 

ecoregions for analysis of this assessment; fig. 1–1). All 

the assessment models were parameterized, and the results 

were calibrated and reported based on these ecoregions. In 

this report, the term region is often used in a general sense, 

depending on the context, whereas the term ecoregion 

refers to the EPA ecoregion hierarchy (U.S. Environmental 

Protection Agency, 2013a). The major terrestrial and aquatic 

ecosystems are analyzed within these ecoregions. The use of 

the ecoregions and the NLCD’s LULC classes chosen for the 

ecosystems in this assessment suggest that the reported results 

are meaningful within the defined ecoregion and ecosystem 

boundaries and may not be directly comparable with other 

national- or regional-level estimates because of the different 

boundary definitions. Further discussion of the ecoregions and 

ecosystems may be found in Zhu and others (2010).
18 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

The EPA ecoregion framework was used to capture 

regionally unique processes and landscape potential (Omernik, 

1987; U.S. Environmental Protection Agency, 2013a). 

Ecoregions represent areas with similar patterns of biotic, 

abiotic, aquatic, and human land-use characteristics and have 

proven to be a useful framework for collecting and synthesizing information about LULC change (Gallant and others, 

2004). Ecoregions as defined by the EPA are hierarchical at 

four spatial scales (levels I, II, III, and IV) for the conterminous 

United States. The 1999 version of level III ecoregions (U.S. 

Environmental Protection Agency, 1999) was used as the base 

map for the assessment. Based on this version of the EPA 1999 

framework, a further modified version consisting of seven 

level II ecoregions was developed to serve as the framework 

for this assessment (fig. 1–1). Condensed versions of the 

ecoregion descriptions are included in the following sections.

2.3.1 Mixed Wood Shield

The Mixed Wood Shield ecoregion covers the northern 

parts of Minnesota, Wisconsin, and Michigan in the United 

States and contains the Northern Minnesota Wetlands and the 

Northern Lakes and Forests level III ecoregions (fig. 1–1). 

Although there are some minor areas of bedrock exposure, this 

is a glaciated region, and most areas are covered with glacial 

drift. The broad landforms are mostly smooth to irregular 

plains with a few areas of hills.

The ecoregion has a severe midlatitude humid continental 

climate, marked by warm summers and very cold winters, 

with no pronounced dry season. The mean annual temperature 

ranges from 2 to 6 degrees Celsius (°C); the mean summer 

temperature is about 16 °C; and the mean winter temperature 

ranges from –10 to –12 °C. The frost-free period ranges 

from less than 100 days to near 160 days in lake-moderated 

areas. The mean annual precipitation ranges from about 

500 to 960 millimeters (mm) within the region (Wiken and 

others, 2011).

The subboreal vegetation includes northern coniferous 

forests, northern hardwood forests, boreal hardwood-conifer 

forests, swamp forests, and peatlands. Changes in stand 

densities and forest composition from hardwood and conifers 

to successional species that have occurred during the past 

150 years have been affected by land use history and management (Albert, 1995; Zhang and others, 2000; Schulte and 

others, 2007). From the mid-1800s through the early 1900s, 

there was intense logging, repeated slash-and-burn fires, and 

a short period of attempted settlement or agricultural use of 

cutover lands and then abandonment. As forests recovered, 

mixed coniferous forests transformed into hardwood 

(especially aspen)-dominated forests; aspen has proven to be 

an early successional species with strong post-disturbance 

sprouting ability (Friedman and Reich, 2005). Fire suppression policies also represent a major change from the natural 

disturbance history in this region.

Land cover in the Mixed Wood Shield ecoregion is 

dominated by forest, wetlands, and water, which account 

for about 87 percent of the region’s area. The region has a 

relatively low human population compared with other regions 

of the Eastern United States. The economy is based primarily 

on forestry, recreation and tourism, hunting and fishing, 

and iron ore mining. Land change is driven by demand for 

resources such as timber, minerals, and in the lower peninsula 

of Michigan, some energy resource extraction from oil and 

natural gas. Forestry activities are the main drive of land use 

and land cover change in the Mixed Wood Shield ecoregion. 

Land change during the past 40 years shows the continued 

dominance of forest land cover, although this land use has 

declined slightly. Wetlands have been relatively stable at about 

20 percent of the region’s land cover since 1973. With the cold 

climate and thin, nutrient-poor soils in the region, agriculture 

is very limited compared with the adjacent Mixed Wood Plains 

and Central USA Plains ecoregions to the south. Agricultural 

lands covered about 7 percent of the region in 2000, a 

relatively stable land use since the early 1970s. Where there 

is agriculture, it is generally in small areas; the most common 

products are hay and grain crops, beef and dairy cattle, and 

potatoes.

2.3.2. Atlantic Highlands

The Atlantic Highlands ecoregion covers forested upland 

areas in Pennsylvania, New Jersey, New York, Connecticut, 

Massachusetts, Vermont, New Hampshire, and Maine in the 

northeastern United States (fig. 1–1). This is a cool, humid, 

forested, and formerly glaciated region that is relatively 

sparsely populated compared with adjacent regions. It has 

higher elevations and more rugged topography than most 

adjacent ecoregions and contains iconic mountain ranges and 

elevated plateau areas of the Northeast, including the White, 

Green, Taconic, Berkshire, Adirondack, Catskill, and Pocono 

Mountains, the Hudson Highlands, and the Allegheny Plateau. 

Elevations reach more than 1,900 meters (m) at Mount 

Washington in New Hampshire. The Atlantic Highlands 

ecoregion contains the Northern Appalachian and Atlantic 

Maritime Highlands (includes the northern Appalachian 

Plateau and uplands) and North Central Appalachians level III 

ecoregions (U.S. Environmental Protection Agency, 2013a).

The Atlantic Highlands ecoregion has a severe 

midlatitude humid continental climate, marked by warm 

summers and cold, snowy winters. The mean annual temperature ranges from about 1 to 8 °C, varying by elevation and 

latitude. The frost-free period ranges from less than 50 days 

at high elevations to near 180 days in low-elevation southeast 

areas. The mean annual precipitation ranges from about 

840 mm to more than 2,000 mm on high peaks (Wiken and 

others, 2011).

As with climate, the vegetation of the region also varies 

by elevation and latitude. Forest types are transitional in a 

broad zone between the boreal forests to the north in Canada 

and the broadleaf deciduous forests to the south (Braun, 1950; 

Goldblum and Rigg, 2010). Forest types include northern 

hardwoods, northern hardwoods-spruce, and northeastern 
Chapter 2 19

spruce-fir forests in the north and Appalachian oak forests in 

the south (Kuchler, 1964). To the south and at low elevations, 

northern hardwood forests give way to transition hardwood 

forests

with more oaks, hickories, and pines on dry sites and northern

hardwoods and hemlock on mesic and north-facing slopes.

The economy of the Atlantic Highlands ecoregion 

today is based primarily on forestry, tourism and recreation, 

hunting and fishing, and some small-scale farming. Forestry 

activities for paper and pulp production, sawlogs, and biomass 

chipping are important, especially in the part of the region that 

is covered by Maine, and some high-quality hardwoods are 

harvested in the Pennsylvanian forests (Napton and others, 

2003). Forest management is influenced by the varied land 

ownership patterns in the region. Private forest land dominates 

the region, from large corporate land holdings in Maine to 

small individual holdings in southern areas.

During the past few decades, the area of forest land cover 

in the region has started to decline, a trend seen in other parts 

of the Northeastern and Eastern United States (Drummond 

and Loveland, 2010; Jeon and others, 2012). Forest cover 

has decreased by nearly 3 percent since 1973 (Sleeter and 

others, 2013).

Agricultural lands are the second largest class of land 

cover in the Atlantic Highlands ecoregion, covering about 

9 percent of the region. With its cold climate and stony soils, 

agriculture is limited in most parts of the Atlantic Highlands 

ecoregion compared with the adjacent Mixed Wood Plains 

ecoregion. The most common products are dairy cattle, hay 

and silage crops, apple orchards, and nursery stock. Similar 

to the Mixed Wood Shield ecoregion, the Atlantic Highlands 

ecoregion also has a relatively low human population 

compared with other regions in the Eastern United States. The 

area covered by developed land increased from 1.9 percent of 

the region in 1973 to 2.4 percent in 2000 (Sleeter and others, 

2013).

2.3.3. Mixed Wood Plains

The Mixed Wood Plains ecoregion covers an area of 

glaciated, rolling to level terrain with mixed land cover that 

extends across parts of Minnesota, Wisconsin, Iowa, Illinois, 

Michigan, Indiana, Ohio, Pennsylvania, New York, New 

Jersey, Connecticut, Massachusetts, Vermont, New Hampshire, 

and Maine (fig. 1–1). The ecoregion is characterized by a 

land cover mosaic of agricultural lands, forest, wetlands, and 

glacial lakes. This is in contrast to the Mixed Wood Shield 

and Atlantic Highlands ecoregions to the north where soils are 

more nutrient-poor and mostly lacking in agricultural lands 

and in contrast to the nutrient-rich Central United States Plains 

and Temperate Prairies ecoregions to the south and west where 

landscapes are dominated by agricultural land use. The Mixed 

Wood Plains ecoregion contains the North Central Hardwood 

Forests, Driftless Area, Southern Michigan/Northern Indiana 

Drift Plains, Northeastern Coastal Zone, Erie Drift Plain, 

Maine/New Brunswick Plains and Hills, and Eastern 

Great Lakes and Hudson Lowlands level III ecoregions 

(U.S. Environmental Protection Agency, 2013a).

The Mixed Wood Plains ecoregion has been glaciated and 

includes a wide variety of deep glacial and marine deposits 

with a few areas of bedrock outcrops. The terrain includes 

flat lake plains, rolling till plains, outwash plains, hummocky 

stagnation moraines, a less-glaciated dissected plateau in the 

Driftless Area (that is, with only some patchy pre-Illinoian 

glacial drift), and some low to high hills, especially in 

New England.

The Mixed Wood Plains ecoregion has a severe 

midlatitude humid continental climate, marked by warm 

summers and cold, snowy winters. There is some maritime 

influence in coastal areas and lake-effect influence near the 

Great Lakes. The mean annual temperature ranges from 

4 to 10 °C. The frost-free period ranges from 110 to 170 days. 

Mean annual precipitation ranges from about 600 mm in the 

far west to more than 1,250 mm in wetter parts of the east 

(Wiken and others, 2011).

Although once mostly forested, because the region 

stretches from near the edge of the Great Plains grasslands 

in the west to coastal New England in the east, the historical 

vegetation of the Mixed Wood Plains ecoregion is varied. 

In Minnesota, Iowa, and Wisconsin, a forest and savanna 

transition zone includes small patches of prairie, oak savannas, 

and maple-basswood forests on mesic sites (Braun, 1950; 

Kuchler, 1964). In south-central Wisconsin, pine barrens with 

jack pine (Pinus banksiana) and scrub oaks were common in 

the sandy, droughty outwash and lake plains, along with areas 

of wet conifer swamps and peatlands (Curtis, 1959). In the 

central section of the Mixed Wood Plains from Lake Michigan 

to Lake Ontario, there are beech-maple forests with some 

oak-hickory forests on drier sites. Further east, in southern 

New England and the lower Hudson Valley of New York, 

Appalachian oak and northeastern oak-pine forests occur, with 

several forest species at the northern limits of their range.

The economy of the Mixed Wood Plains ecoregion 

continues to be diverse, based on manufacturing and 

technology, finance, health research and services, education, 

agriculture, forestry, fishing, and tourism. LULC reflects some 

of this diversity with its mixed uses. Land use is dominated 

slightly by agricultural lands, accounting for about 40 percent 

of the region in 2000, whereas forest cover was nearly as 

extensive at 37 percent of the region. Agricultural lands 

have decreased since 1973 and tend to be a more dominant 

part of the landscape in the western half of the ecoregion, in 

Minnesota, Iowa, Wisconsin, and Michigan. The most typical 

agricultural activities center around dairy and beef cattle, 

hay and silage crops, corn, oats, soybeans, fruit orchards and 

vineyards, and nursery stock. Forests, however, are similar to 

other parts of the Northeastern and Eastern United States, and 

have been declining in the past few decades (Drummond and 

Loveland, 2010; Jeon and others, 2012). Forest land is more 

dominant in the New England part of the region, especially 

in the Maine/New Brunswick Plains and Hills ecoregion of 

Maine, which was about 70 percent covered in forest in 2000.
20 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

2.3.4. Central USA Plains

The Central USA Plains ecoregion is an area of glaciated, 

flat-to-gently-rolling plains that extends across parts of 

Wisconsin, Illinois, Indiana, Michigan, and Ohio (fig. 1–1). The 

ecoregion is characterized by LULC that is dominated by agricultural lands. It is one of the largest areas of the Eastern United 

States with suitable relief and soils for cropland (Hart, 1968). This 

is in contrast to the Mixed Wood Plains to the north that has a 

bimodal mix of agricultural lands and forests and to the nonglaciated Southeastern USA Plains and Ozark, Ouachita-Appalachian 

Forests ecoregions to the south and east that are more densely 

forested and have more irregular or hilly landforms. The Central 

USA Plains ecoregion includes the Southeastern Wisconsin Till 

Plains, Huron/Erie Lake Plains, Central Corn Belt Plains, and 

Eastern Corn Belt Plains level III ecoregions (U.S. Environmental 

Protection Agency, 2013a)

The Central USA Plains ecoregion has a severe 

midlatitude humid continental climate, marked by warm to 

hot summers and cold winters. The mean annual temperature 

ranges from approximately 7 °C in the north to 13 °C in the 

south. Temperatures are moderated in areas near the Great 

Lakes. The frost-free period ranges from 150 to 200 days. 

Mean annual precipitation varies from about 700 mm to 

1,140 mm (Wiken and others, 2011). Tornados are not 

uncommon in late spring and early fall.

The historical vegetation of the Central USA Plains 

ecoregion ranged from mostly prairies and savannas in the 

west to various forest types and some savanna mosaics to 

the east. A large part of the region consists of the eastern 

extent of the Prairie Peninsula region (Transeau, 1935; 

Geis and Boggess, 1968). In southeast Wisconsin, northern 

Illinois, and parts of Indiana and western Ohio, a forest and 

savanna transition zone included small patches of prairie, oak 

savannas, and beech-maple forests on mesic and eastern sites 

(Braun, 1950; Kuchler, 1964; Albert, 1995). The savannas 

contained a tallgrass prairie mosaic along with bur oak 

(Quercus macrocarpa) and other woody species.

Economic restructuring in the steel and other manufacturing 

sectors resulting from a decline in the heavy manufacturing 

industry in the area caused job losses and economic decay in 

parts of the Central USA Plains ecoregion in the late 20th century. 

The economy of the region today is still based primarily on 

manufacturing, agriculture, and financial and other services. The 

ecoregion is dominated by agriculture, although the extent of 

agricultural lands has decreased in response to increased demands 

for urban land uses (Sleeter and others, 2013). The most typical 

agricultural activities center on cash grain farming primarily of 

corn and soybeans, with some wheat and oats. Also important 

in the region are dairy and beef cattle, hogs, poultry, and hay 

and silage crops; these farms are on the small and truck farming 

scales. Dry beans and sugar beets are grown in the Saginaw Lake 

Plain of Michigan. Although forests only cover about 11 percent 

of the region, they cover only slightly more land than developed 

land, which covered slightly less than 11 percent of the region in 

2000. Forest land has seen relatively small but consistent declines 

since 1973, a declining trend similar to other parts of Eastern 

United States (Drummond and Loveland, 2010). Most of the 

loss in forest land area is a result of conversion to developed 

land, with some conversion of forests to agricultural lands in 

certain areas.

2.3.5. Southeastern USA Plains

The Southeastern USA Plains ecoregion is the largest 

level II ecoregion in the Eastern United States, covering the 

inner coastal plains, Piedmont areas, and interior low plateaus 

in parts of 22 States. The ecoregion is characterized by a land 

cover mosaic of forest, pasture, cropland, and developed land. 

The region is generally not as arable as the more nutrient-rich 

Central USA Plains ecoregion to the north or the Great Plains 

region to the west. However, there are several parts of the 

Southeastern USA Plains that are better suited to agriculture 

than the bordering Ozark, Ouachita-Appalachian Forests 

ecoregion, which is typically hilly. The Southeastern USA 

Plains ecoregion contains the East Central Texas Plains, South 

Central Plains, Piedmont, Northern Piedmont, Southeastern 

Plains, Interior Plateau, Interior River Valleys and Hills, and 

Mississippi Valley Loess Plains level III ecoregions (U.S. 

Environmental Protection Agency, 2013a).

The Southeastern USA Plains ecoregion is mostly 

unglaciated except for a few areas in the northern fringes, 

in Iowa, Missouri, Illinois, Indiana, and New Jersey. The 

ecoregion has landforms consisting mostly of smooth to 

irregular plains with some areas of open hills. There are also 

areas of karst plains, dissected plateaus and tablelands, and 

some steep slopes and ravines. Elevations in the region range 

from sea level along Chesapeake Bay to more than 800 m on 

high hills of the inner Piedmont.

Most of the Southeastern USA Plains ecoregion has a 

mild midlatitude humid subtropical climate, marked by hot 

and humid summers and mild winters. The northernmost areas 

are somewhat cooler, located along the boundary of severe 

midlatitude humid continental climates with colder winters. 

The mean annual temperature ranges from 10 °C in the north 

in Illinois to 21 °C in the far southwest in Texas. The frost-free 

period ranges from 160 days in the north to 300 days near 

the Gulf of Mexico in the south. Mean annual precipitation 

ranges from about 680 mm in the dry southwest to more than 

1,650 mm in wet parts of the south (Wiken and others, 2011). 

Precipitation is fairly evenly distributed throughout the year, 

falling as rain from frontal storms in fall, winter, and early 

spring and from convective thunderstorms or tropical storms 

and hurricanes in the warm late spring and summer months. 

Droughts occasionally affect the region.

The historical vegetation of the Southeastern USA 

Plains ecoregion was varied because of its latitudinal 

extent and the diverse landscape elements and ecosystems 

contained within the ecoregion. A mixed oak and oakhickory-pine forest covered much of the Piedmont and parts 

of the upper coastal plains. Longleaf pine (Pinus palustris) 

was one of the most ecologically important tree species in 
Chapter 2 21

the Southern United States, and covered an extensive area, 

although today only a small fraction of the longleaf pine 

ecosystem remains (Earley, 2004). In the interior plateaus and 

far western parts of the ecoregion, oak-hickory forests were 

sometimes intermixed with prairies. In the Interior Plateau 

ecoregion in Tennessee and Kentucky, oak-hickory forest with 

some areas of bluestem prairie, cedar glades, and some mixed 

mesophytic forest were predominant ecosystems (Kuchler, 

1964; Griffith and others, 1997; Wood and others, 2002). In 

the East Central Texas Plains ecoregion, the landscape was 

originally covered by post oak (Quercus stellata) savanna 

vegetation, in contrast to the more open prairie to the west and 

to the pine forests to the east.

Although forest is the most extensive land cover class in 

the Southeastern USA Plains ecoregion, it accounts for less 

than half of the area of the region, which has some variation 

in forest resource lands and areas with different historical 

trends. Overall, forest land cover has been declining for at 

least the past four decades (Drummond and Loveland, 2010). 

However, the ecoregion is one of the most important timber 

production regions of the United States; pine plantation forestry 

is an important driver of land change in parts of the region, 

with tree cutting and regrowth cycles accounting for substantial 

amounts of land conversion. Private land ownership dominates 

the forests of the Southeastern USA Plains ecoregion, consisting 

primarily of small, nonindustrial properties of individual ownership, but there are also some larger corporate land holdings. 

Agricultural lands accounted for an estimated 31 percent of 

the region in 2000 (Sleeter and others, 2013), a decrease from 

33 percent in 1973, part of a long-term regionwide decline 

affected by multiple driving forces and government policies 

(Drummond and Loveland, 2010; Sleeter and others, 2013).

Developed land is the third largest cover type in the 

Southeastern USA Plains ecoregion and the class with the 

largest net gain (Sleeter and others, 2013). Developed land 

covered approximately 10 percent of the region in 2000, 

an increase from 7.5 percent in 1973. Although parts of the 

Southeastern USA Plains ecoregion were still relatively rural 

by the middle of the 20th century, other areas of the region 

were poised for expansive growth. In the northeast, as part 

of the megalopolis between New York City, New York, 

and Washington, D.C., the Northern Piedmont ecoregion 

experienced growth in urban, suburban, and exurban land 

covers at the expense of farms and forest from the New Jersey 

suburbs to Washington (Auch and others, 2012). Further south, 

Piedmont population growth exceeded the national average 

each decade after 1960, and developed land increased from 

less than 12 percent to more than 16 percent of the region by 

2000 (Napton and others, 2010).

2.3.6. Ozark, Ouachita-Appalachian Forests

The Ozark, Ouachita-Appalachian Forests ecoregion 

consists of two separate areas that make up most of the unglaciated forested mountains and upland plateaus in the Central and 

Eastern United States (fig. 1–1). The ecoregion is characterized 

by high elevations, high-relief terrain, high-gradient streams, 

and vegetation and includes some of the most diverse temperate 

forests in the world (Stephenson and others, 1993). About twothirds of the ecoregion land cover of the ecoregion is forested, 

and one-quarter is agricultural lands. The Ozark, OuachitaAppalachian Forests ecoregion contains the Ouachita Mountains, 

Ozark Highlands, Arkansas Valley, Blue Ridge, Ridge and 

Valley, Southwestern Appalachians, Central Appalachians, 

Boston Mountains, Western Allegheny Plateau level III ecoregions (U.S. Environmental Protection Agency, 2013a)

The Ozark, Ouachita-Appalachian Forests ecoregion is 

almost entirely unglaciated except for a few small areas in the 

northeastern fringes, in New York, Pennsylvania, and New 

Jersey. The ecoregion includes landforms consisting mostly of 

high hills and low mountains, with some open high hills and 

plains with hills. The ecoregion has higher elevations and more 

relief than the adjacent Southeastern USA Plains ecoregion. 

Elevations in the region range from about 95 m in the south to 

more than 2,035 m at Mount Mitchell in North Carolina, the 

highest point in the United States east of the Mississippi River.

Most of the Ozark, Ouachita-Appalachian Forests 

ecoregion has a severe midlatitude humid continental climate, 

with warm summers and cold winters. High elevations record 

the coldest temperatures in the region. The south is characterized by a mild midlatitude climate, marked by hot and humid 

summers and mild winters. The mean annual temperature 

ranges from 7 °C at the high elevations in the north to 17 °C 

at the low elevations in the southern and southwestern areas. 

The frost-free period ranges from 125 days to 245 days. Mean 

annual precipitation ranges from about 900 mm in the dry 

valleys to more than 2,500 mm on the high mountain peaks of 

the south (Wiken and others, 2011).

Vegetation in the Ozark, Ouachita-Appalachian Forests 

ecoregion historically varied because of the latitudinal extent 

of the ecoregion and the diversity of landscape elements and 

ecosystems. Appalachian oak, mixed mesophytic, oak-hickory, 

and oak-hickory-pine forests were the primary natural forest types 

(Kuchler, 1964). The region contains one of the richest temperate 

broadleaf forests in the world, with a high diversity of flora and 

fauna. Within the Appalachian oak forests, there is a wide variety 

of oak, hemlock, cove hardwood, and pine communities.

Forest is the most extensive land cover class in the 

ecoregion, accounting for nearly two-thirds of the area of the 

ecoregion in 2000. The ecoregion has some variation in forest 

resource lands and areas with different historical trends, but 

overall, forest land cover has been declining here for at least 

the past four decades (Drummond and Loveland, 2010). Forest 

cover in the Ozark, Ouachita-Appalachian ecoregion decreased 

from 67 percent of the ecoregion in 1973 to 65 percent in 2000, 

the largest change in any of the land cover categories of the 

ecoregion (Sleeter and others, 2013). Forestry is an important 

driver of land change in parts of the ecoregion, with tree cutting 

and regrowth cycles accounting for substantial amounts of 

land-cover change. Agricultural lands were the second most 

extensive cover at 25 percent of the area in 2000. Agricultural 

lands have maintained this approximate areal amount since 
22 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

1973 when 24 percent of the region was in agricultural lands. 

In general, this is one of the more unproductive ecoregions 

for agriculture in the Eastern United States, with some severe 

physical limitations of steepness or poor soils. Developed 

land was the third largest cover type in the Ozark, OuachitaAppalachian Forests ecoregion and was the class with the 

largest net gain. Developed land covered 5.2 percent of the 

region in 2000, an increase from 4.4 percent in 1973.

2.3.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

The Mississippi Alluvial and Southern USA Coastal Plains 

ecoregion covers the outer Atlantic and Gulf of Mexico coastal 

plains and the low-lying, mostly Quaternary alluvial part of the 

Mississippi Embayment, including the floodplains of the lower 

Mississippi, White, Arkansas, Ouachita, and Red Rivers. The 

ecoregion’s land cover is characterized by a mosaic of agricultural lands, wetlands, forests, water, and developed land. The 

region is generally lower, flatter, and warmer than the adjacent 

Southeastern USA Plains ecoregion. The Mississippi Alluvial and 

Southern Coastal Plains ecoregion contains the Middle Atlantic

Coastal Plain, Mississippi Alluvial Plain, Southern Coastal Plain, 

and Atlantic Coastal Pine Barrens level III ecoregions (U.S. 

Environmental Protection Agency, 2013a). For the purposes of 

the analysis for this assessment, the Southern Florida Coastal 

Plain level III ecoregion of the Everglades level II ecoregion has 

been included in the Mississippi Alluvial and Southeast USA 

Coastal Plains ecoregion, as has the Western Gulf Coastal Plain 

level III ecoregion of the Texas-Louisiana Coastal Plain level II 

ecoregion because of the similarities of the ecoregions.

The Mississippi Alluvial and Southern Coastal Plains 

ecoregion is unglaciated except for the terminal moraine and 

glacial outwash materials that formed Long Island, N.Y., 

and Cape Cod, Massachusetts. The ecoregion has landforms 

consisting mostly of flat plains, smooth plains, and a few irregular plains. There are also karst plains, river deltas, floodplains 

and low terraces, oxbows, swamps, bogs, estuaries, barrier 

islands, coral islands and reefs, dunes, and beaches. Elevations 

in the region range from sea level to about 100 m in Illinois, 

and relief is typically only 1 to 10 m. The region consists 

mostly of Quaternary sands, silts, and clays, with a few areas 

of Tertiary and Cretaceous-age marine terrace sediments.

Most of the Mississippi Alluvial and Southern Coastal Plains 

ecoregion has a mild midlatitude humid subtropical climate, 

marked by hot and humid summers and mild winters. The 

northeasternmost areas are somewhat cooler than the southern 

areas and are located along the boundary of severe midlatitude 

humid continental climates with colder winters, whereas southern 

Florida has a humid subtropical to tropical savanna climate with 

hot summers and warm winters. The mean annual temperature 

ranges from 11 °C in the northwest in Illinois to 25 °C in southern 

Florida and southern Texas. Summer heat is tempered by sea 

breezes in coastal areas. The frost-free period ranges from 190 

days in the northeast to 365 days at the southern tip of Florida. 

Mean annual precipitation ranges from about 600 mm in the 

driest southwest part of the region in southern Texas to more 

than 1,760 mm along the wettest parts of the central gulf coast 

(Wiken and others, 2011). Precipitation is fairly evenly distributed 

throughout the year, falling as rain from frontal storms in fall, 

winter, and early spring and from convective thunderstorms 

or tropical storms and hurricanes in the warm late-spring and 

summer months. Southern areas typically have a drier winter 

season than the northern areas. Droughts occasionally affect parts 

of the region, mostly in the south.

The natural vegetation of the Mississippi Alluvial and 

Southern Coastal Plains ecoregion is varied due to the latitudinal 

extent, diverse soil textures, and the subtle but often dynamic 

landforms. Small differences in elevation, water-table levels, or 

exposure to maritime salts can greatly affect vegetation patterns. 

The longleaf pine forest types were diverse and have proven 

difficult to classify given the great variations in disturbance 

history, site conditions, and species composition in the gradients 

from xeric upland sandhill communities to more mesic and wet 

flatwoods and savannas (Christensen, 1988; Peet and Allard, 

1993). With species-rich understories, the longleaf pine historically marked one of the country’s most ecologically important 

forest regions, although today only a fraction of the historical 

extent of the forests of the ecoregion remains (Earley, 2004). 

Another important forest type in the Mississippi Alluvial and 

Southern Coastal Plains ecoregion consists of the bottomland 

hardwood forests of floodplains and river terraces that provide 

crucial habitat for a variety of fish and wildlife. The mostly 

oak-dominated floodplain forest covered the majority of the 

Mississippi Alluvial Plain ecoregion. These forests had a variable 

species mix that was dependent on the tolerances of the species to 

different periods of flooding (Sharitz and Mitsch, 1993).

The landscapes of the Mississippi Alluvial and Southern 

Coastal Plains ecoregion have been greatly altered from the 

early times of Euro-American settlement, especially with 

20th century technology and energy sources. Although the large 

urban centers have typically diverse economies with manufacturing and technology, finance, medical, education, and service 

elements, many of the more rural areas of the region remain 

dependent on agricultural production, forestry, fish and shellfish 

catches, energy production, or tourism. Land cover data reflect 

some of the diversity and dynamism of the region’s mixed 

uses, and there is a high overall footprint of change compared 

with most other ecoregions in the Eastern United States.

Agricultural lands were the most extensive cover class in 

the ecoregion, accounting for approximately one-quarter of the 

ecoregion in 2000. The most typical agricultural activities in 

the Mississippi Alluvial and Southern Coastal Plains ecoregion 

differ by area. Pasture land and hay land occur in several parts 

of the Mississippi Alluvial and Southern Coastal Plains ecoregion, but the traditional cash crop production is concentrated in 

those areas where the land is best suited to cultivation or where 

specialty crops, such as citrus, rice, and sugar cane, can best be 

grown (Hart, 1978). Most of the cropland is in those areas of 

rich and deep soils such as the Mississippi Alluvial Plain ecoregion, where crop production is dominated by soybeans, rice, 
Chapter 2 23

cotton, corn, and wheat, with sugar cane in the south. Wetlands 

are the second most extensive land cover and are one of the 

distinguishing features of the ecoregion’s land cover mosaic. 

Wetlands and surface water combined to account for nearly 36 

percent of the region, with wetlands accounting for 22 percent 

of the area and surface water of the ecoregion, accounting for 

about 14 percent of the area. Forest is the third most extensive 

land cover class in the Mississippi Alluvial and Southern 

Coastal Plains ecoregion and was the largest decreasing land 

cover class in the ecoregion, following the consistent trend 

in other forests in the Eastern United States (Drummond and 

Loveland, 2010). Pine plantation forestry is an important 

driver of forest cover loss in parts of the ecoregion, such as 

the Carolinas, Georgia, and northern Florida, with tree cutting 

and regrowth cycles accounting for substantial amounts of 

logging. Developed land covered about 11 percent of the region 

in 2000, an increase from 8 percent in 1973. Although parts of 

the ecoregion remain relatively rural, other areas, especially in 

Florida and around Houston, Texas, have experienced expansive 

population and development growth.

2.4. Temporal Domain

This assessment was conducted to estimate carbon stock 

and balance and their relation to LULC change and other 

controlling processes for baseline and future projection periods. 

The baseline period covers 1992 through 2005. Projections of 

LULC and carbon stocks and fluxes were modeled for the baseline period using observational data from the USGS and other 

sources (Zhu and others, 2010). The projection period extended 

from the end of the baseline through 2050. Annual projections 

of LULC and carbon stocks and fluxes were produced for the 

projection period across a range of scenarios and input data 

(for example, emission scenarios, ecosystem models, general 

circulation model (GCM) outputs). Whenever possible, 

the estimates are provided as a range of values in order to 

represent the spread of variability in assessment results. The 

ranges of values for baseline estimates were derived from 

the minimum and maximum of averages of model runs for 

2001 through 2005. For projected future values, the ranges 

of values were derived from the means of the unique LULC 

scenarios, climate-change projections, and biogeochemical 

models for 2006 through 2050.

2.5. Scenario Framework

In 2000, the IPCC published the SRES (Nakićenović and 

others, 2000), which documented the development of a global 

set of GHG-emission scenarios based on an underlying set of 

socioeconomic conditions that were consistent with the current (at 

the time) scenario literature. The SRES scenarios were designed 

to assess the effects of alternative GHG-emission pathways on 

coupled human and environmental systems and evaluate future 

vulnerabilities on those systems under various combinations 

of projected change. These scenarios have been used as the 

basis for the IPCC third and fourth assessment reports on future 

projected climate change. The SRES scenarios consist of four 

basic narrative storylines, each of which describe alternative 

developments in the major drivers of GHG emissions, such as 

population growth, economic growth, technological change, 

energy use, globalization, and environmental protection. The 

four scenarios are oriented along two axes with either economic 

growth (denoted as A) or environmental protection (denoted as B) 

aligned along one axis and either global development (denoted as 

1) or regional development (denoted as 2) aligned along the other 

axis; for example, the B1 scenario assumes strong environmental 

protection and global cooperation.

In order to explore sensitivities in the energy sector, the A1 

scenario was subdivided into three subscenarios that focused 

on fossil-fuel use (A1FI), renewable technologies (A1T), and 

a balanced energy sector that did not rely on any particular 

energy source (A1B). Six modeling teams characterized the 

various scenarios, ultimately producing 40 quantified scenarios. 

No probability of occurrence was assigned to any one of the 

SRES scenarios and all should be considered equally plausible 

with none considered more or less preferable. Furthermore, no 

integrated climate-change policies, such as the emissions targets 

of the Kyoto Protocol (United Nations Framework Convention 

on Climate Change, 1998), are incorporated into any of the 

scenarios; therefore, the scenarios serve as reference conditions 

to evaluate the effects of potential mitigation actions and strategies. Since the inception of the SRES scenarios, a suite of future 

climate-change projections (GCM data) have also become 

available and correspond to the major scenarios. At the early 

stage of this assessment, GCM data corresponding to the B2 

scenario were not available. Because this assessment required 

the use of both the LULC scenarios and the climate-change 

projection scenarios, only scenarios A1B, A2, and B1 were used 

in the assessment. Furthermore, although the SRES scenarios 

extend through 2100, this assessment only used projection data 

through 2050. Assumptions about the major driving forces 

associated with each scenario are listed in table 2–1.

2.6. Scenario Downscaling

In order to use the global scenarios, a scenario downscaling process was needed to translate the coarse-scale 

scenario data to fine geographic scales while maintaining 

consistency with the original dataset and with local data 

(van Vuuren and others, 2007, 2010). Land-change scenarios 

were developed using a modular modeling approach. A 

global integrated assessment model (IAM) was used to 

supply future projections of land use at the national scale. An 

accounting model was developed to refine the national-scale 

IAM projections and to downscale to hierarchically nested 

ecoregions. The ecoregion-based projections were then 

converted into annual maps of LULC using a spatially explicit 

LULC change model. The approach used for this assessment 
24 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 2–1. Assumptions about the primary driving forces affecting land-use and land-cover change.

[These assumptions were used to downscale scenarios A1B, A2, and B1 of the Intergovernmental Panel for Climate Change Special Report on Emission 

Scenarios (Nakićenović and others, 2000). Population and per-capita income projections are from Strengers and others (2004)]

Driving forces Scenario A1B Scenario A2 Scenario B1

Population growth (global and 

United States)

Medium; 8.7 billion by 2050, then 

declining; in the United States, 

385 million by 2050

High; 15.1 billion by 2100; in the 

United States 417 million by 

2050

Medium; 8.7 billion by 2050, then 

declining; in the United States, 

385 million by 2050

Economic growth in the 

United States

Very high; per capita income 

$72,531 by 2050

Medium; per capita income 

$47,766 by 2050

High; per capita income

$59,880 by 2050

Regional or global orientation 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

follows the methods described in Zhu and others (2010) and 

more recently in Sohl and others (2012b) and Sleeter and 

others (2012a). The next sections review each of the major 

components of the scenario downscaling process.

2.6.1. Global Integrated Assessment Model

Initial quantities of projected LULC changes were 

formulated in a scenario named “demand” by implementing 

an accounting model for downscaling land-use scenarios 

(described in detail in Sleeter and others, 2012a). National-scale 

LULC projections were based on national-scale projections 

from the Integrated Model to Assess the Global Environment 

(IMAGE, version 2.2; Planbureau voor de Leefomgeving 

[Netherlands Environmental Assessment Agency], 2001), 

land-use histories, and expert knowledge. IMAGE was used to 

simulate future environmental change, including GHG emissions and landuse changes, for the three SRES marker scenarios 

(A1B, A2, B1; Strengers and others, 2004). IMAGE used a 

series of linked modules to project environmental consequences 

resulting from anthropogenic activity (Alcamo and others, 1998; 

Planbureau voor de Leefomgeving [Netherlands Environmental 

Assessment Agency], 2001). Environmental changes were 

projected for 17 world regions (the United States was treated 

as a single region) with LULC data available in a 30-footby-30-foot grid. IMAGE produced projections of demand for 

agriculture and forest harvest, which were incorporated directly 

into the scenario downscaling model described in Sleeter and 

others (2012a). Future projections of development and mining 

were developed through the use of proxy data (population and 

coal usage, respectively) from IMAGE. Land-use histories were 

then used to expand the scenario projections of net change in 

major land-use classes into comprehensive projections of gross 

changes between all major LULC types.

2.6.2. Land-Use Histories

Land-use histories described the recent historical LULC 

changes in ecoregions of the United States. These data came 

primarily from the USGS Land Cover Trends Project, which 

provided ecoregion-based estimates on the rates, extent, and 

types of LULC change for multiple dates between 1973 and 

2000 (Loveland and others, 2002; Sleeter and others, 2012b, 

2013). Maps of land-cover change for the conterminous 

United States were generated through the interpretation and 

classification of satellite data for 2,866 sample sites, stratified 

by ecological region. A comparison of land-cover maps after 

classification was used to produce estimates of change across 

major classes (fig. 2–1).

USGS land cover trends data were incorporated into 

the construction and downscaling of the scenarios in two 

primary ways. First, the data were used to expand projections 

of net change in development, mining, and agricultural lands 

LULC classes into gross conversions between all primary 

LULC classes at the national scale. Second, the data were 

used to downscale proportionally these LULC conversions 

to ecoregions of the conterminous United States. Throughout 

the downscaling process, regional and sectoral experts were 

consulted in a series of workshops and ad-hoc consultations. 

The data served as a default parameter for downscaling, and 

experts were able to modify certain variables in order to 

produce regionally specific scenarios that retained consistency 

with the SRES scenarios. A complete description of the downscaling process can be found in Sleeter and others (2012a).

2.6.3. Mapping Scenarios of Land-Cover Change

Regional LULC scenarios, developed in the process 

as described above, were used as input to the Forecasting 

Scenarios of Land-Use Change (FORE–SCE) model (Sohl and 

Sayler, 2008; Sohl and others, 2012a). The FORE–SCE model 

produced annual, spatially explicit LULC maps from 2006 

through 2050 that were consistent with the scenario assumptions and LULC proportions from the scenario downscaling 

process. The initial LULC map for the beginning of the 

simulation period was the 2005 LULC map produced from the 

baseline LULC modeling described in chapter 3 of this report. 

Suitability-of-occurrence surfaces were used to model baseline 
Chapter 2 25

1973 1980 1986 1992 2000

1973–1980 1980–1986 1986–1992 1992–2000

A. Landsat MSS, TM, and ETM+ images (top row) and interpreted land cover (bottom row)

B. Change/no change images between successive image dates

Water

Developed

Barren

Forest

Grassland/shrubland

Agriculture

Wetland

Ice/snow

Nonmechanical 

 disturbance

Mechanical 

 disturbance

Land use and land cover classification (second row in part A)

No change

Change

Change/no change

(part B)

EXPLANATION

Figure 2.1.

Landsat false-color composite

(first row in part A)—

Wavelength range, in micrometers

Band 5: 1.55 to 1.75

Band 4: 0.76 to 0.90

Band 3: 0.63 to 0.69

Mining

Figure 2–1. Satellite images used in the assessment of carbon sequestration and fluxes in the Eastern United States. A, Land-cover 

change images and B, corresponding interpretation produced from manual interpretation of Landsat data. C, Level of change for four 

time intervals. The example images are from the Texas Blackland Prairies level III ecoregion, which is included in the analysis of the 

Mississippi Alluvial and Southeast USA Coastal Plains ecoregion. EMT+, Enhanced Thematic Mapper Plus; MSS, multispectral scanner; 

TM, Thematic Mapper.

LULC change. These surfaces were developed through a 

logistic regression process to guide the placement of patches 

of change for the 2006 through 2050 scenarios (see chap. 3). 

Each level III ecoregion was individually parameterized and 

modeled by applying the FORE–SCE model for each of the 

three SRES scenarios used in this assessment. The models of 

LULC from 2006 through 2050 provide downscaled spatial 

representations of plausible outcomes that are based on 

the SRES scenarios. When combined with the mapped and 

modeled baseline (1992 through 2005) LULC maps described 

in chapter 3, the baseline and modeled scenarios resulted in 

a continuous, consistent map database for LULC from 1992 

through 2050.
26 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

2.7. Scenario Downscaling Results 

for the Eastern United States

The seven level II ecoregions included in this assessment cover about 3.05 Mkm2

, or approximately 38 percent 

of the land area of the conterminous United States and 

account for 82 percent of the forest area, 87 percent of the 

wetlands, 70 percent of the developed area, and 47 percent 

of the agricultural lands in the conterminous United States. 

Combined, level II ecoregions in the Eastern United States 

comprise 47 percent forest cover, 32 percent agricultural lands, 

9 percent wetlands, 5 percent development, and 2 percent 

grasslands/shrublands (fig. 2–2).

Total overall LULC change in the Eastern United States 

was projected to range between 3 and 5 percent (per 5-year 

time period), depending on scenario. Scenarios A1B and A2 

had the highest rates of change and generally were projected 

to experience an accelerating rate of change through the 

projection period. Scenario B1 remained relatively consistent 

at 3 to 3.6 percent change (per 5-year period) throughout the 

projection period. Table 2–2 provides the range of 5-year 

overall change projections by level II ecoregion.

47

2

32

9

2

5

Forest

Grassland/shrubland

Agriculture

Wetland

Developed

Forest (harvested)

A. Land use and land cover in

 the Eastern United States

B. Land use and land cover in level II ecoregions of the Eastern United States

Forest Grassland/shrubland Agriculture Wetlands

8

11

10

2

38

25

7 7

1

4 2

22

8

56

2 2

17

19

32

12

15 21

2

9

2 22

1

43

Mixed Wood Shield

Atlantic Highlands

Mixed Wood Plains

Central USA Plains

Southeastern USA Plains

Ozark, Ouachita-Appalachian Forests

Mississippi Alluvial and Southeast USA Coastal Plains

Land use and land cover,

 in percent

Level II ecoregion

EXPLANATION

Figure 2.2.

EXPLANATION

Figure 2–2. Pie charts showing A, total composition by land use and land cover (LULC) type and, B, distribution of LULC in level II 

ecoregions of the Eastern United States.

Table 2–2. Projected land-use and land-cover change from 2005 through 2050 in the Eastern United States.

[Values listed in the Intergovernmental Panel for Climate Change Special Report on Emission Scenarios (SRES; Nakićenović and others, 2000) columns are 

the percentage of each level II ecoregion that experienced a change in land use or land cover at least once between 2005 and 2050. Level II ecoregions are from 

U.S. Environmental Protection Agency (1999, 2013). km2, square kilometers]

Ecoregion Area,

in km2

Scenario A1B,

as percentage 

change

Scenario A2,

as percentage 

change

Scenario B1

 as percentage 

change

Mixed Wood Shield 215,648 4.7–7.2 4.6–6.6 2.2–2.5

Atlantic Highlands 187,551 3.6–5.1 3.6–5.1 2.7–3.1

Mixed Wood Plains 388,858 1.7–2.6 1.8–2.7 0.9–1.3

Central USA Plains 239,027 0.7–1.0 0.6–0.8 0.2–0.5

Southeastern USA Plains 994,355 4.7–7.2 4.5–6.3 5.1–6.8

Ozark, Ouachita-Appalachian Forests 520,486 2.8–4.8 3.2–4.5 1.8–2.3

Mississippi Alluvial and Southeast USA Coastal Plain1 506,807 2.1–3.4 1.9–2.5 2.2–2.8

Eastern United States (total) 3,052,732 3.2–5.0 3.2–4.4 3.0–3.6

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.
Chapter 3. Mapping and Modeling of Land Use and Land Cover 

in the Eastern United States From 1992 Through 2050

By Ryan R. Reker,1 Kristi L. Sayler,2 Aaron M. Friesz,3 Terry L. Sohl,2 Michelle A. Bouchard,2

Benjamin M. Sleeter,2

 Rachel R. Sleeter,2 Tamara S. Wilson,2 Glenn E. Griffith,2 and Michelle L. Knuppe2

1

Arctic Slope Regional Corporation Research and Technology Solutions

2

U.S. Geological Survey

3

Information Dynamics

3.1. Highlights

• LULC maps at 250-m resolution were produced for 

each year of the baseline period of 1992 through 2005 

and for three scenarios of future LULC change from 

2006 through 2050. Modeling of terrestrial carbon 

stocks and fluxes, as detailed in chapter 7 of this report, 

used 2001 through 2005 as the baseline period.

• Forested ecosystems with large amounts of clearcutting generally had the highest rates of LULC change 

in all scenarios. Conversion to urban development was 

another significant change, particularly in ecoregions 

with large metropolitan areas. Conversion of forests to 

agricultural lands was significant in scenarios A1B and 

A2.

• The Southeastern USA Plains ecoregion had the greatest amount of change in the baseline period with 14.2 

percent of the ecoregion changing LULC at least once 

and nearly 30 percent or greater changing LULC in all 

three scenarios of projected change.

• The Central USA Plains ecoregion had some of the 

lowest amounts of LULC change throughout the 

Eastern United States with less than 10 percent in each 

scenario and 2.6 percent during the baseline period.

3.2. Introduction

The spatial and temporal frameworks introduced in 

chapter 2 of this report serve as input to the spatial LULC 

modeling component described in this chapter. The mapping 

and modeling of LULC form the spatial foundations of this 

assessment and are used to define the composition of the 

assessed ecosystems. The LULC maps directly feed into other 

components of the assessment, particularly the assessment of 

GHG fluxes of aquatic systems (chaps. 5 and 6) and carbon 

storage and GHG fluxes of terrestrial systems (chap. 7).

LULC in the Eastern United States is diverse. Historically 

dominated by natural forests, the region now consists of a 

fragmented mosaic of urban areas, agricultural lands, areas 

of surface mining, and heavily managed forest lands. LULC 

change is equally as varied, with some areas undergoing rapid 

LULC change and others remaining relatively static, historically and projected into the future. The ecoregions for this 

assessment are defined in chapter 2 of this report.

This assessment uses a thematic classification system that 

represents a mix of LULC classes. The mixed LULC scheme 

enables the mapping and modeling of natural and anthropogenic processes that affect the landscape and, ultimately, 

biogeochemical cycles of GHGs. The temporal foundation of 

this assessment includes baseline data (data available for the 

historical period described in this chapter) and projected future 

data (generated through spatially modeled future scenarios, as 

described in chapter 2 of this report). Baseline and projected 

LULC data were used to guide the assessment of baseline 

and future changes in carbon storage and GHG fluxes. Spatial 

LULC modeling used to produce projected LULC maps 

consistent with the IPCC SRES, as described in chapter 2 of 

this report.

3.3. Methods and Data

3.3.1. Spatial Model Used for Mapping 

and Modeling

The spatial modeling framework FORE–SCE was used 

to produce annual LULC maps from 1992 through 2050. 

FORE–SCE has been used successfully in the past to model 

annual LULC change for large geographic regions (Sohl and 

Sayler, 2008; Sohl and others, 2012a,b). The FORE–SCE 

model uses separate but linked demand and spatial allocation 

components to produce spatially explicit, annual LULC maps. 

The demand component provides aggregate-level quantities 

of LULC change for a region or a prescription for the overall 
28 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

regional LULC proportions. The spatial allocation component 

ingests demand and produces spatially explicit LULC maps 

using a patch-based allocation procedure.

In the spatial allocation component, FORE–SCE uses 

suitability surfaces, unique to each modeled LULC class, 

to guide placement of new patches of LULC change on the 

landscape (Sohl and Sayler, 2008). Suitability surfaces are 

created using logistic regression to quantify empirical relationships between LULC and spatially explicit biophysical and 

socioeconomic variables. Suitability surfaces are made for 

each unique LULC class in every individually parameterized 

region or subregion that is modeled. Individual patches of new 

LULC are placed on the landscape for a given annual model 

run until demand is met for that given year. The process is 

repeated for each successive year until the modeling period 

is completed. Information on land under protected status can 

be used during spatial allocation procedures to restrict the 

placement of specific forms of LULC change on certain types 

of protected lands (for example, restricting urban development 

in national park lands).

The age of forest stands is also tracked spatially and 

temporally and can be estimated in the modeling environment 

in concert with the modeling of forest clearcuts, afforestation, 

and deforestation. In the FORE–SCE model, data about foreststand ages are needed to ensure accurate modeling of clearcutting cycles (based on the typical age when a forest stand is 

ready for harvesting) for a given geographic area and provide 

information on forest structure. The FORE–SCE model tracks 

forest-stand age for each annual model iteration and resets 

the stand age to 0 whenever a new forest area was generated 

or a forest was clearcut. Minimum cutting age thresholds can 

also be established for forest LULC classes to ensure harvest 

does not occur in forested areas of insufficient age. Additional 

details on the FORE–SCE model framework may be found in 

Sohl and Sayler (2008) and Sohl and others (2012a,b)

3.3.2. Starting LULC and Baseline Period

The baseline period permits an examination of recent 

LULC change and the calibration of the LULC and biogeochemical modeling processes before the simulations of 

projected future conditions. A modified version of the 1992 

NLCD (Vogelmann and others, 2001) served as the initial 

LULC data for this work. The start of the baseline period 

was set to 1992 because it marked the earliest year for which 

consistent, nationwide, satellite-derived LULC data were 

available. NLCD data were available for 1992, 2001, and 2006 

(Vogelmann and others, 2001; Homer and others, 2007; Xian 

and others, 2009), but annual maps were not available, and 

different mapping methodologies between NLCD versions 

precluded the use of the NLCD alone for providing LULC 

data for the 1992 through 2005 baseline period. Annual LULC 

maps for the baseline period were required to adequately 

portray gross changes between LULC classes that could be 

missed by a temporal interval longer than 1 year and thus 

could affect carbon and GHG calculations. The endpoint of 

the baseline period was set to 2005. The latest NLCD data 

available at the time of the assessment was conducted were 

from the 2006 NLCD (Xian and others, 2009), but 2005 was 

chosen as the end date for the baseline period to facilitate the 

use of equal 5-year intervals for construction of the projected 

scenarios.

The NLCD thematic classification system could be 

directly generalized to the primary ecosystem types analyzed 

for this assessment (table 1–1). The original resolution of the 

1992 NLCD was 30 m, but the data were resampled to 250 m 

for this assessment to reduce the volume of data and hold the 

modeling requirements to a consistent level. Several adjustments were made to the thematic classes for practical considerations and to improve the ability of the modeling framework 

to address LULC impacts on carbon and GHG fluxes. The four 

urban classes from the 1992 NLCD were collapsed into one 

urban/developed class, because separate categories were not 

required to explicitly model detailed urban class changes for 

the conterminous United States. Similarly, three agricultural 

lands classes from the 1992 NLCD (row crop, small grains, 

and fallow) were collapsed into one agriculture lands class that 

represented cultivated crops.

The classification scheme was also altered to include 

classes representing forest clearcutting (mechanically 

disturbed), because forest management and clearcutting can 

affect significantly not only biogeochemical cycling, but 

other ecological processes as well. The thematic labeling 

of forest clearcuts allowed for tracking and modeling of 

clearcut locations and the resulting effect on forest structure, 

while recognizing that the underlying forest land use had not 

changed. The 1992 NLCD dataset was augmented (fig. 3–1) 

by incorporating information from the Vegetation Change 

Tracker (VCT; Huang and others, 2010) of the Landscape 

Fire and Resource Management Planning Tools Project. The 

VCT data mapped natural and anthropogenic disturbances, 

particularly forest clearcuts and wildland fires, by analyzing 

stacked images from the Landsat Thematic Mapper (TM) 

and Enhanced Thematic Mapper Plus (ETM+). Wildfires 

were extracted from the VCT data using fire locations from 

the Monitoring Trends in Burn Severity Project (MTBS; 

Eidenshink and others, 2007). Data on contemporary rates of 

forest clearcutting and spatial information on clearcut patch 

characteristics from the USGS Land Cover Trends Project 

were subsequently used to filter the VCT data to approximate 

locations of clearcut forest harvests. Clearcut forest locations 

derived from edited VCT data from the baseline period were 

used to populate mechanically disturbed classes 3, 4, and 5 

(table 1–1) for the starting 1992 land cover. The LULC class 

mechanically disturbed derives from the USGS Land Cover 

Trends Project (Auch and others, 2012; Napton and others, 

2010; Sleeter and others, 2012a,b) and is used in this report to 

refer to forest clearcutting for the harvest of timber resources 

exclusively.

The three mechanically disturbed classes are differentiated as national forest, other public land, and private land 
Chapter 3 29

86°30'W 86°15'W 86°00'W 85°45'W

46°

30'

N

46°

20'

N

46°

10'

N

0 100 200 MILES

0 100 200 KILOMETERS

Year of vegetation change (forest cutting)

[Top map]

Ownership of land

1986

1987

1988

1989

1990

Seney National Wildlife Refuge and other wilderness areas

National forest

Other public land

Private land

1996

1997

1998

1999

2000

EXPLANATION

Figure 3.1.

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land cover

[Bottom map]

1991

1992

1993

1994

1995

2001

2002

2003

2004

2005

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

0 5 10 15 KILOMETERS

0 5 10 15 MILES

N

Figure 3–1. Map showing how data from the vegetation change tracker of Landscape Fire and Resource 

Management Planning Tools Project (LANDFIRE) provided information on ecosystem disturbances for the 

assessment of carbon fluxes and storage in the Eastern United States.
30 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

based on ownership and protection status. The Protected Area 

Database of the United States (PAD–US; Protected Areas 

Database of the United States Partnership, 2009) was used 

to spatially distinguish ownership for the three disturbance 

classes. The PAD–US database includes Federal, State, and 

local protected lands, as well as information from national 

nonprofit organizations. The database does not cover all 

protected lands (for example, conservation easements), but 

it is the most comprehensive and accurate protected lands 

database available for the United States. In this framework, 

the mechanically disturbed classification is used strictly to 

represent a temporary land cover status within the overall 

land use class of forest. When a forest parcel is clearcut, the 

mechanically disturbed label is temporarily applied to the 

parcel. Timber on parcels labeled as mechanically disturbed 

can thus be assumed to have been recently harvested, and the 

parcel is still assumed to be managed as a forested land use. 

The framework of the assessment makes the assumption that 

the forest cover regenerates over time after the clearcut, and 

after an average of seven years, the thematic label returns to 

the original forest class. Although forest use does not change 

for areas that have been clearcut, the temporary use of the 

mechanically disturbed label allows tracking, quantifying, and 

modeling forest cutting and forest stand age.

The demand component of FORE–SCE for the LULC 

change in the baseline period was separated into two periods 

to take advantage of temporally specific historical data. 

Demand from 1992 through 2000 was provided by USGS 

land cover trends data (U.S. Geological Survey, 2012). The 

USGS Land Cover Trends Project used a sampling approach 

and the historical archive of Landsat data to produce estimates 

of LULC change for each of 84 level III ecoregions (modified 

from U.S. Environmental Protection Agency, 1999) in the 

conterminous United States (Loveland and others, 2002). 

Although the coarse-scale level II ecoregion framework 

was used for the overall assessment in the Eastern United 

States, the fine-scale level III ecoregion framework served 

as the primary framework for all FORE–SCE-based LULC 

modeling, which improved the representation of spatial LULC 

change patterns in a heterogeneous landscape.

Demand information from the USGS Land Cover Trends 

Project was provided separately for each level III ecoregion, 

and the spatial allocation component of FORE–SCE was 

parameterized individually for each level III ecoregion. From 

1992 through 2000, USGS land cover trends data provided 

baseline regional proportions of LULC change; however, these 

data were thematically less detailed than the LULC classes 

used for this assessment as shown in table 1–1. For example, 

USGS land cover trends only estimated one aggregate forest 

class, whereas this assessment differentiated between deciduous, evergreen, and mixed forest types. To obtain the three 

forest types and their transitions from the USGS land cover 

trends data from 1992 through 2000, the proportions of the 

three forest types from the 1992 NLCD were used to disaggregate the USGS land cover trends single forest class for each 

level III ecoregion. A similar disaggregation of USGS land 

cover trends classes using the 1992 NLCD was performed to 

split the USGS land cover trends class grasslands/shrublands 

into the grassland and shrubland classes, split wetland into 

the herbaceous wetland and woody wetland classes, and split 

agricultural lands into hay/pasture and cultivated crop. Finally, 

the estimates for 1992 through 2000 by ecoregion were 

annualized to produce annual rates of change that served as 

annual demand for the FORE–SCE model.

A similar methodology was used to populate the demand 

component of the model for 2001 through 2005. The demand 

for this period was provided by the 2001 through 2006 NLCD 

change data (Xian and others, 2009). The 2001 and 2006 

NLCD data provided a LULC change product that provided 

consistent, wall-to-wall LULC data for the conterminous 

United States. The level of thematic detail was mostly 

compatible with this assessment, and unlike the USGS land 

cover trends data for 1992 through 2000, no disaggregation 

to a finer thematic scale was necessary. The 2001 through 

2006 NLCD change data were annualized to produce rates of 

change that served as annual demand for 2001 through 2005 

for the FORE–SCE model.

3.3.3. Projection Period

The timeframe of 2006 through 2050 served as the 

projected LULC modeling period. The scenarios (chap. 2) 

served as input to the FORE–SCE spatial modeling 

framework, with FORE–SCE producing spatially explicit 

representations for each of the three SRES scenarios. Spatial 

resolution (250 m), temporal characteristics (annual LULC 

maps), and thematic resolution (table 1–1) were consistent 

with the baseline period, resulting in a consistent, continuous 

series of LULC maps between the baseline and projected 

periods (annual LULC maps from 1992 through 2050).

Regional LULC storylines were downscaled to formulate 

future alternative landscape scenarios (chap. 2). These 

scenarios provided quantitative prescriptions of future landscape composition that were used in conjunction with spatial 

modeling to create a suite of LULC maps from 2006 through 

2050. The FORE–SCE model served as the primary modeling 

framework for ingesting scenario storylines and producing 

the spatially explicit LULC projections. As with the baseline 

LULC modeling, the demand component of the framework 

again supplied overall proportions of LULC change at a 

regional level, and the spatial allocation component ingested 

those proportions and produced spatially explicit LULC maps. 

Demand for the LULC projection period of 2006 through 

2050 was supplied by the quantified scenarios described in 

chapter 2. For this assessment, a version of FORE–SCE was 

used that ingests and models specific LULC transitions; for 

example, each scenario provided a quantified matrix of LULC 

transitions for a given annual model run. FORE–SCE used the 

same suitability surfaces as in the baseline period to guide the 

placement of individual patches of landscape change.

The last year (2005) of the mapped and modeled baseline 

period served as the starting point for the LULC projections. 
Chapter 3 31

The 2005 forest stand age layer from the baseline runs 

provided the starting stand age layer for the projected LULC 

model runs. The PAD–US data again were used to spatially 

partition portions of the landscape with a protected status, but 

for the projected period, the implementation of the PAD–US 

data was dependent upon scenario. More forms of protected 

lands in the PAD–US were protected from LULC change in 

the environmentally focused scenario B1 than in the economically focused scenarios A1B and A2, resulting in a potentially 

different spatial configuration of LULC change between 

scenarios even if the prescribed LULC proportions from the 

scenarios were the same. Parameterization of the FORE–SCE 

model was again conducted for each level III ecoregion, but 

for the projected period, parameterization differed across 

scenarios. For example, a dispersion variable was used to 

determine what proportion of the suitability surface histogram 

is open to LULC change. For a scenario such as A1B, urban 

development may be allowed to occur on a wider part of the 

suitability surface histogram, resulting in a more dispersed 

urban footprint that represents urban sprawl. Conversely, 

for an environmental scenario such as B1, assumptions 

of compact urban development led to a tightening of the 

dispersion variable, resulting in smaller, more compact urban 

footprints. Individual patch characteristics may also differ 

between scenarios, as dictated by the qualitative scenario 

storylines.

Modeling within the FORE–SCE framework was 

conducted with annual model iterations, by level III ecoregion, 

with patches of LULC change placed on the landscape until 

demand for a given annual run was met for that ecoregion. 

Processing then continued to each subsequent year until the 

2006 through 2050 period was complete. The modeled LULC 

change for 2006 through 2050 thus provided plausible spatial 

representations for each of the three SRES scenarios. When 

combined with the mapped and modeled baseline LULC 

maps, the LULC mapping and modeling work described in 

this section resulted in a continuous, annual, consistent LULC 

map database from 1992 through 2050.

3.4. Results and Discussion

3.4.1. Baseline LULC Mapping and Modeling

To understand LULC change in the Eastern United States, 

it is often necessary to discuss changes in individual LULC 

classes nested within the ecosystems. The following discussion of results provides a summary of ecosystems defined 

for this assessment, as well as a discussion of nested LULC 

classes where appropriate. Ecosystem composition in the 

Eastern United States at the beginning of the baseline period 

was dominated by forests with 48.1 percent (1,467,167 km2) 

and agricultural lands with 31.9 percent (972,486 km2), 

combining for 80 percent (2,439,652 km2) of the region. 

Remaining ecosystems consisted of other lands with 9.5 

percent (290,470 km2), wetlands with 8.9 percent (272,486 

km2), and grasslands/shrublands with 1.6 percent (50,168 

km2). Two LULC classes nested within the ecosystem categories warrant special attention for their effects on carbon and 

GHG dynamics and status as primary drivers of land change 

in the Eastern United States. The three mechanically disturbed 

classes are part of the forests ecosystem and represent 3.2 

percent (46,168 km2) of the ecosystem and 1.5 percent of the 

total Eastern United States. Changes in forest ecosystem area 

affect all six nested LULC forest classes (deciduous, evergreen, and mixed forests and the three mechanically disturbed 

classes) to reflect total changes in forest area via conversions 

to other ecosystem classes (that is, developed or agricultural 

lands). LULC changes in the mechanically disturbed class 

reflect transitional changes within the forest lands ecosystem, 

indicating changing rates of forest harvest, but these changes 

do not affect overall ecosystem area. The developed class 

is nested within the other lands ecosystem, making up 

41.1 percent (119,413 km2) of the class and 3.9 percent of the 

total region.

The LULC change footprint is defined as the percentage 

of the Eastern United States or an ecoregion that changed 

LULC type at least once during the time period (Sleeter and 

others, 2012a,b; Wilson and others, 2012). During the baseline 

period, approximately 8 percent (244,331 km2) of the Eastern 

United States changed at least once (fig. 3–2; table 3–1). The 

developed, other lands, and mechanically disturbed classes 

contributed the most to the footprint of LULC change of the 

Eastern United States through unidirectional conversions 

to urban lands and the cyclical nature of forest harvest and 

subsequent regeneration.

Developed land led all LULC classes in net change 

with an increase of 19.4 percent (23,186 km2) in urban 

area (fig. 3–3; table 3–2). LULC change in the other lands 

ecosystem class showed a change of 8.9 percent, with most 

of the categories within the class showing little change, 

aside from mining, which saw a net increase of 29.5 percent 

(1,355 km2). Water area increased by only 0.8 percent 

(1,381 km2), and changes in the barren and ice/snow categories were relatively minor.

LULC change in the forests ecosystem (including deciduous, evergreen, and mixed forest cover types and mechanically

disturbed LULC classes) appeared relatively stable during the 

baseline period, decreasing by only 0.4 percent (5,700 km2

). 

Within the forests ecosystem, LULC in the three mechanically disturbed classes experienced a total net increase of 8.3 

percent (9,525 km2; table 3–2) with most of the gains occurring on private lands (11.4 percent, 4,760 km2

). More than 90 

percent of all forest cutting in the Eastern United States was 

on private lands with that trend continuing throughout the 

baseline period (fig. 3–4). Gains in area by the mechanical 

disturbance classes reflect decreases in area of the forested 

LULC classes, indicating an increase in the rate of forest 

clearcutting. A variety of factors contributed to more intensive 

forest clearcutting in the Eastern United States during the 

baseline period. In the southern half of the region, the shift 

towards increased private industrial forest management of 
32 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

EXPLANATION

Spatial variability of land-use and land-cover 

 change in the Eastern United States, by 

 level III ecoregion, in percent

0 to 5

>5 to 10

>10 to 15

>15 to 20

>20 to 25

Figure 3.2.

Level II Level III

0 200 400 MILES

0 200 400 KILOMETERS

N

Ecoregion boundary

Figure 3–2. Map showing spatial variability of land-use and 

land-cover change in the Eastern United States.

Table 3–1. Land-use and land-cover change footprint in the Eastern United States from 1992 through 2005.

[km2, square kilometers]

Ecoregion Area, in km2 Percentage change

Mixed Wood Shield 215,648 7.6

Atlantic Highlands 187,551 2.3

Mixed Wood Plains 388,858 2.9

Central USA Plains 239,027 2.6

Southeastern USA Plains 994,355 14.2

Ozark, Ouachita-Appalachian Forests 520,486 4.2

Mississippi Alluvial and Southeast USA Coastal 

Plains1

506,807 8.4

Eastern United States (total) 3,052,732 8.0

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

intensely cultivated pine monocultures was already occurring, and the trend continued throughout the baseline period 

(Binford and others, 2006). On a managed pine plantation, 

harvest cycles for timber products can be as short as 20 to 25 

years, enabling multiple harvests in the same amount of time 

it takes to make a single cutting in more temperate forests. 

Production in the Southeastern United States also increased 

due to timber harvest decreases in the Pacific Northwest 

(Haynes, 2003; Drummond and Loveland, 2010).

LULC change in the agricultural lands ecosystem class 

decreased by more than 2.1 percent (20,471 km2) during the 

baseline period. Modern losses in agricultural lands have 

been well documented by contemporary studies of LULC 

change in the Eastern United States (Brown and others, 2005; 

Drummond and Loveland, 2010). Decreasing profitability 

associated with farming marginal land led to losses of nearly 

2 percent each in cropland (10,773 km2) and hay/pasture 

(9,698 km2) to forest lands through abandonment, conversion 

to managed timber production, or government conservation 

programs, such as the Conservation Reserve Program (CRP).

LULC change in the grasslands/shrublands ecosystem 

increased by 2.3 percent (1,138 km2) during the baseline 

period, mostly due to enrollment of agricultural lands into the 

CRP in western parts of the region, such as the East Central 

Texas Plains level III ecoregion, where agricultural lands 

conversion was more suited to native and managed grasses. 

The wetlands ecosystem class encompassed 8.9 percent 

(272,422 km2) of the Eastern United States and remained 

fairly static during the baseline period, with wetland losses of 

0.4 percent (959 km2).

The LULC footprint closely reflected the spatial variability of the two major types of land change, developed and 

forest clearcutting, with each class displaying vastly different 

spatial patterns from the other. Several level III ecoregions 
Chapter 3 33

with large amounts of forest clearcutting and developed lands 

had LULC change footprints of 40 percent or more (fig. 3–5). 

A brief examination of the basic characteristics of the seven 

level II ecoregions and major driving forces of LULC change 

in the ecoregions is provided in chapter 2.

105,000

115,000

125,000

135,000

145,000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 20042005

Area, in square kilometers

Year

Figure 3.3.

Figure 3–3. Chart showing the increasing trend in the areal 

extent of the developed land-use and land-cover class in the 

Eastern United States between 1992 and 2005.

Table 3–2. Mapped and modeled land use and land cover in the Eastern United States from 1992 through 2005.

[km2, square kilometers]

Ecosystem class

Area, in km2 Change between 1992 and 2005

1992 2005 Area, in km2 Percentage

Forests 1,467,167 1,461,467 5,700 – 0.4

Agricultural lands 972,486 952,014 –20,471 –2.1

Other lands 290,470 316,463 25,993 8.9

Wetlands 272,442 271,483 –959 – 0.4

Developed 119,413 142,598 23,186 19.4

Grasslands/shrublands 50,168 51,306 1,138 2.3

Mechanically disturbed 46,168 49,993 3,825 8.3

0

10,000

20,000

30,000

40,000

50,000

60,000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Area, in square kilometers

Figure 3.4.

EXPLANATION

Mechanically disturbed land

National forest

Other public

Private

Year

Figure 3–4. Chart showing the increasing trend of forest 

clearcutting in the Eastern United States between 1992 and 2005. 

Private lands make up the largest share of timber harvest and 

also saw the greatest gains. The greatest declines were noted on 

national forest lands.
34 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Developed

Mechanically 

 disturbed

Forests

Grasslands/

 shrublands

Agriculture

Wetlands

Other

EXPLANATION

Land-use and land-cover class

[Pie charts represent data for 2005]

Land-use or land-cover class

DevelopedMechanically disturbedForestsGrasslands/shrublandsAgricultureWetlandsOther lands

Mixed Wood Shield ecoregion

1992 (area km2

) 1,356 5,407 110,707 3,614 20,977 57,483 22,867

2005 (area km2

) 1,453 5,581 110,556 3,796 20,986 57,336 22,973

Net change in area (in km2

) 97 175 -151 182 9 -147 106

Change in area (in percent) 7.1 3.2 -0.1 5.0 0.0 -0.3 0.5

Atlantic Highlands ecoregion

1992 (area km2

) 3,165 895 155,271 272 18,252 5,120 8,637

2005 (area km2

) 3,385 981 154,954 306 18,195 5,123 8,973

Net change in area (in km2

) 219 86 -316 34 -57 3 336

Change in area (in percent) 6.9 9.6 -0.2 12.4 -0.3 0.1 3.9

Mixed Wood Plains ecoregion

1992 (area km2

) 24,380 1,380 149,476 2,023 162,651 24,257 50,452

2005 (area km2

) 28,401 1,185 147,983 2,247 159,757 24,231 54,639

Net change in area (in km2

) 4,021 -195 -1,493 224 -2,893 -26 4,188

Change in area (in percent) 16.5 -14.1 -1.0 11.1 -1.8 -0.1 8.3

Central USA Plains ecoregion

1992 (area km2

) 14,296 62 24,219 1,001 189,424 4,678 19,706

2005 (area km2

) 18,427 61 23,787 1,175 185,336 4,675 24,055

Net change in area (in km2

) 4,131 -1 -432 174 -4,089 -3 4,349

Change in area (in percent) 28.9 -1.4 -1.8 17.4 -2.2 -0.1 22.1

Southeastern USA Plains ecoregion

1992 (area km2

) 34,573 26,819 550,271 10,999 316,505 60,373 56,207

2005 (area km2

) 42,990 27,757 550,030 11,262 306,678 60,763 65,623

Net change in area (in km2

) 8,418 939 -241 263 -9,828 390 9,416

Change in area (in percent) 24.3 3.5 -0.0 2.4 -3.1 0.6 16.8

Ozark, Ouachita-Appalachian Forests ecoregion

1992 (area km2

) 11,605 3,352 374,361 3,550 118,434 2,571 21,570

2005 (area km2

) 13,611 4,569 372,212 3,903 117,760 2,592 24,020

Net change in area (in km2

) 2,007 1,217 -2,149 353 -674 21 2,450

Change in area (in percent) 17.3 36.3 -0.6 9.9 -0.6 0.8 11.4

Mississippi Alluvial and Southeast USA Coastal Plains ecoregion

1992 (area km2

) 30,037 8,253 102,863 28,709 146,244 117,960 111,032

2005 (area km2

) 34,331 9,858 101,945 28,618 143,304 116,763 116,179

Net change in area (in km2

) 4,293 1,604 -918 -92 -2,940 -1,198 5,147

Change in area (in percent) 14.3 19.4 -0.9 -0.3 -2.0 -1.0 4.6

Figure 3.5. Figure 3–5. Charts showing the proportions of land-use and land-cover (LULC) by level II ecoregion at the end of the 

baseline period (pie charts for 2005) and the net change in the mapped and modeled LULC classes between 1992 and 2005.
Chapter 3 35

3.4.2. Projected LULC Mapping and Modeling

The projected changes in LULC were variable across 

scenarios and ecoregions of the Eastern United States. 

Under the scenarios used for this assessment, the projected 

LULC change footprint ranged from a low of 17.2 percent 

in scenario B1 to the highs of 22.9 percent in scenario A1B 

and 20.8 percent in scenario A2 (fig. 3– 6; table 3–3). The 

scenarios that indicated the greatest (A1B) and smallest (B1) 

amounts of projected LULC change shared the same 

population assumptions; however, the focus on economic 

growth in scenario A1B resulted in higher demand for forest 

products, urban growth, and agricultural intensification 

contributing to greater overall amounts of LULC change 

than in scenario B1. Scenario B1 was characterized by 

strengthening environmental protections and more resourcefriendly lifestyles, which limited anthropogenic conversion of 

natural land covers to urban or agricultural lands LULC. The 

demand for forest products and agricultural commodities was 

reduced in scenario B1 compared with scenario A1B, and the 

environmental emphasis associated with scenario B1 resulted 

in a more compact pattern of urbanization than in scenario 

A1B. Similar to scenario A1B, scenario A2 focused on 

economic growth. However, with a regional focus on growth 

as opposed to globalization that characterized scenario A1B, 

economic growth was muted in comparison to scenario A1B, 

resulting in more moderate amounts of forest cutting, 

agricultural expansion, and urban growth by 2050. The 

LULC change footprints for all scenarios in the Southeastern 

USA Plains ecoregion were near or greater than 30 percent, 

whereas the LULC change footprints for all scenarios in 

the Central USA Plains ecoregion were less than 10 percent 

(fig. 3–5).

Spatial patterns of developed growth varied within 

the Eastern United States as well. Generally, urban growth 

occurred where high-population cities already existed. 

High growth in the developed LULC class occurred in the 

Southeastern USA Plains ecoregion, which contained 30 

percent (42,990 km2) of all developed lands in the Eastern 

United States, and increased by 66 percent in scenario B1 to 

92.9 percent (39,946 km2

) in scenario A1B. Urban growth 

in the ecoregion centered on the large metropolitan centers 

of Atlanta, Georgia; Washington, D.C.; and New York City, 

New York. In contrast, the Mixed Wood Shield ecoregion 

ranked near the bottom in developed growth with a 24.9 

percent (361 km2) increase in scenario B1 to a high of 

49.4 percent (717 km2) in scenario A1B. The Mixed Wood 

Shield ecoregion contained only 1 percent (1,453 km2) of all 

developed lands in the Eastern United States clustered in small 

cities, such as Duluth, Minnesota, and Marquette, Michigan.

Changes in the extent of agricultural lands occurred 

mostly in regions with a mixture of agricultural lands and 

forest ecosystems, such as the Southeastern USA Plains 

ecoregion, which was an area that had the highest LULC 

change footprint in the Eastern United States at nearly 

one-third of the ecoregion in all three scenarios and a fairly 

balanced distribution of agricultural lands (30.8 percent, 

306,678 km2

) and forests (55.3 percent, 550,030 km2) at the 

start of the projection period. In scenario A1B, which had the 

highest LULC change footprint of all scenarios, 88.1 percent 

of the agricultural lands class in 2006 remained stable in 2050. 

In scenario A1B, the Southeastern USA Plains ecoregion had 

a relatively low degree of agricultural stability compared with 

ecoregions where forests or agricultural lands dominate the 

landscape.

The Central USA Plains ecoregion predominantly 

comprises agricultural lands (79.3 percent, 185,336 km2) 

with a small amount of lands as forests (10 percent, 23,787); 

the ecoregion had the lowest LULC change footprint (less 

than 10 percent) across all scenarios. The ecoregion is part of 

the highly fertile Corn Belt, a productive agricultural region 

with little new land available for conversion to agricultural 

lands. Other ecoregions with less fertile soils would be more 

likely to lose agricultural lands before this core area of the 

Corn Belt. As a result, the 98.7 percent of the agricultural 

lands class in 2006 remained unchanged in 2050 in scenario 

A1B. In heavily forested ecoregions, such as the Atlantic 

Highlands ecoregion, which had more forests (82.6 percent, 

154,954 km2) than agricultural lands (9.7 percent, 18,195 km2) 

at the start of the projection period, increases in agricultural 

lands were restricted by poor soils, terrain, and climate. The 

relatively small amounts of agricultural lands LULC at the 

start of the projection period made large-scale decreases in the 

Table 3–3. Projected land-use and land-cover change footprint 

in the Eastern United States.

[IPCC, Intergovernmental Panel on Climate Change; km2, square kilometers; SRES, Special Report on Emission Scenarios (Nakićenović and others, 

2000). km2, square kilometers]

Ecoregion Area,

in km2

IPCC SRES scenario, 

percentage change

A1B A2 B1

Mixed Wood Shield 215,648 29.9 27.6 13.3

Atlantic Highlands 187,551 22.9 22.1 14.2

Mixed Wood Plains 388,858 13.1 13.9 7.3

Central USA Plains 239,027 9.3 7.5 4.6

Southeastern USA Plains 994,355 33.2 29.5 30.6

Ozark, Ouachita-Appalachian 

Forests

520,486 20.5 19.7 10.6

Mississippi Alluvial and Southeast 

USA Coastal Plains1

506,807 16.1 13.1 13.9

Eastern United States (total) 3,052,732 22.9 20.8 17.2

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.
36 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

C. Scenario B1

A. Scenario A1B B. Scenario A2

0 200 400 MILES

0 200 400 KILOMETERS

EXPLANATION

Figure 3.6.

Level II

Ecoregion boundary

0 to 5

>5 to 10

>10 to 20

>20 to 30

>30 to 60

Percentage of ecoregion area 

 projected to experience 

 land-use or land-cover 

 change at least once 

 between 2005 and 2050

Level III

N

Figure 3–6. Maps showing the projected land-use and land-cover change footprint for the level II and III ecoregions of the 

Eastern United States. The footprint represents the percentage of the ecoregion that changed at least once between 2005 and 2050. 

A, Scenario A1B; B, scenario A2; C, scenario B1.
Chapter 3 37

0

100,000

200,000

300,000

400,000

500,000

A1B A2 B1

Area, in square kilometers

Scenarios

EXPLANATION

Figure 3.7.

Developed

Mechanically 

 disturbed

2010 2020

Year

2006 2030 2040

Figure 3.8.

2050

Area, in square kilometers

Scenario A1B

Scenario A2

Scenario B1

40,000

50,000

60,000

70,000

80,000

Figure 3–7. Graph showing comparison between mechanically 

disturbed and developed land use classes in the Eastern United 

States for three land-use and land-cover scenarios (Nakićenović

and others, 2000).

Figure 3–8. Graph showing trends for mechanically disturbed 

land use in the Eastern United States for the projected period 

(2006 through 2050).

agricultural lands class unlikely as well. These factors contributed to the 91 percent of agricultural lands in 2006 remaining 

unchanged by 2050 in scenario A1B.

Aside from conversions to developed lands, change in

the forests class was generally linked to change in agricultural 

lands, so that increases in agricultural lands LULC classes

from forested LULC classes were more than 85 percent

in scenario A1B and more than 90 percent in scenario A2,

leading to net decreases of the forests ecosystem class in both 

scenarios. Forest LULC generally increased across the Eastern 

United States in the more environmentally oriented scenario 

B1 as demand for agricultural goods decreased and focus

shifted to restoration of natural land cover types.

Forest clearcutting was the single largest LULC change 

in the Eastern United States during the projected period,

contributing extensively to the region’s overall LULC change.

Projected forest harvest (the sum of the areas between

2006 and 2050 that experienced clearcut logging) exceeded

net increases in developed lands by a wide margin across 

scenarios (fig. 3–7). Developed LULC change affected a

smaller area of the Eastern United States and was concentrated 

near existing cities; however, this LULC class is unidirectional

and more permanent than forest clearcutting. Forest harvest 

and regrowth is more cyclical developed change because the 

same forest parcel can be harvested multiple times during the 

projection period and harvest widely occurs throughout the 

region. Forest clearcutting rates and trajectories varied across 

scenarios during the projection period (fig. 3–8).

Forest use assumptions associated with scenario A1B led

to a rapid increase in timber harvest early in the projection 

period and remained the highest cutting scenario overall, with

forest harvest increasing by 541.9 percent (5,315 km2) in the

Atlantic Highlands ecoregion and 162.2 percent (7,410 km2)

in the Ozark, Ouachita-Appalachian Forests ecoregion.

Forest cutting increased at a less steep rate in scenario A2 

than in scenario A1B, but narrowed the gap in area of forest

lands harvested toward the end of the projected period to 

end just slightly lower than scenario A1B. Overall forest

cover decreased in scenarios A1B and A2, whereas forest

clearcutting increased. Forest cutting increased slightly early 

in scenario B1, but the overall rate of harvesting decreased by

6.9 percent (3,430 km2) to end up lower than at the beginning

of the projection period. Forest cutting continued a diverse 

regional pattern in scenario B1 with cutting rates increasing by

204.5 percent (2,006 km2) in the Atlantic Highlands ecoregion

but decreasing in other ecoregions by as much as 53.8 percent

(5,305 km2) in the Mississippi Alluvial and Southeast USA

Coastal Plains ecoregion. Details of the projected results for 

the seven ecoregions are presented in the next section.
38 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3. Projections of Land Use and Land Cover 

Patterns in the Eastern United States From 2006 

Through 2050

3.4.3.1. Mixed Wood Shield

During the projected period (2006–2050) for the assessment of carbon fluxes and storage in the Eastern United States, 

the footprint of LULC change in the Mixed Wood Shield ecoregion for each scenario was relatively high compared with that in 

other ecoregions in the Eastern United States. Scenario A1B had 

the greatest amount change, with 29.9 percent of the land area 

changing at least once (table 3–3). Scenario A2 had the second 

highest amount of change (27.6 percent), whereas scenario 

B1 had the lowest amount (13.3 percent). There was little to 

no spatial variability in the amount of LULC change between 

level III ecoregions depending upon the presence and (or) 

amount of forest clearcutting.

The Northern Minnesota Wetlands level III ecoregion 

consists of a mix of LULC types with little forest harvest.

In the highest changing scenario, A1B, only 10.8 percent

(2,590 km2) of LULC changed, whereas in scenarios A2 and B1,

only 9.8 percent (2,359 km2) and 6.2 percent (1,486 km2), 

respectively, of LULC changed.

In the more forested Northern Lakes and Forest ecoregion, 

LULC change was much higher than in other level III ecoregions 

in the Eastern United States due to forest harvest, with change in 

scenarios A1B, A2, and B1 amounting to 32.4 percent (61,984 

km2), 29.8 percent (57,145 km2), and 14.2 percent (27,100 

km2), respectively. In the economically focused scenarios A1B 

and A2, change was driven primarily by the increase in forest 

use (represented by increased rates of clearcutting) and by 

the expansion of developed and agricultural lands within the 

ecoregion. The extent of forest clearcutting in scenario A1B 

increased by 78.3 percent (4,371 km2) between 2006 and 2050 

even though the forests ecosystem (forest and mechanically 

disturbed LULC classes) was reduced by 5.8 percent (6,371 

km2). Scenario A2 had an increase of 64.5 percent (3,602 km2) 

in the extent of forest clearcutting, while also losing 5.8 percent 

(6,411 km2) of the total forest ecosystem area.The economically 

focused scenarios saw increases in the extents of developed and 

agricultural lands. The extent of developed lands in scenario A1B 

increased by 49.4 percent (717 km2), whereas in scenario A2, the 

extent of developed lands increased by 44.4 percent (646 km2). 

The decrease in the extent of the forests ecosystem contributed 

the most to increases in the extent of developed lands. In scenario 

A1B, 85.6 percent (597 km2) of the gains in the extent of 

developed lands came from forests, whereas in scenario A2, 79.4 

percent (553 km2) of the gains came from forests. The story was 

the same for agricultural lands as well. The economic scenarios 

A1B and A2 experienced increases in the extent of agricultural 

lands, undergoing increases of 21.5 percent (4,508 km2) and 21.1 

percent (4,435 km2), respectively, in extent. Gains in the extent of 

agricultural lands from forests totaled 89 percent (4,011 km2) in 

scenario A1B and 91.7 percent in scenario A2.

LULC change in scenario B1 varied from the changes 

experienced in the economic scenarios, with the extent of 

the forested ecosystem decreasing slightly (1.9 percent; 

2,106 km2) and the extent of forest clearcutting reduced 

by 48.1 percent (2,682 km2). In this environmentally 

conscious scenario, the extent of developed lands increased 

by 24.9 percent (361 km2), a much lower extent than in the 

economically focused scenarios, and the extent of agricultural 

lands saw a minor decrease of about 1 percent. Increases in 

the extent of developed lands in this scenario came primarily 

from forests (79.4 percent, 287 km2) and, to a lesser extent, 

from agricultural lands.

An example of typical LULC change in the Mixed 

Wood Shield ecoregion is the area around Duluth, Minnesota, 

and Superior, Wisconsin (fig. 3–9). Encroachment into 

forested areas by developed lands around these cities can 

be seen in all scenarios. Agricultural expansion, due to the 

growing population and the market demand for agricultural 

commodities around Duluth and Superior can be seen in 

all scenarios, although to a lesser extent in scenario B1. 

Differences in the forest characteristics of the economically driven scenarios versus the environmentally oriented 

scenario are stark and driven by scenario assumptions. In 

scenarios A1B and A2, where high pressure is placed on forest 

resources, clearcutting can be seen throughout the entire area. 

In contrast, the extent of forest clearcutting in scenario B1 

is much less dramatic. Although clearcutting in scenario B1 

possesses a similar geographic distribution as in the economic 

scenarios, the large concentrations of clearcut areas are absent, 

and the extent of forest gains are higher, albeit scattered.
Chapter 3 39

92°30' 92°00'

47°

00'

46°

30'

92°30' 92°00'

47°

00'

46°

30'

0 10 20 MILES

0 10 20 KILOMETERS

Superior

Duluth

EXPLANATION

Figure A1–1.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Northern Lakes and Forests

A B

C D

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Map

area

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

Figure 3–9. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios B, A1B, 

C, A2, and D, B1 in 2050 for the area around Duluth, Minnesota, in the Mixed Wood Shield ecoregion. Changes were projected to be 

the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
40 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.2. Atlantic Highlands

The LULC change footprint for each scenario in the 

Atlantic Highlands ecoregion was among the most extensive 

of the ecoregions in the Eastern United States during the 

projected time period. The two economically focused 

scenarios A1B and A2 had similar amounts of change with 

22.9 percent of the ecoregion area changing at least once 

in scenario A1B and 22.1 percent changing in scenario A2 

(table 3–3). In the environmentally oriented scenario B1, 

14.2 percent of the ecoregion changed.

There was significant variation in the amount of LULC 

change projected between the three level III ecoregions 

that make up the Atlantic Highlands ecoregion. During the 

projected period, high amounts of change occurred in the 

Northern Appalachian and Atlantic Maritime Highlands and 

the North Central Appalachians level III ecoregions where 

large amounts of forest cutting and conversion to developed 

was common. Conversely, the Northern Appalachian Plateau 

and Uplands level III ecoregion experienced slight changes in 

LULC, largely because of the area’s lack of clearcutting.

A majority of the change in the economically focused 

scenarios A1B and A2 was due to clearcutting activities 

in forests and increases in other anthropogenic land uses. 

Between 2006 and 2050, increases in forest harvest activities 

were dramatic. In scenario A1B, the extent of clearcutting 

increased by 541.9 percent (5,315 km2

), whereas in scenario 

A2, the extent of clearcutting increased by 492 percent 

(4,825 km2). The increase in the extent of developed lands was 

substantial as well. In the A1B scenario, the extent of developed lands increased by 107.1 percent (3,625 km2

), whereas 

in scenario A2, the extent of developed lands increased by 

42.5 percent (1,438 km2). The extent of agricultural lands also 

expanded in scenario A2 (28.5 percent, 5,193 km2) and to a 

lesser extent in scenario A1B. The extent of mining expanded 

in the two scenarios as well, increasing by 66.5 percent 

(158 km2) in scenario A1B and by 91.1 percent (217 km2) 

in scenario A2. In both scenarios, conversion from forests 

contributed to more than 90 percent of the gains seen by each 

of these anthropogenic LULC types.

In scenario B1, clearcutting activities and expansion 

of developed lands contributed the most to land changes 

that were observed for the projected period. The extents 

of clearcutting and developed lands were lower compared 

with those in the economic scenarios but still high overall. 

The extent of clearcutting of forested lands expanded by 

204.5 percent (2,006 km2), whereas the extent of developed 

lands increased by 25.1 percent (848 km2). Most of the gains 

in developed were at the expense of forests (79.9 percent, 

678 km2); however, this did not result in an overall loss in 

forest cover. Scenario B1 was the only scenario to have an 

increase in the extent of the forests ecosystem, albeit a minor 

gain (1.2 percent, 1,895 km2). This was mostly due to the 

large declines in the extent of agricultural lands (15.2 percent, 

2,771 km2) during the projected time period.

Figure 3–10 represents typical LULC patterns between 

scenarios. The expansion in all directions near and around 

the cities of Pittsfield and Springfield, Massachusetts, can 

clearly be discerned in all scenarios as the area’s population 

increases. In this example, gains in the extent of developed 

lands are considerably less in scenario B1 where growth 

is more compact compared with the more sprawling 

scenarios A1B and A2. Agricultural lands expand considerably 

in scenarios A1B and A2, particularly in lowland valleys 

and adjacent to existing cultivated areas. In an attempt to 

accommodate the increased demand for agricultural products, 

agriculture stretches into the west and northeast to increasingly 

marginal lands. In scenario B1, agriculture LULC does 

expand slightly into new areas; however, agricultural lands 

are also being converted to other ecosystem classes, leading 

to an overall decrease in the extent of agricultural lands in 

this scenario. Between scenarios, the spatial distributions 

of clearcut forest are the same for this area. The density of 

clearcutting, however, varies between economic and environmental scenarios. In the economic scenarios, forest resources 

are in high demand with less of a focus on preservation 

and responsible forest use. Conversely, the environmental 

scenarios put a value on forest preservation and restoration, 

leading to lower amounts of clearcutting as well as increases 

in total forested area, particularly as the lower demand for 

agricultural commodities result in conversions of agricultural 

lands to forests ecosystems.
Chapter 3 41

73°00' 72°30'

43°

00'

42°

30'

73°30' 73°00' 72°30'

42°

30'

Springfield

Pittsfield

Northeastern Highlands

Northeastern 

Coastal Zone

Eastern 

Great Lakes

and Hudson 

Lowlands

A B

C D

0 10 20 MILES

0 10 20 KILOMETERS

Figure A1–2. 

Logging

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Map

area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

EXPLANATION

Level III ecoregion boundary

Projected gains in land use and

 land cover (B, C, and D)

Land use and land cover (A)

Figure 3–10. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios 

B, A1B, C, A2, and D, B1 in 2050 for the area near Springfield, Massachusetts, in the Atlantic Highlands ecoregion. Changes 

were projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
42 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.3. Mixed Wood Plains

Each of the scenarios for the Mixed Wood Plains ecoregion exhibited some of the lowest amounts of LULC change 

in the Eastern United States. By contrast to many other eastern 

ecoregions, the LULC change footprint in the Mixed Wood 

Plains ecoregion was greatest in the in scenario A2 rather than 

scenario A1B. In scenario A2, 13.9 percent of the ecoregion’s 

LULC changed at least one time between 2006 and 2050, with 

scenario A1B similar at 13.1 percent (table 3–3). Scenario B1 

had the least amount of LULC change, with only 7.3 percent.

There is spatial variability in the amount and types 

of LULC change across the ecoregion. For example, the 

Maine/New Brunswick Plains and Hills level III ecoregion 

continued the historical trend of having the highest amount 

of spatial change across all scenarios due to high amounts of 

forest cutting. The Driftless Area ecoregion in Wisconsin 

and Minnesota had the lowest amount of change in 

scenarios A1B and A2, but in the environmental scenario 

B1, this area had the second highest amount of change due 

to afforestation.

In both the economically focused scenarios, the extents of 

developed lands and forest clearcutting increased dramatically 

during the projected time period. In scenarios A1B and A2, 

the extents of developed lands increased by 47.1 percent 

(13,366 km2) and 37.9 percent (10,764 km2 ), respectively. 

Forests and agricultural lands combined to contribute more 

than 90 percent of the land converted to developed lands; 

however, the contributions of forest and agricultural lands 

to developed lands varied by scenario. In scenario A1B, 

39.6 percent (5,293 km2 ) of the land converted to developed 

lands came from forests, and 55.9 percent (7,472 km2) came 

from agricultural lands, whereas in scenario A2, the trend was 

reversed. The variation in conversions reflects where new 

developed occurred. In scenario A1B, there was more development in agricultural areas in the western part of the ecoregion, 

whereas in scenario A2, the extent of developed lands grew 

more in the more forested eastern regions.

The economically focused scenarios saw increases in 

forest clearcutting of 279.1 percent (3,308 km2 ) in scenario 

A1B and 249.3 percent (2,955 km2) in scenario A2. Although 

forest harvest increased, the areal extent of the overall forest 

ecosystem decreased by 7.9 percent (11,747 km2) in scenario 

A1B and 13.8 percent (20,430 km2) in scenario A2 due to 

conversion to other LULC types. From 2006 through 2050, 

the extent of agricultural lands in scenario A1B experienced 

a small net loss of less than 200 km2, with losses occurring to 

developed lands but gains coming from forests. Scenario A2 

saw a 6.6 percent (10,533 km2) increase in the extent of 

agricultural lands, predominantly from forests.

The environmental scenario B1 had the smallest increase 

in the extent of developed lands at 29 percent (8,239 km2). 

Conversions to developed lands were similar to scenario A1B 

where more agricultural lands (60 percent, 4,943 km2) were 

converted than forests (36.4 percent, 2,999 km2). The extent 

of the forests ecosystem in scenario B1 changed little, but 

forest clearcutting did increase by nearly 20 percent. Finally, 

agricultural lands saw a decrease of 5.3 percent (8,463 km2) 

during the projected time period due to conversions to 

developed lands and forests

Figure 3–11 illustrates typical projected LULC change 

in the Bangor, Maine, area between 2006 and 2050. In all 

scenarios, developed lands expanded into the agricultural 

lands and forested areas around the city, but to a lesser degree 

in scenario B1 than in scenarios A1B and A2. A drastic 

increase in the amount of forest clearcutting can be seen 

throughout the surrounding area in the economically focused 

scenarios. Conversely, the expansion of forest cutting in 

scenario B1 was more moderate than in scenarios A1B and A2, 

and small patches of new forested land can be observed. The 

economic scenarios also showed a large gain in the amount 

of agricultural lands, which helped offset losses to developed 

areas. However, in scenario B1, gains in the extent of agricultural LULC are much smaller, and losses to developed lands 

result in an overall decrease in the extent of agricultural lands 

in the area between 2006 and 2050.
Chapter 3 43

68°00'

68°30'

68°30'

69°00'

45°

00'

44°

30'

44°

30'

Bangor Laurentian Plains and Hills

A B

C D

0 10 20 MILES

0 10 20 KILOMETERS

Figure A1–3.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Map

area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

EXPLANATION

Figure 3–11. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios 

B, A1B, C, A2, and D, B1 in 2050 for the area around Bangor, Maine, in the Mixed Wood Plains ecoregion. Changes were 

projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
44 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.4. Central USA Plains

The Central USA Plains ecoregion was the least active 

ecoregion in the Eastern United States in terms of LULC 

change, with LULC change footprints in each scenario 

the lowest of the entire Eastern United States. With little 

existing forest, there are only small amounts of clearcutting 

to contribute to LULC change, and much of the area 

has stabilized as a highly productive agricultural region. 

Scenario B1 had the least amount of LULC change in the 

Central USA Plains ecoregion with only 4.6 percent of the 

ecoregion changing at least once between 2006 and 2050 

(table 3–3). Scenario A2 saw 7.5 percent of the ecoregion 

change at least once, and scenario A1B saw 9.3 percent of 

the ecoregion change at least once. The spatial variability 

of LULC change across the ecoregion was low. Between 

level III ecoregions in the Central USA Plains, variability in 

footprint change did not exceed 4 percent.

The economically focused scenarios saw considerable 

increases in the extents of developed lands and decreases 

in the extents of agricultural lands and forests. In scenario 

A1B, the extent of developed lands increased by 80.1 percent 

(14,757 km2

), whereas scenario A2 saw an increase of 

55.8 percent (10,275 km2). In both economic scenarios, gains 

in the extent of developed lands came primarily from the 

conversion of agricultural lands. Despite these conversions, 

agricultural lands retained almost 90 percent of the extent 

in 2005 during the projected time period, experiencing only 

small net decreases of 6.1 percent (11,214 km2

) in scenario 

A1B and 3.6 percent (6,759 km2) in scenario A2. Natural 

ecosystems saw reductions in their extent as well, with forests 

declining by similar amounts (13.9 percent, 3,316 km in 

scenario A1B and 13.9 percent, 3,311 km2 in scenario A2).

In scenario B1, increases in the extent of developed lands 

were modest compared with those in scenarios A1B and A2. 

The extent of developed lands increased by 24.2 percent 

(4,458 km2) between 2006 and 2050, with most of the increase 

coming from agricultural lands (86 percent, 3,834 km2). 

Similar to the economically focused scenarios, the extent of 

agricultural lands experienced a net decrease (2.5 percent, 

4,712 km2) in scenario B1, but this decrease was smaller than 

that in the other two scenarios due to scenario assumptions 

limiting expansion of developed lands. The extent of the 

forests ecosystem saw a slight increase of less than 1 percent. 

Types of LULC change in the Chicago, Illinois, area are 

typical of the changes in the Central USA Plains from 2006 

through 2050. In all scenarios, developed lands expanded into 

agricultural lands along the fringes of the metropolitan areas 

(fig. 3–12). In scenarios A1B and A2, development was more 

apt to occur in the remaining forested areas within the city. 

However, in scenario B1, development was more compact 

and, coupled with an increased focus on preserving natural 

cover types, kept developed lands from encroaching as much 

into the city’s forested areas.
Chapter 3 45

87°30'

88°00'

88°00'

88°30'

88°30'

42°

00'

42°

00'

41°

30'

41°

30'

A B

C D

Chicago

Central Corn Belt Plains

Figure A1–4.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Map

area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

0 10 20 MILES

0 10 20 KILOMETERS

EXPLANATION

Figure 3 –12. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios 

B, A1B, C, A2, and D, B1 in 2050 for the area around Chicago, Illinois, in the Central USA Plains ecoregion. Changes were 

projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
46 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.5. Southeastern USA Plains

The Southeastern USA Plains ecoregion continued 

the recent historical trend of being the highest changing 

level II ecoregion in the Eastern United States in every 

scenario due to high rates of clearcutting and LULC conversions. The ecoregion is very dynamic and had the largest 

LULC change footprints in the Eastern United States, with 

33.2 percent in scenario A1B, 30.6 percent in scenario B1, 

and 29.5 percent in scenario A2 (table 3–3). Within the heart 

of the Southeastern USA Plains, the LULC change footprint 

exceeded 40 percent in the South Central Plains, Piedmont, 

Southeastern Plains, and Mississippi Valley Loess Plains 

level III ecoregions.

In both the economically focused scenarios, change was 

driven by high amounts of anthropogenic land uses in the 

form of forest clearcutting, developed lands, and agriculture. 

The ecoregion accounted for more than half (55.5 percent) of 

all forest clearcutting in the Eastern United States at the start 

of the projection period, and in both scenarios, clearcutting 

maintained an approximately 50 percent share in 2050. The 

extent of forest cutting increased by 34.6 percent (9,614 km2) 

over the projection period in scenario A1B, which was 

a modest rate increase with the ecoregion being heavily 

harvested already. In addition to the increases in forest cutting, 

the extent of the forest ecosystem decreased by 13.8 percent 

(75,816 km2), putting further conversion pressure on the 

region’s forests. In scenario A2, forest clearcutting increased 

by 24.2 percent (6,724 km2), and the extent of the overall 

forests ecosystem decreased by 12.4 percent (68,367 km2).

In scenario A1B, the developed LULC class had the 

highest rate of increase across all ecoregions and scenarios 

with a 92.9 percent (39,946 km2) gain. Conversions to 

developed lands in scenario A1B primarily came from forests 

(59.6 percent, 23,808 km2) and agricultural lands (35 percent, 

13,981 km2). Scenario A2 experienced a 76.4 percent 

(32,848 km2) increase in the extent of developed lands, with 

a greater percentage of new developed lands coming from 

forests (67.3 percent, 22,107 km2

) than in scenario A1B and 

a smaller share (26.1 percent, 8,573 km2) from agricultural 

lands. Increases in the extent of developed lands were 

primarily concentrated in level III ecoregions with high LULC 

change footprints and in existing large urban corridors, with 

the Piedmont and Southeastern Plains level III ecoregions 

accounting for approximately 65 percent of all urban growth in 

both economic scenarios. In the Piedmont, the urban corridor 

stretching from Atlanta, Georgia; Washington, D.C.; Charlotte, 

Winston-Salem, and Raleigh-Durham, North Carolina, saw 

the greatest increases with 119.7 percent (14,480 km2

) in 

scenario A1B and 103.2 percent (12,484 km2) in scenario A2. 

Developed lands in the Southeastern Plains level III ecoregion 

focused in cities along the edge of the Piedmont from 

Montgomery, Alabama; Macon, Ga.; Colombia, South 

Carolina; and Richmond, Virginia; to the southeastern fringes of 

Baltimore, Maryland, with increases of 92.7 percent (8,969 km2) 

in scenario A1B and 80.8 percent (7,821 km2) in scenario A2.

Gains in the extent of the agricultural lands ecosystem 

were also the largest by area in scenarios A1B and A2 

compared with changes in the other level II ecoregions in 

the Eastern United States. The extent of agricultural lands 

increased by 11.7 percent (35,828 km2

) in scenario A1B and 

11 percent (33,810 km2) in scenario A2, with gains in both 

scenarios predominantly coming from forests and to a lesser 

degree from grasslands/shrublands.

Scenario B1 had the second highest LULC change 

footprint in this ecoregion due to continued high amounts of 

forest cutting, growth in developed lands, and restoration of 

forests and wetlands ecosystems. Forest cutting continued to 

increase (7.9 percent, 2,200 km2), although at a rate much lower 

than scenarios A1B and A2. This increase is a deviation from 

much of the Eastern United States in scenario B1 where timber 

harvest generally decreased in the Eastern United States under 

this scenario. As a result of conversion of agricultural lands 

to forest, the overall extent of the forests ecosystem increased 

by 2.9 percent (15,829 km2) during the projection period. 

The increases in the extent of developed lands (66 percent, 

28,387 km2) remained the highest of any ecoregion in the 

Eastern United States, but was still lower than either of the 

economic scenarios. New developed lands came more from 

agricultural lands (53.1 percent, 15,074 km2) than from forests 

(42.4 percent 12,036 km2) in scenario B1, with similar patterns 

of new urban growth as the economic scenarios except for a 

greater share of increases in the extent of developed lands in 

cities of the Interior Plateau and Interior River Valleys and Hills 

level III ecoregions, such as St. Louis, Missouri; Nashville, 

Tennessee; and Louisville, Kentucky. The extent of the wetlands 

ecosystem experienced a 7.8 percent (4,722 km2) increase in 

scenario B1, with conversions to wetlands types predominantly from agricultural lands (67.7 percent, 3,197 km2

) and 

forests (30.6 percent, 1,445 km2).

LULC change in the Augusta, Ga., region that borders 

the Piedmont and Southeastern Plains level III ecoregions is 

typical of the Southeastern USA Plains (fig. 3 –13). Increases 

in the extent of developed lands from forested areas around 

Augusta were similar in the A1B and A2 scenarios; however, 

increases in the extent of developed lands were much lower 

in scenario B1, which has assumptions that focus more on 

compact urban growth. Higher rates of forest clearcutting 

and conversion to new agricultural lands are apparent in 

scenario A1B, with the higher densities of new agriculture 

visible in the areas northwest and southwest of Augusta. In 

scenario B1, forest restoration is noticeable in the cropped 

areas south of Augusta and in isolated patches in the western 

portion of the image. Use of the Protected Area Database 

of the United States in the LULC modeling is also apparent 

as seen near Fort Gordon, Ga., on the outskirts of Augusta. 

Anthropogenic LULC change is restricted on military lands 

in all scenarios as illustrated by the lack of new agricultural 

lands, forest clearcutting, and developed lands on base. 

However, conversions to natural LULC types such as forest 

and wetland are allowed in scenario B1 as seen by the new 

patches of forest in the center of the base.
Chapter 3 47

82°00'

82°00'

82°30'

82°30'

34°

00'

33°

30'

33°

30'

A B

C D

Augusta

Southeastern Plains

Piedmont

Fort

Gordon

Figure A1–5.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Map area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

0 10 20 MILES

0 10 20 KILOMETERS

EXPLANATION

Figure 3–13. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios 

B, A1B, C, A2, and D, B1 in 2050 for the area around Augusta, Georgia, in the Southeastern USA Plains ecoregion. Changes 

were projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
48 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.6. Ozark, Ouachita-Appalachian Forests

The LULC change footprint in the Ozark, OuachitaAppalachian Forests ecoregion closely resembles the 

average pattern for the Eastern United States during the 

projection period, especially in the economic scenarios. The 

A1B and A2 scenarios had change rates of 20.5 percent and 

19.7 percent, respectively, compared with the Eastern United 

States with 22.9 percent in scenario A1B and 20.8 percent in 

scenario A2 (table 3–3). However, the rate of LULC change 

in scenario B1 was considerably lower (10.6 percent) than 

the Eastern United States average (17.2 percent). However, 

several level III ecoregions were noticeable exceptions to the 

trend in the ecoregion, with the Southwestern Appalachians 

and Ouachita Mountains level III ecoregions showing a rate 

of LULC change of about 40 percent or more due to the high 

amounts of forest clearcutting in these areas. The Ouachita 

Mountains ecoregion in particular had the highest rate of 

any level III ecoregion in the entire Eastern United States, 

with nearly half of the area changing at least once during 

the projection period in every scenario (48.1 percent in 

scenario A1, 55.4 percent in scenario A2, and 44.7 percent 

in scenario B1; fig. 3–5).

In both the economically focused scenarios, LULC 

change was driven primarily by forest clearcutting, moderate 

growth in major urban centers, and agricultural expansion in 

the Ozark Highlands ecoregion and the lowland valleys of the 

Appalachian Mountains. Forest clearcutting in scenario A1B 

increased by 162.2 percent (9,129 km2

) and 145.5 percent 

(7,426 km2) in scenario A2 during the projected period, 

an increase three times that of the Eastern United States 

averages of 49.2 percent (24,593 km2) in scenario A1B and 

38 percent (18,978 km2) in scenario A2. The extent of the 

forests ecosystem decreased by 7.2 percent (26,898 km2) in 

scenario A1B and 8.2 percent (30,599 km2) in scenario A2 

due to conversions to agricultural and developed lands. Both 

scenarios saw increases in the extent of the developed agricultural lands, with the extent of developed lands increasing by 

67.1 percent (9,129 km2

) primarily from forests (67.6 percent, 

6,171 km2) compared with agricultural lands (29.9 percent, 

2,730 km2

) in scenario A1B. In scenario A2, the extent of 

developed lands increased by 54.6 percent (7,426 km2), with 

a greater proportion of new urban lands coming from forests 

(77.7 percent, 5,770 km2) than agricultural lands (19.9 percent, 

1,478 km2). Compared with developed lands, increases in 

agricultural lands were higher in scenario A2 (19.1 percent, 

22,520 km2) than in scenario A1B (14.8 percent, 17,425 km2), 

with almost all new agricultural lands converted from forests.

Scenario B1 saw few net changes in the ecoregion with 

the extents of forest, agriculture, and forest clearcutting 

LULC all remaining relatively stable throughout the projection period. The extent of developed lands saw a moderate 

increase of 31.1 percent (4,229 km2) for the largest scenario 

change. Gains in developed lands were balanced between 

forests (56.2 percent, 2,377 km2) and agricultural lands 

(40.2 percent, 1,700 km2). Forest clearcutting was considerably lower in scenario B1 compared with the economically 

oriented scenarios but did increase slightly by a little more 

than 3 percent during the projection period, in contrast with 

the Eastern United States as a whole, which saw clearcutting 

decrease by 6.9 percent (3,430 km2). The extent of the forests 

ecosystem decreased overall by 0.8 percent (2,991 km2) due 

primarily to conversions to new urban growth. Decreases in 

agricultural lands were minimal.

LULC change in the Birmingham, Alabama, area is 

indicative of changes in the region (fig. 3–14), with high 

amounts of forest clearcutting seen in scenarios A1B and A2 

and less so in scenario B1. Growth of large cities such as 

Birmingham is similar between scenarios A1B and A2, with 

slightly higher increases in scenario A1B. Smaller cities 

such as Jasper, Ala., follow similar growth patterns as the 

larger towns, whereas increases in the extent of developed 

lands are much lower in metropolitan and rural areas in 

scenario B1. Conversions of forests to agricultural lands are 

highest in scenario A2, with greater concentrations of new 

agricultural lands northwest and southwest of Birmingham. 

Increases in agricultural lands are slightly lower in scenario 

A1B, following similar patterns as scenario A2. Widespread 

forest restoration is visible throughout scenario B1, as isolated 

patches of agricultural lands are converted to forests.
Chapter 3 49

86°30'

86°30'

87°00'

87°00'

87°30'

34°

00'

34°

00'

33°

30'

33°

30'

A B

C D

Birmingham

Jasper

Southwestern Appalachians

Ridge and Valley

Figure A1–6.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Mixed Wood

Shield

Level II ecoregions

Southeastern

USA Plains

Mixed 

Wood 

Plains

Map area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

0 10 20 MILES

0 10 20 KILOMETERS

EXPLANATION

Figure 3–14. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios 

B, A1B, C, A2, and D, B1 in 2050 for the area Birmingham, Alabama, in the Ozark, Ouachita-Appalachian Forests ecoregion. 

Changes were projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest clearcutting.
50 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.4.3.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

From 2006 through 2050, the Mississippi Alluvial and 

Southeast USA Coastal Plains ecoregion experienced modest 

amounts LULC change in each scenario. Scenario A1B had 

the greatest amount of LULC change with 16.1 percent of 

the ecoregion changing at least once during the projected 

period (table 3–3). Scenario B1 experienced the second 

largest LULC change with 13.9 percent and scenario A2 was 

a close third with 13.1 percent. The amount of LULC change 

varied spatially by level III ecoregions within the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion. As in 

the baseline results, the Southern Coastal Plain ecoregion in 

the southeastern part of the ecoregion saw large amounts of 

LULC change in all scenarios. The Middle Atlantic Coastal 

Plain and the Western Gulf Coastal Plain (of the TexasLouisiana Coastal Plains level III ecoregion) ecoregions 

saw comparatively modest amounts of change, whereas the 

Atlantic Coastal Pine Barrens and the Mississippi Alluvial 

Plain ecoregions saw relatively little change (fig. 3 –5).

The economically focused scenarios experienced drastic 

increases in the extent of developed lands and substantial 

decreases in the extents of natural ecosystems (forests, 

wetlands, and grasslands/shrublands) during the projected time 

period. In scenario A1B, the extent of developed lands increased 

by 84.1 percent (28,872 km2), most of which came from agricultural lands (38.3 percent, 11,058 km2

), forests (35.1 percent, 

10,134 km2), and wetlands (16.6 percent, 4,793 km2). The 

extent of developed lands in scenario A2 saw a 69.6 percent 

(23,891 km2) increase, with a greater share of conversions from 

forests in this scenario (40.4 percent, 4,793 km2) and relatively 

equal amounts from agricultural lands (23.7 percent, 5,662 km2) 

and wetlands (22.8 percent, 5,447 km2).

In scenarios A1B and A2, the extent of the forests 

ecosystem saw decreases of 21.5 percent (21,919 km2) 

and 15.9 percent (16,258 km2) respectively. However, 

forest clearcuts increased by 54.9 percent (5,412 km2) 

in scenario A1B and by 58.4 percent (5,758 km2) in 

scenario A2 in a decreasing forested land base. The extent 

of the wetlands ecosystem also decreased, by 7.2 percent 

(8,403 km2) in scenario A1B and by 6 percent (7,018 km2) 

in scenario A2. The Mississippi Alluvial and Southeast 

USA Coastal Plains ecoregion was one of the few areas of 

the Eastern United States that had substantial amounts of 

the grasslands/shrublands ecosystem, which decreased by 

7.5 percent (2,142 km2) in scenario A1B and 8.7 percent 

(2,490 km2) in scenario A2. Conversions from these natural 

ecosystems were primarily to developed and agricultural 

lands, which also saw slight increases of 2.2 percent 

(3,106 km2) in scenario A1B and 0.9 percent (1,327 km2) 

in scenario A2.

In scenario B1, the extent of developed lands continued 

to grow, with a 43.6 percent (14, 959 km2) increase being the 

second highest in the Eastern United States (scenario B1), 

which came primarily from agricultural lands (43.6 percent, 

6,522 km2), forests (37.5 percent, 5,610 km2), and 

grasslands/shrublands (9.1 percent, 1,361 km2). Forests and 

grassland/shrublands ecosystems continued to experience net 

declines in scenario B1, with the extents of forests decreasing 

by 5.1 percent (5,173 km2) and grasslands/shrublands by 

almost 3 percent. Forest clearcutting decreases were the 

greatest across the entire Eastern United States, with a decrease 

of 53.8 percent (5,305 km2) during the projection period. 

The wetlands ecosystem, contrary to the economic scenarios, 

experienced a net increase of 5.1 percent (5,985 km2), with 

conversions to wetlands almost exclusively from agricultural 

lands. Agricultural lands experienced a net loss of 11.2 percent 

(16,067 km2) to developed lands and wetlands.

LULC changes in the Jacksonville, Florida, area are 

representative of the variety of changes that took place 

in the Mississippi Alluvial and Southeast USA Coastal 

Plains ecoregion. The extent of developed lands in 

scenarios A1B and A2 showed similar patterns of expansion 

into the forests and wetlands surrounding Jacksonville, 

whereas increases in developed lands in scenario B1 were 

much lower (fig. 3–15). Other developed lands expanded to 

the south of Camp Blanding, Fla., and came from forests, 

agricultural lands, and wetlands. Gains in agriculture are 

greatest in scenario A1B and can be seen expanding into the 

forest lands north of Camp Blanding. In scenario B1, agricultural losses to forested lands can be seen in several areas. Both 

Camp Blanding and Okefenokee Swamp were protected under 

all scenarios and thus growth was restricted in those places.
Chapter 3 51

81°30'

81°30'

82°00'

82°00'

82°30'

30°

30'

30°

30'

30°

00'

30°

00'

Jacksonville

Southern Coastal Plain

A B

C D

Middle Atlantic

Coastal Plain

Camp

Blanding

Figure A1–7.

Logging

Level III ecoregion boundary

Forest (deciduous, 

 evergreen, mixed)

Developed

Cultivated crops 

 and hay/pasture

Projected gains in land use and

 land cover (B, C, and D)

Mixed Wood

Shield

Level II ecoregions

Mixed 

Wood 

Plains

Map area

Atlantic

Highlands

Everglades Mississippi Alluvial

and Southeast USA

Coastal Plains

Central

USA Plains

Ozark, QuachitaAppalachian Forests

Water

Developed

Mechanically disturbed 

 (national forest)

Mechanically disturbed 

 (other public protected)

Evergreen forest

Mixed forest

Grassland

Shrubland

Cultivated crops

Land use and land cover (A)

Mechanically disturbed 

 (private land)

Mining

Barren

Deciduous forest

Hay/pasture

Woody wetland

Herbaceous 

 wetland

Ice/snow

0 10 20 MILES

0 10 20 KILOMETERS

Okefenokee Swamp

Southeastern

USA Plains

EXPLANATION

Figure 3–15. Maps showing A, land use and land cover (LULC) and a comparison of the projected LULC change in scenarios B, 

A1B, C, A2, and D, B1 in 2050 for the area around Jacksonville, Florida, in the Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregion. Changes were projected to be the result of land-use change (agricultural lands, forests, developed lands) or forest 

clearcutting.
52 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

3.5. Validation and Uncertainty

3.5.1. Baseline Validation and Uncertainty

For a historical period, a formal, quantitative validation of 

the 1992 through 2005 LULC maps is theoretically possible. 

Quantitative validation of LULC model output typically examines measurements of quantitative disagreement to ensure that 

the model produced the correct LULC quantities over a study 

area and allocation disagreement to ensure that the model place 

LULC change in the correct locations (Pontius and Millones, 

2012). The quantity of mapped LULC change for the baseline 

period was informed and calibrated by data derived from the 

USGS Land Cover Trends Project for 1992 through 2000 and 

by the NLCD2006 land cover change product for LULC change 

from 2001 through 2005. The FORE–SCE model has the ability 

to precisely match prescribed proportions of LULC change and 

thus replicate the historical amounts of LULC change provided 

by the two datasets. Quantitative disagreement was therefore 

not an issue, because model runs were rejected if FORE–SCE 

could not accurately replicate the prescribed quantities of LULC 

change for any reason.

Conversely, allocation disagreement is potentially subject 

to validation. Given that all level III ecoregions were parameterized and modeled independently, allocation disagreement 

was partially mitigated because the proportions of LULC 

change were spatially distributed to the appropriate ecoregion. 

Allocation disagreement was thus only an issue within a level 

III ecoregion. Forest clearcuts were not modeled, but were 

mapped using the LANDFIRE VCT data. All other types of 

LULC change were modeled by FORE–SCE and were subject 

to allocation disagreement assessment. However, there were 

difficulties in assessing allocation disagreement using USGS 

trends and NLCD data. The USGS trends data are sample 

based, limiting our ability to make spatial comparisons with the 

wall-to-wall maps. The starting 1992 NLCD used a different 

classification scheme and mapping methodology than the 2001 

NLCD, and the two datasets cannot be directly compared to 

determine change between 1992 and 2001. A retrofit product 

of LULC change between 1992 and 2001 was produced for 

NLCD, remapping the 1992 NLCD using 2001 methodologies; 

the retrofitted 1992 data represent a different product than the 

1992 NLCD used for this work. The 2001 and 2006 NLCD data 

were produced using a consistent methodology and theoretically 

could be used to evaluate the allocation disagreement of the 

modeled LULC change for that period. However, the 2001 and 

2006 NLCD products are not directly comparable to the starting 

1992 NLCD, again making direct comparison with the modeled 

2001 through 2006 maps of little use.

There is no single standardized methodology for judging 

all LULC models (Rykiel, 1996), and quantitative validation 

cannot serve as the sole basis for judging a model to be 

valid or invalid (Verburg and others, 2006). Given the lack 

of quantitative disagreement (with FORE–SCE matching 

prescribed LULC proportions), model assessment boils down 

to the question of whether LULC change is being placed 

in suitable locations. With quantitative validation difficult 

due to characteristics of the available historical LULC data, 

model assessment thus focused on qualitative assessment of 

model input parameters controlling suitability of the land to 

support a given LULC type and of model results. Suitability 

surfaces were constructed for each LULC class in every level 

III ecoregion of the Eastern United States, each serving to 

control the location of LULC change. For each and every 

suitability surface, the quality of the surface and the fidelity of 

the regressions used to create those surfaces were reviewed. 

Suitability surfaces with perceived statistical or representational issues were recreated and subject to further review. A 

similar qualitative assessment was used to judge final model 

performance. During the modeling process, the performance 

of the model from 1992 through 2005 was evaluated independently for each level III ecoregion using a visual assessment 

of the LULC change distribution. The assessment was based 

on historical and current patterns of change, LULC patch size 

characteristics, spatial arrangement and context, and dispersion patterns. An unacceptable distribution of LULC change 

resulted in a reparameterization of the FORE–SCE model, and 

a subsequent new model run was initiated, with the process 

repeating until model performance was deemed acceptable.

3.5.2. Projected Validation and Uncertainty 

A formal validation of the projected LULC changes was 

not possible because there were no reference data for a future 

timeframe. Although a validation cannot be performed for 

the projected period, sources of uncertainty may be examined 

in the projections for the future. There are many sources of 

uncertainty for LULC projections into the future and may 

include data sources, modeling assumptions regarding future 

driving forces, misrepresentation of processes within the 

model, incomplete knowledge and unknowns, and uncertainty 

propagation between model components (Dendocker and 

others, 2008; Verburg and others, 2012). Sensitivity analyses 

on the effects of individual contributors to overall uncertainty 

have been performed by land cover modelers, but accounting 

for all sources of uncertainty and how they propagate through 

a LULC modeling framework remains a daunting challenge.

For this assessment, the proportions of the projected 

LULC change in the scenarios themselves were used to bound 

overall uncertainties regarding future LULC proportions. 

Although not all sources of uncertainty that contribute to 

the final maps of projected LULC could be quantitatively 

assessed, the same quantity and allocation disagreement 

measurements discussed previously may be used to examine 

sources of uncertainty between the modeled scenarios. In 

this context, a quantitative disagreement measurement can be 

used to examine the differences in projected LULC proportions between scenarios. The spatial modeling component 

of FORE–SCE introduced allocation disagreement between 

scenarios in that the spatial pattern of change at a pixel level 
Chapter 3 53

may differ between two scenarios even if the prescribed 

scenario LULC proportions were similar. Applications of 

quantitative and allocation disagreement measurements to 

each pair of the three scenarios allowed for a determination 

of whether the per-pixel differences between scenarios maps 

were because of the scenario LULC prescriptions themselves 

or were a result of the spatial modeling and the placement of 

LULC change (Sohl and others, 2012a).

Total disagreement, the per-pixel measurement of 

differences between paired scenario images, was relatively 

similar between each scenario pair, topping out at around 

14 percent by 2050 (fig. 3–16). However, the contributions 

of quantitative disagreement and allocation disagreement differ between scenario pairs. Even by 2050, the 

prescribed proportions of LULC change, as provided by 

scenarios A1B and A2, are quite similar, because quantitative 

disagreement is quite low. Most of the per-pixel differences 

between scenarios A1B and A2 are due to exact placement 

of change from the spatial allocation model, and not from 

prescribed scenario differences. Given the similarity between 

scenarios A1B and A2, results of comparisons of those 

scenarios to scenario B1 are quite similar. The comparisons 

between scenarios A1B and B1 and between scenarios A2 

and B1 show significant levels of quantitative disagreement, 

with quantitative disagreement reaching similar levels as 

allocation disagreement by 2050. This is a similar pattern 

to past FORE–SCE model runs with per-pixel differences 

in the placement of LULC change outweighing differences 

early in simulations due to the scenarios themselves, but with 

scenario differences becoming increasingly important as 

the model iterates forward in time (Sohl and others, 2012a). 

Although this assessment only analyzed change through 

2050, LULC model runs were completed through 2100. In 

all scenario pairs, quantitative disagreement significantly 

increases after 2050, including in the A1B and A2 scenario 

pair, suggesting that in this modeling framework, long 

simulation periods are most effective for evaluating prescribed 

scenario differences. It should be noted, however, that 

allocation disagreement measures are calculated at the pixel 

level, and allocation disagreement will be measured even 

if LULC change is placed in very close proximity between 

two scenarios. Given the emphasis that FORE–SCE places 

on LULC change in suitable locations for a given LULC 

type, it is not expected that per-pixel differences between 

exact placement of LULC change patches would result in 

significant differences in reported carbon and GHG fluxes in 

this assessment, although future sensitivity analyses may be 

needed to confirm this.

Differences between scenarios can also be examined 

spatially to identify areas where future LULC is more certain 

(for example, same LULC type regardless of future modeled 

scenario) or more uncertain (for example, different LULC 

type due to either scenario or spatial allocation of LULC 

change). The spatial diversity image (fig. 3–17) indicates 

where the three scenarios are the same in 2050 and where they 

are different, at the pixel level. When examining differences 

between the three scenarios for 2050, 19.7 percent of all pixels 

for the Eastern United States are different between two or 

more scenarios, whereas 80.3 percent are the same between 

all scenarios. The scenarios are clearly the most different in 

forested ecoregions of the Southeast. Levels of forest clearcutting differ between the scenarios, and scenario differences 

clearly show up in the forested ecoregions of the Southeastern 

United States with high levels of timber activity. Quantitative 

disagreement (scenario differences) undoubtedly contributes 

to much of the per-pixel diversity, but allocation disagreement 

(pixel-level differences of where LULC change patches are 

placed) is also an important factor in differences between 

modeled scenarios (fig. 3 –17). Forested regions with low 

amounts of cutting, such as those in the Northeast and upper 

Midwest, show lower diversity between scenarios than do the 

ecoregions in the Southeast. Scenarios are the most similar in 

heavily agricultural ecoregions, such as in the Corn Belt of the 

Midwest and the Mississippi Alluvial Valley.

0

2

4

6

8

10

12

14

16

2006 2010 2020 2030 2040 2050

Agreement, in percent

2006 2010 2020 2030

Year

2040 2050 2006 2010 2020 2030 2040 2050

Quantity

Total

Allocation

Figure 3.9.

A B C

Figure 3–16. Graphs showing comparisons of quantity and allocation disagreement for land-use and land-cover 

scenarios (Nakićenović and others, 2000) A, A1B and A2, B, A2 and B1, and C, A1B and B1 for the Eastern United States 

from 2006 through 2050. The total disagreement between scenario pairs is relatively similar for the three scenario pairs. 

However, scenarios A1B and A2 are clearly similar through 2050, with allocation disagreement making up most of the 

disagreement between scenarios A1B and A2 even by 2050.
54 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

EXPLANATION

Scenario diversity of land-use 

 and land-cover class between 

 scenarios A1B, A2, and B1, 2050

Different between two or 

 more scenarios

Same between all scenarios

Level II ecoregion

0 100 200 300 400 MILES

0 100 200 300 400 KILOMETERS

N

Figure 3.10. Figure 3 –17. Map showing the spatial diversity between three land-use and land-cover scenarios (Naki enovi and 

others, 2000) in the Eastern United States in 2050.

ć ć
Chapter 4. Wildland Fire Occurrence and Emissions in the 

Eastern United States From 2001 Through 2050

By Todd Hawbaker and Zhiliang Zhu

4.1. Highlights

• During the baseline period of the wildland fire part 

of the assessment (2001 through 2008), the median 

of area burned by wildland fires was 1,355 square 

kilometers per year (km2

/yr), and the 95th percentile 

was 4,092 km2/yr.

• During the baseline period of the wildland fire part 

of the assessment, the median of emissions from 

wildland fires was 5.2 teragrams of carbon dioxide 

equivalent per year (TgCO2-eq/yr; 1.4 TgC/yr), 

and the 95th percentile was 17.3 TgCO2-eq/yr 

(4.7 TgC/yr).

• During the baseline period of the wildland fire part 

of the assessment, the median of wildfire emissions 

in the Eastern United States was equivalent to 

0.51 percent of the average NEP (279 TgC/yr; 

chap. 7), and the 95th percentile was 1.59 percent 

of the average NEP.

• During the baseline period of the wildland fire 

assessment, the median of wildfire emissions in the 

Eastern United States was equivalent to 0.09 percent 

of fossil fuel emissions for the United States, and 

the 95th percentile was 0.31 percent of fossil fuel 

emissions for the United States.

• For the projected period (2009 through 2050), 

wildland fire occurrence and greenhouse-gas 

emissions increased in the Eastern United States 

under all of the climate-change scenarios considered 

in this assessment.

• For the projected period, there was substantial 

variability in the direction and magnitude of change 

among ecoregions, and not all ecoregions experienced increases in wildland fire occurrence and 

emissions under all climate-change scenarios.

• The projected median amount of area burned 

annually from 2041 through 2050 was as much as 

51 percent greater than the median amount of area 

burned annually during the baseline years. The 

median annual emissions were projected to increase 

by as much as 41 percent from the baseline median 

annual emissions.

• Extreme fire years (years in which the amount of area 

burned are ranked the 95th percentile) are projected 

to become more extreme. The annual area burned 

in extreme fire years was projected to increase by 

43 to 122 percent from the early conditions, and the 

95th percentile of annual wildland fire emissions was 

projected to increase by 41 to 111 percent from the 

baseline 95th percentile estimate.

• Prescribed fire is an important land management 

tool in the Eastern United States; however, the lack 

of accurate and consistent data about prescribed 

fires limits understanding of their contributions to 

GHG emissions.

4.2. Introduction

The methodology for this assessment (Zhu and others, 

2010) explicitly addressed ecosystem disturbances, including 

human- and natural-caused wildland fires, as required by the 

EISA (U.S. Congress, 2007). Estimates for the baseline and 

projected biomass combustion emissions from wildland fires 

and effects in long-term carbon balance were components 

of the assessment (fig. 1–2). Biomass combustion emission 

results are presented in this chapter. The baseline burned areas 

and the projected future potential burned areas for wildland 

fires and their severity, described in this chapter, were used as 

input into the assessment of the long-term effects of ecosystem 

carbon balances (chap. 5).

Wildland fires are a critical component of the global 

carbon cycle because they produce an immediate release of 

GHGs—CO, CO2

, and CH4—when biomass is consumed 

through combustion (Seiler and Crutzen, 1980). However, 

previous estimates of emissions from wildland fires in the 

United States were highly variable, and after converting the 

reported emissions to carbon-dioxide equivalents, they were 

as follows:

• from 15 TgCO2-eq/yr in 2001 to 73 TgCO2-eq/yr 

in 2008 (Giglio and others, 2010; van der Werf and 

others, 2010; Oak Ridge National Laboratory, 2012),

• from 29 TgCO2-eq/yr in 2001 to 199 TgCO2-eq/yr 

in 2008 (French and others, 2011; Michigan Tech 

Research Institute, 2012), and
56 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

• from 157 TgCO2-eq/yr in 2002 to 283 TgCO2-eq/yr 

in 2006 (Wiedinmeyer and Neff, 2007).

When compared with the 2010 estimate of 

1,075 TgCO2-eq/yr of net carbon flux from ecosystems in 

the continental United States reported by the EPA (U.S. 

Environmental Protection Agency, 2012), the annual emissions from wildland fires were equivalent to 1 to 26 percent 

of the ecosystem’s total annual net carbon flux. In contrast, 

the combustion of fossil fuels produced 5,642 TgCO2/yr from 

2001 to 2008 (U.S. Environmental Protection Agency, 2012), 

and emissions increased at a rate of 1 percent per year (Pacala 

and others, 2007). Based on these rates, the annual emissions 

from wildland fires were equivalent to 0.3 to 5.1 percent of the 

emissions from fossil-fuel consumption.

The differences among the variability and quality of these 

results, the spatial and temporal resolution of the data, and 

assumptions about variations in combustion efficiency were 

the primary sources of uncertainties in wildland fire emissions 

estimates (Larkin and others, 2009; French and others, 

2011). The assumptions about the proportion of aboveground 

biomass consumed by wildland fire, especially aboveground 

woody biomass in forests, can have a substantial influence on 

emission estimates (Campbell and others, 2007; Meigs and 

others, 2009). The methods used to calculate emissions relied 

on estimates of the area that was burned, fuel loads (volume 

of live and dead biomass available for burning), combustion 

efficiency, and emission factors (Seiler and Crutzen, 1980; 

Albini and others, 1995; Wiedinmeyer and Neff, 2007; Ottmar 

and others, 2008). For example, the Global Fire Emissions 

Database (GFED; Giglio and others, 2010; van der Werf and 

others, 2010) estimates biomass consumption and emission 

at fire locations (including agricultural fires) detected by the 

National Aerospace and Space Administration’s (NASA) 

moderate resolution imaging spectroradiometer (MODIS; 

Roy and others, 2002; Giglio and others, 2003) based on 

land-cover types, combustion completeness, soil moisture, 

and land-cover-specific emission factors. The GFED also 

incorporates changes in fuel loads using the Carnegie Ames 

Stanford approach to characterize biomass production 

(Potter and others, 1993, 2012). Wiedinmyer and Neff (2007) 

also used active wildland fire observations from MODIS 

satellite sensors (Giglio and others, 2003), but calculated the 

emissions based on static land-cover types, percentage of land 

cover, and biomass at 1-kilometer (km) resolution. French 

and others (2011) used the Forest Service Consume model 

(Ottmar and others, 2008), which calculated fuel consumption and emission using fuel loads derived from the Forest 

Service Fuel Characteristic Classification System (FCCS; 

Ottmar and others, 2007) and fuel moistures derived from 

weather-station data.

Wildland fires also have long-term effects on ecosystem 

carbon balance by influencing the rate of carbon sequestration after combustion, through the decomposition of dead 

vegetation (which can provide nutrients to help establish 

new vegetation), and by reducing the rate of photosynthesis 

per unit area. Because of those effects, years to decades can 

pass before carbon stocks return to conditions before the fire 

(Turner and others, 1998; Cleary and others, 2010; Hurteau 

and Brooks, 2011; Kashian and others, 2012). If fire regimes 

are stable and assuming no other land management, the longterm effects of wildland fires on ecosystem carbon balance 

are typically negligible because carbon sequestration through 

growth of new vegetation and carbon loss through wildland 

fire emissions cancel out each other over long periods (Balshi 

and others, 2009a,b; Flannigan and others, 2009). However, if 

a fire regime changes, then the vulnerability for carbon storage 

is high because the amount of carbon stored in the ecosystem 

can be altered or lost through emissions.

Substantial evidence is available to document that fire 

regimes have not been static in modern times. For example, 

the frequency of wildland fires has been greatly reduced since 

settlement of the United States began mainly due to land-use 

changes and the success of fire suppression in the past century 

(Cleland and others, 2004). In the Western United States, the 

frequency of wildland fires has been increasing since the 1990s 

because of climate changes leading to an increasingly earlier 

snowmelt (Westerling and others, 2006). Wildland fires and 

emissions to carbon cycling are likely to be less important in 

the Eastern United States than in the Western United States, but 

limited research has been conducted quantifying relationships 

between wildfire occurrence and climate change in the Eastern 

United States. If the climate shifts to warmer and drier conditions, then increases in fire frequency and emissions are likely. 

Therefore, any comprehensive assessment of carbon storage 

and fluxes in ecosystems through time must account for the 

potential changes in wildland fire occurrence and emissions.

Wildland fire regimes are a function of the interactions 

between vegetation, land use, and ultimately, the climate 

(Swetnam and Betancourt, 1990; Gedalof and others, 2005; 

Westerling and others, 2006; Falk and others, 2007). A 

changing climate may result in changes in wildland fire 

regimes, including their occurrence and severity. Hessl (2011) 

outlined the primary pathways through which climate change 

may alter wildland fire regimes, including (1) altered fuel 

conditions, such as a change in fuel moisture; (2) altered fuel 

loads; and (3) changes in ignition patterns. The effects of 

climate change on wildland fires in the Eastern United States 

are expected to be significant and result in changes in weather 

patterns that would alter (1) ignition patterns, (2) wildland 

fire behavior, and (3) to a lesser extent, the distribution of 

vegetation. No single study, however, has addressed all three 

types of changes simultaneously at the scale required by this 

assessment (Flannigan and others, 2009).

Previous studies provided an estimate of the effects of 

wildland fires on carbon balance at a national scale but lacked 

the regional detail required by this assessment. Furthermore, 

there were few projections of future potential wildland fire 

emissions that were consistent with the existing baseline 

emission estimates. Therefore, a set of baseline emissions and 

projected future potential emissions was developed to ensure 

consistency throughout this and other regional assessments. 
Chapter 4 57

The primary questions addressed in this chapter include: 

(1) what were the patterns of wildland fire occurrence and 

emissions in the Eastern United States? (2) what may be 

the potential changes in wildland fire occurrence and emissions for the Eastern United States under climate change? 

(3) how did recent wildland fire occurrence and emissions 

vary temporally and spatially among the ecoregions of the 

Eastern United States? and (4) which ecoregions of the 

Eastern United States have the greatest potential for changes 

in fire-occurrence and emissions?

4.3. Methods and Data

The wildfire modeling and estimation study for the 

carbon sequestration assessment of ecosystems of the Eastern 

United States had two tasks: (1) calculating the baseline 

quantities for wildland fire occurrence and emissions and 

(2) simulating future (2009 –2050) projections of wildland fire 

occurrence and emissions under climate-change scenarios. The 

methods used for this study are the same methods (Hawbaker 

and Zhu, 2012) used in the USGS western ecosystem carbon 

sequestration assessment (Zhu and Reed, 2012). Some specific 

technical processes are common to both parts of the study; 

other methods are specific for a single task. The baseline 

estimates for the number of wildland fires, the area burned, 

and emissions were derived from the Wildland Fire Leadership 

Council’s Monitoring Trends in Burn Severity (MTBS) 

database (Eidenshink and others, 2007) and the Forest 

Service’s First Order Fire Effects Model (FOFEM; Reinhardt 

and others, 1997) for the major GHGs CO2 , CO, and CH4 . 

This method was applied to each wildland fire in the region 

that was in the MTBS database to produce estimates of CO2, 

CO, and CH4 emissions (converted to CO2 equivalents).

The wildland fire modeling approach used for the future 

projections in this assessment incorporated three primary 

components: wildfire ignitions, spread, and effects. The 

parameters for the ignition and spread components were 

selected through a calibration process using the baseline 

observed data, which were used to simulate future potential 

wildfires and burned areas and then FOFEM was used to 

estimate emissions for the simulated burned areas. Results 

of the baseline and future projections of wildland fires were 

aggregated to produce estimates of emissions for the Eastern 

United States as a whole and for each level II ecoregion within 

it (fig. 1–1; as described in detail in chapter 2 of this report). 

The datasets and methods used by the various wildland fire 

modeling components are described briefly in Hawbaker and 

Zhu (2012) and in more detail in the following sections.

4.3.1. MTBS Wildland Fire Data

The locations of wildland fires were taken from MTBS 

data (Eidenshink and others, 2007) and were used for baseline 

observations and to calibrate the ignition and spread components of the wildland fire modeling system. The MTBS data 

described fires that occurred from 1984 to 2008 and covered 

areas that were larger than 404 hectares (ha; 1,000 acres) 

in the Western United States and 202 ha (500 acres) in the 

Eastern United States. The MTBS data did not include small 

fires but captured the majority of the area burned because 

they included the largest fires, which contributed most to total 

area burned (Strauss and others, 1989; Stocks and others, 

2002). Some prescribed fires are included in the MTBS 

database; however, they were excluded from this assessment 

because the completeness of prescribed fire coverage in the 

MTBS database was uncertain and likely underestimated 

actual prescribed fire use in the Eastern United States. Each 

wildland fire detailed in the MTBS database was identified 

in State or Federal fire records, and its burn scar and severity 

were manually mapped using Landsat imagery from before 

and after the fire. Because of the MTBS methodology, there 

was a high degree of confidence in the spatial and temporal 

accuracy of the wildland fire data, whereas other wildland fire 

databases had known problems, including duplicate records 

and erroneous locations (Brown and others, 2002), which 

would require laborious error checking before use.

4.3.2. Fuels and Topography

The methodology for the baseline and projected wildfires 

relied on vegetation, fuels, and topography data from the 

Landscape Fire and Resource Management Planning Tools 

(LANDFIRE) Program (Rollins, 2009) of the U.S. Department 

of the Interior (DOI) and the U.S. Department of Agriculture 

(USDA). These data included information about existing 

vegetation, fire-behavior fuel models, and tree canopy fuels 

(cover, height, base height, and bulk density), as well as 

the elevation, slope, and aspect of the terrain. To calculate 

emissions from wildland fires, the Fuel-Loading Model (FLM; 

Lutes and others, 2009) data layer of the LANDFIRE program 

was used. Vegetation and fuels were held static throughout the 

simulations for future wildfires and were not altered by simulated disturbances and other types of LULC change. All raster 

data were aggregated to 250-meter (m) resolution in order to 

improve the processing efficiency using a nearest-neighbor 

rule (Lillesand and others, 2007). The nearest-neighbor 

aggregation was desirable because it preserved the proportion 

of vegetative-cover types within the study area, whereas other 

aggregation methods were more likely to result in common 

vegetative-cover types being overrepresented and uncommon 

vegetative-cover types being underrepresented.

4.3.3. Weather and Climate Data

The assessment methodology required daily weather 

data, including temperature, precipitation, relative humidity, 

and wind speeds, for the baseline and future periods. For the 

baseline period, gridded daily weather data for the conterminous United States with 0.125-degree (°) spatial resolution 

(approximately 12 km) were used (Maurer and others, 2002). 
58 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

These data were interpolated from weather stations and 

included the minimum and maximum daily temperature and 

daily precipitation from 1950 to 2010. The data on afternoon 

wind speed and direction from the 0.333° (approximately 

32 km) North American regional reanalysis (Mesinger and 

others, 2006) were joined to the 0.125° daily temperature and 

precipitation data.

In order to simulate the effects of the climate-change 

scenarios on wildland fire occurrence and emissions, 

downscaled monthly climate data provided by the World 

Climate Research Programme (WCRP) Coupled Model 

Intercomparison Project phase 3 (CMIP3) multimodel dataset 

were used. The CMIP3 data were corrected for bias and 

spatially downscaled to match the 0.125°-resolution baseline 

weather data (Maurer and others, 2007). For this analysis, 

the downscaled data from the Climate Research Branch 

of Environment Canada’s Canadian Centre for Climate 

Modelling and Analysis (CCCma) third generation coupled 

global climate model (CGCM 3.1; Flato and Boer, 2001), 

the Australian Bureau of Meteorology’s Commonwealth 

Scientific and Industrial Research Organisation Mark 3.0 

(CSIRO Mk3.0; Gordon and others, 2000) climate model, 

and the Model for Interdisciplinary Research on Climate 

version 3.2 medium resolution (MIROC 3.2-medres; Hasumi 

and Emori, 2004) for each of climate-change scenarios 

A1B, A2, and B1 (chap. 2) were downloaded from the bias 

corrected and downscaled WCRP CMIP3 climate projections 

archive (Maurer and others, 2007; Meehl and others, 2007). 

The GCMs and scenarios were selected on the basis of their 

ability to capture past climate patterns (Balshi and others, 

2009a,b). Additionally, the range of variability among the 

projections generally bracketed the extremes of temperature 

and precipitation projections for the conterminous United 

States (Gonzalez and others, 2010). Seasonal summaries of 

the climate projections were generated for the 1991 through 

2020 and 2031 through 2060 periods. These 30-year periods 

are after the baseline period (2001–2008) and the last decade 

of the projections (2041–2050) used in this assessment. 

Differences in temperature and precipitation among the 1991 

through 2020 and 2031 through 2060 periods for the different 

climate-change scenario and GCMs used in this assessment 

are shown in figure 4–1.

The downscaled climate data only provided monthly 

temperature and precipitation values, so a temporal 

disaggregation algorithm (Wood and others, 2002) was 

implemented to produce the daily values necessary for 

wildland fire simulations. This algorithm randomly rearranged 

year-long sequences of the baseline weather data for each 

future year and then adjusted the disaggregated daily values 

of temperature and precipitation so that the monthly means 

matched the values provided by the monthly climate forecasts. 

Using this methodology, three replicate weather sequences 

were generated for each GCM and climate-change scenario 

combination for a total of 27 simulation runs. The number 

of GCMs used and replicate runs was ultimately limited by 

computing power and processing times.

For the baseline and future climate change scenarios, 

additional processing steps were taken to produce the live and 

dead fuel moisture variables required for simulating wildland fire 

spread and behavior. First, the University of Montana mountain 

climate simulator (MT–CLIM) algorithms (Glassy and Running, 

1994) were used to calculate relative humidity based on minimum 

and afternoon daily temperatures (Kimball and others, 1997). 

Once humidity was estimated, the National Fire Danger Rating 

System (NFDRS) algorithms were used to estimate daily values 

for live and dead fuel moistures, as well as wildland fire behavior 

indices, such as an energy release component (ERC; Deeming 

and others, 1977; Bradshaw and others, 1983; Burgan, 1988). 

The NFDRS algorithms required information about the beginning 

of spring (green, up) and fall (brown, down) to estimate live 

fuel moistures. To account for potential shifts in phenology, a 

technique was implemented that determined the dates of seasonal 

0

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Scenario A1B Scenario A2 Scenario B1 Scenario A1B Scenario A2 Scenario B1 Scenario A1B Scenario A2 Scenario B1

Number of wildland fires per year Area burned, in

square kilometers per year

Emissions, in carbon dioxide

equivalent per year

5th percentile

Minimum

95th percentile

50th percentile (median)

Maximum

EXPLANATION

Figure 4–1. Graphs showing summaries of projected wildland fire ignitions, burned area, and 

emissions for the Eastern United States by decade from 2001 through 2050. The x-axis labels indicate 

the last year in the decade; for example, 2010 on the graph corresponds to the decade from 2001 

through 2010. Scenarios A1B, A2, and B1 are from Nakićenović and others (2000).
Chapter 4 59

changes based on green-up and brown-down dates using an index 

that incorporated the daily photoperiod, minimum temperature, 

and the vapor-pressure deficit (Jolly and others, 2005).

4.3.4. Baseline Wildland Fire Occurrence 

and Emissions

Baseline wildland fire emissions were calculated for each 

burned pixel in the MTBS database using FOFEM, which used 

fuel loads along with fuel moistures to estimate the amount 

of forest litter and downed deadwood that was consumed 

(Albini and Reinhardt, 1995, 1997; Albini and others, 1995). 

The consumption of duff (decaying forest litter), trees, plants, 

and shrubs was estimated as a function of the region, season, 

fuel moistures, and fuel loads. Canopy fuel consumption was 

estimated as a function of the burn severity provided by the 

MTBS data. The emissions of CO, CO2, and CH4 were then 

calculated based on the amount of fuel consumed, the organicmatter content of the fuel, and how efficiently it burned. The 

required input data for FOFEM included fuel loads, burn 

severity, and dead and live fuel moistures. To simplify the 

reporting of results, the emission estimates were summarized 

for all carbon-containing constituents to CO2 equivalents using 

the following equation:

CO2-eq=CO2+(2.33×CO)+(21×CH4 ). (4 –1)

Fuel-load data provided an estimate of the amount of 

biomass that was available for consumption and were derived 

from the LANDFIRE Project’s FLM data layer (Lutes and 

others, 2009). These fuel-load data were categorized by 

1-, 10-, 100-, and 1,000-hour fuel classes for dead, decaying 

(duff and forest litter), and live (grass, shrubs, and tree 

canopy) biomass. In the FOFEM, the amount of tree canopy 

that was consumed was a direct function of burn-severity 

values from the MTBS data. The amounts of canopy foliage 

consumed in the high, moderate, and low burn-severity 

categories were assumed to be 100 percent, 60 percent, 

and 20 percent, respectively. Similarly, the consumption of 

the canopy’s branch wood in the high, moderate, and low 

burn-severity categories was set at 50 percent, 30 percent, 

and 10 percent, respectively. These values were based on 

previously published estimates (Spracklen and others, 2009; 

Zhu and others, 2010) and on a comparison of FOFEM 

emissions with previously published results for selected 

wildland fires. The emissions were calculated for wildland 

fires between 2001 and 2008. Wildland fires before 2001 were 

excluded because the LANDFIRE fuels data were derived 

from Landsat imagery from about 2001.

After calculating emissions, summaries of the wildland 

fire data for each level II ecoregion and for the entirety of the 

Eastern United States were generated for the baseline period. 

These summaries include the minimum, mean, median, 95th 

percentile, and maximum values for number of fires per year, 

area burned per year, and emissions per year. The median and 

95th percentile summary statistics were assumed to represent 

typical and extreme fire years, respectively.

4.3.5. Projected Future Wildland Fire 

Occurrence and Emissions

Past studies generally suggest that the area affected by 

large wildland fires and emissions from the fires was a function of ignition patterns and fire behavior, primarily spread; 

ignition patterns and fire behavior were largely influenced 

by weather conditions, fuels, and topography (Cary and 

others, 2009) and in some regions, ignitions were influenced 

by human activity (Cardille and others, 2001; Syphard and 

others, 2007). Projecting the potential changes in wildfire 

patterns, therefore, required an understanding and accurate 

characterization of the drivers that created the observed 

patterns of ignitions, spread, and emissions (Keane and others, 

2003; Flannigan and others, 2009; Hessl, 2011). Accordingly, 

the wildland fire modeling approach used for this assessment 

incorporated three primary components: wildfire ignitions, 

spread, and effects. The parameters for the ignition and spread 

components were selected through a calibration process using 

the baseline observed data, which were used to simulate future 

potential wildfires.

4.3.5.1. Ignitions

General linear models (GLMs) with a binary response 

were constructed to predict daily ignition probabilities within 

each 0.125° weather grid cell. From the data described above, a 

suite of potential predictor variables was compiled that included 

daily weather statistics (minimum and maximum temperature 

and ERC), monthly weather summaries (temperature and 

precipitation), seasonal weather summaries (temperature and 

precipitation), and monthly and seasonal regional summaries of 

temperature and precipitation. Also included within the 0.125° 

weather grid cells as potential predictors in the GLM modeling 

were the proportions of land area classified as public or urban and 

existing vegetation type groups from the LANDFIRE database.

Most observations (grid cells with daily weather data) 

had no data on ignitions; therefore, a subsample was selected 

using a case-control sampling design. Any observation with 

precipitation greater than 0.25 centimeters (cm) was removed; 

this was done to ensure that ignition probabilities were zero 

on days with substantial precipitation that would limit fire 

spread. All observations with ignition data were retained along 

with a randomly selected set of observations without ignition 

data. The number of observations without ignition data was 

10 times the number of observations with ignition data. The 

choice of design was somewhat arbitrary, but justified because 

the predictive performance of models using case-control 

sampling designs has been shown to increase with the ratio 

of cases to controls (Hastie and others, 2009). The intercept 

of the GLM was adjusted using equation 4–2 to account 

for unequal proportions of cases (ignitions) and controls 
60 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

(non-ignitions) in the sample compared with the population 

(Preisler and others, 2004; Hastie and others, 2009).

gol gol elpmas elpmas

noitalupop noitalupop

non ignitions ignitions

non ignitions ignitions

− − − (4–2)

To build the GLMs, an initial set of predictor variables 

was selected using forward stepwise regression, including only 

variables with p-values less than or equal to 0.05 and limiting 

the number of predictors to one-tenth the number of wildland 

fire observations. Each GLM was then evaluated and modified as needed to ensure that the selected predictor variables 

accurately described weather and climate conditions known 

to affect wildland fire occurrence in a given ecoregion. The 

overall performance of the final GLM was judged using the 

area under the curve (AUC) of a receiver-operator characteristic plot (Hanley and McNeil, 1982). The AUC measured the 

probability of correctly classifying a random pair of fire and 

nonfire observations; an AUC value of 0.5 indicated that the 

model predictions were equivalent to a random guess, and an 

AUC value of 1.0 indicated perfect predictions. AUC values 

greater than 0.8 were generally considered to be good.

4.3.5.2. Spread

During the simulations, the minimum travel time (MTT) 

algorithm (Finney, 2002) was used to simulate the spread of 

wildland fires after ignition. The MTT algorithm has been used 

extensively for local- and national-scale simulations of burn 

probability (Calkin and others, 2011; Finney and others, 2011). 

In addition to an ignition location, the MTT algorithm relied on 

fuels (surface and canopy), topography (elevation, slope, and 

aspect), weather (wind speed and direction), and live and dead 

fuel moisture data. The MTT algorithm also required a specified 

number of days and minutes per day that a wildland fire can 

spread. The outputs produced by the MTT algorithm included the 

arrival time (duration of the wildland fire since ignition) of every 

pixel representing burned area, as well as wildland fire-behavior 

metrics, such as fire line intensity and crown-fire activity.

4.3.5.3. Emissions

To calculate emissions, the FOFEM (Reinhardt and 

others, 1997; Reinhardt and Keane, 2009) was applied to each 

pixel burned by the simulated wildland fires using the same 

processing steps that were used for the baseline emissions.

4.3.5.4. Calibration

A number of calibration simulations were required 

to determine the appropriate number of days and minutes 

per day to allow wildland fires to spread using the MTT 

algorithm. The initial values for the minimum and maximum 

number of days to allow the spread and minutes of spread 

per day were selected based on values derived from Federal 

fire records. Nine replicate simulations were run using the 

available weather data (1984–2008). After the simulations 

were complete, a two-sided t-test was used to determine if the 

annual average area burned during the simulations differed 

significantly from the annual average area burned based on 

observations from the MTBS database. If the differences were 

significant, the number of days of fire spread and the minutes 

of spread per day were altered and the calibration process 

was repeated until the p-value of the t-test was less than 0.05, 

indicating that the calibration simulations reproduced the 

baseline fire patterns.

4.3.5.5. Simulations of Future Fires

After calibration, future potential wildland fire ignitions, 

spread, and emissions were generated for three replicate 

simulations for each of the climate-change scenarios and 

GCMs, starting in 2001 and ending in 2050. The replicate 

simulations were run to help quantify uncertainty because of 

the stochastic nature of the models; more replicate simulations would have been ideal, but processing times limited the 

number of replicates to three. The simulated annual number of 

wildland fires, area burned, and emissions were summarized 

across the GCMs and replicates and were shown as the 

median and 95th percentile for each climate-change scenario 

for each decade, which represented typical and extreme fire 

years, respectively. The relative change between 2001–2010 

and 2041–2050 were reported. Significance of each change 

was assessed at a 0.05-alpha (α) level using Monte Carlo 

permutation test with 1,000 permutations (Hesterberg and 

others, 2012).

4.3.6. Limitations and Uncertainties

The results generated for this assessment for baseline 

wildfire emissions differed and were generally lower than 

estimates of emissions from the peer-reviewed literature for 

the Eastern United States. The highest estimates of emissions from peer-reviewed literature were from Wiedinmyer 

and Neff (2007), who used the active fire product from 

the MODIS sensors from 2002 to 2006 and estimated the 

mean of annual emissions to be 71 TgCO2

-eq for Alabama, 

Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, 

Kentucky, Louisiana, Maine, Maryland, Massachusetts, 

Michigan, Mississippi, New Hampshire, New Jersey, New 

York, North Carolina, Ohio, Pennsylvania, Rhode Island, 

South Carolina, Tennessee, Vermont, Virginia, West Virginia, 

and Wisconsin. The area used in Wiedinmyer and Neff (2007) 

is not the same as the area used for this analysis of the Eastern 

United States.

French and others (2011) calculated emissions using the 

MTBS data and Consume model (Ottmar and others, 2008) 

for wildland fires occurring from 2001 to 2008; the results 

are available as ecoregion-level summaries (Michigan Tech 

Research Institute, 2012). When the results from French and 
Chapter 4 61

others (2011) were summarized across the Eastern United 

States, the average and standard deviation of annual emissions 

were 20.5 TgCO2-eq and 12 TgCO2-eq, respectively. The emission estimates from these analyses were substantially greater 

than the estimates produced for this assessment. The differences 

among results are likely due to differences in methods, data, and 

the resolution of the data used. Wiedinmyer and Neff (2007) 

relied on 1-km-resolution active wildland fire data from MODIS 

and fuel data from the FCCS (U.S. Department of Agriculture, 

Forest Service, 2012a), which typically have higher fuel loads 

then the FLM data used for this assessment. They also assumed 

that all the available biomass could potentially burn, and that 

is often not the case, especially for woody fuels (Campbell and 

others, 2007; Meigs and others, 2009). French and others (2011) 

also used the 1-km-resolution FCCS fuels data and aggregated 

1-km-resolution MTBS data. The fuel data in their report 

differed from the data layer used in this assessment in terms of 

information and resolution. The methods used in this assessment 

made use of fuel moistures, which are based on gridded daily 

weather data. The methods used by French and others (2011) 

also made use of fuel moistures, but recommended 10 percent 

levels for 1,000-hour availability and duff moistures, which are 

very favorable conditions for combustion. The full effects of 

the differences in fuel maps and moisture levels on the accuracy 

of fire modeling were difficult to assess, but these comparisons 

suggest that the results in this assessment are more conservative 

than previously published estimates of wildland fire emissions.

The MTBS data used in this assessment did not include 

small wildland fires, but they still captured the majority of 

the area burned because they included the largest wildland 

fires that contributed most to the amount of area burned 

(Strauss and others, 1989; Stocks and others, 2002). A 

comparison of the MTBS data with the Federal wildland 

fire-occurrence database (U.S. Department of Interior, 2012) 

showed that the MTBS listed only 3 percent of all wildland 

fires but accounted for 88 percent of the area burned in the 

Eastern United States. Therefore, the results of this assessment 

captured the general patterns and trends of wildland fires in 

the Eastern United States, but in all likelihood, underestimated 

wildland fire emissions.

In this assessment, the baseline and projected estimates 

of area burned and emissions also did not include the influence 

of prescribed and agricultural fires (for example, burning crop 

residues); however, the emissions produced by those types 

of fires were suspected to be low relative to the wildland fire 

emissions (Liu, 2004; van der Werf and others, 2010). The 

influence of prescribed fires on emissions was difficult to 

assess because the data characterizing prescribed fires were 

generally poor based on inconsistent reporting about them 

across the country. The existing estimates of emissions from 

prescribed fires suggested that they produced only 10 percent 

of the emissions from wildland fires (Liu, 2004), in part 

because prescribed fires usually burn under less extreme 

meteorological conditions than wildland fires. The influence of 

agricultural fires was also estimated to be about 10 percent of 

the wildland fire emissions in the GFED database.

In the Eastern United States, the relative amount of 

emissions produced by prescribed and agricultural fires is likely 

to be more substantial than in other parts of the United States, 

because agricultural fires are common in the region (Korontzi 

and others, 2006; Tulbure and others, 2011). Prescribed fires 

in the Eastern United States, especially those in the Southeast, 

accounted for 70 percent of the area burned by prescribed fires 

nationwide, and area burned by prescribed fires in the study area 

was more than 2.5 times the area burned by wildfires (based on 

2002 to 2008 data from the National Interagency Fire Center, 

2012). Unfortunately, records for many prescribed fires are not 

entered into national-level databases, so accurate data characterizing the individual locations of each prescribed fire are not 

available. In the MTBS database, the area burned by prescribed 

fires was only 18 percent of the total area burned in the Eastern 

United States. Because prescribed fires burn under less extreme 

meteorological conditions, severity and emissions are expected 

to be lower than wildfires (Liu, 2004; Outcalt and Wade, 2004; 

van der Werf and others, 2010). However, speculating about 

the locations of prescribed fires, the fuels they burn though, and 

the conditions under which they burn would likely introduce 

a greater amount of uncertainty into the emission estimates 

provided in this assessment. These uncertainties will remain 

difficult to resolve until better information is available on the 

timing and location of prescribed fires.

In spite of the uncertainties about the timing and 

location of prescribed fires in the Eastern United States, 

emission estimates have been included in the EPA’s National 

Greenhouse Gas Emissions Inventory (U.S. Environmental 

Protection Agency, 2012) and can provide some indication 

of the impact of prescribed fires on emissions. The EPA 

methods for wildland and prescribed fire emissions focus on 

forests and apply emission factors to carbon stock-changes. 

The EPA estimates for 2005, 2007, and 2008 for wildfire 

emissions averaged 144.3 TgCO2

-eq/yr and for prescribed 

emissions averaged 20.3 TgCO2-eq/yr for the conterminous 

United States. Assuming that 70 percent of prescribed fires 

occur in the Eastern United States (National Interagency 

Fire Center, 2012), then average annual emissions from 

prescribed fires would be 14.2 TgCO2

-eq/yr, a value 

substantially larger than the median wildfire emissions from 

the baseline period of this assessment (5.2 TgCO2-eq/yr). 

However, the EPA’s wildfire estimates for the continental 

United States (144.3 TgCO2-eq/yr) are also substantially 

larger than those produced for this assessment, the Great 

Plains assessment (Zhu and others, 2011), and the Western 

assessment (Hawbaker and Zhu, 2012), which collectively 

had a median value of 56.0 TgCO2-eq/yr and a maximum 

value of 101.4 TgCO2-eq/yr.

Throughout the wildland fire simulations, vegetation and 

fuels remained static, which introduced some limitations into 

the assessment. Because of succession and disturbances (especially anthropogenic, such as LULC change), the composition 

and structure of vegetation may change substantially during 

the 50-year span used in this assessment (Foster and others, 

1998; Gallant and others, 2004; Rhemtulla and others, 2009). 
62 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

These changes could result in altered surface and canopy 

fuels that influence wildland fire behavior and emissions. By 

holding land use, vegetation, and fuels static, the interactions 

among wildland fire and LULC change were oversimplified, 

which are limitations that are shared by many broad-scale 

studies of projected climate change and wildland fires.

Vegetation dynamics have often been ignored in 

climate-change projections in part because of the difficulty 

of parameterizing the successional trajectories of each 

individual ecosystem type and the lack of information about 

how ecosystems may shift across the landscape under climate 

change. The influence that vegetation dynamics might have 

had on the results of this assessment is uncertain. In spite of the 

projected increases in wildland fire ignitions and area burned 

simulated for this assessment, the extent of the area burned 

each year was projected to be quite small relative to the extent 

of area that could potentially burn in an ecoregion. Thus, in 

the ecoregions included in this portion of the assessment, 

it is unlikely that the amount and arrangement of burnable 

vegetation on the landscape will limit wildland fires. Shifts in 

vegetation, however, might affect the type of vegetation and the 

amount of fuel available to burn; thus, past wildland fires and 

LULC change might alter the fuels, behavior, and emissions of 

future wildland fires (Bachelet and others, 2001, 2003).

Specifically, in the Eastern United States these processes 

could include the mesophication of forests in the mid-Atlantic 

throughout southern Appalachia that is reducing understory 

vegetation density and the flammability of litter fuels (Nowacki 

and Abrams, 2008). In the Southeastern USA Plains and the 

Mississippi Alluvial and Southeast USA Coastal Plains ecoregions, crown fire occurrence and wildland fire emissions could 

potentially increase with shifts in the acreage of short-rotation 

pine forests, with little fire management. Defoliation and 

mortality following insect outbreaks can affect substantial areas 

in the Eastern United States. The effects of insect outbreaks 

on carbon cycling in the Eastern United States could include 

short-term decreases in primary productivity and increases in 

respiration through altered decomposition rates (Hicke and 

others, 2012). However, the effects of insect disturbances on 

carbon cycling at ecoregional scales are not well understood, 

and models to predict where future outbreaks are likely are 

not currently available, thus the effects of insect disturbances 

were not incorporated into this assessment. These processes 

and vegetation dynamics into the ecosystem-disturbance model 

component should be considered for incorporation into future 

carbon assessments (Running, 2008; Goetz and others, 2012).

4.4. Results

Results for the baseline and future potential projections 

of wildland fire occurrence and emissions are presented using 

summary statistics of the median and 95th percentile values. 

These were assumed to represent typical and extreme fire 

years respectively.

4.4.1. Baseline Wildland Fires and Emissions

During the baseline period (2001–2008) in the Eastern 

United States, the number of wildland fires per year in a 

typical fire year was 112, but year-to-year variability was 

high, and as many as 186 wildfires occurred during extreme 

fire years (fig. 4–2; table 4–1). The area burned by wildfires 

in a typical fire year was 1,355 km2, but was more than twice 

that in extreme fire years (4,092 km2). Annual emissions from 

wildfires were 5.2 teragrams of carbon dioxide equivalent 

(TgCO2-eq) during typical fire years, but more than four times 

that amount in extreme fire years (17.3 TgCO2-eq).

Combined, the Mixed Wood Shield and Mixed Wood 

Plains ecoregions had the least amount of fire activity among 

2001 2002 2003

Year

2004 2005 2006 2007 2008

0

50

150

200

100

0

2,000

1,000

4,000

3,000

5,000

0

5

10

15

20

25

Figure 4–1.

Number of wildfires Area burned, in square kilometers Emissions, in teragrams of carbon dioxide equivalent

Figure 4 –2. Graphs showing the annual number of wildland fires, 

area burned, and emissions for the baseline (2001–2008) period 

of the carbon flux and storage assessment of the Eastern United 

States.
Chapter 4 63

Table 4–1. Summary statistics for the number of wildland fires, area burned, and emissions.

Metric

Mixed Wood Shield

and

Mixed Wood Plains

Southeastern

USA Plains

Ozark, OuachitaAppalachian Forests

Mississippi Alluvial 

and Southeast USA 

Coastal Plains1

Eastern

United States

Number of wildfires per year

Mean 9 17 37 58 121

Minimum 1 2 13 24 60

Median 9 10 41 49 112

95th percentile 17 49 54 97 186

Maximum 18 57 59 110 200

Area burned, in square kilometers per year

Mean 173 137 311 1,225 1,851

Minimum 3 21 60 499 974

Median 81 52 382 746 1,355

95th percentile 497 417 462 3,164 4,092

Maximum 523 526 470 4,035 5,048

Emissions, in teragrams of carbon dioxide equivalent per year

Mean 2.0 0.3 0.8 4.2 7.3

Minimum 0.0 0.0 0.2 0.6 2.9

Median 1.2 0.1 0.9 2.8 5.2

95th percentile 5.8 1.0 1.3 11.9 17.3

Maximum 6.0 1.3 1.3 15.6 22.3

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

the four ecoregions analyzed in the Eastern United States 

(fig. 4–3A; table 4–1). The median number of wildfires per 

year was 9, and the 95th percentile of wildfires per year was 

17. Area burned during typical fire years was 81 km2 and was 

as high as 497 km2 in extreme fire years. Emissions were very 

low at 1.2 TgCO2-eq/yr in a typical fire year. However, in 

spite of having the least amount of wildfires and area burned, 

emissions during extreme fire years were relatively high in this 

ecoregion at 5.8 TgCO2-eq/yr.

In the Southeastern USA Plains ecoregion, the median 

values for wildfire occurrence, area burned, and emissions 

were 10 wildfires per year, 52 km2/yr, and 0.1 TgCO2-eq/yr, 

respectively (fig. 4–3B; table 4–1). The number of wildfires, 

area burned, and emissions were much greater during 

extreme fire years at 49 wildfires per year, 417 km2/yr, and 

1 TgCO2-eq/yr, respectively.

Fire occurrence in the Ozark, Ouachita-Appalachian 

Forests ecoregion during typical fire years was greater than in 

the Southeastern USA Plains, Mixed Wood Shield, and Mixed 

Wood Plains ecoregions (fig. 4–3C; table 4–1). The number 

of wildfires, area burned, and emissions were 41 per year, 

382 km2/yr, and 0.9 TgCO2-eq/yr, respectively. Rates of wildfire occurrence and emissions were not substantially different 

during extreme fire years when the number of wildfires, area 

burned, and emissions were 54 per year, 462 km2/yr, and 

1.3 TgCO2-eq/yr, respectively.

Among the four ecoregions of the Eastern United States, 

the Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregion had the greatest number of wildfires, area burned, 

and emissions (fig. 4–3D; table 4–1). During typical fire years, 

the number of wildfires, area burned, and emissions were 

49 per year, 746 km2

/yr, and 2.8 TgCO2-eq/yr, respectively. 

During extreme fire years, there were nearly twice as many 

wildfires (97 per year), with more than four times as much 

area burned (3,164 km2/yr) and emissions (11.9 TgCO2-eq/yr).

4.4.2. Climate-Change Trends in the Eastern 

United States

In the Eastern United States, general warming trends 

were projected across all seasons and almost all GCMs 

between the 1991 through 2020 and 2031 through 2060 

periods (fig. 4–4). As noted before, to compare results of 

baseline and future projection years, these intervals were used 

because they were centered over the adjusted baseline period 

(2001 through 2010, ±10 years) and the last decade of the 

projections (2041 through 2050, ±10 years). Temperatures 

were projected to increase by 1 to 2 degrees Celsius (°C) 

for all ecoregions and all seasons across the Eastern United 

States. Precipitation changes were more variable, but appeared 

to have a decreasing trend (drier) in the summer and fall. 

Patterns of winter and spring precipitation varied depending 

on the ecoregion and scenario, but tended toward increased 

moisture in the Northeast, Upper Great Lakes, and the Ozark 

and Appalachian Mountains and decreased in the Southeastern 

USA Plains and Mississippi Alluvial and Southeast USA 

Coastal Plain ecoregions (fig. 4–4). For temperature and 

precipitation, the spatial patterns of change were generally 

similar for a given GCM across scenarios, but appeared to be 

more variable across the GCMs than the scenarios.

In the Mixed Wood Shield ecoregion, average seasonal 

temperature change was projected to be 1.9 °C, 1.4 °C, 1.4 °C, 

and 1.2 °C for the winter, spring, summer, and fall seasons, 

respectively. Precipitation changes were more variable and 

depended on the specific scenario and GCM. In general, 
64 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

20

15

10

5

0

60

50

40

30

20

10

0

60

50

40

30

20

10

0

600

500

400

300

200

100

0

600

500

400

300

200

100

0

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

0

40

20

80

60

120

100

0

2,000

5,000

4,000

3,000

1,000

20

15

10

5

0

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

B. Southeastern USA Plains

C. Ozark, Ouachita−Appalachian Forests

D. Mississippi Alluvial and Southeast USA Coastal Plains

2001 2002 2003

Year

2004 2005 2006 2007 2008 2001 2002 2003

Year

2004 2005 2006 2007 2008 2001 2002 2003

Year

2004 2005 2006 2007 2008

Figure 4–2.

A. Mixed Wood Shield and Mixed Wood Plains

Number of wildfires Area burned,

in square kilometers

Emissions, in teragrams of

carbon dioxide equivalent

600

500

400

300

200

100

0

7

6

5

4

3

2

1

0

Figure 4–3. Graphs showing the annual number of wildland fires, area burned, and emissions for the baseline (2001–2008) period 

for the A, Mixed Wood Shield and Mixed Wood Plains, B, Southeastern USA Plains, C, Ozark, Ouachita-Appalachian Forests, and D, 

Mississippi Alluvial and Southeast USA Coastal Plains ecoregions (U.S. Environmental Protection Agency, 2013a) in the Eastern United 

States.
Chapter 4 65

Temperature change

Scenario A1B

Precipitation change

Scenario A2

Scenario B1

Winter Spring Summer Fall

CCCMA CGCM 3.1 CSIRO Mk 3.0 MIROC 3.2 (medres) CCCMA CGCM 3.1 CSIRO Mk 3.0 MIROC 3.2 (medres) CCCMA CGCM 3.1 CSIRO Mk 3.0 MIROC 3.2 (medres)

Winter Spring Summer Fall

Temperature change, in degrees Celsius Precipitation change, in centimeters

–1 0 1 2 3 4 8 –7.5 –5 –2.5 –0.5 0 0.5 2.5 5 7.5

EXPLANATION

Figure 4.3.

Figure 4–4. Maps showing the projected changes in mean daily temperature and total precipitation by season, calculated 

using the difference in mean values from 2031 through 2060 and from 1991 through 2021. Climate data are from Maurer and 

others (2007). Scenarios A1B, A2, and B1 are from Nakićenović and others (2000). CCCMA CGCM 3.1, Canadian Centre for 

Climate Modelling and Analysis third generation coupled global climate model; CSIRO Mk 3.0, Commonwealth Scientific and 

Industrial Research Organisation Mark 3.0; MIROC 3.2 (medres), Model for Interdisciplinary Research on Climate version 3.2 

medium resolution.
66 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

winter and spring seasons were projected to have increases in 

precipitation of 0.5 cm and 2.4 cm, respectively. Precipitation 

decreased in the summer season by 0.5 cm, and fall precipitation changed little.

Warming trends were also projected by most scenarios 

and GCMs in the Southeastern USA Plains ecoregion. Average 

winter, spring, summer, and fall temperatures were 1 °C, 

1.3 °C, 1.4 °C, and 1.3 °C, respectively, greater in the 2031 

through 2050 period than they were in the 1991 through 2020 

period. Winter and spring precipitation decreased by 1.1 cm 

and 1.9 cm, respectively. Summer and fall projections were 

more mixed depending on the GCM and scenario. Average 

summer precipitation decreased by 0.9 cm, but average fall 

precipitation increased slightly by 0.3 cm.

Similar to the Southeastern USA Plains ecoregion, 

temperature changes in the Ozark, Ouachita-Appalachian 

Forests ecoregion increased the least in winter (1.2 °C) and 

the most in spring (1.4 °C), summer (1.5 °C), and fall (1.3 °C). 

In all seasons, the GCMs and scenarios were evenly split 

with about how precipitation would change: about half of 

the simulations projected drier conditions, and half projected 

wetter conditions. When the precipitation change projections 

were averaged across GCMs and scenarios, winter precipitation decreased minimally by 0.04 cm, spring precipitation 

decreased by 0.5 cm, summer precipitation decreased by 

0.9 cm, and fall precipitation increased slightly by 0.3 cm.

Projected changes in temperature for the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion were the 

smallest among all ecoregions in the Eastern United States. 

Winter, spring, summer, and fall temperatures were projected 

to increase by 0.9 °C, 1.2 °C, 1.3 °C, and 1.2 °C, respectively, 

between the 1991 through 2020 and 2031 through 2050 

periods. Precipitation changes were consistently drier in the 

winter and spring seasons (decreases of 1.4 cm and 2.5 cm, 

respectively) and to some extent in the summer season 

(decrease of 1.6 cm), but precipitation increased slightly in 

the fall season (0.6 cm).

4.4.3. Projected Future Wildland Fires 

and Emissions

GLMs were fit to each level III ecoregion and used 

to generate daily ignition probabilities in the simulations. 

In general, the model fits were good with the AUC values 

averaging 0.87 and ranging from 0.72 to 0.93. The best 

model fits were in the level III ecoregions within the Ozark, 

Ouachita-Appalachian Forests and the worst model fit was 

for the Southern Florida Coastal Plain level III ecoregion 

(reported with the Mississippi Alluvial and Southeast USA 

Coastal Plains ecoregion in this report). Most models included 

the energy release component (ERC) or 10- or 100-hour dead 

fuel moistures as a predictor as well as monthly and seasonal 

weather summaries (which captured seasonal and year-toyear variability). Most ecoregions also included at least 

one vegetation predictor. Developed land (which included 

high- and low-density urban areas, golf courses, urban parks, 

and highways), public lands, and (or) area of wildland vegetation and agriculture were often included as predictors too. 

When included, developed land and agriculture had negative 

relationships with wildfire ignition probability, and public 

lands and wildland vegetation area had positive relationships 

with ignition probability.

Calibration simulations were run for each level III 

ecoregion to ensure that the patterns of wildland fire occurrence from 1984 through 2008 could be reproduced. For all 

the ecoregions, there was no significant difference in the 

average annual burned area between the calibration simulation 

and the observed values from the MTBS database, assuming 

that differences were not significant when a p-value of 0.05 or 

greater was calculated using a two-sided t-test that assumed 

unequal variance. After the calibration process, the simulated 

wildland fires were allowed to spread between 4 to 24 hours 

per day, and the burn durations ranged from 1 to 6 days, 

depending on the ecoregion.

For the entire Eastern United States and across all the 

climate-change scenarios, the projection simulations resulted 

in an increase in wildland fire ignitions, area burned, and 

emissions between the first (2001–2010) and last (2041–2050) 

decades (fig. 4–1; table 4–2). In typical fire years, the 

median number of ignitions was projected to increase, 

ranging from 36 percent under scenario B1 to 42 percent 

under scenario A2 and 75 percent under scenario A1B. 

The area burned was projected to increase, ranging from a 

17 percent increase in scenario B1 to a 51 percent increase 

in scenario A1B. Wildland fire emissions followed similar 

patterns, with projected increases of 41 percent under 

scenario A1B, 25 percent under scenario A2, and 1 percent 

under scenario B1. However, the changes in median area 

burned and emissions were not statistically significant at a 

0.05 α level under scenario B1. Characteristics of scenarios 

A1B, A2, and B1 are provided in chapter 2.

The simulated changes in ignitions, area burned, and 

emissions were significantly greater in extreme fire years 

(95th percentile); across the Eastern United States, ignitions 

were projected to increase by 45 to 153 percent, the area 

burned was projected to increase by 43 to 122 percent, and 

emissions were projected to increase by 41 to 111 percent 

(fig. 4 –1; table 4–2). The rate of change was generally 

nonlinear, and the greatest increases in ignitions, area burned, 

and emissions were projected in 2021 to 2030 for scenario 

A1B, 2041 to 2050 in scenario A2, and 2031 to 2040 in 

scenario B1.

There was a large amount of variability in the projections 

of future potential wildfires, area burned, and emissions in 

the Mixed Wood Shield and Mixed Wood Plains ecoregions 

(fig. 4–5A; table 4–2) and not all the changes were statistically 

significantly different from zero. Ignitions increased by as 

much as 71 percent during typical fire years and as much 

as 43 percent during extreme fire years. Area burned and 

emissions did not necessarily increase even though ignitions 

did under all scenarios. During typical fire years, the change 
Chapter 4 67

0

20

40

30

10

A. Mixed Wood Shield and Mixed Wood Plains

0

200

100

300

400

500

0

4

8

2

6

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80

20

60

B. Southeastern USA Plains

0

400

800

200

600

1.0

1.5

0.5

0

0

400

200

1,000

800

600

C. Ozark, Ouachita−Appalachian Forests

0

6,000

4,000

2,000

12,000

10,000

8,000

0

20

40

10

30

0

100

200

50

150

D. Mississippi Alluvial and Southeast USA Coastal Plains

0

3,000

2,000

1,000

7,000

6,000

5,000

4,000

0

20

10

30

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

2010

2020

2030

2040

2050

Scenario A1B Scenario A2 Scenario B1 Scenario A1B Scenario A2 Scenario B1 Scenario A1B Scenario A2 Scenario B1

Figure 4–5. 

Number of wildland fires per year Area burned, in

square kilometers per year

Emissions, in carbon dioxide

equivalent per year

5th percentile

Minimum

95th percentile

50th percentile (median)

Maximum

Figure 4–5. Graphs showing summaries of projected wildland fire ignitions, burned area, and EXPLANATION

emissions for the A, Mixed Wood Shield and Mixed Wood Plains, B, Southeastern USA Plains, C, 

Ozark, Ouachita-Appalachian Forests, and D, Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregions (U.S. Environmental Protection Agency, 2013a) by decade from 2001 through 2050. The 

x-axis labels indicate the last year in the decade; for example, 2010 on the graph corresponds to 2001 

through 2010. Scenarios A1B, A2, and B1 are from Nakićenović and others (2000).
68 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

in the amount of area burned ranged as much as 234 percent 

in scenario A2, and in emissions, as much as 246 percent in 

scenario A2. The large amount of change is likely an artifact 

of the small amount of area burned and emissions during the 

baseline period (5 km2 and 0.04 TgCO2-eq, respectively), so 

that even small increases resulted in large relative change. The 

projected changes for ignitions, area burned, and emissions 

were less in extreme fire years than in typical fire years and 

were mostly not statistically significant. Ignitions increased 

by as much as 43 percent, area burned changed by as much as 

65 percent, and emissions changed by as much as 26 percent, 

but only changes in ignitions under scenario A2 were statistically significant.

In the Southeastern USA Plains ecoregion, projected 

changes were only statistically significant for typical fire 

years under scenario A1B (fig. 4–5B; table 4–2), during which 

ignitions increased by as much as 40 percent, burned area 

increased by as much as 36 percent, and emissions increased 

by as much as 72 percent. Projected changes for the number 

of wildfires, area burned, and emissions were not statistically 

significant for scenario A1B for extreme fire years and for 

scenarios A2 and B1 for typical and extreme fire years.

The Ozark, Ouachita-Appalachian Forests ecoregion 

experienced the greatest amount of change in the Eastern 

United States in fire occurrence and emissions in typical and 

extreme fire years, and the all the projected changes were 

substantial (fig. 4–5C; table 4–2). The number of ignitions 

was projected to change by 56 to 123 percent in typical fire 

years and 55 to 207 percent in extreme fire years. Projected 

changes in burned area were similar and increased by 

66 to 133 percent in typical fire years and 49 to 197 percent 

in extreme fire years. Changes in emissions largely followed 

changes in burned area and increased by 64 to 132 percent in 

typical fire years and 48 to 209 percent in extreme fire years.

Projected changes in fire occurrence and emissions in the 

Mississippi Alluvial and Southeast USA Coastal Plains ecoregion generally increased (fig. 4–5D; table 4–2). Decreases 

were projected under some climate-change scenarios, but were 

not statistically significant. In typical fire years, the amounts 

of change ranged by as much as 30 percent for ignitions, 

47 percent for area burned, and 36 percent for emissions. The 

decreases occurred under scenario B1, and increases were 

projected for scenarios A1B and A2. In extreme fire years, 

fire occurrence and emissions increased under all scenarios. 

Changes ranged by as much as 51 percent for ignitions, 

55 percent for area burned, and 67 percent for emissions.

4.4.4. Summary of Wildland Fire Occurrence 

and Emissions of Carbon Fluxes

From 2001 through 2008, wildland fire activity in 

the Eastern United States was less substantial than other 

regions of the United States. Large wildland fires numbered 

between 60 and 200 per year and burned 974 to 5,048 km2

each year. These wildland fires in the Eastern United States 

Table 4–2. Relative projected changes in the 50th and 95th percentiles for wildfire ignitions, area burned, and emissions between 

2001–2010 and 2041–2050.

Scenario Metric

Mixed Wood Shield 

and

Mixed Wood Plains

Southeastern

USA Plains

Ozark, OuachitaAppalachian Forests

Mississippi Alluvial 

and Southeast USA 

Coastal Plains1

Eastern

United States

Percentage of number of wildfires per year

A1B Median 50* 40* 123* 26* 75*

A2 Median 71* 18 64* 30* 42*

B1 Median 20 8 56* 12 36*

A1B 95th percentile 5 15 198* 41* 141*

A2 95th percentile 43* 28 207* 51* 153*

B1 95th percentile 16 42 55* 25* 45*

Percentage of area burned per year

A1B Median –10 36* 133* 47* 51*

A2 Median 234* 27 66* 12 34*

B1 Median 79 –4 70* –2 17

A1B 95th percentile –2 –16 162* 19 52*

A2 95th percentile 65* –9 197* 55* 122*

B1 95th percentile 1 –5 49* 22 43*

Percentage of emissions per year

A1B Median 9 72* 132* 36* 41*

A2 Median 246* 20 64* 9 25*

B1 Median 127 2 70* –17 1

A1B 95th percentile –16 –8 209* 10 41*

A2 95th percentile 26 5 164* 67* 111*

B1 95th percentile 20 –12 48* 12 45*

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

*

Change was significant at 0.05 alpha levels in permutation tests.
Chapter 4 69

represented 23 percent of all wildland fires that occurred in the 

conterminous United States from 2001 through 2008 and are 

mapped in the MTBS database and accounted for 10 percent 

of all the area burned. The median of annual emissions was 

5.2 TgCO2-eq/yr, which was equivalent to 0.48 percent of 

NEP (chap. 5). The interannual variability in emissions was 

high and ranged from 2.9 to 22.3 TgCO2-eq in the Eastern 

United States, which was equivalent to 0.27 to 2.05 percent 

of NEP. In 2010, the median of annual wildfire emissions was 

5,594 TgCO2-eq/yr, equivalent to 0.09 percent of nationwide 

fossil-fuel emissions (U.S. Environmental Protection Agency, 

2012); minimum wildfire emissions were 0.13 percent 

and maximum wildfire emissions were 0.40 percent of the 

nationwide fossil-fuel emissions. The relative contribution 

of wildland fires of the Eastern United States to nationwide 

greenhouse-gas emissions was small when compared that 

of other regions of the United States (Zhu and others, 

2010; Hawbaker and Zhu, 2012) and to the contribution of 

fossil-fuel emissions.

Wildland fire ignitions and area burned were projected 

to increase across all the climate-change scenarios for the 

Eastern United States as a whole, and the changes were larger 

for extreme fire years than for typical fire years. The annual 

amount of area burned by wildfires in the Eastern United 

States was projected to increase by as much as 51 percent from 

1,355 km2 in typical fire years and as much as 122 percent 

from 4,092 km2 in extreme fire years. The projected changes 

in burned area resulted in similar changes in emissions, 

which increased by as much as 41 percent from 5.2 TgCO2-eq 

in typical fire years and between 41 and 111 percent from 

17.3 TgCO2-eq in extreme fire years. These changes were 

driven by the influence of climate change on live and dead fuel 

moisture levels, which in turn had a large influence on ignition 

probabilities, fire spread, fuel consumption, and emissions.

The magnitude and direction of change of the wildfire 

projections varied among ecoregions and climate-change 

scenarios. The only ecoregion that had consistent increases 

in the number of wildfires, area burned, and emissions across 

all climate-change scenarios was the Ozark, OuachitaAppalachian Forests ecoregion. The Mixed Wood Shield and 

Mixed Wood Plains ecoregions projected increases in ignitions 

under all scenarios and increases in area burned and emissions 

under scenarios A2 and B1, but not for scenario A1B where 

precipitation projections suggested a wetter climate. The 

Southeastern USA Plains ecoregion had mixed results, with 

typical fire years experiencing mostly increases in ignitions, 

area burned, and emissions, but with extreme fire years 

experiencing decreases in area burned and emissions. The 

Mississippi Alluvial and Southeast USA Coastal Plains ecoregion experienced increases in fire occurrence and emissions 

under scenarios A1B and A2, but not under scenario B1.

In this study area, wildfire emissions were relatively low 

compared with carbon sequestered in terrestrial ecosystems 

(chap. 5), but this does not necessarily mean that wildfires do 

not have important effects on carbon balance in the Eastern 

United States. Instead, the largest effects of wildfires on 

carbon balance in the Eastern United States might be localized to specific ecosystems that currently have high carbon 

storage, but are at risk of losing carbon because of increased 

fire activities or altered fire behavior. These ecosystems may 

include pocosin wetlands with deep peat deposits along the 

Atlantic coast (Messina and Conner, 1998), southern boreal 

forests also with deep peat deposits and high fuel loads in the 

Upper Great Lakes (Heinselman, 1973), pine barrens in the 

Upper Great Lakes and New Jersey (Forman, 1998; Radeloff 

and others, 2000), and pine forests in the Southeastern USA 

Coastal Plains (Christensen, 1999). Management efforts 

designed to maintain or increase carbon storage in these 

ecosystems may be challenged by the potential carbon losses 

due to the projected climate-driven increases in wildland fire 

activities. In other ecosystems in the Eastern United States, 

wildfire plays a smaller role in carbon cycling, and other 

natural disturbances may be more important, for example, 

wind throw (Canham and Loucks, 1984; Frelich and Lorimer, 

1991) and anthropogenic LULC change (chap. 3).

Chapter 5. Carbon Sequestration, Transport, and Emission From 

Inland Aquatic Ecosystems in the Eastern United States

By Sarah Stackpoole,1 David Butman,1 David Clow,1 Cory McDonald,2 Edward Stets,1 and Robert Striegl1

1

U.S. Geological Survey

2

Wisconsin Department of Natural Resources

5.1. Highlights

• The total surface area of inland waters in the 

Eastern United States was 93,000 km2, which 

represented about 3.1 percent of the total land surface 

area of the region.

• Riverine ecosystems of the Eastern United States were 

carbon sources, exporting dissolved inorganic carbon and 

total organic carbon to coastal areas at a rate of 6.4 gC/m2/

yr and emitting 14 gC/m2/yr as CO2 to the atmosphere.

• Lacustrine systems of the Eastern United States were 

sources and sinks of carbon with 3.3 gC/m2/yr emitted 

as CO2 to the atmosphere and 3.1 gC/m2/yr of carbon 

sequestered in sediments.

• There was considerable variability in the estimated 

carbon fluxes of inland waters among the seven ecoregions in the Eastern United States. This was likely due 

to the differences in the size and abundance of water 

bodies, topography, climate, and land cover associated 

with each ecoregion.

5.2. Introduction

Section 712 of the EISA specifically required an assessment of carbon fluxes related to freshwater aquatic ecosystems. 

This chapter focuses on freshwater ecosystems, including 

streams, rivers, perennial ponds, lakes, and impoundments, 

which collectively are categorized as inland waters. Carbon 

sequestration rates in coastal estuaries, which are transition 

zones between rivers and oceans, are presented in chapter 6, and 

carbon fluxes from wetland systems are addressed in chapter 7. 

Carbon fluxes associated with aquatic ecosystems (this chapter) 

were assessed separately from those of terrestrial ecosystems 

(chap. 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 waters are active sites for transport, transformation, 

and storage of carbon between terrestrial landscapes and oceans 

(Cole and others, 2007; Striegl and others, 2007; Tranvik and 

others, 2009). On a global scale, inland waters cover only 

about 1 percent of the land surface (Battin and others, 2009), 

but carbon emissions from inland waters to the atmosphere are 

comparable in magnitude with terrestrial NEP values (Tranvik 

and others, 2009). So although inland aquatic systems represent 

less than 3 percent of the total land surface of the United States 

(Zhu and others, 2010), they may play a significant role in the 

regional carbon budget in the Eastern United States.

The objective of this chapter is to provide baseline 

estimates of carbon sequestration, carbon transport, and gaseous 

carbon emissions from inland waters for the seven ecoregions of 

the Eastern United States. In addition to national maps depicting 

the spatial extent of water bodies, datasets of water chemistry, 

flow, and sedimentation rates, spanning from 1920 through 

2011, were used to calculate the following fluxes: (1) lateral 

transport of DIC and total organic carbon (TOC) in riverine 

systems, (2) gaseous carbon emissions in the form of CO2

 from 

riverine systems, (3) gaseous carbon emissions in the form of 

CO2 from lacustrine systems, and (4) carbon burial rates in 

sediments of lacustrine systems. All ecoregion boundaries used 

in this chapter are consistent with those presented in chapter 1 

(U.S. Environmental Protection Agency, 2013a).

Differences in physiography, land cover, and climate 

associated with each ecoregion are expected to have an effect 

on carbon storage, transport, and loss to the atmosphere. Flux 

values presented in this chapter were normalized to total 

land surface area to produce yield estimates. These baseline 

estimates of carbon yields were then used in two integrated 

analyses to compare (1) relative magnitudes of yield values 

of inland waters among ecoregions and (2) yields of inland 

waters to terrestrial carbon storage.

5.3. Methods and Data

5.3.1. Lateral Carbon Transport in Riverine 

Systems

Lateral carbon fluxes in riverine systems account for 

carbon derived from terrestrial ecosystems, groundwater, and 

in-stream production (photosynthesis) minus the losses from 

sedimentation and CO2 flux to the atmosphere. Water-quality 
72 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

data for estimating lateral fluxes were obtained from the 

National Water Information Service (NWIS; U.S. Geological 

Survey, undated). In this chapter, lateral fluxes are represented 

by calculations derived from two different datasets, the 

Coastal Export Dataset and the Ecoregion Dataset.

Flux values calculated using the Coastal Export Dataset 

were categorized by the receiving body of water. For the 

Eastern United States, this included the Atlantic Ocean, Great 

Lakes, Gulf of Mexico, and Rainy River. The dataset represents the most downstream streamgages on rivers draining to 

the coast that had continuous stream discharge data. Therefore, 

areas heavily influenced by tides were not included, nor were 

the small coastal watersheds outside of the coastal boundary. 

Some of the watersheds that drain to coastal areas crossed 

political boundaries into Canada, but in most instances the 

area outside the United States border was small and no 

correction was made. However, since 56 percent of the Great 

Lakes total drainage area was in Canada, only watersheds 

inside the United States border were used to calculate carbon 

flux to the Great Lakes. Ultimately, the Coastal Export Dataset 

included DIC estimates from 122 NWIS sites and TOC 

estimates from 109 NWIS sites (fig. 5–1). These sites covered 

approximately 2.5 Mkm2 or 75 percent of the total drainage 

area for the regions of these receiving waters.

The Ecoregion Dataset included only drainage basins 

contained entirely within a single ecoregion of the Eastern 

United States. These estimates were useful to illustrate 

ecoregional variability in carbon fluxes across the entire 

Eastern United States. There was some overlap with the 

Coastal Export Dataset for streamgages located upstream from 

coastal areas that had watersheds entirely within ecoregional 

boundaries. The Ecoregion Dataset included DIC estimates 

from 1,001 NWIS sites and TOC estimates from 511 NWIS 

sites (fig. 5–1A). The flux estimates associated with this 

dataset were derived from small drainage basins, ranging in 

size from 3.4 to 59,000 km2. About 33 percent of the total 

A. Riverine within-ecoregion carbon fluxes B. Riverine carbon dioxide emissions

0 to 5.2

>5.2 to 8

>8 to 10

>10 to 13

>13

Carbon yield, in grams of carbon

 per square meter per year

EXPLANATION EXPLANATION

Total organic carbon

Dissolved inorganic carbon

Carbon sampling site

Partial pressure of carbon dioxide,

 in microatmospheres

Carbon yield, in grams of carbon

 per square meter per year

0 to 750

>750 to 1,000

>1,000 to 2,000

>2,000 to 3,000

>3,000 to 6,000

>6,000 to 12,000

>12,000 to 30,000

0 to 5

>5 to 10

>10 to 14.1

>14.1 to 26.2

0 200 400 MILES

0 200 400 KILOMETERS

N

Figure 5–1. Maps showing the estimated relative magnitude of carbon yields. A, Within-ecoregion lateral carbon transport by riverine 

systems. B, Carbon dioxide (CO2

) emissions from riverine systems. C, Lateral carbon export to coasts by riverine systems. D, CO2

emissions from lacustrine systems. E, Carbon burial rates in lacustrine systems. The maps show locations of calibrated sample data. 

Panels B and D also indicate the estimated relative magnitude of the partial pressure of CO2

 (pCO2) concentrations at the sampling 

locations.
Chapter 5 73

D. Lacustrine carbon dioxide emissions E. Lacustrine carbon burial

Figure 5.1.—Continued

0 to – 4

–4.1 to –8

–8.1 to –12

Carbon yield, in grams of carbon

 per square meter per year

EXPLANATION EXPLANATION

Partial pressure of carbon dioxide,

 in microatmospheres

Carbon yield, in grams of carbon

 per square meter per year

0 to 1.1

1.2 to 2

2.1 to 4

4.1 to 5

<400

>400 to 750

>750 to 1,000

>1,000 to 1,500

>1,500 to 2,000

>2,000 to 2,500

>2,500

Dataset and modeling method 

 for sedimentation estimate

Sedimentation rate location

Sediment carbon location

C. Riverine lateral carbon fluxes to coastal waters

EXPLANATION

Total organic carbon

Dissolved inorganic carbon

Carbon sampling site

5.3

9.5

11.6

13.5

Carbon yield, in grams of carbon

 per square meter per year

Watershed boundary

Figure 5–1.—Continued
74 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

ecoregional area of the Eastern United States was accounted 

for by the drainage areas associated with these sites.

For all lateral carbon flux estimates, the DIC concentration was estimated from pH, temperature, and either filtered 

or unfiltered alkalinity. The estimated TOC concentration 

was taken directly from water-quality data or was calculated 

as the sum of dissolved and particulate organic carbon 

(Stets and Striegl, 2012). Carbon fluxes were estimated 

from water-quality and daily streamflow data using the 

USGS Load Estimator Model (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 

LOADEST model uses Akaike Information Criterion to select 

the best combination of coefficients at each streamgage station 

from the full model, which is based on the following equation:

ln LOAD=a0+a1

lnQ+a2lnQ2+a3sin(2πdtime)

 +a4cos(2πdtime)+a5dtime+a6dtime 2+ ε, (5–1)

where

ln LOAD is the natural log of the constituent load,

Q is the discharge,

dtime is time in decimal years,

a0 , a1,...a6 are regression coefficients, and

ε is an independent and normally 

distributed error.

The model calibration required at least 12 paired water-quality

and daily streamflow values. The input data were log-transformed 

to avoid bias and centered to avoid multicollinearity.

For the Coastal Export Dataset, daily carbon fluxes 

were converted to annual fluxes and then summed across the 

contributing area. Carbon export (Total EC) was estimated 

by extrapolating to the drainage area not captured by sites 

included in the dataset (Stets and Striegl, 2012) by using the 

following equation:

Total EC =EC(IN)×(ATOT /AIN), (5–2)

where

EC(IN) is the carbon export estimated from sites 

included in the database,

ATOT is the total exorheic drainage area (draining 

to coastal areas), and

AIN is the total drainage for which lateral flux 

estimates could be made.

This extrapolation assumed an equivalent areal carbon 

flux from the remaining (unmeasured) drainage area. The 

5th and 95th confidence intervals were calculated from 

associated standard errors. Coastal carbon yields were calculated by dividing total carbon fluxes by associated drainage 

area. Ungaged area from the Coastal Export Dataset ranged 

from 0 percent at the Rainy River to 35 percent at the Atlantic 

Ocean, with an average of 15 percent ungaged area for the 

entire Eastern United States.

Large river basins in the Coastal Export Dataset included 

source areas that lay within ecoregions that were considered in 

the Central (Zhu and others, 2011) or Western (Zhu and Reed, 

2012) United States assessment reports, which complicated 

flux estimates based on ecoregional boundaries due to 

potential double counting. For the assessment of the Eastern 

United States, total coastal export to the receiving waters was 

considered a part of the assessment if the coastal outlet was 

within the assessment boundaries of the Eastern United States; 

some additional explanation was provided to establish the 

significance of carbon and water entering the Eastern United 

States from ecoregions outside of this assessment area. For 

example, the Mississippi-Atchafalaya River Basin was the 

largest in the United States (total basin area of 3.18 Mkm2) and 

had its coastal outlet in the Eastern United States. However, 

only 40 percent of the total drainage area of the MississippiAtchafalaya River Basin is within the assessment area of the 

Eastern United States; 54 percent of the basin is in ecoregions 

that were considered in the report for the Central United States 

(Temperate Prairies, West-Central Semiarid Prairies, and 

South-Central Semiarid Prairies ecoregions), and 6 percent 

is within ecoregions considered in the report for the Western 

United States (Western Cordillera, Cold Deserts, and Warm 

Deserts ecosystems). The majority of the area outside the 

study area was encompassed by the Missouri River, Arkansas 

River, and Red River Basins. Total carbon yields from these 

rivers were 1.3 gC/m2/yr and 2.4 gC/m2/yr for the Arkansas and 

Missouri Rivers, respectively (Stackpoole and Stets, unpub. 

data, June 25, 2013). Ecoregions in the Eastern United States 

had much higher runoff and terrestrial primary production 

than ecoregions in the Central and Western United Sates and 

therefore had higher carbon yields as well. For example, total 

carbon yields for the Ohio River, which was entirely within 

the Eastern United States assessment area, had a total carbon 

yield of 10.2 gC/m2/yr (Sarah M. Stackpoole and Edward G. 

Stets, U.S. Geological Survey, unpub. data, June 25, 2013).

Rivers draining to the Gulf of Mexico west of the 

Mississippi River Basin also crossed regional boundaries. 

The headwaters of many of these rivers were in the SouthCentral Prairies and Southern Texas Plain ecoregions, but 

the outlets to the Gulf of Mexico were in the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion. Carbon 

yields in the South-Central Prairies and Southern Texas 

Plain ecoregions were smaller than those in the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion, 

suggesting that the absolute magnitude of carbon flux from 

the Southeast USA Coastal Plain ecoregion was small and 

had a minor effect on the total carbon export estimates for the 

Eastern United States.

Individual site fluxes from the Ecoregion Dataset were 

summed by ecoregion (EC(IN) ), and the ecoregional carbon flux 

estimate (Total EC) was calculated using equation 5–2, where 

ATOT represented the total ecoregional area and AIN represented 

the sum of the drainage areas associated with sites included in 

the dataset. This correction also assumed an equivalent areal 

carbon flux from the remaining (unmeasured) drainage area. 
Chapter 5 75

To calculate 5th and 95th confidence intervals for the mean 

ecoregional fluxes, a bootstrap with replacement method was 

run for 1,000 iterations. Total carbon fluxes represent the 

sum of DIC and TOC fluxes. The total carbon yields for an 

ecoregion were calculated by dividing annual fluxes by area 

of the ecoregion.

5.3.2. CO2 Flux From Riverine Systems

Three values were required to measure the 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 vertical flux of CO2 from riverine systems in 

the Eastern United States was modeled according to established methods (Butman and Raymond, 2011) and as outlined 

in the following equation:

CO2 flux=(CO2-water–CO2-air)×kCO2×SA, (5–3)

where

CO2 flux is the total net emission of CO2 from riverine 

systems of the Eastern United States,

CO2-water is the riverine CO2 concentration,

CO2-air is the CO2 concentration in the atmosphere,

kCO2 is the gas transfer velocity of CO2 across 

the air-water interface, and

SA is the riverine surface area.

The total flux was estimated by summing all of the mean annual 

fluxes for a stream order (Strahler, 1952) within an ecoregion.

The dissolved CO2 concentrations [CO2-water] were 

estimated from stream and river alkalinity data available 

through NWIS using the Mathworks, Inc. CO2SYS program 

(van Heuven and others, 2009). CO2SYS used temperature, pH, 

and alkalinity to estimate the dissolved CO2 concentrations by 

incorporating disassociation constants for carbonic acid (H2CO3) 

into its calculations. Disassociation constants are mathematical 

constants that describe the tendency of a large molecule, such 

as H2

CO3, to disassociate into smaller molecules, such as 

bicarbonate (HCO3

–

), carbonate (CO3

2–), and CO2

 in an aqueous 

environment. The disassociation constants used in the CO2SYS 

equations for this assessment were from Millero (1979).

Water-chemistry data were compiled from the late 1920s 

through 2011, and only daily measurements of pH paired with 

temperature and alkalinity measurements were used to estimate dissolved CO2

. For the seven ecoregions in the Eastern 

United States, 4,040 USGS streamgages had an adequate 

chemistry record, and their data were used for the CO2 flux 

estimate (fig. 5–1). At least 12 sampling dates were required 

for inclusion in this analysis. A total of 232,751 daily chemical 

measurements were identified. The concentration of CO2 in the 

atmosphere (CO2-air) was assumed to be constant at 390 parts 

per million (ppm) in equation 5–3 for all the ecoregions in the 

Eastern United States.

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 small 

to midsized river systems in the United States (Melching and 

Flores, 1999; Raymond and others, 2012). The variation in gas 

transfer velocities within riverine systems 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, according to the 

following equation:

k600=S×V×2,841.6+2.03, (5 – 4)

where

k600 is the gas transfer velocity of CO2 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 standard temperature (20 °C) 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

A total of 563 independent gas tracer injection measurements 

were included in the development of this model.

Average slope was derived from the NHDPlus (Horizon 

Systems Corporation, 2005) dataset for each stream order 

within each ecoregion of the Eastern United States. Velocity 

estimates were calculated from hydraulic geometry coefficients 

derived for each of the ecoregions in the Eastern United States 

using the NWIS database of measured slope and velocity 

at 4,900 streamgages from 1940 through 2011. A total of 

630,182 discharge measurements were used to derive hydraulic 

geometry coefficients specific to each ecoregion. Hydraulic 

geometry of stream reaches showed remarkable consistency 

within and across watersheds to approximate channel width, 

velocity, and depth from measured discharge (Leopold and 

Maddock, 1953; Park, 1977). All scaling relationships derived 

by the calculation of hydraulic geometry exponents were 

statistically significant (p-value less than 0.001). However, 

velocity measurements had coefficient of determination (R2) 

values ranging from 0.23 to 0.56, whereas width measurements 

had R2 values ranging from 0.86 to 0.95. Modeled average 

annual discharge from the NHDPlus data was used to estimate 

average channel width and velocity by utilizing the ecoregion 

specific hydraulic geometry coefficients. Average slope and 

velocity were then calculated by stream order to estimate the 

gas transfer velocity of CO2 based on equation 5–4.

Stream surface area was calculated based on the same 

hydraulic geometry coefficients discussed in the previous 

paragraph. Average discharge was utilized to then calculate an 

average width for each stream order within an ecoregion. The 

total stream length was then calculated for each stream order 

within an ecoregion. Stream and river surface area was than 

calculated as the product of the average width and total length 

of streams by stream order.

Error propagation and uncertainty analyses were 

performed for each component of equation 5–3. To estimate 
76 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

error, a bootstrapping technique as outlined in Efron and 

Tibshirani (1993) and Butman and Raymond (2011) was 

utilized. Bootstrap with replacement (α=0.05) was run for 

1,000 iterations to calculate 5th and 95th confidence intervals 

for the concentrations of the partial pressure of CO2 (pCO2 ) 

for each stream order within an ecoregion. Similarly, bootstrap 

with replacement was utilized 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 the stream 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 mean value for 

each stream order. Similarly, the effect of bootstrapping the 

hydraulic geometry parameters produced minimal bias.

A Monte-Carlo simulation was performed for each stream 

order estimate of the total carbon flux from riverine surfaces 

(equation 5–3). The 5th and 95th confidence intervals derived 

from the bootstrapping discussed above were used to set the 

boundary conditions of the Monte Carlo simulation for each 

parameter of equation 5–3. One thousand replications of the 

total flux calculation were performed. This approach is considered conservative because it allowed for the same probability of 

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 riverine evasion flux.

All estimates for the total carbon flux within an ecoregion 

were presented with the 5th and 95th confidence intervals derived 

from the Monte Carlo simulation. In general, this conservative 

approach biased the range of estimate high due to a slight skew 

in the distribution of pCO2

 concentration within a stream order 

and ecoregion. It should be noted that the mean concentration by 

stream order was chosen rather than the median values because 

incorporating mean values approximated a broader spatial representation of pCO2

 concentrations in the Eastern United States. All 

estimates derived from the Monte Carlo simulation were adjusted 

to account for monthly temperatures below freezing under the 

assumption that stream and river flux did not occur when monthly 

temperatures averaged less than 0 °C. This adjustment reduced 

the flux for the Mixed Wood Shield ecoregion by 40 percent, the 

Atlantic Highlands ecoregion by 32 percent, the Mixed Wood 

Plains ecoregion by 30 percent, the Central USA Plains ecoregion 

by 24 percent, the Ozark, Ouachita-Appalachian Forests ecoregion by 6 percent, and the Southeastern USA Plains ecoregion by 

1 percent; the adjustment had no effect on the Mississippi Alluvial 

and Southeast USA Coastal Plains ecoregion.

5.3.3. CO2 Flux From Lacustrine Systems

Water-chemistry data used to estimate lacustrine CO2

emissions were obtained from the 2007 national lakes assessment (NLA) of the EPA (U.S. Environmental Protection 

Agency, 2009). The NLA used a probability-based survey 

design to select lakes and reservoirs that met the following 

criteria: (1) greater than 4 hectares (ha) in area, with a 

minimum of 0.1 ha of open water; (2) at least 1 m deep; and 

(3) not classified or described as treatment or disposal ponds 

or as brackish-water or ephemeral bodies (U.S. Environmental 

Protection Agency, 2009). Following outlier removal 

(McDonald and others, 2013), data from 549 lakes were used 

in the following calculations (fig. 5–1D); 45 of these lakes 

were sampled twice.

Sampling took place during summer 2007; 50 percent of 

the samples were obtained between July 12 and August 23, 

and nearly all (99 percent) were obtained between June 1 

and September 30. The data were classified according to the 

seven ecoregions in the Eastern United States. The number of 

lakes ranged from 37 to 143 per ecoregion. Various biological, 

physical, and chemical indicators were measured during the 

NLA (U.S. Environmental Protection Agency, 2009), and only 

a subset of water-chemistry and physical data was used in this 

assessment and included acid-neutralizing capacity (ANC, 

assumed to be equal to alkalinity), pH, temperature, and 

dissolved organic carbon (DOC).

The estimated CO2

 flux from lacustrine systems was 

calculated using the general equation 5–3. The estimated 

dissolved CO2 (CO2-water) was computed using the 

equilibrium geochemical model PHREEQC version 2 

(Parkhurst and Appelo, 1999). This model is similar to 

CO2SYS in that parameters such as water temperature, pH, 

and alkalinity were used to estimate CO2 concentrations.

The gas transfer velocity for lacustrine systems is largely 

a function of wind speed (Cole and Caraco, 1998). The 

estimated mean wind speeds in summer (June to September) 

were determined for each ecoregion from surface meteorology 

and solar energy data of the National Aeronautics and Space 

Administration (NASA; 2012). The surface area of lakes and 

reservoirs was tabulated for each ecoregion, as in McDonald 

and others (2012).

Many of the parameters involved in these calculations 

violated normality assumptions; therefore, nonparametric 

5th and 95th confidence intervals were determined on 

1 million ordinary bootstrap replicates. The confidence 

intervals for the estimated fluxes were determined by 

propagation of uncertainty, except for the total values 

(for example, the sum of the regional estimates). In those 

cases, the confidence intervals were assumed to be additive 

(uncertainty was not propagated) because potential errors in 

the regional estimates are likely to be systematic.

5.3.4. Carbon Burial in Lacustrine Systems

Carbon burial in lacustrine systems includes carbon 

buried in the sediments of lakes and reservoirs; it is a function of sedimentation rates, sediment carbon concentrations, 

and the areal extent of lacustrine systems. Carbon burial in 

lacustrine systems of the Eastern United States was modeled 

according to the following equation:

Cburial=SedRt×Cconc×SAWB (5–5)
Chapter 5 77

where

Cburial is the carbon burial rate,

SedRt is the sedimentation rate,

Cconc is the concentration of carbon in sediments, 

and

SAWB is the surface area of the water body.

Data on sedimentation rates and carbon concentrations in 

sediments were sparse, necessitating an empirical approach 

that relied on existing data to build geostatistical models, 

which were then used to estimate carbon burial rates in 

unsampled lakes and reservoirs. The input data included 

(1) sedimentation rates derived from a national database 

(for reservoirs) and peer-reviewed literature (for lakes) and 

(2) carbon concentrations obtained from measurements on 

sediment samples collected as part of the 2007 NLA (U.S. 

Environmental Protection Agency, 2009, §10.3.3).

The areal extents of lacustrine systems were derived 

from the high-resolution (1:24,000) USGS National 

Hydrography Dataset (NHD; U.S. Geological Survey, 2012c). 

Sedimentation rates in lakes and carbon concentrations in 

lake sediments usually are different from those in reservoirs 

(Mulholland and Elwood, 1982; Dean and Gorham, 1998); 

thus, the water bodies were separated into lake and reservoir 

classes. Water bodies were classified as reservoirs if they met 

any of the following criteria: (1) the water body was tagged as 

a reservoir in the NHD, (2) the water-body name included the 

word “reservoir” in it, or (3) the water body was included in 

the National Inventory of Dams database (U.S. Army Corps 

of Engineers, 2012). Water bodies that were not classified 

as reservoirs were assumed to be lakes. A comparison with 

ground-based observations on the 697 lakes that were sampled 

for carbon in sediments during the 2007 NLA indicated that 

this classification scheme was correct 80 percent of the time; 

however, misclassification rates might have been higher for 

small water bodies (area less than or equal to 4 ha), such as 

farm ponds, which were not sampled during the NLA.

The best available national dataset of reservoir sedimentation rates was the Reservoir Sedimentation Database 

(RESSED), which included sedimentation-rate data on more 

than 1,800 georeferenced reservoirs in the United States 

(fig. 5–1E; Mixon and others, 2008; Ackerman and others, 

2009). The sedimentation rates in the RESSED database were 

derived from repeat bathymetric surveys with the purpose of estimating losses in reservoir storage. On the basis of the hypothesis 

that sedimentation rates were related to land use, topography, 

soils, and vegetation characteristics in the area surrounding 

the reservoirs, a geographic information system (GIS) analysis 

was performed to quantify these characteristics for each 

hydrologic unit (represented by a 12-digit hydrologic unit code 

(HUC); U.S. Department of Agriculture, Natural Resources 

Conservation Service, 2012) adjacent to each reservoir. The 

sedimentation rates in the RESSED database strongly correlated 

with the net contributing area (R2

 equal to 0.94). However, the 

values for the net contributing area were not available for most 

reservoirs in the United States; therefore, a reservoir’s surface 

area, which should scale with the net contributing area, was 

used as a surrogate for the net contributing area.

The RESSED dataset was split evenly into calibration 

and validation datasets, and a stepwise multiple-linearregression (MLR) analysis was performed on the calibration 

data, where the sedimentation rate was the dependent variable, 

and the land use and basin characteristics were independent, 

explanatory variables. The independent variable that explained 

the most variance in the sedimentation rate entered the model 

first. The variances explained by the remaining explanatory 

variables were recalculated, and the variable that explained the 

next greatest amount of variance entered the model next. This 

iterative process was repeated until no additional variables 

showed statistically significant correlations to sedimentation 

rates, using a p-value less than or equal to 0.1. The multicollinearity among explanatory variables was evaluated using 

the variance inflation factor (calculated as 1/(1 –R2); Hair and 

others, 2005), which had a threshold for exclusion of 0.2. The 

resulting MLR equation was used to estimate the sedimentation rates for all the reservoirs in the NHD. The standard 

error of the equation was used to calculate uncertainty at 

95-percent confidence for the predicted sedimentation rates 

for sites in the validation dataset.

A national dataset of lake sedimentation rates does not 

exist; therefore, sedimentation rates in lakes were estimated 

from data in peer-reviewed literature. Lake sedimentation rates 

have been calculated for more than 80 lakes around the world 

using lead-210 and cesium-137 isotope dating techniques on 

sediment cores; in most studies, multiple cores were collected 

from each lake. A review of peer-reviewed literature identified data for sites in Africa, Antarctica, Asia, Europe, New 

Zealand, and North America. The data were compiled and a 

statistical analysis was performed to characterize a probability 

density function of lake sedimentation rates. A sedimentation 

rate was assigned to each lake from the probability density 

function using random sampling with replacement. This 

procedure was repeated 100 times, drawing a new value 

from the statistical distribution each time in order to obtain 

100 possible sedimentation rate values for each lake. Each of 

these values was used to calculate a carbon burial rate using 

equation 5–5, providing a range of carbon burial estimates for 

each lake. Uncertainty at the 95-percent confidence level was 

calculated as 2×F-pseudosigma, where F-pseudosigma is a 

nonparametric equivalent to the standard deviation.

Carbon concentrations were measured on sediment 

samples collected from 697 bodies of water during the 2007 

NLA (fig. 5–1E; U.S. Environmental Protection Agency, 

2009). The data were split into calibration and validation 

datasets, and a stepwise MLR analysis was performed using 

the same methods and explanatory variables as in the reservoir 

sedimentation-rate analysis. The resulting equation was used 

to estimate carbon concentrations in lake and reservoir sediments in unsampled water bodies across the Eastern United 

States. Uncertainty and model performance were evaluated in 

the same way as the reservoir sedimentation-rate analysis.
78 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

5.3.5. Exclusion of the Great Lakes From 

the Analysis

In this study, the surface area, CO2 emissions, and carbon 

burial in sediments were not considered for the Laurentian 

Great Lakes, although their combined surface area is about 

equal to 160 percent of all other inland lakes in the contiguous 

United States combined (McDonald and others, 2012). Even 

using the most current modeling techniques, large uncertainty 

is still associated with carbon fluxes on these large water 

bodies (Bennington and others, 2012). CO2 emission estimates 

for the entire system remain very poorly constrained, currently 

ranging from less than 1 to more than 30 TgC/yr (McKinley 

and others, 2011). There is also the additional difficulty of 

dividing gas emissions and carbon burial for four of the Great 

Lakes between the borders of Canada and the United States.

5.3.6. Limitations and Uncertainties

The within-ecoregion lateral flux results presented in 

this chapter were based on empirical data, but there were 

two main limitations of the associated methods used to 

calculate these fluxes. The lateral flux values determined from 

the Ecoregion Dataset (table 5–1) represented only small 

watersheds with drainage areas between 3 and 59,000 km2, 

because watershed boundaries had to be entirely within 

ecoregional boundaries to be included in this analysis. Lateral 

fluxes represent carbon concentrations multiplied by flow, 

and because flow from smaller watersheds is generally lower 

than flow from large ones, the fluxes from this dataset may 

be biased toward the low end of the range of lateral fluxes. 

Additionally, watersheds included in the Ecoregion Dataset 

ranged from 18 to 40 percent of the total ecoregional area, 

and an equivalent areal carbon flux from the remaining 

(unmeasured) drainage area was a major assumption in the 

calculated flux. Additionally, large river basins included in the 

Coastal Export Dataset included source areas that lay within 

ecoregions that were considered in the Central (Zhu and 

others, 2011) or Western (Zhu and Reed, 2012) United States 

assessment reports, which complicated flux estimates based on 

ecoregional boundaries.

In this assessment, the estimated CO2

 flux rates from 

riverine systems dominated the estimated aquatic carbon 

fluxes. Validation data to support fluxes 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 meters per day (m/d) in the large main channels up to 

338 m/d in rapids (Hall and others, 2012). 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 application of this model in highly diverse 

riverine landscapes must be done with appropriate caution.

The contribution of organic acids to the calculation 

of total alkalinity potentially could have caused an overestimation of the dissolved pCO2

 concentrations (Tishchenko 

and others, 2006; Hunt and others, 2011). In typical naturally 

occurring fresh water, the only major contributors to 

noncarbonate alkalinity are organic acids, primarily humic 

and fulvic acids (Lozovik, 2005). The concentration of free 

organic ions was estimated for the lakes included in the 

2007 NLA (U.S. Environmental Protection Agency, 2009) 

using the empirical relations of Oliver and others (1983). 

The estimated organic anion concentration for each lake or 

reservoir was subtracted from the measured alkalinity before 

performing an analysis of pCO2; however, an appropriate 

correction algorithm has not been developed for the dataset 

that was used for the flux calculation in riverine systems 

because of the limited locations of paired DOC and alkalinity 

Table 5–1. Estimated within-ecoregion carbon fluxes and yields from riverine systems in the Eastern United States.

[Sites represent U.S. Geological Survey streamgages in endorheic and exorheic basins for which data were available to calculate estimated dissolved inorganic 

carbon (DIC) and total organic carbon (TOC) fluxes, respectively. Values presented in parentheses represent errors associated with total flux and total yield 

(fluxes normalized to watershed areas) at the bootstrapped 5th and 95th confidence intervals. Total carbon fluxes and mean yields were calculated by summing 

the estimated DIC and TOC. gC/m2/yr, grams of carbon per square meter per year; TgC/yr, teragrams of carbon per year]

Ecoregion

Number of sites Estimated total flux,

in TgC/yr

Estimated total yield,

in gC/m2

/yr

Estimated flux as DIC,

DIC yields TOC yields in percent of total flux

Mixed Wood Shield 25 18 1.3 (0.6, 2.1) 6.4 (2.9, 10.0) 55.7

Atlantic Highlands 61 21 0.2 (0.1, 0.4) 1.2 (0.6, 6.6) 75.1

Mixed Wood Plains 73 50 2.1 (1.0, 3.1) 5.5 (2.5, 8.3) 80.3

Central USA Plains 77 38 2.3 (1.6, 3.1) 9.8 (6.6, 12.9) 82.9

Southeastern USA Plains 321 203 5.6 (3.7, 7.4) 5.6 (3.8, 7.4) 48.9

Ozark, Ouachita-Appalachian Forests 338 78 5.5 (3.2, 7.8) 10.7 (6.3, 15.1) 86.1

Mississippi Alluvial and Southeast 

USA Coastal Plains1

106 103 3.0 (1.5, 4.4) 6.6 (3.3, 9.9) 43.2

Eastern United States (total) 1,001 511 19.4 (15.2, 23.6) 6.5 (5.1, 8.0) 57.1

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.
Chapter 5 79

measurements within the NWIS database. Because the current 

methodology for estimating alkalinity in riverine systems does 

not account for organic acids, some of the existing estimates 

of riverine fluxes may be high. Uncertainties in the estimates 

may be reduced by accounting for noncarbonate alkalinity 

(organic acids) when deriving p CO2 concentration from total 

alkalinity measurements.

The stream and river surface-area estimates for each 

ecoregion ranged from 0.3 to 0.9 percent of the total area and 

are consistent with other published values (Aufdenkampe and 

others, 2011; Downing and others, 2012); however, the accuracy of stream and river surface area estimates may improve 

by using remote-sensing techniques to further constrain the 

hydraulic geometry parameters that are appropriate at the 

ecoregion scale (Striegl and others, 2012). Specifically, there 

is a need to constrain the surface areas of first-order stream 

systems (headwaters areas) that may be poorly characterized 

within the NHDPlus dataset and that may exhibit strong 

seasonal variation in water flow. Regional efforts to physically 

map first-order stream surface areas in combination with 

scaling laws would reduce uncertainties.

The locations of USGS streamgages, which were used 

to calculate the hydraulic geometry coefficients, introduced 

a bias because the streamgages were placed in a location that 

was best suited for accurate discharge measurements (Leopold 

and Maddock, 1953; Park, 1977). Therefore these streamgage 

locations most likely do not represent the entire range of 

variability in the relationships among stream depth, width, and 

velocity that exists along the flow paths of rivers in the Eastern 

United States. The results from the Monte Carlo simulation 

suggested levels of uncertainty approaching 52 percent for the 

Mixed Wood Shield ecoregion and between 32 and 40 percent 

for each of the six other ecoregions. In addition, the current 

application of bootstrapping and simulation was considered 

very conservative; however, as suggested above, without 

extensive efforts in field validation for both the gas transfer 

velocity and dissolved CO2

 concentration in small stream 

environments, the model estimates reported in this assessment 

represent the most comprehensive to date.

Using the available data, it was not possible to accurately 

model the impact of seasonality on estimated mean CO2

 flux 

from lacustrine systems. 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 hypolimnion 

(bottom waters) during stratification as a result of heterotrophic respiration and are degassed rapidly during mixing 

(Michmerhuizen and others, 1996; Riera and others, 1999). 

Because the available data for the assessment were collected 

from surface waters only during the summer, this aspect of the 

seasonal p CO2

 dynamics was not included in the estimates.

There were numerous sources of uncertainty in the 

carbon burial estimates. The datasets used to calibrate the 

regression models of sedimentation rates in reservoirs and 

carbon concentrations in sediments were derived from national 

databases; however, they represented less than 0.2 percent of 

the total number of water bodies in the conterminous United 

States. A national database of sedimentation rates in lakes 

does not exist, necessitating a statistical probability approach 

to estimating lake sedimentation rates, which probably misses 

substantial regional variability. Carbon concentrations in 

water body sediments were derived from surface sediment 

samples collected at single points within each water body 

and may not represent average carbon concentrations in the 

surface sediments. Important constants used in the carbon 

burial calculations include sediment bulk density and porosity; 

these parameters were assigned values based on information 

in the literature (Dean and Gorham, 1998), but the values may 

vary widely among lacustrine systems. Combining all these 

sources of uncertainty yields a conservative error estimate of 

approximately ±50 percent in carbon burial rates. It may be 

possible to reduce the uncertainty in these estimates in the 

future, as additional data on sedimentation rates and sediment 

carbon concentrations become available.

5.4. Results

5.4.1. Lateral Carbon Transport in 

Riverine Systems

The total carbon export from exorheic basins, calculated 

using the Coastal Export Dataset, was estimated to be 

36.5 TgC/yr (with lower and upper confidence intervals of 

28.1 TgC/yr and 44.8 TgC/yr, respectively; table 5–2), with 

74 percent of the export occurring as DIC. The Mississippi 

Table 5–2. Estimated coastal carbon exports and yields from riverine systems in the Eastern United States.

[Sites represent U.S. Geological Survey streamgages for which data were available to calculate estimated carbon fluxes from exorheic basins. Values presented 

in parentheses represent the 5th and 95th confidence intervals. Total exports and total yields were calculated by summing dissolved inorganic carbon (DIC) and 

total organic carbon (TOC). gC/m2/yr, grams of carbon per square meter per year; TgC/yr, teragrams of carbon per year]

Waterway

Number of sites Estimated total export,

in TgC/yr

Estimated total yield,

in gC/m2

/yr

Estimated flux as DIC,

DIC yields TOC yields as percentage of total export

Atlantic Ocean 55 52 4.1 (3.7, 4.6) 5.3 (4.8, 5.9) 50

Great Lakes 30 20 5.4 (5.3, 5.6) 18.7 (18.3, 19.1) 85

Gulf of Mexico 36 36 26.3 (18.0, 34.6) 6.45 (4.41, 8.49) 75

Rainy River 1 1 0.2 (0.1, 0.3) 11.6 (6.3, 16.8) 38

All regions 122 109 36.5 (28.1, 44.8) 6.4 (5.0, 7.9) 74
80 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 5–3. Estimated vertical fluxes and yields of CO2 from riverine systems in the Eastern United States.

[Sites are U.S. Geological Survey streamgages for which data were available to calculate the estimated partial pressure of carbon dioxide (pCO2 ). Values 

presented in parentheses represent errors associated with total flux and total yield (fluxes normalized to watershed areas) at the 5th and 95th percentiles derived 

from a Monte Carlo simulation. Total yields were calculated by dividing the estimated total flux by the ecoregion area. gC/m2/yr, grams of carbon per square 

meter per year; km2, square kilometers; TgC/yr, teragrams of carbon per year]

Ecoregion Number

of sites

Stream area, 

in km2

Estimated total flux,

in TgC/yr

Estimated total yield,

in gC/m2

/yr

Mixed Wood Shield 59 630 0.6 (0.3, 1.0) 2.9 (1.6, 4.9)

Atlantic Highlands 194 861 2.6 (1.6, 3.8) 13.1 (8.6, 20.2)

Mixed Wood Plains 367 1,598 2.8 (1.9, 3.8) 7.1 (5.0, 10.2)

Central USA Plains 223 807 1.1 (0.7, 1.6) 4.6 (3.1, 6.6)

Southeastern USA Plains 1,444 5,617 14.5 (9.9, 19.4) 14.0 (10.0, 19.4)

Ozark, Ouachita-Appalachian Forest 1,149 3,054 13.8 (9.2, 19.2) 26.2 (17.9, 37.3)

Mississippi Alluvial and Southeast USA Coastal Plains1 622 3,901 6.4 (3.9, 8.9) 14.5 (9.2, 20.7)

Eastern United States (total) 4,040 16,467 41.6 (27.5, 57.7) 14.0 (7.9, 17.0)

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

River was the single largest source of carbon to the coastal 

ocean (22.1 TgC/yr), and therefore, the majority of carbon 

export from the Eastern United States was to the Gulf of 

Mexico, which received 26.3 TgC/yr. The Great Lakes 

received 5.4 TgC/yr, whereas the Atlantic Ocean received 

4.1 TgC/yr. The Rainy River in the Mixed Wood Shield 

ecoregion exported 0.2 TgC/yr.

The estimated total fluxes (table 5–1), calculated using 

the Ecoregion Dataset, were highest in the Southeastern USA 

Plains ecoregion (5.6 TgC/yr) and lowest in the Atlantic 

Highlands ecoregion (0.2 TgC/yr). The estimated mean 

yields were highest in the Ozark, Ouachita-Appalachian 

Forests ecoregion (10.7 gC/m2/yr) and lowest in the Atlantic 

Highlands ecoregion (1.2 gC/m2/yr; fig. 5–1; table 5–1). 

The mean DIC concentration in the Central USA Plains 

ecoregion (51 milligrams per liter (mg/L)) was eight times 

higher than the estimated mean DIC concentration in the 

Atlantic Highlands ecoregion (6 mg/L). The range of TOC 

concentrations were narrower, with the highest estimated 

mean concentration in the Mississippi Alluvial and Southeast 

USA Coastal Plains ecoregion (16 mg/L) and the lowest in the 

Atlantic Highlands ecoregion (4 mg/L). There was substantial 

variability in the mean runoff among the ecoregions, with 

the greatest mean runoff in the Atlantic Highlands ecoregion 

(627 millimeters per year (mm/yr)) and the smallest mean 

runoff was in the Mixed Wood Shield ecoregion (297 mm/yr).

5.4.2. CO2 Flux From Riverine Systems

The mean concentration of dissolved CO2 in streams and 

rivers across the Eastern United States exceeded atmospheric 

concentrations, indicating that these ecosystems represent sources 

of carbon to the atmosphere. Mean p CO2 concentration was 

greatest in the Southeastern USA Plains ecoregion at 4,902 microatmospheres (μatm), which is 12.5 times greater than atmospheric

p CO2 concentration, and smallest in the Atlantic Highlands 

ecoregion at 2,088 μatm, which is 5.3 times greater than 

atmospheric p CO2 concentration. The mean p CO2 concentration 

for all regions was 3,265 μatm, which was 8.3 times greater than 

atmospheric pCO2 concentration (fig. 5–1).

Stream surface area ranged from 630 km2 in the Mixed 

Wood Shield ecoregion (representing 0.3 percent of the total 

land surface area of the Eastern United States) to 5,617 km2

in the Southeastern USA Plains ecoregion (0.56 percent; 

table 5–3). The total stream surface area for the Eastern 

United States was 16,467 km2 representing 0.56 percent of the 

land surface. Stream and river surface area was greatest in the 

Mississippi Alluvial and Southeast Coastal Plain ecoregion at 

23,465 km2, or 0.9 percent.

The total riverine vertical flux of carbon for the Eastern 

United States was 152.1 TgCO2-eq/yr with 5th and 95th 

percentiles of 101 and 211, respectively. Ecoregion specific fluxes 

ranged from a high of 49.4 TgCO2-eq/yr (5th and 95th percentiles 

of 33.7 to 70.3 TgCO2-eq/yr) in the Ozark, Ouachita-Appalachian 

Forests ecoregion to 2.2 TgCO2-eq/yr (5th and 95th percentiles 

of 1.1 to 3.7 TgCO2-eq/yr) in the Mixed Wood Shield ecoregion. 

Riverine flux on an area basis for ecoregions in the Eastern 

United States was 14 gC/m2/yr (5th and 95th percentiles of 7.9 

and 17 gC/m2/yr, respectively), ranging from 2.93 gC/m2/yr (5th 

and 95th percentiles of 1.6 and 4.9 gC/m2/yr, respectively) in the 

Mixed Wood Shield ecoregion to 26.2 gC/m2/yr (with 5th and 

95th percentiles of 17.9 and 37.3 gC/m2/yr, respectively) in the 

Ozark, Ouachita-Appalachian Forests ecoregion (fig. 5–1).

5.4.3. CO2 Flux From Lacustrine Systems

The estimated mean concentration of p CO2 in lacustrine 

systems of the Eastern United States was 1,184 μatm with 

a median of 740 μatm, which was greater than atmospheric 

concentrations for all eastern ecoregions. Thus, all ecoregions 

were net sources of carbon to the atmosphere. The mean p CO2

was greatest in the Southeastern USA Plains ecoregion at 

1,866 μatm, which was 4.8 times greater than the atmospheric 

concentration of CO2, and smallest in the Mixed Wood Shield 

ecoregion at 714 μatm, which was 1.8 times greater than the 

atmospheric concentration of CO2 (fig. 5–1).
Chapter 5 81

Table 5–4. Estimated vertical flux of carbon dioxide from lacustrine systems in the Eastern United States.

[Sites are from the 2007 National Lakes Assessment (U.S. Environmental Protection Agency, 2009). The data from the 2007 NLA were used in the calculation 

of the partial pressure of carbon dioxide (pCO2 ). Values presented in parentheses represent errors associated with total flux and total yield (fluxes normalized 

to watershed areas) at the bootstrapped 5th and 95th confidence intervals. Carbon yields were calculated by dividing the estimated total flux by the ecoregion 

area. gC/m2/yr, grams of carbon per square meter per year; km2, square kilometers; TgC/yr, teragrams of carbon per year]

Ecoregion Number

of sites

Lake and

reservoir area, 

in km2

Estimated total flux,

in TgC/yr

Estimated total yield,

in gC/m2

/yr

Mixed Wood Shield 58 12,300 0.7 (0.4, 1.2) 3.5 (1.8, 6.0)

Atlantic Highlands 62 5,170 0.5 (0.3, 0.7) 2.7 (1.8, 3.9)

Mixed Wood Plain 143 11,700 1.7 (1.4, 2.2) 4.6 (3.6, 5.8)

Central USA Plains 37 3,170 0.3 (0.1, 0.4) 1.1 (0.6, 1.8)

Southeastern USA Plains 124 17,400 3.9 (2.9, 5.1) 3.9 (2.9, 5.1)

Ozark, Ouachita-Appalachian Forests 72 7,080 0.7 (0.5, 0.9) 1.4 (0.9, 1.8)

Mississippi Alluvial and Southeast USA Coastal Plains1 53 19,800 1.9 (1.2, 3.4) 4.6 (2.6, 7.6)

Eastern United States (total) 549 76,620 9.7 (6.8, 13.9) 3.3 (2.9, 4.0)

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

The estimated mean flux of CO2 across the air-water 

interface was primarily determined by the gradient between 

the dissolved and atmospheric concentrations of carbon. 

The estimated gas transfer velocity was less variable than 

the estimated p CO2 among all of the ecoregions—smallest 

in the Atlantic Highlands ecoregion (0.99 m/d) and 

greatest in the Central USA Plains ecoregion (1.21 m/d). 

The ecoregional estimates of total annual CO2 flux from 

lacustrine systems (table 5–4) ranged from 0.3 TgC/yr 

(1.2 TgCO2-eq/yr) in the Central USA Plains ecoregion to 

3.9 TgC/yr (14.3 TgCO2-eq/yr) in the Southeastern USA 

Plains ecoregion. The total CO2 flux from the Eastern United 

States was estimated to be 9.7 TgC/yr (35.6 TgCO2-eq/yr). 

The estimated ecoregional flux values were directly related to 

the surface area of the lacustrine systems (table 5– 4), which 

varied among the ecoregions, partially because of differences 

in regional morphology and climate but mainly due to differences in the size of the ecoregions.

In order to facilitate a direct comparison between lake 

and reservoir gas fluxes, lateral carbon transport, carbon 

burial, and terrestrial processes, the estimated CO2 flux 

values were normalized to the total land surface area in each 

ecoregion to provide the carbon yield (fig. 5–1; table 5– 4). 

The estimated carbon yields ranged from 1.1 gC/m2/yr in the 

Central USA Plains ecoregion to 4.6 gC/m2/yr in the Mixed 

Wood Plains and the Mississippi Alluvial and Southeast USA 

Coastal Plains ecoregions. The estimated mean carbon yield 

(expressed as CO2 flux per unit of total land and water area) 

from lacustrine systems in the Eastern United States was 

3.3 gC/m2/yr.

5.4.4. Carbon Burial in Lacustrine Systems

The estimated net flux of carbon due to burial in lacustrine sediments of the Eastern United States was –9.2 TgC/yr 

(negative values represent a carbon sink; table 5–5). The 

Mixed Wood Shield and the Mississippi Alluvial and 

Southeast USA Coastal Plain ecoregions had the highest 

estimated carbon burial rates, accounting for net fluxes of 

–2.5 TgC/yr and –2.7 TgC/yr, respectively. The Central USA 

Plains and the Ozark, Ouachita-Appalachian Forest ecoregions 

had the lowest estimated carbon burial rates, with net flux 

of– 0.4 TgC/yr each (table 5–5).

When normalized to the area of each ecoregion (yield), 

the estimated net flux due to carbon burial in lacustrine 

systems was –3.1 gC/m2/yr (fig. 5–1; table 5–5). The 

ecoregion-normalized fluxes ranged from –0.8 gC/m2/yr 

in the Ozark, Ouachita-Appalachian Forest ecoregion to 

–11.9 gC/m2/yr in the Mixed Wood Shield ecoregion.

5.5. Discussion

5.5.1. Coastal Export, Lateral Transport, and

CO2

 Flux From Riverine Systems

The highest riverine within-ecoregion lateral carbon 

fluxes and CO2 emissions occurred in the Ozark, OuachitaAppalachian Forests ecoregion. Moderate DIC and TOC 

concentrations (15.3 mg/L and 4 mg/L, respectively) coupled 

with high annual precipitation (1,218 mm/yr) and average 

annual runoff (average streamflow divided by drainage area, 

533.8 mm/yr) produced within-ecoregion riverine lateral 

carbon fluxes of 10.7 gC/m2/yr. Elevated p CO2 concentrations 

(4,950 μatm) were also estimated for this ecoregion, where 

terrestrial soil processes may have a significant impact on 

stream water chemistry. Direct input of terrestrial soil and root 

respiration is often facilitated by increased levels of precipitation and water throughput as groundwater enters a stream 

environment (Jones and Mulholland, 1998). In fact, Jones and 

Mulholland (1998) measured pCO2 concentration in excess 

of 6,000 μatm within forested headwater catchments of the 

Ozark, Ouachita-Appalachian Forests ecoregion, 20 percent 

greater than the pCO2 concentrations estimated across similar 

headwaters systems presented in this report. The high riverine 
Table 5–5. Estimated carbon burial in lacustrine sediments in the Eastern United States.

[Sites were those in the 2007 National Lakes Assessment (U.S. Environmental Protection Agency, 2009) that were used to develop a regression equation to 

estimate carbon concentrations in sediment. Values presented in parentheses represent errors associated with total flux and total yield (fluxes normalized to 

watershed areas) at the bootstrapped 5th and 95th confidence intervals. Carbon yields were calculated by dividing the estimated total flux divided by the ecoregion area. gC/m2

/yr, grams of carbon per square meter per year; TgC/yr, teragrams of carbon per year]

Ecoregion Number

of sites

Estimated total flux,

in TgC/yr

Estimated total yield,

in gC/m2

/yr

Mixed Wood Shield 21 –2.5 (–1.2, –3.7) –11.9 (– 6.0, –17.9)

Atlantic Highlands 47 –0.6 (– 0.3, –1.0) –3.4 (–1.7, –5.1)

Mixed Wood Plains 73 –1.4 (–0.7, –2.1) –3.7 (–1.9, –5.6)

Central USA Plains 30 –0.4 (–0.2, –0.5) –1.5 (–0.8, –2.3)

Southeastern USA Plains 95 –1.3 (–0.6, –1.9) –1.3 (–0.6, –2.0)

Ozark, Ouachita-Appalachian Forests 53 –0.4 (–0.2, –0.7) – 0.8 (– 0.4, –1.3)

Mississippi Alluvial and Southeast USA Coastal Plains1 31 –2.7 (–1.3, –4.0) –6.2 (–3.1, –9.3)

Eastern United States (total) 350 –9.2 (–4.6, –13.8) –3.1 (–1.6, – 4.7)

1

Includes the Everglades and Texas-Louisiana Coastal Plain level II ecoregions for the analysis of this assessment.

CO2 emissions in the Ozark, Ouachita-Appalachian Forests 

ecoregion were also likely related to physical parameters of the 

riverine systems in this ecoregion. The slopes of streambeds 

in the headwaters were high, averaging 3 percent, which when 

coupled with high stream velocities, resulted in gas transfer 

velocities upwards of 22 m/d. The combination of elevated 

CO2 concentrations and gas transfer rates resulted in large areal 

riverine CO2 flux estimates of 26.2 gC/m2/yr in this ecoregion.

Similarly, the riverine CO2 yield of 13.1 gC/m2/yr calculated for the Atlantic Highlands ecoregion, which includes 

much of the White Mountains throughout New England, 

was a result of the physical characteristics of the landscape. 

Estimated headwater slope averaged 3.5 percent, and 

calculated gas transfer rates were 25.6 m/d. The differences 

in climate, with a mean annual temperature of 6.1 °C in the 

Atlantic Highlands ecoregion compared with 12.6 °C for the 

Ozark, Ouachita-Appalachian Forests ecoregion, could explain 

the change in flux from 2.6 TgC/yr to 13.8TgC/yr between 

the two ecoregions, respectively. At the lower temperatures 

that are characteristic of the Atlantic Highlands ecoregion, the 

mineralization and soil weathering rates are comparatively 

low, which would also lower the carbon yields (Post and 

others, 1982; Lauerwald and others, 2012). Additionally, the 

dilute water chemistry of this ecoregion also likely played a 

role in low riverine CO2

 emissions, as there were relatively 

low p CO2 concentrations (2,088 microatmospheres) in the 

Atlantic Highlands ecoregion.

The headwaters of the Susquehanna River, one of the 

larger rivers in the Northeastern United States, are in the 

Atlantic Highlands and the Ozark, Ouachita-Appalachian 

Forests ecoregions. Coastal exports from this river are 

ultimately delivered to the Chesapeake Bay. This river 

accounted for about 1 percent of the total coastal carbon 

exports in the Eastern United States, with a total carbon yield 

of 5.8 gC/m2/yr. This value represents an average of the carbon 

yields of the three ecoregions that this river encounters along 

its flow path: the Atlantic Highlands ecoregion (main branch 

of the Susquehanna River), the Ozark, Ouachita-Appalachian 

Forests ecoregion (west branch of the Susquehanna River), 

and the Southeastern USA Plains ecoregion (coastal outlet), 

which have ecoregional yields of 1.2 g/m2/yr, 10.7 g/m2/yr, 

and 5.6 g/m2/yr, respectively. These coastal export values for 

the Eastern United States are similar to previously reported 

(Mulholland and Watts, 1982; Shih and others, 2010) estimated TOC export values for the North Atlantic (3.4 gC/m2

/yr) 

and southern Atlantic gulf (5.9 gC/m2/yr).

The Mississippi Alluvial and Southeast USA Coastal 

Plains ecoregion had the highest riverine p CO2 concentrations at 4,902 μatm. The riverine DIC (15.2 mg/L) and TOC 

(16 mg/L) concentrations for the ecoregion were also high, 

indicating that both inorganic and organic carbon could 

have contributed to the high p CO2 values. Additionally, the 

coastal outlet of the Mississippi River is in the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion, and the 

combination of the Mississippi River and Atchafalaya River 

Basins represented more than 60 percent of the total carbon 

export from the Eastern United States. The flux of 20.6 TgC/yr 

estimated for the Mississippi River Basin is similar to reported 

flux values (Raymond and Cole, 2003; Turner and Rabalais, 

2004). The high average concentrations and fluxes in this 

southern ecoregion may be supported by direct inputs from 

highly productive wetland and riparian vegetation that cover 

more than 30 percent of the total land area and average annual 

precipitation exceeds 1.3 m. Additionally, the areal extent 

of streams and rivers within the Mississippi Alluvial and 

Southeast USA Coastal Plains ecoregion was the greatest 

across the Eastern United States at about 1 percent of the 

total ecoregion area. The connectivity of wetland and riparian 

sources of dissolved carbon gasses to stream and river ecosystems has not been well established along spatial and temporal 

gradients; however, a study in other wetland-dominated 

systems suggests very high concentrations of CO2

 at these 

riparian interfaces (Clilverd and others, 2008). Although 

research does suggest that, in regions with high groundwater 

recharge rates, the penetration of root respiration derived CO2

from wetlands can be enhanced (Hunt and others, 1999), the 

analysis presented in this chapter is unable to quantify this 

effects across ecoregions.
Chapter 5 83

The Mixed Wood Shield (6.4 gC/m2/yr), Mixed Wood 

Plains (5.5 gC/m2/yr), and Central USA Plains (9.8 gC/m2/yr) 

ecoregions had substantial within-ecoregion lateral carbon 

fluxes. The Mixed Wood Shield ecoregion, which is dominated 

by wetlands and forests, represented an ecoregion where 

total carbon was divided relatively equally between DIC 

and TOC. Both these major land cover classes (forests and 

wetlands) deliver substantial amounts of organic matter to 

riverine systems (Mulholland, 2003; Creed and others, 2008). 

In contrast, the Mixed Wood Plains and Central USA Plains 

ecoregions had more than 80 percent total carbon export 

as DIC. Fluvial export of terrestrial alkalinity is a major 

component of riverine DIC concentrations (Raymond and 

Cole, 2003; Stets and Striegl, 2012), but sedimentary rock 

coverage in either ecoregion was not substantial (less than 

10 percent). However, agricultural lands and urban development did dominate the land cover in this ecoregion; these 

two landscape types have been linked with elevated DIC 

concentrations (Barnes and Raymond, 2009). Interestingly, the 

magnitude of riverine CO2

 emissions was not as high in these 

two ecoregions, most likely because of the flat topography of 

these glacially impacted ecoregions.

5.5.2. CO2 Flux From and Carbon Burial in 

Lacustrine Systems

Similar to the riverine flux results, the average lacustrine 

CO2 emissions (4.6 gC/m2/yr) and carbon burial fluxes 

(– 6.2 gC/m2/yr) were high in the Mississippi Alluvial and 

Southeast USA Coastal Plains ecoregion. However, unlike 

the riverine CO2 emissions, the northern ecoregions of the 

Eastern United States (Mixed Wood Shield and Mixed Wood 

Plains ecoregions) also had relatively high lacustrine CO2

emissions (4.6 gC/m2/yr and 3.5 gC/m2/yr, respectively) and 

carbon burial fluxes (–11.9 gC/m2/yr and –3.7 g C/m2/yr, 

respectively). The high fluxes in these ecoregions reflected the 

size and density of lakes in those areas. The southern part of 

the Eastern United States contains a higher density of smaller 

water bodies, many of which are artificial, compared with the 

northern part of the Eastern United States where there is a 

relatively low density of lakes, but the lakes that are present 

have large surface areas, particularly in the Mixed Wood 

Shield ecoregion. Water body area alone explained 60 percent 

of the variation in carbon burial rates among ecoregions.

In general, the ecoregions with higher CO2

 emissions 

were also ecoregions with high carbon burial rates, indicating 

that excess carbon entering lacustrine systems that is not 

processed and emitted as CO2 may otherwise be buried. 

Lacustrine systems with higher carbon burial rates also had 

a strong positive correlation between ecoregional average 

soil organic carbon and sediment carbon concentration. 

Higher lacustrine burial rates were related to the prevalence 

of wetlands particularly in the Mixed Wood Shield and the 

Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregions. As with the riverine fluxes, this reflects the 

importance of allochthonous (terrestrially derived) inputs of 

carbon to lakes and reservoirs in freshwater carbon budgets 

(Stets and others, 2009).

Variations in total annual carbon burial among ecoregions 

reflected differences in the sedimentation rates and sediment 

carbon concentrations of lacustrine systems as well as the 

prevalence and size of water bodies within each ecoregion. 

In the Eastern United States, sedimentation rates in reservoirs 

were highest in the Central USA Plains ecoregion, where the 

dominant land use was cultivated crops, a land cover that is 

associated with high erosion rates (Crowder, 1987; McIntyre, 

1993). These results further emphasize the important influence 

that land cover can have on inland water carbon fluxes, as 

the relationship between high within-ecoregion lateral fluxes 

for the Central USA Plains ecoregion was also linked to the 

combined effects of agricultural lands and urban development 

in the previous section.

Regions with higher total lacustrine CO2

 emissions 

(table 5– 4) tended to also exhibit higher total riverine CO2

emissions (table 5–3). A notable exception is the Ozark, 

Ouachita-Appalachian Forests ecoregion, which had relatively 

low lacustrine emissions. This was likely due to the greater 

abundance of streams relative to lakes and reservoirs in this 

mountainous region. Lacustrine CO2 emissions (table 5– 4) 

normalized to ecoregion area (yields) produced values that 

range from 6 to 52 percent of lateral inorganic carbon flux 

and from 28 to 126 percent of lateral organic carbon flux 

(table 5–1). Although there was not a strong relationship 

between the magnitude of lateral fluxes and lake emissions, 

there was a positive relationship between the fraction of 

the lateral flux present as inorganic carbon (table 5–1) and 

ecoregional CO2 yield from lakes and reservoirs, so that 

ecoregions with high proportions of total carbon as DIC had 

low CO2 emissions. This suggests that the carbon composition, as indicated by the ratio of DIC to TOC, rather than the 

overall amount of allochthonous-derived carbon, controlled 

lacustrine CO2 flux.

5.5.3. Summary and Conclusions of Carbon 

Storage From Inland Aquatic Systems

The considerable variability in the estimated inland 

waters’ carbon fluxes among the seven ecoregions in the 

Eastern United States was likely due to the differences in the 

size and abundance of water bodies, topography, climate, 

and land cover associated with each ecoregion. In general, 

those ecoregions across the Eastern United States that had 

the highest annual temperature, natural vegetation cover, and 

precipitation had the highest total carbon fluxes, suggesting 

strong linkages across the terrestrial to aquatic interface. 

Overall, these results highlight the impact that climate and 

land cover can have on annual carbon transfer, storage, and 

flux from inland waters.

Riverine systems and lacustrine systems covered 

0.55 and 2.6 percent of the total ecoregional area of the 
84 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Eastern United States, respectively, excluding the Laurentian 

Great Lakes. Despite the small surface area that these inland 

waters cover, they accounted for considerable carbon losses. 

Riverine CO2 emissions were the largest carbon sources 

to the atmosphere (14 gC/m2/yr). Additionally, a total of 

6.3 gC/m2/yr as DIC and TOC was exported to coastal areas. 

The lacustrine CO2 losses (3.3 gC/m2/yr) were balanced 

by nearly equivalent fluxes of carbon buried in lacustrine 

sediments (–3.1 gC/m2/yr). Accounting for the three carbon 

loss terms (riverine and lacustrine CO2 loss, and lateral 

carbon coastal export) and one sequestration term (lacustrine 

carbon burial) resulted in a net inland waters’ carbon yield 

of 23.5 gC/m2/yr.

The results presented in this study indicate that the 

magnitude of aquatic fluxes in inland waters is significant, and 

that integration of these fluxes into the traditional terrestrial 

carbon cycle is needed. In this study, the terrestrial NEP value 

defined as carbon stored in terrestrial biomass, soil organic 

carbon, or harvested wood is equivalent to 99.9 gC/m2/yr 

(chap. 7). The sum of carbon transported, emitted, or buried in 

aquatic ecosystems (29.7 g C/m2/yr) was equivalent to about 

30 percent of this terrestrial NEP estimate. This percentage 

value must be interpreted with caution, as it is possible that 

some of the carbon considered as a part of the terrestrial soil 

organic carbon pool, may in fact have been lost to lacustrine 

and riverine systems via erosion or leaching processes, 

which would already be accounted for in the inland aquatic 

ecosystems’ yield values. However, this comparison indicates 

that the linkage between terrestrial and aquatic ecosystems 

is critically important to understand fully the role natural 

ecosystems play in greenhouse-gas storage and cycling.

This chapter focused on current carbon fluxes from inland 

aquatic systems, but projected climate and land-use changes are 

likely to influence aquatic fluxes in future decades. Predicted 

changes to the hydrologic cycle include increased precipitation and streamflow (Mearns and others, 2003; Hayhoe and 

others, 2007; Huntington and others, 2009), reduction in the 

number of ice cover days on lakes and rivers (Magnuson and 

others, 2000; Jensen and others, 2007), and decreases in snow 

depth (Hayhoe and others, 2007; Burakowski and others, 

2008). Delivery of carbon to aquatic ecosystems will also be 

changed by expected alterations to both terrestrial and aquatic 

biogeochemical cycling of carbon. Increases in net primary 

productivity (Freeman and others, 2004; Wrona and others, 

2006) and organic matter decomposition rates (Davidson and 

Janssens 2006; Craine and others, 2010), as well as land-use 

changes such as urbanization and changes in agricultural 

management practices (Raymond and Cole, 2003; Barnes and 

Raymond, 2009; Aufdenkampe and others, 2011) are all likely 

to affect the amount and composition of carbon delivered to 

aquatic systems. Coupling both hydrologic and biogeochemical 

reactions in a systematic fashion over space and time will 

be key to accurately predicting changes in the magnitude of 

aquatic carbon fluxes in the future.
Chapter 6. Terrestrial Fluxes of Nutrients and Sediment to 

Coastal Waters and Their Effects on Coastal Carbon Storage 

in the Eastern United States

By Brian A. Bergamaschi,11 Richard A. Smith,1 Michael J. Sauer,1 Jhih-Shyang Shih,2 and Lei Ji1

1

U.S. Geological Survey

2

 Resources for the Future

6.1. Highlights

• Model estimates indicate that nutrient and sediment 

fluxes from terrestrial environments of the Eastern 

United States contribute significantly to the uptake and 

storage of carbon in coastal waters.

• Changes in population and land use are projected to 

result in significantly greater fluxes of nutrients and 

sediments to coastal waters by 2050 relative to the 

baseline years (2001–2005). However, total organic 

carbon flux to coastal areas is projected to increase 

only slightly. For example, projected nitrate fluxes 

for 2050 are 16 to 52 percent higher than the baseline 

year, depending on the region and LULC scenario 

modeled. As a consequence, an associated increase in 

the frequency and duration of coastal and estuarine 

hypoxia events and harmful algal blooms could be 

expected.

• The estimated annual coastal carbon storage flux 

related to continental inputs was 7.9 TgC/yr, or 

3 percent of the estimated average annual terrestrial 

flux based on LULC in 2005.

• Although variable by region, about 60 percent of 

coastal carbon storage related to terrestrial inputs is 

buried in sediments and 40 percent is stored in deep 

ocean waters, below the surface ocean mixed layer.

• Annual rates of coastal carbon storage are projected to 

increase by 18 to 56 percent between 2005 and 2050, 

based on several modeled LULC scenarios. This is in 

contrast to terrestrial rates of carbon storage, which 

are projected to decrease by 20 percent. The differing 

trends in coastal and terrestrial storage result from 

projected increases in nutrient and sediment runoff 

from urban and agricultural lands and from decreases 

in forest cover.

• Fluxes of nutrients and sediments from the Eastern 

United States in 2005 could account for about 

9,100 TgC of burial in the active coastal sediment 

carbon pool (the upper 1 m) and about 6,000 TgC in 

deep ocean waters. This represents about two thirds 

of the mass of carbon stored in the major terrestrial 

carbon pools and is in addition to coastal carbon 

storage related to oceanic fluxes of nutrients and 

sediments.

6.2. Introduction

The current and projected LULC maps of the United States 

that form the centerpiece of the USGS land carbon national 

assessment (chap. 3) provide a unique opportunity to examine 

the extent to which LULC affects carbon storage in coastal 

oceans, given the influence of riverborne fluxes of nutrients and 

sediments to the coastal oceans, and the role of nutrients and 

sediments in carbon storage processes. Coastal oceans remove 

a greater amount of carbon from the atmosphere globally than 

terrestrial biomass, largely through photosynthetic uptake of 

atmospheric carbon by phytoplankton and sequestration of 

this organic material from the surface ocean through either 

burial in sediments or removal to the deep ocean (Walsh and 

others, 1985; Hedges and Keil, 1995; Sarmiento and Gruber, 

2002; Dunne and others, 2007). More than 90 percent of global 

phytoplankton productivity occurs in coastal areas of the 

oceans, in part because of the elevated nutrient supply from 

terrestrial sources (Smith and Hollibaugh, 1993; Behrenfeld 

and Falkowski, 1997). These fluxes of sediment from the 

continents act to sink and bury this phytoplankton production 

(Milliman and Syvitski, 1992; Armstrong and others, 2001; 

Dunne and others, 2005; Syvitski and others, 2005). Coastal 

carbon preservation in sediments is strongly coupled to the 

availability of nutrients and the sediment supply (Boudreau 

and Ruddick, 1991; Dagg and others, 2004; Meybeck and 

others, 2006). Thus, changes in the continental fluxes of nutrients and sediments will affect the coastal storage of carbon.

It is well established that LULC distributions are a primary 

determinant of terrestrial fluxes of sediments and nutrients 

to coastal oceans (Seitzinger and others, 2005; Howarth, 

2008; Mayorga and others, 2010). It is also well known 

that both sediment and nutrient delivery to coastal waters has 
86 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

been significantly altered by changes in population and land use 

(Syvitski and others, 2005; da Cunha and others, 2007; Glibert, 

2010), resulting in modified patterns of coastal production and 

carbon burial (Walsh and others, 1985; Rabalais and others, 

1996; Leithold and others, 2005; Middelburg and Levin, 2009). 

This increasing nutrient supply has raised coastal productivity 

enough to increase commercial fish harvests and to expand the 

areas where hypoxic conditions occur (Howarth and others, 

1996; Nixon and others, 1996; Rabalais and others, 1996; Boyer 

and others, 2006; Swaney and others, 2012). Continued population 

growth and increased areal extent and intensity of agricultural lands 

are expected to accelerate these changes (Syvitski and others, 

2005; Mayorga and others, 2010; Seitzinger and others, 2010; 

Vörösmarty and others, 2010).

The goals of this coastal carbon storage analysis were 

to evaluate how terrestrial fluxes of sediment and nutrients 

contribute to carbon storage in the coastal oceans of the Eastern 

United States and how changing sediment and fluxes associated 

with different prospective future LULC conditions may alter 

rates of carbon storage. Terrestrial fluxes of sediment, nutrients, 

and carbon were quantified for baseline conditions and a range 

of projected LULC distributions associated with different SRES 

storylines (chaps. 2 and 3), and the resulting coastal carbon 

storage for each LULC distribution was inferred using a 

simple biogeochemical model. These projected changes in 

terrestrial flux are also useful for assessing potential future 

coastal water quality conditions (Whitehead and others, 2009), 

forecasting fish harvests (Oczkowski and Nixon, 2008; Breitburg 

and others, 2009), gaging the potential for coastal and estuarine 

hypoxia (Rabalais and others, 2010; Zhang and others, 2010), 

and evaluating the vulnerability of coastal ecosystems to harmful 

algal blooms (Glibert and others, 2005; Litchman and others, 

2006). The spatially explicit nature of the assessment permits the 

use of economic models for evaluating and comparing among 

different policy and land management decisions made to prevent 

further degradation of aquatic habitats (Paerl, 2006; Shepherd 

and others, 2007). The coastal carbon storage and burial 

portion of the assessment will likely help guide scientific 

studies and monitoring programs in the coastal ocean as well 

as provide the basis for economic analysis of this important 

carbon resource (Burdige, 2007; Williams and others, 2009).

Continental flux and coastal carbon storage models were 

prepared for baseline LULC and projected future LULC under 

SRES scenarios A1B, A2, and B1 (chaps. 2 and 3) to explore a 

range of potential effects of LULC on carbon flux and storage. 

The geographic extent of the coastal assessment was from the 

northern border to the southern border of the Eastern United 

States and was divided into the Great Lakes, Gulf of Maine, 

Mid-Atlantic Bight, South Atlantic Bight, and Gulf of Mexico 

regions (fig. 6–1). The model includes all the watersheds 

that drain from these regions into the adjacent coastal waters, 

composing the majority of the continental United States. The 

coastal carbon storage model considers only coastal processes 

affected by the terrestrial nutrients and sediments delivered 

to coastal waters within a particular region, with an arbitrary 

offshore oceanic boundary at the 2,000-m isobath.

Bathymetry, in meters

 above sea level

200 to 2,000

<10

100

Coastal region catchments

EXPLANATION

Great Lakes

Gulf of Maine

Mid-Atlantic Bight

South Atlantic Bight

Gulf of Mexico

No data or not draining 

 to coast

N

Figure 6.1. 

0 250 500 MILES

0 250 500 KILOMETERS

GULF OF MEXICO

A

TLANTIC

OCEAN

Lake

Michigan

Lake Superior

Lake Huron

Lake Erie

Lake

Ontario

Figure 6–1. Map showing the geographic extent of the five regions used in the assessment of coastal carbon storage in the 

Eastern United States. The geographic extent of the regions includes all catchments in the Eastern United States draining to waters 

of the Atlantic Ocean, the Gulf of Mexico, and the Great Lakes and extends in coastal waters to a water depth of 2,000 meters (m). 

Catchment areas within the model domain that do not drain to the coast or have insufficient data to accurately model are indicated.
Chapter 6 87

6.3. Coastal Carbon Storage Model

6.3.1. Background and Overview

The analysis described in this chapter comprises two 

distinct modeling efforts. One assesses the riverine fluxes of 

nutrients and sediments to coastal waters and changes caused 

by changing LULC, and a separate one assesses the effect 

those fluxes may have on carbon storage in coastal waters, as 

well as how carbon storage may change under scenarios of 

altered terrestrial fluxes.

The first model relies on the databases described in chapter 

3 of this report that document present and future projected 

LULC under different SRES scenarios (A1B, A2, and B1). 

For the baseline LULC and for the projected LULC for each 

scenario in 2050, riverine fluxes of TOC, total nitrogen (TN), 

total phosphorus (TP), and total suspended sediment (TSS) 

to coastal waters were estimated using the hybrid statisticalmechanical Spatially Referenced Regression on Watershed 

Attributes (SPARROW) model (Smith and others, 1997; 

Schwarz and others, 2006). The SPARROW model consists of 

regressions of large water quality datasets to LULC, processbased mass-transport components for water flow paths and 

in-stream processing, and mass-balance constraints (Schwarz 

and others, 2006). Relations between land use and flux are 

statistically robust, provided water quality records used for 

model development cover a sufficient length of time and include 

accurate streamflow measurements (Cohn and others, 1992). 

The SPARROW model is routinely used for regional assessments of contaminant sources and fluxes and is now a routine 

part of the USGS water-quality assessment activities (Preston 

and others, 2011). The advantage of using the SPARROW 

model is that it permits assessment using projected future LULC 

and population conditions. Results from the SPARROW model 

based on SRES storylines and spatially detailed modeling 

represent an important product of this analysis because they 

project changes in loads of nutrients and sediment to estuaries 

and coastal areas and may be useful in separate assessments of 

water quality for a range of potential future conditions.

The second part of this analysis uses a simple heuristic 

biogeochemical model to assess the potential magnitude of 

coastal carbon storage directly associated with the riverine 

fluxes of sediments, carbon, and nutrients. The model structure 

follows that of Dunne and others (2007), who demonstrated 

that the rate of carbon burial in shallow ocean waters may be 

much higher than previously recognized. The goal of the coastal 

model is to provide the basis for comparing coastal carbon 

storage estimates associated with baseline and projections of 

individual SRES scenarios. To permit such a comparison, the 

model was developed to provide estimates of millennial carbon 

storage associated with a single year of riverine inputs under 

different scenarios. This approach necessarily involves evaluation of the flux of a single year through processes occurring over 

timescales of days to weeks (for example, primary production), 

months to decades (for example, sediment transport), and 

decades to centuries (for example, carbon burial). Given this 

construct, the coastal model is intended to be representational 

rather than realistic and focuses specifically on how LULC 

may be related to coastal carbon storage. The model makes no 

attempt to reproduce or project coastal ocean carbon cycling 

and does not account for carbon cycling and carbon storage 

associated with upwelling, downwelling, advective transport of 

carbon, and other oceanic processes. It should be noted that the 

carbon storage estimates provided by the model represent only a 

fraction of the total carbon stored in coastal sediments.

For the purpose of this analysis, carbon storage is defined as 

burial in coastal sediments below the zone of pore-water oxygen 

penetration or removal of carbon from the surface ocean across 

the maximum depth of the upper mixed layer and away from 

contact with the atmosphere. Separate components of the model 

estimate retention of nutrients and sediment in estuaries, “new 

production” of phytoplankton biomass, transit and degradation 

of biomass through the water column, and burial of biomass in 

sediments or loss below the deep mixed layer into the deep ocean, 

depending on the location of the original biomass production. It 

also estimates the amount of terrestrial TOC stored. Because the 

amount of surface productivity that is eventually stored depends 

largely on the depth of the water column and the sediment burial 

rate, carbon storage in coastal areas is sensitive to the spatial 

distribution of nutrients and sediments in the surface waters. The 

model presented here uses a spatial distribution developed from 

averaged remote sensing data to represent the physical processes 

leading to spatial variability in coastal waters.

Climate-related changes, such as those to river discharge 

and vegetation, were not estimated in the LULC coverages and 

thus were not part of this analysis. The potential effects of climate 

change on nutrients or biological processes in the coastal ocean 

also were not included. Although changing climate would be 

expected to have some influence on future fluxes of nutrients and 

suspended sediment to coastal waters, LULC is expected to be the 

main driver of the variability in these parameters between 2005 

and 2050 (Allan, 2004). Potential effects of temperature rise on 

TN flux can be estimated from the characteristics of the temperature term in the SPARROW TN model (Alexander and others, 

2008). For example, the TN flux from a typical watershed in the 

Eastern United States was projected to decrease by 13 percent 

because of increased denitrification associated with the higher 

temperature for an air temperature increase of 2 °C between 2005 

and 2050. The effects of temperature on TP, TSS, and TOC flux 

would be expected to be smaller. Effects of changing streamflow 

on nutrients and sediment are more difficult to anticipate given 

the large current uncertainty about the direction and magnitude 

of changes in the hydrologic cycle in the Eastern United States 

(Najjar and others, 2000, 2010)

6.3.2. Methods and Data

6.3.2.1. Terrestrial Flux Model

Estimates of TN, TP, TSS, and TOC fluxes were prepared 

for the baseline year and for SRES scenarios A1B, A2, and B1 
88 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

for 2050 using SPARROW models calibrated to 1992 LULC 

conditions (see chap. 3 for description of modeling of LULC 

changes from 2005 through 2050). SPARROW models describe 

the steady-state balance between contaminant supply rates and 

average streamflow and water quality conditions (Schwarz and 

others, 2006; Shih and others, 2010; Smith and others, 1997). Use 

of the TN, TP, TSS, and TOC models to project fluxes through 

2050 assumes that contaminant sources change in accordance 

with projected LULC scenarios and that water quality conditions 

reach a new steady state based on current streamflow rates.

Modeled estimates of TSS, TN, TP, and TOC fluxes were 

produced for 2,174 tributary drainages to the Great Lakes 

and the east and gulf coasts of the United States. Estimates of 

TOC that were produced using a different modeling approach 

(chap. 5) agree with those presented here.

For surface-water monitoring stations that had sufficient 

data on discharge and water quality, water-quality parameters 

were estimated by spatially correlating the stream data with 

georeferenced data on the constituent sources (for example, 

atmospheric deposition, fertilizers, human and animal wastes) 

and delivery factors (for example, precipitation, topography, 

vegetation, soils, and water routing). Parameter estimation 

ensured that the calibrated model would not be more complex 

than can be supported by the data.

SPARROW models describe mass transport in watersheds as 

three sequential processes — (1) source supply, (2) land-to-water 

transport, and (3) channel-network transport (Smith and others, 

1997). Data describing these processes are developed on a 

stream reach and associated catchment basis. There are about 

63,000 reaches or catchments in the national-scale dataset used to 

calibrate the models. Table 6–1 provides information on the TN,

TP, TSS, and TOC models used in this assessment to quantify the 

flux of material to coastal waters.

The source variables (table 6–1) were of particular 

importance because they served as the basis for translating the 

baseline and projected LULC into coastal delivery of TOC, 

TN, TP, and TSS from 2005 through 2050. Table 6–2 summarizes the correspondence between the LULC classes and the 

source categories from the SPARROW model. An underlying 

assumption made in modeling projected changes in coastal flux 

was that the rate of the source supply will change in proportion 

to the LULC changes in each modeled catchment. The change 

in the value of the population variable in the TN model within 

each catchment was approximated by the projected change 

in the developed land area (chap. 3). The modeled SRES 

scenarios are thus reflected in the LULC and SPARROW 

modeling (chaps. 2 and 3; Sohl and others, 2012b).

The 90-percent confidence intervals for the coastal flux 

estimates in table 6–3 were developed through a bootstrap 

procedure in which 200 equally likely estimates for each entry 

in the table were randomly generated based on the error characteristics of the model determined during calibration. The width 

of the confidence intervals surrounding the 1992 and 2050 

flux estimates includes coefficient and residual (that is, model 

specification) errors (Schwarz and others, 2006). The residual 

errors of the flux estimates for individual coastal rivers within 

each region were assumed to reflect idiosyncrasies of the river 

watersheds. However, the estimated errors surrounding the 

“percent-change” estimates for each coastal river were assumed 

to arise only from coefficient error, based on the further 

assumption that the idiosyncrasies of a given river can be 

assumed to be the same in 2005 and 2050. Thus, the confidence 

intervals for the percent-change estimates are smaller than 

those for the separate 2005 and 2050 flux estimates.

6.3.2.2. Coastal Carbon Storage Model

The approach used for modeling coastal processes for 

storing carbon was based largely on the model approach of 

Dunne and others (2007) that captured the fate of surface 

productivity as it moves through the water column and into the 

deep ocean and sediments. This model approach was adapted 

to analyze only the sensitivity of carbon storage associated 

with the potential new production that results from terrestrial 

inputs from the Eastern United States.

Table 6–1. Variables used in the SPARROW models of total nitrogen, total phosphorous, total suspended sediment, and total organic 

carbon fluxes in the assessment of coastal carbon storage and fluxes in the Eastern United States.

[R2, coefficient of determination; SPARROW, spatially referenced regressions of watershed attributes water-quality model; TN, total nitrogen; TP, total

phosphorus; TSS, total suspended solids; TOC, total organic carbon]

Model Number

of sites R2 Source variables Reference

TN 425 0.933 Population, atmospheric nitrate deposition, corn or soybean fertilizer, 

alfalfa fertilizer, wheat fertilizer, other crop fertilizer, farm animal 

waste, forest, barren land, shrubland

Alexander and others (2008)

TP 425 0.871 Population, corn or soybean fertilizer, alfalfa fertilizer, other crop fertilizer, farm animal waste, forest, barren land (transitional), shrublandAlexander and others (2008)

TSS 1,828 0.711 Urban area, forest, crop and pasture land, federal land, other marginal 

land, channel storage and erosion

Schwarz (2008)

TOC 1,125 0.928 Cultivated land, pasture, deciduous forest, evergreen forest, mixed

forest, rangeland, urban land, wetlands, in-stream photosynthesis

Shih and others (2010)
Chapter 6 89

Table 6–2. Assumed correspondences between the source categories of the SPARROW model and the LULC classes used in the 

assessment of coastal carbon storage and fluxes in the Eastern United States.

[See chapter 3 of this report for definitions of the land use and land cover (LULC) classes. NA, not available; 

SPARROW, spatially referenced regressions of watershed attributes water-quality model]

Model source LULC class

Total nitrogen (TN) model

Population Developed land

Fertilizer nitrogen applied to agriculture Agriculture

Forest Deciduous, evergreen, mixed forests

Nitrogen content of farm animal waste Grassland and shrubland

Barren land Barren land

Shrubland Shrubland

Atmospheric nitrate (NO3 ) deposition Separate deposition layer; future changes assumed proportional to developed land

Total phosphorus (TP) model

Population Developed land

Fertilizer phosphorous applied to agriculture Agriculture

Forest Deciduous, evergreen, mixed forests

Phosphorous content of farm animal waste Grassland and shrubland

Barren land Barren land

Shrubland Shrubland

Total suspended solids (TSS) model

Developed land Developed land

Crop and pasture land Agriculture

Forest Deciduous, evergreen, mixed forests

Federally managed land Barren land, grassland and shrubland

Other land Barren land, grassland and shrubland

Stream channels No LULC correspondence; assumed to be constant

Total organic carbon (TOC) model1

Developed land Developed land

Cultivated land Agriculture

Deciduous forest Deciduous forest

Evergreen forest Evergreen forest

Mixed forest Mixed forest

Rangeland Grassland and shrubland

Wetlands Herbaceous and woody wetland

Pasture Hay/pasture

1

The TOC model includes in-stream photosynthesis as a TOC source which does not correspond directly to a LULC class. However, the in-stream photosynthesis estimates depend, in part, on predictions from the TP model, which does incorporate the LULC classes, as indicated in this table.

The model is a simple heuristic one-dimensional model 

of productivity, carbon burial in sediments, and deep water 

remineralization implemented with a 4-km resolution over the 

10- to 2,000-m bathymetry of the coastal oceans and Great 

Lakes of the Eastern United States. A 4-km model resolution 

was used to correspond with the spatial resolution of the 

remote sensing imagery and used to establish a realistic spatial 

distribution of nutrients and sediments in the coastal ocean.

A representative spatial field for production and for 

sediment dispersion in the coastal ocean was developed 

from a composite image of chlorophyll a (Chl a) and of total 

suspended sediment (TSS) derived from European Space 

Agency medium resolution imaging spectrometer (MERIS) 

data at nominal 4-km resolution for the entire coastal waters 

of the United States in 2011. The Chl2 algorithm from MERIS 

was specifically used to avoid overestimation of Chl a due to 

colored dissolved organic matter present in nearshore coastal 

zones where typical empirical ocean color algorithms fail (for 

example, algorithms OC4 and OC3 from the SeaWiFS Data 

Analysis System (SeaDAS); Szeto and others, 2011; Sauer and 

others, 2012; Siegel and others, 2013). The resulting ocean 

color imagery was divided into study regions and aligned 

with NOAA ETOPO1 bathymetry. Loading to each pixel in 

the model domain was estimated by unit-normalizing the 

integrated value across the region and multiplying by the input 

mass to that region.

The purpose of the remote sensing imagery is to 

conceptualize coastal physical dynamics in the model while 
90 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 6 –3. Modeled baseline (2005) and projected (2050) estimates of total nitrogen, total phosphorus, total suspended sediment, 

total organic carbon fluxes to coastal waters, and estimated inventory of carbon in the upper 1 meter of sediment directly attributable to 

terrestrial inputs in the Eastern United States.—Continued

[Data in parentheses are 90-percent confidence intervals. Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report 

on Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios A1B, A2, and B1. Values may not add to totals shown due to independent rounding. 

CI, confidence intercval; Tg/yr, teragrams per year; kg/km2/yr, kilograms per square kilometer per year; km2, square kilometers; —, no data]

A. Fluxes and yields—Continued

Region

Baseline, 2005 estimate 

(90-percent CI)

Scenario A1B Scenario A2 Scenario B1

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change Flux,

in Tg/yr

Yield,

in kg/km2

/yr

Total nitrogen

Great Lakes 0.3

(0.1– 0.8)

888

(326–2,280)

0.4

(0.1– 0.9)

22.9

(22.6–23.1)

0.4

(0.1– 0.9)

21.1

(20.8–21.4)

0.4

(0.1– 0.8)

15.7

(15.4–15.9)

Gulf of Maine 0.1

(0.0 – 0.1)

505

(168–1,094)

0.1

(0.0 – 0.2)

32.7

(32.1–33.3)

0.1

(0.0 – 0.2)

42.5

(41.5–43.5)

0.4

(0.2–1.0)

22.2

(21.8–22.7)

Mid-Atlantic Bight 0.4

(0.1– 0.8)

1,030

(401–2,318)

0.5

(0.2–1.1)

31.2

(30.9–31.4)

0.5

(0.2–1.0)

28.2

(28.9–28.5)

0.2

(0.1– 0.4)

20.6

(20.3–20.8)

South Atlantic Bight 0.1

(0.1– 0.3)

473

(169–1,114)

0.2

(0.1– 0.5)

51.0

(50.6–51.4)

0.2

(0.1– 0.5)

45.3

(44.9–45.7)

0.1

(0.0 – 0.2)

26.1

(25.7–26.5)

Gulf of Mexico 1.8

(0.7–4.1)

375

(149–643)

2.2

(0.8–4.9)

19.3

(19.0 –19.6)

2.1

(0.8–4.9)

17.7

(17.4–17.9)

2.1

(0.8–4.8)

16.8

(16.5–17.0)

Total 2.7

(1.0 – 6.0)

450

(175–1,019)

3.3

(1.3–7.5)

23.3

(23.0 –23.6)

3.2

(1.2–7.4)

21.5

(21.2–21.8)

3.2

(1.2–7.2)

17.8

(17.5–18.0)

Total phosphorus

Great Lakes 0.0

(0.0 – 0.0)

59

(0–89)

0.0

(0.0 – 0.0)

12.3

(12.0 –12.5)

0.0

(0.0 – 0.0)

9.6

(9.4–9.8)

0.0

(0.0 – 0.0)

5.8

(5.6–5.9)

Gulf of Maine 0.0

(0.0 – 0.0)

34

(0–84)

0.0

(0.0 – 0.0)

11.5

(11.2–11.8)

0.0

(0.0 – 0.01

18.2

(17.5–18.9)

0.0

(0.0 – 0.0)

5.1

(5.0 –5.2)

Mid-Atlantic Bight 0.0

(0.0 – 0.1)

86

(29–172)

0.0

(0.0 – 0.1)

15.1

(14.9–15.3)

0.0

(0.0 – 0.1)

13.3

(13.1–13.5)

0.0

(0.0 – 0.1)

8.7

(8.6–8.9)

South Atlantic Bight 0.0

(0.0 – 0.0)

34

(0–101)

0.0

(0.0 – 0.0)

19.7

(19.4–20.0)

0.0

(0.0 – 0.0)

17.8

(17.4–18.1)

0.0

(0.0 – 0.0)

7.9

(7.7–8.2)

Gulf of Mexico 0.3

(0.1– 0.4)

52

(10–87)

0.3

(0.1– 0.4)

2.0

(2.0 –2.0)

0.3

(0.1– 0.4)

1.4

(1.4–1.4)

0.3

(0.1– 0.4)

0.6

(0.6– 0.7)

Total 0.3

(0.1– 0.6)

54

(10–93)

0.3

(0.1– 0.6)

4.7

(4.6 – 4.8)

0.3

(0.1– 0.6)

3.9

(3.9–4.0)

0.3

(0.1– 0.6)

2.1

(2.0 –2.1)

Total suspended solids

Great Lakes 12.8

(0.4–39.8)

37,769

(1,243–11,7943)

15.7

(0.5–49.4)

23.4

(22.9–23.9)

15.1

(0.5–47.5)

18.7

(18.4–19.0)

13.9

(0.5–43.4)

9.0

(8.8–9.3)

Gulf of Maine 3.0

(0.1–8.5)

25,397

(673–71,707)

3.3

(0.1–9.3)

10.1

(9.7–10.5)

3.3

(0.1–9.5)

10.7

(10.3–11.1)

3.1

(0.1–8.9)

4.2

(4.0 –4.3)

Mid-Atlantic Bight 25.6

(0.8–70.9)

73,103

(2,175–202,772)

32.2

(1.0 –91.5)

26.2

(25.5–26.8)

31.1

(0.9–88.2)

21.8

(21.2–22.3)

28.5

(0.9–80.1)

11.7

(11.3–12.1)

South Atlantic Bight 13.5

(0.4–43.4)

45,395

(1,350–146,346)

20.6

(0.6–67.7)

52.8

(51.3–54.3)

19.5

(0.6–63.9)

44.9

(43.6–46.2)

16.8

(0.5–55.2)

25.0

(24.1–25.8)

Gulf of Mexico 361.6

(5.5–920.0)

74,900

(1,133–190,568)

453.7

(7.3–1,199.1)

25.5

(24.2–26.8)

424.9

(6.6–1,120.7)

17.5

(16.6–18.4)

429.2

(6.6–1134.9)

18.7

(17.7–19.7)

Total 416.4

(7.1–1,082.6)

70,214

(1,204 –182,560)

525.6

(9.5–1,416.9)

26.2

(24.9–27.5)

494.0

(8.8 –1,329.6)

18.6

(17.8 –19.5)

491.6

(8.5 –1132.4)

18.1

(17.1–19.0)
Chapter 6 91

Table 6 –3. Modeled baseline (2005) and projected (2050) estimates of total nitrogen, total phosphorus, total suspended sediment, 

total organic carbon fluxes to coastal waters, and estimated inventory of carbon in the upper 1 meter of sediment directly attributable to 

terrestrial inputs in the Eastern United States.—Continued

[Data in parentheses are 90-percent confidence intervals. Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report 

on Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios A1B, A2, and B1. Values may not add to totals shown due to independent rounding. 

CI, confidence intercval; Tg/yr, teragrams per year; kg/km2/yr, kilograms per square kilometer per year; km2, square kilometers; —, no data]

A. Fluxes and yields—Continued

Region

Baseline, 2005 estimate 

(90-percent CI)

Scenario A1B Scenario A2 Scenario B1

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change Flux,

in Tg/yr

Yield,

in kg/km2

/yr

Total organic carbon

Great Lakes 1.3

(0.5–2.7)

3,709

(1,480–7,964)

1.3

(0.5–2.8)

2.8

(2.7–2.8)

1.3

(0.5–2.8)

2.8

(2.8–2.9)

1.3

(0.5–2.8)

2.3

(2.3–2.4)

Gulf of Maine 0.3

(0.1– 0.7)

2,862

(1,094–6,144)

0.4

(0.1– 0.8)

1.9

(1.8–1.9)

0.4

(0.1– 0.7)

2.4

(2.4–2.5)

0.4

(0.1– 0.7)

0.8

(0.7– 0.8)

Mid-Atlantic Bight 1.5

(0.6–3.1)

4,201

(1,631–8,870)

1.6

(0.6–3.3)

5.9

(5.8–6.0)

1.5

(0.6–3.2)

4.5

(4.4–4.6)

1.5

(0.6–3.2)

3.5

(3.5–3.6)

South Atlantic Bight 1.7

(0.7–3.8)

5,665

(2,228–12,825)

1.8

(0.7–4.0)

5.7

(5.6–5.8)

1.8

(0.7–3.9)

4.0

(3.9–4.1)

1.8

(0.7–4.0)

6.3

(6.2–6.3)

Gulf of Mexico 13.7

(5.5–29.6)

2,830

(1,148–6,139)

13.8

(5.6–29.9)

1.0

(0.9–1.0)

13.8

(5.6–30.0)

1.2

(1.2–1.3)

14.2

(5.8–30.7)

3.8

(3.8–3.9)

Total 18.4

(7.4– 40.0)

3,104

(1,250–6,738)

18.8

(7.6– 40.7)

1.9

(1.9–2.0)

18.8

(7.6–40.7)

1.9

(1.8 –1.9)

19.1

(7.7–41.4)

3.9

(3.8 –3.9)

B. Total terrestrial carbon storage

Region

Drainage 

area, in 

km2

Sediment 

CT

inventory, 

in TgC

Baseline 

(2005),

in Tg/y

Scenario A1B Scenario A2 Scenario B1

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change

Projected

(2050),

in Tg/yr

Percentage 

change

Great Lakes 337,789 2,900 0.4

(0.0 –1.3)

0.5

(0.0 –1.7)

31 0.5

(0.0 –1.6)

27.5 0.5

(0.1–1.5)

18.6

Gulf of Maine 118,817 1,200 0.1

(0.0 – 0.2)

0.1

(0.0 – 0.3)

33.8 0.1

(0.0 – 0.3)

42.3 0.1

(0.0 – 0.3)

22.1

Mid-Atlantic Bight 349,505 1,400 0.7

(0.1–1.9)

1.0

(0.2–2.6)

35.8 1.0

(0.2–2.5)

32 0.9

(0.1–2.4)

22.3

South Atlantic Bight 296,285 1,300 0.5

(0.2–1.2)

0.7

(0.2–1.8)

55.7 0.7

(0.2–1.7)

49.2 0.6

(0.2–1.5)

28.5

Gulf of Mexico 4,827,771 2,300 6.2

(1.6–17.8)

7.5

(1.9–18.5)

21.3 7.3

(1.9–18.1)

19 7.3

(1.8–18.0)

18.4

Total 5,930,167 9,100 7.8

(1.8 –22.4)

9.8

(2.3–24.9)

25.4 9.6

(2.3–24.7)

22.7 9.3

(2.3–24.0)

19.4

still permitting use of the one-dimensional carbon storage 

modeling framework across the model domain. There is no 

horizontal transport in the model. This is an oversimplification 

that neglects to account in any way for processes not present 

in the composite images, such as near-bottom cross shelf 

transport of sediment and carbon (Hales and others, 2008). 

However, explicitly modeling the effects of physical dynamics 

is beyond the scope of this analysis.

The model input for the coastal portion of the analysis 

was the output from the SPARROW fluxes to estuaries and 
92 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

coastal areas, modified to account for nutrient and sediment 

retention in estuaries and for direct diffusive denitrification 

of nitrate from coastal waters in coastal sediments. Direct 

coastal erosion is not included in the analysis, but nutrient 

and sediment fluxes from the land surface adjacent to coasts 

and estuaries are included in the SPARROW model output 

from the modeling scenarios that were run for this assessment 

even if the coasts and estuaries are not part of a watershed 

included in the database. Nutrient retention in estuaries was 

estimated separately for nitrogen and phosphorus according to 

the global relations presented by Nixon and others (1996) and 

the residence time estimates in the NOAA National Estuarine 

Eutrophication Assessment (NEEA) estuaries database 

(http://ian.umces.edu/neea/siteinformation.php). Sediment 

retention was assumed to be similar to phosphorus retention. 

No retention was assumed for the Mississippi River because 

of the discharge geometry and the time scale of the analysis. 

This assumption was necessary because no models exist for 

long-term carbon burial in estuaries, and estuaries are subject 

to substantial reworking of sedimentary carbon on decadal 

time scales (Keil and others, 1997).

Denitrification is a strong sink for nitrogen in the coastal 

oceans, resulting in a loss of nitrogen potentially available 

to support photosynthetic production (Nixon and others, 

1996; Seitzinger and Giblin, 1996), with the bulk of removal 

occurring through either denitrification during remineralization of organic matter or direct denitrification of nitrate in the 

overlying water. Separate approaches were used to account 

for each of these processes in the model. Before calculating 

primary production, the demand from shallow water column 

and direct denitrification was removed from the totalized TN 

mass as an inverse geometric function of water depth and 

TN concentration. The shallow water TN demand used in the 

model ranged from 1,200 millimoles per square meter per year 

(mmol/m2/yr) in shallow waters to 6 mmol/m2/yr at 200 m. 

Denitrification during remineralization within the sediments 

was accounted for by assuming that organic nitrogen from new 

production arriving at the sediment surface is either buried or 

denitrified and thus was not available to support additional 

primary production in the surface waters (Murray and Parslow, 

1999; Piña-Ochoa and Alvarez-Cobelas, 2006). Phosphate was 

assumed to be liberated by remineralization and consequently 

was not found to be limiting to modeled production.

Following reduction of TSS mass estimated by the 

SPARROW model for estuary retention, the resulting lower 

TSS mass was converted to pixel loadings across each region 

by multiplying the total input mass by each normalized pixel 

value of the TSS MERIS images across the 30- to 2,000-m 

bathymetry (fig. 6–2). For the purpose of this assessment, 

it was assumed that no net sediment deposition (or carbon 

burial) occurred at mean water depths less than 30 m over 

time scales of decades, owing to sediment resuspension and 

transport due to wind waves, internal waves, storms, and flood 

events (Blair and others, 2004).

Nutrient loading to each pixel was established using the 

unit normalized satellite Chl a imagery (fig 6–2) multiplied 

by the adjusted input mass of TN. Due to the denitrification 

demand, TN distribution was performed in two iterations. 

In the first iteration, the TN mass adjusted for retention by 

estuaries was distributed across the 10- to 2,000-m bathymetry 

for the purpose of calculating the diffusive and shallow water 

denitrification demand (see below). After the calculation 

and removal of TN lost through these processes, a second 

iteration redistributed the remainder of the TN mass across the 

30- to 2,000-m bathymetry.

Nutrient inputs to the surface ocean were assumed to be 

converted by photosynthetic activity into “new production” 

according to the stoichiometric balance described by Anderson 

and Sarmiento (1994), where the ratio of carbon to nitrogen is 

7:1 and the ratio of carbon to phosphorus is 117:1. Nutrients 

released into the surface waters during water-column 

remineralization were assumed to be available to support 

additional new production.

The fate of this modeled new production depended on 

the depth of the water column and the deep mixed layer. 

The deep mixed layer was assigned a uniform thickness of 

200 m, based on the statistical analysis of global ocean annual 

maximum mixed layer depths (Kara and others, 2000; de 

Boyer Montégut and others, 2004). New production in areas 

with water column depths shallower than 200 m was presumed 

to undergo the process of sediment burial, with remineralized 

CO2 equilibrated with the atmosphere. Where water depths 

exceeded 200 m, new production sank out of the deep mixed 

layer, and the carbon and associated nutrients were considered 

removed from the surface ocean and stored in the deep ocean. 

Remineralization of new production below the mixed layer 

was modeled using the relationship derived by the Martin 

curve (Martin and others, 1987) as employed in Dunne and 

others (2007), yielding a fraction of new production that is 

subject to burial in sediments and a fraction that is stored 

as dissolved inorganic carbon in the deep ocean. However, 

because there is no millennial deep-water reservoir of carbon 

in the Great Lakes, all carbon storage in the Eastern United 

States is presumed to be through sediment burial and all 

CO2

 resulting from remineralization presumed equilibrated 

with the atmosphere.

Nutrients supporting new organic production were 

presumed to regenerate until fully consumed and removed 

from the mixed layer by sinking or to the sediments. Once 

new production was removed to the sediments, it was not 

considered buried until it was buried below the zone of oxygen 

penetration and resistant to remineralization over millennial 

timescales (Hedges and Keil, 1995; Dunne and others, 2007). 

Burial of the fraction of material arriving at the sediment 

surface was estimated using the empirical formulation of 

Dunne and others (2007, equation 2), using data from Alperin 

and others (2002), Thomas and others (2002), and Hofmann 

and others (2011). Organic carbon of terrestrial origin was 

assumed to be 5 percent of the total carbon buried (Blair and 

others, 2003; Burdige, 2005). Terrestrial carbon buried in 

sediments represents about 0.4 percent of the SPARROWestimated terrestrial TOC flux under baseline conditions. All 
Chapter 6 93

Figure 6.2. 

A. Sediment dispersion

B. Nutrient dispersion

N

0 250 500 MILES

0 250 500 KILOMETERS

Lake

Michigan

Lake Superior

Lake Huron

Lake Erie

Lake

Ontario

Coastal region catchments

EXPLANATION

Great Lakes

Gulf of Maine

Mid-Atlantic Bight

South Atlantic Bight

Gulf of Mexico

No data or not draining 

 to coast

High

Low

Normalized dispersion

Coastal delineation of

 regional catchment

 boundary

Figure 6–2. Maps showing dispersion fields for A, nutrients and B, sediments in the coastal carbon sequestration model 

for the Eastern United States. Heavy black lines indicate the boundaries between the Great Lakes, east coast, and Gulf of 

Mexico.
94 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

remaining TOC mass that originates from terrestrial flux is 

presumed remineralized in the surface ocean and the resulting 

CO2 equilibrated with the atmosphere (Hedges and Keil, 1995; 

Hedges and others, 1997).

The effects of increased hypoxia on carbon burial were 

not considered; generally, increased hypoxia results in higher 

rates of organic carbon preservation (Bergamaschi and 

others, 1997; Green and others, 2006). Thus, carbon storage 

in this model is defined as the rate at which carbon fixed by 

new production is either buried to the extent that it escapes 

remineralization over millennial timescales or is exported 

to the deep ocean where it is buried or prevented from 

equilibration with the atmosphere over timescales of ocean 

circulation. Burial was calculated over the entire model 

domain, whereas carbon was stored in deep ocean waters 

only where water depths exceed the depth of the permanent 

mixed layer.

To permit comparison to terrestrial carbon stocks and to 

assess the process of long-term sediment carbon accumulation, 

the amount of carbon that would accumulate in the upper 1 m 

of sediments due to the modeled storage rates was estimated 

by applying the organic content derived from the model to 

the upper meter of sediment, assuming a porosity of 0.7 and 

a density of 2.5 grams per cubic meter (g/cm3

; Dunne and 

others, 2007). The organic content was given by dividing the 

carbon burial rate by the sediment mass accumulation rate. It 

should be noted that this heuristic calculation yields a result 

for the conditions of the modeled year (2005) although the 

timescale of accumulation of the upper 1 m of sediment can 

span thousands of years.

6.4. Results and Discussion

The primary active pools of carbon storage in the oceans 

are organic carbon buried in surficial sediment and dissolved 

inorganic carbon in seawater (Hedges and Keil, 1995; 

Sarmiento and Gruber, 2002). This study focuses exclusively on how nutrient and sediment fluxes from terrestrial 

environments affect carbon storage within both these pools. 

To understand the model results, the carbon derived from 

terrestrial process must be clearly distinguished from carbon 

derived from other processes that store carbon in the coastal 

ocean, such as primary production supported by nutrients from 

the deep ocean (Sarmiento and Gruber, 2002). The model 

presented here tracks the fraction of carbon associated with 

primary production that is specifically supported by nutrients 

from the continents (terrestrial carbon; CT) and ultimately 

estimate the fraction of this carbon eventually stored in the 

coastal ocean through either the transport and burial of that 

primary production by riverborne sediments or through the 

transport to the deep ocean, with both processes isolating 

carbon from the atmosphere for millennial timescales. The 

symbol CT is introduced here to distinguish this subset of 

terrestrially supported carbon from the much larger carbon 

pool in the ocean.

6.4.1. Flux of Nutrients, Suspended Sediment, 

and TOC to Coastal Waters

Sources in common for TN and TP include treated 

municipal sewage, industrial wastewater, urban runoff, 

and crop and animal agricultural activity. Although the 

atmosphere contributes significant amounts of TN, it is 

a negligible source of TP in most watersheds. Principal 

sources for TSS are erosion and channel scour as well as 

urban, crop, and pasture lands. The principal source for 

TOC is in-stream photosynthesis, followed by wetlands and 

cultivated lands.

6.4.1.1. Total Nitrogen

The SPARROW model estimated a baseline TN flux of 

2.7 Tg/yr (fig. 6 –3; table 6 –3) for the Eastern United States. 

About two thirds of the total flux originated in the Gulf of 

Mexico drainage, nearly one quarter originated in the east 

coast drainages, and about one tenth originated in the Great 

Lakes drainage. However, after adjusting for drainage area, 

the flux per unit area (yield) of TN in the Gulf of Mexico 

drainage was lowest among the five regions (table 6–3). This 

reflects the much longer distances nitrogen must travel to the 

Gulf of Mexico from sources in the large Mississippi Basin, 

which leads to elevated losses of TN to denitrification in 

stream and river channels. By contrast, the highest yields of 

TN came from the Mid-Atlantic Bight part of the east coast 

drainages (Gulf of Maine, Mid-Atlantic Bight, and South 

Atlantic Bight) where urban, agricultural, and atmospheric 

sources are high and travel distances to the east coast are 

relatively short.

The estimated TN flux from all five coastal regions 

was projected to increase significantly by 2050 under 

all three SRES scenarios (fig. 6 –4; table 6 –3), primarily 

because of increasing population and resulting increases 

in atmospheric sources of CO2 in those areas (table 6 – 4). 

Under scenarios A1B and A2, U.S. and global population and 

per-capita income were projected to increase substantially by 

2050. Although U.S. agricultural production was expected to 

increase substantially to meet elevated worldwide demand, 

the increase was assumed to occur largely through increases 

in yield rather than in agricultural land area. The estimated 

TN flux from all five regions was projected to increase 

by more than 20 percent in scenarios A1B and A2, with 

increases in the South Atlantic Bight region alone projected 

to increase by 45 percent and 51 percent, respectively 

(figs. 6 –3 and 6 – 4; table 6 –3). By contrast, under scenario 

B1, a greater focus on biodiversity protection than under 

scenarios A1B and A2 results in a smaller rise in the land 

area devoted to urban development, despite increases in 

population and wealth. Hence, TN flux to the five coastal 

regions of the Eastern United States is projected to increase 

by smaller percentages under scenario B1 than under 

scenario A1B or A2.
Chapter 6 95

Gulf of Mexico

(1.8 Tg/yr)

Great Lakes

(0.3 Tg/yr) Gulf of Maine

(0.1 Tg/yr)

Mid-Atlantic Bight

(0.4 Tg/yr)

South Atlantic

Bight

(0.1 Tg/yr)

Figure 6.3.

A. Estimated delivered total nitrogen yield

B. Major sources of total nitrogen in model catchments

N

0 250 500 MILES

0 250 500 KILOMETERS

Regional catchment boundary

EXPLANATION

0 to 0.5

>0.5 to 1.3

>1.3 to 2.4

>2.4 to 6.0

>6.0

No data

Urban

Atmospheric deposition

Corn and soybean

Alfalfa

Wheat

Other crops

Animal waste

Forest

Barren

Shrub

No data

Total nitrogen delivered

 incremental yield in 2005, in

 kilograms per hectare per year

Major source of total nitrogen,

 by land use, land cover, or process

Regional catchment boundary

Figure 6–3. Maps showing A, estimated delivered total nitrogen (TN) yield to coastal waters and B, major sources of TN in 

model catchments under baseline (2005) conditions in the Eastern United States. Delivered yield reflects the effects of in-stream 

losses that occur during transport from the outlet of a catchment through the stream and river system to coastal waters. Values 

shown in parentheses indicate net flux of TN for each region. LULC, land cover and land use; Tg/yr, teragrams per year; >, more 

than.
N

0 250 500 MILES

0 250 500 KILOMETERS

Regional catchment boundary

EXPLANATION

<–50

>–50 to –25

>–25 to 0

>0 to 25

>25 to 50

>50

No data

Gulf of Mexico

(19.3%)

Great Lakes

(22.9%)

Gulf of Maine

(32.7%)

Mid-Atlantic Bight

(31.2%)

South Atlantic

Bight

(51.0%)

Gulf of Mexico

(16.8%)

Great Lakes

(15.7%)

Gulf of Maine

(22.2%)

Mid-Atlantic Bight

(20.6%)

South Atlantic

Bight

(26.1%)

Difference in total nitrogen yield

 between baseline (2005) and

 projected (2050) conditions,

 in percent (%) 

Figure 6.4.

A. Difference in total nitrogen yield between baseline and scenario A1B

B. Difference in total nitrogen yield between baseline and scenario B1

Figure 6–4. Maps showing difference between estimated delivered total nitrogen (TN) yield to coastal waters under 

baseline (2005) and projected (2050) conditions for Intergovernmental Panel on Climate Change Special Report on Emissions 

Scenarios (SRES; Nakićenović and others, 2000) scenarios A, A1B and B, B1 in the Eastern United States. Values shown in 

parentheses indicate regional difference from baseline. <, less than; >, more than.
Chapter 6 97

Table 6– 4. Estimates of total nitrogen, total phosphorous, total suspended sediment, and total organic carbon fluxes to the coastal 

waters of the Eastern United States by source of the fluxes, under baseline and projected conditions.—Continued 

[Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and

others, 2000) scenarios A1B, A2, and B1. Data may not add to totals shown due to independent rounding. Gg/yr, billion grams per year]

Source Baseline

(2005)

Scenario A1B Scenario A2 Scenario B1

2050 Change

in mass 2050 Changein mass 2050 Changein mass

Great Lakes

Total nitrogen (TN)

Population 78.1 117.7 39.6 107.7 29.7 97.6 19.6

Atmospheric deposition 69.5 102.9 33.4 102.9 33.4 102.9 33.4

Corn and soybean 94.8 93.1 –1.8 96.0 1.1 91.8 –3.0

Alfalfa 16.4 16.5 0.2 17.5 1.1 15.6 – 0.7

Wheat 9.1 8.9 – 0.1 9.2 0.1 8.9 – 0.2

Other crops 15.5 15.8 0.3 16.9 1.4 14.8 – 0.7

Farm animal waste 4.6 3.8 – 0.8 3.6 – 0.9 3.8 – 0.7

Forest 15.2 13.8 –1.5 13.4 –1.8 15.2 0.0

Barren land 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 303.3 372.6 69.3 367.2 64.0 350.8 47.5

Total phosphorus (TP)

Population 7.7 10.9 3.2 10.1 2.4 9.3 1.6

Corn and soybean 3.3 3.0 – 0.3 3.1 – 0.2 3.1 – 0.2

Alfalfa 2.4 2.2 – 0.2 2.4 – 0.1 2.3 – 0.2

Other crops 1.6 1.5 – 0.1 1.6 0.0 1.5 – 0.1

Farm animal waste 0.9 0.7 – 0.2 0.7 – 0.2 0.7 – 0.2

Forest 1.7 1.5 – 0.2 1.4 – 0.3 1.6 0.0

Barren land 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 17.6 19.8 2.2 19.3 1.7 18.6 1.0

Total suspended solids (TSS)

Urban 4,401.9 7,463.3 3,061.4 6,606.7 2,204.8 5,761.5 1,359.6

Forest 138.2 121.3 –16.9 115.5 –22.7 137.6 – 0.7

Federal land 13.9 13.6 – 0.3 13.5 – 0.4 13.5 – 0.3

Crop and pasture land 4,676.2 4,636.0 –40.2 4,898.5 222.3 4,490.7 –185.4

Grassland, shrubland,

barren land

92.6 75.6 –17.0 72.9 –19.7 74.1 –18.4

Channel storage or erosion 3,435.1 3,435.1 0.0 3,435.1 0.0 3,435.1 0.0

Total 12,757.9 15,744.8 2,987.0 15,142.2 2,384.3 13,912.6 1,154.7

Total organic carbon (TOC)

Cultivated land 183.8 183.5 – 0.3 190.7 6.9 177.3 –6.5

Deciduous forest 81.6 81.6 0.0 81.6 0.0 81.6 0.0

Evergreen forest 20.5 20.5 0.0 20.5 0.0 20.5 0.0

Mixed forest 51.1 51.1 0.0 51.1 0.0 51.1 0.0

Urban land 65.8 102.1 36.3 93.0 27.2 83.7 17.9

Wetlands 729.7 728.2 –1.6 731.0 1.3 747.5 17.7

In-stream photosynthesis 120.2 120.2 0.0 120.2 0.0 120.2 0.0

Total 1,252.7 1,287.1 34.4 1,288.2 35.4 1,281.9 29.2
98 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 6– 4. Estimates of total nitrogen, total phosphorous, total suspended sediment, and total organic carbon fluxes to the coastal 

waters of the Eastern United States by source of the fluxes, under baseline and projected conditions.—Continued 

[Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and

others, 2000) scenarios A1B, A2, and B1. Data may not add to totals shown due to independent rounding. Gg/yr, billion grams per year]

Source Baseline

(2005)

Scenario A1B Scenario A2 Scenario B1

2050 Change

in mass 2050 Changein mass 2050 Changen mass

Gulf of Maine

Total nitrogen (TN)

Population 18.6 24.7 6.2 23.8 5.3 21.6 3.1

Atmospheric deposition 21.0 31.1 10.1 31.1 10.1 31.1 10.1

Corn and soybean 0.7 0.8 0.1 1.2 0.4 0.6 – 0.1

Alfalfa 0.6 0.7 0.1 1.0 0.4 0.6 – 0.1

Wheat 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Other crops 3.3 7.3 4.0 13.5 10.2 3.7 0.4

Farm animal waste 0.3 0.3 0.0 0.3 0.0 0.3 0.0

Forest 13.6 12.3 –1.3 12.1 –1.5 13.3 – 0.3

Barren land 0.3 0.3 0.0 0.3 0.0 0.3 0.0

Shrubland 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Total 58.5 77.7 19.2 83.4 24.9 71.5 13.0

Total phosphorus (TP)

Population 2.5 3.2 0.6 3.1 0.6 2.9 0.4

Corn and soybean 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Alfalfa 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Other crops 0.2 0.5 0.2 0.9 0.7 0.2 0.0

Farm animal waste 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Forest 1.7 1.4 – 0.3 1.3 – 0.4 1.6 – 0.1

Barren land 0.2 0.1 0.0 0.1 0.0 0.2 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 4.8 5.3 0.5 5.7 0.9 5.0 0.2

Total suspended solids (TSS)

Urban 637.5 915.7 278.1 852.3 214.7 778.5 141.0

Forest 113.2 102.1 –11.1 99.8 –13.4 110.6 –2.6

Federal land 3.6 3.6 0.0 3.6 0.0 3.6 0.0

Crop and pasture land 173.8 210.6 36.8 295.1 121.3 159.8 –14.0

Grassland, shrubland,

barren land

158.9 160.3 1.4 159.7 0.8 160.2 1.3

Channel storage or erosion 1,930.6 1,930.6 0.0 1,930.6 0.0 1,930.6 0.0

Total 3,017.6 3,322.8 305.2 3,341.0 323.4 3,143.3 125.7

Total organic carbon (TOC)

Cultivated land 6.9 8.4 1.5 11.4 4.5 6.4 – 0.6

Deciduous forest 26.6 26.6 0.0 26.6 0.0 26.6 0.0

Evergreen forest 35.7 35.7 0.0 35.7 0.0 35.7 0.0

Mixed forest 83.3 83.3 0.0 83.3 0.0 83.3 0.0

Urban land 21.6 29.2 7.5 28.0 6.4 25.5 3.8

Wetlands 135.0 132.4 –2.6 132.4 –2.6 134.4 – 0.7

In-stream photosynthesis 34.3 34.3 0.0 34.3 0.0 34.3 0.0

Total 343.6 350.0 6.4 351.9 8.3 346.2 2.6
Chapter 6 99

Table 6– 4. Estimates of total nitrogen, total phosphorous, total suspended sediment, and total organic carbon fluxes to the coastal 

waters of the Eastern United States by source of the fluxes, under baseline and projected conditions.—Continued 

[Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and

others, 2000) scenarios A1B, A2, and B1. Data may not add to totals shown due to independent rounding. Gg/yr, billion grams per year]

Source Baseline

(2005)

Scenario A1B Scenario A2 Scenario B1

2050 Change

in mass 2050 Changein mass 2050 Changen mass

Mid-Atlantic Bight

Total nitrogen (TN)

Population 170.1 242.2 72.2 226.4 56.3 214.2 44.1

Atmospheric deposition 84.3 124.7 40.5 124.7 40.5 124.7 40.5

Corn and soybean 50.9 52.8 1.9 54.5 3.6 45.1 –5.8

Alfalfa 11.4 11.5 0.2 13.3 1.9 10.1 –1.2

Wheat 5.1 5.5 0.4 5.4 0.3 4.5 – 0.6

Other crops 16.1 17.4 1.3 19.7 3.6 14.2 –1.9

Farm animal waste 0.5 0.6 0.0 0.4 – 0.1 0.5 – 0.1

Forest 25.5 22.6 –2.9 22.2 –3.3 25.3 – 0.2

Barren land 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 364.0 477.6 113.5 466.9 102.8 438.9 74.8

Total phosphorus (TP) 

Population 19.7 26.1 6.4 24.9 5.2 23.8 4.1

Corn and soybean 2.0 1.8 – 0.1 1.9 0.0 1.7 – 0.3

Alfalfa 2.3 2.0 – 0.3 2.3 0.0 1.9 – 0.5

Other crops 1.4 1.3 – 0.1 1.5 0.1 1.2 – 0.2

Farm animal waste 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Forest 4.5 3.2 –1.3 3.3 –1.2 4.0 – 0.5

Barren land 0.1 0.1 0.0 0.1 0.0 0.1 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 30.1 34.7 4.6 34.1 4.0 32.7 2.6

Total suspended solids (TSS) 

Urban 9,559.2 15,903.7 6,344.4 13,980.6 4,421.3 13,500.6 3,941.3

Forest 693.0 617.9 –75.2 602.6 –90.4 693.8 0.8

Federal land 7.6 7.6 0.0 7.6 0.0 7.6 0.0

Crop and pasture land 8,072.5 8,486.9 414.4 9,306.2 1,233.7 7,110.3 –962.2

Grassland, shrubland,

barren land

44.8 45.8 1.0 43.5 –1.3 43.5 –1.3

Channel storage or erosion 7,173.1 7,173.1 0.0 7,173.1 0.0 7,173.1 0.0

Total 25,550.3 32,234.9 6,684.7 31,113.5 5,563.3 28,528.9 2,978.6

Total organic carbon (TOC)

Cultivated land 117.0 124.6 7.7 133.1 16.1 103.5 –13.4

Deciduous forest 134.4 134.4 0.0 134.4 0.0 134.4 0.0

Evergreen forest 39.6 39.6 0.0 39.6 0.0 39.6 0.0

Mixed forest 132.7 132.7 0.0 132.7 0.0 132.7 0.0

Urban land 155.1 236.9 81.8 219.5 64.4 202.8 47.8

Wetlands 557.8 555.0 –2.8 543.8 –14.0 575.5 17.7

In-stream photosynthesis 331.6 331.6 0.0 331.6 0.0 331.6 0.0

Total 1,468.3 1,554.9 86.6 1,534.7 66.4 1,520.3 52.0
100 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 6– 4. Estimates of total nitrogen, total phosphorous, total suspended sediment, and total organic carbon fluxes to the coastal 

waters of the Eastern United States by source of the fluxes, under baseline and projected conditions.—Continued 

[Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and

others, 2000) scenarios A1B, A2, and B1. Data may not add to totals shown due to independent rounding. Gg/yr, billion grams per year]

Source Baseline

(2005)

Scenario A1B Scenario A2 Scenario B1

2050 Change

in mass 2050 Changein mass 2050 Changen mass

South Atlantic Bight

Total nitrogen (TN)

Population 54.5 108.1 53.6 101.7 47.2 85.4 30.9

Atmospheric deposition 29.3 43.3 14.1 43.3 14.1 43.3 14.1

Corn and soybean 29.9 35.1 5.2 33.5 3.7 25.7 – 4.2

Alfalfa 0.4 0.5 0.1 0.5 0.1 0.3 – 0.1

Wheat 4.3 4.9 0.6 4.8 0.5 3.7 – 0.7

Other crops 12.2 13.9 1.7 13.5 1.3 9.8 –2.4

Farm animal waste 0.7 0.6 – 0.1 0.6 – 0.1 0.7 – 0.1

Forest 11.1 8.9 –2.2 9.4 –1.8 11.0 – 0.1

Barren land 0.7 0.7 0.0 0.7 0.0 0.7 0.0

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 143.2 216.2 73.0 208.1 64.9 180.6 37.4

Total phosphorus (TP)

Population 6.3 10.0 3.8 9.8 3.5 8.7 2.4

Corn and soybean 2.1 2.2 0.1 2.1 0.0 1.7 – 0.4

Alfalfa 0.2 0.1 0.0 0.2 0.0 0.1 0.0

Other crops 1.8 1.8 0.0 1.8 – 0.1 1.4 – 0.5

Farm animal waste 0.5 0.5 – 0.1 0.4 – 0.1 0.5 0.0

Forest 2.2 1.4 – 0.8 1.5 – 0.7 1.9 – 0.2

Barren land 0.9 0.7 – 0.2 0.7 – 0.1 0.8 – 0.1

Shrubland 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 14.0 16.7 2.8 16.5 2.5 15.1 1.1

Total suspended solids (TSS)

Urban 4,515.8 11,150.3 6,634.5 10,092.2 5,576.4 8,424.2 3,908.4

Forest 314.3 253.9 –60.4 261.4 –52.8 309.2 –5.1

Federal land 5.1 5.0 – 0.1 5.0 – 0.1 5.0 – 0.1

Crop and pasture land 3,128.1 3,655.9 527.7 3,643.7 515.5 2,583.7 –544.5

Grassland, shrubland,

barren land

562.2 562.6 0.4 561.7 – 0.5 561.8 – 0.5

Channel storage or erosion 4,925.0 4,925.0 0.0 4,925.0 0.0 4,925.0 0.0

Total 13,450.5 20,552.6 7,102.1 19,489.0 6,038.5 16,808.7 3,358.2

Total organic carbon (TOC)

Cultivated land 93.8 107.7 13.9 103.9 10.1 76.9 –16.9

Deciduous forest 49.2 49.2 0.0 49.2 0.0 49.2 0.0

Evergreen forest 93.3 93.3 0.0 93.3 0.0 93.3 0.0

Mixed forest 64.2 64.2 0.0 64.2 0.0 64.2 0.0

Urban land 85.3 177.9 92.6 167.6 82.3 141.2 56.0

Wetlands 977.7 966.6 –11.2 952.6 –25.2 1,043.7 66.0

In-stream photosynthesis 315.0 315.0 0.0 315.0 0.0 315.0 0.0

Total 1,678.5 1,773.8 95.3 1,745.8 67.3 1,783.5 105.0
Chapter 6 101

Table 6– 4. Estimates of total nitrogen, total phosphorous, total suspended sediment, and total organic carbon fluxes to the coastal 

waters of the Eastern United States by source of the fluxes, under baseline and projected conditions.—Continued 

[Projected (2050) conditions are based on Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and

others, 2000) scenarios A1B, A2, and B1. Data may not add to totals shown due to independent rounding. Gg/yr, billion grams per year]

Source Baseline

(2005)

Scenario A1B Scenario A2 Scenario B1

2050 Change

in mass 2050 Changein mass 2050 Changen mass

Gulf of Mexico

Total nitrogen (TN)

Population 250.8 463.1 212.3 416.1 165.4 366.1 115.3

Atmospheric deposition 330.7 489.5 158.8 489.5 158.8 489.5 158.8

Corn and soybean 791.9 756.1 –35.7 777.4 –14.4 815.8 23.9

Alfalfa 47.1 46.4 – 0.7 48.2 1.1 49.4 2.3

Wheat 60.1 60.8 0.7 61.3 1.2 63.0 2.9

Other crops 168.4 184.8 16.4 184.3 15.9 167.1 –1.3

Farm animal waste 58.3 64.7 6.4 59.0 0.7 58.0 – 0.3

Forest 87.8 77.3 –10.5 78.1 –9.7 88.7 0.9

Barren land 1.4 1.4 0.0 1.4 0.0 1.4 0.0

Shrubland 8.6 9.5 0.9 8.7 0.1 8.6 0.0

Total 1,805.1 2,153.7 348.5 2,124.2 319.0 2,107.6 302.5

Total phosphorus (TP) 

Population 48.6 74.7 26.1 69.5 20.9 64.6 16.0

Corn and soybean 51.6 44.7 –6.9 47.2 – 4.3 49.2 –2.3

Alfalfa 22.2 18.9 –3.2 20.4 –1.7 21.0 –1.2

Other crops 29.9 28.3 –1.6 29.0 – 0.8 26.8 –3.0

Farm animal waste 61.5 60.2 –1.2 58.4 –3.1 56.2 –5.3

Forest 31.1 23.1 –8.0 23.9 –7.2 28.9 –2.2

Barren land 1.2 1.1 – 0.1 1.1 – 0.1 1.1 – 0.1

Shrubland 4.9 4.9 0.0 4.7 – 0.2 4.5 – 0.4

Total 250.8 255.8 5.0 254.3 3.5 252.4 1.6

Total suspended solids (TSS) 

Urban 76,076.7 146,043.3 69,966.5 128,042.0 51,965.3 118,550.3 42,473.6

Forest 5,230.3 4,497.7 –732.6 4,613.0 –617.3 5,247.7 17.3

Federal land 4,484.5 4,958.9 474.4 4,654.5 170.0 4,533.0 48.5

Crop and pasture land 160,678.3 158,912.7 –1,765.6 162,415.7 1,737.4 180,778.6 20,100.3

Grassland, shrubland, 

barren land

26,362.3 50,535.4 24,173.1 36,394.9 10,032.7 31,333.3 4,971.0

Channel storage or erosion 88,768.5 88,768.5 0.0 88,768.5 0.0 88,768.5 0.0

Total 361,600.6 453,716.5 92,115.8 424,888.7 63,288.0 429,211.4 67,610.7

Total organic carbon (TOC)

Cultivated land 1,771.5 1,771.0 – 0.5 1,802.4 30.9 1,879.5 108.1

Deciduous forest 550.0 550.0 0.0 550.0 0.0 550.0 0.0

Evergreen forest 277.7 277.7 0.0 277.7 0.0 277.7 0.0

Mixed forest 441.7 441.7 0.0 441.7 0.0 441.7 0.0

Urban land 385.5 694.4 308.9 633.6 248.1 560.6 175.1

Wetlands 3,694.6 3,520.1 –174.5 3,585.6 –109.0 3,936.5 241.9

In-stream photosynthesis 6,544.0 6,544.0 0.0 6,544.0 0.0 6,544.0 0.0

Total 13,664.9 13,798.8 133.9 13,834.9 170.0 14,189.9 525.0
102 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

6.4.1.2. Total Phosphorus

The results for TP flux to coastal waters followed a 

generally similar pattern to those for TN, reflecting similar 

environmental sources. The total baseline (2005) flux of 

TP from the five regions was 0.3 Tg/yr (figs. 6 –5 and 

6 –6; table 6 –3), with 79 percent originating in the Gulf of 

Mexico drainage, 15 percent originating in the combined 

east coast drainages, and 6 percent originating in the Great 

Lakes drainage. Again, the mid-Atlantic Bight part of 

the east coast drainage had the highest yield of the three 

regions of the east coast due to large sources from urban and 

agricultural lands and relatively short travel distances to the 

east coast. In contrast to the pattern for TN yield, however, 

the Mid-Atlantic Bight TP yield was not dramatically larger 

than that from the gulf coast drainage. This is because, 

whereas atmospheric sources of TN were especially high 

in the Mid-Atlantic Bight, atmospheric sources of TP were 

negligible in both regions.

The estimated TP flux from all five coastal regions 

was projected to increase between 2005 and 2050 under all 

three future projection scenarios (fig. 6 – 6; table 6 –3), but 

only minimally so for some regions and scenarios. Most 

notably, TP flux from the Gulf of Mexico drainage was 

projected to increase by only 2.0 percent, 1.4 percent, and 

0.6 percent under scenarios A1B, A2, and B1, respectively, 

during the 45 years of study (fig. 6–6; table 6–3). By 

contrast, TP flux from the South Atlantic Bight drainage 

was projected to increase by 19.7 percent, 17.9 percent, and 

7.9 percent under scenarios A1B, A2, and B1, respectively, 

during the same period. Projected increases in TP flux 

under all scenarios are consistently smaller than those for 

TN flux because of the lack of an atmospheric source of 

phosphorus. The effects of population and developed land 

growth on TN are compounded by associated increases in 

the atmospheric sources, which are an important source in 

many basins in the Eastern United States. Moreover, rising 

population and urban development under all scenarios 

tended to have the greatest influence on the coastal flux of 

both nutrients and is most pronounced outside of the Gulf 

of Mexico drainage.

6.4.1.3. Total Suspended Sediment

The baseline TSS flux to the five coastal regions averaged 

416.5 Tg/yr (fig. 6–7; table 6–3). Similar to TN and TP, the 

Gulf of Mexico drainage produced 87 percent of TSS, the 

three east coast regions together contributed 10 percent, and 

the Great Lakes region contributed about 3 percent. In contrast 

to the patterns for TN and TP, however, the large TSS yield 

from the Gulf of Mexico drainage (74,900 kilograms per 

square kilometer per year (kg/km2

/yr)) was highest among the 

five regions due to large areas of erodible cropland in the basin 

(table 6– 4). Lowest in yield terms were the Gulf of Maine and 

Great Lakes regions (25,397 kg/km2/yr and 37,769 kg/ km2/yr, 

respectively) due to large areas of less erodible forest and 

pasture in those regions.

The estimated TSS flux from all five regions was 

projected to increase between 4 percent and 53 percent by 

2050 under scenarios A1B and A2, with the largest increases 

in the South Atlantic Bight region (fig. 6–8; table 6–3). In all 

regions, urban development contributed most to increases in 

TSS. By contrast, under scenario B1, greater environmental 

and biodiversity protection than in scenario A1B or A2 

results in a smaller increase in the land area devoted to urban 

development; consequently, an increase in sources from 

agricultural lands explained a greater fraction of the increase 

(nearly one third) than under other scenarios. Hence, TSS flux 

to the five coastal regions is projected to increase by smaller 

percentages under scenario B1 than under either scenario A1B 

or scenario A2.

6.4.1.4. Total Organic Carbon

The baseline (2005) coastal flux of TOC from the five 

regions was 18.5 Tg/yr (table 6 –3). Seventy-four percent 

originated in the Gulf of Mexico drainage, 18 percent 

originated in the combined east coast drainages, and 8 percent 

originated in the Great Lakes drainage (fig. 6 –9). The South 

Atlantic Bight part of the east coast drainage had the highest 

yield (5,665 kg/km2/yr) due to large wetland and forest 

sources (table 6 –4), and relatively short travel distances 

to the east coast. Other regions with high delivered TOC 

yield were the Mid-Atlantic Bight (4,201 kg/ km2/yr) and 

Great Lakes (3,709 kg/km2/yr) drainages and stem from the 

extensive forest cover and relatively short travel distances 

in the basin. The Gulf of Mexico drainage had the lowest 

yield (2,830 kg/km2/yr) due to longer travel times and larger 

in-stream losses than in any other region of the Eastern 

United States.

Predicted increases in TOC flux by 2050 for the five 

regions were small, ranging from 0.8 to 6.3 percent among 

the three scenarios (fig.6 –10). These changes are significantly smaller than those projected for TN, TP, and TSS 

and reflect the fact that changes in forest and wetland cover 

were predicted to be generally small under all scenarios. The 

largest changes in TOC in the five regions were predicted 

under scenario B1 (3.9 percent overall increase) because 

efforts to reverse urban development and nurture natural 

areas led to more modest predicted increases in wetland areas 

(chap. 2). For example, coastal TOC flux from wetlands in 

the Gulf of Mexico drainage was projected to increase by 

about 6 percent under scenario B1, whereas, coastal TOC 

flux was predicted to decrease by 5 percent under scenario 

A1B.
Chapter 6 103

Gulf of Mexico

(0.3 Tg/yr)

Great Lakes

(0 Tg/yr)

Gulf of Maine

(0 Tg/yr)

Mid-Atlantic Bight

(0 Tg/yr)

South Atlantic

Bight

(0 Tg/yr)

0

>0 to 0.1

>0.1 to 0.2

>0.2 to 0.5

>0.5

No data

Urban

Corn and soybean

Alfalfa

Other crops

Animal waste

Forest

Barren

Shrub

No data

Total phosphorus delivered

 incremental yield in 2005, in

 kilograms per hectare per year

Major source of total 

 phosphorus, by land use 

 or land cover

Regional catchment boundary

EXPLANATION

Regional catchment boundary

N

0 250 500 MILES

0 250 500 KILOMETERS

A. Estimated delivered total phosphorous yield

B. Major sources of total phosphorous in model catchments

Figure 6.5.

Figure 6–5. Maps showing A, estimated delivered total phosphorus (TP) yield to coastal waters and B, major sources of 

TP in model catchments under baseline (2005) conditions in the Eastern United States. Delivered yield reflects the effects of 

in-stream losses that occur during transport from the outlet of a catchment through the stream and river system to coastal 

waters. Values shown in parentheses indicate net flux of TP for each region. Tg/yr, teragrams per year; >, more than.
104 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

N

0 250 500 MILES

0 250 500 KILOMETERS

Regional catchment boundary

EXPLANATION

<–50

>–50 to –25

>–25 to 0

>0 to 25

>25 to 50

>50

No data

Gulf of Mexico

(2.0%)

Great Lakes

(12.3%)

Gulf of Maine

(11.5%)

Mid-Atlantic Bight

(15.1%)

South Atlantic

Bight

(19.7%)

Gulf of Mexico

(2.1%)

Great Lakes

(5.8%)

Gulf of Maine

(5.1%)

Mid-Atlantic Bight

(8.7%)

South Atlantic

Bight

(0.6%)

Difference of total phosphorus yield 

 between baseline (2005) and 

 projected (2050) conditions,

 in percent (%)

A. Difference in total phosphorus yield between baseline and scenario A1B

B. Difference in total phosphorus yield between baseline and scenario B1

Figure 6.6.

Figure 6–6. Maps showing difference between estimated delivered total phosphorus (TP) yield to coastal waters under 

baseline (2005) conditions and projected (2050) conditions for Intergovernmental Panel on Climate Change Special Report on 

Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios A, A1B and B, B1 in the Eastern United States. Values 

shown in parentheses indicate regional difference from baseline. <, less than; >, more than.
Chapter 6 105

Urban

Forest

Federal land

Crop and pasture

Grass, shrub, and barren

Channel storage or erosion

No data

0 to 54.8

>54.8 to 223.7

>223.7 to 592.6

>592.6 to 1,337.2

>1,337.2

No data

Gulf of Mexico

(361.6 Tg/yr)

Great Lakes

(12.8 Tg/yr)

Gulf of Maine

(3.0 Tg/yr)

Mid-Atlantic Bight

(25.6 Tg/yr)

South Atlantic

Bight

(13.5 Tg/yr)

Total suspended sediment

 delivered incremental yield in 2005,

 in kilograms per hectare per year

Major source of total 

 suspended sediment, by land

 use, land cover, or process

Regional catchment boundary

EXPLANATION

Regional catchment boundary

N

0 250 500 MILES

0 250 500 KILOMETERS

A. Estimated delivered total suspended sediment yield

B. Major sources of total suspended sediment in model catchments

Figure 6.7. Figure 6–7. Maps showing A, estimated delivered total suspended sediment (TSS) yield to coastal waters and 

B, major sources of TSS in model catchments under baseline (2005) conditions in the Eastern United States. Delivered yield 

reflects the effects of in-stream losses that occur during transport from the outlet of a catchment through the stream and river

system to coastal waters. Values shown in parentheses indicate net flux of TSS for each region. Tg/yr, teragrams per year; >, more 

than.
106 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

N

0 250 500 MILES

0 250 500 KILOMETERS

Regional catchment boundary

EXPLANATION

<–50

>–50 to –25

>–25 to 0

>0 to 25

>25 to 50

>50

No data

Gulf of Mexico

(25.5%)

Great Lakes

(23.1%)

Gulf of Maine

(10.1%)

Mid-Atlantic Bight

(26.2%)

South Atlantic

Bight

(52.8%)

Gulf of Mexico

(18.7%)

Great Lakes

(9.1%)

Gulf of Maine

(4.2%)

Mid-Atlantic Bight

(11.7%)

South Atlantic

Bight

(25.0%)

Difference of total suspended

 sediment yield between baseline

 (2005) and projected (2050)

 conditions, in percent (%)

Figure 6.8.

A. Difference in total suspended sediment yield between baseline and scenario A1B

B. Difference in total suspended sediment yield between baseline and scenario B1

Figure 6–8. Maps showing difference between estimated delivered total suspended sediment (TSS) yield to coastal waters 

under baseline (2005) conditions and projected (2050) conditions for Intergovernmental Panel on Climate Change Special 

Report on Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios A, A1B and B, B1 in the Eastern United States. 

Values shown in parentheses indicate regional difference from baseline. <, less than; >, more than.
Chapter 6 107

0 to 3.9

>3.9 to 9.9

>9.9 to 17.1

>17.1 to 33.4

>33.4

No data

Urban

Cultivated land

Deciduous forest

Evergreen forest

Mixed forest

Wetland

In-stream photosynthesis

No data

Gulf of Mexico

(13.7 Tg/yr)

Great Lakes

(1.3 Tg/yr)

Gulf of Maine

(0.3 Tg/yr)

Mid-Atlantic Bight

(1.5 Tg/yr)

South Atlantic

Bight

(1.7 Tg/yr)

Total organic carbon yield to 

 coastal waters, in kilograms

 per hectare per year

Major source of total 

 organic carbon, by land

 use, land cover, or process

Regional catchment boundary

EXPLANATION

Regional catchment boundary

N

0 250 500 MILES

0 250 500 KILOMETERS

A. Estimated delivered total organic carbon yield

B. Major sources of total organic carbon in model catchments

Figure 6.9.

Figure 6–9. Maps showing A, estimated delivered total organic carbon (TOC) yield to coastal waters and B, major sources 

of TOC in model catchments under baseline (2005) conditions in the Eastern United States. Delivered yield reflects the 

effects of in-stream losses that occur during transport from the outlet of a catchment through the stream and river system 

to coastal waters. Values shown in parentheses indicate net flux of TOC for each region. Tg/yr, teragrams per year; >, more 

than.
108 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

N

0 250 500 MILES

0 250 500 KILOMETERS

Regional catchment boundary

EXPLANATION

<–50

>–50 to –25

>–25 to 0

>0 to 25

>25 to 50

>50

No data

Gulf of Mexico

(1.0%)

Great Lakes

(2.8%)

Gulf of Maine

(1.9%)

Mid-Atlantic Bight

(5.9%)

South Atlantic

Bight

(5.7%)

Gulf of Mexico

(3.8%)

Great Lakes

(2.3%)

Gulf of Maine

(0.8%)

Mid-Atlantic Bight

(3.5%)

South Atlantic

Bight

(6.3%)

Difference of total organic carbon

 yield between baseline (2005) 

 and projected (2050) conditions,

 in percent (%)

Figure 6.10.

A. Difference in total organic carbon yield between baseline and scenario A1B

B. Difference in total organic carbon yield between baseline and scenario B1

Figure 6–10. Maps showing difference between estimated delivered total organic carbon (TOC) yield to coastal waters 

under baseline (2005) conditions and projected (2050) conditions for Intergovernmental Panel on Climate Change Special 

Report on Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios A, A1B and B, B1 in the Eastern United States. 

Values shown in parentheses indicate regional difference from baseline. <, less than; >, more than.
Chapter 6 109

6.4.2. Carbon Storage in Coastal Waters

The coastal model used the adjusted nutrient and 

sediment inputs to estimate the millennial CT storage rates 

in coastal waters—burial in sediments and storage in deep 

ocean waters. This provides an estimate of the amount of 

CO2 removed from the atmosphere into coastal oceans that is 

specifically attributable to the terrestrial fluxes of nutrients and 

sediments. The model also estimated burial rates across the 

entire domain to facilitate comparisons to regional sediment 

carbon budgets. Finally, the “active” sedimentary inventory 

of CT was also estimated as the mass of CT in the upper 1 m 

of sediment (Hedges and Keil, 1995) using burial conditions 

based on the fluxes in the baseline year. This was done to 

permit comparisons to terrestrial soil carbon and biomass 

carbon pool size estimates (chap. 7).

6.4.2.1. Estuary Retention and Denitrification

The SPARROW-modeled fluxes of nutrients and sediments were converted to annual rates of carbon storage in 

sediments and in the deep ocean by separately estimating 

primary production, sediment burial, and remineralization in 

the deep ocean using the model framework described above. 

However, the SPARROW fluxes were adjusted for retention 

within estuaries before being used in the coastal CT storage 

model because the SPARROW model typically ends at the last 

nontidal reach. Sediment and phosphate retention in estuaries 

rates ranged up to 63 percent in the Chesapeake Bay, with 

an average of 11 percent among major estuaries (those in the 

NEEA database). TN retention rates in estuaries ranged up 

to 50 percent in Chesapeake Bay and averaged 22 percent 

among major estuaries. The information available to calculate 

retention in the Great Lakes region was insufficient, so median 

values for retention in coastal estuaries were used.

Similarly, an adjustment for nitrogen loss due to diffusive 

denitrification was also made to the SPARROW fluxes 

before use in the model. Modeled nitrogen loss ranged from 

4.3 percent in the Gulf of Maine to 11 percent in the South 

Atlantic Bight. The effective estimated rates of diffusive 

denitrification in shallow waters ranged up to more than 

1,000 mmol/m2/yr, which was consistent (on an areal basis) 

with literature estimates (Seitzinger and Giblin, 1996; PiñaOchoa and Alvarez-Cobelas, 2006). It should be noted that, as a 

sink for nitrogen, diffusive denitrification represents the lesser 

of the two ways denitrification was accounted for in the model.

6.4.2.2. Coastal CT Storage

Storage of CT in coastal waters of the Eastern United 

States was estimated to be 7.8 Tg/yr for the baseline year. 

This is roughly four times the CT storage estimate for coastal 

waters Pacific United States, calculated using similar methods 

(Zhu and others, 2010). The Gulf of Mexico alone accounted 

for 79 percent of the total CT for the Eastern United States, or 

about three times the CT storage of the Pacific United States, 

owing to the large flux of nutrients and sediments from the 

Mississippi River. High nutrient and suspended sediment fluxes 

originating from the Mississippi River resulted in high model 

estimates of primary production and high burial efficiencies, 

magnifying the CT storage in shallow gulf coast waters.

Much of the modeled CT storage in the Gulf of Mexico 

was due to burial near the mouth of the Mississippi River, 

with the highest modeled burial rates near the Mississippi 

River Delta (fig. 6–11). This is consistent with global assessments showing that the majority of carbon stored in coastal 

sediments is found in large river deltas (Hedges and Keil, 

1995). The model also showed that burial along the coastline 

to the west of the Mississippi and along the coast of western 

Florida were important (fig. 6–11). The influence of fluxes 

from the Mississippi River on modeled CT storage extended 

to the western model boundary and offshore all the way to 

the 2,000-m boundary (figs. 6–2 and 6 –11). High burial rates 

along the coast west of the Mississippi River are consistent 

with high carbon accumulation rates and high modern content 

of carbon observed in this region (Gordon and others, 2001; 

Turner and others, 2007; Sampere and others, 2008). Along 

the coastline of western Florida, modeled CT storage rates 

decreased as the shelf becomes wider and as TN, TP, and 

TSS concentrations decreased. As a result, 58 percent of CT

stored in the gulf coast region was buried in the sediments; 

84 percent of this burial was at water depths less than 200 m.

Nevertheless, it should be noted that, despite the high 

burial in shallow zones near the mouth of the Mississippi 

River, modeled CT storage in the deep ocean for the Gulf of 

Mexico represented 42 percent of the total storage, largely 

in the deeper areas of the northeastern gulf coast (fig. 6–11). 

This highlights the importance of bathymetry in determining 

CT storage. Deep waters have a disproportionately large 

influence on carbon storage rates because transport of primary 

production below the deep mixed layer and into deep ocean 

waters is the most efficient form of CT storage in the model. 

Remineralization within sediments during burial reduces 

the amount of carbon stored considerably compared areas of 

similar productivity over deep water. In areas of low sediment 

flux, calculated burial efficiencies were near zero.

For the eastern seaboard, total modeled CT storage was 

1.3 Tg/yr (table 6 – 4), accounting for about 17 percent of the 

total for the Eastern United States. CT storage along the east 

coast was about 36 percent lower than that of the Pacific coast, 

corresponding to lower nutrient and sediment fluxes on the 

east coast. The sediment, terrestrial carbon and nutrient fluxes 

on the east coast were also much lower than for the Gulf of 

Mexico, but the contrast in modeled CT storage was even 

greater; although the nutrient flux along the eastern seaboard 

comprised one third of the gulf coast nutrient flux, CT storage 

in the eastern seaboard was only one fifth as large as that of 

the gulf coast. This lower relative CT storage was due largely to 

lower relative suspended sediment flux in the region (about 5 

percent), which resulted in lower modeled burial efficiencies. 

Similar to the Gulf of Mexico, regional bathymetry played 
110 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

0 250 500 MILES

0 250 500 KILOMETERS

N

25 

0

3 

0

10 

0

2 

0

5 

0

1

0

1

0

5 

0

5 

5 0

0

5 

0

5 

0

1

0

1 

0

5 

0

B. Difference in carbon storage rate between baseline

 and scenario A1B

C. Difference in carbon storage rate between baseline

 and scenario B1

Great Lakes

(0.4 TgC/yr)

Great Lakes

(31%)

Great Lakes

(19%)

Gulf of Mexico

(6.2 TgC/yr)

Gulf of Mexico

(21%)

Gulf of Mexico

(18%)

Gulf of Maine

(0.1 TgC/yr)

Gulf of Maine

(34%)

Gulf of Maine

(22%)

Mid-Atlantic Bight

(0.7 TgC/yr)

Mid-Atlantic 

Bight

(36%)

Mid-Atlantic 

Bight

(22%)

South Atlantic Bight

(0.5 TgC/yr)

South Atlantic Bight

(56%)

South Atlantic Bight

(28%)

A. Baseline (2005) carbon storage rate from terrestrial input

Figure 6.11.

Coastal region catchments

EXPLANATION

Great Lakes

Gulf of Maine

Mid-Atlantic Bight

South Atlantic Bight

Gulf of Mexico

No data or not draining 

 to coast

Coastal delineation of

 regional catchment

 boundary

Carbon storage rate, in grams

 per year per square meter

High value

Low value

Figure 6–11. Maps showing model-derived millennial carbon storage rates for carbon storage directly attributable to terrestrial inputs 

(CT) in coastal waters A, under baseline (2005) conditions and as differences for projected (2050) conditions based on Intergovernmental 

Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakićenović and others, 2000) scenarios B, A1B and C, B1 in the 

Eastern United States. Values shown in parentheses indicate total carbon storage rate A, for each region or B and C, as a difference for 

each region. Color scales are unique to each region. TgC/yr, teragrams of carbon per year; %, percent.
Chapter 6 111

an important role; a shallower average bathymetry along the 

eastern seaboard resulted in a greater proportion of CT storage 

as burial rather than deep ocean storage.

There is considerable bathymetric diversity among the 

eastern seaboard regions; whereas the South Atlantic Bight 

has relatively steep bathymetric gradients, the Mid-Atlantic 

Bight has relatively broad shallow bathymetry, and the Gulf of 

Maine and Great Lakes have irregular bathymetric gradients. 

Modeled storage of CT in the deep (more than 200 m) offshore 

waters was dominant in the South Atlantic Bight, accounting 

for 75 percent of CT storage as DIC in deep waters (remineralized remains of the phytoplankton production from the surface 

ocean) in the South Atlantic Bight. By contrast, sediment CT

burial in the relatively shallow bathymetry of the Mid-Atlantic 

Bight accounted for 73 percent of total CT; much of the CT

stored in the Mid-Atlantic Bight was buried in shallow shelf 

sediments rather than stored in deep ocean waters.

There was considerable diversity in modeled CT storage 

among the physiographic regions on the eastern seaboard 

due to bathymetric differences as well as differences in 

sediment and nutrient inputs. The highest rates of coastal 

CT storage were in the Mid-Atlantic Bight, which accounted 

for 57 percent of the total, followed by the South Atlantic 

Bight (37 percent) and the Gulf of Maine (6 percent). The 

higher rates of CT storage in the Mid-Atlantic Bight were 

attributable primarily to elevated modeled primary production 

resulting from high nutrient inputs (fig. 6–11; table 6–3). 

The Mid-Atlantic Bight had the highest modeled terrestrial 

organic carbon and nutrient inputs, largely due to the higher 

population and urban land use in the Hudson River Basin and 

the Chesapeake Bay (figs. 6–3 and 6 –5; table 6– 4; Malone 

and others, 1996; Gibson, 1998). These inputs were elevated 

in the coastal ocean despite the comparatively high modeled 

estuarine CT retention in the Mid-Atlantic Bight.

The lowest modeled CT storage rates in the Eastern 

United States were in the Gulf of Maine (6 percent; 

fig. 6 –11). Terrestrial organic carbon and nutrient flux was 

nearly five times lower in the Gulf of Maine compared with 

the Mid-Atlantic Bight and only half as much in the South 

Atlantic Bight, resulting in lower modeled primary production and CT storage. Reduced riverine suspended sediments 

entering the Gulf of Maine compounded the effect of lower 

nutrient flux by contributing to reduced burial efficiencies 

and CT burial rates in shallow waters (table 6–3). Further, the 

variable bathymetry of the Gulf of Maine resulted in a higher 

proportion of CT storage (43 percent) through burial rather 

than through transport to the deep ocean relative to other 

regions (fig. 6 –11).

The Great Lakes accounted for about 5 percent of 

the total modeled CT storage in the Eastern United States 

(table 6–3), driven by nutrient and sediment dispersion 

among the lakes. In contrast to the other regions, there was 

no permanent deep mixed layer under which CO2 is presumed 

to accumulate and thus contribute to total CT storage; all 

storage is through sediment burial. Consequently, the effects 

of bathymetry in model results were less important than for the 

other regions, and burial efficiency was the primary determinant of CT storage. Maximum modeled rates of burial were 

in Lake Ontario, followed by the small deep basin of eastern 

Lake Erie and the shorelines of eastern Lake Michigan and 

southern Lake Superior (fig. 6–11), although the assessment 

extends only to the border of the United States. All these areas 

are characterized by dense population and high levels of urban 

and agricultural lands use, characteristics that lead to elevated 

riverine export of nutrients and total suspended sediment 

(table 6– 4). Relatively little CT storage was found to occur in 

the deep waters of northern Lake Huron where low watershed 

population densities and agricultural lands use correspond 

with relatively low riverine terrestrial organic carbon, nutrient, 

and sediment inputs.

6.4.2.3. Sediment CT Inventory

Organic carbon in the upper meter of sediment is 

considered one of the primary “active” pools of carbon storage 

in the marine system and can be considered a rough analog 

of the terrestrial soil organic carbon pool that is commonly 

used in comparisons and aggregations of active carbon pools 

(Hedges and Keil, 1995). Based on the results of the model, 

the integrated total mass of CT that would accumulate as a pool 

of carbon in the upper meter of sediment under baseline flux 

conditions from the Eastern United States is 9,100 teragrams 

(Tg; fig. 6–12; table 6–3), which represents one third of 

terrestrial carbon pools (chap. 7). However, the distribution 

of this sediment CT “inventory” among the coastal regions 

was different from the distribution of storage rates (fig. 6–3), 

highlighting the importance of burial rates in determining the 

long-term sediment repository for carbon. For example, the 

majority of CT inventory of coastal sediments in the Eastern 

United States was in the sediments of the eastern seaboard, 

whereas the highest rates of CT storage were in the Gulf of 

Mexico. The modeled CT inventory of the eastern seaboard 

was about 4,000 Tg (fig. 6 –12) compared with about 2,300 Tg 

for the Gulf of Mexico and 2,900 Tg for the Great Lakes.

There are two main reasons why the distribution of 

sediment CT inventory does not correspond to the rates of 

storage. First, the sediment CT inventory is dependent on the 

percentage of organic carbon in buried sediments, which is 

determined by the local sediment flux, the ratio of productivity 

to sediment flux, and the bathymetry. Very high inorganic 

sediment fluxes can result in dilution of the organic carbon 

buried (Tyson, 2001). Second, the modeled CT storage rate is 

not simply a function of the burial rate. Rather, it is the sum 

of the burial rate and the accumulation rate of remineralized 

CT in the deep ocean water, which can be substantial. For 

example, despite the high rates of sediment burial, a significant amount of carbon is also stored as DIC in the deep ocean 

waters (more than 200 m) of the Gulf of Mexico, the result of 

remineralization combined with the steep bathymetry of the 

northeastern Gulf of Mexico (fig. 6–1).

The allocation of total CT storage rates between sediment 

burial and storage in deep ocean water as DIC provides a 
112 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

0 250 500 MILES

0 250 500 KILOMETERS

N

Figure 6.12. Eastern United States showing estimated inventory of carbon directly attributable to terrestrial inputs 

(CT) that would be stored in the upper meter of sediment under baseline (2005) flux conditions, Numbers in 

parentheses indicate total mass for the region, in teragrams (Tg). 

Great Lakes

(2,900 Tg)

Gulf of Mexico

(2,300 Tg)

Gulf of Maine

(1,200 Tg)

Mid-Atlantic Bight

(1,400 Tg)

South Atlantic Bight

(1,300 Tg)

Coastal region catchments

EXPLANATION

Great Lakes

Gulf of Maine

Mid-Atlantic Bight

South Atlantic Bight

Gulf of Mexico

No data or not draining 

 to coast

Coastal delineation of

 regional catchment

 boundary

0.1

0

Carbon inventory in upper 1 meter

 of sediment in 2005, in teragrams

 per square kilometer

Figure 6–12. Map showing estimated inventory of carbon directly attributable to terrestrial inputs (CT) that would be 

stored in the upper 1 meter of sediment under baseline (2005) flux conditions in the Eastern United States. Values shown in 

parentheses indicate total carbon storage for each region. Tg, teragrams.

means to crudely assess the carbon inventory accumulated 

in both pools together for the time during which the upper 

1 m of sediment accreted. Assuming that this ratio, which 

is largely determined by bathymetry, is consistent for the 

period of deposition, an amount of carbon equivalent to two 

thirds of buried carbon is stored as DIC in the deep ocean. 

This suggests that, under baseline flux conditions, a total 

CT inventory of nearly 15,000 Tg would result. This represents 

a pool size more than half the estimated size of the terrestrial 

carbon pool (26,962 TgC) and about equal to the size of the 

terrestrial soil carbon pool (chap. 7).

However, the time scales of carbon accumulation in these 

two pools are very different. The rate of carbon accumulation as 

well as the rate of response to change are much lower in coastal 

systems than in terrestrial systems (Sarmiento and Gruber, 

2002). The CT burial rate in marine sediments (4.7 Tg/yr) is far 

less than that in terrestrial soils (65.4 Tg/yr; chap. 7). The CT

storage rate in coastal waters of the Eastern United States total 

is only 3 percent of the estimated terrestrial flux (chap. 7).

6.4.2.4. Projected Changes in CT Storage in 

Coastal Waters

CT storage and burial rates in coastal systems increased 

under all three modeled SRES scenario LULC projections. 

The projected future rise in CT storage rate was driven by 

substantially elevated nutrient and sediment fluxes, which 

interact synergistically to increase modeled carbon storage 

rate. The relative changes in these fluxes and in carbon storage 

rate was highest for SRES scenario A1B, with rates of carbon 

storage in the Eastern United States projected to increase 

by an average of 25 percent by 2050. The South Atlantic 

Bight showed the highest increase (56 percent), followed 

by the Mid-Atlantic Bight (36 percent), the Gulf of Maine 

(34 percent), the Great Lakes (31 percent), and the Gulf of 

Mexico (21 percent). Scenario B1 had the lowest projected 

rise in CT storage rate of the three scenarios tested, with the 

average storage rate in 2050 estimated to be 19 percent higher 

than the baseline; this was still only about 76 percent of the 

increase projected for scenario A1B (with a similar proportional change between regions).

The amount of change in CT storage rates (fig. 6 –11) 

reflects the spatial variability and relative change in urban, 

agricultural, and atmospheric sources (figs. 6–3, 6 –5, 6 –7, 

and 6 –11; table 6–4). The projected changes in CT storage 

rates under all SRES scenarios were greatest in the eastern 

seaboard, particularly in the South Atlantic Bight. This was 

largely the result of large projected increases in nutrient and 

total suspended sediment flux to coastal waters, but these 

increases were exacerbated by relatively low watershed 

and estuarine retention and steep increases in the depth of 

coastal waters. Although the Gulf of Mexico dominated 
Chapter 6 113

total CT storage rates in the Eastern United States due to the 

large fluxes of nutrients and suspended sediments from the 

Mississippi River, the increase in these fluxes compared with 

the baseline flux was low for all scenarios tested. Because the 

Mississippi River sediment and nutrient fluxes account for 

about 80 percent of the input to coastal waters for the Eastern 

United States, the aggregate average increase for the Eastern 

United States as a whole is biased toward the low projected 

change in the Gulf of Mexico.

The projected changes in CT storage rates under 

scenario B1 were smaller than those estimated for scenarios 

A1B and A2 (fig. 6 –11; table 6–3) because less land area is 

devoted to urban development under scenario B1 (fig. 6–5). 

This resulted in a lower nutrient and suspended sediment flux 

for scenario B1 than for scenario A1B, particularly along 

the eastern seaboard where there are many major population 

centers. Although relative differences in CT storage projections for scenario B1 were greatest on the eastern seaboard, 

the differences between regions were less than for the other 

scenarios (fig. 6 –11), indicating that the differences in patterns 

of development and land use embodied in the different 

scenarios can substantially affect coastal CT storage rates, 

thereby altering the carbon cycle of the ocean significantly.

6.4.3. Summary of Carbon Storage Related 

to Coastal Processes

The majority of the portion of coastal carbon storage 

directly related to terrestrial processes in the Eastern United 

States resulted from the burial of organic carbon in sediments 

rather from than transport and storage in the deep ocean. 

However, transport to the deep ocean was also significant 

and was more sensitive to projected changing inputs. Of the 

total carbon stored, the model results suggested that about 

60 percent of the coastal CT was buried in coastal sediments, 

whereas about 40 percent was stored as carbonate in deep 

ocean waters. The rate of CT storage was about 3 percent of 

the rate of terrestrial carbon storage, but the amount of CT

that would be stored in the active coastal carbon pools under 

baseline flux conditions was on the same order as that stored 

in terrestrial carbon pools because of the longer timescales 

of carbon accumulation in the ocean compared with carbon 

accumulation by terrestrial processes.

Coastal bathymetry plays an important role in coastal 

carbon storage. Deep areas export primary production beneath 

the deep mixed layer, keeping dissolved inorganic carbon 

produced as a consequence of remineralization isolated from 

exchange with the atmosphere and resulting in relatively 

reduced remineralization rates upon organic carbon burial. 

By contrast, shallow regions, even those with high incident 

sediment loads, tend to bury more of their production, which 

is less efficient because the continued slow degradation in 

shallow (less than 200 m) sediments results in return of the 

remineralized carbon to the atmosphere.

According to the model, the amount of CT buried in sediments was partitioned by water depth as follows: 38 percent 

was buried in waters less than 50 m deep, 32 percent was 

buried between 50 m and 100 m deep, 15 percent was buried 

between 100 and 200 m deep, and the remainder (about 

15 percent) was stored in sediments more than 200 m deep. 

The decreased burial in the deep zones was because of the 

low sediment flux in this region and the remineralization that 

occurs during settling through a deep-water column. These 

results agree with Dunne and others (2007), who suggested 

that previous ocean models of carbon storage rates did not 

account for the appreciable carbon storage that occurs in 

shallow coastal sediments.

CT storage in coastal waters was found to be sensitive 

to projected changes in fluxes of sediment and nutrients to 

coastal waters. Under all scenarios modeled, carbon storage 

in coastal systems was projected to increase. The projected 

increase in carbon storage is driven by substantially elevated 

projected nutrient fluxes and elevated sediment fluxes. These 

two fluxes interact synergistically to increase modeled carbon 

storage. Projections for scenario A1B exhibited the greatest 

proportional change in coastal CT storage. Rates of total 

carbon storage for the Eastern United States were projected 

to increase by 25 percent for this scenario; the South Atlantic 

Bight showed the highest increase (56 percent), followed by 

the Mid-Atlantic Bight, the Great Lakes, the Gulf of Maine, 

and the Gulf of Mexico. Changes in CT storage in the Gulf of 

Mexico were primarily driven by changes in sedimentation, 

whereas the changes in the east coast and the Great Lakes 

were driven primarily by changes in population density and 

corresponding increases in nutrient and total suspended 

sediment transport via rivers to coastal waters.

6.5. Conclusions and Implications

Coastal ocean primary productivity has been changing 

in response to increased nutrient loading. Increased primary 

production (Herbert, 1999) and fisheries landings (Breitburg 

and others, 2009) and a greater areal extent of hypoxia 

resulting from terrestrial inputs (Bianchi and others, 2010) are 

well documented. The model results presented in this report 

project a continued increase in nutrient fluxes associated with 

increasing population and agricultural intensity (Howarth, 

2008). This continued increase has important implications for 

management of coastal resources because of the increased 

incidence of coastal and estuarine hypoxia (Bianchi and 

others, 2010) and harmful algal blooms (Anderson and others, 

2002; Glibert and others, 2005). Increased areas of hypoxic 

sediments will likely increase the rates of carbon storage 

(Bergamaschi and others, 1997; Middelburg and Levin, 2009).

This assessment highlights the notion that processes 

controlling carbon storage in coastal oceans are not in steady 

state; rates of nutrient and sediment input are continuously 

changing in response to land use modification and population 

increase, among the many other drivers of change. A debate 

exists in the published literature regarding whether a change 

in coastal carbon burial rates has been or will be observed 
114 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

in coastal sediments. Walsh and others (1985) believe that 

population and land use change have already led to increased 

coastal carbon burial rates. The Arabian Sea Carbon Flux 

Group and others (Lee and others, 1998) found evidence of 

changes to sediment burial due to anthropogenic activity in 

waters off the Pacific coast. Middelburg and Levin (2009) 

summarized the geochemical evidence for the levels of 

increased carbon preservation in coastal sediments that may 

be expected. Alternatively, some studies have found that 

modern carbon burial rates are similar to long-term rates, and 

thus no change has occurred; no evidence of recent changes in 

carbon burial rates was found in east coast sediments (Alperin 

and others, 2002; Thomas and others, 2004) or in the Gulf of 

Mexico (Allison and others, 2007). The results of the current 

study suggest that changes in CT burial—the part of the burial 

rate subject to change—are spatially variable and potentially 

obscured by competing processes. Changes in sediment 

carbon burial rates may be due to changes in phytoplankton 

production as well as sediment delivery, with elevated sediment delivery potentially leading to a reduced organic content 

rather than enrichment because of dilution (Tyson, 2001). The 

location of studies intended to document changes resulting 

from altered terrestrial fluxes must be carefully chosen to 

focus on the nutrient or sediment supply only.

This assessment underscores the need for progress in our 

understanding of carbon storage in coastal systems. Although 

there have been significant strides in understanding the role 

of denitrification and other nitrogen cycling processes in the 

coastal ocean, there is a high degree of uncertainty about 

the relative importance of different processes (Seitzinger 

and others, 2006; Burgin and Hamilton, 2007; Trimmer and 

Engström, 2011). Large-scale datasets that permit simple 

model synthetic reproduction are not available. Additional 

research in coastal nutrient cycling is necessary to accurately 

evaluate the effects of changing continental fluxes.

Finally, this assessment also highlights the need for a 

greater understanding of carbon storage and cycling in estuarine sediments. CT and the total inventory of carbon in estuaries was assumed to be constant in this assessment because 

no models were available to relate carbon burial efficiency in 

estuaries to sediment accumulation rates and because remote 

sensing data still cannot be interpreted reliably in these areas. 

Furthermore, based on a simple empirical model formulation, 

the results of this assessment indicate that sediment carbon 

burial rates in estuaries represent 1 to 6 percent of total coastal 

carbon storage rates. However, this is likely an underestimate 

because it presumes no long-term carbon sequestration. 

A clearer picture of carbon accumulation in estuaries is 

important for understanding the effects and mechanisms of 

estuarine processing that influence terrestrial efflux and coastal 

carbon storage.
Chapter 7. Baseline and Projected Future Carbon Storage, 

Carbon Sequestration, and Greenhouse-Gas Fluxes in 

Terrestrial Ecosystems of the Eastern United States

By Shuguang Liu,1 Jinxun Liu,2 Yiping Wu,3 Claudia J. Young,2 Jeremy M. Werner,1 Devendra Dahal,2

Jennifer Oeding,2

 and Gail L. Schmidt 2

1

U.S. Geological Survey

2

Stinger Ghaffarian Technologies, Inc.

3

ASRC Federal InuTeq

7.1. Highlights

• From 2001 through 2005, the average total carbon 

stored in major pools (live biomass, soils (up to 

20 cm in depth) and dead biomass) in the Eastern 

United States was estimated to be 26,962 TgC, ranging 

from 25,069 to 28,497 TgC. SOC in the top 20-cm 

soil, live biomass, and dead biomass (such as litter and 

woody debris) accounted for 43 percent, 42 percent, 

and 15 percent, respectively, of the total carbon stored 

in the Eastern United States.

• From 2001 through 2005, the average annual net 

carbon flux (which is equivalent to the NECB) in 

the terrestrial ecosystems of the Eastern United States 

was estimated to be –279.4 TgC/yr, ranging from 

– 405.5 to –112.5 TgC/yr (negative values denote a 

carbon sink). Of the total NECB, live biomass 

accumulation accounted for –188.7 TgC/yr, followed 

by SOC at –65.4 TgC/yr and dead biomass pool at 

–25.2 TgC/yr.

• The average annual net fluxes of GHGs were estimated to be –1,024.6 TgCO2

-eq/yr for CO2, 174.7 

TgCO2

-eq/yr for N2O, and 193 TgCO2-eq/yr for CH4

in the Eastern United States, with a sum of –656.9 

TgCO2-eq/yr for the baseline period, which was 

equivalent to 11.7 percent of the 5,594 TgCO2-eq/

yr of nationwide fossil-fuel emissions in 2010 (U.S. 

Environmental Protection Agency, 2012).

• Based on the LULC scenarios, climate-change projections, and biogeochemical models used in the terrestrial assessment, the total carbon stored in the Eastern 

United States in 2050 was projected to be 37,082 TgC, 

ranging from 25,512 to 46,002 TgC, an increase of 

37.5 percent from the average baseline carbon storage.

• Between 2006 and 2050, the NECB in the terrestrial 

ecosystems of the Eastern United States was projected 

to be –224.9 TgC/yr, ranging from −403.7 to 1.4 

TgC/yr, a potential decrease of 54.5 TgC/yr (or 19.5 

percent reduction in the magnitude of the carbon 

sink). On average, about 64.3 percent of the total 

carbon was projected to accumulate in live biomass, 

20.3 percent in SOC, and the remaining 15.4 percent 

in dead biomass.Forests were projected to be the 

primary carbon sinkwith an average value of −157.6 

TgC/yr. The projected future GHG fluxes averaged 

–824.6 TgCO2-eq/yr for CO2, 174.7 TgCO2-eq/yr for 

N2O, and 198.7 TgCO2-eq/yr for CH4.

• Only a partial attribution analysis was produced on 

effects of controlling processes (for example, effects 

of wildland fire [chap. 4], effects of timber production 

[this chapter], general attribution of LULC change 

[chap. 3 and this chapter], and uncertainty contribution from the three biogeochemical models, LULC 

scenarios, and GCMs [this chapter]).

• Results of this assessment suggested a wide range of 

uncertainty in the estimated carbon sequestration rates 

across models, LULC scenarios, and GCM projections in ecoregions and in the Eastern United States. 

In addition, the results showed that the uncertainty 

from models were far greater than the uncertainties 

from LULC and GCMs. These results are important 

but they are high-level observations without a detailed 

cause-and-effect analysis, which require a further 

effort to explain the differences among models, LULC 

scenarios, and GCM projections.

7.2. Introduction

Many inventory- and modeling-based studies that use 

atmospheric (top-down) and ground-based (bottom-up) 

methods have been conducted to quantify carbon stock and 

changes in the United States in the past decade. These studies 

agree on the presence of a carbon sink in the ecosystems of 
116 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

the conterminous United States (Houghton and others, 1999; 

Pacala and others, 2001; Pan and others, 2011). For example, 

Turner and others (1995) estimated that the total carbon stock 

in forests of the conterminous United States (at the beginning 

of 1990s) was about 36,700 TgC, with half of that amount in 

the soils. In 2011, the annual net carbon flux from ecosystems 

amounted to –227.3 TgC/yr from forested lands and 

–246.9 TgC/yr from all lands in conterminous United States 

(U.S. Environmental Protection Agency, 2012). Woodbury and 

others (2007) estimated that the forest sector (including forests 

and wood products) sequestered an average –162 TgC/yr 

in the United States from 1990 through 2005, providing an 

offset equal to 10 percent of the national total CO2 emissions 

in 2005. Climate change and land-use change have profound 

effects on the ability of ecosystems to sequester carbon and 

maintain a stable carbon stock (Goward and others, 2008; 

Houghton, 2010; Liu and others, 2011; Pan and others, 2011). 

Several recent studies suggested that a lower rate of carbon 

sequestration by ecosystems than these contemporary rates 

is possible in future years as the result of climate change and 

land-use change (Hurtt and others, 2002; Birdsey and others, 

2006; Liu and others, 2012b), whereas other studies (such 

as Woodbury and others, 2007) estimated that forests in the 

United States would continue to sequester carbon in the near 

future at a rate similar to those of recent years.

As described in chapter 1 of this report, the scope of the 

assessment required a methodology that integrated several 

technical components, including LULC change, wildland fire 

disturbances, and modeling of terrestrial and aquatic carbon 

fluxes (fig. 1–2). The objective of this chapter is to describe 

methods used to estimate carbon stock, carbon fluxes, and 

the rate of sequestration in terrestrial ecosystems in the 

Eastern United States and present the results of the terrestrial 

assessment.

7.3. Methods and Data

7.3.1. Ecosystem Biogeochemical Models

Consistent with the approach used for the regional assessments conducted for the Great Plains (Zhu and others, 2011) 

and the Western United States (Zhu and Reed, 2012), the 

Century version 4.0 (Parton and others, 1987, 1994; Metherell 

and others, 1993), Erosion Deposition Carbon Model (EDCM; 

appendix 1; Liu and others, 2003), and Land Greenhouse-Gas 

Accounting Tool (LGAT; appendix 2) biogeochemical models 

were used for simulating ecosystem biogeochemical cycles 

and estimating carbon stock and flux values. 

Major model-data intercomparison studies have shown 

diverse results of different models on estimating carbon 

stocks and fluxes (Schwalm and others, 2010; Huntzinger 

and others, 2012). These studies suggested that a collective 

use of multiple models would yield more useful information 

than any single model. Perhaps a major advantage of using 

several models together is the consideration that the range 

of results obtained from the models could serve to illustrate 

uncertainties stemming from inherent biases of the individual 

models. Based on the above considerations, an ensemble 

modeling strategy was adopted and implemented in the 

General Ensemble Biogeochemical Modeling System (GEMS; 

Liu, 2009; Liu and others, 2012c). For this assessment, the 

three biogeochemical models were run in an ensemble fashion 

on the GEMS platform to share input data and produce the 

range of results to quantify uncertainties of model outputs 

(appendix 3).

Running GEMS or any biogeochemical models over 

a large area is challenging because of the high computation 

load. To speed model simulations, a sampling approach can 

be implemented in GEMS in addition to the conventional 

approach that supports only wall-to-wall or per-pixel 

simulations. Using this sampling approach, users can choose 

different sampling densities to run GEMS to meet their needs. 

For example, EDCM and Century models can be both run 

with a 10×10 subsample factor to allow for adequate time of 

processing, generating statistics, and assessing the results. 

With this sampling scheme, only one pixel is simulated and 

processed for every 100 pixels (that is, 1 out of 10 pixels in 

each the x and y directions). The adequacy of sample intensity 

should be evaluated before full deployment over large areas.

The concept of a joint frequency distribution (JFD) table 

was originally used and implemented in GEMS to speed up 

model simulations and address input data uncertainty (see 

Liu and others, 2004; Liu, 2009). Containing one or multiple 

pixels, each JFD record in the table represents a unique 

combination of environmental conditions derived from overlay 

operations of multiple geospatial data layers, such as LULC, 

soil, and climate. The JFD approach is most efficient when 

the number of strata is relatively small and the spatial resolutions of the geospatial data layers are coarse. However, this 

efficiency decreases as the study area, resolution of the spatial 

data layers, and number of spatial data layers increase. The 

extreme of the JFD approach is the case that each JFD record 

contains only one pixel (that is, each pixel is uniquely defined 

by the spatial data layers), which then becomes the per-pixel 

model simulation. In this case, there is no need for explicit 

JFD tables in per-pixel GEMS simulations because operating 

on the geospatial data layers directly can gain efficiency by 

eliminating some searching and computing algorithms.

The major biogeochemical processes of the carbon cycle 

simulated by the two process-based models (that is, Century 

and EDCM) include NPP, photosynthetic allocation, litter fall, 

mortality, decomposition of plant tissues, and SOC. There 

is no need to predetermine endpoints of maximum carboncarrying capacity or predefine paths to describe how the 

endpoints are approached because the dynamics of vegetative 

and soil carbon pools are controlled by the fluxes of inputs 

and outputs. The endpoints and paths, varying in space with 

specific site conditions, are tightly coupled with and regulated 

by the nitrogen and water cycles, disturbances, and management activities.
Chapter 7 117

7.3.2. Input Data

Major input datasets included climate, LULC, soils, elevations, biomass, land management activities, and natural disturbances such as wildland fires. These datasets were obtained from 

different sources (table 7–1) and converted to standard spatial and 

temporal resolutions, projections, and data formats. Examples of 

input data layers (maps) are provided for the baseline (fig. 7–1) 

and projected future (fig. 7–2) periods of this assessment.

As with the previous assessments (Zhu and others, 2011; 

Liu and others, 2012a), this assessment relied on nationwide 

geospatial data layers to characterize the spatial and temporal 

distributions of land-management activities and natural disturbances. Examples of processing and formatting techniques for 

the data layers are given in Schmidt and others (2011). Major 

land-management activities and natural disturbances included 

in model runs for this assessment are listed in table 7–2, with 

examples for 2005 given in figure 7–3.

The spatial resolution of some data layers listed in 

table 7–2, especially those derived from censuses and inventories, was at the county, State, or FIA-unit level. These data 

layers were further downscaled to pixels to generate spatially 

explicit map layers using a Monte Carlo approach and some 

other additional information (techniques described in Schmidt 

and others (2011)). The most common pixel resolution among 

all the map layers was 250 m. The map series had individual 

maps for each year from 1992 through 2050. Annual maps 

showing areas of forest clearcuts were produced as part of the 

LULC change modeling detailed in chapter 3 of this report. 

Annual maps of wildland fire disturbances were modeled as 

described in chapter 4 of this report.

The county-level crop management information used in 

this assessment included crop type, crop rotation, fertilization, 

manure addition, tillage practices, irrigation, and harvesting 

practices. Crop management activities were downscaled to 

pixel level on the LULC maps using a probability-based 

Monte Carlo approach and the crop composition information derived from the USDA agricultural census data (U.S. 

Department of Agriculture, National Agricultural Statistics 

Service, 2011). All the crop management data layers (except 

irrigation) were subsequently generated from these land-cover 

data layers, and more than 20 major crops were presented 

consistently for the United States (Schmidt and others, 2011). 

The tabular data about manure application were derived from 

the USDA census (U.S. Department of Agriculture, National 

Agricultural Statistics Service, 2011), which included, for each 

crop type in each State, the Federal Information Processing 

Standard (FIPS) code for each State, the year the crop was 

planted, the total planted area, the percentage of the planted 

area that was treated with manure, the amount of manure that 

was applied, the rate at which the manure was applied, the 

rate at which the nitrogen in the manure was applied, and the 

rate at which the carbon in the manure was applied. A gridded 

manure dataset for all agricultural lands in the region was 

generated from this tabular data along with the land-cover 

maps using a Monte Carlo approach.

The information about tillage practices was acquired from 

the Conservation Technology Information Center (CTIC) in 

tabular format. The tabular data included the FIPS code for each 

State, the year the area was tilled, the total planted area that 

was tilled, the total percentage of residue on all tilled areas, the 

planted area for each tillage type, and the percentage of residue 

for each tillage type by crop type within the State. The tillage 

practices included in the database included conventional, mulch, 

no-till, reduced, and ridge tillage. A gridded dataset showing the 

spatial and temporal changes of tillage practices for all agricultural lands was generated from these tabular data along with the 

land-cover maps using a Monte Carlo approach. An irrigation 

map derived from the MODIS (U.S. Geological Survey, 2010) 

for the United States was used to characterize the locations of 

irrigated land. Because of the lack of data showing the temporal 

changes in irrigation across the Eastern United States, this 

assessment assumed that the locations of irrigated land did not 

change over time during the assessment period.

Only nitrogen fertilization on agricultural lands was 

considered in this assessment. Forest fertilization was not 

included in the assessment due to the lack of spatially explicit 

information. A nationally consistent procedure was put in place 

to generate crop- and location-specific nitrogen-fertilization 

data for all agricultural lands (Schmidt and others, 2011). The 

tabular dataset included the FIPS code for each State, the year 

the planted area was fertilized, the total planted area where 

nitrogen fertilizer was applied, the percentage of total area that 

was fertilized with nitrogen, the rate of application for nitrogen 

fertilizer, and the total amount of nitrogen fertilizer applied 

for each crop type within each State. Because several States in 

the Eastern United States did not report this information, this 

assessment assumed that agricultural lands were automatically 

fertilized every year in order to satisfy growth requirements.

The selective or partial forest clearcutting (thinning) 

information used in this assessment included thinning ratio, 

thinning age, and thinning intensity, which were calculated from 

the FIA database (U.S. Department of Agriculture, 2012a) at the 

FIA-unit level (see appendix 4). Mortality caused by disturbances 

(insects, disease, fire, animals, weather, vegetation, or other) was 

also included in the assessment of the Eastern United States. A 

mortality ratio was calculated for each FIA unit by forest type 

from the FIA database. Because of the lack of data showing 

the temporal changes in partial cutting and mortality across the 

Eastern United States, this assessment assumed that the ratios did 

not change over time during the assessment period.

7.3.3. Model Run Setup

7.3.3.1. Model Initialization

The soil properties that were initialized based on data from

the SSURGO database (U.S. Department of Agriculture, Natural 

Resources Conservation Service, 2009) included soil thickness, 

organic carbon storage, texture (fractions of sand, silt, and clay), 

bulk density, and drainage. The total SOC pool was partitioned 

into active (5 percent), slow (45 percent), and passive (55 percent) 
118 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–1. Input data used in the model runs for the assessment of carbon fluxes and storage in the Eastern United States.

[Most of the input data have a 250-meter spatial resolution and variable temporal characteristics, although most data cover the first decade of the 21st century. 

LGAT, Land Greenhouse Gas Accounting Tool; EDCM, erosion deposition carbon model; LULC, land use and land cover; MIROC 3.2-medres, Model for 

Interdisciplinary Research on Climate version 3.2 medium resolution; CGCM3.1, The Third Generation Coupled Global Climate Model; CSIRO Mk3.5, Commonwealth Scientific and Industrial Research Organisation Mark 3.5; Db 0.33 bar H2

O, the oven-dry weight of the less than 2 millimeters of soil material per 

unit volume of soil at a water tension of 0.33 bar (as used in the Soil Survey Geographic database); FIA, Forest Inventory and Analysis; USDA, U.S. Department of Agriculture; GCM, general circulation model; K factor, an erodibility factor that quantifies the susceptibility of soil particles to detachment by water; 

mm, millimeter; MODIS, moderate resolution imaging spectrometer; NASA, National Aeronautics and Space Administration; PRISM, parameter-elevation 

regressions on independent slopes model]

Data category Data types and characteristics Data source Model

LGAT EDCM Century

LULC LULC classes Chapter 3 of this report X X X

Climate Past (baseline) and future climate 

datasets: Monthly minimum and 

maximum temperature, monthly total 

precipitation 

Past climate: PRISM Climate Group (2012)

Projected future climate: MIROC 3.2-medres, 

CSIRO Mk3.5, and CGCM3.1 GCMs, Canadian 

Forest Service (Joyce and others, 2011) 

X X

Soil Total sand Soil Survey Geographic database (U.S. Department of Agriculture, Natural Resources Conservation Service, 2009)X X

Total clay X X

Total silt X X

Soil thickness X

Soil organic carbon X X X

Available water capacity X

DB 0.33 bar H2

O X

K factor X X

Forest Biomass Geodata (U.S. Department of Agriculture, 

Forest Service, 2012b)

X

Stand age Chapter 3 of this report X X X

FIA species growth curves, height, 

diameter, and biomass measurements

Forest Inventory and Analysis (U.S. Department

of Agriculture, Forest Service, 2012a)

X

Timber product output Renewable Resources Planning Act of 1974 timber 

product output (U.S. Department of Agriculture, 

Forest Serivce, 2011d)

X

Crops and crop 

management

Derived crop type Schmidt and others (2011) X X X

USDA crop yield table U.S. Department of Agriculture, National 

Agricultural Statistics Service (2011e)

X X

Derived fertilizer spatial data using

USDA fertilization table

U.S. Department of Agriculture, Economic 

Research Service (2011b); Schmidt and 

others (2011)

X X X

Derived manure spatial data using

USDA manure table

U.S. Department of Agriculture, Economic 

Research Service (2011a); Schmidt and 

others (2011)

X X X

Derived tillage spatial data using

Conservation Technology 

Information Center tillage table

Conservation Technology Information Center 

(2012); U.S. Department of Agriculture, 

Economic Research Service (2011a); 

Schmidt and others (2011)

X X X

Irrigation U.S. Geological Survey (2010) X X X

Elevation Elevation U.S. Geological Survey (2012b) X X

Remote sensing Net primary production Zhao and others (2005) X X

Fire Fire severity Eidenshink and others (2007); chapter 4 

of this report

X X

Reference

information

State and county Federal information 

processing standard

U.S. Census Bureau (2012) X X X

Initial conditions Forest litter biomass Forest Inventory and Analysis (U.S. Department 

of Agriculture, Forest Service, 2012a)

X X X

Above ground live biomass X X X

Below ground live biomass X X X

Down deadwood biomass X X X

Standing dead biomass X X X
Chapter 7 119

A. Soil organic carbon—Top layer B. Total annual precipitation in 2005

C. Land use and land cover in 2005

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

N

Figure 7.1. 

100 Barley

101 Beans

102 Canola

103 Corn grain

104 Corn sillage

105 Cotton

106 Flaxseed

109 Lentils

110 Sorghum silage

111 Oats

112 Peanuts

113 Peas

114 Potatoes

115 Rice

116 Rye

117 Safflower

118 Sorghum

119 Soybeans

120 Sugarbeets

121 Sugarcane

122 Sunflowers

123 Sweet corn

124 Tobacco

125 Tomatoes

126 Wheat durum

127 Wheat spring

128 Wheat winter

Soil organic carbon, in

 kilograms of carbon

 per square meter

High: 40.5231

Low: 0

Precipitation, in

 millimeters

<600

601 to 800

801 to 1,000

1,001 to 1,200

1,201 to 1,400

1,401 to 2,000

>2,000

1 Water

2 Developed

3 Mechanically

 disturbed 

 national forest

4 Mechanically 

 disturbed other

 public land

5 Mechanically 

 disturbed 

 private land

6 Mining

7 Barren

8 Deciduous forest

9 Evergreen forest

10 Mixed forest

11 Grassland

12 Shrubland

14 Hay/pasture

16 Herbaceous wetland

15 Woody wetland

17 Perennual ice/snow

Level II

 ecoregion

 boundary

Land use and land cover

EXPLANATION

Figure 7–1. Examples of maps showing input data for the Eastern United States. A, Soil organic carbon (SOC) for the top 0 to 5 centimeters of the soil layer; data were derived from the Soil Survey Geographic (SSURGO) database (U.S. Department of Agriculture, 

Natural Resources Conservation Service, 2009). B, Total precipitation in 2005 (PRISM Climate Group, 2012). C, Land use and land cover 

(LULC) in 2005, from chapter 3 of this report with the agricultural lands class downscaled to the crop types. Level II ecoregions are 

shown in figure 1–1.
120 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

A. Precipitation, 2050—MIROC scenario A1B B. Precipitation, 2050—MIROC scenario A2

C. Precipitation, 2050—MIROC Scenario B1 D. Land use and land cover, 2050—Scenario A1B

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

Figure 7.2. 

N

Figure 7–2. Maps showing projected total annual precipitation under Intergovernmental Panel on Climate Change Special Report 

on Emission Scenarios (SRES; Nakićenović and others, 2000) scenarios A, A1B, B, A2, and C, B1 in 2050 and D, projected land use and 

land cover (LULC) under SRES scenario A1B in the Eastern United States in 2050. Precipitation data were projected by the Model for 

Interdisciplinary Research on Climate version 3.2 medium resolution (MIROC 3.2–medres) general circulation model (Joyce and others, 

2011). Projected LULC change was from chapter 6 of this report with downscaling of agriculture to crop types by Schmidt and others 

(2011). Level II ecoregions are shown in figure 1–1.
Chapter 7 121

Table 7–2. Major land-management activities and natural disturbances included in model runs for the assessment of carbon fluxes 

and storage in the Eastern United States.

[m, meters; NA, not applicable; PRISM, parameter-elevation regressions on independent slopes model; FIA, Forest Inventory Analysis]

Management activities or natural 

disturbances

Data source Spatial

resolution Time period References

Forest harvesting 

or clearcuts

Stand age, chapter 3 of this report 250 m 1992–2050 U.S. Geological Survey (2012a)

U.S. Department of Agriculture Forest Inventory Analysis Resource Planning Act timber 

product output

State 2002 U.S. Department of Agriculture, 

Forest Service (2011d)

Forest thinning Thinning ratio from forest inventory data FIA unit Average for 

1997–2010

U.S. Department of Agriculture, 

Forest Service (2012a)

Forest mortality Mortality ratio from forest inventory data FIA unit Average for 

1997–2010

U.S. Department of Agriculture, 

Forest Service (2012a)

Wildland fire: 

extent, severity, 

frequency

Chapter 4 of this report 250 m 1992–2050 Chapter 4 of this report

Drought Precipitation from PRISM and the Canadian 

Forest Service

250 m 1992–2050 Canadian Forest Service (2012); 

PRISM Climate Group (2012)

Crop yield U.S. Department of Agriculture crop yield table County 1992–2050 U.S. Department of Agriculture National 

Agricultural Statistics Service (2011e)

Fertilization U.S. Department of Agriculture Economic Research Service fertilization tableCounty 1992–2050 U.S. Department of Agriculture Economic Research Service (2011b)

Manure U.S. Department of Agriculture manure table County 1992–2050 U.S. Department of Agriculture 

Economic Research Service (2011a)

Tillage Conservation Technology Information Center 

tillage table

County 1992–2050 Conservation Technology Information 

Center (2012); U.S. Department of 

Agriculture Economic Research 

Service (2011a)

Irrigation U.S. Geological Survey 250 m Static U.S. Geological Survey (2010)

100 Barley

101 Beans

102 Canola

103 Corn grain

104 Corn sillage

105 Cotton

106 Flaxseed

109 Lentils

110 Sorghum silage

111 Oats

112 Peanuts

113 Peas

114 Potatoes

115 Rice

116 Rye

117 Safflower

118 Sorghum

119 Soybeans

120 Sugarbeets

121 Sugarcane

122 Sunflowers

123 Sweet corn

124 Tobacco

125 Tomatoes

126 Wheat durum

127 Wheat spring

128 Wheat winter

Precipitation, in millimeters

<600

601 to 800

801 to 1,000

1,001 to 1,200

1,201 to 1,400

1,401 to 2,000

>2,000

1 Water

2 Developed

3 Mechanically

 disturbed 

 national forest

4 Mechanically 

 disturbed other

 public land

5 Mechanically 

 disturbed 

 private land

6 Mining

7 Barren

8 Deciduous forest

9 Evergreen forest

10 Mixed forest

11 Grassland

12 Shrubland

14 Hay/pasture

16 Herbaceous wetland

15 Woody wetland

17 Perennial ice/snow Level II ecoregion

 boundary

Land use and land cover

EXPLANATION

Figure 7–2.—Continued
122 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

A. Land use and land cover, 2005 B. Manure, 2005

N

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

Figure 7.3. 

100 Barley

101 Beans

102 Canola

103 Corn grain

104 Corn sillage

105 Cotton

106 Flaxseed

109 Lentils

110 Sorghum silage

111 Oats

112 Peanuts

113 Peas

114 Potatoes

115 Rice

116 Rye

117 Safflower

118 Sorghum

119 Soybeans

120 Sugarbeets

121 Sugarcane

122 Sunflowers

123 Sweet corn

124 Tobacco

125 Tomatoes

126 Wheat durum

127 Wheat spring

128 Wheat winter

1 Water

2 Developed

3 Mechanically

 disturbed 

 national forest

4 Mechanically 

 disturbed other

 public land

5 Mechanically 

 disturbed 

 private land

6 Mining

7 Barren

8 Deciduous forest

9 Evergreen forest

10 Mixed forest

11 Grassland

12 Shrubland

14 Hay/pasture

16 Herbaceous wetland

15 Woody wetland

17 Perennial ice/snow

Level II ecoregion boundary

Land use and land cover

EXPLANATION

Total manure application

 per level II ecoregion,

 in kilograms

14,275 2,623

8,511

53,484

10,072

73,901

5,944

Southeastern 

USA Plains

Central USA 

Plains

Mixed

Wood Shield

Atlantic 

Highlands

Ozark, QuachitaAppalachian Forests

Mississippi Alluvial

and Southeast USA

Coastal Plains 

Mixed Wood

Plains

984

162

527

615

20,000

5,000

3,279

4,407

335

Figure 7–3. Maps showing examples of A, land use and land cover; B, manure; C, tillage; D, irrigation; and E, stand age data layers for 

land-management activities and natural disturbances in the Eastern United States for 2005. Level II ecoregions are shown in figure 1–1.
Chapter 7 123

C. Tillage, 2005 D. Irrigation, 2005

E. Stand age, 2005

N

Level II ecoregion boundary

EXPLANATION

Irrigation

Non-irrigated

Irrigated

Tillage

None

Ridge

Mulch

Reduced

Conventional

Stand age, in years

0

1 to 5

6 to 10

11 to 15

16 to 30

31 to 50

51 to 100

101 to 250

>250

Figure 7–3.—Conitnued
124 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

classifications for Century and EDCM initialization (Liu and 

others, 2003). Forest biomass carbon pools (aboveground and 

belowground live biomass or dead biomass consisting of forest 

litter and dead, woody debris) were initialized using the initial 

forest-age map (derived from FIA data; U.S. Department of 

Agriculture, Forest Service, 2012b), forest type (evergreen, 

broadleaf, and mixed), and the relation between forest age and 

carbon stock. For consistency and to avoid potential errors, the 

initialization of the SOC and biomass was done using the LGAT, 

and the outputs from the LGAT for 1992 (the first year of the 

model simulations) were then read directly by the Century model 

and the EDCM as initial conditions.

7.3.3.2. Model Calibration and Validation

Model calibration, the process of adjusting model 

parameters to minimize the difference between simulations and 

observations, was only applied to Century and the EDCM as all 

coefficients of the LGAT could be derived directly from field 

measurements. The observed data for calibration (from 2001 

through 2005) included county-based grain yield survey data by 

crop type (U.S. Department of Agriculture, National Agricultural 

Statistics Service, 2011) and 250-m-resolution NPP data from 

the MODIS for other LULC types, such as forests and grasslands 

(Zhao and others, 2005). The MODIS NPP was found to lack 

consistent performance for calibrating crop production. As the 

result, crop yield data from the USDA were used. An automated 

calibration was implemented for EDCM using the Shuffled 

Complex Evolution (SCE–UA) method (Duan and others, 1992) 

and the R-language Flexible Modeling Environment (R–FME) 

software package (Soetaert and Petzoldt, 2010; Wu and Liu, 

2012). On the other hand, manual calibration was used for 

Century model. The potential maximum production parameter 

(PRDX) was adjusted by comparing the modeled grain yield with 

the USDA county-level statistics of grain yield and the forest NPP 

with the county-level MODIS-derived NPP from 2001 through 

2005. Appendix 5 summarizes the derived PRDX values of 13 

main ecosystem types by county across all ecoregions in the 

Eastern United States.

Observational data used for validation included USDA 

forest biomass values (U.S. Department of Agriculture, Forest 

Service, 2012a), aboveground biomass from the Woods Hole 

Research Center National Biomass and Carbon Dataset for the 

Year 2000 (Kellndorfer and others, 2004), the MODISderived 

NPP (Zhao and others, 2005), and the USDA grain yield (U.S. 

Department of Agriculture, National Agricultural Statistics 

Service, 2011) for 2006, 2008, and 2010. Maps, binned scatterplots, and correlation plots were generated for different ecosystems in each ecoregion of the Eastern United States in order to 

compare the simulated results of the process-based models with 

observational data. Simple linear-regression modeling, the R2

, 

and the root mean square error (RMSE) between the observed 

and modeled data were calculated to evaluate the performance 

of the models. Some of the results of the validation are shown 

in figure 7–4 and table 7–3. Figure 7–4 shows the comparison 

between NPP estimated by MODIS and NPP simulated by 

Century and EDCM in all seven ecoregions of the Eastern 

United States in 2006. Table 7–3 summarizes validation metrics 

from different models in the Mixed Wood Shield ecoregion in 

2006 and serves as an example of statistics used in the models; 

other ecoregions, not shown in this report, show similar results.

7.3.3.3. Ensemble Modeling

Multiple GEMS simulations were run continuously for 

1992 through 2050 with the following setup:

• Three models were run on the GEMS platform. 

EDCM and Century were run at monthly time steps 

with a sampling intensity of 1 percent (or 1 pixel for 

each 10 pixels in the x direction and 10 pixels in the 

y direction) for this report. The validity of the sampling 

rate was confirmed by comparing results with those 

produced with per-pixel simulations (see appendix 6). 

The LGAT was run at annual time steps on a per-pixel 

basis because the time for each run was much shorter 

than the other two process-based models.

• Three LULC scenarios were incorporated. Each of 

the scenarios was developed (chapters 2 and 3 of this 

Table 7–3. Biogeochemical models in the General Ensemble Modeling System in the Eastern United States for 2006.

[Based on aggregated results at the county level. RMSE, root mean squared error; gC/m2/yr, grams of carbon per square meter per year; R2, coefficient of 

determination; EDCM, Erosion Deposition Carbon Model; LGAT, Land Greenhouse Gas Accounting Tool]

Observation Model System RMSE,

in gC/m2

/yr R2

National Biomass and Carbon Dataset (Kellndorfer and others, 2012) live biomass LGAT Forest 0.819 0.95

U.S. Department of Agriculture, Forest Service live biomass LGAT Forest 0.698 0.97

Moderate resolution imaging spectroradiometer-derived net primary production Century Forest 0.139 0.90

EDCM Forest 0.172 0.86

Century Grassland/shrubland 0.005 0.96

EDCM Grassland/shrubland 0.008 0.92

U.S. Department of Agriculture grain yield Century Winter wheat 0.001 0.90

EDCM Winter wheat 0.001 0.72
Chapter 7 125

A. MODIS NPP

C. GEMS-EDCM MIROC A1B NPP

B. GEMS-CENTURY MIROC A1B NPP

N 0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

Figure 7.4. 

EXPLANATION

0 to 0.12

0.13 to 0.25

0.26 to 0.49

0.50 to 1.00

1.01 to 1.41

1.42 to 2.00

2.01 to 2.28

Net primary production, 

 in kilograms of carbon

 per square meter

Level II ecoregion boundary

Figure 7–4. Maps showing a comparison of net primary production (NPP) in the Eastern United States for 2006 estimated by A, the

moderate resolution imaging spectroradiometer (MODIS), B, the Century model run in conjunction with a General Ensemble Biogeochemical Modeling System (GEMS) model under Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios 

(SRES; Nakićenović and others, 2000) scenario A1B using the Model for Interdisciplinary Research on Climate version 3.2 medium 

resolution (MIROC 3.2–medres) general circulation model (GCM), and C, the Erosion Deposition Carbon Model (EDCM) in conjunction 

with GEMS run under SRES scenario A1B using the MIROC 3.2-medres GCM. Level II ecoregions are shown in figure 1–1.
126 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

report) in accordance with scenario A1B, A2, or B1 

from the SRES (Nakićenović and others, 2000).

• Three GCM (MIROC 3.2-medres, CSIRO Mk3.0, 

and CCCma CGCM3.1; table 7–1) climate change 

projections associated with each LULC scenario 

were processed. Each of the GCMs corresponded 

to one of the SRES scenarios. 

The models were run for the same land base from 1992 

through 2050, with 1992 through 2000 used as model spin-up, 

2001 through 2005 as the baseline period, and 2006 through 

2050 as the scenario or projection period. Although a longer 

spin-up time window would be desirable (for example, 

thousands of years could be used to reach a quasisteady state 

of SOC under natural conditions (Liu and others, 2003)), the 

process would also require corresponding historical LULC 

data, which were not available for this analysis.

A total of 21 model runs were performed based on the 

combinations of models, LULC scenarios, and GCM projections. It was not 27 model runs (that is, three models for each 

of the three LULC scenarios for each of the three GCMs) 

because the LGAT is not designed to simulate the effects of 

climate change and therefore only had three runs (one for 

each of the three LULC scenarios). It should be noted that 

only three unique model simulations, generated by the three 

models with no variation in LULC and climate, existed from 

1992 through 2006 because there were no alternative scenarios 

for climate and LULC data during the historical period. All 

three models produce all the individual carbon pools. For this 

assessment, CH4

 and N2O were simulated only by the LGAT 

due to extreme challenges in simulating hydrological conditions, a critical controlling factor for N2

O and CH4, in many 

wetlands over large areas using the process-based models 

Century and EDCM. The emission factors of N2O and CH4

compiled from literature are provided in appendix 7.

7.3.4. Definitions, Output, and Analysis

7.3.4.1. Definitions of Carbon Stocks and Fluxes 

and Uncertainty

The key concepts and terminology of carbon stocks 

and fluxes used in this chapter, including net carbon flux, 

NPP, NEP, and NECB, consistent with previous reports for 

the Great Plains (Zhu and others, 2011) and Western (Zhu 

and Reed, 2012) regions of the United States, are defined in 

chapter 1 of this report and follow conventions used in the 

published literature (Chapin and others, 2006).

Three measures of uncertainty, where appropriate, were 

used in this chapter—standard deviation, range, and relative 

uncertainty. Uncertainty, meaning “doubt about the validity of 

a measurement”, can be measured by a “parameter, associated 

with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the 

measurand” (International Organization for Standardization, 

1995). An example of the parameter is the standard deviation, 

which is referred as the “standard uncertainty.” Ranges of 

values can be used as a measure of the dispersion or variation.

A “variability index” (V), a measure of relative uncertainty, was defined for this report and calculated to represent 

the relative dispersion of responses of carbon stock or flux to 

different models, LULC, or GCMs:

V=(Cmax –Cmin)/Cmean , (7–1)

where

Cmax is the maximum carbon stock or flux among 

all models, LULC scenarios, or GNMs; 

Cmin is the minimum carbon stock or flux among 

all models, LULC scenarios, or GNMs; 

and

Cmean is the average carbon stock or flux among 

all models, LULC scenarios, or GNMs.

The variability index is similar to the relative sensitivity as 

both quantify the response of model result to the changing 

conditions. The larger the variability index, the more sensitive 

the GEMS to that variable and the greater the contribution of 

that variable to the overall uncertainty of GEMS results.

7.3.4.2. Output and Further Processing

For this assessment, all the carbon stocks reported were 

the carbon storage at the end of each year, and CH4 and 

N2O fluxes were the annual total fluxes. Annual maps of the 

following variables were generated from each model run:

• the total live biomass carbon (forest total carbon, 

FRSTC), including both aboveground and belowground

• SOC in the top 20-cm layer

• other components, including those that were not 

counted in the live biomass and SOC, such as 

coarse woody debris, litter, and understory

• CH4 and N2O only from the LGAT

• carbon removal from fields by timber and grain 

harvest or land cover conversion

The amounts of carbon removed from ecosystems by timber 

and grain production were tracked in GEMS. However, the 

fate of the offsite carbon in timber and grain products was not 

tracked. Therefore, the offsite contribution of the harvests was 

not included in this assessment. Fire emissions were tracked by 

GEMS according to the extent and severity data layers generated 

in chapter 4. When a land was converted from type A to type 

B, for example, the emissions of carbon were added to cover 

type B, consistent to IPCC good practice guidance (Watson and 

others, 2000). The following variables were calculated, when 

appropriate, based on the model output variables listed above:

• The minimum, maximum, standard deviation, and 

average of carbon stocks and fluxes in FRSTC, SOC, 

other pools, and all system carbon pools (the sum 

of the first three carbon stocks), as simulated by the 

3 (baseline period) and 21 (projection period) model 
Chapter 7 127

simulations, were summarized by ecoregion and 

ecosystem type.

• The annual carbon stock change in a given year

(t; in other words, the net carbon flux and the NECB at 

ecosystem level) was calculated as the stock difference 

between year t and the previous year (t –1) as Ct–1–Ct.

• The average NECB during the baseline period was 

calculated as the difference of total system carbon 

stock between 2001 and 2005 divided by the duration, 

which is 5 years), as follows: NECB=(C2001 –C2005)/5, 

where C2001 and C2005 represent the carbon storage at the 

beginning of 2001 and the end of 2005, respectively.

• Similarly, the average NECB during the projection 

period was calculated as the difference of total 

system carbon stock between 2006 and 2050 divided 

by the duration, which is 45 years, as follows: 

NECB=(C2006–C2050 )/45, where C2006 and C2050

represent the carbon storage at the beginning

of 2006 and the end of 2050, respectively.

Based on these calculations, negative NECB would 

indicate carbon sequestration in terrestrial ecosystems, and 

this notion is consistent with previous reports of the national 

assessment (Zhu and others, 2011; Zhu and Reed, 2012).

Global warming potentials (GWP) of CH4 and N2O 

fluxes in CO2-eq were calculated using 21 as a factor for CH4

and 310 as a factor for N2O (U.S. Environmental Protection 

Agency, 2012). CO2 flux was calculated from the NECB using 

a molecular factor of 3.667 to convert carbon to CO2. The total 

annual GWP of GHG fluxes was calculated as the sum of the 

GWP of CO2, CH4, and N2O.

7.3.4.3. Analysis

The outcome of assessing carbon sequestration and GHG 

emissions is often influenced by diverse factors, including 

models, land use, disturbances, and climate. Many aspects of 

the carbon dynamics and GHG fluxes quantified in this assessment can be analyzed at a range of spatial and temporal scales. 

In this report, the focuses of our analysis are the following:

• Present and analyze the minimum, maximum, and 

average of the carbon stocks and GHG fluxes, estimated by ensemble modeling, during the baseline and 

projection periods for all ecosystems in each ecoregion 

of the Eastern United States. This helps answer the 

following questions:

• What are the spatial patterns of carbon storage 

and GHG fluxes in the Eastern United States?

• How much carbon could be sequestered in vegetation and soils by ecosystem and ecoregion?

• How will carbon sequestration strengths of 

different ecosystems change over time (that is, 

between the baseline and the projection period)?

• What are the uncertainties of the estimates?

• Examine and compare the projected average carbon 

stocks in 2050, projected average annual NECB from 

2006 through 2050, and the variability index by model, 

LULC scenario, and GCM for each ecoregion and for 

the entire Eastern United States. This helps answer the 

following questions:

• What are the differences in the estimated carbon 

sequestration potentials across ecoregions and 

within the Eastern United States?

• What are the results of using different models, 

LULC scenarios, and GCMs on the estimated 

carbon sequestration?

• What is the major contributor among models, 

LULC, and GCMs to the uncertainty in the 

estimated carbon sequestration in various 

ecoregions and the entire Eastern United States?

• Examine the effects of major land use activities and 

disturbances (fire (chap. 4 of this report) and forest 

harvesting activities, including clearcutting and 

partial cutting) on carbon dynamics.

• Perform an integrated analysis of carbon stocks and 

fluxes for the baseline period by synthesizing results 

from fire emissions and aquatic systems. Similar 

integration could not be done for the projection period 

owing to the lack of data for aquatic systems.

• Identify major limitations of this assessment and future 

directions for carbon cycle research, assessment, and 

monitoring in the region.

7.4. Results

7.4.1. Baseline Ecosystem Carbon Stocks

Maps of estimated annual carbon stocks by the terrestrial 

ecosystems and ecoregions from 2001 through 2005 were 

produced using the three models. The magnitude and spatial 

pattern of the carbon stock estimated from 2001 through 2005 

remained relatively stable; for this reason, the estimates for 

2005 (the last year of the baseline period) are presented in this 

report. The map in figure 7–5 shows the spatial distribution 

and uncertainty estimates of total ecosystem carbon stock 

(carbon in live and dead biomass plus SOC in the top 20-cm 

soil layer) in the Eastern United States in 2005. The high 

carbon storage locations are shown mostly in the northern 

States where soil carbon content was high (carbon in 20-cm 

soil layer more than 5 kgC/m2

) and along the Atlantic Ocean 

and Gulf of Mexico coastal regions where wetlands were 

dominant. The Blue Ridge hydrographic province also had 

high carbon storage because of high forest biomass. The 

uncertainty map, which shows the standard deviation, 

shows that high carbon storage regions usually had higher 

model uncertainty.
Table 7–4 lists the range (minimum to maximum) and the 

average of the estimated amounts of carbon stored as estimated 

by the three models (LGAT, Century, and EDCM) for 2005, the 

last year of the baseline conditions. The total estimated carbon 

storage averaged 26,961.8 TgC (ranged from 25,068.8 to 28,497 

TgC across the three models) for the Eastern United States. 

Among all the ecoregions within the Eastern United States, the 

Southeastern USA Plains ecoregion stored the most carbon with 

more than 7,794.2 TgC (29 percent), followed by the Ozark, 

Ouachita-Appalachian Forests (18 percent), Mississippi Alluvial 

and Southeast USA Coastal Plains (15 percent), Mixed Wood 

Plains (13 percent), Atlantic Highlands (10 percent), Mixed 

Wood Shield (10 percent), and Central USA Plains (6 percent) 

ecoregions. SOC in the top 20-cm soil, live biomass, and other 

carbon pool (such as litter and woody debris) accounted for 43 

percent, 42 percent, and 15 percent of the total carbon storage in 

the Eastern United States, respectively. Breaking down different 

ecosystems, forests, agricultural lands, wetland, grassland/

shrubland, and other lands stored 68 percent, 15 percent, 15 

percent, 1 percent, and 1 percent of the total carbon, respectively. Among different ecosystems, forest was the dominant 

carbon storage location for the Mixed Wood Shield, Atlantic 

Highlands, Mixed Wood Plains, Southeastern USA Plains, 

and Ozark, Ouachita-Appalachian Forests ecoregions. Carbon 

storage in the Central USA Plains ecoregion was predominantly 

in agricultural lands, whereas carbon storage in the Mississippi 

Alluvial and Southeast USA Coastal Plains ecoregion was 

predominantly in wetlands.

Carbon density (that is, carbon storage per unit area) for a 

specific ecosystem varied substantially between ecoregions. The 

Atlantic Highlands ecoregion had the highest carbon storage 

density (15 kgC/m2), followed by the Mixed Wood Shield (12 

kgC/m2), Ozark, Ouachita-Appalachian Forests (9.2 kgC/m2), 

Mixed Wood Plains (9.1 kgC/m2), Southeastern USA Plains (7.8 

kgC/m2), Mississippi Alluvial and Southeast USA Coastal Plains 

(7.7 kgC/m2), and Central USA Plains (6.3 kgC/m2) ecoregions. 

A B

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

N

Figure 7.5. 

EXPLANATION

Average carbon storage in 2005, in 

 kilograms of carbon per square meter

EXPLANATION

Standard deviation of the mean carbon storage

 for 2005, in kilograms of carbon per square meter

≤1

1.1 to 2.0

2.1 to 4.0

≤2.0

2.1 to 10.0

10.1 to 20.0

20.1 to 30.0

30.1 to 50.0

50.1 to 100.0

>100

4.1 to 8.0

8.1 to 10.0

>10.0

Level II ecoregion boundary

Level II ecoregion boundary

Figure 7–5. Maps showing the A, average amount and B, standard deviation from the average amount of carbon stored in the Eastern 

United States in 2005. The estimated average amount of carbon stored in 2005 was derived by averaging the results from three General 

Ensemble Modeling System (GEMS) models (Land GHG Accounting Tool, Century, and Erosion Deposition Carbon Model). Level II 

ecoregions are shown in figure 1–1.
Chapter 7 129

For forest, the highest carbon density was in the Atlantic 

Highlands ecoregion (16.8 kgC/m2) and the lowest carbon density 

was in the Southeast USA Coastal Plains ecoregion (11 kgC/

m2). For grassland/shrubland, the highest carbon density was in 

the Atlantic Highlands ecoregion (8.2 kgC/m2) and the lowest 

carbon density was in the Ozark, Ouachita-Appalachian Forests 

ecoregion (3.9 kgC/m2). For agricultural lands, the Central USA 

Plains ecoregion had the highest carbon density (5.7 kgC/m2), 

whereas the Ozark, Ouachita-Appalachian Forests ecoregion 

had the lowest carbon density (3 kgC/m2). For wetlands, the 

Atlantic Highlands ecoregion had the highest carbon density 

(19.2 kgC/m2), whereas the Ozark, Ouachita-Appalachian Forests 

ecoregion had the lowest carbon density (9.4 kgC/m2).

7.4.1.1. Mixed Wood Shield

In this northern ecoregion of the Eastern United States, 

the total carbon storage in 2005 was 2,596 TgC, ranging 

between 2,470 and 2,745 TgC across the three models, of 

which 32 percent was in live biomass, 53 percent was in soil, 

and 15 percent was in ground litter and dead woody biomass. 

Among the different ecosystems, forest occupied 51 percent of 

the total land area and held 52 percent of the total carbon stock 

(1,360 TgC). Grassland/shrubland occupied 2 percent of the 

land area and took 1 percent of the carbon storage (26 TgC). 

Agricultural land area occupied 10 percent of land area with 

4 percent of the total carbon storage (105 TgC). The wetland 

system occupied 27 percent of land area, but accounted for 

42 percent of the total carbon storage (1,100 TgC). Carbon 

densities were 19.2 kgC/m2, 12.3 kgC/m2, 6.7 kgC/m2, 

5 kgC/m2, and for wetland, forest, grassland/shrubland, and 

agricultural land, respectively.

7.4.1.2. Atlantic Highlands

The total carbon storage of this ecoregion in 2005 was 

2,808 TgC, ranging between 2,619 and 3,008 TgC across 

models, of which 45 percent was in live biomass, 37 percent 

was in soil, and 17 percent was in ground litter and dead 

woody biomass. Forest had the major portion of carbon 

stock (2,608 TgC, 93 percent of the total), followed by 

agricultural land (95 TgC, 3 percent of the total) and wetland 

(92 TgC, 3 percent of the total). The grassland/shrubland 

only had 2.5 TgC (0.1 percent of the total). This ecoregion 

had the highest forestland area percentage (83 percent) in 

the eastern ecoregions. The forest carbon density was also 

the highest (16.9 kgC/m2). The carbon densities of wetland, 

grassland/shrubland, and agricultural land were 17.9 kgC/m2, 

8.2 kgC/m2, and 5.2 kgC/m2, respectively.

7.4.1.3. Mixed Wood Plains

This northern ecoregion was dominated by wetland and 

forest ecosystems, which accounted for 41 and 38 percent, 

respectively, of total land area. The total carbon storage 

in this ecoregion in 2005 was 3,550 TgC, ranging from 

3,369 and 3,786 TgC across all models, of which 55 percent 

was in soil, 32 percent was in live biomass, and 13 percent 

was in ground litter and dead woody biomass. Forest had 

the major portion of carbon storage (2,234 TgC, 63 percent 

of the total), followed by agricultural land (883 TgC, 

25 percent of the total) and wetland (345 TgC, 10 percent of 

the total). Grassland/shrubland occupied 0.6 percent of the 

total land area and accounted for only 0.4 percent (14 TgC) 

of total carbon storage. Carbon densities were 15.1 kgC/m2, 

14.3 kgC/m2, 6.1 kgC/m2, and 5.5 kgC/m2 for forest, wetland, 

grassland/shrubland, and agricultural land, respectively.

7.4.1.4. Central USA Plains

As a primarily agricultural region, the Central USA 

Plains mainly consisted of agricultural and pasture lands. 

Together, they accounted for 78 percent of the total land area. 

Forest, wetland, and grassland/shrubland covered 10 percent, 

2 percent, and 0.5 percent, respectively, of total land area. The 

total carbon storage of this ecoregion in 2005 was 1,500 TgC, 

ranging from 1,442to 1,565 TgC across models, of which 

74 percent was in soil, 17 percent was in live biomass, and 

9 percent was in ground litter and dead woody biomass. Crop 

land had the greatest portion of carbon stock (1,057 TgC, 

70 percent of the total), followed by forest (320 TgC, 

21 percent of the total), wetland (68 TgC, 5 percent of the 

total) and grassland/shrubland (8 TgC, 1 percent of the total). 

Carbon densities were 14.6 kgC/m2, 13.5 kgC/m2, 6.6 kgC/m2, 

and 5.7 kgC/m2 for wetland, forest, grassland/shrubland, and 

agricultural land, respectively.

7.4.1.5. Southeastern USA Plains

This is the largest ecoregion in the Eastern United 

States with significant forest and agricultural lands, covering 

55 and 31 percent, respectively, of total land area. Wetland 

covered 6 percent of the total land area, and grassland/

shrubland covered only 1 percent of the total land area. The 

total carbon storage of this ecoregion in 2005 was 7,794 TgC, 

ranging from 7,065 to 8,266 TgC across the three models, 

of which 51 percent was in live biomass, 33 percent was in 

soil, and 16 percent was in ground litter and dead woody 

biomass. Forest land had the greatest portion of carbon stock 

(6,052 TgC, 78 percent of the total), followed by cropland 

(895 TgC, 11 percent of the total), wetland (732 TgC, 

9 percent of the total), and grassland/shrubland (48 TgC, 

1 percent of the total). Carbon densities were 12.1 kgC/m2, 

11 kgC/m2, 4.3 kgC/m2, 3 kgC/m2 for wetland, forest, 

grassland/shrubland, and agricultural land, respectively.

7.4.1.6. Ozark, Ouachita-Appalachian Forests

Approximately 72 percent of this ecoregion is forest. 

The total carbon storage of this ecoregion in 2005 was 
130 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7– 4. Carbon stored in the Eastern United States in 2005.

[Carbon storage is by carbon pool for each ecoregion and ecosystem. Only soil organic carbon (SOC) in the top 20 centimeters of the soil layer was calculated. 

km2, square kilometers; max, maximum; min, minimum; TgC, teragrams (or 1012 grams) of carbon]

Ecoregion Ecosystem Area,

in km2

Biomass, in TgC SOC, in TgC 

Min Max Average Min Max Average 

Mixed Wood 

Shield

Forests 110,556 508.3 534.9 530.2 534.2 600.8 564.5 

Grass/shrub 3,796 0.9 4.5 2.9 18.1 19.7 19.4 

Agriculture 20,986 0.0 6.8 3.0 88.7 102.8 94.3 

Wetlands 57,336 241.2 306.6 279.4 676.3 724.2 704.2 

Other 22,926 0.0 0.0 0.0 0.6 27.3 6.3 

Total 215,599 750.5 852.7 815.6 1,317.9 1,474.8 1,388.6 

Atlantic Highlands Forests 154,954 1,206.4 1,273.2 1,238.8 836.1 989.4 904.1 

Grass/shrub 306 0.1 1.0 0.5 1.6 1.7 1.6 

Agriculture 18,194 0.0 3.3 1.7 82.8 92.8 88.1 

Wetlands 5,123 30.8 36.9 33.8 44.6 47.9 46.0 

Other 8,973 0.0 0.2 0.1 1.9 26.2 10.3 

Total 187,550 1,237.3 1,314.7 1,274.9 967.0 1,158.0 1,050.1 

Mixed Wood Plains Forests 147,983 958.4 999.5 977.5 821.7 943.6 876.2 

Grass/shrub 2,247 0.4 2.6 1.5 10.4 11.2 10.6 

Agriculture 159,756 0.0 66.3 37.1 761.5 846.1 793.9 

Wetlands 24,231 97.2 110.6 103.4 194.4 211.9 201.7 

Other 54,639 0.0 2.0 0.9 19.2 162.2 70.3 

Total 388,858 1,056.0 1,181.0 1,120.3 1,807.3 2,175.0 1,952.6 

Central USA Plains Forests 23,787 153.7 170.5 160.0 97.3 115.8 108.2 

Grass/shrub 1,175 0.0 1.0 0.6 6.3 6.9 6.6 

Agriculture 185,336 0.0 109.5 62.4 884.5 953.0 919.5 

Wetlands 4,675 21.7 25.5 23.6 35.8 38.5 37.0 

Other 24,055 0.0 2.2 0.9 18.3 83.1 44.7 

Total 239,027 175.4 308.7 247.5 1,042.2 1,197.3 1,116.1 

Southeastern 

USA Plains

Forests 550,022 3,434.0 3,567.7 3,502.3 1,141.4 1,546.1 1,477.7 

Grass/shrub 11,262 9.4 15.2 12.7 23.5 30.9 28.3 

Agriculture 306,678 0.3 85.2 47.2 670.1 831.8 745.3 

Wetlands 60,762 361.4 421.5 390.5 213.1 256.7 241.9 

Other 65,622 0.0 4.1 1.9 26.4 102.8 56.4 

Total 994,346 3,805.1 4,093.8 3,954.6 2,074.6 2,768.4 2,549.6 

Ozark, OuachitaAppalachian 

Forests

Forests 372,212 2,542.7 2,645.3 2,567.9 849.1 1,143.3 1,069.2 

Grass/shrub 3,903 0.7 4.2 2.3 9.7 12.1 11.1 

Agriculture 117,760 0.1 23.0 13.0 273.4 337.8 303.8 

Wetlands 2,592 11.5 12.6 12.1 7.5 9.0 8.6 

Other 24,020 0.0 1.1 0.5 7.9 37.9 20.0 

Total 520,486 2,554.9 2,686.2 2,595.8 1,147.6 1,540.1 1,412.7 

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Forests 101,944 607.1 750.9 708.7 445.3 492.3 479.6 

Grass/shrub 28,618 13.0 33.1 24.5 85.2 90.3 87.9 

Agriculture 143,291 0.1 36.7 20.1 459.8 524.2 493.4 

Wetlands 116,763 466.5 615.5 565.0 999.0 1,059.2 1,009.4 

Other 116,144 0.0 2.1 0.9 21.1 201.9 70.1 

Total 506,760 1,086.7 1,438.2 1,319.2 2,010.3 2,367.9 2,140.3 

Eastern United 

States

Forests 1,461,458 9,410.6 9,942.1 9,685.5 4,725.2 5,831.3 5,479.4 

Grass/shrub 51,306 24.5 61.6 44.9 154.8 172.8 165.4 

Agriculture 952,000 0.4 330.8 184.5 3,220.8 3,688.4 3,438.5 

Wetlands 271,482 1,230.3 1,529.3 1,407.8 2,170.8 2,347.5 2,248.8 

Other 316,380 0.0 11.7 5.3 95.3 641.4 278.0 

Total 3,052,626 10,665.9 11,875.4 11,328.0 10,366.9 12,681.5 11,610.0 
Chapter 7 131

Table 7– 4. Carbon stored in the Eastern United States in 2005.—Continued

[Carbon storage is by carbon pool for each ecoregion and ecosystem. Only soil organic carbon (SOC) in the top 20 centimeters of the soil layer was calculated. 

km2, square kilometers; max, maximum; min, minimum; TgC, teragrams (or 1012 grams) of carbon]

Ecoregion

Mixed Wood Shield

Ecosystem Area, 

in km2

Others, in TgC Total, in TgC

Min Max Average Min Max Average

Forests 110,556 232.1 295.2 265.6 1,299.7 1,424.3 1,360.3

Grass/shrub 3,796 0.0 4.9 3.2 22.5 28.9 25.5

Agriculture 20,986 0.0 12.4 7.3 98.4 109.6 104.7

Wetlands 57,336 107.4 123.9 116.0 1,048.9 1,154.6 1,099.6

Other 22,926 0.0 0.2 0.1 0.8 27.3 6.4

Total 215,599 339.5 436.6 392.2 2,470.3 2,744.5 2,596.4

Atlantic Highlands Forests 154,954 390.9 531.4 464.8 2,437.5 2,779.3 2,607.7

Grass/shrub 306 0.0 0.7 0.4 1.8 3.0 2.5

Agriculture 18,194 0.0 9.3 5.1 89.0 102.1 94.9

Wetlands 5,123 11.1 13.2 12.2 87.4 97.7 91.9

Other 8,973 0.0 1.3 0.8 3.2 26.2 11.2

Total 187,550 402.0 555.9 483.2 2,619.0 3,008.2 2,808.2

Mixed Wood Plains Forests 147,983 347.4 416.7 380.6 2,127.8 2,344.7 2,234.3

Grass/shrub 2,247 0.0 2.4 1.5 12.0 15.1 13.6

Agriculture 159,756 0.0 75.2 51.6 873.6 907.7 882.6

Wetlands 24,231 39.1 40.3 40.1 332.0 356.2 345.2

Other 54,639 0.0 4.5 2.8 23.8 162.2 74.0

Total 388,858 386.6 539.1 476.6 3,369.1 3,785.9 3,549.6

Central USA Plains Forests 23,787 50.1 52.7 51.8 310.3 330.2 319.9

Grass/shrub 1,175 0.0 0.9 0.6 6.9 8.8 7.8

Agriculture 185,336 0.0 97.6 75.1 1,039.7 1,072.4 1,057.0

Wetlands 4,675 7.4 8.2 7.7 65.7 70.8 68.4

Other 24,055 0.0 1.0 0.7 19.3 83.1 46.4

Total 239,027 57.5 160.5 135.9 1,441.9 1,565.3 1,499.5

Southeastern Forests 550,022 880.3 1,228.9 1,072.0 5,525.4 6,338.1 6,052.0

USA Plains Grass/shrub 11,262 0.0 12.7 7.3 38.4 58.9 48.2

Agriculture 306,678 0.0 180.2 102.7 805.8 1,012.3 895.3

Wetlands 60,762 80.9 124.8 99.9 655.4 753.7 732.4

Other 65,622 0.0 13.0 8.1 39.7 102.8 66.3

Total 994,346 961.2 1,559.7 1,290.0 7,064.7 8,265.8 7,794.2

Ozark, Ouachita- Forests 372,212 720.8 769.2 744.3 4,215.2 4,417.0 4,381.4

Appalachian 

Forests

Grass/shrub

Agriculture

3,903

117,760

0.0

0.0

2.7

56.8

1.7

30.6

12.5

311.3

18.1

394.6

15.1

347.4

Wetlands 2,592 3.3 4.1 3.6 23.2 24.6 24.3

Other 24,020 0.0 3.8 2.3 11.8 37.9 22.8

Total 520,486 724.1 836.7 782.5 4,574.0 4,892.2 4,791.0

Mississippi Alluvial Forests 101,944 172.6 269.0 230.2 1,225.1 1,497.6 1,418.6

and Southeast 

USA Coastal 

Plains

Grass/shrub

Agriculture

Wetlands

28,618

143,291

116,763

0.0

0.0

107.2

24.0

120.5

179.0

14.1

70.4

145.6

108.0

537.6

1,633.0

147.4

644.6

1,743.6

126.4

583.8

1,720.0

Other 116,144 0.0 4.9 3.0 26.1 201.9 74.0

Total 506,760 279.9 597.4 463.3 3,529.8 4,235.1 3,922.8

Eastern United Forests 1,461,458 2,794.2 3,563.1 3,209.4 17,141.1 19,131.1 18,374.2

States Grass/shrub 51,306 0.0 48.4 28.7 202.1 280.2 239.0

Agriculture 952,000 0.0 552.1 342.8 3,755.3 4,243.2 3,965.7

Wetlands 271,482 356.4 493.4 425.2 3,845.5 4,201.0 4,081.8

Other 316,380 0.0 28.8 17.8 124.8 641.4 301.1

Total 3,052,626 3,150.7 4,685.8 4,023.8 25,068.8 28,497.0 26,961.8
132 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

4,791 TgC, ranging from 4,574 to 4,892 TgC across models, 

of which 54 percent was in live biomass, 29 percent was in 

soil, and 16 percent was in ground litter and dead woody 

biomass. Forest land had the greatest portion of carbon stock 

(4,381 TgC, 91 percent of the total), followed by cropland 

(347 TgC, 7 percent of the total), wetland (24 TgC, 1 percent 

of the total) and grassland/shrubland (15 TgC, 0.3 percent of 

the total). Carbon densities were 11.8 kgC/m2, 9.4 kgC/m2, 

3.9 kgC/m2, and 3 kgC/m2 for forest, wetland, grassland/

shrubland, and agricultural land, respectively.

7.4.1.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

In this largest coastal ecoregion in the United States, 

cropland, wetland, forest, and grassland/shrubland occupied 

28 percent, 23 percent, 20 percent, and 6 percent, respectively, 

of total land area. Other lands mostly have no natural 

vegetation cover (such as, barren and urban) and encompassed 

23 percent of the total land area. The total carbon storage 

of this ecoregion in 2005 was 3,923 TgC, ranging from 

3,530 to 4,235 TgC across models, of which 54 percent was 

in soil, 34 percent was in live biomass, and 12 percent was 

in ground litter and dead woody biomass. Wetland had the 

greatest portion of carbon stock (1,720 TgC, 44 percent of 

the total), followed by forest land (1,419 TgC, 36 percent of 

the total), cropland (584 TgC, 15 percent of the total) and 

grassland/shrubland (126 TgC, 3 percent of the total). Carbon 

densities were 14.7 kgC/m2, 13.9 kgC/m2, 4.4 kgC/m2, and 

4.1 kgC/m2 for forest, wetland, grassland/shrubland, and 

agricultural land, respectively.

7.4.2. Baseline Net Ecosystem Carbon Fluxes

The magnitude and spatial distribution of the average net 

carbon fluxes across the Eastern United States are shown in 

figure 7–6, which indicates a strong carbon sink associated with 

forest areas in the region. The standard deviations were generally positively correlated with carbon gains, as was expected.

Table 7–5 lists the minimum, maximum, and average 

of the carbon stock change (that is, the NECB) by carbon 

pool (live biomass, dead biomass, and soil), ecosystem 

type, and ecoregion in the Eastern United States averaged 

from 2001 through 2005. The overall NECB ranged 

from –405.5 to –112.5 TgC/yr among the three models, with 

an average of –279.4 TgC/yr, of which –188.7 TgC was 

attributed to live biomass accumulation, –65.4 TgC to soil 

carbon pool, and –25.2 TgC to dead biomass carbon pool. The 

forest ecosystem was the largest carbon sink (81 percent of 

the total), followed by wetland (13 percent), agricultural lands 

(4 percent), and grassland/shrubland (1 percent). On a per-unit 

area basis, the magnitude of the carbon sink in forests, wetlands, 

grassland/shrubland, and agricultural lands was –155 gC/m2/yr, 

–132 gC/m2/yr, –41 gC/m2/yr, and –12 gC/m2/yr, respectively. 

Although all the ecoregions were carbon sinks from 2001 

through 2005, certain individual ecosystems in specific 

ecoregions were not. For example, agricultural lands in the 

Mixed Wood Shield ecoregion and grassland/shrubland in the 

Central USA Plains ecoregion were estimated to be carbon 

neutral, and agricultural lands in the Atlantic Highlands and 

Mixed Wood Plains ecoregions were estimated to lose carbon 

at a rate of 0.2 TgC/yr and 1.5 TgC/yr, respectively.

7.4.2.1. Mixed Wood Shield

The average estimate for net ecosystem carbon flux in 

this ecoregion was approximately –14.5 teragrams of carbon 

per year (TgC/yr), ranging from –18.6 to –6.3 TgC/yr across 

models, of which 76 percent was allocated to live biomass, 

13 percent to soil, and 11 percent to ground litter and dead 

woody biomass. Among the different ecosystems, the forest 

ecosystem sequestered –9.7 TgC/yr (67 percent of the total), 

wetland –4.5 TgC/yr (31 percent), grassland/shrubland 

–0.3 TgC/yr (2 percent of the total), and agricultural land 

(carbon neutral).

7.4.2.2. Atlantic Highlands

The average estimate for net carbon flux in this 

overwhelmingly forested ecoregion was –24.7 TgC/yr, 

ranging from –28.6 to –15.0 TgC/yr across models, of which 

81 percent was allocated to live biomass, 18 percent to soil, 

and 1 percent to ground litter and dead woody biomass. The 

forest ecosystem sequestered –24.2 TgC/yr (98 percent of the 

total), followed by wetland with –0.6 TgC/yr (2 percent of the 

total). Agricultural land lost carbon at a small rate of 0.2 Tg/yr.

7.4.2.3. Mixed Wood Plains

The average estimate for net carbon flux in this ecoregion 

was –22.4 TgC/yr, ranging from –26.2 to –12.4 TgC/yr 

across the three models, of which 84 percent was allocated 

to live biomass, 9 percent to ground litter and dead 

woody biomass, and 7 percent to soil. Forest sequestered 

–20.9 TgC/yr (93 percent of the total), followed by wetland 

with –2.1 TgC/yr (9 percent of the total) and grassland/

shrubland with –0.1 TgC/yr (0.5 percent of the total). 

Agricultural land was a carbon source at a rate of 1.5 TgC/yr.

7.4.2.4. Central USA Plains

The ecoregion was dominated by agricultural lands 

(78 percent of the total land area). The average estimate for net 

carbon flux was –5.2 TgC/yr, ranging from –7.4 to –2.9 TgC/yr 

across models, of which 46 percent was allocated to ground 

litter and dead woody biomass, 44 percent to live biomass, and 

10 percent to soil. Forest sequestered –2.7 TgC/yr (52 percent 

of the total), followed by agricultural land (–1.6 TgC/yr, 

31 percent of the total), wetland (–0.4 TgC/yr, 8 percent of 

the total), and grassland/shrubland (carbon neutral).
Chapter 7 133

A B

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

N

Figure 7.6. 

EXPLANATION EXPLANATION

Standard deviation of the mean carbon storage

 for 2005, in kilograms of carbon per square meter

≤1

1.1 to 2.0

2.1 to 4.0

4.1 to 8.0

8.1 to 10.0

>10.0

Level II ecoregion boundary

Total mean net carbon flux from 2001 to 2005, in 

 kilograms of carbon per square meter per year

≤–5

–5.0 to –2.1

–2.0 to –0.6

–0.5 to 0.4

0.5 to 1.9

2.0 to 4.9

≥5

Level II ecoregion boundary

Figure 7–6. Maps showing carbon flux in ecosystems of the Eastern United States. A, The average net carbon flux derived from each 

of the three General Ensemble Modeling System (GEMS) models (Land GHG Accounting Tool, Century, and Erosion Deposition Carbon 

Model) and averaged for the baseline years (2001 through 2005). B, The standard deviation of the three models for the baseline years. 

Negative values indicate net carbon gains and positive values indicate net carbon losses. Level II ecoregions are shown in figure 1–1.

7.4.2.5. Southeastern USA Plains

This largest ecoregion in this assessment was dominated 

by forests and agricultural lands (55 percent and 31 percent, 

respectively, of total land area). The average estimate 

for net carbon flux was –112.2 TgC/yr, ranging from 

–176.7 to –39.4 TgC/yr across the three models, of which 

62xpercent was allocated to live biomass, 29 percent to soil, 

and 9 percent to ground litter and dead woody biomass. Forest 

sequestered –93.4 TgC/yr (83 percent of the total), followed 

by wetland (–12.6 TgC/yr, 11 percent of the total), cropland 

(–4.7 TgC/yr, 4 percent of the total), and grassland/shrubland 

(–0.6 TgC/yr, 0.5 percent of the total).

7.4.2.6. Ozark, Ouachita-Appalachian Forests

In this heavily forested ecoregion (72 percent of the 

total area), the average estimate for net carbon flux was 

–60.7 TgC/yr, ranging from –84.6 to –30.4 TgC/yr across 

models, of which 65 percent was allocated to live biomass, 

29percent to soil, and 6 percent to ground litter and dead woody 

biomass. Forest sequestered –58 TgC/yr (96 percent of the 

total), followed by agricultural land (–1.8 TgC/yr, 3 percent 

of the total), wetland (–0.4 TgC/yr, 1 percent of the total), and 

grassland/shrubland (–0.2 TgC/yr, less than 0.5 percent of the 

total).

7.4.2.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

The costal ecoregion consisted of agricultural lands 

(28 percent), wetlands (23 percent), other lands (23 percent), 

forests (20 percent), and grassland/shrubland (6 percent). The 

mean estimate for net carbon flux was –39.7 TgC/yr, ranging 

from –63.4 to –6.2 TgC/yr across models, of which 70 percent 

was allocated to live biomass, 18 percent to soil, and 
134 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–5. Net ecosystem carbon balance in the Eastern United States from 2001 through 2005.

[Data are by carbon pool for each ecoregion and ecosystem Negative numbers indicate carbon sequestration; positive numbers indicate a loss of carbon to the 

atmosphere. Only soil organic carbon (SOC) in the top 20 centimeters of the soil layer was calculated. km2, square kilometers; max, maximum; min, minimum; 

TgC/yr, teragrams (or 1012 grams) of carbon per year]

Ecoregion Ecosystem Area,

in km2

Biomass, in TgC/yr SOC, in TgC/yr 

Min Max Average Min Max Average 

Mixed Wood Shield Forests 110,556 –7.2 –3.3 –6.6 –3.1 –0.1 –2.1 

Grass/shrub 3,796 0.0 0.1 0.0 –0.3 –0.2 –0.2 

Agriculture 20,986 –0.1 0.0 0.0 –0.1 0.4 0.2 

Wetlands 57,336 –5.9 –1.5 –4.5 –0.4 0.9 0.2 

Other 22,926 0.0 0.0 0.0 0.0 0.1 0.0 

Total 215,599 –13.3 –4.7 –11.0 –3.9 1.1 –1.9 

Atlantic Highlands Forests 154,954 –21.0 –12.1 –19.3 –8.8 0.1 –4.6 

Grass/shrub 306 0.0 0.0 0.0 –0.1 0.0 –0.1 

Agriculture 18,194 0.0 0.0 0.0 0.0 0.5 0.3 

Wetlands 5,123 –0.7 –0.2 –0.6 –0.1 0.0 –0.1 

Other 8,973 0.0 0.0 0.0 –0.1 0.1 0.0 

Total 187,550 –21.8 –12.3 –19.9 –9.1 0.8 –4.4 

Mixed Wood Plains Forests 147,983 –18.1 –11.1 –17.0 –5.7 0.7 –3.2 

Grass/shrub 2,247 0.0 0.0 0.0 –0.1 –0.1 –0.1 

Agriculture 159,756 –0.4 0.1 0.0 0.8 3.1 2.5 

Wetlands 24,231 –2.0 –1.1 –1.7 –0.5 –0.1 –0.3 

Other 54,639 –0.1 0.0 –0.1 –1.3 0.2 –0.6 

Total 388,858 –20.7 –12.1 –18.8 –6.8 3.9 –1.6 

Central USA Plains Forests 23,787 –2.0 –1.6 –1.8 –1.0 0.1 –0.7 

Grass/shrub 1,175 0.0 0.0 0.0 0.0 0.1 0.0 

Agriculture 185,336 –1.4 0.1 –0.1 0.1 1.2 0.7 

Wetlands 4,675 –0.4 –0.2 –0.3 –0.1 0.0 –0.1 

Other 24,055 –0.1 0.0 0.0 –1.1 0.0 –0.5 

Total 239,027 –3.9 –1.6 –2.3 –2.1 1.4 –0.5 

Southeastern 

USA Plains

Forests 550,022 –85.6 –32.5 –61.3 –36.8 0.7 –24.4 

Grass/shrub 11,262 –0.1 0.0 0.0 –0.7 0.0 –0.4 

Agriculture 306,678 –0.7 0.8 0.2 –11.0 1.4 –3.6 

Wetlands 60,762 –9.9 –3.6 –7.9 –5.1 –0.7 –3.3 

Other 65,622 –0.2 0.0 –0.1 –1.4 –0.3 –0.9 

Total 994,346 –96.6 –35.2 –69.2 –55.0 1.2 –32.6 

Ozark, OuachitaAppalachian 

Forests

Forests 372,212 –52.0 –26.2 –39.3 –18.6 0.2 –15.4 

Grass/shrub 3,903 0.0 0.0 0.0 –0.2 0.0 –0.1 

Agriculture 117,760 –0.3 0.0 0.0 –4.3 1.0 –1.3 

Wetlands 2,592 –0.3 –0.1 –0.2 –0.2 0.0 –0.1 

Other 24,020 –0.1 0.0 0.0 –0.5 0.0 –0.3 

Total 520,486 –52.6 –26.2 –39.6 –23.8 1.2 –17.3 

Mississippi Alluvial 

and Southeast 

USA Coastal 

Plains

Forests 101,944 –20.9 –3.0 –15.1 –4.3 1.1 –2.1 

Grass/shrub 28,618 –0.2 0.0 –0.1 –0.8 0.1 –0.5 

Agriculture 143,291 0.0 0.1 0.0 –5.1 1.3 –2.1 

Wetlands 116,763 –15.9 –3.0 –12.7 –4.6 1.5 –1.5 

Other 116,144 –0.1 0.0 –0.1 –1.6 –0.2 –1.0 

Total 506,760 –37.2 –5.9 –27.9 –16.4 3.8 –7.1 

Eastern United 

States

Forests 1,461,458 –206.8 –89.7 –160.4 –78.2 2.9 –52.3 

Grass/shrub 51,306 –0.3 0.2 –0.1 –2.2 –0.1 –1.3 

Agriculture 952,000 –3.0 1.2 –0.1 –19.7 9.1 –3.5 

Wetlands 271,482 –35.2 –9.8 –27.9 –11.1 1.6 –5.0 

Other 316,380 –0.7 0.0 –0.3 –6.0 –0.2 –3.2 

Total 3,052,626 –246.1 –98.1 –188.7 –117.2 13.3 –65.4 
Chapter 7 135

Table 7–5. Net ecosystem carbon balance in the Eastern United States from 2001 through 2005.—Continued

[Data are by carbon pool for each ecoregion and ecosystem Negative numbers indicate carbon sequestration; positive numbers indicate a loss of carbon to the 

atmosphere. Only soil organic carbon (SOC) in the top 20 centimeters of the soil layer was calculated. km2, square kilometers; max, maximum; min, minimum; 

TgC/yr, teragrams (or 1012 grams) of carbon per year]

Ecoregion

Mixed Wood Shield

Ecosystem Area, 

in km2

Others, in TgC/yr Total, in TgC/yr

Min Max Average Min Max Average

Forests 110,556 –3.5 1.6 –1.0 –12.4 –4.7 –9.7

Grass/shrub 3,796 – 0.2 0.0 – 0.1 – 0.4 – 0.2 – 0.3

Agriculture 20,986 – 0.4 0.0 – 0.2 – 0.4 0.4 0.0

Wetlands 57,336 – 0.8 0.4 – 0.3 –5.3 –1.7 –4.5

Other 22,926 0.0 0.0 0.0 – 0.1 0.1 0.0

Total 215,599 –5.0 2.0 –1.6 –18.6 –6.3 –14.5

Atlantic Highlands Forests 154,954 –3.3 2.3 – 0.2 –27.5 –15.3 –24.2

Grass/shrub 306 0.0 0.0 0.0 – 0.1 0.0 – 0.1

Agriculture 18,194 – 0.2 0.0 – 0.1 – 0.1 0.5 0.2

Wetlands 5,123 – 0.1 0.1 0.0 – 0.7 – 0.3 – 0.6

Other 8,973 0.0 0.0 0.0 – 0.1 0.1 0.0

Total 187,550 –3.7 2.4 – 0.3 –28.6 –15.0 –24.7

Mixed Wood Plains Forests 147,983 –3.0 1.7 – 0.8 –22.6 –13.5 –20.9

Grass/shrub 2,247 – 0.1 0.0 0.0 – 0.2 – 0.1 – 0.1

Agriculture 159,756 –1.7 0.0 –1.0 0.3 2.6 1.5

Wetlands 24,231 – 0.3 0.1 – 0.1 –2.4 –1.5 –2.1

Other 54,639 – 0.2 0.0 – 0.1 –1.3 0.1 – 0.7

Total 388,858 –5.2 1.8 –2.0 –26.2 –12.4 –22.4

Central USA Plains Forests 23,787 – 0.4 0.0 – 0.2 –3.0 –2.1 –2.7

Grass/shrub 1,175 0.0 0.0 0.0 0.0 0.1 0.0

Agriculture 185,336 –2.9 0.0 –2.1 –2.8 – 0.4 –1.6

Wetlands 4,675 – 0.1 0.0 – 0.1 – 0.5 – 0.3 – 0.4

Other 24,055 0.0 0.0 0.0 –1.1 – 0.2 – 0.6

Total 239,027 –3.4 0.0 –2.4 –7.4 –2.9 –5.2

Southeastern Forests 550,022 –19.3 3.4 –7.7 –141.7 –37.7 –93.4

USA Plains Grass/shrub 11,262 – 0.3 0.0 – 0.2 –1.0 0.1 – 0.6

Agriculture 306,678 –3.9 1.0 –1.2 –14.9 3.2 –4.7

Wetlands 60,762 –2.4 – 0.6 –1.4 –17.5 –4.9 –12.6

Other 65,622 – 0.2 0.4 0.1 –1.6 – 0.1 – 0.9

Total 994,346 –26.2 4.3 –10.4 –176.7 –39.4 –112.2

Ozark, Ouachita- Forests 372,212 –7.4 1.6 –3.2 –78.0 –31.1 –58.0

Appalachian 

Forests

Grass/shrub

Agriculture

3,903

117,760

– 0.1

– 0.9

0.0

0.0

– 0.1

– 0.4

– 0.3

–5.2

0.0

0.9

– 0.2

–1.8

Wetlands 2,592 – 0.1 0.0 – 0.1 – 0.6 – 0.1 – 0.4

Other 24,020 – 0.1 0.0 0.0 – 0.5 0.0 – 0.3

Total 520,486 –8.6 1.6 –3.8 –84.6 –30.4 –60.7

Mississippi Alluvial Forests 101,944 –1.6 1.3 – 0.3 –26.8 –2.6 –17.4

and Southeast 

USA Coastal 

Plains

Grass/shrub

Agriculture

Wetlands

28,618

143,291

116,763

– 0.7

–5.9

–1.9

0.0

0.0

– 0.5

– 0.3

–2.9

–1.1

–1.5

–11.0

–22.3

0.0

1.3

–4.4

– 0.9

–5.0

–15.2

Other 116,144 – 0.2 0.0 – 0.1 –1.8 – 0.5 –1.2

Total 506,760 –10.2 0.8 –4.7 –63.4 –6.2 –39.7

Eastern United Forests 1,461,458 –38.6 11.8 –13.5 –312.1 –107.0 –226.2

States Grass/shrub 51,306 –1.4 0.0 – 0.7 –3.5 – 0.1 –2.1

Agriculture 952,000 –15.8 1.0 –7.9 –34.1 8.4 –11.5

Wetlands 271,482 –5.7 – 0.4 –3.0 –49.3 –13.3 –35.9

Other 316,380 – 0.8 0.5 – 0.2 –6.6 – 0.5 –3.7

Total 3,052,626 –62.3 12.9 –25.2 –405.5 –112.5 –279.4
136 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

12 percent to ground litter and dead woody biomass. Forest 

sequestered –17.4 TgC/yr (44 percent of the total), followed 

by wetland (–15.2 TgC/yr, 38 percent of the total), agricultural 

land (–5 TgC/yr, 13 percent of the total) and grassland/

shrubland (–0.9 TgC/yr, 2 percent of the total).

7.4.3. Baseline GHG Fluxes

The minimum, maximum, and average estimates of 

GHG fluxes for the baseline years are listed in table 7–6. To 

illustrate the spatial distribution, example maps of the GHG 

fluxes in 2005 are presented in figure 7–7.

A B

C D

N

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

Figure 7.7. 

Level II ecoregion 

 boundary

< –250

–250 to < – 75

–75 to <75

75 to 250

>250

EXPLANATION

Carbon dioxide, in 

 grams of carbon 

 dioxide equivalent 

 per square meter

 per year

Level II ecoregion 

 boundary

<10

10 to < 25

25 to 50

>50

EXPLANATION

Nitrous oxide, in 

 grams of carbon 

 dioxide equivalent 

 per square meter

 per year

Level II ecoregion 

 boundary

< –15

–15 to <– 5

– 5 to <5

5 to 15

>15

EXPLANATION

Methane, in grams

 of carbon dioxide

 equivalent per

 square meter

 per year

Level II ecoregion 

 boundary

< –250

–250 to < – 75

–75 to <75

75 to 250

>250

EXPLANATION

Global warming 

 potential, in grams

 of carbon dioxide

 equivalent per

 square meter per year

A B

C D

N

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

Figure 7.7. 

Level II ecoregion 

 boundary

< –250

–250 to < – 75

–75 to <75

75 to 250

>250

EXPLANATION

Carbon dioxide, in 

 grams of carbon 

 dioxide equivalent 

 per square meter

 per year

Level II ecoregion 

 boundary

<10

10 to < 25

25 to 50

>50

EXPLANATION

Nitrous oxide, in 

 grams of carbon 

 dioxide equivalent 

 per square meter

 per year

Level II ecoregion 

 boundary

< –15

–15 to <– 5

– 5 to <5

5 to 15

>15

EXPLANATION

Methane, in grams

 of carbon dioxide

 equivalent per

 square meter

 per year

Level II ecoregion 

 boundary

< –250

–250 to < – 75

–75 to <75

75 to 250

>250

EXPLANATION

Global warming 

 potential, in grams

 of carbon dioxide

 equivalent per

 square meter per year

Figure 7–7. Maps showing the spatial distribution of the average annual carbon dioxide, methane, and nitrous oxide fluxes and the 

total global warming potential from 2001 through 2005 in the Eastern United States. Level II ecoregions are shown in figure 1–1.
Chapter 7 137

Overall, the Eastern United States served as a GHG sink at 

an average rate of –656.9 TgCO2-eq/yr (ranging from –1,122.3 

to –41.3 TgCO2-eq/yr across the three models). On average, 

the net CO2 sink strength was –1,024.6 TgCO2-eq/yr. However, 

the CO2 sink was partially offset by N2O and CH4 emissions 

with averages of 174.7 TgCO2-eq/yr and 193 TgCO2-eq/yr, 

respectively. Among all ecosystems, forest was the largest 

sink of GHG (–776.6 TgCO2-eq/yr), whereas wetlands and 

cropland were net GHG sources emitting 69.6 TgCO2-eq/yr 

and 45.9 TgCO2-eq/yr, respectively. Forest and cropland were 

major sources of N2O with average annual contributions of 76.4 

TgCO2-eq/yr and 74.7 TgCO2-eq/yr, respectively. Wetlands 

contributed the highest amount of CH4 with 186.2 TgCO2-eq/yr 

on emission, followed by cropland with 12.9 TgCO2-eq/yr, but 

forests took up CH4 with an average rate of –23.2 TgCO2-eq/yr.

7.4.4. Projected Future Carbon Stock Distributions

A total of 21 maps resulted from the 21 simulation 

model runs described in the “Ensemble Modeling” and 

“Output and Further Processing” sections, which depict the 

spatial patterns of carbon storage in 2050 (the end year of the 

scenario period), were produced for the Eastern United States. 

The maps showing the average and standard deviation of the 

21 simulation model runs are shown in figure 7–8. Similar 

to the baseline carbon stock maps (fig. 7–5), the projected 

future carbon stock maps show that forest ecosystems have the 

highest carbon density (that is, carbon storage per unit area), 

and grass/shrublands and agricultural lands have the lowest 

carbon densities. The spatial pattern of carbon storage in 2050 

is in general agreement with that in 2005.

A B

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

N

Figure 7.8. 

EXPLANATION

Average carbon storage in 2050, in 

 kilograms of carbon per square meter

EXPLANATION

Standard deviation of the mean carbon storage

 for 2050, in kilograms of carbon per square meter

≤1

1.1 to 2.0

2.1 to 4.0

≤2.0

2.1 to 10.0

10.1 to 20.0

20.1 to 30.0

30.1 to 50.0

50.1 to 100.0

>100

4.1 to 8.0

8.1 to 10.0

>10.0

Level II ecoregion boundary

Level II ecoregion boundary

Figure 7–8. Maps showing the projected A, average amount and B, standard deviation of carbon stored in the Eastern United States 

in 2050. Projected average carbon stored in 2050 was derived from 21 simulation model runs using biogeochemical models Land GHG

Accounting Tool, Century, and Erosion Deposition Carbon Model under Intergovernmental Panel on Climate Change Special Report on 

Emissions Scenarios (Nakićenović and others, 2000) scenarios A1B, A2, and B1 and general circulation models Third Generation Coupled 

Global Climate Model of the Canadian Centre for Climate Modelling and Analysis, Australia’s Commonwealth Scientific and Industrial 

Research Organisation Mark 3.0, and Model for Interdisciplinary Research on Climate 3.2, medium resolution. Level II ecoregions are 

shown in figure 1–1.
138 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–6. Annual fluxes and total global warming potential from 2001 through 2005 in the Eastern United States.

[Data are by greenhouse-gas type for each ecosystem in each ecoregion. Estimates of methane (CH4 ) and nitrous oxide (N2O) were generated by the land GHG 

accounting tool. Carbon dioxide (CO2) was calculated using net ecosystem carbon balance values from table 7–5. Global warming potential is the sum of CO2, 

CH4, and N2O. TgCO2-eq/yr, teragrams (or 1012 grams) of carbon dioxide equivalent per year]

Ecoregion Ecosystem Area,

in km2

CO2, in TgCO2-eq/yr N2O, in TgCO2-eq/yr 

Min Max Average Min Max Average 

Mixed Wood 

Shield

Forests 110,556 –45.47 –17.2 –35.6 0.5 0.5 0.5 

Grass/shrub 3,796 –1.5 –0.7 –1.1 0.3 0.3 0.3 

Agriculture 20,986 –1.5 1.5 0.0 1.5 1.6 1.5 

Wetlands 57,336 –19.4 –6.2 –16.5 2.9 2.9 2.9 

Other 22,926 –0.4 0.4 0.0 0.1 0.1 0.1 

Total 215,599 –68.2 –23.1 –53.2 5.2 5.2 5.2 

Atlantic Highlands Forests 154,954 –100.8 –56.1 –88.7 1.8 1.8 1.8 

Grass/shrub 306 –0.4 0.0 –0.4 0.0 0.0 0.0 

Agriculture 18,194 –0.4 1.8 0.7 1.2 1.2 1.2 

Wetlands 5,123 –2.6 –1.1 –2.2 0.3 0.3 0.3 

Other 8,973 –0.4 0.4 0.0 0.2 0.2 0.2 

Total 187,550 –104.9 –55.0 –90.6 3.5 3.5 3.5 

Mixed Wood Plains Forests 147,983 –82.9 –49.5 –76.6 1.5 1.5 1.5 

Grass/shrub 2,247 –0.7 –0.4 –0.4 0.0 0.0 0.0 

Agriculture 159,756 1.1 9.5 5.5 9.7 9.7 9.7 

Wetlands 24,231 –8.8 –5.5 –7.7 1.5 1.5 1.5 

Other 54,639 –4.8 0.4 –2.6 1.1 1.1 1.1 

Total 388,858 –96.1 –45.5 –82.1 13.8 13.9 13.8 

Central USA Plains Forests 23,787 –11.0 –7.7 –9.9 0.8 0.8 0.8 

Grass/shrub 1,175 0.0 0.4 0.0 0.0 0.0 0.0 

Agriculture 185,336 –10.3 –1.5 –5.9 26.4 27.2 26.7 

Wetlands 4,675 –1.8 –1.1 –1.5 0.3 0.3 0.3 

Other 24,055 –4.0 –0.7 –2.2 0.2 0.2 0.2 

Total 239,027 –27.1 –10.6 –19.1 27.8 28.5 28.1 

Southeastern USA 

Plains

Forests 550,022 –519.6 –138.3 –342.5 59.9 60.7 60.3 

Grass/shrub 11,262 –3.7 0.4 –2.2 0.5 0.5 0.5 

Agriculture 306,678 –54.6 11.7 –17.2 20.7 21.4 20.9 

Wetlands 60,762 –64.2 –18.0 –46.2 3.5 3.5 3.5 

Other 65,622 –5.9 –0.4 –3.3 3.4 3.4 3.4 

Total 994,346 –648.0 –144.5 –411.4 87.9 89.5 88.6 

Ozark, OuachitaAppalachian 

Forests

Forests 372,212 –286.0 –114.0 –212.7 11.2 11.2 11.2 

Grass/shrub 3,903 –1.1 0.0 –0.7 0.2 0.2 0.2 

Agriculture 117,760 –19.1 3.3 –6.6 5.4 5.5 5.4 

Wetlands 2,592 –2.2 –0.4 –1.5 0.1 0.1 0.1 

Other 24,020 –1.8 0.0 –1.1 1.4 1.4 1.4 

Total 520,486 –310.2 –111.5 –222.6 18.2 18.3 18.3 

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Forests 101,944 –98.3 –9.5 –63.8 0.5 0.5 0.5 

Grass/shrub 28,618 –5.5 0.0 –3.3 1.4 1.4 1.4 

Agriculture 143,291 –40.3 4.8 –18.3 8.7 9.8 9.2 

Wetlands 116,763 –81.8 –16.1 –55.7 6.0 6.0 6.0 

Other 116,144 –6.6 –1.8 –4.4 0.1 0.1 0.1 

Total 506,760 –232.5 –22.7 –145.6 16.6 17.8 17.2 

Eastern United 

States

Forests 1,461,458 –1,144.1 –392.4 –829.8 76.0 76.8 76.4 

Grass/shrub 51,306 –12.8 –0.4 –8.1 2.4 2.5 2.4 

Agriculture 952,000 –125.0 31.2 –41.8 73.6 76.3 74.7 

Wetlands 271,482 –180.8 –48.4 –131.3 14.6 14.6 14.6 

Other 316,380 –23.8 –1.8 –13.6 6.5 6.5 6.5 

Total 3,052,626 –1,487.0 –412.9 –1,024.6 173.1 176.6 174.7 
Chapter 7 139

Table 7–6. Annual fluxes and total global warming potential from 2001 through 2005 in the Eastern United States.—Continued

[Data are by greenhouse-gas type for each ecosystem in each ecoregion. Estimates of methane (CH ) and nitrous oxide (N O) were generated by the land GHG 4 2

accounting tool. Carbon dioxide (CO ) was calculated using net ecosystem carbon balance values from table 7–5. Global warming potential is the sum of CO2, 2 

CH4, and N2O. TgCO2

Ecoregion

Mixed Wood Shield

-eq/yr, teragrams (or

Ecosystem

Forests

 1012 grams) of carb

Area, 

in km2

110,556

on dioxide e

Min

–1.7

quivalent per year]

CH , in TgCO -eq/yr 4 2

Max

–1.6

Average

–1.6

GWP, in TgCO -eq/yr 2

Min Max Average

–46.7 –18.4 –36.8

Grass/shrub 3,796 0.0 0.0 0.0 –1.2 – 0.5 – 0.9

Agriculture 20,986 0.0 0.0 0.0 0.0 3.0 1.5

Wetlands 57,336 47.1 47.2 47.1 30.5 43.8 33.5

Other 22,926 2.2 2.2 2.2 1.9 2.6 2.3

Total 215,599 47.5 47.6 47.6 –15.5 29.7 – 0.4

Atlantic Highlands Forests 154,954 –3.1 –3.1 –3.1 –102.2 –57.4 –90.1

Grass/shrub 306 0.0 0.0 0.0 – 0.4 0.0 – 0.4

Agriculture 18,194 0.0 0.0 0.0 0.8 3.0 1.9

Wetlands 5,123 3.6 3.6 3.6 1.3 2.8 1.7

Other 8,973 0.6 0.6 0.6 0.4 1.1 0.8

Total 187,550 1.0 1.0 1.0 –100.4 –50.5 –86.0

Mixed Wood Plains Forests 147,983 – 0.9 – 0.9 – 0.9 –82.3 –49.0 –76.1

Grass/shrub 2,247 0.0 0.0 0.0 – 0.7 – 0.4 – 0.4

Agriculture 159,756 – 0.3 – 0.3 – 0.3 10.5 19.0 14.9

Wetlands 24,231 19.5 19.5 19.5 12.2 15.5 13.3

Other 54,639 2.7 2.7 2.7 –1.0 4.2 1.2

Total 388,858 20.9 20.9 20.9 –61.4 –10.7 –47.4

Central USA Plains Forests 23,787 – 0.5 – 0.5 – 0.5 –10.6 –7.3 –9.5

Grass/shrub 1,175 0.0 0.0 0.0 0.0 0.4 0.0

Agriculture 185,336 – 0.1 – 0.1 – 0.1 16.1 25.6 20.8

Wetlands 4,675 3.3 3.3 3.3 1.7 2.5 2.1

Other 24,055 0.6 0.6 0.6 –3.2 0.1 –1.4

Total 239,027 3.3 3.3 3.3 4.0 21.2 12.3

Southeastern Forests 550,022 –6.6 –6.5 –6.5 –466.3 –84.1 –288.7

USA Plains Grass/shrub 11,262 – 0.1 – 0.1 – 0.1 –3.2 0.8 –1.7

Agriculture 306,678 – 0.1 0.1 0.0 –34.1 33.2 3.7

Wetlands 60,762 43.4 43.9 43.7 –17.3 29.5 1.0

Other 65,622 2.1 2.1 2.1 – 0.4 5.1 2.2

Total 994,346 39.0 39.5 39.2 –521.1 –15.4 –283.6

Ozark, Ouachita– Forests 372,212 –9.9 –9.9 –9.9 –284.8 –112.8 –211.4

Appalachian 

Forests

Grass/shrub

Agriculture

3,903

117,760

0.0

– 0.1

0.0

0.3

0.0

0.1

– 0.9 0.2 – 0.6

–13.8 9.0 –1.1

Wetlands 2,592 1.7 1.7 1.7 – 0.4 1.5 0.4

Other 24,020 0.9 0.9 0.9 0.4 2.2 1.1

Total 520,486 –7.4 –7.1 –7.3 –299.4 –100.2 –211.6

Mississippi Alluvial Forests 101,944 – 0.7 – 0.7 – 0.7 –98.5 –9.8 –64.1

and Southeast 

USA Coastal 

Plains

Grass/shrub

Agriculture

Wetlands

28,618

143,291

116,763

– 0.2

12.5

67.3

– 0.2

14.6

67.5

– 0.2

13.3

67.4

–4.3 1.2 –2.1

–19.2 29.2 4.2

–8.5 57.4 17.7

Other 116,144 8.4 8.4 8.4 1.9 6.7 4.1

Total 506,760 87.3 89.7 88.2 –128.5 84.7 –40.2

Eastern United Forests 1,461,458 –23.3 –23.2 –23.2 –1,091.4 –338.7 –776.6

States Grass/shrub 51,306 – 0.3 – 0.3 – 0.3 –10.7 1.8 –5.9

Agriculture 952,000 11.8 14.5 12.9 –39.6 121.9 45.9

Wetlands 271,482 185.8 186.7 186.2 19.6 152.9 69.6

Other 316,380 17.3 17.4 17.4 0.0 22.0 10.3

Total 3,052,626 191.7 195.0 193.0 –1,122.3 –41.3 –656.9
140 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

The projected minimum, maximum, and average amounts 

of stored carbon from the 21 simulation model runs are listed 

in table 7–7 and are by carbon pool, ecosystem, and ecoregion 

in the Eastern United States for 2050. The overall carbon 

stored in all seven ecoregions was projected to be 37,083 TgC, 

averaged across all scenarios, GCMs, and models used. The 

variability ranged from 25,513 to 46,002 TgC across all 

21 model simulations, which was considerably wider than the 

range of 25,069 to 28,497 TgC for the baseline period. Among 

the ecoregions, the Southeastern USA Plains ecoregion was 

projected to have the most carbon stored by 2050, accounting 

for about 31 percent of the total carbon stored in the Eastern 

United States in terms of the average estimates, followed 

by the Ozark, Ouachita-Appalachian Forests (18 percent of 

the total), Mississippi Alluvial and Southeast USA Coastal 

Plains (14 percent), Mixed Wood Plains (13 percent), Atlantic 

Highlands (10 percent), Mixed Wood Shield (9 percent), and 

Central USA Plains (5 percent) ecoregions. Among the different 

ecosystems, forests were projected to store the most carbon 

(69 percent) in terms of total amount in the Eastern United 

States, followed by wetlands (16 percent) and agricultural 

lands (13 percent). About 48 percent and 37 percent of the total 

carbon stock were projected to be allocated to the live biomass 

and SOC pools, respectively, and the remaining 15 percent was 

projected to be stored in dead biomass (such as forest litter and 

dead, woody debris). Compared with the baseline period, the 

projected allocation indicates that the carbon stock in the live 

biomass pools grew a little faster than the SOC pools.

The average carbon density of the Eastern United 

States was projected to be about 12.2 kgC/m2. However, by 

ecosystems, the projected average future carbon densities 

varied substantially (fig. 7–8; table 7–7), as follows: wetlands 

(21.8 kgC/m2), forests (18.7 kgC/m2), grasslands/shrublands 

(5.7 kgC/m2), agricultural lands (5.1 kgC/m2), and other 

lands (1.2 kgC/m2). Geographically, the projected average 

future carbon density in forests varied among the Atlantic 

Highlands (23.7 kgC/m2), Mississippi Alluvial and Southeast 

USA Coastal Plains (22.5 kgC/m2), Mixed Wood Plains 

(22.2 kgC/m2), Central USA Plains (18.6 kgC/m2), Ozark, 

Ouachita-Appalachian Forests (17.6 gC/m2

), Southeast 

USA Coastal Plains (16.9 kgC/m2), and Mixed Wood Shield 

(16 kgC/m2) ecoregions. For grasslands/shrublands, the 

highest carbon density was projected to be found in the 

Central USA Plains ecoregion (9.1 kgC/m2) and the lowest 

carbon density was projected to be found in the Atlantic 

Highlands ecoregion (4.9 kgC/m2).

7.4.4.1. Mixed Wood Shield

The total carbon stored in the Mixed Wood Shield 

was projected to range between 2,512 and 3,783 TgC in 

2050 across 21 model simulations used in this assessment 

(table 7–7). Live biomass, soil organic carbon (SOC), 

and dead biomass were projected to store an average of 

40 percent, 43.5 percent, and 16.5 percent, respectively, of the 

total carbon. Among the different ecosystems, forests were 

projected to store the most carbon (average of 50.7 percent of 

the total) followed by wetlands (44 percent) and agricultural 

lands (4.2 percent). The projected allocation of carbon varied 

substantially between the three carbon pools (live biomass, 

SOC, and dead biomass) across ecosystems. Live biomass was 

projected to account for 44.4 percent of the total carbon stored 

in forests, whereas SOC was projected to be the dominant 

storage pool for other lands (92.1 percent), agricultural lands 

(82.9 percent), and grassland/shrubland (78.9 percent) in 2050.

7.4.4.2. Atlantic Highlands

The estimated carbon stored in the Atlantic Highlands in 

2050 was projected to range from 2,875 to 4,401 TgC across 

21 model simulations used in this assessment (table 7–7). Live 

biomass and SOC were projected to contain 51.5 percent and 

31.1 percent, respectively, of this total amount. This being 

a predominantly forested region, forests were projected to 

store the most carbon (93.2 percent of the projected total 

carbon), followed by wetlands (3.4 percent) and agricultural 

lands (2.8 percent). The live biomass carbon pool was 

projected to contain the most carbon in wetlands, accounting 

for 49 percent, whereas the SOC pool was projected to be 

the largest for agricultural lands, other lands ecosystems, 

and grasslands/shrublands, accounting for 85.1 percent, 

80.9 percent, and 73.3 percent, respectively, in 2050.

7.4.4.3. Mixed Wood Plains

The estimated carbon stored in the Mixed Wood Plains 

ecoregion was projected to range from 3,401 to 5,426 TgC 

in 2050 across 21 model simulations used in this assessment (table 7–7). Live biomass and SOC were projected to 

contain 40.1 percent and 45 percent, respectively, of this total 

amount. Forests were projected to serve as the primary carbon 

storage pool (66.2 percent), followed by agricultural lands 

(21 percent) and wetlands (10.2 percent). The total percentage 

of carbon stored in grasslands/shrublands and other lands was 

projected to be less than 3 percent. Live biomass was projected 

to serve as the major carbon pool in forests (52.6 percent of 

the total forests), but for the other ecosystems, most carbon 

was projected to be stored in the SOC pool, ranging from 

45 percent (for wetlands) to 90.7 percent (for other lands).

7.4.4.4. Central USA Plains

For the Central USA Plains ecoregion, the projected total 

carbon ranged from 1,464 to 2,055 TgC from the 21 model 

simulations used in this assessment (table 7–7). Unlike in the 

Mixed Wood Shield, Atlantic Highlands, and Mixed Wood Plains 

ecoregions, SOC was projected to be the primary carbon pool 

in the Central USA Plains ecoregion, by storing 69.9 percent 

of the total carbon, and live biomass was projected to store 

only 19.1 percent in this agricultural ecoregion. Croplands were 

projected to store the most carbon (67.4 percent of the total), 

followed by forests (22.8 percent) and other lands (5.3 percent). 
Chapter 7 141

Live biomass was projected to account for 56.3 percent of the 

total carbon stock in forests, and SOC was projected to be the 

primary pool in grasslands/shrublands (85.3 percent), agricultural 

lands (84.7 percent), and others lands (93.4 percent).

7.4.4.5. Southeastern USA Plains

For the Southeastern USA Plains ecoregion, the 

total carbon stored in 2050 was projected to range from 

6,863 to 15,311 TgC by the 21 model simulations used in this 

assessment (table 7–7). Live biomass was projected to be the 

primary carbon pool (storing 53.5 percent of the total carbon) 

and SOC was projected to store 30.7 percent. The majority of 

the stored carbon was projected to be in forests (75.4 percent 

of the total), followed by wetlands (11.8 percent) and 

agricultural lands (11.1 percent). Live biomass was projected 

to be the primary carbon pools for forests and wetlands, 

accounting for 60.1 percent and 62.9 percent of their totals, 

whereas in other ecosystems, most carbon was projected 

to be stored in the SOC pool, ranging from 60.9 percent in 

grasslands/shrublands to 80.8 percent in other lands.

7.4.4.6. Ozark, Ouachita-Appalachian Forests

The estimated carbon stored in the Ozark, OuachitaAppalachian Forests ecoregion was projected to range from 

4,847 to 8,328 TgC in 2050 according to the 21 model 

simulations used in this assessment (table 7–7). Live 

biomass carbon was projected to be the primary carbon 

pool (accounting for 56.8 percent of the projected total 

amount of carbon), followed by SOC (27.6 percent). Forests 

were projected to serve as the primary carbon storage pool 

(91.2 percent), followed by agricultural lands (7.3 percent). 

The total percentage of carbon stored in grasslands/

shrublands, wetlands, and other lands was projected to be 

less than 2 percent. Live biomass was projected to serve as 

the major carbon pool in forests (61.5 percent of the total 

carbon in forests) and wetlands (56.5 percent of the total 

carbon in wetlands), but for the other ecosystems, most 

carbon was projected to be stored in the SOC pool, ranging 

from 79 percent (for grasslands/shrublands) to 82.4 percent 

(for agricultural lands).

7.4.4.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

The total carbon stored in the Mixed Wood Shield was 

projected to range between 3,549 and 6,698 TgC in 2050 

according to the 21 model simulations used in this assessment 

(table 7–7). Live biomass, SOC, and dead biomass were 

projected to store an average of 44.4 percent, 43.6 percent, 

and 12 percent, respectively, of the total carbon. Among the 

different ecosystems, wetlands were projected to store the 

most carbon (average of 44.2 percent of the total), followed 

by forests (36.6 percent) and agricultural lands (14.2 percent). 

The carbon stored in grasslands/shrublands and other lands 

(combined) was projected to be the remaining 5 percent of 

the total carbon. The projected allocation of carbon varied 

substantially between the three pools (live biomass, SOC, 

and dead biomass) across ecosystems. Live biomass was 

projected to account for 60.2 percent of the total carbon stored 

in forests, whereas SOC was projected to be the dominant 

storage pool for other lands (87.9 percent), agricultural lands 

(82.1 percent), and grasslands/shrublands (68.5 percent) 

in 2050.

7.4.5. Projected Future Carbon Flux Estimates

The projected average annual NECB and the standard 

uncertainty between 2006 and 2050 are shown in figure 7–9. 

The average annual NECB and standard deviation were calculated from all 21 simulation model runs. The projected high 

carbon sequestration rates (fig. 7–9, negative NECB, shown 

by green hues on the map) were strongly associated with the 

presence of forest ecosystems; the simulated disturbances, 

such as clearcutting, were projected to be responsible for a 

large number of carbon-release hot spots (fig. 7–9, positive 

NECB, indicated by red hues on the map). Carbon sequestration was also projected to occur in the agricultural lands. The 

standard deviation map, as shown in figure 7–9, was spatially 

similar to the pattern of the average annual NECB, suggesting 

the spread of NECB estimates was projected to be generally 

greater in areas experiencing large changes in carbon storage.

The projected minimum, maximum, and average of 

average annual net carbon fluxes—from the 21 simulations 

and averaged annually between 2006 and 2050—are listed 

in table 7–8 by carbon pool, ecosystem, and ecoregion in 

the Eastern United States. The annual NECB estimates were 

projected to vary between −403.7 and 1.4 TgC/yr across 

21 simulations in the Eastern United States with an average 

value of −224.9 TgC/yr.

As shown in table 7–8, the average annual NECB in the 

ecoregions of the Eastern United States was projected to be 

highly variable, although all ecoregions within the Eastern 

United States were projected to be carbon sinks on average. 

Among the seven ecoregions, the Southeast USA Coastal 

Plains ecoregion was projected to be the greatest carbon sink 

with an average of −79.4 TgC/yr, followed by the Ozark, 

Ouachita-Appalachian Forests (−44.2 TgC/yr), Mississippi 

Alluvial and Southeast USA Coastal Plains (−32.2 TgC/yr), 

Atlantic Highlands (−23.5 TgC/yr), Mixed Wood Plains 

(−23.4 TgC/yr), Mixed Wood Shield (−16.3 TgC/yr), and 

Central USA Plains (−5.8 TgC/yr) ecoregions. Among all 

ecosystems, forests were projected to remain strong terrestrial 

carbon sinks, accounting for approximately 70 percent of the 

projected total average NECB. The other ecosystems were 

also projected to have the potential to sequester carbon, but 

the interannual variability was high. Wetlands were projected 

to have the highest average annual NECB per unit of area 

(−150.5 gC/m2

/yr), compared with forests (−115.6 gC/m2/yr), 
142 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–7. Projections of carbon stored in the Eastern United States in 2050.

[Results are based on 21 simulation model runs and are listed by ecosystem and ecoregion. Only soil organic carbon (SOC) in the top 20 centimeters of the soil 

layer was calculated. Data may not add to totals shown due to independent rounding. km2, square kilometers; max, maximum; min, minimum; TgC, teragrams 

(or 1012 grams) of carbon]

Ecoregion Ecosystem Area,

in km2

Biomass, in TgC SOC, in TgC

Min Max Average Min Max Average

Mixed Wood Shield Forests 105,593 421.4 988.9 750.3 498.5 670.0 594.4

Grass/shrub 3,822 1.4 4.5 2.5 18.0 23.5 22.0

Agriculture 23,888 0.1 9.3 4.1 78.5 148.9 115.5

Wetlands 58,742 334.4 682.6 577.4 656.9 739.1 707.7

Other 23,554 0.0 0.3 0.1 2.4 30.5 8.2

Total 215,599 757.3 1,685.6 1,334.5 1,254.4 1,612.0 1,447.7

Atlantic Highlands Forests 151,901 1,352.3 2,252.9 1,923.1 802.9 1,264.0 1,041.2

Grass/shrub 304 0.0 1.0 0.3 1.0 1.7 1.1

Agriculture 19,145 0.1 7.1 2.7 54.9 145.3 92.7

Wetlands 5,124 42.2 75.1 64.8 43.0 55.8 49.0

Other 11,076 0.0 3.1 0.8 5.8 44.0 16.5

Total 187,550 1,394.6 2,339.2 1,991.7 907.6 1,510.8 1,200.5

Mixed Wood Plains Forests 137,258 1,037.1 1,958.0 1,602.1 723.3 1,125.4 927.9

Grass/shrub 2,049 0.5 2.4 1.2 10.2 12.8 11.9

Agriculture 160,383 0.2 74.6 40.0 651.3 1,016.3 826.7

Wetlands 23,494 136.3 232.1 201.0 185.5 230.8 209.2

Other 65,674 0.0 8.7 3.4 45.8 230.8 96.4

Total 388,857 1,174.1 2,275.8 1,847.7 1,616.1 2,616.0 2,072.1

Central USA Plains Forests 21,621 195.9 253.2 225.7 83.0 143.0 112.6

Grass/shrub 827 0.0 0.8 0.4 4.2 7.8 6.4

Agriculture 177,774 0.1 125.3 62.7 864.6 1,155.1 1,004.3

Wetlands 4,658 32.4 53.8 43.9 34.2 41.4 38.2

Other 34,147 0.0 11.1 3.5 27.4 156.6 68.1

Total 239,027 228.4 444.2 336.2 1,013.3 1,504.1 1,229.7

Southeastern USA 

Plains

Forests 507,236 3,369.8 6,851.9 5,149.8 981.5 2,786.5 2,019.2

Grass/shrub 10,377 12.0 20.7 16.3 20.0 47.3 38.0

Agriculture 313,514 1.4 123.6 60.8 616.1 1,504.3 1,001.4

Wetlands 63,410 551.8 955.7 846.3 219.1 405.7 335.9

Other 99,809 0.0 22.9 9.1 69.4 189.6 100.3

Total 994,346 3,934.9 7,974.8 6,082.3 1,906.1 4,933.4 3,494.9

Ozark, OuachitaAppalachian 

Forests

Forests 352,049 2,939.7 4,689.9 3,803.0 777.6 1,595.7 1,402.1

Grass/shrub 3,804 1.2 4.1 2.2 9.4 17.8 15.0

Agriculture 130,834 0.5 37.5 18.5 254.8 605.6 410.1

Wetlands 2,671 17.1 26.7 24.0 7.6 14.8 12.9

Other 31,128 0.0 5.8 2.2 17.0 64.2 31.7

Total 520,486 2,958.4 4,763.9 3,850.0 1,066.4 2,298.1 1,871.9

Mississippi Alluvial 

and Southeast 

USA Coastal 

Plains

Forests 87,495 581.6 1,463.9 1,183.3 359.3 612.2 495.8

Grass/shrub 26,811 21.6 34.6 28.1 77.7 107.3 96.0

Agriculture 139,412 0.3 45.6 22.8 453.1 846.7 623.9

Wetlands 113,617 643.9 1,353.4 1,146.5 948.7 1,094.1 1,012.9

Other 139,424 0.0 17.9 6.7 48.6 311.0 114.4

Total 506,758 1,247.4 2,915.5 2,387.4 1,887.5 2,971.4 2,343.1

Eastern United 

States

Forests 1,363,153 9,897.8 18,458.6 14,637.3 4,226.2 8,196.8 6,593.2

Grass/shrub 47,992 36.7 68.0 51.0 140.5 218.2 190.5

Agriculture 964,948 2.6 423.0 211.6 2,973.3 5,422.4 4,074.6

Wetlands 271,717 1,758.1 3,379.4 2,904.0 2,094.9 2,581.8 2,365.9

Other 404,813 0.0 69.8 26.0 216.4 1,026.6 435.6

Total 3,052,623 11,695.2 22,399.0 17,829.8 9,651.3 17,445.7 13,659.8
Chapter 7 143

Table 7–7. Projections of carbon stored in the Eastern United States in 2050.—Continued

[Results are based on 21 simulation model runs and are listed by ecosystem and ecoregion. Only soil organic carbon (SOC) in the top 20 centimeters of the soil 

layer was calculated. Data may not add to totals shown due to independent rounding. km2, square kilometers; max, maximum; min, minimum; TgC, teragrams 

(or 1012 grams) of carbon]

Ecoregion Ecosystem Area,

in km2

Others, in TgC Total, in TgC

Min Max Average Min Max Average

Mixed Wood Shield Forests 105,593 254.1 390.5 345.8 1,174.1 1,929.0 1,690.5

Grass/shrub 3,822 0.0 5.1 3.4 22.5 30.4 27.9

Agriculture 23,888 0.0 37.0 19.8 93.3 184.6 139.4

Wetlands 58,742 154.5 197.9 179.5 1,218.7 1,608.3 1,464.7

Other 23,554 0.0 1.5 0.6 3.1 30.5 8.9

Total 215,599 408.6 631.9 549.2 2,511.8 3,782.9 3,331.4

Atlantic Highlands Forests 151,901 543.4 690.1 638.9 2,699.8 4,023.7 3,603.2

Grass/shrub 304 0.0 0.2 0.1 1.1 2.7 1.5

Agriculture 19,145 0.0 36.7 13.6 61.9 182.5 108.9

Wetlands 5,124 16.9 21.0 18.3 103.7 148.1 132.1

Other 11,076 0.0 9.2 3.1 8.3 44.0 20.4

Total 187,550 560.4 757.2 674.0 2,874.8 4,401.0 3,866.1

Mixed Wood Plains Forests 137,258 410.2 588.1 515.3 2,170.7 3,469.8 3,045.3

Grass/shrub 2,049 0.0 2.6 1.7 12.5 16.9 14.8

Agriculture 160,383 0.0 176.9 100.5 778.3 1,192.5 967.2

Wetlands 23,494 48.9 70.5 59.7 386.3 516.4 469.8

Other 65,674 0.0 14.9 6.5 53.3 230.8 106.3

Total 388,857 459.1 853.0 683.6 3,401.0 5,426.4 4,603.4

Central USA Plains Forests 21,621 53.5 74.3 62.8 332.6 460.3 401.0

Grass/shrub 827 0.0 1.1 0.6 4.8 9.6 7.5

Agriculture 177,774 0.0 168.9 119.1 1,019.0 1,324.2 1,186.2

Wetlands 4,658 9.4 12.9 10.9 79.5 104.0 92.9

Other 34,147 0.0 3.0 1.2 28.0 156.6 72.9

Total 239,027 62.8 260.2 194.6 1,463.9 2,054.7 1,760.5

Southeastern USA 

Plains

Forests 507,236 811.0 1,976.3 1,407.2 5,162.3 11,613.9 8,576.2

Grass/shrub 10,377 0.0 15.3 8.1 32.0 78.6 62.4

Agriculture 313,514 0.0 410.7 199.6 697.0 1,919.5 1,261.8

Wetlands 63,410 115.3 211.7 162.3 886.2 1,510.0 1,344.6

Other 99,809 0.0 30.7 14.8 85.5 189.6 124.2

Total 994,346 926.4 2,644.7 1,791.9 6,863.1 15,311.4 11,369.1

Ozark, OuachitaAppalachian 

Forests

Forests 352,049 753.8 1,222.3 975.9 4,471.0 7,450.3 6,181.1

Grass/shrub 3,804 0.0 2.8 1.7 13.5 22.9 19.0

Agriculture 130,834 0.0 134.9 69.3 312.1 742.2 497.9

Wetlands 2,671 4.3 7.4 5.6 29.0 48.6 42.5

Other 31,128 0.0 10.3 4.7 21.8 64.2 38.6

Total 520,486 758.0 1,377.8 1,057.2 4,847.3 8,328.1 6,779.1

Mississippi Alluvial 

and Southeast 

USA Coastal 

Plains

Forests 87,495 156.7 396.2 288 1,097.6 2,472.3 1,967.1

Grass/shrub 26,811 0 28.8 16.1 108.1 169.7 140.2

Agriculture 139,412 0 231.4 113.4 487.9 1,079.6 760.1

Wetlands 113,617 137.3 291.8 216.6 1,799.1 2,665.1 2,376.0

Other 139,424 0 20.7 9 56.9 311 130.2

Total 506,758 294 968.9 643.1 3,549.6 6,697.7 5,373.6

Eastern United 

States

Forests 1,363,153 2,982.6 5,337.8 4,233.9 17,108.1 31,419.3 25,464.3

Grass/shrub 47,992 0 55.9 31.7 194.6 330.8 273.3

Agriculture 964,948 0 1,196.40 635.2 3,449.5 6,625.0 4,921.4

Wetlands 271,717 486.5 813.3 652.8 4,502.4 6,600.5 5,922.7

Other 404,813 0 90.3 39.9 256.9 1,026.6 501.5

Total 3,052,623 3,469.2 7,493.7 5,593.5 25,511.6 46,002.2 37,083.2
144 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

A B

0 100 200 300 400MILES

0 100 200 300 400KILOMETERS

N

Figure 7.9. 

Level II ecoregion boundary

Level II ecoregion boundary

EXPLANATION

Total mean net carbon flux averaged annually 

 from 2006 to 2050, in kilograms of carbon

 per square meter per year

EXPLANATION

Standard deviation of mean net carbon flux

 averaged annually from 2006 to 2050, in 

 kilograms of carbon per square meter per year

<1

1.1 to 2

2.1 to 4

≤–5

–5 to –2.1

–2 to –0.6

–0.5 to 0.4

0.5 to 1.9

2.0 to 4.9

≥5

4.1 to 8

8.1 to 10

>10

Figure 7–9. Maps showing the projected A, average annual and B, standard deviation of net ecosystem carbon balance (NECB) in 

the Eastern United States, averaged annually from 2006 to 2050. Projected average annual NECB was derived from 21 simulation model 

runs using biogeochemical models Land GHG Accounting Tool, Century, and Erosion Deposition Carbon Model, Intergovernmental 

Panel on Climate Change Special Report on Emissions Scenarios (Nakićenović and others, 2000) scenarios A1B, A2, and B1, and 

general circulation models Third Generation Coupled Global Climate Model of the Canadian Centre for Climate Modelling and Analysis, 

Australia’s Commonwealth Scientific and Industrial Research Organisation Mark 3.0, and Model for Interdisciplinary Research on 

Climate 3.2, medium resolution. Negative average annual NECB values indicate projected carbon sinks or carbon gains by terrestrial 

ecosystems, and positive values denote projected carbon losses. Level II ecoregions are shown in figure 1–1.

between 2006 and 2050 of the 21 model simulations used in 

this assessment ranged from −26 to 0.6 TgC/yr , depending on 

land-use and land-cover scenario, climate-change projection, 

and biogeochemical model (table 7–8). On average, this 

ecoregion can be a carbon sink with the sequestration rate 

of −16.3 TgC/yr. Among the different ecosystems, wetlands 

were projected to gain −8.1 TgC/yr (49.7 percent of the total) 

averaged across all model runs, followed by forests with an 

average of −7.3 TgC/yr (44.8 percent of the total), agricultural 

lands with an average of −0.8 TgC/yr (4.9 percent of the 

total), and the sum of the rest of the ecosystems averaging 

−0.2 TgC/yr (less than 1 percent of the total).

agricultural lands (−22 gC/m2

/yr), grasslands/shrublands 

(−16.7 gC/m2/yr), and other lands (−11.1 gC/m2/yr). On 

average, about 64.3 percent of the total carbon was projected 

to accumulate in live biomass, 20.3 percent in soil organic 

carbon, and about 15.4 percent in dead biomass (forest litter 

and dead, woody debris). Forests were projected to be the 

primary carbon sink with −157.6 TgC/yr in the Eastern 

United States in the future.

7.4.5.1. Mixed Wood Shield

In the Mixed Wood Shield ecoregion, the projected 

mean annual net ecosystem carbon balance (NECB) values 
Chapter 7 145

7.4.5.2. Atlantic Highlands

The projected mean annual NECB values in the Atlantic 

Highlands ecoregion between 2006 and 2050 ranged from 

−34.8 to −1.8 TgC/yr across 21 model simulations used in this 

assessment (table 7–8) with an average of −23.5 TgC/yr. The 

projected mean annual NECB for forests in this ecoregion was 

–22.1 TgC/yr (or 94 percent of the total) across all model runs, 

followed by wetlands (3.8 percent of the total) and agricultural 

lands (1.3 percent of the total).

7.4.5.3. Mixed Wood Plains

The projected mean annual NECB values in the 

Mixed Wood Plains between 2006 and 2050 ranged from 

−41.7 to 2.5 TgC/yr across all 21 model simulations used 

in this assessment (table 7–8). The projected mean annual 

NECB for forests (–18 TgC/yr) in this ecoregion accounted for 

76.9 percent of the total, followed by wetlands (12 percent of 

the total) and agricultural lands (8.1 percent of the total).

7.4.5.4. Central USA Plains

The projected means of annual NECB in the Central 

USA Plains ecoregion between 2006 and 2050 ranged 

from −11.7 to 0.6 TgC/yr (table 7–8) across all 21 model 

simulations used in this assessment. Generally, this ecoregion 

was projected to be a carbon sink, with a mean carbon 

sequestration rate of −5.8 TgC/yr. The dominant ecosystem in 

the ecoregion, agricultural lands, was projected to contribute 

about 50 percent of the total carbon sequestration, followed by 

forests (31 percent of the total), other lands (10.4 percent), and 

wetlands (8.6 percent).

7.4.5.5. Southeastern USA Plains

The projected means of annual NECB in the Southeastern 

USA Plains ecoregion between 2006 and 2050 ranged from 

−157.3 to 5 TgC/yr according to 21 model simulations used in 

this assessment. The projected average NECB of −79.4 TgC/yr 

makes this ecoregion the greatest carbon sink in the Eastern 

United States. Among different ecosystems, forests were 

projected to accumulate the most carbon (70.7 percent of the 

total), followed by wetlands (17.1 percent), agricultural lands 

(10.2 percent), and grasslands/shrublands (0.4 percent). The 

total carbon sequestration the rest ecosystems contributed 

about 2 percent.

7.4.5.6. Ozark, Ouachita-Appalachian Forests

In the Ozark, Ouachita-Appalachian Forests ecoregion, 

the projected means of annual NECB between 2006 and 2050 

as simulated by the 21 model runs used in this assessment 

ranged from −76.9 to −5.7 TgC/yr (table 7–8) with an average 

estimate of −44.2 TgC/yr, which makes this ecoregion the 

second largest carbon sink in the Eastern United States. 

Among the different ecosystems, forests were projected to 

gain −40 TgC/yr, which contributed 90.1 percent of the total 

carbon sequestration, and agricultural lands, which accounted 

for about 7.5 percent.

7.4.5.7. Mississippi Alluvial and Southeast USA 

Coastal Plains

In the Mississippi Alluvial and Southeast USA Coastal 

Plains ecoregion, the projected meansof annual NECB between 

2006 and 2050 representing the 21 model simulations used in 

this assessment ranged from −55.3 to 0.2 TgC/yr. Of this total, 

wetlands were projected to accumulate the most carbon (45.3 

percent of the total), followed by forests (37.9 percent) and 

agricultural lands (12.1 percent). Grasslands/shrublands and 

other lands were projected to account for about 4.7 percent of 

the total mean annual NECB in this ecoregion.

7.4.6. Projected GHG Fluxes

The projected minimum, maximum, and average of annual 

GHG fluxes from 2006 to 2050 are listed by ecoregion and 

ecosystem in table 7–9. Temporal trends of the projected future 

fluxes of the three GHG between 2000 and 2050 are shown in 

figure 7–10. The projected future GHG fluxes for CO2, N2O, 

and CH4 averaged –824.6 TgCO2-eq/yr, 174.7 TgCO2-eq/yr, and 

198.71 TgCO2-eq/yr, respectively. The overall GWP from 2006 

through 2050 averaged −451.2 TgCO2-eq/yr.

7.5. Discussion

7.5.1. Effects of Uncertainty of Models, LULC 

Scenarios, and GCMs on Carbon Sequestration 

Estimates

Table 7–10 lists the projected estimates of average 

carbon stocks in 2050, average annual NECB from 2006 

to 2050, and corresponding relative variability (variability 

index, as described in the Definitions of Carbon Stocks and 

Fluxes and Uncertainty section) by model, LULC scenario, 

and GCM. The variability index of the carbon stocks and 

NECB estimated by the three models indicates the uncertainty 

introduced by these models. Among the three biogeochemical 

models for the Eastern United States, the Century model and 

the LGAT gave a highest and lowest projected estimates, 

respectively, whereas the projection by the EDCM model 

fell generally in the middle although a lot closer to that of 

the Century model. For simulating carbon stock, the models 

performed differently across ecoregions with the smallest 

discrepancy found in the Central USA Plains ecoregion 

(15.1 percent variability across three models), and the highest, 

in the Southeastern USA Plains ecoregion (49.1 percent). For 
146 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–8. Projected net ecosystem carbon balance values simulated in 21 model runs and averaged between 2006 and 2050 in the 

Eastern United States.

[Negative net ecosystem carbon balance (NECB) values indicate carbon uptake or sequestration by ecosystems. Only soil organic carbon (SOC) in the top

20 cm of the soil layer was calculated. Data may not add to totals shown due to independent rounding. km2, square kilometers; max, maximum; min, minimum; 

TgC, teragrams (or 1012 grams) of carbon]

Ecoregion Ecosystem Area,

in km2

Biomass, in TgC/yr SOC, in TgC/yr 

Min Max Average Min Max Average 

Mixed Wood 

Shield

Forests 105,593 –10.1 1.9 –4.9 –1.5 0.8 –0.7 

Grass/shrub 3,822 0.0 0.0 0.0 –0.1 0.0 –0.1 

Agriculture 23,888 –0.1 0.0 0.0 –1.2 0.2 –0.5 

Wetlands 58,742 –8.4 –2.1 –6.6 –0.4 0.5 –0.1 

Other 23,554 0.0 0.0 0.0 –0.1 0.0 0.0 

Total 215,599 –18.6 –0.1 –11.5 –3.3 1.6 –1.3 

Atlantic Highlands Forests 151,901 –23.3 –2.6 –15.2 –6.1 0.7 –3.0 

Grass/shrub 304 0.0 0.0 0.0 0.0 0.0 0.0 

Agriculture 19,145 –0.1 0.0 0.0 –1.2 0.6 –0.1 

Wetlands 5,124 –0.9 –0.3 –0.7 –0.2 0.0 –0.1 

Other 11,076 –0.1 0.0 0.0 –0.4 0.1 –0.1 

Total 187,550 –24.3 –2.8 –15.9 –7.9 1.5 –3.3 

Mixed Wood Plains Forests 137,258 –22.2 –1.4 –13.9 –4.0 2.4 –1.1 

Grass/shrub 2,049 0.0 0.0 0.0 0.0 0.0 0.0 

Agriculture 160,383 –0.3 0.0 –0.1 –4.6 2.5 –0.7 

Wetlands 23,494 –2.7 –0.8 –2.2 –0.6 0.2 –0.2 

Other 65,674 –0.1 0.0 –0.1 –1.5 0.1 –0.6 

Total 388,857 –25.3 –2.2 –16.2 –10.8 5.3 –2.7 

Central USA Plains Forests 21,621 –2.2 –0.6 –1.5 –0.6 0.3 –0.1 

Grass/shrub 827 0.0 0.0 0.0 0.0 0.0 0.0 

Agriculture 177,774 –0.4 0.1 0.0 –4.7 1.3 –1.9 

Wetlands 4,658 –0.6 –0.2 –0.5 –0.1 0.0 0.0 

Other 34,147 –0.2 0.0 –0.1 –1.6 0.2 –0.5 

Total 239,027 –3.4 –0.6 –2.0 –7.1 1.8 –2.5 

Southeastern 

USA Plains

Forests 507,236 –73.0 3.0 –36.6 –27.7 3.6 –12.0 

Grass/shrub 10,377 –0.2 0.1 –0.1 –0.4 0.1 –0.2 

Agriculture 313,514 –0.9 0.0 –0.3 –15.2 2.4 –5.7 

Wetlands 63,410 –11.9 –4.2 –10.1 –3.3 –0.1 –2.1 

Other 99,809 –0.4 0.0 –0.2 –1.9 –0.2 –1.0 

Total 994,346 –86.4 –1.1 –47.3 –48.5 5.7 –21.0 

Ozark, OuachitaAppalachian 

Forests

Forests 352,049 –47.2 –6.5 –27.4 –11.7 1.6 –7.4 

Grass/shrub 3,804 0.0 0.0 0.0 –0.1 0.0 –0.1 

Agriculture 130,834 –0.3 0.0 –0.1 –6.0 0.4 –2.4 

Wetlands 2,671 –0.3 –0.1 –0.3 –0.1 0.0 –0.1 

Other 31,128 –0.1 0.0 0.0 –0.6 0.1 –0.3 

Total 520,486 –48.0 –6.6 –27.9 –18.6 2.1 –10.2 

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Forests 87,495 –15.8 0.6 –10.5 –3.0 1.9 –0.4 

Grass/shrub 26,811 –0.2 0.1 –0.1 –0.4 0.2 –0.2 

Agriculture 139,412 –0.2 0.0 –0.1 –7.2 1.2 –2.9 

Wetlands 113,617 –16.4 –3.9 –12.9 –2.1 1.2 –0.1 

Other 139,424 –0.4 0.0 –0.1 –2.4 –0.1 –1.0 

Total 506,758 –33.1 –3.3 –23.7 –15.1 4.4 –4.5 

Eastern United 

States

Forests 1,363,153 –193.8 –5.7 –110.0 –54.7 11.3 –24.8 

Grass/shrub 47,992 –0.5 0.2 –0.1 –1.0 0.3 –0.6 

Agriculture 964,948 –2.2 0.2 –0.6 –40.2 8.5 –14.1 

Wetlands 271,717 –41.2 –11.6 –33.2 –6.8 1.9 –2.6 

Other 404,813 –1.3 0.0 –0.5 –8.6 0.2 –3.5 

Total 3,052,623 –239.0 –16.8 –144.5 –111.3 22.3 –45.6 
Chapter 7 147

Table 7–8. Projected net ecosystem carbon balance values simulated in 21 model runs and averaged between 2006 and 2050 in the 

Eastern United States.—Continued

[Negative net ecosystem carbon balance (NECB) values indicate carbon uptake or sequestration by ecosystems. Only soil organic carbon (SOC) in the top

20 cm of the soil layer was calculated. Data may not add to totals shown due to independent rounding. km2, square kilometers; max, maximum; min, minimum; 

TgC, teragrams (or 1012 grams) of carbon]

Ecoregion

Mixed Wood Shield

Ecosystem Area, 

in km2

Others, in TgC/yr Total, in TgC/yr

Min Max Average Min Max Average

Forests 105,593 –3.5 0.9 –1.8 –14.0 3.7 –7.3

Grass/shrub 3,822 0.0 0.0 0.0 – 0.1 0.0 – 0.1

Agriculture 23,888 – 0.6 0.0 – 0.3 –1.7 0.1 – 0.8

Wetlands 58,742 –2.0 – 0.8 –1.4 –10.1 –3.2 –8.1

Other 23,554 0.0 0.0 0.0 – 0.1 0.0 – 0.1

Total 215,599 –6.1 0.1 –3.5 –26.0 0.6 –16.3

Atlantic Highlands Forests 151,901 –6.4 – 0.3 –3.9 –31.2 –2.2 –22.1

Grass/shrub 304 0.0 0.0 0.0 0.0 0.0 0.0

Agriculture 19,145 – 0.6 0.0 – 0.2 –1.8 0.6 – 0.3

Wetlands 5,124 – 0.2 – 0.1 – 0.1 –1.1 – 0.3 – 0.9

Other 11,076 – 0.2 0.0 – 0.1 – 0.6 0.0 – 0.2

Total 187,550 –7.4 – 0.3 –4.2 –34.8 –1.8 –23.5

Mixed Wood Plains Forests 137,258 –5.3 0.1 –3.0 –29.7 1.1 –18.0

Grass/shrub 2,049 0.0 0.0 0.0 – 0.1 0.0 0.0

Agriculture 160,383 –2.3 0.0 –1.1 –6.9 2.1 –1.9

Wetlands 23,494 – 0.7 – 0.2 – 0.4 –3.6 – 0.8 –2.8

Other 65,674 – 0.2 0.0 – 0.1 –1.5 0.0 – 0.7

Total 388,857 –8.6 – 0.1 –4.6 –41.7 2.5 –23.4

Central USA Plains Forests 21,621 – 0.5 – 0.1 – 0.2 –3.0 – 0.3 –1.8

Grass/shrub 827 0.0 0.0 0.0 0.0 0.1 0.0

Agriculture 177,774 –1.6 0.0 –1.0 –6.3 0.9 –2.9

Wetlands 4,658 – 0.1 0.0 – 0.1 – 0.7 – 0.2 – 0.5

Other 34,147 0.0 0.0 0.0 –1.6 0.1 – 0.6

Total 239,027 –2.2 – 0.1 –1.3 –11.7 0.6 –5.8

Southeastern Forests 507,236 –16.6 1.5 –7.4 –117.2 8.1 –56.1

USA Plains Grass/shrub 10,377 – 0.1 0.0 0.0 – 0.7 0.1 – 0.3

Agriculture 313,514 –5.3 0.0 –2.2 –20.6 2.4 –8.1

Wetlands 63,410 –1.9 – 0.8 –1.4 –16.8 –5.1 –13.6

Other 99,809 – 0.4 0.0 – 0.1 –2.0 – 0.5 –1.3

Total 994,346 –24.3 0.8 –11.2 –157.3 5.0 –79.4

Ozark, Ouachita– Forests 352,049 –10.1 – 0.7 –5.1 –67.7 –5.7 –40.0

Appalachian 

Forests

Grass/shrub

Agriculture

3,804

130,834

0.0

–1.8

0.0

0.0

0.0

– 0.9

– 0.1

–7.8

0.0

0.0

– 0.1

–3.3

Wetlands 2,671 – 0.1 0.0 0.0 – 0.5 – 0.1 – 0.4

Other 31,128 – 0.1 0.0 – 0.1 – 0.7 0.0 – 0.4

Total 520,486 –12.1 – 0.7 –6.1 –76.9 –5.7 –44.2

Mississippi Alluvial Forests 87,495 –2.8 0.4 –1.3 –21.7 2.8 –12.2

and Southeast 

USA Coastal 

Plains

Grass/shrub

Agriculture

Wetlands

26,811

139,412

113,617

– 0.1

–2.5

–2.5

0.0

0.0

– 0.7

0.0

–1.0

–1.6

– 0.6

–9.8

–20.9

0.2

1.1

–3.7

– 0.3

–3.9

–14.6

Other 139,424 – 0.4 0.0 – 0.1 –2.4 – 0.2 –1.2

Total 506,758 –8.3 – 0.3 –4.0 –55.3 0.2 –32.2

Eastern United Forests 1,363,153 –45.2 1.9 –22.8 –284.5 7.5 –157.6

States Grass/shrub 47,992 – 0.2 0.1 – 0.1 –1.6 0.5 – 0.8

Agriculture 964,948 –14.6 0.0 –6.5 –54.9 7.4 –21.2

Wetlands 271,717 –7.5 –2.6 –5.1 –53.7 –13.4 –40.9

Other 404,813 –1.4 0.0 – 0.5 –9.0 – 0.5 –4.5

Total 3,052,623 –68.9 – 0.7 –34.9 –403.7 1.4 –224.9
148 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–9. Projected average annual nutrient fluxes and total global warming potential (GWP), averaged from 2006 to 2050, in the 

Eastern United States.

[Nutrient fluxes are for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Projected fluxes of methane and nitrous oxide were estimated by the 

land GHG accounting tool (LGAT), and projected flux of carbon dioxide was calculated using net ecosystem carbon balance (NECB) values from table 7–8. 

TgCO2-eq, teragrams (or 1012 grams) of carbon dioxide equivalent per year]

Ecoregion Ecosystem Area,

in km2

CO2, in TgCO2-eq/yr N2O, in TgCO2-eq/yr 

Min Max Average Min Max Average 

Mixed Wood 

Shield

Forests 105,593 –51.3 13.6 –26.8 0.4 0.5 0.4 

Grass/shrub 3,822 –0.4 0.0 –0.4 0.3 0.3 0.3 

Agriculture 23,888 –6.2 0.4 –2.9 1.5 1.9 1.6 

Wetlands 58,742 –37.0 –11.7 –29.7 2.9 3.0 2.9 

Other 23,554 –0.4 0.0 –0.4 0.1 0.1 0.1 

Total 215,599 –95.3 2.2 –59.8 5.2 5.6 5.4 

Atlantic Highlands Forests 151,901 –114.4 –8.1 –81.0 1.6 1.8 1.7 

Grass/shrub 304 0.0 0.0 0.0 0.0 0.0 0.0 

Agriculture 19,145 –6.6 2.2 –1.1 1.0 1.6 1.3 

Wetlands 5,124 –4.0 –1.1 –3.3 0.3 0.3 0.3 

Other 11,076 –2.2 0.0 –0.7 0.2 0.2 0.2 

Total 187,550 –127.6 –6.6 –86.2 3.3 3.8 3.5 

Mixed Wood 

Plains

Forests 137,258 –108.9 4.0 –66.0 1.2 1.5 1.4 

Grass/shrub 2,049 –0.4 0.0 0.0 0.0 0.0 0.0 

Agriculture 160,383 –25.3 7.7 –7.0 9.4 10.7 9.9 

Wetlands 23,494 –13.2 –2.9 –10.3 1.4 1.6 1.5 

Other 65,674 –5.5 0.0 –2.6 1.1 1.1 1.1 

Total 388,857 –152.9 9.2 –85.8 13.6 14.6 13.9 

Central USA 

Plains

Forests 21,621 –11.0 –1.1 –6.6 0.7 0.9 0.8 

Grass/shrub 827 0.0 0.4 0.0 0.0 0.0 0.0 

Agriculture 177,774 –23.1 3.3 –10.6 27.1 29.1 28.0 

Wetlands 4,658 –2.6 –0.7 –1.8 0.3 0.3 0.3 

Other 34,147 –5.9 0.4 –2.2 0.2 0.2 0.2 

Total 239,027 –42.9 2.2 –21.3 28.5 30.5 29.3 

Southeastern 

USA Plains

Forests 507,236 –429.7 29.7 –205.7 52.2 64.1 59.1 

Grass/shrub 10,377 –2.6 0.4 –1.1 0.5 0.6 0.5 

Agriculture 313,514 –75.5 8.8 –29.7 17.6 25.5 21.0 

Wetlands 63,410 –61.6 –18.7 –49.9 3.6 3.8 3.7 

Other 99,809 –7.3 –1.8 –4.8 3.4 3.6 3.4 

Total 994,346 –576.8 18.3 –291.1 83.2 90.4 87.7 

Ozark, OuachitaAppalachian 

Forests

Forests 352,049 –248.2 –20.9 –146.7 10.0 11.2 10.8 

Grass/shrub 3,804 –0.4 0.0 –0.4 0.2 0.2 0.0 

Agriculture 130,834 –28.6 0.0 –12.1 5.2 6.8 5.8 

Wetlands 2,671 –1.8 –0.4 –1.5 0.1 0.2 0.2 

Other 31,128 –2.6 0.0 –1.5 1.4 1.4 1.4 

Total 520,486 –282.0 –20.9 –162.1 18.0 18.5 18.2 

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Forests 87,495 –79.6 10.3 –44.7 0.4 0.5 0.5 

Grass/shrub 26,811 –2.2 0.7 –1.1 1.3 1.4 1.4 

Agriculture 139,412 –35.9 4.0 –14.3 7.9 9.6 8.8 

Wetlands 113,617 –76.6 –13.6 –53.5 5.6 6.3 6.0 

Other 139,424 –8.8 –0.7 –4.4 0.1 0.1 0.1 

Total 506,758 –202.8 0.7 –118.1 16.0 17.4 16.7 

Eastern United 

States

Forests 1,363,153 –1,043.2 27.5 –577.9 66.6 80.3 74.6 

Grass/shrub 47,992 –5.9 1.8 –2.9 2.2 2.5 2.4 

Agriculture 964,948 –201.3 27.1 –77.7 69.7 85.3 76.3 

Wetlands 271,717 –196.9 –49.1 –150.0 14.2 15.4 14.9 

Other 404,813 –33.0 –1.8 –16.5 6.5 6.7 6.5 

Total 3,052,623 –1,480.2 5.1 –824.6 167.7 180.8 174.7 
Chapter 7 149

Table 7–9. Projected average annual nutrient fluxes and total global warming potential (GWP), averaged from 2006 to 2050, in the 

Eastern United States.—Continued

[Nutrient fluxes are for carbon dioxide (CO ), methane (CH ), and nitrous oxide (N O). Projected fluxes of methane and nitrous oxide were estimated by the 2 4 2

land GHG accounting tool (LGAT), and projected flux of carbon dioxide was calculated using net ecosystem carbon balance (NECB) values from table 7–8. 

TgCO -eq, teragrams (or 1012 grams) of carbon dioxide equivalent per year] 2

Ecoregion Ecosystem Area, 

in km2

CH , in TgCO -eq/yr 4 2 GWP, in TgCO -eq/yr 2

Min Max Average Min Max Average

Mixed Wood 

Shield

Atlantic Highlands

Mixed Wood 

Plains

Central USA 

Plains

Southeastern 

USA Plains

Ozark, OuachitaAppalachian 

Forests

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Eastern United 

States

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

Forests

Grass/shrub

Agriculture

Wetlands

Other

Total

105,593

3,822

23,888

58,742

23,554

215,599

151,901

304

19,145

5,124

11,076

187,550

137,258

2,049

160,383

23,494

65,674

388,857

21,621

827

177,774

4,658

34,147

239,027

507,236

10,377

313,514

63,410

99,809

994,346

352,049

3,804

130,834

2,671

31,128

520,486

87,495

26,811

139,412

113,617

139,424

506,758

1,363,153

47,992

964,948

271,717

404,813

3,052,623

–1.7

0.0

– 0.1

47.3

2.2

47.8

–3.1

0.0

0.0

3.6

0.6

1.0

– 0.9

0.0

– 0.3

18.3

2.7

19.8

– 0.5

0.0

– 0.1

3.2

0.6

3.3

–6.8

– 0.1

0.0

44.6

2.1

40.3

–9.9

0.0

– 0.1

1.7

0.9

–7.4

– 0.8

– 0.2

13.7

63.2

8.4

85.5

–23.6

– 0.3

13.1

182.0

17.4

190.4

–1.5

0.0

0.0

48.6

2.2

49.1

–2.9

0.0

0.0

3.6

0.6

1.3

– 0.8

0.0

– 0.3

19.7

2.7

21.2

– 0.4

0.0

– 0.1

3.4

0.6

3.4

–5.4

– 0.1

0.2

47.5

2.2

43.0

–8.9

0.0

0.0

1.8

0.9

–6.2

– 0.6

– 0.2

15.2

71.3

8.5

92.7

–20.4

– 0.3

15.0

195.9

17.6

204.5

–1.6

0.0

0.0

48.2

2.2

48.7

–3.0

0.0

0.0

3.6

0.6

1.1

– 0.9

0.0

– 0.3

19.2

2.7

20.7

– 0.4

0.0

– 0.1

3.3

0.6

3.3

–6.2

– 0.1

0.1

46.0

2.2

42.0

–9.5

– 0.5

– 0.1

1.8

0.9

–7.0

– 0.7

– 0.2

14.3

67.9

8.5

89.8

–22.3

– 0.3

13.9

189.9

17.5

198.7

–52.6

– 0.1

–4.8

13.2

1.9

–42.3

–115.9

0.0

–5.6

– 0.1

–1.4

–123.3

–108.6

– 0.3

–16.2

6.5

–1.7

–119.5

–10.8

0.0

3.9

0.9

–5.1

–11.1

–384.4

–2.2

–58.0

–13.4

–1.8

–453.3

–248.1

– 0.2

–23.5

0.0

– 0.3

–271.4

–79.9

–1.1

–14.3

–7.8

– 0.3

–101.2

–1,000.2

–3.9

–118.5

– 0.8

–9.2

–1,122.1

12.6

0.3

2.3

39.8

2.3

56.9

–9.2

0.0

3.8

2.8

0.8

–1.6

4.7

0.0

18.2

18.3

3.8

45.0

– 0.6

0.4

32.4

2.9

1.2

36.1

88.4

0.9

34.5

32.6

3.9

151.8

–18.6

0.2

6.8

1.6

2.3

–8.7

10.2

2.0

28.8

64.0

7.9

110.8

87.4

4.1

127.4

162.1

22.5

390.3

–27.9

– 0.1

–1.3

21.4

1.9

–5.7

–82.3

0.0

0.1

0.6

0.0

–81.6

–65.5

0.0

2.6

10.5

1.2

–51.2

–6.2

0.0

17.3

1.7

–1.4

11.4

–152.8

– 0.7

–8.6

– 0.2

0.8

–161.5

–145.4

– 0.9

–6.4

0.4

0.8

–150.8

–45.0

0.1

8.8

20.4

4.2

–11.6

–525.5

– 0.8

12.4

54.9

7.5

–451.2
150 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

−1,000

−800

−900

−700

−600

−500

−400

−300

190

192

194

196

198

200

202

204

172

170

174

176

178

180

Nitrous oxide, in teragrams of

carbon dioxide equivalent per year

Methane, in teragrams of

 carbon dioxide equivalent per year

Carbon dioxide, in teragrams of

carbon dioxide equivalent per year

2000 2010 2020 2030 2040 2050

2000 2010 2020 2030 2040 2050

2000 2010 2020 2030 2040 2050

Year

Year

Figure 7.10.

Scenario A1B

Scenario A2

Scenario B1

Mean

EXPLANATION

A

C

B

Figure 7–10. Graphs showing the baseline and projected 

temporal changes in global warming potential of A, carbon 

dioxide, B, methane, and C, nitrous oxide fluxes in the Eastern 

United States from 2006 through 2050. TgCO2

-eq/yr, teragrams of 

carbon dioxide equivalent per year.

the projected average annual NECB estimates, the Century 

model almost always yielded the highest estimates, followed 

by the EDCM and the LGAT. The relative variability among 

the models in projecting the average annual NECB was very 

high, ranging from 110.2 percent in the Mixed Wood Plains to 

154.6 percent in the Central USA Plains.

The three LULC scenarios (A1B, A2, and B1) were 

adapted for the assessment based on social, economic, and 

biophysical conditions embedded in the SRES scenarios 

(chaps. 2 and 3). Among the three scenarios, the relative 

variability of the estimated carbon stock was small, ranging 

from 2.9 to 12.1 percent across the ecoregions. The relative 

variability of the projected average annual NECB under 

these scenarios, higher than that of carbon stock, ranged 

from 13.3 percent in the Central USA Plains to 32.5 percent 

in the Southeastern USA Plains ecoregion. The higher 

variability of the projected average annual NECB across 

scenarios in some ecoregions compared with the carbon 

stock variability did not necessarily indicate that there was a 

large difference among the results of the scenario modeling. 

The high variability may have been simply related to the 

low projected average annual NECB estimates and how the 

percent variability was defined.

The effect of climate uncertainty introduced by the 

GCMs on projected carbon sequestration is reflected by the 

variability of carbon estimates across GCMs. The relative 

variability that may be attributed to GCM uncertainty ranged 

from 1 to 4.1 percent for projected carbon stocks and from 

7.9 to 15.9 percent for the projected average annual NECB 

across the ecoregions (table 7–10).

Comparing the relative variability or uncertainty 

of carbon estimates demonstrated by the models, LULC 

scenarios, and GCMs (table 7–10), the biogeochemical models 

introduced the highest relative uncertainty, varying from 

110.2 to 148.8 percent, followed by LULC scenarios (from 

13.3 to 32.5 percent) and GCMs (from 7.9 to 15.9 percent). 

The uncertainty of biogeochemical models overwhelmed the 

uncertainty from the other two.

7.5.2. Comparison of Results With Other Studies

On average, the terrestrial ecosystems (forests, agricultural lands, grasslands and shrublands, wetlands, and other 

lands) in the seven ecoregions of the Eastern United States 

stored a total 26,962 TgC (table 7–4) during the baseline 

period. Carbon in biomass pools (such as live and dead 

vegetative materials aboveground and belowground, except for 

those removed from agricultural fields and forests) accounted 

for 15,352 TgC (57 percent) of the total, and the rest was 

stored in the top 20 cm of the soil layer. Carbon stored in other 

pools (such as grain and woody biomass removed from the 
Chapter 7 151

Table 7–10. Comparison of projected average carbon stocks in 2050 and projected average annual net ecosystem carbon balance 

from 2006 to 2050 in the Eastern United States.

[Derived from combinations of three biogeochemical models, three land-use and land-change scenarios, and three general circulation models. The “variability 

index,” which is also presented, was calculated as a percentage measure for each of the three subsets of the model runs by dividing the range of the minimum 

and maximum estimates of the subset by their average and multiplying by 100]

Source

of data

Mixed Wood 

Shield

Atlantic

Highlands

Mixed Wood 

Plains

Central USA 

Plains

Southeastern 

USA Plains

Ozark,

OuachitaAppalachian 

Forests

Mississippi 

Alluvial and 

Southeast USA 

Coastal Plains

Eastern

United

States

Projected average carbon stock in 2050, in teragrams of carbon

Century 3,345.2 3,809.9 4,552.7 1,693.9 12,732.8 7,282.1 5,562.8 38,979.4

EDCM 3,505.4 4,147.0 4,835.8 1,868.4 11,115.2 6,696.7 5,597.2 37,765.7

Spreadsheet 2,693.3 3,040.4 3,922.4 1,607.8 7,592.2 5,138.3 3,982.9 27,977.4

Variability 25.5 30.2 20.6 15.1 49.1 33.6 32.0 31.5

Scenario A1B 3,254.4 3,775.1 4,534.7 1,730.2 10,719.6 6,522.5 5,165.5 35,702.0

Scenario A2 3,268.2 3,795.5 4,462.3 1,758.4 11,107.0 6,629.3 5,350.2 36,370.8

Scenario B1 3,439.6 3,962.7 4,755.0 1,780.5 12,088.9 7,023.1 5,540.0 38,589.8

Variability 5.6 4.9 6.4 2.9 12.1 7.4 7.0 7.8

CGCM3 3,223.4 3,727.7 4,488.3 1,732.5 10,641.9 6,526.4 5,098.0 35,438.2

CSIRO 3,129.6 3,594.7 4,367.8 1,715.4 10,489.6 6,328.2 5,082.3 34,707.6

MIROC 3,190.9 3,674.8 4,454.9 1,722.2 10,308.8 6,262.5 4,962.6 34,576.7

Variability 2.9 3.6 2.7 1.0 3.2 4.1 2.7 2.5

Projected average annual NECB from 2006 to 2050, in teragrams of carbon per year

Century –19.0 –26.2 –24.7 –3.9 –104.3 –55.7 –35.0 –268.8

EDCM –17.7 –26.0 –27.1 –8.9 –74.8 –41.0 –37.3 –232.8

Spreadsheet –2.6 –5.0 –5.9 –1.5 –10.3 –11.8 –5.9 –43.1

Variability 124.9 111.3 110.2 154.6 148.8 121.4 120.2 124.3

Scenario A1B –12.3 –18.1 –18.6 –4.4 –54.4 –33.3 –23.2 –164.3

Scenario A2 –12.4 –18.4 –17.5 –4.9 –60.1 –34.9 –26.1 –174.3

Scenario B1 –14.7 –20.7 –21.6 –5.1 –74.9 –40.3 –29.0 –206.3

Variability 18.4 13.3 21.8 15.3 32.5 19.4 22.2 23.1

CGCM3 –14.0 –20.4 –20.3 –5.0 –66.5 –39.5 –27.1 –192.9

CSIRO –11.9 –17.4 –17.7 –4.6 –63.1 –35.1 –26.8 –176.6

MIROC –13.4 –19.3 –19.7 –4.8 –59.8 –34.0 –24.3 –175.2

Variability 15.9 15.5 13.9 7.9 10.7 15.2 11.1 9.7

landscape) was not estimated in this assessment, although its 

influx was calculated.

For the baseline period, the estimated forest live biomass 

carbon for the entire Eastern United States was 9,686 TgC 

on 1.461 Mkm2 (146.1 million hectares (Mha)) of forested 

areas, which was lower than a recent Forest Service estimate 

of 11,249 TgC on a larger land base of 155.4 Mha (U.S. 

Department of Agriculture, 2008). In terms of carbon density, 

forest live biomass from this assessment (6.6 kgC/m2) 

was about 10 percent lower than that of the Forest Service 

study at 7.3 kgC/m2. The dead biomass (dead wood and 

forest floor) from this assessment was estimated to be 

3,209 TgC (2.2 kgC/m2), which was very close to the Forest 

Service estimate of 3,188 TgC (2.1 kgC/m2).

The average carbon sequestration rate or NECB of 

all terrestrial ecosystems in the Eastern United States was 

estimated to decline from –279.4 TgC/yr during the baseline 

period to –224.9 TgC/yr during the projection period. The 

decrease of the carbon sink strength of Eastern United States 

over time may be attributed largely to potential forest aging 

and reduced soil organic carbon accumulation. Several studies 

have noted the maturity of recovered forest lands in the 

Eastern United States (particularly in the northeastern region) 

since the early 20th century, hence the reduced contribution 

of active carbon production by the forests. Major processes 

contributing to the reduced SOC accumulation in the Eastern 

United States may include (1) the re-equilibration of soils to 

the rates of addition of organic matter as soils slowly recover 

from the soil organic matter that was lost following abandonment of agriculture around the beginning of the 20th century, 

(2) acceleration of the waning of the sink strength due to 

climate warming (as soil temperatures and biological activities 

increase with climate warming, rates of soil organic matter 

decomposition increase northwards; Melvin and Goodale, 
152 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

2013), and (3) genetic improvement of plants, especially 

crops, that will enhance organic matter production and 

therefore likely increase carbon storage in soils.

The average rate of sequestration by forests in the Eastern 

United States estimated in this assessment was –155 gC/m2/yr 

(–226.2 TgC/yr on 146.1 Mha), including –36 gC/m2/yr for 

soil carbon increase and –119 gC/m2/yr for live and dead 

biomass carbon increase. By comparison, the Forest Service 

estimate (U.S. Department of Agriculture, 2008) estimated 

the forest sequestration rate in combined aboveground and 

belowground live and dead biomass pool to be –60 gC/m2/yr 

(–93.3 TgC/yr on 155.4 Mha). Therefore, the per-unit-area 

estimate for carbon sequestration in the biomass pool is twice 

as large from this assessment compared with that from the 

Forest Service study. Causes to the difference in the estimates 

of carbon sequestration are many, including different forest 

areas, definitions, years of studies, and methods, and can 

be complex; the variability and associated uncertainties in 

many existing studies summarized in table 1–2 range from 

–82.8 gC/m2/yr for major forest types in the Eastern United 

States (Williams and others, 2012) to –243 gC/m2/yr for all 

forests in the Eastern United States (Turner and others, 1995). 

Carbon removals, especially clearcutting and thinning, are also 

critical components that affect the calculation of the rate of 

sequestration. The carbon removal amount (clear and partial 

cuttings) was estimated in this assessment to be 41 TgC/yr 

for the baseline period, which is significantly lower than in 

some earlier studies; for example, Williams and others (2012) 

estimated carbon removal to be about 100 TgC/yr in the 

Eastern United States.

NPP values from MODIS for 2001 through 2005 were 

used to constrain the spatial variability of GEMS NPP 

simulations. For the entire Eastern United States, the average 

baseline NPP was about 640 gC/m2/yr, generally matching that 

of other regional studies. Mickler and others (2002) reported 

that forest NPP of the Southeastern United States was between 

644 and 711 gC/m2/yr (converted from biomass according 

to a standard conversion factor of 0.5 given by Eggleston 

and others (2006)). One challenge in validating NPP simulations is the scarcity of the NPP estimates compared with the 

aboveground NPP (ANPP) estimates because of the difficulties in measuring belowground NPP. In order to compare 

belowground NPP estimates with available ANPP estimates, 

ANPP from this assessment was estimated to be 448 gC/m2/yr, 

using the forest NPP estimate of 640 gC/m2/yr and a conversion factor of 0.7 from forest NPP to ANPP according to field 

studies (Whittaker and Woodwell, 1969; Harris and others, 

1975; Benecke and Nordmeyer, 1982; Gholz and Fisher, 1982; 

Ryan and others, 1996, 1997; Curtis and others, 2002; Maier 

and others, 2004). This value for ANPP would agree well with 

other estimations generated using inventory or site-specific 

methods. For example, Brown and Schroeder (1999) reported 

that the ANPP in forests in the Eastern United States averaged 

435 gC/m2/yr and 485 gC/m2/yr for hardwood and softwood 

types, respectively. Jenkins and others (2001) reported that 

the average forest ANPPs in the mid-Atlantic region were 

393 gC/m2/yr and 430 gC/m2/yr for hardwood and softwood 

types, respectively.

Another comparison for ecosystem carbon sequestration may be made with CO2

 NEE for an entire ecosystem, 

estimated with the eddy covariance technique (table 7–11). 

The eddy covariance technique has been central to measuring 

the magnitude and variation of NEE in various ecosystems 

and the effects of disturbances and climate change (Loescher 

and others, 2006). Although NEE values listed in table 7–11 

showed large variations across sites, most ecosystems showed 

Table 7–11. Net ecosystem exchange of carbon dioxide measured in various ecosystems in the Eastern United States using eddy 

covariance techniques.

[Negative net ecosystem exchange (NEE) of carbon dioxide (CO2) indicates carbon uptake by ecosystems. gC/m2/yr, grams per square meter per year]

Forest ecosystem NEE, in

gC/m2

/yr Location of site Location Reference

Deciduous broad-leaved forest –370 Harvard Forest, Mass. 42°54'N,72°18'W Wofsy and others (1993)

Deciduous broad-leaved forest –140 Harvard Forest, Mass. 42°54N,72°18'W Goulden and others (1996)

Slash pine plantation –740 Gainesville, Fla. 29°44 N, 82°09' W Clark and others (1999)

Scrub oak ecosystem –287 Kissimmee-St. Cloud, Fla. 28°36'N, 80°42'W Powell and others (2006)

Deciduous forest –525 Oak Ridge, Tenn. 35°57'N, 84°17'W Greco and Baldocchi (1996)

Deciduous broad-leaved forest –574 Walker Branch Watershed, Tenn. 35°57'N, 84°17'W Wilson and Baldocchi (2000)

Slash pine plantation –425 Duke Forest, N.C. 35°98'N, 79°08'W Oren and others (2006)

Mixed hardwood and boreal forests –119 University of Michigan 

Biological Station, Mich.

45°35'N, 84°42'W Curtis and others (2002)

Mix of upland forests and wetlands –220 Willow Creek, Wisc. 45°47'N, 90°05'W Curtis and others (2002)

Deciduous broad-leaved forest –280 Harvard Forest, Mass. 42°54'N,72°17'W Urbanski and others (2007)

Loblolly pine plantation 106 Southeast Tree Research and

Education Site, Scotland 

County, N.C.

34°48'N, 79°12'W Lai and others (2002)

Young loblolly pine plantation –1,010 Scotland, N.C. 35°00'N, 79°00'W Albaugh and others (1998)
Chapter 7 153

carbon sequestration. For example, Wofsy and others (1993) 

reported an NEE of –370 gC/m2/yr at the Harvard Forest. 

Goulden and others (1996) reported that the annual net uptake 

of CO2 by a deciduous forest in New England (the Atlantic 

Highlands and Mixed Wood Plains ecoregions) ranged from 

–140 gC/m2/yr to 280 gC/m2/yr. Falge and others (2002) 

estimated the NEE of a temperate deciduous forest to be 

–181 gC/m2/yr. 

Bracho and others (2012) used eddy covariance and 

biometric approaches to measure carbon dynamics in two 

slash pine plantations (Pinus elliottii var. elliottii Englm) in 

northern Florida (in the Mississippi Alluvial and Southeast 

USA Coastal Plains ecoregion) over 9 years with frequent 

drought events and observed that the NEE magnitude 

fluctuated with environmental conditions (for example, 

drought events) from –800 to –400 gC/m2/yr and documented a drought-induced reduction of 20 percent in NEE. 

Similarly, Clark and others (2010) reported that invasive 

insects led to defoliation in a mixed stand in New Jersey (in 

the Mississippi Alluvial and Southeast USA Coastal Plains 

ecoregion) from 2005 through 2007 and resulted in a 

20 to 41 percent NEE reduction.

After analyzing a global database, Luyssaert and others 

(2007) found that the average NEP was –311 gC/m2/yr 

±38 gC/m2/yr for temperate humid deciduous forests. In 

addition, the study found that the global pattern for NEP 

was insensitive to climate and was mainly determined by 

nonclimatic conditions, such as successional stage, site 

management, site history, and site disturbance.

In the USGS assessment of the Eastern United States, 

the average forest carbon sequestration in the baseline period 

was –155 gC/m2/yr, with soil carbon change contributing 

–36 gC/m2/yr and the live and dead biomass carbon increase 

representing –119 gC/m2/yr. This average value was in the 

lower range of the measurements made by eddy covariance 

in the Eastern United States (table 7–11). With support of 

data obtained from 128 cold temperate and boreal forests 

across the United States, Reich (2012) found that stand-scale 

forest productivity is a function of leaf area index and canopy 

nitrogen concentration, which together explain more than 

75 percent of the variation in ANPP among forests.

7.5.3. Soil Carbon Sequestration Measurements 

in Agricultural Ecosystems

The agricultural soils were estimated to be a small 

SOC sink at an average rate of –4 gC/m2/yr (derived from 

table 7–5) ranging from a small source of 10 gC/m2/yr to a 

sink of –21 gC/m2/yr for the baseline years. The average value 

is somewhat smaller than the observed values reported by 

Tan and others (2006) and Franzluebbers (2010) but shows a 

relatively higher uncertainty than the reported field measurements. These estimated rates from previous studies came with 

an assumption that all cropped lands were under conservation 

tillage, but in reality only about 70 percent of all cropped soils 

were managed with conservation practices (Conservation 

Technology Information Center, 2012). The estimated values 

from this assessment included cropped soils that would be 

managed with conventional tillage, which likely led to higher 

SOC carbon source estimates (West and Post, 2002). Projected 

average net carbon flux in SOC pool for the agricultural lands 

was a SOC sink at –15 gC/m2

/yr, ranging from an almost 

neutral state to a moderate SOC sink of –36 gC/m2/yr.

7.5.4. Carbon Removal From Forest Harvesting

Carbon removal from clearcutting and partial forest cuts 

was estimated to be 41 TgC/yr from 2001 through 2005, which 

was lower than an estimate by the Forest Service (Adams 

and others, 2006) that showed about 110 to 120 TgC/yr of 

clearcutting and partial forest cuts by the end of the 1990s. 

Comparing annual harvest statistics from 2001 through 2005 

between the Forest Service RPA studies (Smith and others, 

2004, 2009) for the north and south regions of the Forest 

Service and this assessment (table 7–12) clearly indicates that 

the issue was the difference in estimating areas of harvesting.

Although forest areas used in the two studies were 

similar, annual areas harvested were quite different, with 

Smith and others (2009) reporting approximately 2.5 times the 

harvest area in this assessment. Area affected from clearcutting 

and partial cutting in the RPA was reported as 38,091 km2 or 

2.45 percent of the total forest area, whereas it was 15,037 km2

or 1.03 percent of the total forest area used in this assessment. 

Both clear cutting and partial cutting were more than twice 

as high in the RPA study as in this assessment. The RPA 

study relied on the approach of forest inventory to estimate 

harvesting area (Smith and others, 2009), whereas this assessment used datasets derived from remote sensing techniques as 

described in chapter 3 of this report.

Carbon removal rates, on the other hand, were similar 

between the RPA study and this assessment at 2.8 kilograms 

of carbon per square meter per event (kgC/m2/event) 

and 2.7 kgC/m2/event, respectively. The RPA study used 

3.6 kgC/m2

/event and 2.3 kgC/m2/event for clearcutting 

and partial cutting, compared with 3.8 kgC/m2/event and 

1.8 kgC/m2/event used in this assessment.

As the result, using the approach described in McKinley 

and others (2011) and the data from the RPA (Smith and 

others, 2009), the total carbon removal from the RPA study 

would be approximately 108 TgC/yr for the two Forest Service 

regions in the Eastern United States, compared with 41 TgC/yr 

reported in this assessment. The magnitude of difference is 

similar for both clearcutting and partial cutting (table 7–12).

Evidence from this assessment shows that the effect of 

discrepancies in estimating areas of forest cutting (clearcutting 

and partial cutting) on estimating net forest carbon flux is 

substantial. Current efforts that merge remotely sensed spatial 

forest cutting footprints with estimates derived from forest 

inventories have shown promising results (Hicke and others, 

2007; Goward and others, 2008; Huang and others, 2010; 
154 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table 7–12. Annual average forest harvests from 2001 through 

2005 in the Eastern United States.

[Comparison of forest harvest results between the Forest Service Resource 

Planning Act (RPA) results reported in Smith and others (2004; 2009) for the 

Forest Service north and south regions and produced for the U.S. Geological 

Survey (USGS) assessment. km2, square kilometers; gC/m2/event, grams of 

carbon per square meter per event; TgC/yr, teragrams of carbon per year]

Forest harvest variable RPA USGS

assessment

Forest area, in km2 1,555,163 1,461,458

Clearcut and partial cut area, in km2 38,091 15,037

Forest area clearcut and partial cut, in 

percent

2.45 1.03

Clearcut area, in km2 14,810 6,683

Forest area clearcut, in percent 0.95 0.46

Partial cut area, in km2 23,280 8,354

Forest area partial cut, in percent 1.50 0.57

Carbon removal from clearcut and 

partial cut, in TgC/yr

108 41.4

Carbon removal from clearcut, in 

TgC/yr

54.0 25.4

Carbon removal from partial cut, in 

TgC/yr

54.0 15.6

Rate of carbon removal from clearcut 

and partial cut, in gC/m2

 per event

2,830 2,750

Rate of carbon removal from clearcut, 

in gC/m2

 per event

3,640 3,800

Rate of carbon removal from partial 

cut, in gC/m2

 per event

2,320 1,800

Turner and others, 2011; Williams and others, 2012). The 

combined approach is one that was used for this assessment 

(described in chapter 3 of this report). Both remotely sensed 

products and ground-based estimation carry uncertainties 

in their results. This kind of uncertainty is one of the major 

areas that deserve much attention to reduce the uncertainty in 

estimating carbon dynamics over large areas (Liu and others, 

2012b; Kasischke and others, 2013).

7.5.5. Summary of Baseline Carbon Fluxes in 

the Assessment of the Eastern United States 

On average, the terrestrial ecosystems (forests, croplands, 

grasslands, shrublands, wetlands, and other lands) in the 

seven ecoregions of the Eastern United States stored a total of 

26,962 TgC during the baseline period (2001–2005). Carbon 

in biomass pools (such as live and dead vegetative materials 

aboveground and belowground, except for those removed 

from agricultural fields and forests) accounted for 15,352 TgC 

(57 percent) of the total, and the rest was stored in the top 

20 cm of the soil layer. Carbon stored in other pools (such 

as grain and woody biomass removed from the landscape) 

was not estimated in this assessment, although the influx of 

carbon from these sources was calculated. The regional NECB 

was estimated to be –288.6 TgC/yr, including NECB from 

lacustrine systems (9.2 TgC/yr) in the Eastern United States. 

This estimate represented the sum of carbon sequestered in 

terrestrial pools and in sediments in aquatic ecosystems in 

this region; however, carbon removal from rivers and coastal 

waters was not included in this assessment. Of the total NECB 

in the region, the terrestrial ecosystems were responsible for 

an average of –279.4 TgC/yr, including –214 TgC/yr and 

–65.4 TgC/yr in biomass and soils, respectively (fig. 7–11).

Carbon fluxes and burials from major terrestrial sources 

(this chapter) and in inland waters and coastal waters 

(chaps. 5 and 6) for baseline years are summarized together 

in figure 7–11. For simplicity, the estimated carbon stocks in 

all terrestrial ecosystems are lumped together in this diagram 

within two carbon pools: one for biomass carbon and one for 

soil organic carbon. The baseline years varied for different 

components of the assessment as limited by available input 

data (chap. 1). As the result, figure 7–11 should be interpreted 

as a composite representation of contemporary carbon cycle 

processes in the region estimated using different methods 

described in various chapters of this report. The common time 

period for all the components was from 2001 to 2005, which is 

the nominal baseline period for this assessment.

Among the various fluxes, the largest were NPP and 

heterotrophic respiration of the terrestrial ecosystems 

(fig. 7–11). The NPP and heterotrophic respiration were 

1,690.1 TgC/yr and 1,369.1 TgC/yr, respectively. About 

16.5 percent of the annual NPP was sequestered in biomass 

and soils. The amount of carbon removed by grain harvesting 

from agricultural lands was 103.8 TgC/yr. The amount of 

carbon removed by timber harvesting from forest lands was 

41.4 TgC/yr. Wildland fires also emitted an average of 1.6 

TgC/yr from ecosystems in the Eastern United States. On the 

aquatic side, rivers and streams transported an average of 36.5 

TgC/yr through various ecosystems in the Eastern United 

States, and 51.3 TgC/yr was emitted from all inland water 

bodies.

7.5.6. Limitation of This Assessment and 

Future Directions

For this assessment, efforts were made to process and 

produce spatially and temporally explicit input data of LULC 

(chaps. 2 and 3), wildland fire (chap. 4), and land management activities (this chapter) and to include the effects of 

these processes in carbon estimates presented in this chapter. 

Nevertheless, only a partial attribution analysis was produced 

on effects of controlling processes (for example, effects of 

wildland fire (chap. 4), effects of timber production (this 

chapter), general attribution of LULC change (chap. 3 and this 

chapter), and uncertainty contribution from the three biogeochemical models, LULC scenarios, and GCMs (this chapter).

Other ecosystem processes and land-use activities not 

included in this assessment could also potentially introduce 

uncertainties in the results. Although carbon fluxes related 

to timber and grain harvesting were estimated, the offsite 
Chapter 7 155

Atmosphere

Biomass

(15,352)

SOC

(11,610) Lakes Rivers Coast

NPP

(1,690.1)

HR

(1,369.1)

Fire

(0.29)

CH4

(24.1)

Decay

(NA)

Evasion

(41.6)

Evasion

(9.7)

Wood

product

(41.4)

(36.5)

214 65.4 9.2 NA

Grain

(103.8)

(19.4)

Stock

Stock change

Flux

Carbon stocks and flux, in 

 teragrams of carbon per year

EXPLANATION

Figure 7–11.

NA NA NA

Figure 7–11. Flow diagram showing average carbon stocks and fluxes and changes in average carbon stock for primary carbon 

pools in the Eastern United States during the baseline years (2001 through 2005). Only those carbon stocks and fluxes that were 

examined in the assessment are shown. Changes in carbon storage rates in lacustrine systems (lakes and reservoirs) and in 

coastal waters (by burial in sediment) were included, but the carbon stocks in these ecosystems were not included. In quantifying 

the changes in average carbon stocks of soils and biomass (all dead and live biomass), carbon combustion by fire and transfer 

to products by harvesting were considered, but not their export to the aquatic ecosystems. There was no coupling between 

the estimates of carbon stocks in the terrestrial and aquatic systems. Positive carbon stock change indicates a carbon storage 

increase and therefore represents carbon sequestration. The arrow with the dashed line under the “Rivers” box indicates the 

lateral flux of carbon within the streams and rivers. CH4

, methane; HR, heterotrophic respiration of terrestrial ecosystems; NA, not 

applicable, due to either a lack of input data or the choice of methods; NPP, net primary production of terrestrial ecosystems; SOC, 

soil organic carbon.

dynamics of timber and grain products were not accounted in 

this assessment because no life-cycle analysis was conducted 

to evaluate the long-term decomposition rates of the harvests. 

Also not explicitly included in this assessment were the 

carbon implications of other ecosystem disturbances, such as 

the carbon fluxes related to the temporal dynamics of forest 

defoliation and mortality from insects and windstorm-caused 

mortalities, particularly in the Great Lake and southern States. 

The dynamics of these land-management activities and natural 

disturbances, although highly relevant to the carbon cycle in 

the Eastern United States, were not supported with sufficient 

input data and scientific understanding. As a result, their 

exclusion introduced uncertainty in the assessment.

Results of this assessment suggested a wide range of 

uncertainty in the estimated carbon sequestration rates across 

models, LULC scenarios, and GCM projections in ecoregions 

and in the Eastern United States. In addition, the results showed 

that the uncertainty from models dominated the uncertainties 

from LULC and GCMs. These results are important but they 

are high-level observations without a detailed cause-and-effect 

analysis, which require a further effort to explain the differences among models, LULC scenarios, and GCM projections.

It should be recognized that the uncertainty analysis 

presented in this chapter is partial and conditional to the 

methods and input data used, specifically, the three biogeochemical models, climate projections of the three GCMs, 
156 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

and the three LULC scenarios. For example, the general 

analysis did not include uncertainties from input data layers 

and model parameters. Model simulations were constrained 

by nationally consistent ground-based measurements and 

census data (for example, forest inventory data from FIA and 

grain yield and crop management practices from the USDA) 

and satellite-derived estimates of processes such as NPP 

and disturbances. The development of additional data layers 

should be beneficial in constraining model simulations. For 

example, GEMS models should be calibrated and validated, 

with measures of parameter uncertainty, at site scales, using 

various measurements collected from diverse ecosystems 

and are available from FLUXNET (http://fluxnet.ornl.gov/). 

Developing additional data layers from FIA data through 

collaboration with the Forest Service and from literature 

review and meta-analysis may help reduce uncertainties in the 

data. It is also important to develop a scheme that can improve 

the understanding and quantification of the export of carbon 

from terrestrial to aquatic systems using the law of mass 

conservation, which was not implemented in this assessment. 

A continuing national effort should be undertaken to add more 

data streams and fields to constrain GEMS model behavior 

more tightly and therefore improve the understanding and 

quantification of the carbon cycle and reduce the uncertainty 

of the estimated carbon sources and sinks over large areas in 

the United States.

This assessment has created a nationwide and consistent 

framework capacity for quantifying carbon dynamics and 

GHG emissions under the effects of LULC, disturbances, and 

climate for the Eastern United States at 250-m resolution. 

Future efforts should be orchestrated around the following 

major themes to strengthen this capacity: (1) improving 

understanding and quantification of the carbon cycle at landscape to national scales, as described earlier in this section, 

with fundamental advancement in process understanding, 

and (2) transitioning GEMS into a real-time carbon dynamics 

monitoring system for the country and the ecoregions. With 

regard to the second theme, efforts should replace part of 

the databases developed in this assessment. Specifically, the 

model simulated dynamics of climate, land use, and disturbances from 2006 to present (part of the projection period in 

the assessment) should be replaced with real observations. 
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Appendixes 1–6

Appendix 1. The Erosion Deposition Carbon Model 

Appendix 2. The Land Greenhouse-Gas Accounting Tool

Appendix 3. Coefficients of Forest Partial Cutting and Biomass Mortality

Appendix 4. Optimized Maximum Monthly Gross Primary Production

Appendix 5. Comparison of Sampling-Based and Per-Pixel Model Runs

Appendix 6. Emission Factors of Nitrous Oxide and Methane in the Eastern United States
180 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Appendix 1. The Erosion Deposition Carbon Model

The Erosion Deposition Carbon Model (EDCM; Liu 

and others, 2003) is an ecosystem biogeochemical model 

developed from the well-established ecosystem model 

Century version 4.0 (Parton and others, 1987, 1994). Although 

the EDCM retains the basic input and output file structures of 

Century, major changes have been made on model structure 

and underlying biogeochemical processes.

A new algorithm has been implemented in EDCM 

for this national assessment to account for the effects of 

cropping improvement on the carbon cycle of an ecosystem. 

Crop biomass production, crop yield, and residue return to 

the soil greatly affect the carbon cycle in cropland systems. 

Crop yield has experienced continuous improvement 

over the past century and continuing into the future due 

to genetic engineering and improved management practices.

We analyzed the temporal trends of yield from 

1866 through 2009 for 23 major crops in the United 

States based on the census data of the U.S. Department of 

Agriculture at the State and county levels (U.S. Department 

of Agriculture, Economic Research Service, 2011). To 

quantify the overall temporal change of each crop, the 

reported yield for a given crop was averaged across the 

country and normalized to the yield in 2000. In addition, the 

projected yield changes from Integrated Model to Assess 

the Greenhouse Effect (IMAGE) for various crops (Alcamo 

and others, 1998) were used to constrain the future paths 

and potentials of these crops for the next 50 years. These 

normalized yields of any given crop were then fitted to the 

following logistic growth curve:

( ) 1 exp n

k Y t c

a b t d

= +

+ × × − , (A1–1)

where

c, k, a, b, and d are fitted coefficients (table A1–1), and

Yn( t) is the yield in year t normalized to 2000.

After examining the normalized temporal yield curves of the 

all the crops, these changes were grouped into seven major 

categories (table A1–1). It should be noted that the curves 

(fig. A1–1) were developed for applications across the country, 

and some crops (for example, sugarcane and rice) might not 

exist in all ecoregions across the country. These curves are 

embedded in the General Ensemble Biogeochemical Modeling 

System (GEMS; Liu, 2009; Liu and others, 2012).

1850 1900 1950 2000 2050 2100

Year

Barley

EXPLANATION

Corn

Soybean

Wheat

Rice

Hay

Sugarcane

0

0.5

1

1.5

2

0

0.5

1

1.5

2

A. Corn

B. Major crop groups

Figure A2.1

Normalized yield [dimensionless] Normalized yield [dimensionless]

Figure A1–1. Normalized curves of grain yield in the United 

States for A, corn and B, major crop groups.
Appendixes 181

Table A1–1. Coefficients of the logistic curves describing the grain yield change for major crops in the United States.

[Values of c, k, a, b, and d are fitted coefficients of the logistic growth curves; see text for the equation]

Group c k a b d Crops in the group

Barley 0.3853 1.9614 8.6459 –0.038 1960 Barley, oats

Corn 0.235 1.2095 4.2095 –0.0602 1960 Corn, corn silage, sugar beets, sweet corn, potatoes, tomatoes, cotton

Soybean 0.413 0.6681 1.5526 –0.06 1960 Soybeans, peanuts

Wheat 0.0852 1.6525 2.3209 –0.0291 1955 Winter wheat, spring wheat, durum wheat, sunflowers

Rice 0.0846 2.1279 3.4339 –0.0256 1955 Rice, sorghum

Hay 0.2832 1.939 6.6342 –0.0365 1955 Hay, rye, tobacco, beans

Sugarcane 0.4044 0.6252 0.0575 –0.09 1960 Sugarcane, sorghum silage

References Cited

Alcamo, Joseph, Leemans, Rick, and Kreileman, Eric, 1998, 

Global change scenarios of the 21st century—Results from 

the IMAGE 2.1 model: Oxford, Elsevier Science Ltd., 296 p.

Liu, Shuguang, 2009, Quantifying the spatial details of 

carbon sequestration potential and performance, in

McPherson, B.J., and Sundquist, E.T., eds., Carbon 

sequestration and its role in the global carbon cycle: 

American Geophysical Union Monograph 183, p. 117–128, 

http://dx.doi.org/10.1029/2006GM000524.

Liu, Shuguang, Bliss, Norman, Sundquist, Eric, and 

Huntington, T.G., 2003, Modeling carbon dynamics in 

vegetation and soil under the impact of soil erosion and 

deposition: Global Biogeochemical Cycles, v. 17, no. 2, 

1074, 24 p., http://dx.doi.org/10.1029/2002GB002010.

Liu, Shuguang, Tan, Zhengxi, Chen, Mingshi, Liu, Jinxun, 

Wein, Anne, Li, Zhengpeng, Huang, Shengli, 

Oeding, Jennifer, Young, Claudia, Verma, S.B., 

Suyker, A.E., Faulkner, Stephen, and McCarty, G.W., 2012, 

The general ensemble biogeochemical modeling system 

(GEMS) and its applications to agricultural systems in the 

United States, chap. 18 of Liebig, Mark, Franzluebbers, A.J.,

and Follett, Ronald, eds., Managing agricultural greenhouse 

gases—Coordinated agricultural research through GRACEnet

to address our changing climate: London, Academic Press, 

p. 309–323. (Also available at http://dx.doi.org/10.1016/

B978-0-12-386897-8.00018-8.)

Parton, W.J., Schimel, D.S., Cole, C.V., and Ojima, D.S., 

1987, Analysis of factors controlling soil organic-matter 

levels in Great Plains grasslands: Soil Science Society of 

America Journal, v. 51, no. 5, p. 1173–1179, http://dx.doi.

org/10.2136/sssaj1987.03615995005100050015x.

Parton, W.J., Ojima, D.S., and Schimel, D.S., 1994, Environmental change in grasslands—Assessment using 

models: Climatic Change, v. 28, nos. 1–2, p. 111–141, 

http://dx.doi.org/10.1007/BF01094103.

U.S. Department of Agriculture, Economic Research Service, 

2011a, ARMS farm financial and crop production practices—

Tailored reports: U.S. Department of Agriculture, Economic

Research Service database, accessed August 16, 

2011, at http://www.ers.usda.gov/Data/ARMS/app/. 

[Database moved and accessed June 6, 2014, at 

http://www.ers.usda.gov/data-products/arms-farm-financialand-crop-production-practices/tailored-reports-farmstructure-and-finance.aspx#.U5GYp_mwLMo.]
182 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Appendix 2. The Land Greenhouse-Gas Accounting Tool

Spreadsheet-based or bookkeeping models have been 

used to simulate various biogeochemical processes (for 

example, Houghton and Hackler, 2000; Verburg and Johnson, 

2001). The Land Greenhouse-Gas Accounting Tool (LGAT) 

is developed based on a set of rules and implemented using 

C++ to calculate carbon budgets and greenhouse gas (GHG) 

emissions, similar to a spreadsheet model in algorithms but 

different in form. Instead of performing calculations according 

to tables in a spreadsheet environment, the LGAT generates 

spatial data layers of carbon stocks and GHG emissions on a 

pixel basis according to the rules and conditions of each pixel. 

Changes in pixel conditions can lead to changes in carbon 

stocks and GHG emissions over time.

The LGAT requires the following input data:

• land cover map

• forest age distribution map

• soil carbon map

• potential aboveground and belowground biomass maps 

for grassland and shrubland

• forest carbon density by age and forest type

• lookup tables for methane (CH4) and nitrous oxide 

(N2O) emission factors by region 

and land cover

The LGAT generates maps of 12 variables on an annual 

basis (table A2–1). Additional data layers, such as temporal 

changes of total carbon, can be calculated handily at the pixel 

level by looking at the differences between years. The LGAT 

calculates carbon stocks and GHG fluxes of all pixels in a 

region using the following rules:

• Soil organic carbon (SOC) stocks.—Assign SOC 

stocks according to static map (no SOC change is 

tracked because lack of data at present).

• Biomass carbon stocks.—

• If the land cover is forest—

• If there is no disturbance, assign carbon stock 

values according to region (for example, the 

U.S. Forest Service Forest Inventory and Analysis 

(FIA) unit), forest type, and age.

• If forest harvesting occurs—

• Calculate carbon removal (as β×CAGLB , where 

β is the mass transfer coefficient from aboveground live carbon to harvested wood and CAGLB

is the aboveground live biomass carbon).

• Assign carbon stock values according to region 

(for example, FIA unit), forest type, and age 

(=0).

• No procedure has been implemented for other 

disturbances.

• If the land cover is cropland—

• If cover is corn, assign corn-specific constant 

carbon stock values.

• If cover is not corn, assign specific constant carbon stock values.

• If the land cover is grassland, assign carbon stock 

values according to a potential grassland biomass 

carbon stock map.

• If land cover is shrubland, assign carbon stock values according to a potential shrubland biomass 

carbon stock map.

• CH4 flux.—Assign CH4 emission factor according to 

region and land cover.

• N2O flux.—Assign N2O emission factor according to 

region and land cover.

Table A2–1. List of output variables from the Land Greenhouse-Gas Accounting Tool.

[CH4, methane; gC/ha/yr, grams of nitrogen per hectare per year; MgC/ha, megagrams of carbon per hectare; MgC/ha/yr, megagrams 

of carbon per hectare per year; N2O, nitrous oxide]

Index Variable Definition Unit Pool/flux Scale

1 Aglc_tr Aboveground live carbon MgC/ha Pool Pixel

2 Bglc_tr Belowground live carbon MgC/ha Pool Pixel

3 Aglc_ntr Understory aboveground carbon MgC/ha Pool Pixel

4 Bglc_ntr Understory belowground carbon MgC/ha Pool Pixel

5 CWD_st Standing dead carbon MgC/ha Pool Pixel

6 CWD_dn Down wood carbon MgC/ha Pool Pixel

7 Litter Litter carbon MgC/ha Pool Pixel

8 roots_dead Dead roots MgC/ha Pool Pixel

9 soc Soil organic carbon MgC/ha Pool Pixel

10 systemc Total system carbon MgC/ha Pool Pixel

11 N2

O N2O emissions gN/ha/yr Flux Pixel

12 CH4 CH4

 flux MgC/ha/yr Flux Pixel
Appendixes 183

References Cited

Houghton, R.A., and Hackler, J.L., 2000, Changes in terrestrial carbon storage in the United States. 1. The roles of 

agriculture and forestry: Global Ecology and Biogeography, 

v. 9, no. 2, p. 125–144, accessed December 10, 2013, at 

http://dx.doi.org/10.1046/j.1365-2699.2000.00166.x.

Verburg, P.S.J., and Johnson, D.W., 2001, A spreadsheet-based 

biogeochemical model to simulate nutrient cycling processes in forest ecosystems: Ecological Modelling, v. 141, 

nos. 1–3, p. 185–200, accessed December 10, 2013, at 

http://dx.doi.org/10.1016/S0304-3800(01)00273-3.
184 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Appendix 3. Coefficients of Forest Partial Cutting and Biomass Mortality

The U.S. Forest Service Forest Inventory and Analysis (FIA) 

Program is known to provide forest inventory data for the United 

States. The FIA database for each individual State can be downloaded from the FIA DataMart (http://fia.fs.fed.us/tools-data/) as 

a Microsoft Access database. We downloaded the forest inventory 

information for the period that reported the annualized data (from 

2002 through 2010) to estimate the coefficients related to partial 

Table A3–1. Forest partial cutting and biomass mortality in the Eastern United States.—Continued

[Forest partial cutting is the percent of forestland area being partially cut per year, and biomass mortality is the percentage of live biomass becoming dead 

each year, averaged from Forest Inventory and Analysis (FIA) observations from 2002 through 2010. FIPS ID, Federal Information Processing Standard 

identification number]

State

FIPS ID FIA unit

Partial cutting, in percent Mortality, in percent

Softwood

forest

Hardwood

forest

Mixed

forest

Softwood

forest

Hardwood

forest

Mixed

forest

1 1 3.82 2.76 2.79 0.91 1.19 0.85

1 2 4.65 3.07 4.35 1.44 1.35 0.85

1 3 3.80 2.84 3.15 1.15 0.91 0.75

1 4 3.56 2.75 2.73 1.41 1.14 1.56

1 5 2.36 2.33 1.53 1.56 1.48 1.36

1 6 1.78 1.83 1.55 1.03 1.54 2.00

2 0 NA NA NA NA NA NA

2 1 NA NA NA NA NA NA

5 1 2.92 1.73 4.24 1.04 0.11 0.37

5 2 2.98 1.76 3.36 1.36 0.32 NA

5 3 4.61 3.62 4.67 0.97 0.61 0.45

5 4 2.83 2.57 1.90 1.09 0.74 0.64

5 5 1.86 1.65 1.25 0.85 0.56 0.74

9 1 NA 1.80 1.41 0.70 0.09 0.39

10 1 0.67 0.80 3.78 0.63 0.30 0.79

11 1 NA NA NA NA NA NA

12 1 3.80 2.39 2.99 0.77 0.77 0.85

12 2 2.98 2.76 2.80 0.71 0.80 0.39

12 3 0.97 1.45 0.83 1.06 1.23 0.82

12 4 1.02 0.79 0.55 0.27 0.26 2.00

13 1 2.69 2.69 2.21 1.43 0.71 0.51

13 2 3.57 3.11 2.41 1.44 0.84 0.78

13 3 2.86 3.31 2.30 0.92 0.94 0.66

13 4 2.25 2.37 1.86 0.91 0.77 0.95

13 5 1.92 1.17 1.24 0.62 1.39 1.67

15 1 NA NA NA NA NA NA

17 1 0.89 1.24 NA 1.52 0.35 1.09

17 2 NA 1.45 NA 1.17 NA NA

17 3 0.62 1.14 6.35 1.41 0.39 1.82

18 1 NA 2.44 NA 0.64 0.64 2.00

18 2 2.41 1.59 2.82 0.97 0.21 0.66

18 3 NA 0.76 1.44 0.84 0.54 1.42

18 4 4.79 1.79 NA 1.10 0.09 1.22

19 1 NA 3.00 NA 1.12 0.15 NA

19 2 NA 1.37 NA 1.20 0.58 NA

19 3 NA 1.77 NA 1.46 0.39 2.00

cutting and biomass mortality in the Eastern United States at 

FIA plot level. These coefficients were then averaged by FIA

unit, defined as a group of counties by State, and delineated by 

forest type (tables A3–1 to A3–4). This work was primarily done 

by Decheng Zhou from Beijing University and Jennifer Oeding

of Stinger Ghaffarian Technolgoes Inc. under contract for the 

U.S. Geological Survey, in consultation with FIA staff.
Appendixes 185

Table A3–1. Forest partial cutting and biomass mortality in the Eastern United States.— Continued

[Forest partial cutting is the percent of forestland area being partially cut per year, and biomass mortality is the percentage of live biomass becoming dead 

each year, averaged from Forest Inventory and Analysis (FIA) observations from 2002 through 2010. FIPS ID, Federal Information Processing Standard 

identification number]

State 

FIPS ID FIA unit

Partial cutting, in percent Mortality, in percent

Softwood 

forest

Hardwood 

forest

Mixed 

forest

Softwood 

forest

Hardwood 

forest

Mixed 

forest

19

20

20

20

21

21

21

21

21

21

21

22

22

22

22

22

23

23

23

23

23

23

23

23

23

24

24

24

24

25

26

26

26

26

27

27

27

27

28

28

28

28

28

29

29

29

29

4

1

2

3

1

2

3

4

5

6

7

1

2

3

4

5

1

2

3

4

5

6

7

8

9

2

3

4

5

1

1

2

3

4

1

2

3

4

1

2

3

4

5

1

2

3

4

NA

2.26

1.11

NA

NA

1.82

NA

1.18

2.71

0.65

NA

4.41

3.45

4.33

3.42

4.94

3.60

2.22

3.09

1.94

2.54

2.67

1.87

3.64

2.77

1.47

2.50

3.81

NA

1.29

0.74

0.87

1.12

0.57

0.65

1.09

1.26

NA

1.60

3.56

3.98

3.47

3.63

1.58

2.98

2.04

NA

0.68

1.29

1.00

0.59

1.50

1.35

1.46

0.86

1.32

1.07

0.79

1.34

1.24

2.79

2.25

3.97

2.52

3.27

3.04

3.02

3.07

2.03

4.05

2.99

3.09

1.98

1.73

1.86

1.94

1.71

2.03

1.97

1.66

1.46

1.99

2.43

1.95

1.32

2.29

2.40

3.17

2.87

2.59

2.39

3.19

2.36

1.96

NA

NA

2.49

NA

2.09

NA

0.90

0.81

1.19

1.80

7.00

2.07

5.68

3.86

4.80

4.77

10.00

NA

6.78

NA

NA

2.01

3.64

3.40

3.80

2.82

4.26

4.66

11.35

1.65

1.51

1.59

1.29

1.76

1.34

1.86

3.44

NA

3.87

2.10

3.26

3.60

3.57

2.39

1.78

1.72

2.01

2.00

1.30

1.00

2.00

1.10

1.28

0.77

0.80

0.72

0.50

0.87

1.16

1.57

1.55

2.00

0.68

0.89

1.28

0.99

1.37

1.32

1.00

1.48

0.52

1.06

1.00

0.51

0.95

0.99

0.76

0.99

0.85

0.75

1.24

2.00

1.41

1.25

1.31

0.95

0.83

1.27

2.00

2.00

0.94

0.92

1.43

1.16

NA

1.12

NA

1.06

0.56

0.97

1.72

0.54

0.77

1.08

0.21

0.64

NA

1.56

1.49

0.08

2.00

NA

0.12

0.05

1.89

0.63

1.19

0.98

0.33

0.62

1.84

0.67

1.03

0.68

0.43

0.59

0.66

1.15

2.00

1.22

1.46

0.27

1.30

1.38

0.88

2.00

0.41

0.68

0.51

0.37

1.47

NA

2.00

0.78

NA

1.37

0.99

2.00

1.75

1.40

0.73

0.74

0.45

2.00

1.54

0.49

0.43

0.76

0.99

0.87

1.15

1.02

1.33

0.99

0.74

0.85

0.49

1.02

0.67

0.19

0.52

0.95

1.18

0.71

2.00

1.61

1.23

1.28

0.75

0.08

0.34

0.95

1.56

0.46

0.33

0.50

0.06

0.92
186 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table A3–1. Forest partial cutting and biomass mortality in the Eastern United States.— Continued

[Forest partial cutting is the percent of forestland area being partially cut per year, and biomass mortality is the percentage of live biomass becoming dead 

each year, averaged from Forest Inventory and Analysis (FIA) observations from 2002 through 2010. FIPS ID, Federal Information Processing Standard 

identification number]

State 

FIPS ID FIA unit

Partial cutting, in percent Mortality, in percent

Softwood 

forest

Hardwood 

forest

Mixed 

forest

Softwood 

forest

Hardwood 

forest

Mixed 

forest

29

31

31

33

33

34

36

36

36

36

36

36

36

36

37

37

37

37

38

39

39

39

39

39

39

40

40

40

40

40

40

40

42

42

42

42

42

42

44

45

45

45

46

46

47

47

47

5

1

2

2

3

1

1

2

3

4

5

6

7

8

1

2

3

4

1

1

2

3

4

5

6

1

2

3

4

5

6

7

0

5

6

7

8

9

1

1

2

3

1

2

1

2

3

4.00

1.18

0.29

2.10

2.93

0.19

0.99

0.57

1.46

0.46

3.44

1.19

1.37

0.98

4.61

3.31

1.69

0.98

NA

0.37

NA

NA

2.84

NA

2.73

4.57

2.31

NA

NA

NA

NA

NA

NA

2.68

0.66

NA

3.28

NA

0.49

3.24

2.85

3.01

NA

2.08

1.67

3.44

0.56

1.85

0.84

0.58

2.24

2.12

1.01

1.76

1.64

1.53

0.92

2.17

2.33

2.05

1.00

3.14

2.93

2.10

1.12

0.59

1.23

1.15

1.01

1.22

0.48

1.10

1.89

0.98

NA

NA

NA

NA

NA

1.97

2.24

1.65

2.13

1.41

1.90

0.96

1.92

2.37

1.95

0.59

3.40

2.09

2.03

1.87

1.50

2.57

NA

2.87

3.74

1.43

2.21

3.77

NA

2.52

NA

3.22

1.51

NA

3.24

3.58

2.50

1.83

NA

2.79

2.12

NA

NA

NA

NA

2.97

1.52

NA

NA

NA

NA

NA

NA

NA

1.02

4.87

1.86

NA

NA

1.91

2.37

2.02

NA

NA

2.25

2.29

1.20

1.11

1.35

2.00

1.05

0.75

0.81

1.26

0.70

0.95

1.12

0.47

0.60

0.58

0.84

1.76

1.24

0.95

0.64

2.00

1.00

0.90

1.11

1.08

0.86

0.95

0.83

2.00

1.07

1.07

1.07

1.07

1.07

1.14

0.86

0.70

0.69

0.79

0.76

0.51

0.82

0.75

0.88

2.00

1.18

1.15

0.88

0.82

0.64

1.44

0.83

1.15

0.48

0.61

1.17

0.30

0.95

0.90

0.90

0.79

0.50

0.22

1.00

1.68

1.49

1.28

NA

1.14

2.00

2.00

NA

0.65

1.89

0.23

2.00

0.87

0.87

0.87

0.87

0.87

1.66

2.00

0.50

0.42

2.00

0.07

0.08

0.79

0.60

1.12

NA

NA

0.70

0.96

0.56

0.34

0.41

0.86

2.00

0.72

0.59

1.16

1.22

0.84

0.88

1.68

0.67

0.52

0.78

0.69

1.14

1.53

1.41

0.74

0.41

0.48

0.81

0.31

NA

1.70

0.71

NA

0.92

0.92

0.92

0.92

0.92

0.33

1.00

0.71

2.00

1.34

0.89

0.72

0.61

0.59

0.96

0.22

1.14

0.82

0.28

0.40
Appendixes 187

Table A3–1. Forest partial cutting and biomass mortality in the Eastern United States.— Continued

[Forest partial cutting is the percent of forestland area being partially cut per year, and biomass mortality is the percentage of live biomass becoming dead 

each year, averaged from Forest Inventory and Analysis (FIA) observations from 2002 through 2010. FIPS ID, Federal Information Processing Standard 

identification number]

State 

FIPS ID FIA unit

Partial cutting, in percent Mortality, in percent

Softwood 

forest

Hardwood 

forest

Mixed 

forest

Softwood 

forest

Hardwood 

forest

Mixed 

forest

47

47

48

48

48

48

48

48

48

50

50

51

51

51

51

51

54

54

54

55

55

55

55

55

4

5

1

2

3

4

5

6

7

2

3

1

2

3

4

5

2

3

4

1

2

3

4

5

2.34

0.98

3.84

4.52

NA

NA

NA

NA

NA

3.83

2.88

2.21

1.92

2.64

0.63

1.07

0.11

NA

0.77

1.42

1.23

3.19

1.75

0.58

2.13

1.23

2.75

3.12

NA

NA

NA

NA

NA

3.02

2.33

1.59

2.10

1.32

0.81

1.17

2.30

2.85

2.18

2.39

2.08

2.80

2.41

1.92

1.96

1.13

4.52

3.64

NA

NA

NA

NA

NA

2.68

NA

1.84

1.99

1.86

0.67

1.15

3.52

2.45

3.02

2.43

1.49

2.45

2.75

3.47

0.85

0.96

1.43

0.94

1.07

1.07

1.07

1.07

1.07

0.88

1.06

1.34

0.61

0.87

0.64

0.51

0.77

0.66

0.90

0.79

1.11

1.16

1.43

1.26

2.00

2.00

1.05

0.65

0.87

0.87

0.87

0.87

0.87

0.63

0.35

1.00

1.03

1.34

0.49

0.53

0.65

0.40

0.56

0.51

1.26

1.16

1.57

2.00

2.00

2.00

0.76

0.98

0.92

0.92

0.92

0.92

0.92

1.15

0.45

0.81

0.74

0.77

1.85

2.00

0.51

0.96

1.41

0.60

0.60

0.75

0.48

0.87

Table A3–2. Age distribution of forest mortality in the Eastern United States.

[Values are as a percentage of the total forest type. Columns represent age class: age10, 0 to 10 years old; age20, 11 to 20 years old; age30, 21 to 30 years old; 

age40 =31 to 40 years old; age50, 41 to 50 years old; age60, 51 to 60 years old; age70, 61 to 70 years old; age80, 71 to 80 years old; age90, 81 to 90 years old; 

age 100, 91 to 100 years old; age 110, > 100 years old]

Forest type age10 age20 age30 age40 age50 age60 age70 age80 age90 age100 age110

Hardwood 0.69 1.42 2.74 6.82 13.61 17.24 20.29 15.39 10.74 5.77 5.29

Mixed 1.19 3.25 5.08 8.87 18.29 23.47 15.71 13.29 8.10 1.71 1.03

Softwood 1.45 6.78 15.57 10.94 16.59 15.52 12.25 9.24 4.74 2.45 4.48

Table A3–3. Age distribution of partial forest cutting in the Eastern United States.

[Values are as a percentage of the total forest type. Columns represent age class: age10, 0 to 10 years old; age20, 11 to 20 years old; age30, 21 to 30 years old; 

age40 =31 to 40 years old; age50, 41 to 50 years old; age60, 51 to 60 years old; age70, 61 to 70 years old; age80, 71 to 80 years old; age90, 81 to 90 years old; 

age 100, 91 to 100 years old; age 110, more than 100 years old]

Forest type age10 age20 age30 age40 age50 age60 age70 age80 age90 age100 age110

Hardwood 1.23 1.29 3.05 7.22 12.50 19.43 20.93 15.48 8.94 4.39 5.56

Mixed 0.92 3.30 11.19 16.93 16.35 24.84 10.33 8.23 3.06 2.47 2.39

Softwood 0.51 15.66 28.53 17.71 15.68 10.24 5.56 2.96 1.52 0.60 1.03
188 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table A3–4. Frequency distribution of forest cutting in the Eastern United States.

[Values are as a percentage of the total forest type. Columns represent removal rate of aboveground biomass: ci0, 0 to 10 percent removal; ci20, 11 to 20 percent 

removal; ci30, 21 to 30 percent removal; ci40 =31 to 40 percent removal; ci50, 41 to 50 percent removal; ci60, 51 to 60 percent removal; ci70, 61 to 70 percent 

removal; ci80, 71 to 80 percent removal; ci90, 81 to 90 percent removal; ci100, 91 to 100 percent removal (that is, clearcut).

Forest Type ci10 ci20 ci30 ci40 ci50 ci60 ci70 ci80 ci90 ci100

Hardwood 3.741 7.489 9.647 9.486 10.44 11.15 9.335 7.911 13.1 17.7

Mixed 3.286 5.521 4.801 6.879 10.25 6.89 15.08 11.35 16.53 19.41

Softwood 1.018 2.897 5.83 7.527 9.354 9.768 8.314 9.152 9.948 36.19
Appendixes 189

Appendix 4. Optimized Maximum Monthly Gross Primary Production

The maximum gross primary production rates (PRDX) 

for all land cover types within each county in the Eastern 

United States were calibrated using county-based grainyield-survey data by crop type and 250-meter resolution net 

primary production data from the moderate resolution imaging 

spectroradiometer (MODIS) for other land-use and land-cover 

types such as forests and grasslands from 2001 through 2005. 

Figure A4–1 shows the distribution of PRDX for the major 

land cover types across more than 1,990 counties in the 

Eastern United States.

0

500

1,000

1,500

Gross primary production, in grams of biomass

per square meter per month

8

Forest 

deciduous

9

Forest

evergreen 

10

Forest

mixed

11

Grass

12

Shrub

14

Hay

15 

Wetland

herbaceous

16

Wetland

woody

103

Corn

grain

104

Corn

silage

119

Soybean

127

Wheat,

spring

128

Wheat,

winter

5th percentile

Minimum

95th percentile

50th percentile

 (median)

Maximum

EXPLANATION

Figure A4.1.

Figure A4–1. Graph showing the distribution of the optimized parameter potential monthly gross primary production (PRDX), in 

grams of biomass per square meter per month, in the Erosion Deposition Carbon Model (EDCM) for each major land cover across 

1,990 counties in the Eastern United States. Each box-and-whisker represents the distribution of the PRDX values derived for 

1,990 counties. The line inside the box shows the median, the upper and lower ends of the box are the 75th and 25th percentiles, 

respectively, and the whiskers show the maximum and minimum values.
190 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Appendix 5. Comparison of Sampling-Based and Per-Pixel Model Runs

Biogeochemical modeling over large areas is computationally intensive. In order to accelerate turnover time 

of modeling results for repeated analysis and debugging, 

sampling-based model simulations are implemented in 

the General Ensemble Biogeochemical Modeling System 

(GEMS; Liu, 2009; Liu and others, 2012). Not all pixels will 

be simulated using the sampling approach. Instead, GEMS 

systematically selects the pixels to simulate according to user 

input. For example, simulating every fifth pixel in both x and 

y directions would result in a sampling rate of 1:25, every 

tenth pixel would result in a rate of 1:100, and so on. Users 

can select sampling rates according to situation (for example, 

the size of study area and the running time they can afford). 

The running time for the Century version 4.0 model (Parton 

and others, 1987, 1994; Metherell and others, 1993) and the 

Erosion Deposition Carbon Model (EDCM; appendix 1; Liu 

and others, 2003) can be shortened by more than 98 percent 

with a sampling rate of 1:100.

To illustrate the effects of sampling on model simulations, figures A5–1 and A5–2 compare model-simulated 

carbon stocks and fluxes using sampling-based and per-pixel 

simulation approaches in the Mixed Wood Shield ecoregion 

(other ecoregions showed similar results). The sampling rate of 

1:100 used for this assessment was sufficient to represent the 

overall dynamics of carbon stocks and fluxes compared with 

the per-pixel simulation approach.

References Cited

Liu, Shuguang, 2009, Quantifying the spatial details of 

carbon sequestration potential and performance, in

McPherson, B.J., and Sundquist, E.T., eds., Carbon 

sequestration and its role in the global carbon cycle: 

American Geophysical Union Monograph 183, p. 117–128, 

http://dx.doi.org/10.1029/2006GM000524.

Liu, Shuguang, Bliss, Norman, Sundquist, Eric, and Huntington, T.G., 2003, Modeling carbon dynamics in vegetation 

and soil under the impact of soil erosion and deposition: 

Global Biogeochemical Cycles, v. 17, no. 2, 1074, 24 p., 

http://dx.doi.org/10.1029/2002GB002010.

Liu, Shuguang, Tan, Zhengxi, Chen, Mingshi, Liu, Jinxun, 

Wein, Anne, Li, Zhengpeng, Huang, Shengli, Oeding, Jennifer, Young, Claudia, Verma, S.B., Suyker, A.E., Faulkner, 

Stephen, and McCarty, G.W., 2012, The general ensemble 

biogeochemical modeling system (GEMS) and its applications to agricultural systems in the United States, chap. 18 

of Liebig, Mark, Franzluebbers, A.J., and Follett, Ronald, 

eds., Managing agricultural greenhouse gases—Coordinated 

agricultural research through GRACEnet to address our 

changing climate: London, Academic Press, p. 309–323. 

(Also available at http://dx.doi.org/10.1016/B978-0-12-

386897-8.00018-8.)

Metherell, A.K., Harding, L.A., Cole, C.V., and Parton, W.J., 

1993, Century soil organic matter model environment, 

technical documentation, Agroecosystem version 4.0: U.S. 

Department of Agriculture Technical Report 4, accessed 

December 16, 2013, at http://www.nrel.colostate.edu/

projects/century/.

Parton, W.J., Schimel, D.S., Cole, C.V., and Ojima, D.S., 

1987, Analysis of factors controlling soil organic-matter 

levels in Great Plains grasslands: Soil Science Society of America Journal, v. 51, no. 5, p. 1173–1179, 

http://dx.doi.org/10.2136/sssaj1987.036159950051000500

15x.

Parton, W.J., Ojima, D.S., and Schimel, D.S., 1994, 

Environmental change in grasslands—Assessment using 

models: Climatic Change, v. 28, nos. 1–2, p. 111–141, 

http://dx.doi.org/10.1007/BF01094103.
Appendixes 191

0.20 0.52 0.84

0.20

0.36

0.52

0.68

0.84

1.00

Rate=1:25

0.20 0.52 0.84

Rate=1:100

0.20 0.52 0.84

Rate=1:400

0.20 0.52 0.84

Rate=1:10,000

0 0.01 0.02 0.03

0

0.01

0.02

0.03

0 0.01 0.02 0.03 0 0.01 0.02 0.03 0 0.01 0.02 0.03

6.80 7.08 7.36

6.80

6.94

7.08

7.22

7.36

7.50

6.80 7.08 7.36 6.80 7.08 7.36 6.80 7.08 7.36

11.0 13.8 16.6

11.0

12.4

13.8

15.2

16.6

18.0

11.0 13.8 16.6 11.0 13.8 16.6 11.0 13.8 16.6

Per-pixel simulations

Figure A6.1.

A. NPP, in kg/m2/yr 

B. Grain yield, in kg/m2

/yr

C. SOC, in kg/m2

 

D. Carbon stock, in kg/m2

Sample-based simulations

Figure A5 –1. Comparison of simulations in the Century version 4.0 model (Parton and others, 1987, 1994; Metherell and others, 

1993) of net primary production (NPP), grain yield, soil organic carbon (SOC), and total ecoregion carbon storage in the Mixed Wood 

Shield ecoregion using per-pixel and sampling-based approaches. Each circle represents the carbon stock or flux for a given year 

between 1992 and 1950; the circles on or near the per-pixel simulation trend (red line) show the deviation of results of the samplingbased simulations compared with results from the per-pixel simulation. kg/m2

/yr, kilograms per square meter per year.
192 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0

0 0.02 0.04

0

0.01

0.02

0.03

0.04

0.05

0 0.02 0.04 0 0.02 0.04 0 0.02 0.04

7.3 7.6 7.9 8.2 8.5

7.3

7.6

7.9

8.2

8.5

7.3 7.6 7.9 8.2 8.5 7.3 7.6 7.9 8.2 8.5 7.3 7.6 7.9 8.2 8.5

13.0 15.8 18.6

13.0

14.4

15.8

17.2

18.6

20.0

13.0 15.8 18.6 13.0 15.8 18.6 13.0 15.8 18.6

Figure A6.2.

NPP, in kg/m2/yr Grain yield, in kg/m2/yr SOC, in kg/m2 Carbon stock, in kg/m2

Rate=1:25 Rate=1:100 Rate=1:400 Rate=1:10,000

Per-pixel simulations

A. NPP, in kg/m2/yr 

B. Grain yield, in kg/m2

/yr

C. SOC, in kg/m2

 

D. Carbon stock, in kg/m2

Sample-based simulations

Figure A5–2. Comparison of simulations from the Erosion Deposition Carbon Model (EDCM; appendix 1; Liu and others, 2003) of 

net primary production (NPP), grain yield, soil organic carbon (SOC), and total ecoregion carbon storage in the Mixed Wood Shield 

ecoregion using per-pixel and sampling-based approaches. Each circle represents the carbon stock or flux for a given year between 

1992 and 1950; the circles on or near the per-pixel simulation trend (red line) show the deviation of results of the sampling-based 

simulations compared with results from the per-pixel simulation. kg/m2

/yr, kilograms per square meter per year.
Appendixes 193

Appendix 6. Emission Factors of Nitrous Oxide and Methane in the Eastern 

United States

Nitrous oxide (N2O) and methane (CH4) are major greenhouse gases; in terms of global warming potentials (GWP), 

N2O and CH4 have 310 and 21 times, respectively, more of 

an effect than carbon dioxide (CO2; U.S. Environmental 

Protection Agency, 2012). Atmospheric concentrations of 

these gases are increasing in exponential rate (Corre and 

others, 1999); human activities are the only reason for these 

increases. Agricultural activities are major emitter of anthropogenic CH4

 (27 percent) and N2O (70 percent) to the global 

atmosphere, although natural systems, such as wetlands and 

forests, emit a significant amount of these gases (Smith and 

others, 2007).

N2O flux is highly dependent on nitrogen input and 

precipitation and only slightly on temperature. However, 

soil parameters, including pH, organic carbon content, and 

nitrogen content, have a significant effect (Mosier and others, 

1997b; Gleason and others, 2009; Anderson and others, 

2010). CH4

 is produced when organic materials decompose in 

oxygen-deprived conditions, notably from fermentative digestion by ruminant livestock, from stored manures, and from rice 

grown under flooded conditions (Mosier and others, 1998).

The compilation of the emission factors was conducted 

using an Intergovernmental Panel on Climate Change data 

collection approach (Goodwin and others, 2006). For this 

approach, published emission factors of various land use 

and land cover types in the United States were collected, and 

type of land use (types of crop, forest, wetland, or grassland), 

management practice, annual mean flux (if multiple years 

were reported, we took average annual flux), and location 

were recorded. Collected values were averaged when there 

were multiple values for any ecoregion and ecosystem. If no 

published value was found for any particular region, the value 

for a nearby similar region was used. Tables A6–1 and A6–2 

list the compiled results of N2O and CH4 emission factors by 

ecoregion and ecosystem type with data source flag.
194 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table A6–1. N2O emission factors by ecosystem and level II ecoregion.

[LC ID, land cover name and identification (U.S. Geological Survey Land Carbon Project designation for the purposes of the assessment). Values are in 

kilograms of nitrous oxide as nitrogen per hectare per year. Values in flag columns explain if mean is measured or surrogated: 0, measured, 1, assumed, 

2, average of all measured value for the land cover type. Data are from the sources listed in the References Cited section of this appendix]

LC name LC ID ER_52 Flag_52 ER_53 Flag_53 ER_62 Flag_62 ER_71 Flag_71 ER_81 Flag_81 ER_82 Flag_82 ER_83 Flag_83 

Water 1 0.080 0 0.825 2 0.825 2 1.581 0 0.880 0 0.880 0 3.430 0 

Developed 2 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, 

national forest

3 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, other 

public lands

4 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, 

private lands

5 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mining 6 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Barren 7 0.484 2 0.484 2 0.484 2 0.484 2 0.484 2 0.484 2 0.484 2 

Deciduous forest 8 0.020 0 0.253 0 0.649 2 0.649 2 0.110 0 0.649 2 0.420 0 

Evergreen forest 9 0.170 0 0.180 0 0.572 2 0.572 2 0.050 0 2.430 0 6.200 0 

Mixed forest 10 0.253 0 0.230 0 0.492 2 0.492 2 0.555 0 0.492 2 1.350 0 

Grassland 11 1.999 0 1.027 2 1.921 0 1.027 2 0.185 0 0.250 0 1.027 2 

Shrubland 12 0.150 0 0.928 2 0.150 0 0.928 2 0.630 0 0.928 2 0.928 2 

Agriculture 13 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 

Hay pasture 14 0.813 2 0.813 2 0.813 2 0.813 2 0.813 2 0.813 2 0.813 2 

Herbaceous 

wetland 

15 0.010 0 0.846 2 0.846 2 0.846 2 1.450 0 1.450 0 0.846 2 

Woody wetland 16 1.222 2 1.222 2 1.222 2 1.222 2 1.222 2 1.222 2 1.222 2 

Perennial ice/snow 17 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Barley 100 2.459 2 2.459 2 2.459 2 2.459 2 2.800 0 0.900 0 2.459 2 

Beans 101 1.310 2 1.310 2 1.310 2 1.310 2 1.310 2 1.310 2 1.310 2 

Canola 102 2.400 2 2.400 2 2.400 2 2.400 2 2.500 0 2.400 2 2.400 2 

Corn grain 103 2.680 2 2.680 2 2.680 2 2.680 2 1.873 0 5.111 0 2.680 2 

Corn silage 104 2.680 2 2.680 2 2.680 2 2.680 2 1.873 0 5.111 0 2.680 2 

Cotton 105 0.489 2 0.489 2 0.489 2 0.489 2 0.489 2 0.489 2 0.543 0 

Flaxseed 106 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 

Forage 107 2.572 2 2.572 2 2.572 2 2.572 2 3.470 0 2.572 2 2.572 2 

Hay 108 0.320 2 0.320 2 0.320 2 0.320 2 0.320 2 0.320 2 0.320 2 

Lentils 109 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 

Sorghum silage 110 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 

Oats 111 0.160 2 0.160 2 0.160 2 0.160 2 0.160 2 0.160 2 0.160 2 

Peanuts 112 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 

Peas 113 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2 

Potatoes 114 3.095 2 3.095 2 3.095 2 3.095 2 3.095 2 3.095 2 3.095 2 

Rice 115 0.117 2 0.117 2 0.117 2 0.117 2 0.117 2 0.117 2 0.117 2 

Rye 116 0.038 2 0.038 2 0.038 2 0.038 2 0.038 2 0.038 2 0.038 2 

Safflower 117 1.200 2 1.200 2 1.200 2 1.200 2 1.200 2 1.200 2 1.200 2 

Sorghum 118 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 

Soybeans 119 1.981 2 1.981 2 1.981 2 1.981 2 1.070 0 1.981 2 1.981 2 

Sugarbeets 120 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 

Sugarcane 121 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 

Sunflowers 122 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 

Sweet corn 123 3.790 2 3.790 2 3.790 2 3.790 2 3.790 2 3.790 2 3.790 2 

Tobacco 124 2.258 2 2.258 2 2.258 2 2.258 2 2.258 2 2.258 0 2.258 2 

Tomatoes 125 7.375 2 7.375 2 7.375 2 7.375 2 7.375 2 0.750 0 7.375 2 

Wheat durum 126 2.011 2 2.011 2 2.011 2 2.011 2 2.265 0 2.011 2 2.011 2 

Wheat, spring 127 2.011 2 2.011 2 2.011 2 2.011 2 2.265 0 2.011 2 2.011 2 

Wheat, winter 128 2.011 2 2.011 2 2.011 2 2.011 2 2.265 0 2.011 2 2.011 2 
Appendixes 195

Table A6–1. N O emission factors by ecosystem and level II ecoregion.—Continued 2

[LC ID, land cover name and identification (U.S. Geological Survey Land Carbon Project designation for the purposes of the assessment). Values are in 

kilograms of nitrous oxide as nitrogen per hectare per year. Values in flag columns explain if mean is measured or surrogated: 0, measured, 1, assumed, 

2, average of all measured value for the land cover type. Data are from the sources listed in the References Cited section of this appendix]

ER_84 Flag_84 ER_85 Flag_85 ER_92 Flag_92 ER_93 Flag_93 ER_94 Flag_94 ER_101 Flag_101 ER_102 Flag_102 ER_111 Flag_111

3.430 2 0.022 0 2.370 0 0.850 0 0.825 2 0.825 2 0.825 2 0.825 2

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

0.484 2 0.484 2 0.484 2 0.400 0 0.353 0 0.103 0 0.484 2 0.484 2

0.649 2 0.030 0 0.649 2 0.649 2 0.649 2 0.649 2 0.649 2 0.649 2

0.572 2 0.030 0 0.572 2 0.572 2 0.572 2 0.572 2 0.572 2 0.572 2

0.535 0 0.492 2 0.492 2 0.030 0 0.492 2 0.492 2 0.492 2 0.492 2

1.027 2 1.027 2 0.465 0 2.040 0 0.800 0 0.245 0 1.027 2 1.027 2

0.928 2 0.928 2 0.928 2 0.928 2 0.928 2 0.230 0 0.928 2 0.928 2

1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2 1.300 2

0.813 2 0.813 2 0.813 2 1.410 0 0.450 0 0.813 2 0.813 2 0.813 2

0.846 2 0.790 0 1.845 0 0.740 0 0.846 2 0.846 2 0.846 2 0.846 2

1.222 2 1.222 2 3.150 0 0.495 0 1.222 2 1.222 2 1.222 2 1.222 2

0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1

2.459 2 2.459 2 2.459 2 2.443 0 3.692 0 2.459 2 2.459 2 2.459 2

1.310 2 1.310 2 1.310 2 1.310 2 1.310 0 1.310 2 1.310 2 1.310 2

2.400 2 2.400 2 2.400 2 2.370 0 2.400 2 2.400 2 2.400 2 2.400 2

2.050 0 1.140 0 5.684 0 0.180 0 1.786 0 2.680 2 2.680 2 1.892 0

2.050 0 1.140 0 5.684 0 0.180 0 1.786 0 2.680 2 2.680 2 1.892 0

0.489 2 0.516 0 0.489 2 0.489 2 0.624 0 0.489 2 0.489 2 0.489 0

0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1 0.500 1

2.572 2 2.572 2 4.207 0 0.040 0 2.572 2 2.572 2 2.572 2 2.572 2

0.320 2 0.320 2 0.320 2 0.320 2 0.320 0 0.320 2 0.320 2 0.320 2

0.510 2 0.510 2 0.510 0 0.510 2 0.510 2 0.510 2 0.510 2 0.510 2

2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2

0.160 2 0.160 2 0.160 2 0.160 0 0.160 2 0.160 2 0.160 2 0.160 2

6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1 6.200 1

0.510 2 0.510 2 0.510 2 0.510 0 0.510 2 0.510 2 0.510 2 0.510 2

3.095 2 3.095 2 3.095 2 3.095 2 3.095 2 3.095 0 3.095 2 3.095 2

0.117 2 0.117 0 0.117 2 0.117 2 0.117 2 0.117 2 0.117 2 0.117 2

0.040 0 0.035 0 0.038 2 0.038 2 0.038 2 0.038 2 0.038 2 0.038 2

1.200 2 1.200 2 1.200 2 1.200 0 1.200 2 1.200 2 1.200 2 1.200 2

2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2 2.200 2

1.450 0 2.235 0 3.168 0 1.981 2 1.981 2 1.981 2 1.981 2 1.981 2

1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1 1.000 1

0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2 0.280 2

2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1 2.000 1

3.790 2 3.790 2 3.790 2 3.790 2 3.790 2 3.790 0 3.790 2 3.790 2

2.258 2 2.258 2 2.258 2 2.258 2 2.258 2 2.258 2 2.258 2 2.258 2

7.375 2 7.375 2 7.375 2 7.375 2 7.375 2 7.375 2 7.375 2 14.000 0

1.880 0 2.011 2 4.150 0 0.863 0 0.898 0 2.011 2 2.011 2 2.011 2

1.880 0 2.011 2 4.150 0 0.863 0 0.898 0 2.011 2 2.011 2 2.011 2

1.880 0 2.011 2 4.150 0 0.863 0 0.898 0 2.011 2 2.011 2 2.011 2
196 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

Table A6–2. CH4 emission factors by ecosystem and level II ecoregion.

[LC ID, land cover name and identification (U.S. Geological Survey Land Carbon Project designation for the purposes of the assessment). Values are in 

kilograms of nitrous oxide as nitrogen per hectare per year. Values in flag columns explain if mean is measured or surrogated: 0, measured, 1, assumed, 

2, average of all measured value for the land cover type. Data are from the sources listed in the References Cited section of this appendix]

LC name LC ID ER_52 Flag_52 ER_53 Flag_53 ER_62 Flag_62 ER_71 Flag_71 ER_81 Flag_81 ER_82 Flag_82 ER_83 Flag_83 

Water 1 37.308 0 37.308 2 6.570 0 42.431 0 37.698 2 37.698 2 37.698 2 

Developed 2 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, 

national forest

3 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, other

public lands

4 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mechanically 

disturbed, 

private lands

5 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Mining 6 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Barren 7 –1.182 2 –1.182 2 –1.182 2 –1.182 2 –1.182 2 –1.182 2 –1.182 2 

Deciduous forest 8 –6.955 2 –8.547 0 –9.230 0 –6.955 2 –2.212 0 –6.955 2 –6.955 2 

Evergreen forest 9 –2.770 2 –6.993 0 –1.060 0 –2.770 2 –0.857 0 –2.770 2 –2.770 2 

Mixed forest 10 –3.245 2 –4.413 0 –4.650 0 –3.245 2 –3.245 2 –3.245 2 –1.920 0 

Grassland 11 –1.833 2 –1.833 2 –3.380 0 –1.833 2 –1.500 0 0.240 0 –1.833 2 

Shrubland 12 –2.670 2 –2.670 2 –2.670 2 –2.670 2 –2.670 2 –2.670 2 –2.670 2 

Agriculture 13 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Hay pasture 14 –0.609 2 –0.609 2 –0.609 2 –0.609 2 –0.609 2 –0.609 2 –0.609 2 

Herbaceous 

wetland 

15 373.029 0 149.450 2 167.860 0 149.450 2 306.800 0 149.450 2 19.040 0 

Woody wetland 16 277.309 2 277.309 2 16.260 0 277.309 2 277.309 2 277.309 2 277.309 2 

Perennial ice/snow 17 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 0.001 1 

Barley 100 0.040 2 0.040 2 0.040 2 0.040 2 0.040 2 0.040 2 0.040 2 

Beans 101 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Canola 102 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Corn grain 103 –0.813 2 –0.813 2 –0.813 2 –0.813 2 –0.360 0 0.103 0 –0.813 2 

Corn silage 104 –0.813 2 –0.813 2 –0.813 2 –0.813 2 –0.360 0 0.103 0 –0.813 2 

Cotton 105 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Flaxseed 106 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Forage 107 –1.557 2 –1.557 2 –1.557 2 –1.557 2 –2.045 0 –1.557 2 –1.557 2 

Hay 108 –2.380 2 –2.380 2 –2.380 2 –2.380 2 –2.380 2 –2.380 2 –2.380 2 

Lentils 109 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Sorghum silage 110 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 

Oats 111 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Peanuts 112 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Peas 113 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Potatoes 114 0.983 2 0.983 2 0.983 2 0.983 2 2.677 0 0.983 2 0.983 2 

Rice 115 253.773 2 253.773 2 253.773 2 253.773 2 253.773 2 253.773 2 253.773 2 

Rye 116 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Safflower 117 –0.510 2 –0.510 2 –0.510 2 –0.510 2 –0.510 2 –0.510 2 –0.510 2 

Sorghum 118 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 –1.230 2 

Soybeans 119 –0.538 2 –0.538 2 –0.538 2 –0.538 2 –1.750 0 –0.010 0 –0.538 2 

Sugarbeets 120 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Sugarcane 121 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Sunflowers 122 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Sweet corn 123 –0.545 2 –0.545 2 –0.545 2 –0.545 2 –0.545 2 –0.545 2 –0.545 2 

Tobacco 124 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Tomatoes 125 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 –0.571 2 

Wheat durum 126 –1.055 2 –1.055 2 –1.055 2 –1.055 2 –1.626 0 –1.055 2 –1.055 2 

Wheat, spring 127 –1.055 2 –1.055 2 –1.055 2 –1.055 2 –1.626 0 –1.055 2 –1.055 2 

Wheat, winter 128 –1.055 2 –1.055 2 –1.055 2 –1.055 2 –1.626 0 –1.055 2 –1.055 2 
Appendixes 197

Table A6–2. CH emission factors by ecosystem and level II ecoregion.—Continued 4

[LC ID, land cover name and identification (U.S. Geological Survey Land Carbon Project designation for the purposes of the assessment). Values are in 

kilograms of nitrous oxide as nitrogen per hectare per year. Values in flag columns explain if mean is measured or surrogated: 0, measured, 1, assumed, 

2, average of all measured value for the land cover type. Data are from the sources listed in the References Cited section of this appendix]

ER_84 Flag_84 ER_85 Flag_85 ER_92 Flag_92 ER_93 Flag_93 ER_94 Flag_94 ER_101 Flag_101 ER_102 Flag_102 ER_111 Flag_111

37.698

0.001

0.001

0.001

0.001

0.001

–1.182

–11.895

–2.770

–3.245

–1.833

–2.670

–0.571

–0.609

106.935

277.309

0.001

0.040

–0.571

–0.571

–0.813

–0.813

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

253.773

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.055

–1.055

–1.055

2

1

1

1

1

1

2

0

2

2

2

2

2

2

0

2

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

37.698

0.001

0.001

0.001

0.001

0.001

–1.182

–6.955

–1.770

–1.750

–1.833

–2.670

–0.571

–0.609

35.238

314.406

0.001

0.040

–0.571

–0.571

–0.813

–0.813

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

321.390

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.055

–1.055

–1.055

2

1

1

1

1

1

2

2

0

0

2

2

2

2

0

0

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

0

2

2

2

2

2

2

2

2

2

2

2

2

2

47.180

0.001

0.001

0.001

0.001

0.001

–1.182

–6.955

–2.770

–3.583

–1.635

–2.670

–0.571

–0.609

185.530

277.309

0.001

0.040

–0.571

–0.571

–0.343

–0.343

–0.571

–0.571

–0.245

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

253.773

–0.571

–0.510

–1.230

0.148

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–0.675

–0.675

–0.675

0

1

1

1

1

1

2

2

2

0

0

2

2

2

0

2

1

2

2

2

0

0

2

2

0

2

2

2

2

2

2

2

2

2

2

2

0

2

2

2

2

2

2

0

0

0

37.698

0.001

0.001

0.001

0.001

0.001

–1.182

–6.955

–2.770

–3.245

–1.530

–2.670

–0.571

–2.145

19.170

16.260

0.001

0.040

–0.571

–0.571

–0.890

–0.890

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

253.773

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–0.330

–0.330

–0.330

2

1

1

1

1

1

2

2

2

2

0

2

2

0

0

0

1

2

2

2

0

0

2

2

2

2

2

2

2

2

2

2

2

2

0

2

2

2

2

2

2

2

2

0

0

0

37.698

0.001

0.001

0.001

0.001

0.001

–0.663

–6.955

–2.770

–3.245

–2.107

–2.670

–0.571

0.928

149.450

277.309

0.001

0.040

–0.571

–0.571

–2.575

–2.575

–0.571

–0.571

–2.380

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

253.773

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.588

–1.588

–1.588

2

1

1

1

1

1

0

2

2

2

0

2

2

0

2

2

1

0

2

2

0

0

2

2

0

0

2

0

2

2

2

2

2

2

2

0

2

2

2

2

2

2

2

0

0

0

37.698

0.001

0.001

0.001

0.001

0.001

–1.700

–6.955

–4.160

–3.245

–1.960

–3.830

–0.571

–0.609

149.450

277.309

0.001

0.040

–0.571

–0.571

–0.813

–0.813

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

–0.710

253.773

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.055

–1.055

–1.055

2

1

1

1

1

1

0

2

0

2

0

0

2

2

2

2

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

0

2

2

2

2

2

2

2

2

0

2

2

2

2

2

37.698

0.001

0.001

0.001

0.001

0.001

–1.182

–6.955

–2.770

–3.245

–1.833

–2.670

–0.571

–0.609

149.450

277.309

0.001

0.040

–0.571

–0.571

–0.813

–0.813

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

253.773

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.055

–1.055

–1.055

2

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

37.698

0.001

0.001

0.001

0.001

0.001

–1.182

–6.955

–2.770

–3.245

–1.833

–2.670

–0.571

–0.609

149.450

277.309

0.001

0.040

–0.571

–0.571

–0.813

–0.813

–0.571

–0.571

–1.557

–2.380

–0.571

–1.230

–0.571

–0.571

–0.571

0.983

186.155

–0.571

–0.510

–1.230

–0.538

–0.571

–0.571

–0.571

–0.545

–0.571

–0.571

–1.055

–1.055

–1.055

2

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

0

2

2

2

2

2

2

2

2

2

2

2

2

2
198 Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States

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Manuscript approved for publication June 2, 2014

Publishing support provided by the U.S. Geological Survey Science Publishing Network, 

Pembroke, Raleigh, Reston, and Tacoma Publishing Service Centers

Edited by Anna Glover, Pat Lee, and Ann Marie Squillacci

Illustrations by Caryl Wipperfurth and layout by Anna Glover

Cover by Bill Gibbs

For more information concerning the research in this report, contact the

Land Change Science Program

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Zhu and Reed, eds.—Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Eastern United States—Professional Paper 1804

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http://dx.doi.org/10.3133/pp1804