U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5233

A Method for Assessing Carbon Stocks, 

Carbon Sequestration, and Greenhouse-Gas Fluxes 

in Ecosystems of the United States Under 

Present Conditions and Future Scenarios
Front of cover.  Photograph of a ranch in the Green River valley in the Rocky Mountains of

western Wyoming. Note the willow along the river, sagebrush around the outbuilding and

at lower elevations, the open meadow, and aspen trees transitioning to conifers at higher

elevations. Forests, grasslands/shrublands, and aquatic ecosystems are represented in this view.

Photograph by Donald O. Ohlen, U.S. Geological Survey.

Inside of cover.  Panoramic photograph of a forested area in Yellowstone National Park,

northwestern Wyoming, showing the regeneration of lodgepole pines after the 1988 wildland

fires. Photograph by Donald O. Ohlen, U.S. Geological Survey

Back of cover.  Photograph of the Park Range Mountains in the Routt National Forest, south of

Steamboat Springs, north-central Colorado, showing various shades of autumn foliage in a mixed

forest consisting of lodgepole pine, Douglas fir, and aspen. Photograph by Zhiliang Zhu, U.S.

Geological Survey.
A Method for Assessing Carbon Stocks, 

Carbon Sequestration, and GreenhouseGas Fluxes in Ecosystems of the United 

States Under Present Conditions and Future 

Scenarios

By Zhiliang Zhu (editor), Brian Bergamaschi, Richard Bernknopf, David Clow,

Dennis Dye, Stephen Faulkner, William Forney, Robert Gleason, Todd Hawbaker,

Jinxun Liu, Shuguang Liu, Stephen Prisley, Bradley Reed, Matthew Reeves,

Matthew Rollins, Benjamin Sleeter, Terry Sohl, Sarah Stackpoole, Stephen

Stehman, Robert Striegl, Anne Wein, and Zhiliang Zhu

Scientific Investigations Report 2010–5233

U.S. Department of the Interior

U.S. Geological Survey

Supersedes Open-File Report 2010–1144
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

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, 188 p. (Also available at http://pubs.usgs.gov/sir/2010/5233/.) (Supersedes U.S. 

Geological Survey Open-File Report 2010–1144.)

ISBN 978–1–4113–3031–3
iii

Acknowledgments

The authors express their gratitude towards the following individuals for their invaluable 

contributions to this manuscript: Members of an interagency science panel (Richard Birdsey, 

U.S. Forest Service (USFS); Jennifer Jenkins, U.S. Environmental Protection Agency (EPA); 

Linda Langner, USFS; Thomas Loveland, U.S. Geological Survey (USGS); David McGuire, 

USGS; Tilden Meyers, National Oceanic and Atmospheric Administration; Ramakrishna 

Nemani, National Aeronautics and Space Administration; Donald Reicosky, U.S. Department 

of Agriculture (USDA) Agricultural Research Service; Benjamin Simon, U.S. Department of 

the Interior; and Susan Southard, USDA Natural Resources Conservation Service) for their 

technical and science guidance; Eric Sundquist, Jennifer Harden, and Stephen Perakis, all of the 

U.S. Geological Survey, for their review and coordination; and the twelve peer reviewers (Sandra Brown, Winrock International; Richard Conant, Colorado State University; John Downing, 

Iowa State University; Thomas Huntington, USGS; Robert Keane, USFS; James McClelland, 

University of Texas at Austin; Karen Murphy, U.S. Fish and Wildlife Service; Emilio Mayorga, 

University of Washington; Sarah Bushey Ohrel, U.S. Environmental Protection Agency; David 

Schimel, National Ecological Observatory Network; Bill Sommers, George Mason University; 

and Michael Wimberly, South Dakota State University) for their careful, constructive, and critical reviews. June Thormodsgard (USGS) and Beatrice Van Horne (USFS, formerly with USGS) 

were instrumental in leading communication with our Federal partners and providing technical advice, as well as leading coordination of science support. The authors especially thank 

William Werkheiser of the USGS for his active leadership in guiding the overall methodology 

development process.

v

Contents

Acknowledgments........................................................................................................................................iii

Energy Independence and Security Act of 2007 ...................................................................................xvi

Executive Summary.......................................................................................................................................1

1. Introduction.................................................................................................................................................5

1.1. Requirements of Section 712 of the Energy Independence and Security Act of 2007.......5

1.2. Understanding the Concepts and Requirements of the Energy Independence and

Security Act of 2007 ................................................................................................................5

1.2.1. Assessment.........................................................................................................................5

1.2.2. Ecosystems.........................................................................................................................5

1.2.3. Adaptation and Mitigation Strategies ............................................................................6

1.2.4. Carbon-Sequestration Capacity......................................................................................6

1.2.5. Processes That Control the Flux of Covered Greenhouse Gases..............................6

1.2.6. Management Activities and Restoration Activities .....................................................6

1.2.7. Range of Policies ...............................................................................................................7

1.2.8. Use of Native Plant Species ............................................................................................7

1.2.9. Measuring, Monitoring, and Quantifying.......................................................................7

1.2.10. Use of Economic and Other Systems Models, Analyses, and Estimates...............7

1.3. Summary..........................................................................................................................................8

2. Review of Concepts and Literature ........................................................................................................9

2.1. Major Carbon-Cycle Processes and Pools ...............................................................................9

2.2. Current Knowledge of the Carbon Cycle and Greenhouse-Gas Fluxes in the United

States.........................................................................................................................................9

2.3. Effects of Major Controlling Processes ...................................................................................13

2.3.1. Effects of Changes in Land Use, Land Cover, and Land Management ...................13

2.3.2. Effects of Ecosystem Disturbances..............................................................................14

2.3.3. Effects of Climate Change, Elevated Carbon Dioxide, and Nitrogen Fertilization.14

2.4. Carbon Sequestration and Ecosystem Services ....................................................................15

2.5. Ongoing Global and National Carbon and Greenhouse-Gas Inventories and

Assessments..........................................................................................................................16

2.5.1. Intergovernmental Panel on Climate Change Scenarios and Guidelines ..............16

2.5.2. Examples of Continental-Scale Greenhouse-Gas Inventories and Assessments17

2.5.3. Existing National-Scale Inventories and Assessments in the United States ........18

2.5.4. Uncertainty Assessment and Reporting in Existing National Assessments..........19

2.5.5. Economic Analysis and Its Use in Existing National Assessments.........................20

3. Methodology for the National Assessment.........................................................................................21

3.1. Introduction...................................................................................................................................21

3.1.1. Design Requirements and Goals for the Assessment ...............................................21

3.1.2. Scope of Assessment .....................................................................................................22

3.1.3. Methodology Constraints ...............................................................................................25

3.1.4. Collaborations for the Assessment...............................................................................26

3.1.5. Methodology Organization.............................................................................................26

3.2. Methodology Framework............................................................................................................26

3.2.1. Framework for Assessing Current Carbon Stocks, Carbon Sequestration, and

Greenhouse-Gas Fluxes..............................................................................................27
vi

3.2.2. Framework for Assessing Future Potential Carbon Stocks, Carbon Sequestration,

and Greenhouse-Gas Fluxes ......................................................................................29

3.2.3. Methodology Framework Summary .............................................................................34

3.3. Introduction to Assessment Methods......................................................................................34

3.3.1. Technical Plan for Key National Datasets ...................................................................35

3.3.2. Land-Use and Land-Cover Change...............................................................................38

3.3.3. Ecosystem Disturbances................................................................................................40

3.3.4. Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Terrestrial

Ecosystems ...................................................................................................................45

3.3.5. Carbon Sequestration and Greenhouse-Gas Fluxes of Aquatic Ecosystems .......55

3.3.6. Analyses of Assessment Results—Mitigation Activities, Ecosystem Services,

Costs, and Benefits ......................................................................................................57

3.3.7. Validation Methods..........................................................................................................62

3.3.8. Methods for Assessing and Reporting Uncertainty...................................................62

3.3.9. Requirements of Section 712 of the Energy Independence and Security Act for

Measuring and Monitoring.........................................................................................63

3.4. Data Products, Deliverables, and Reports ..............................................................................64

3.4.1. Data Products...................................................................................................................64

3.4.2. Assessment Reporting....................................................................................................64

4. Conducting the National Assessment..................................................................................................67

4.1. Operational Issues.......................................................................................................................67

4.2. Major Scientific Research and Development Needs ............................................................67

4.3. Intended Applications.................................................................................................................69

References Cited..........................................................................................................................................71

Glossary.........................................................................................................................................................87

Appendix A. Reference and Alternative Mitigation Scenarios............................................................89

Appendix B. Land-Use and Land-Cover Modeling...............................................................................103

Appendix C. Characterization and Modeling of Major Ecosystem Disturbances...........................111

Appendix D. Methods for Assessing Carbon Stocks, Carbon Sequestration, and GreenhouseGas Fluxes of Terrestrial Ecosystems .......................................................................................119

Appendix E. Methods for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas

Fluxes in Aquatic Ecosystems....................................................................................................141

Appendix F. Methods for Analyzing Ecosystem Services and Benefits and Costs of Mitigation

Activities ........................................................................................................................................167

Appendix G. Methods for Validation and Uncertainty Assessment..................................................171

Appendix H. Methods for Energy Independence and Security Act Measuring and Monitoring

Requirements................................................................................................................................179

Appendix I. Data Management Technical Plan ....................................................................................185
vii

Figures

2.1. Diagram showing the terrestrial ecosystems covered in the assessment and

the major carbon pools..............................................................................................................10

2.2. Diagrams showing examples of measured greenhouse-gas fluxes in

representative ecosystems.......................................................................................................11

2.3. Conceptual diagram of the relations among ecosystem structure, function, and

services ........................................................................................................................................16

3.1. Map showing level II ecoregions of the U.S. Environmental Protection Agency............23

3.2. Diagram showing fluxes of carbon and nitrogen and exchanges among the

primary carbon pools .................................................................................................................25

3.3. Graph showing hypothetical emission ranges for each of three scenarios.....................29

3.4. Diagram showing the assessment framework for each IPCC SRES storyline,

using storyline A1B as an example..........................................................................................32

3.5. Diagram showing relations among major methods that are designed to run

scenarios and produce assessment deliverables ................................................................35

3.6. Graphs showing the net change for the modeled land-use and land-cover types

in the two U.S. Environmental Protection Agency level III ecoregions used in

the test study ...............................................................................................................................41

3.7. Maps showing the results of a land-use-modeling test for the A1B scenario.................42

3.8. Graphs showing the observed and simulated number of wildfires per year, and

the observed and simulated number of hectares of area burned by wildfire

per year ........................................................................................................................................46

3.9. Map of the test area showing locations of simulated wildfires in two EPA level III

ecoregions for 2001 through 2050, using the A1B storyline.................................................49

3.10. Map showing study area for a test using terrestrial methods to assess carbon

stocks, carbon sequestration, and greenhouse-gas fluxes ................................................53

3.11. Graph showing comparisons of annual carbon-sequestration rates of biomass

carbon stock among the three methods using the “enhanced land use and land

cover with reference land management” (L) scenario for the whole test area from

2001 to 2050..................................................................................................................................53

3.12. Graphs showing comparisons of emissions between the GEMS-spreadsheet

model and the GEMS–EDCM model, showing the difference between the

“reference land use, land cover, and land management” scenario and the

“enhanced land use and land cover with reference land management” scenario,

from 2001 to 2050.........................................................................................................................55

3.13. Chart showing a comparison of ecosystem service changes using the

ecosystem service change indicator......................................................................................59

Tables

2.1. Carbon stocks in the conterminous United States and Alaska, and greenhousegas emissions from major ecosystems in the conterminous United States only,

as reported by recent studies...................................................................................................12

2.2 Comparison of forest stocks and net forest-stock changes from three

continental-scale studies of temperate forest zones...........................................................18

2.3. Selected estimates and uncertainties reported from recent national-scale

assessments of carbon sequestration....................................................................................20
viii

3.1. Ecosystems, descriptions, and thematic components of source datasets ......................22

3.2. Broad-level definitions of relevant carbon pools to be included for carbonassessment products.................................................................................................................24

3.3. Time periods, land use and land cover, ecosystem disturbances, and landmanagement activities used for assessments of current and future potential

carbon stocks, carbon sequestration, and greenhouse-gas fluxes ..................................27

3.4. Thematic land-cover classes used to describe current conditions ..................................28

3.5. Candidate mitigation activities to be considered for the assessment...............................31

3.6. Summary of the assessment framework for linking climate-change mitigation

scenarios to changes in ecosystem capacities for carbon stocks and carbon

sequestration and to changes in greenhouse-gas fluxes ...................................................34

3.7. Data needs, sources, variables, spatial and temporal resolution, and uses in the

assessment methodology..........................................................................................................36

3.8. Examples of general circulation models that are suitable for use by the national

assessment..................................................................................................................................38

3.9. Major natural and anthropogenic ecosystem disturbances, selected attributes,

and data sources ........................................................................................................................43

3.10. Preliminary methods or models, quantifying parameters, technical processes,

target ecosystems, and data needs or sources that will be used to assess

parameters of carbon stocks, carbon sequestration, and greenhouse-gas flux ............50

3.11. Total carbon stocks and cumulative and additional carbon sequestration within

the test area (Tensas Parish, La., and Claiborne County, Miss.), calculated using

the specified method, and using the “reference land use, land cover, and land

management” (R) and “enhanced land use and land cover with reference land

management” (L) scenarios......................................................................................................54

3.12. Annual emission rates of methane and nitrous oxide and their total differences

(between 2001 and 2050), for the “reference land use, land cover, and land

management” (R) and the “enhanced land use and land cover with reference

land management” (L) scenarios.............................................................................................55

3.13. Example of a table format for reporting the effectiveness of mitigation activities

for sequestering carbon, by the duration (years) of the implementation..........................57

3.14. Ecosystem services that are likely to be affected by mitigation activities and

will be analyzed, their functions, and the assessment data products that will

be used to analyze the effects of mitigation activities .........................................................58

3.15. Preliminary ecosystem service estimates for a test in Tensas Parish, La., and

Claiborne County, Miss., using the A1B storyline..................................................................59

3.16. Partial list of deliverable and intermediate data products targeted for validation,

and the corresponding reference data sources and needs................................................62

3.17. A subset of primary deliverable or intermediate products for the national

assessment..................................................................................................................................65

3.18. Example of a table format for reporting the results of the assessment of carbon

stocks, carbon sequestration, and greenhouse gases, including uncertainties,

for current or future scenarios depicted in table 3.6............................................................66

3.19. Example of a table format for reporting the effects of wildfires and manmade

fires on carbon stocks for a given assessment unit, for all ecosystems ..........................66

3.20. Example of a table format for reporting validation results of comparing the

reference data with the 2010 assessment estimates (present conditions) for

selected assessment parameters, for a given assessment unit ........................................66
ix

Conversion Factors

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

Area

square meter (m2) 10.76 square foot (ft2)

hectare (ha) 2.471 acre

square kilometer (km2) 0.3861 square miles (mi2)

megahectare (Mha) 3,861 square mile (mi2

)

Volume

cubic meter (m3) 35.31 cubic foot (ft3)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

megagram (Mg) [metric ton (t)] 1.102 ton, short (2,000 lb)

gigagram (Gg) 1.102 × 103 ton, short (2,000 lb)

teragram (Tg) 1.102 × 106 ton, short (2,000 lb)

petagram (Pg) 1.102 × 109 ton, short (2,000 lb)

Irradiance

watts per square meter (W/m2) 0.09294 watts per square foot (W/ft2)

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

°F = (1.8 × °C) + 32

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

°C = (°F - 32) / 1.8

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

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

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

of n meters × n meters.

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

1 megagram (Mg) = 1 million grams (106

 g) = 1 metric ton (t)

1 gigagram (Gg) = 1 billion grams (109

 g) = 1,000 metric tons

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

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

Abbreviations, Acronyms, and Chemical Symbols

ADAGE Applied Dynamic Analysis of the Global Economy

AFOLU agriculture, forestry, and other land uses

AmeriFlux American flux network

API appication programming interface

AR4 IPCC fourth assessment report

AR5 IPCC fifth assessment report

ArcFVS ArcInfo’s Forest Vegetation Simulator

ArcSDE ArcInfo’s Spatial Database Engine

ARMS Agricultural Resource Management Survey

ARS USDA Agricultural Research Service

AVHRR Advanced Very High Resolution Radiometer

BAER Burned Area Emergency Recovery

BCC–BCM2.0 Bjerknes Centre for Climate Research, Bergen Climate Model 2.0

BGC biogeochemical

BMPs best management practices

BPS biophysical settings

C carbon

Ced carbon erosion and deposition

Cs carbon stock

Csr carbon sequestration

CASA Carnegie-Ames-Stanford Approach model

CBI Conservation Biology Institute

CCSP U.S. Climate Change Science Program

CEAP Conservation Effects Assessment Project

CH4 methane

CLUE Conversion of Land Use and Its Effects

CMIP3 World Climate Research Programme’s Coupled Model Intercomparison

Project (phase 3)

CO2 carbon dioxide

COLE carbon online estimator

COMET-VR Voluntary Reporting of Greenhouse Gases-Carbon Management Evaluation

Tool

CONUS conterminous United States

CRP Conservation Reserve Program

CSIRO–Mk3.0 Australia’s Commonwealth Scientific and Industrial Research Organisation

Mark 3.0

CSP Conservation Security Program

CSW Catalogue Service for Web

DCF discounted cash flow

DED duck energy day

DEM digital elevation model

DIC dissolved inorganic carbon

DLG digital line graph
xi

DNDC denitrification-decomposition model

DOC dissolved organic carbon

DOI U.S. Department of the Interior

DON dissolved organic nitrogen

EAC equivalent annual cost

EcoServ ecosystem services model

EDCM Erosion-Deposition-Carbon Model

EDNA Elevation Derivatives for National Applications

EF emission factor

EISA Energy Independence and Security Act of 2007

EnKF Ensemble Kalman Filter

EPA U.S. Environmental Protection Agency

EQIP Environmental Quality Incentives Program

ER ecosystem respiration

ERF1 Enhanced River Reach File (USGS)

EROS USGS Center for Earth Resources Observation and Science

ERS Economic Research

ESCI ecosystem services change indicator

ESRI Environmental Systems Research Institute

FACE Free-Air CO2 Enrichment

FARSITE Fire Area Simulator

FASOM–GHG Forest and Agriculture Sector Optimization Model with Greenhouse Gases

FCCS fuel characteristic classification system

FGDC Federal Geographic Data Committee

FIA U.S. Forest Service Forest Inventory and Analysis Program

FlamMap fire behavior mapping and analysis program (USFS)

FLM fuel loading model

FLUXNET flux network

FOFEM First Order Fire Effects Model

FORE–SCE “forecasting scenarios of land cover change” model

FPA fire program analysis

FPAR Fraction of Photosynthetically Active Radiation

FRLPP Farm and Ranch Lands Protection Program

FSPro Fire Spread Probability model

FVS Forest Vegetation Simulator

FWS U.S. Fish and Wildlife Service

GAP Gap Analysis Program

GCM Global Circulation Model

GCP Global Carbon Project

GDP Gross Domestic Product

GEMS General Ensemble Modeling System

GeoEye commercial Earth-observation satellite

GeoMPI Geospatial Model Programming Interface

GeoMSI Geospatial Model Sharing Interface

GHG greenhouse gas
xii

GIS geographic information system

GPP gross primary productivity

GRACEnet “greenhouse gas reduction through agricultural carbon enhancement”

network

GRP Grassland Reserve Program

GSG Global Scenario Group

GUI graphical user interface

GWP global warming potential

HCO3

– bicarbonate

HFRP Healthy Forests Reserve Program

HR heterotrophic respiration

HUC hydrologic unit codes

IBIS Integrated Biosphere Simulator

IC inorganic carbon

ID identifier

IKONOS Earth-observing satellite

INM–CM3.0 Institute of Numerical Mathematics, Climate Model 3.0

InTec Integrated Terrestrial Ecosystem model

INT–GTR Intermountain Research Station-General Technical Report

IPCC Intergovernmental Panel on Climate Change

ISO International Organization for Standardization

J2EE Java Platform Enterprise Edition

JERS–1 Japanese Earth Resources Satellite 1

JFD joint frequency distribution

K soil erodibility factor

LAI leaf area index

LANDFIRE Landscape Fire and Resource Management Planning Tools Project

LANDIS–II Disturbance and Succession forest landscape model

Landsat USGS and NASA Satellite Program

LANDSUM Landscape Succession Model

LCCs U.S. Fish and Wildlife Service’s Landscape Conservation Cooperatives

LIDAR Light detection and ranging

LOADEST USGS Load Estimator program

LTER Long Term Ecological Research Network

LULC land use and land cover

LULCC land-use and land-cover change

LULUCF Land Use, Land-Use Change, and Forestry

MCMC Markov Chain Monte Carlo method

MIROC3 Model for Interdisciplinary Research on Climate 3

MODFLOW groundwater flow model

MODIS Moderate Resolution Imaging Spectroradiometer

MRLC Multi-Resolution Land Characteristics Consortium

MTBS Monitoring Trends in Burn Severity project (LANDFIRE)

MTT minimum-travel time

N2O nitrous oxide
xiii

NADP National Atmospheric Deposition Program

NARR North American Regional Reanalysis (NOAA)

NASA National Aeronautics and Space Administration

NASS National Agricultural Statistics Service

NASQAN USGS National Stream Quality Accounting Network

NBP net biome productivity

NCAS National Carbon Accounting System

NCDC National Climatic Data Center (NOAA)

NCEP National Centers for Environmental Prediction

NCWin NetCDF program for processing and visualizing NetCDF data

NDVI Normalized Difference Vegetation Index

NECB net ecosystem carbon balance

NED National Elevation Dataset

NEE net ecosystem exchange

NEON National Ecological Observatory Network

NEP net ecosystem production

NetCDF network Common Data Form

NEWS Nutrient Export from WaterSheds model

NFPORS National Fire Plan Operations and Reporting System

NHD National Hydrography Dataset

NID National Inventory of Dams

NLCD National Land Cover Database

NLM Nitrogen Loading Model

NO3

– nitrate

NOAA National Oceanic and Atmospheric Administration

NPP net primary productivity

NPCDI National Coastal Pollutant Discharge Inventory

NPDES National Pollutant Discharge Elimination System

Nr reactive nitrogen

NRCS Natural Resources Conservation Service

NRI National Resources Inventory (NRCS)

NSDI National Spatial Data Infrastructure

NSF National Science Foundation

NWI National Wetlands Inventory

NWIS National Water Information System

OC organic carbon

OGC Open Geospatial Consortium, Inc.

OM organic matter

OSU Oregon State University

P phosphorus

PAD–US (CBI) Protected Areas Database of the United States (maintained by the

Conservation Biology Institute of Oregon)

PAO proportion of area occupied

PDF probability distribution function

PEST model-independent parameter estimation application (EPA)
xiv

PIC particulate inorganic carbon

PNV potential natural vegetation

POC particulate organic carbon

PPR Prairie Pothole region

PRISM parameter-elevation regressions on independent slopes model

PRMS Precipitation-Runoff Modeling System

PVB present value of the benefits

PVC present value of the costs

QC/QA quality control and quality assurance

r

2 coefficient of determination

RAC Oracle’s Real Application Clusters database

RDBMS Oracle’s Enterprise Edition Relational Database Management System

RCP representative concentration pathways

REDD reducing emissions from deforestation and forest degradation

redox reduction-oxidation

RESIS–II Reservoir Sedimentation Survey Information System (updated version)

RESSED Reservoir Sedimentation Database

RPA U.S. Forest Service Forest and Rangeland Renewable Resources Planning

Act of 1974

RSLC remote sensing of landscape change activities at U.S. Geological Survey

SAR synthetic aperture radar

SEnKF Smoothed Ensemble Kalman Filter

SIC soil inorganic carbon

SOC soil organic carbon

SOCCR State of the Carbon Cycle Report

SOM soil organic matter

SPARROW “spatially referenced regressions on watershed attributes” water-quality

model

SRES IPCC’s Special Report on Emission Scenarios

SRTM Shuttle Radar Topography Mission

SSURGO Soil Survey Geographic database (NRCS)

STATSGO2 U.S. General Soil Map (formerly STATSGO)

STORET EPA’s Storage and Retrieval Data Warehouse

SWAT Soil and Water Assessment Tool (USDA)

TDN total dissolved nitrogen

TIGER® Topologically Integrated Geographic Encoding and Referencing system of the

U.S. Census Bureau

TM Landsat Thematic Mapper

TN total nitrogen

TOC total organic carbon

TP total phosphorous

TRIPLEX monthly time-step model of forest growth and carbon dynamics that

integrates three BGC models

TSS total suspended sediments

UMD University of Maryland
xv

UMCES University of Maryland, Center for Environmental Science

UNEP United Nations Environment Programme

USDA U.S. Department of Agriculture

USDC U.S. Department of Commerce

USFS U.S. Forest Service

USGS U.S. Geological Survey

USLE Universal Soil Loss Equation

USPED Unit Stream Power Erosion and Deposition

VCT vegetation change tracker (a product of LANDFIRE)

VDDT Vegetation Dynamics Development Tool

WCS Web Coverage Service

WFS Web Feature Service

WHIP Wildlife Habitats Incentives Program

WMS Web Map Service

WRP Wetland Reserve Program

WUE water-use efficiency
xvi

Energy Independence and Security Act of 2007

H. R. 6—222 

SEC. 712. ASSESSMENT OF CARBON SEQUESTRATION AND METHANE 

AND NITROUS OXIDE EMISSIONS FROM ECOSYSTEMS. 

(a) DEFINITIONS.—In this section: 

(1) ADAPTATION STRATEGY.—The term ‘‘adaptation strategy’’ 

means a land use and management strategy that can be used— 

(A) to increase the sequestration capabilities of covered 

greenhouse gases of any ecosystem; or 

(B) to reduce the emissions of covered greenhouse gases 

from any ecosystem. 

(2) ASSESSMENT.—The term ‘‘assessment’’ means the 

national assessment authorized under subsection (b). 

(3) COVERED GREENHOUSE GAS.—The term ‘‘covered greenhouse gas’’ means carbon dioxide, nitrous oxide, and methane 

gas. 

(4) ECOSYSTEM.—The term ‘‘ecosystem’’ means any terrestrial, freshwater aquatic, or coastal ecosystem, including an 

estuary. 

(5) NATIVE PLANT SPECIES.—The term ‘‘native plant species’’ 

means any noninvasive, naturally occurring plant species 

within an ecosystem. 

(6) SECRETARY.—The term ‘‘Secretary’’ means the Secretary 

of the Interior. 

(b) AUTHORIZATION OFASSESSMENT.—Not later than 2 years 

after the date on which the final methodology is published under 

subsection (f)(3)(D), the Secretary shall complete a national assessment of— 

(1) the quantity of carbon stored in and released from 

ecosystems, including from man-caused and natural fires; and 

(2) the annual flux of covered greenhouse gases in and 

out of ecosystems. 

(c) COMPONENTS.—In conducting the assessment under subsection (b), the Secretary shall— 

(1) determine the processes that control the flux of covered 

greenhouse gases in and out of each ecosystem; 

(2) estimate the potential for increasing carbon sequestration in natural and managed ecosystems through management 

activities or restoration activities in each ecosystem; 

(3) develop near-term and long-term adaptation strategies 

or mitigation strategies that can be employed— 

(A) to enhance the sequestration of carbon in each 

ecosystem; 

(B) to reduce emissions of covered greenhouse gases 

from ecosystems; and 

(C) to adapt to climate change; and 

(4) estimate the annual carbon sequestration capacity of 

ecosystems under a range of policies in support of management 

activities to optimize sequestration. 

(d) USE OF

NATIVEPLANTSPECIES.—In developing restoration 

activities under subsection (c)(2) and management strategies and 

adaptation strategies under subsection (c)(3), the Secretary shall 

emphasize the use of native plant species (including mixtures of 

many native plant species) for sequestering covered greenhouse 

gas in each ecosystem. 

(e) CONSULTATION.— 

(1) IN GENERAL.—In conducting the assessment under subsection (b) and developing the methodology under subsection 

(f), the Secretary shall consult with— 
xvii

H. R. 6—223 

(A) the Secretary of Energy; 

(B) the Secretary of Agriculture; 

(C) the Administrator of the Environmental Protection 

Agency; 

(D) the Secretary of Commerce, acting through the 

Under Secretary for Oceans and Atmosphere; and 

(E) the heads of other relevant agencies. 

(2) OCEAN AND COASTAL ECOSYSTEMS.—In carrying out this 

section with respect to ocean and coastal ecosystems (including 

estuaries), the Secretary shall work jointly with the Secretary 

of Commerce, acting through the Under Secretary for Oceans 

and Atmosphere. 

(f) METHODOLOGY.— 

(1) IN GENERAL.—Not later than 1 year after the date 

of enactment of this Act, the Secretary shall develop a methodology for conducting the assessment. 

(2) REQUIREMENTS.—The methodology developed under 

paragraph (1)— 

(A) shall— 

(i) determine the method for measuring, monitoring, and quantifying covered greenhouse gas emissions and reductions; 

(ii) estimate the total capacity of each ecosystem 

to sequester carbon; and 

(iii) estimate the ability of each ecosystem to 

reduce emissions of covered greenhouse gases through 

management practices; and 

(B) may employ economic and other systems models, 

analyses, and estimates, to be developed in consultation 

with each of the individuals described in subsection (e). 

(3) EXTERNAL REVIEW AND PUBLICATION.—On completion 

of a proposed methodology, the Secretary shall— 

(A) publish the proposed methodology; 

(B) at least 60 days before the date on which the 

final methodology is published, solicit comments from— 

(i) the public; and 

(ii) heads of affected Federal and State agencies; 

(C) establish a panel to review the proposed methodology published under subparagraph (A) and any comments 

received under subparagraph (B), to be composed of members— 

(i) with expertise in the matters described in subsections (c) and (d); and 

(ii) that are, as appropriate, representatives of Federal agencies, institutions of higher education, nongovernmental organizations, State organizations, 

industry, and international organizations; and 

(D) on completion of the review under subparagraph 

(C), publish in the Federal Register the revised final methodology. 

(g) ESTIMATE; REVIEW.—The Secretary shall— 

(1) based on the assessment, prescribe the data, information, and analysis needed to establish a scientifically sound 

estimate of the carbon sequestration capacity of relevant ecosystems; and 

(2) not later than 180 days after the date on which the 

assessment is completed, submit to the heads of applicable 
xviii

H. R. 6—224 

Federal agencies and the appropriate committees of Congress 

a report that describes the results of the assessment. 

(h) DATA ANDREPORTAVAILABILITY.—On completion of the 

assessment, the Secretary shall incorporate the results of the assessment into a web-accessible database for public use. 

(i) AUTHORIZATION.—There is authorized to be appropriated to carry out this section $20,000,000 for the period of fiscal years 

2008 through 2012. 

SEC. 713. CARBON DIOXIDE SEQUESTRATION INVENTORY. 

Section 354 of the Energy Policy Act of 2005 (42 U.S.C. 15910) 

is amended— 

(1) by redesignating subsection (d) as subsection (e); and 

(2) by inserting after subsection (c) the following: 

‘‘(d) RECORDS AND

INVENTORY.—The Secretary of the Interior, 

acting through the Bureau of Land Management, shall maintain 

records on, and an inventory of, the quantity of carbon dioxide 

stored within Federal mineral leaseholds.’’. 

SEC. 714. FRAMEWORK FOR GEOLOGICAL CARBON SEQUESTRATION 

ON PUBLIC LAND. 

(a) REPORT.—Not later than 1 year after the date of enactment 

of this Act, the Secretary of the Interior shall submit to the Committee on Natural Resources of the House of Representatives and 

the Committee on Energy and Natural Resources of the Senate 

a report on a recommended framework for managing geological 

carbon sequestration activities on public land. 

(b) CONTENTS.—The report required by subsection (a) shall 

include the following: 

(1) Recommended criteria for identifying candidate 

geological sequestration sites in each of the following types 

of geological settings: 

(A) Operating oil and gas fields. 

(B) Depleted oil and gas fields. 

(C) Unmineable coal seams. 

(D) Deep saline formations. 

(E) Deep geological systems that may be used as engineered reservoirs to extract economical quantities of heat 

from geothermal resources of low permeability or porosity. 

(F) Deep geological systems containing basalt formations. 

(G) Coalbeds being used for methane recovery. 

(2) A proposed regulatory framework for the leasing of 

public land or an interest in public land for the long-term 

geological sequestration of carbon dioxide, which includes an 

assessment of options to ensure that the United States receives 

fair market value for the use of public land or an interest 

in public land for geological sequestration. 

(3) A proposed procedure for ensuring that any geological 

carbon sequestration activities on public land— 

(A) provide for public review and comment from all 

interested persons; and 

(B) protect the quality of natural and cultural resources 

of the public land overlaying a geological sequestration 

site. 

(4) A description of the status of Federal leasehold or 

Federal mineral estate liability issues related to the geological 

subsurface trespass of or caused by carbon dioxide stored in 
1

◦ Climate-change effects and other controlling processes (including wildland fires) on carbon and GHG 

uptake and emissions from ecosystems

The legislation requires that an assessment of carbon storage and GHG fluxes in the Nation’s ecosystems be 

performed, including an evaluation of potential policies for 

climate-change mitigation. Such an assessment is as complex 

as it is geographically broad, encompassing high ecological 

diversity and influenced by many present and future potential 

consequences of climate change, population change, landcover change, ecosystem disturbances, and land-management 

activities. This document defines the scope and methods of 

the assessment in terms of the ecosystems, pools, assessment 

units, and scale of applications; and explains the interdisciplinary framework and the individual methods and models used to 

develop assessment reports.

The concepts of ecosystems, carbon pools, and GHG 

fluxes used for the assessment follow conventional definitions 

in use by major national and international assessment or inventory efforts such as the Intergovernmental Panel on Climate 

Change (IPCC), the U.S. Global Change Research Program’s 

State of the Carbon Cycle Report, and the U.S. Environmental Protection Agency (EPA) Greenhouse Gas Inventory 

Report. Ecosystems defined in the methodology are forests, 

Executive Summary

This methodology14 was developed to fulfill a requirement by the Energy Independence and Security Act of 2007 

(EISA). The EISA legislation mandates the U.S. Department 

of the Interior (DOI) to develop a methodology and conduct an 

assessment of carbon storage, carbon sequestration, and fluxes 

of three principal greenhouse gases (GHG) for the Nation’s 

ecosystems. The three principal GHG are carbon dioxide 

(CO2), methane (CH4), and nitrous oxide (N2O). Section 712 

of this legislation (provided in the front of this report) asks 

DOI to develop the following:

• A methodology that includes quantifying, measuring, 

and monitoring carbon sequestration and GHG fluxes 

using current science and available, suitable national 

datasets

• A resource assessment of the Nation’s ecosystems—

terrestrial (forests, croplands, wetlands, and others) 

and aquatic (freshwater systems, estuaries, coastal 

waters)—focusing on the evaluation of the following:

◦ A range of mitigation activities for a potential 

increase in carbon-sequestration capacity and reduction of GHG fluxes to inform policy analysis

A Method for Assessing Carbon Stocks, 

Carbon Sequestration, and Greenhouse-Gas Fluxes 

in Ecosystems of the United States Under 

Present Conditions and Future Scenarios

By Zhiliang Zhu1 (editor), Brian Bergamaschi,2 Richard Bernknopf,3 David Clow,4 Dennis Dye,5

Stephen Faulkner,6 William Forney,3 Robert Gleason,7 Todd Hawbaker,4 Jinxun Liu,8 Shuguang Liu,9

Stephen Prisley,10 Bradley Reed,1 Matthew Reeves,11 Matthew Rollins,9 Benjamin Sleeter,3 Terry Sohl,9

Sarah Stackpoole,4 Stephen Stehman,12 Robert Striegl,13 Anne Wein,1 and Zhiliang Zhu1

1

U.S. Geological Survey, Reston, Va.

2

U.S. Geological Survey, Sacramento, Calif.

3

U.S. Geological Survey, Menlo Park, Calif.

4

U.S. Geological Survey, Denver, Colo.

5

U.S. Geological Survey, Flagstaff, Ariz.

6

U.S. Geological Survey, Leetown, W. Va.

7

U.S. Geological Survey, Jamestown, N.D.

8

Stinger Ghaffarian Technologies, Inc., Sioux Falls, S.D.

9

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

10Virginia Polytechnic Institute and State University, Blacksburg, Va.

11U.S. Forest Service, Missoula, Mont.

12State University of New York, College of Environmental Science and 

Forestry, Syracuse, N.Y.

13U.S. Geological Survey, Boulder, Colo.

14Supersedes U.S. Geological Survey Open-File Report 2010–1144.
2 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

grasslands/shrublands, croplands, wetlands, and aquatic ecosystems (which include inland impoundments, estuaries, and 

coastal waters). Terrestrial carbon pools include aboveground 

and belowground biomass, nonliving woody debris and litter, 

soil organic matter, and harvested wood. Aquatic pools include 

dissolved organic and inorganic carbon as well as particulate 

and sedimentary organic carbon. Across the Nation, the EPA’s 

level II ecoregions map (which delineates 24 ecoregions for 

the Nation) is the practical instrument for developing and 

delivering assessment results. Consequently, the ecoregion is 

the scale of the assessment because the mitigation scenarios, 

assessment results, validation, and uncertainty analysis are 

produced at this scale.

For a given landscape, an ecosystem’s capacity 

to increase carbon stocks and reduce GHG flux can be 

enhanced through changes in land use and land cover 

(LULC) (such as converting marginal cropland to forest or 

wetland) and changes in land management (such as increased 

use of prescribed burning to manage wildland fires). In 

order to estimate current ecosystem carbon stocks and GHG 

fluxes and to understand the potential capacity and effects 

of mitigation strategies, two time periods are used for the 

assessment: 2001 through 2010, which is used to establish a 

current ecosystem GHG baseline and validate models; and 

2011 through 2050, which is used to assess future potential 

conditions.

The method for conducting the assessment of future 

potential conditions uses IPCC storylines and climate scenarios. A reference and three mitigation scenarios are constructed 

for each storyline. The reference scenario projects LULC and 

land-management change in the absence of climate-mitigation 

policy. Alternative mitigation scenarios apply combinations 

of LULC changes and management activities to enhance 

carbon sequestration and reduce GHG emissions. In addition, 

the assessment also will consider the concept of potential 

natural vegetation as a scenario. Input from regional experts 

and stakeholders will be solicited to construct realistic and 

meaningful scenarios.

The methods used in the assessment include a current 

(2010) baseline component and a future potential component. 

The baseline component uses existing inventory and remotesensing data to measure and analyze spatial and temporal 

distributions of carbon stocks and GHG fluxes. The future 

potential component starts with the IPCC scenarios and 

examines the underlying economic and policy assumptions. 

The economic and policy assumptions are then translated into 

spatially and temporally resolved projections of future LULC 

in annual steps. Projections of future climate under different scenarios are obtained by downscaling data from global 

climate models. Future potential ecosystem disturbances, 

such as wildfires, are modeled in a similar manner. Using a 

geographic information system (GIS), data on the projected 

climate, LULC, and ecosystem disturbances are integrated to 

generate georeferenced layers of information that describe the 

future distribution of ecosystems and vegetation. The product 

of these analyses is a map of future ecosystems and ecosystem 

conditions for each future year and each scenario. These 

annual maps form the basis for calculating carbon storage and 

GHG emissions.

The carbon storage, carbon-sequestration capacities, 

and GHG emissions from terrestrial and wetland ecosystems 

under projected future conditions are assessed quantitatively 

from these GIS-produced maps using a spatially explicit 

biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or 

harvesting) and properties of individual ecosystems (such as 

elevation, vegetation characteristics, and soil attributes) and 

integrates them with the LULC and climate data. In addition 

to carbon storage and GHG fluxes, this model also provides 

important ancillary information about water use and other 

ecosystem services. Assessment of aquatic ecosystems also 

is based on the maps, but uses empirical models. The export 

of carbon in rivers, the flux of carbon and GHG into or out 

of inland basins (such as ponds, lakes, and reservoirs), and 

the impact of modified nutrient and sediment loads on carbon 

storage and GHG flux into or out of estuarine and coastal 

waters are assessed for the projected future conditions. 

Validation and uncertainty analysis of the assessment results 

follows established technical protocols, such as IPCC guidelines on assessing and reporting uncertainties. The assessment 

results (for each annual map of projected future ecosystem 

conditions and associated uncertainties) (1) permit the reporting of probability distributions of effects and the effectiveness 

of controlling processes and potential mitigation activities, and 

(2) support an analysis of potential policy applications.

The success of the assessment will depend on the 

methods and models used and the availability of suitable 

observational data. A wide variety of datasets for input are 

needed: carbon and GHG measurements (such as forest inventory or flux-tower data), streamgage data, remote-sensing data 

(such as precipitation, land-cover maps, and wildfires), soil 

attributes, current and future projected climates, agriculture 

and forestry production data, and a host of other input data. In 

addition, an approach for ecosystem GHG-flux monitoring is 

outlined in the methodology.

Implementation of the methods and access to datasets 

requires collaborations among various Federal agencies, State 

agencies, nongovernmental organizations, and the science 

community. For example, sharing or developing input data 

will be critical to the assessment, thereby ensuring a common 

basis to produce consistent assessment results, which then 

becomes important when making comparisons to other existing inventory or assessment efforts. Participation by experts or 

stakeholders in understanding the needs of policymakers and 

developing realistic mitigation scenarios will lead to improved 

accuracy in assessment results. Collaborative research on 

carbon cycling, GHG fluxes, ecosystem services, and model or 

method comparisons will help improve the methodology and 

enhance user confidence in assessment results. Applications 

of the assessment for mitigation planning or creating other 

land-management policies also will provide opportunities for 

validating the assessment results and for monitoring future 
Executive Summary 3

mitigation performances. Not all data and information needs 

can be met adequately. Further research and development will 

be needed as described in the document.

Using the method described in this document, the assessment can be completed in approximately three years. The 

primary deliverables will be assessment reports that present 

the results in the form of tables, charts, and maps. Changes in 

carbon stocks, net ecosystem carbon balance, GHG fluxes, and 

other services in ecoregions will be reported annually for 2001 

through 2050 by ecosystem, pool, and scenario. These results 

will be used to examine policy- or research-relevant questions, 

such as the following:

• What are estimates of the ecological carbon-sequestration capacity and GHG flux of the Nation’s ecosystems 

under different future climate scenarios, and how do 

these estimates vary geographically and temporally?

• How effective are management practices, such as notill agriculture or fire suppression, on short- and longterm carbon sequestration and GHG fluxes?

• How effective are deliberate changes in land use, such 

as reforestation or wetland restoration, on carbonsequestration capacity and GHG emissions?

• What might be the most effective and economically 

feasible regional mitigation strategies?

• How might other ecosystem services, such as water 

yield or wildlife habitat conditions, be affected by mitigation strategies to enhance carbon sequestration?

• How will changes in the terrestrial supply of carbon, 

nutrients, and sediments to inland basins, estuaries, and 

coastal oceans affect carbon sequestration and GHG 

production, including potential effects on natural processes and mitigation actions such as enhanced algal 

production and wetland restoration?

In short, the methods described in this document represent a nationally consistent, comprehensive effort to assess 

carbon storage and GHG fluxes covering the ecosystems 

of all 50 States. The assessment will rely on the contribution of agencies and scientists for expert evaluation of data, 

models, and validation, thereby linking to the best available 

approaches at each phase of the assessment. The results will 

permit (1) an evaluation of a range of policies and mitigation 

options, and (2) an evaluation of the effects that changes in 

demography, LULC, and climate will have on carbon stocks 

and GHG fluxes in ecosystems.
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5

1. Introduction

1.2. Understanding the Concepts and 

Requirements of the Energy Independence and 

Security Act of 2007

1.2.1. Assessment

The EISA requires an assessment of resources. In this 

assessment, the resources are the three greenhouse gases 

covered by the EISA: carbon dioxide, methane, and nitrous 

oxide. A resource assessment is a measurement or an estimate 

that determines the amount of a resource. The requirement 

for an ecosystem-based assessment by the legislation suggests, accordingly, a quantitative evaluation of the ecological 

processes and ecosystem capacities of carbon sequestration 

and GHG fluxes. The assessment needs to establish baseline 

conditions and therefore overlaps with existing resource (such 

as forest and rangeland) inventories; however, the focus of the 

assessment is on the estimates of future potential ecosystem 

capacities for fluxes of the three gases.

1.2.2. Ecosystems

An ecosystem is generally defined as a functional unit of 

the environment consisting of physical and biological components (Heal and others, 2005). Examples of ecosystems are 

provided in the EISA in terms of terrestrial systems, freshwater 

systems, and coastal aquatic systems (including estuaries). This 

context is consistent with the definitions of the ecosystems that 

are used in other global and national studies, which are reviewed 

in chapter 2 of this report; those studies consistently used forests, 

wetlands, croplands, grasslands/shrublands, and aquatic ecosystems as ecosystems for assessment and reporting purposes. The 

specific definitions, boundaries, and scale of ecosystems for this 

assessment are discussed in chapter 3 of this report.

Among the major functions of ecosystems is land cover and 

land use. Land use is generally defined as the anthropogenic use 

of land resources, typically in terms of economic decisions for 

the land. Land use can be referred to in terms of types of land use 

(such as agricultural or forest land) and management conducted 

within a type of land use (such as fertilization of agricultural 

land or controlled burning of forest land). Land cover refers to 

the actual vegetative or other surface cover at any given time. 

Land cover is related to land use in that it is often the result of 

economic land-use decisions. The effects of changes in both land 

use and land cover often need examination. Given the inextricable relationship between land use and land cover (LULC), 

changes in LULC often are considered simultaneously.

This chapter briefly summarizes DOI’s responsibilities 

and explains the concepts and requirements contained in Section 712 of the Energy Independence and Security Act of 2007 

(U.S. Congress, 2007). A firm understanding of these concepts 

and requirements is necessary because they form the foundation upon which to construct the methodology for carrying out 

the assessment.

1.1. Requirements of Section 712 of the Energy 

Independence and Security Act of 2007

In 2007, the U.S. Congress passed Public Law 110–140, 

the Energy Independence and Security Act (EISA). Section 

712 of the EISA (provided in the front of this report; U.S. 

Congress, 2007) authorizes the U.S. Department of the Interior 

(DOI) to develop a methodology and conduct an assessment of 

the Nation’s ecosystems for (1) carbon storage and sequestration, and (2) the fluxes of three greenhouse gases (GHG)—

carbon dioxide (CO2), methane (CH4), and nitrous oxide 

(N2O). Ecosystems (such as forests, wetlands, croplands, 

grasslands/shrublands, and aquatic ecosystems) both sequester and emit greenhouse gases and, to certain extent, can be 

managed in order to increase carbon sequestration or decrease 

emissions to help mitigate the effects of burning fossil fuels. 

The EISA also states that a purpose of the assessment is “to 

promote research on and deploy greenhouse gas capture and 

storage options.”

Section 712 begins with the definition of terms used in 

the law. Some of these terms, as well as others used in this 

report, are included in the glossary found at the end of this 

report. Section 712 also contains specific requirements—

mainly that DOI must develop a methodology, complete a 

national assessment, and report on that assessment; in the process, DOI must use native plant species and consult with other 

government agencies. Within the sections entitled “Authorization of Assessment,” “Components,” and “Methodology,” 

the law specifies the information that the methodology and 

assessment must include.

To understand the requirements of section 712 of the 

EISA and maintain the usage and intent of the terminology, 

key concepts in the legislative language are defined below. 

These concepts and requirements include assessment, ecosystems, mitigation and adaptation strategies, carbon-sequestration capacity, major processes that control greenhouse-gas 

fluxes, management and restoration activities, range of policies, the use of native plant species, and components of the 

methodology.
6 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

1.2.3. Adaptation and Mitigation Strategies

The EISA requires the development of “near-term and 

long-term adaptation strategies and mitigation strategies;” 

however, the law only defines adaptation strategy, not mitigation strategy. Adaptation strategy is defined as “a land use 

and management strategy that can be used (A) to increase the 

sequestration capabilities of covered greenhouse gases of any 

ecosystem, or (B) to reduce the emissions of covered greenhouse gases from any ecosystem.”

This definition, however, is more consistent with the 

standard definition of mitigation, which is the taking of action 

to avoid, reduce, minimize, rectify, or compensate for adverse 

impacts (see National Environmental Policy Act of 1970; U.S. 

Congress, 1970). In contrast, adaptation refers to changes in 

natural systems or “actions taken to enhance the resilience of 

vulnerable systems, thereby reducing damages to human and 

natural systems from climate change and variability” (Scheraga and Grambsch, 1998, p. 85). Therefore, it is reasonable to 

consider mitigation strategies as portfolios of land-use change 

and land-management activities that are implemented over 

time and across landscapes to enhance carbon sequestration 

and reduce GHG emissions. Estimation of carbon sequestration and GHG fluxes for various climates scenarios should 

inform the development of strategies to adapt to climate 

change.

1.2.4. Carbon-Sequestration Capacity

EISA requires that the assessment shall “estimate the 

annual carbon sequestration capacity of ecosystems.” The 

term “carbon sequestration” is defined in this methodology 

as the removal of CO2 from the atmosphere and its storage in 

ecological sinks (terrestrial and aquatic ecosystems). Carbon 

sequestration can be quantified as a change in the amount of 

carbon stocks either in an ecosystem or between ecosystems. 

The term “carbon-sequestration capacity” can refer to both the 

maximum rate of carbon storage (such as the rate of growth 

measured for an actively managed forest) and the maximum 

amount of carbon that can be stored (such as in an old-growth 

forest or a boreal soil pool).

The reporting of annual rates of carbon storage and 

changes in carbon stocks is questionable given the amount of 

annual variance in climate and in vegetation productivity. All 

ecosystems have a finite storage capacity for a given climate 

that is limited by ecophysiological constraints on primary 

productivity, respiration, and decomposition, resulting in a 

net carbon balance (Chapin and others, 2006). The storage 

capacity for a given landscape or region can be determined by 

the extent of specific factors or processes, including changes 

in LULC and changes in land management within the defined 

area.

1.2.5. Processes That Control the Flux of Covered

Greenhouse Gases

The EISA requirement to “determine the processes that 

control the flux of covered greenhouse gases” is understood as 

a requirement to determine the effects of the processes rather 

than to conduct experiments to identify the processes; these 

processes (such as photosynthesis, respiration and decomposition, LULC, land management, and ecosystem disturbance) 

are generally well understood and have been extensively 

documented in the scientific literature. A general review of 

the processes and their effects is provided in chapter 2. A 

key controlling process for GHG fluxes in ecosystems is fire 

caused by natural and human processes, which is generally 

considered as either a function of ecosystems or a disturbance. 

The legislation requires that wildland fire be assessed for its 

effect on carbon storage and releases. The methodology thus 

will incorporate existing scientific knowledge to quantify the 

effects of the relevant controlling processes on carbon sequestration and GHG flux.

1.2.6. Management Activities and Restoration

Activities

The EISA requires that the assessment shall “estimate the 

annual carbon sequestration capacity of ecosystems under 

a range of policies in support of management activities to 

optimize sequestration.” As defined above for mitigation 

strategies, management and restoration activities are considered components of mitigation strategic portfolios that are 

developed in order to increase ecological carbon sequestration 

and (or) reduce GHG emissions. Changes in management or 

restoration activities occur within a LULC class (for example, 

reduced tillage on croplands, wildland fuel treatments, ricepaddy management, or controlled water flow in freshwater 

systems). For the purpose of assessing ecological carbonsequestration capacity, land-use change and land-management 

activities are limited to those that directly increase carbon 

sequestration in soils, vegetation, wood products, and sediments. Not included are (1) the indirect effects on climate 

mitigation from the generation of energy from biomass; (2) 

technological actions that can aid in ecologically sequestering 

carbon but are not explicitly a land-use or management change 

(for example, growing algae in industrial fluxes); (3) activities 

to reduce downstream or life-cycle GHG fluxes (for example, 

GHG emissions from hauling and processing of timber are 

not assessed even though they are the result of harvest rotation changes); and (4) GHG emissions from livestock. To 

address these broader implications, it will be necessary to use 

results of the assessment in other life-cycle analyses, such as 

the various ongoing efforts that analyze biomass for energy 

applications.
1. Introduction 7

The consideration of mitigation activities will require 

information on potential ancillary effects for other ecosystem 

services because these services may either limit or enhance 

the implementation of a particular land-use change or management activity, thus changing the potential for increasing 

carbon sequestration. In addition, losses and gains to ecosystem services can be expressed as (nonmarket) social values 

(Brookshire and others, 2010; Jenkins and others, 2010). 

Evaluating indirect or ancillary effects of mitigation strategies on ecosystem services is a necessary and critical part of 

the assessment and is directly relevant to informing the policy 

process, particularly because of the environmental impact 

review requirements by the National Environmental Policy 

Act of 1970 (U.S. Congress, 1970).

1.2.7. Range of Policies

The EISA requirement “to estimate the annual carbon 

sequestration capacity of ecosystems under a range of policies 

in support of management activities to optimize sequestration” 

is understood as estimating the carbon-sequestration capacity 

of ecosystems for a range of land-use change and management 

activities which will in turn inform policy analyses. Policy 

analyses of management activities and land-use change alone 

would be suboptimal; the results of this assessment should be 

considered together with climate-mitigation-policy analyses 

that include other mitigation options besides ecological carbon 

sequestration (for example, Creyts and others, 2007) that pertain to other sectors (such as energy) for informing policymaking. These broader analyses accommodate (1) multiple and 

competing uses of land for carbon sequestration, food, fiber, 

and energy; (2) interactions between multiple sectors; and (3) 

international impacts (for example, Lewandrowski and others, 

2004; Murray and others, 2005; U.S. Environmental Protection Agency, 2008; Larsen, 2009; Ross and others, 2009). 

This national assessment can evaluate mitigation activities 

and strategies for carbon-sequestration capacity and effects on 

GHG emissions, and ancillary ramifications on cost and ecosystem services, but otherwise needs to link to the other policy 

analyses, such as the three examples given above.

1.2.8. Use of Native Plant Species

The EISA requires that the assessment should “emphasize 

the use of native plant species.” The assessment requires that 

the plants will be used in the restoration, management, and 

mitigation activities. In this methodology, when plant species 

are evaluated as part of assessing management activities, only 

native plant species are considered.

1.2.9. Measuring, Monitoring, and Quantifying

The EISA stipulates that the methodology for the 

national assessment shall include methods for “measuring, 

monitoring, and quantifying covered greenhouse gases emissions and reductions.” In the context of the national assessment, these three closely related activities are defined as 

follows:

“Measuring” is applying effective tools and techniques for collecting primary data that address information 

requirements of the national assessment. Measurement can 

be subsidiary to the quantification task defined below (for 

example, providing data for input into a model) or independent of it (for example, providing data for validation 

or monitoring). Measurement products to be used by the 

national assessment include past (archived), current, and 

future data records. Measurement products may be provided 

by ongoing national inventory programs (such as plot-based 

biomass measurements by the U.S. Forest Service’s Forest 

Inventory and Analysis Program) or by the use of remotesensing methods (such as fire perimeters defined by using 

satellite imagery).

“Monitoring” is the continual, systematic repetition of 

measurement defined above. The objectives of monitoring for 

this assessment are to enable the following:

• Quantification through time of carbon stocks, carbon 

sequestration, GHG emissions, and related ecological 

processes by providing the data required for calibrating, updating, and improving the accuracy of methods 

and assessment results

• Validation or assessment of the accuracy and precision 

of assessment results

• Evaluation of the effectiveness of applied LULC 

changes, management activities, and mitigation strategies for increasing carbon sequestration, reducing 

GHG emission, and related goals

“Quantification” is the determination of numerical values 

for variables specified in the national assessment for specific 

ecosystems, including current and projected future potential 

carbon sequestration, GHG emissions, and reductions in those 

emissions due to LULC change and management activities. 

Quantification in the national assessment is achieved primarily 

through the methods described in chapter 3.

1.2.10. Use of Economic and Other Systems

Models, Analyses, and Estimates

The EISA notes that the methodology may involve the 

use of “economic and other systems models, analyses, and 

estimates.” In order to select appropriate models, certain 

factors will be considered, such as (1) a consensus by the 

scientific community that the model is of a high enough quality, (2) technical practicality or operational considerations, (3) 

availability of input data to support the particular method, and 

(4) whether the models can be integrated with each other and 

produce results that are consistent with other similar ongoing 

assessment efforts.
8 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

1.3. Summary

In summary, the components of the assessment required 

by section 712 of the EISA represent a progression from 

science to policy: (1) existing scientific knowledge is incorporated in order to quantify the effects of the relevant controlling processes on carbon sequestration and GHG flux, (2) 

increased carbon sequestration and reduced GHG emissions in 

ecosystems from LULC change and land-management activities are estimated, (3) mitigation strategies under a range of 

climate-change projections are examined, and (4) activities to 

enhance sequestration capacity are identified and their costs 

and effects on ecosystem services are examined as contributions to the policymaking process. The methodology criteria 

require the preparation of data products to support the informational needs of the assessment (measuring); an estimation 

of the current and projected future potential carbon sequestration, GHG emissions, and reductions in those emissions due 

to LULC change and management activities (quantifying); 

and the calibration, validation, and updating of results (monitoring). Consultation with other agencies is integral to the 

assessment, as are productive partnerships for implementing 

the methodology.
9

2. Review of Concepts and Literature

gas in the atmosphere (Ramaswamy and others, 2001). Carbon 

dioxide is the standard to which other gases are compared, so it 

has a GWP of 1. Methane has a GWP of 21, and nitrous oxide 

is the most potent greenhouse gas with a GWP of 310 (U.S. 

Environmental Protection Agency, 2010).

Terrestrial and aquatic ecosystems play an important role 

in the carbon cycle (fig. 2.1). Major ecosystems that are commonly considered in both global and national carbon assessments and in inventories include forests, croplands, grasslands/

shrublands, and wetlands. Carbon cycling in aquatic ecosystems (such as rivers, lakes, and coastal waters) has not received 

as much attention, particularly in inventories at the national 

level (Cole and others, 2007; Battin and others, 2009). Plant 

biomass, both aboveground and belowground, is a main pool of 

carbon. The amount of carbon stored in plant biomass is influenced by land use. For example, forest clearing for cropland 

greatly reduces the amount of carbon stored in the vegetative 

biomass. In a natural system, most of the biomass production 

contained in living plant material is eventually transferred 

to dead organic matter pools, such as dead wood and litter. 

Dead organic matter on the ground and plant biomass below 

the ground decompose and transform into soil organic matter 

(SOM), which is another primary pool and can have varying 

residence times in the soil. Decomposition of SOM releases 

CO2 back into the atmosphere (Chapin and others, 2006; 

Paustian, Ravindranath, van Amstel and others, 2006). Rivers 

receive dissolved particulate inorganic carbon (PIC), particulate 

organic carbon (POC), dissolved inorganic carbon (DIC), and 

dissolved organic carbon (DOC) from terrestrial ecosystems 

and transport it downstream. A fraction of this carbon is emitted 

as GHGs during transport and most of the remainder is buried 

in aquatic sediments in inland basins, waterways, coastal areas, 

and oceans (Cole and others, 2007; Tranvik and others, 2009).

By assessing carbon fluxes among all the major pools, it 

is possible to summarize all resulting quantities as the net ecosystem carbon balance (NECB) for each ecosystem (Chapin, 

and others, 2006). This value accounts for net ecosystem 

production (NEP), which is calculated by subtracting ecosystem respiration (ER) from gross primary productivity (GPP). 

Net biome productivity (NBP) is based on NEP, but further 

accounts for ecosystem disturbances. The NECB integrates all 

carbon flux terms, including lateral runoff and river transport 

of carbon (Chapin and others, 2006).

2.2. Current Knowledge of the Carbon Cycle and 

Greenhouse-Gas Fluxes in the United States

Recent studies indicate that terrestrial ecosystems in 

the United States represent a sizeable and globally important 

carbon sink (Potter and others, 2007). Forests are a large 

The intent of this chapter is to summarize current knowledge about the carbon cycle and GHG fluxes in the Nation’s 

ecosystems and associated controlling processes. A review 

of large-scale (continental or national-scale) inventories and 

assessments also is provided.

2.1. Major Carbon-Cycle Processes and Pools

Carbon research, covering global to local scales, informs 

our understanding of the potential role of ecological sequestration in offsetting carbon emissions. Observations and 

modeling indicate that annual rates of CO2 accumulation in 

the atmosphere are far larger than can be balanced by natural 

ecological processes that remove CO2 from the atmosphere 

(U.S. Climate Change Science Program (CCSP), 2007; Le 

Quéré and others, 2009). Global carbon sinks vary annually, 

but from 1990 to 2000, on average the land sink accumulated 

2.6±0.7 petagrams of carbon per year (PgC/yr) and the ocean 

sink accumulated 2.2±0.4 PgC/yr. In 2008, the global average 

uptake rate for land was 4.7±1.2 PgC/yr and for oceans was 

2.3±0.4 PgC/yr, but annual CO2 emissions from fossil-fuel 

combustion for the same year were estimated to be 8.7 PgC 

(Global Carbon Project (GCP), 2009; Le Quéré and others, 

2009). Therefore, mitigation of net global carbon emissions 

ultimately will require both a reduction in the sources of CO2

to the atmosphere as well as maintaining and increasing terrestrial and aquatic sinks (CCSP, 2007).

Although biological and anthropogenic controls over 

carbon cycling and GHG flux vary among major ecosystems, 

the basic ecophysiological processes controlling the accumulation and loss of carbon to and from ecosystems are similar. 

The primary CO2 fluxes between the atmosphere and ecosystems are uptake through plant photosynthesis and release by 

respiration, decomposition, and combustion of organic matter 

(Paustian, Ravindranath, van Amstel, and others, 2006). Carbon 

fluxes associated with aquatic ecosystems occur through lateral 

transfer via rivers and streams, sedimentation and burial in 

inland and coastal waters, and emission of GHGs from water 

bodies (CCSP, 2007; Tranvik and others, 2009). Both CH4 and 

N2O emissions are caused largely by microbial processes and 

combustion of organic materials in fires. For example, CH4 is 

released through methanogenesis under anaerobic conditions in 

soils and during incomplete combustion of organic matter, and 

N2O is a byproduct of nitrification and denitrification (Faulkner, 

2004; Wiedinmyer and Neff, 2007). These GHGs (CO2, CH4, 

and N2O) have atmospheric consequences and the IPCC developed the global warming potential (GWP) concept to compare their impacts on the climate. The GWP is a measure that 

combines the effects of the radiative influence of a gas into the 

atmosphere relative to CO2 as well as the residence time of the 
10 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

carbon sink, but they are ecosystems that gain and lose carbon 

continually (fig. 2.2A). Photosynthesis is the driving process 

behind carbon storage in biomass, and the stored biomass 

eventually ends up in soils and dead organic matter pools. 

Respiration, decomposition, and combustion (fire) release 

CO2 and CH4 back into the atmosphere (see section 2.3.2 for 

more information on the impacts of ecosystem disturbances on 

forests). A forest will show a net gain or loss of carbon based 

on the balance of these processes. One forest may be highly 

variable in its carbon-storage capacity if it is measured over a 

long time period, in part because of natural disturbances and 

harvest events; however, when considering many different 

forests in a large region, such variability in carbon storage will 

not be as apparent because the region is composed of forests 

that are in different stages of recovery and regrowth. In the 

conterminous United States, forests cover about 246 million 

hectares, with an additional 52 million hectares in Alaska 

(Goodale and others, 2002). The forest carbon stock in the 

conterminous United States is 41 Pg and Alaska has an additional 16 Pg, as estimated by forest inventories (Birdsey and 

Heath, 1995; Goodale and others, 2002). The forest product 

pool is a considerable carbon sink that sequesters 57 teragrams 

of carbon per year (TgC/yr) (CCSP, 2007, also known as the 

first State of the Carbon Cycle Report, or SOCCR, throughout 

this report), but individual wood products can have widely 

varying decomposition rates (Ryan and others, 2010).

Croplands can be very productive ecosystems, and often 

this productivity is measured in terms of crop yield; however, 

the accumulation of carbon in plant material is transient, as 

the plants are mostly herbaceous, and often the plants have an 

annual life cycle and a constrained growing season. Therefore, 

the majority of carbon in croplands actually is held in the soil 

as annual litter additions slowly decompose and become part 

of the soil organic matter (CCSP, 2007). To some extent, fire 

plays a role in the combustion of carbon from these lands 

because farmers sometimes burn plant residues on the soil 

surface to release nutrients back into the soil. In croplands, 

N2O emissions are driven by a combination of factors including fertilization levels, crop type, and soil-drainage capacity 

(Del Grosso and others, 2005). In the conterminous United 

Figure 2.1.  Diagram showing the terrestrial ecosystems covered in the assessment and the major carbon pools (in boxes).

Abbreviations as follows: DIC, dissolved inorganic carbon; DOC; dissolved organic carbon; POC, particulate organic carbon.

Cropland

Wetland 

River

Lake

Forest

Impoundment

Grassland/shrubland Coast

Estuary
2. Review of Concepts and Literature 11

CH4

CO2

CO2

CO2

CO2

CH4

CO2

CO2

Figure 2.2.  Diagrams showing examples of measured greenhousegas (GHG) fluxes in representative ecosystems. The three GHGs

covered in the assessment are carbon dioxide (CO2), nitrous oxide

(N2O) and methane (CH4). Across all ecosystems, CO2 is emitted

via biomass decomposition and respiration and CO2 is taken up by

photosynthesis. A, Croplands. Because of fertilizer amendments to

cropland soils, emission of N2O via denitrification often occurs from

this ecosystem. B, Grasslands/shrublands. Belowground biomass

States and Alaska, croplands cover about 134 million hectares, 

and the cropland carbon stock for these regions is about 16 Pg 

(Bliss, 2003).

Grasslands/shrublands are similar to croplands in that 

most of the carbon stock is stored in the soil. Plant roots provide the primary input of carbon into grassland soils, but some 

of the carbon is oxidized by soil microbes and is released 

back into the atmosphere. Grasslands/shrublands can be net 

sinks for carbon, although the capacity of these ecosystems 

to store carbon is variable across the landscape (Reeder and 

others, 2000). Grasslands/shrublands are subject to woody 

encroachment, which is the invasion of woody species into 

grasslands, or of trees into shrublands. In the conterminous 

United States and Alaska, grasslands/shrublands cover about 

345 million hectares, and the grassland/shrubland carbon stock 

for these regions is about 20 Pg (Bliss, 2003). Many grassland/

shrubland ecosystems are used as rangelands or pasturelands 

in the United States. Rangelands, which are dominant in the 

Western United States, have native grasses, forbs, or shrubs. 

Pasturelands, which are more dominant in the eastern part 

of the United States, contain introduced forage plant species 

rather than native plants. On rangelands and pasturelands, N2O 

and litter incorporation into the soil contribute to large carbon stocks

in grassland soils. C, Aquatic ecosystems. Surface water-atmosphere

exchange of CO2 occurs in water bodies. Methane results from

anaerobic respiration of buried sediments. D, Forest. Large amounts

of CO2 are stored in forest biomass, but carbon can be emitted

through combustion of organic matter in forest fires. E, Wetlands.

Wetlands store large amounts of carbon in organic matter, but

anaerobic conditions drive CH4 emissions into the atmosphere.
12 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

emissions are largely influenced by the presence of livestock 

(Follett and others, 2010).

Wetlands are transitional areas between uplands and 

aquatic ecosystems and generally can be defined as lands that 

are inundated periodically or permanently with water, or have 

saturated soils, and support vegetation adapted to anaerobic 

conditions (fig. 2.2B). Carbon is stored mainly in the soil 

carbon pool, which is the result of saturated, anaerobic soils 

that slow the decomposition of biomass production; however, 

both woody and nonwoody vegetation and sediments also 

contribute to sequestered carbon in wetlands. The primary 

productivity in wetlands can be highly variable; wetlands 

that receive most of their water from precipitation have low 

primary productivity, but wetlands (such as river floodplains) 

that receive pulses of nutrients typically are very productive 

(Reddy and DeLaune, 2008). Carbon is lost from wetlands 

through methanogenesis in anaerobic soils and through oxidation of organic matter when wetlands are drained. Therefore, 

wetland carbon sequestration is a balance of soil and plant carbon sequestration, loss of carbon through methanogenesis, and 

loss of carbon due to drainage of wetlands. Only 48 percent 

of the original wetland area in the conterminous United States 

still exists (CCSP, 2007). The current wetland acreage in the 

conterminous United States is 70 million hectares with 43 

million hectares more in Alaska. Wetlands in the conterminous 

United States store 20 PgC. An additional 42 PgC are stored in 

Alaskan wetlands (Bridgham and others, 2006).

Global estimates exist for GHG fluxes from inland waters 

(Tranvik and others, 2009), and national estimates exist for 

the export of carbon from rivers to oceans (Pacala and others, 2001); however, national estimates of GHG fluxes from 

inland waters (for example, lakes and impoundments), coastal 

systems, and estuarine systems are lacking. Rivers (fig. 2.2C) 

in the conterminous United States export an estimated 30 to 

40 TgC/yr to the oceans (Pacala and others, 2001) and global 

exports have been estimated at 0.9 PgC/yr (Tranvik and others, 2009). Exports are the sum of four carbon fractions: PIC, 

POC, DIC, and DOC. Globally, approximately 46 percent of 

riverine carbon is in organic form (25% dissolved and 21% 

particulate) and 38 percent is transported as dissolved CO2

(CCSP, 2007).

Considerable amounts of dissolved carbon and sediment are transported and then stored in inland water bodies, 

estuaries, and coastal waters (fig. 2.2D). For example, global 

estimates state that lakes stored 820 Pg of carbon during the 

Holocene Epoch (Einsele and others, 2001), which is comparable to the global estimates of carbon currently stored in the 

soil surface layer (1,350 to 1,576 Pg) and in terrestrial biomass 

(460 Pg) (Post and others, 1982; Raich and Schlesinger, 1992; 

Eswaran and others, 1993). Thus, quantification of inland 

aquatic processes related to carbon, nutrient, and sediment 

transport is critical for accurately quantifying regional and 

national carbon budgets and assessing aquatic carbon cycling. 

Lakes and impoundments (reservoirs and farm ponds) sequester carbon through burial of organic matter in sediments (Cole 

and others, 2007; Tranvik and others, 2009). Tranvik and 

others (2009) estimated that global burial of organic carbon in 

lakes and impoundments may account for 0.6 PgC/yr. Global 

emissions of CO2 from lakes and reservoirs have been estimated at approximately 0.8 PgC/yr (Tranvik and others, 2009). 

Methane emissions from lakes and impoundments could be 

even more important than CO2 in terms of GWP. The magnitude of GHG emissions from lakes, ponds, and reservoirs in 

the United States alone is unknown.

The transport of carbon, sediment, and nutrients to 

coastal waters stimulates primary productivity and leads to 

carbon burial in coastal sediments and sequestration in the 

deep ocean (fig. 2.2E). Nutrient additions to coastal systems 

cause an increase in the CO2 uptake in coastal systems (van 

Geen and others, 2000; Hales and others, 2005). Seitzinger 

and Mayorga (2008) estimated that the carbon production in 

coastal waters that are specifically fueled by nitrogen loading had a total global estimate of 20 TgC/yr. The fate of this 

new coastal primary production of carbon and the terrestrial 

organic carbon exported by rivers is related to both its composition and the rate of sediment supply by rivers (Boudreau 

and Ruddick, 1991; Hedges and Keil, 1995; Dagg and others, 

2004). Finally, this influx of nutrients and coastal productivity 

can result in the production of significant amounts of CH4 and 

N2O (Bange, 2006; Hirota and others, 2007). The estimates of 

current carbon stocks and GHG emissions for each ecosystem 

described above are shown in table 2.1.

Table 2.1.  Carbon stocks in the conterminous United States and

Alaska, and greenhouse-gas emissions from major ecosystems in

the conterminous United States only, as reported by recent studies.

[For each carbon, methane, and nitrous-oxide flux value, a carbon source 

is indicated by a positive value and a sink is indicated by a negative value. 

Abbreviations and symbols are as follows: LULUCF, land use, land-use 

change, and forestry; TgC, teragrams of carbon; TgC/yr, teragrams of carbon 

per year; TgCH4/yr, teragrams of methane per year; TgN2O/yr, teragrams of 

nitrous oxide per year; –, negligible value; NA, data not currently available]

Ecosystem

Carbon 

stock

TgC

Carbon flux

TgC/yr

Methane 

flux

TgCH4/yr

Nitrous 

oxide flux

TgN2O/yr

Forests 57,000a -162d – –

Grasslands/shrublands 16,000b -0.05d 0.03d 0.09d

Croplands 20,000b -8.8d 0.1d 0.16d

Wetlands 62,000c -9.5e 3.1e NA

Riversf NA -30 to -40g NA NA

a

Source: Goodale and others (2002). Forest and woodland pools include 

aboveground and belowground live vegetation for trees and understory 

vegetation, dead wood, litter, and soil organic matter below the litter layer to a 

depth of 1 meter.

b

Source: Bliss (2003).

c

Source: Bridgham and others (2006). This estimate accounts for vegetation 

and soil organic carbon pools.

d

Source: U.S. Department of Agriculture (USDA, 2008).

e

Source: CCSP (2007).

f

Refers only to lateral flux via rivers.

g

Source: Pacala and others (2001).
2. Review of Concepts and Literature 13

2.3. Effects of Major Controlling Processes

2.3.1. Effects of Changes in Land Use, Land

Cover, and Land Management

The examination of carbon sequestration and emissions 

requires an analysis of changes in both land use (for example, 

conversion of agricultural land to urban development) and land 

cover (for example, harvesting trees on a parcel used for forestry). Changes in LULC influence biogeochemical cycles and 

the carbon and GHG status of an ecosystem (Meyer and Turner, 

1992; Houghton and others, 1999). Changes to the Earth’s 

surface that are caused by human activity can have significant 

effects on ecosystem composition, structure, and function. For 

example, current global-change studies estimate that approximately 50 percent of the Earth’s ice-free land surface has been 

transformed. This land transformation was caused by major 

changes in land use and land cover, such as clearing forests for 

agriculture. If this estimate also included land that was in its 

“wild” state before being altered by human activity, this number 

would be much larger (B.L. Turner and others, 2007). When 

forests or other ecosystems are degraded or cleared, stored carbon is released into the atmosphere. Tropical deforestation alone 

released roughly 15 to 25 percent of annual global GHG emissions during the 1990s (Gibbs and others, 2007). Changes in 

LULC generally take two forms: (1) conversion from one landcover type to another (for example, forest to agricultural use) 

or (2) modification of a condition within a type (for example, 

timber harvest with subsequent regeneration of forest).

During the period from 1700 to the 1930s, major LULC 

changes in the United States occurred when native forests and 

prairies were converted to agricultural lands. In the 20th century, the trend reversed due to the following: (1) as farms were 

abandoned, both managed and unmanaged forests were regenerated; (2) the demand for harvested wood for fuel decreased; 

and (3) fire-suppression methods increased the forest biomass 

(Houghton and others, 1999; CCSP, 2007). These historical 

LULC changes were identified mainly by inventory or survey 

methods, but more current large-scale LULC change studies 

have been based on a combination of inventories, surveys, and 

remote-sensing techniques (Meyer and Turner, 1992; Sleeter 

and others, 2010). Using these methods, studies have shown that 

there are strong regional differences driving LULC change in 

the United States. For example, apart from ecosystem disturbance, both agricultural intensification and urbanization have 

been the major land-use changes in regions such as California 

during recent decades (Sleeter and others, 2010). In contrast, 

economic gain fostered an increase in agricultural land cover 

at the expense of native grasslands in the western Great Plains 

between 1973 and 1986; however, between 1986 and 2000, 

public policy, which encouraged native grassland restoration, 

drove a conversion from agriculture back to grassland (Drummond, 2007). In the Eastern United States, trends, causes, and 

consequences for LULC change were far more complex. A 

recent study found that recent LULC changes were associated 

with forest harvesting and regrowth, agricultural abandonment, 

and development (Drummond and Loveland, 2010). These 

findings in LULC changes have significant implications on the 

capacities of ecosystems to sequester carbon in these regions.

A contemporary driver of LULC change is land management. By applying changes in the types of land management 

(for example, change in cropland tillage) or in the intensity of 

land-management activities (for example, active use of prescribed burning), it is possible to manage forests and other ecosystems to enhance carbon sequestration. Recent studies showed 

that the active use of prescribed burning in fire-dependent forest 

ecosystems helps to increase the rate of carbon sequestration 

(Wiedinmyer and Hurteau, 2010). In the Pacific Northwest, 

increasing the time between harvests and reducing the total 

number of acres to be harvested are two management activities 

that would enable forests to store theoretically up to 40 percent 

more carbon (Hudiburg and others, 2009).

Grasslands/shrublands in the Western United States are 

frequently used for livestock grazing, and the lands store and 

process far less carbon than forests (Negra and others, 2008); 

however, with sustainable grazing intensity, grasslands/shrublands cumulatively have the potential to sequester a significant 

quantity of carbon when integrated over approximately 350 

million hectares in the United States (Joyce, 1989; Baron 

and others, 2002; Elmore and Asner, 2006). Reducing grazing intensity also contributes to a reduction in the emissions 

of N2O and CH4 (Baron and others, 2002). Two-thirds of the 

grasslands/shrublands in the United States are identified as 

having some limits on productivity and carbon storage; therefore, increases in potential soil carbon pools on these lands 

would be variable and possibly slow (Bruce and others, 1999).

Changes in crop-management practices, such as implementing crop rotation, planting winter cover crops, and setting 

aside land according to the Conservation Reserve Program 

(CRP) and Wetland Reserve Program (WRP) agreements, have 

great potential to increase carbon stock in croplands (McLachlan and Knispel, 2005; Rees and others, 2005; Lal and others, 

2007). The CRP offers incentives to encourage the reclamation 

of former agricultural lands by converting it to other vegetation 

(often grasses), and this change has resulted in increased carbon 

storage of approximately 0.6 megagrams of carbon per hectare per year in the United States (Schuman and others, 2002). 

Additionally, implementation of conservation practices, such 

as residue management and reductions of summer fallow lands 

coupled with no-till and reduced-tillage, may be possible landmanagement activities that may help enhance carbon sequestration and reduce GHG emission in croplands (Tan and others, 

2006, 2007). These activities also may help improve soil quality 

and crop productivity (Causarano and others, 2006).

Land-management activities that affect carbon cycling 

in terrestrial ecosystems also influence carbon processes in 

aquatic ecosystems. For example, reservoirs and farm ponds can 

sequester carbon through the burial of organic matter in aquatic 

sediments (Cole and others, 2007; Tranvik and others, 2009); 

the number of farm ponds in agricultural areas of the United 

States has been increasing 1 to 2 percent annually (Downing 
14 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

and others, 2006). Carbon burial is influenced by rates of erosion and carbon concentration in upland soils, which in turn 

are influenced by land use. For example, tillage increases the 

erosion of agricultural land, and sediment resulting from the 

erosion is deposited in downstream water bodies. Farm ponds 

can be created to capture sediment that otherwise would enter 

streams and rivers; however, the utility of farm ponds as carbonsequestration tools depends on their longevity. Numerous future 

land-management activities may intentionally or unintentionally 

alter sediment loads to coastal systems as well. Land-management activities that may positively or negatively affect sediment 

flux to coastal areas include building new reservoirs, fortifying 

river channels and banks, and trapping farm sediment (Syvitski 

and others, 2003). Sediment diverted for inland wetland or 

floodplain restoration or coastal wetland creation will lower 

sediment supply to the ocean (Khalil and Finkl, 2009).

2.3.2. Effects of Ecosystem Disturbances

Ecosystem disturbances are episodic events that may 

affect the composition, structure, and (or) function of an ecosystem (Pickett and White, 1985; E.A. Johnson and Miyanishi, 

2001; M.G. Turner and others, 2001). The effects of ecosystem 

disturbances are treated differently from the effects of global 

environmental change, which includes sustained alterations in 

climate that may arise from increasing CO2 in the atmosphere 

or nitrogen deposition (B.L. Turner and others, 1990). The 

effects of ecosystem disturbances also are separate from the 

effects of LULC changes, such as the conversion of forest 

to cropland. Major ecosystem disturbances are one of the 

primary mechanisms that have the potential to reset carbonsequestration pathways and change ecosystems from carbon 

sinks to sources (Baldocchi, 2008; Running, 2008). Examples 

of such disturbances include wildland or prescribed fires, hurricanes and storms, and insect or disease outbreaks.

Wildland fire contributes to the loss of stored carbon in 

terrestrial ecosystems and the release of both CO2 and CH4 emissions into the atmosphere (Simpson and others, 2006; Wiedinmyer and Neff, 2007). A study using a global air-sample dataset 

indicated that burning biomass has contributed to an increase in 

atmospheric methane levels (Simpson and others, 2006). A study 

using satellite imagery showed that, between 2002 and 2006, the 

average annual CO2 emissions were estimated at 213±50 Tg/yr 

for the conterminous United States and 80±89 Tg/yr for Alaska 

(Wiedinmyer and Neff, 2007). The EPA estimated that total CO2

emissions in the United States from forest fires amounted to 318 

Tg/yr in 2007 and 189.7 Tg/yr in 2008 (EPA, 2010). These current estimates of carbon emissions actually may underestimate 

the amount of carbon historically emitted by fires because firereturn intervals (the number of years between two successive 

fire events at a specific site or an area of a specified size) actually 

have increased by an order of magnitude in many areas relative 

to historic fire regimes (Rollins and others, 2001; Cleland and 

others, 2004; Grissino-Mayer and others, 2004).

Greenhouse-gas emissions from wildland fires are difficult 

to estimate because of the temporal and spatial variability of 

their occurrences, the long-term effects of fires of mixed severity, and the differing degrees of combustion of aboveground 

biomass and soil organic matter stocks (Neff and others, 2005; 

Wiedinmyer and Neff, 2007). In the Western United States, an 

increase in fire-suppression activities during the 20th century is 

partially responsible for the increase in forest biomass in firedependent ecosystems (McKelvey and Busse, 1996; Houghton 

and Hackler, 2000; Canadell and Raupach, 2008); however, shifts 

in climate have been correlated with longer wildfire seasons and 

an increase in the frequency of large fires (those that cover more 

than 9,400 ha) (Westerling and others, 2006). One result of very 

large wildfires is that a severe fire season lasting only one or two 

months can release a considerable amount of carbon dioxide and 

possibly cancel out the effects of carbon sequestration in forests 

(Wiedinmyer and Neff, 2007). Because many of these ecosystems 

are adapted to fire, suppression of wildfires to reduce GHG emissions may not yield greater long-term emissions reductions when 

compared with GHG emissions from areas where fire is retained 

or is re-established in its functional ecosystem role.

Other disturbances, such as windstorms or insect outbreaks, 

do not cause the same direct and rapid emissions of CO2 as fires, 

but they do change trees from live carbon sinks to dead and 

slowly decaying carbon sources over large areas (Running, 2008). 

In the Eastern United States, strong hurricanes usually occur 

in two out of every three years. Just one storm can change the 

equivalent of 10 percent of the total annual carbon sequestrated 

by forests in the United States into dead and downed biomass 

(McNulty, 2002). Generally, limited amounts of destroyed timber 

are salvaged following a major hurricane, and eventually the carbon stored in the trees returns to the atmosphere (McNulty, 2002). 

Insect outbreaks, such as the mountain pine beetle epidemic in 

forest ecosystems of the Rocky Mountains, have the same effect. 

Large amounts of carbon emissions from forests are lost either 

directly (because live biomass has been converted to dead organic 

matter) or indirectly (because the death of the forest leads to lost 

carbon-sequestration capacity). Because of the changing climate 

regime, these types of insect outbreaks, together with high-severity fires and storm damage, could put forest carbon sinks at risk.

2.3.3. Effects of Climate Change, Elevated Carbon

Dioxide, and Nitrogen Fertilization

Climate change, increasing atmospheric CO2 concentrations, and increasing reactive nitrogen deposition have a strong 

potential to influence carbon-cycling processes in terrestrial and 

aquatic environments (Canadell and others, 2007; Reay and others, 2008; McMahon and others, 2010). The fourth assessment 

report (AR4) by the IPCC (2007) stated that the best estimates of 

likely increases in the mean global surface-air temperature by the 

end of the 21st century are between 1.1°C and 2.9°C for the “low 

scenario” and 2.4°C and 6.4°C for the “high scenario,” and that 

the major cause of global warming is the human-induced increase 

of GHG in the atmosphere. Climate change may influence the 

frequency of extreme events, such as droughts, fires, heat waves, 

flooding, and hurricanes, thereby releasing additional carbon into 

the atmosphere. One of the most profound effects of increasing 
2. Review of Concepts and Literature 15

temperatures may be a thaw of permafrost in the northern latitudes (Camill, 2005; Lawrence and Slater, 2005). Climate change 

can also bring gradual changes to the length of the growing season and shifts in the geographical ranges for some plants (IPCC, 

2007). Studies of the effects of climate change on both permafrost 

and the growing seasons and geographical ranges of plants contain large uncertainties. An increase in the length of the growing 

season may promote more crop and tree growth, especially of 

plants in northern regions and higher elevations that act as carbon 

sinks (Euskirchen and others, 2006; IPCC, 2007); however, many 

studies indicate that ecosystem respiration has increased due to 

warming (Bond-Lamberty and Thomson, 2010). Therefore, the 

carbon loss from ecosystem respiration may substantially reduce 

or even outweigh the gain from the increase in the length of the 

growing season (Piao and others, 2008).

Increases in atmospheric CO2 may enhance crop production and water-use efficiency (WUE; the ratio of CO2

 uptake to 

evapotranspiration) (Allen and others, 1996). For forests, the 

Free-Air CO2 Enrichment (FACE) experiments by Norby and 

others (2005), tree-ring studies by Soulé and Knapp (2006), and 

improved field-data analysis by McMahon and others (2010) 

all suggest that the growth rates for trees may increase with 

increasing atmospheric CO2; however, other studies have shown 

that the magnitude of growth enhancement can vary from 0 to 

60 percent when atmospheric CO2 is doubled (Running, 2008).

Reay and others (2008) studied the possible effects of 

nitrogen deposition on global carbon sinks; they noted that emissions of reactive nitrogen (Nr; for example, nitric oxide (NO) 

and nitrogen dioxide (NO2)) currently are three to five times 

the global preindustrial levels (mid-1800s) due to fossil-fuel 

combustion and agricultural activities (Galloway and others, 

2004). Under the SRES A2 storyline, worldwide Nr deposition 

will increase by between 50 and 100 percent by 2030 relative 

to 2000 (Dentener and others, 2006; Reay and others, 2008). 

When deposited on land and water, Nr has a stimulating effect 

on primary productivity in ecosystems that are nitrogen-limited 

(Vitousek and Howarth, 1991; Elser and others, 2007). On land, 

an increase in Nr can result in a net increase in forest biomass 

(and hence, carbon sequestration), except in areas that already 

receive high levels of atmospheric nitrogen. Similarly, agricultural lands, which often are heavily fertilized, are not expected 

to see an increase in crop biomass. Increases in primary productivity in oceans can lead to increased burial of organic matter; 

however, increasing greenhouse-gas emissions from the ocean 

into the atmosphere may largely offset the carbon-sequestration 

effect. Reay and others (2008) concluded that carbon uptake by 

both northern and tropical forests might increase by up to 0.67 

Pg/yr and 0.14 Pg/yr, respectively, by 2030. This would amount 

to an additional 10 percent of projected CO2 emissions, but the 

increase was considered to be an upper limit; an increased uptake 

of 1 to 2 percent of CO2 emissions was considered more likely 

(Reay and others, 2008). The enhancement of CO2 uptake in 

oceans by nitrogen deposition was estimated to be less than 0.3 

PgC/yr (Reay and others, 2008). The potential for increased carbon sequestration in freshwater systems (lakes, impoundments, 

and wetlands) due to the addition of nutrients was thought to be 

potentially significant but required further investigation (Elser 

and others, 2007; Reay and others, 2008).

Complicated interactions exist among climatic and atmospheric factors and among carbon-nitrogen-water cycles. The 

combined effect (synergies and tradeoffs) of driving forces on an 

ecosystem biogeochemical cycle and productivity needs detailed 

analysis. For example, research results based on measurements 

made at hundreds of European forest-monitoring plots indicate 

that an increase in carbon-sequestration rates in response to 

increased Nr deposition will only occur if the site already is nitrogen-limited (de Vries and others, 2009). The AR4 (IPCC, 2007) 

also indicated that all regions of the world show an overall net 

negative impact of climate change on water resources and freshwater ecosystems and that water resources will depend on trends 

in both climatic and nonclimatic factors. Because an increase in 

carbon sequestration may require more water supplies, tradeoffs 

between carbon and water resources must also be assessed.

2.4. Carbon Sequestration and Ecosystem 

Services

In order to properly evaluate appropriate management 

actions to enhance carbon sequestration, it is important to 

consider the effects of these actions on ecosystem services. 

Ecosystem services are the benefits that people and societies derive from the natural processes that sustain ecosystems 

(Daily, 1997), and they can be generally cataloged into four 

broad areas: supporting, regulating, provisioning, and cultural 

(Millennium Ecosystem Assessment, 2003). Supporting services include basic ecosystem functions such as soil formation, 

whereas provisioning services are important sources of food and 

fiber. Regulating services help control climate change through 

carbon sequestration. Cultural services include recreation and 

education. The concept of ecosystem services is inherently 

based on the value or importance to humans, but the expression 

of those services is controlled by the underlying complex ecological structure and processes. (Daily and Matson, 2008; Fisher 

and others, 2008) (fig. 2.3). In some cases, the links between 

structure, processes, and resulting services is fairly straightforward. For example, the degree to which a specific plant community can support a given wildlife population can be determined 

directly by measuring community attributes, such as species 

composition, height, and age. Other services, such as improving 

water quality by converting nitrate to nitrogen gas through denitrification, are controlled by more complex interactions between 

multiple ecosystem attributes (for example, carbon, reductionoxidation (or redox) status, soil microbial population, and 

temperature) that are more difficult to measure. These relations 

also are altered by temporal and condition gradients (fig. 2.3), 

which result in dynamic processes and significant variability 

across and within different ecosystems. This makes the relations 

difficult to measure and quantify at large spatial scales. Ecological production function models based on biophysical inputs 

are often used to produce spatial estimates of specific services 

(Nelson and others, 2009).
16 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

An explicit recognition of the complex relations among 

ecosystem structure, processes, and services is critical to understanding the potential ancillary effects of carbon-sequestration 

strategies. Any change, either anthropogenic or naturally occurring, that affects structural components (such as plant-community composition) or processes (such as nutrient cycling) may 

affect the quality, quantity, and types of services produced from 

that ecosystem. The quantification of the effect is a difficult task 

because some services, such as biodiversity, can be both a cause 

of the way an ecosystem functions and a response that varies 

with changing management activities (Hooper and others, 2005; 

Costanza and others, 2007); therefore, the effects of carbonspecific components may be hard to separate.

Another problem is that the responses of multiple 

services to specific carbon-related land-management activities are not well studied. Nelson and others (2008) concluded 

that policies aimed at increasing carbon sequestration did 

not necessarily increase species conservation and that highly 

targeted policies were not necessarily better than more general 

policies. The study by Nelson and others (2008) demonstrates 

the likelihood that many of the possible management activities and sequestration strategies may affect those ecosystem 

services that are of direct importance to landowners and land 

managers. For example, an afforestation plan that is designed 

to increase carbon sequestration may alter migratory bird habitat depending on the location and the variety of species chosen 

for that forest (Hamilton and others, 2005; Twedt and others, 

2006); therefore, ecological tradeoffs may be necessary when 

planning land-management activities.

2.5. Ongoing Global and National Carbon and 

Greenhouse-Gas Inventories and Assessments

Currently, there are many ongoing national and international carbon inventories and assessments. This section 

describes some of the objectives and methods of these largescale projects. The terms “inventory” and “assessment” are 

similar in that they both provide estimates of resource conditions; however, the inventory methods focus on measurements 

of present resource conditions rather than providing an estimate of future carbon-sequestration capacity and GHG fluxes.

2.5.1. Intergovernmental Panel on Climate

Change Scenarios and Guidelines

The Intergovernmental Panel on Climate Change (IPCC) 

is an international scientific body charged with conducting 

global assessments related to climate change. Since 1990, 

the IPCC has produced four comprehensive assessment 

reports (IPCC, 1990, 1992, 2001, 2007). In 2000, a special 

report on emission scenarios (SRES) was produced by the 

Figure 2.3.  Conceptual

diagram of the relations among

ecosystem structure, function,

and services.
2. Review of Concepts and Literature 17

IPCC (Nakicenovic and others, 2000). The SRES framework 

provides assumptions about future potential socioeconomic, 

demographic, and technological changes that serve as storylines or pathways to project future potential GHG emissions 

and changes in climate. The SRES does not set forth policies 

that explicitly address climate change; it provides a reference 

for the evaluation of potential mitigation activities. The SRES 

includes four main storylines that produce four families of scenarios: A1, A2, B1, and B2. In brief, the A1 family describes 

a future with fast economic growth, a population increase that 

peaks in mid-20th century and then declines, and a rapid introduction of new technologies until the middle of the 21st century, after which there is a decline. The A2 family describes a 

future with economic growth that is tied to regional interests, a 

slow and steady change in population, and technological adaptation that is not as consistent and widespread as that described 

in A1. The B1 family describes a future where the economy 

is focused on the service and information sectors of society; 

there is a peak in the population with a drop in the middle of 

the 21st century, as in A1, but this population uses fewer material goods and strives to introduce more environmentally sustainable technologies. Finally, the B2 family describes a world 

in which there is a focus on developing local economies and 

promoting environmental sustainability; the population growth 

is slow, and the development and acceptance of new technologies also are slow. The IPCC projected future emissions and 

climate change on the basis of the four scenarios in order to 

allow adequate representation of the inherent uncertainties 

associated with predicting future climate change.

In 2006, the IPCC published guidelines for agriculture, 

forestry, and other land uses (AFOLU) that defined three tiers, 

three different approaches, and two methods for assessing and 

reporting GHG emissions (IPCC, 2006). The tier 1 method is 

a basic method that uses default values by LULC type and is 

most suitable for nations with limited inventory and remotesensing capabilities. The tier 2 (inventory and bookkeeping) 

and tier 3 (inventory and process-based models) methods 

represent more demanding technical capabilities, accuracy, 

and data requirements. Three different IPCC approaches were 

described in the guidelines for handling LULC and changes, 

ranging from simple to more complex treatments of those 

changes. In describing the approaches, the IPCC recommended six standard AFOLU categories for consistent and 

comparable reporting: forest land, cropland, grassland, wetlands, settlements, and other land. Finally, the IPCC elaborated 

on two different methods for GHG emission accounting: the 

gain-loss method and the stock-difference method.

2.5.2. Examples of Continental-Scale

Greenhouse-Gas Inventories and Assessments

Continental-scale inventories and assessments of carbon and GHG have been conducted for various countries 

and regions in the world; a few examples reported in recent 

literature are summarized below for Australia, the European 

Union, and China. In the Australian inventory, carbon storage 

and GHG emissions (CO2, CH4, and N2O) have been assessed 

for terrestrial ecosystems by using a prototype National Carbon Accounting System (NCAS; Richards and Brack, 2004). 

Carbon stocks and GHG fluxes are inventoried and forecasted 

in spatially and temporally explicit fashions for major ecosystems (such as forests, grasslands, and croplands) by considering major controlling processes, including climate change, soil 

productivity, land-cover change, soil decomposition, and landmanagement activities. The NCAS methodology is based on 

the combined use of Landsat remote-sensing imagery, current 

and future potential climate-model estimates, soil, inventory, 

and land-management databases. At the core of the NCAS 

methodology is an ecosystem biogeochemical (BGC) model 

that uses accounting algorithms implemented for both nonspatial and spatial applications (Richards and Evans, 2000). 

Although the prototype NCAS will be enhanced during the 

next several years, the assessment results have been incrementally reported. In 2007, the most recent year for which reporting was conducted, the net GHG emissions for Australia from 

agricultural land use, deforestation, and net uptake from forest 

plantation accounted for 24, 21, and -5.8 TgC, respectively 

(Australian Government, Department of Climate Change, 

2009). The development of a new phase of the NCAS is based 

on the use of remote sensing for tracking LULC changes and 

a process-based ecosystem biogeochemical model to estimate 

GHG emissions (IPCC tier 3 and approach 3 in IPCC (2006)). 

It is worth noting that the NCAS methodology is used in Australia for both GHG accounting and monitoring purposes.

A recent European Union-wide assessment was conducted for terrestrial ecosystems to assess carbon and GHG 

(N2O, CH4) fluxes into and out of forest and cropland ecosystems. A compilation approach for the assessment used different methods (inventory accounting, process-based ecosystem models, and direct remote sensing) and source datasets 

(national forest or soil inventory, flux tower, and remote-sensing sources, all of which were collected over a five-year period 

between 2003 and 2009) (Ciais and others, 2010; Luyssaert 

and others, 2010). For both forest and cropland ecosystems 

of the European Union’s 25 nations, different carbon pools, 

fluxes, and processes (such as harvesting, decomposition, and 

wildfire) were analyzed using the approach. Uncertainties 

were quantified (where feasible) and communicated following IPCC guidelines. Results from the different methods and 

datasets then were analyzed and compared, and average values 

were derived. Results showed that, overall, forest ecosystems 

in the European Union had an average net primary productivity (NPP) of 520±75 grams of carbon per square meter per 

year. The total forested area included in this estimate was 

1.32 × 106 square kilometers (km2) to 1.55 × 106 km2. The net 

biome productivity (NBP) was 75±20 grams of carbon per 

square meter per year. For cropland systems, the average NPP 

ranged between 490 to 846 grams of carbon per square meter 

per year, and the NBP (estimated by using flux-tower or soilinventory data) averaged a net loss of 23 grams of carbon per 

square meter per year. Cropland assessment also considered 
18 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

the N2O and CH4 fluxes, which resulted in a combined global 

warming potential (GWP) range of 42 to 47 grams carbon 

equivalents per square meter per year (Ciais and others, 2010; 

Luyssaert and others, 2010). As a result, the study showed that 

European Union forests and croplands ecosystems are a net 

carbon sink of 52 grams of carbon per square meter per year.

A recent study on China’s carbon balance found that 

China’s terrestrial ecosystems (forests, grasslands/shrublands, 

and croplands) also were a net carbon sink and averaged 

between 0.19 and 0.26 PgC/yr (Piao and others, 2009). The 

study evaluated aboveground vegetation and soil organic carbon 

by using a methodology in which forest inventory data were (1) 

analyzed and interpolated together with satellite imagery, (2) 

calibrated with an atmospheric CO2 inversion method, and (3) 

attributed with the use of five process-based models. Because 

soil data were very limited, the amount of carbon in the soil was 

estimated by using a regression approach. In addition to estimating the overall carbon-sequestration capacity, the study also 

reported per-ecosystem estimates of carbon-sequestration capacity and compared those estimates with estimates for the United 

States, by ecosystem. For example, the study found that aboveground biomass accumulation in forests, on a per-area basis, was 

57±26 grams of carbon per square meter per year for China; 

the same type of accumulation in forests in the United States is 

reported to be 52 to 71 grams of carbon per square meter per 

year. Woody encroachment (the invasion of woody plants into 

grasslands and trees into shrublands) in China was estimated at 

approximately 30 percent of total forest biomass accumulation. 

In the United States, woody encroachment represents about 30 

percent of the total terrestrial carbon sink (CCSP, 2007). Overall, the study noted that the total terrestrial carbon sink of China 

is comparable to that of continental Europe but is about half the 

size of the sink in the United States. One significant weakness, 

as noted in the report, was that the study did not account for 

land-use change, which is a significant factor for China (Piao 

and others, 2009). These studies indicate the global-scale impact 

from biological carbon sequestration and possible methods 

and techniques to follow to produce a successful assessment. 

Additionally, it is useful to compare carbon stock numbers from 

ecosystems in different parts of the world. Table 2.2 compares 

the forest stocks from studies in China and the European Union 

to forest stocks from the United States.

2.5.3. Existing National-Scale Inventories and

Assessments in the United States

As of 2006, the United States has been identified as the 

world’s second largest cumulative national source of fossil-fuelrelated CO2

 emissions with a source of 1.6 Pg of carbon (Marland and others, 2009). A considerable amount of work already 

has been done within the United States to account for the 

potential effect of ecological carbon sequestration in offsetting 

these emissions. The most comprehensive assessment related to 

the carbon cycle and budget is that of the first State of the Carbon Cycle Report (SOCCR) (CCSP, 2007), which is discussed 

above in section 2.2. Two other ongoing national-scale studies 

also contribute to the state of knowledge about the Nation’s carbon and GHG in ecosystems: the EPA’s annual U.S. Greenhouse 

Gas Inventory Report (EPA, 2009, 2010), and the U.S. Forest 

Service’s Forest and Rangeland Renewable Resources Planning 

Act (RPA) assessment (U.S. Department of Agriculture (USDA) 

Forest Service, 2000), which is conducted on a 10-year cycle.

In the EPA’s annual GHG inventory reports (EPA, 2009, 

2010), GHG emission estimates are reported from a range of 

sectors including energy, industry, waste, LULC, forestry, and 

agriculture. The primary data sources for the annual emission 

reports related to LULC, forestry, and agriculture are the Forest Inventory and Analysis (FIA) program of the U.S. Forest 

Service (USFS) and the National Resources Inventory (NRI) 

of the National Resource Conservation Service (both within 

the USDA); these databases are augmented by the National 

Land Cover Database (NLCD) produced by the Multi-Resolution Land Characteristics Consortium (MRLC) (Homer and 

others, 2004). Because of the source data used, the annual 

GHG inventory reports were compiled using a tier 3 and 

approach 2 methodology, according to the IPCC (2006) terminology. The estimates in the GHG inventory reports address 

land-use change, as well as carbon emissions from agricultural 

and forest fires on managed lands. Alaska and Federal lands in 

Hawaii are not included in the current reports.

The USDA also produces periodic GHG inventory reports, 

which are incorporated into the EPA’s annual inventories. The 

most recent agriculture and forestry greenhouse-gas inventory 

(USDA, 2008) spans the time period from 1990 to 2005, and it 

complements EPA’s Inventory of U.S. Greenhouse Gas Emissions and Sinks (EPA, 2007). The USDA report provides a more 

in-depth review of emissions from forestry and agricultural 

Table 2.2 Comparison of forest stocks and net forest-stock

changes from three continental-scale studies of temperate forest

zones.

[Abbreviations are as follows: M km2, millions of square kilometers; PgC, 

petagrams of carbon; TgC/yr, teragrams of carbon per year]

Study regions

Forest 

area

(M km2)

Stocks

(PgC)

Net forest 

stock 

change

(TgC/yr)

Uncertainty

(percent)

China 1.75 27.1a -92b 47b

European Union 1.46 23.1a -110c 27c

Conterminous United States 2.69 41.3a -162d 18d

a

Source: Goodale and others (2002). Forest and woodland pools include 

aboveground and belowground live vegetation for trees and understory 

vegetation, dead wood, litter, and soil organic matter below the litter layer to a 

depth of 1 meter.

b

Source: Piao and others (2009). Pools included in their stock change 

estimate are not clear.

c

Source: Luyssaert and others (2010). Pools included in their stock change 

estimate are not clear.

d

Source: U.S. Environmental Protection Agency (EPA) (2010). Pools 

included in the stock change estimate are aboveground and belowground 

biomass, litter, and soil organic carbon.
2. Review of Concepts and Literature 19

lands in the United States than what is presented in the EPA 

reports. The USDA relies on the Century (Parton and others, 

1993) and Daycent (Parton and others, 1998) ecosystem models 

to estimate direct and indirect GHG emissions for major croplands in the United States. These models simulate fluxes of carbon and nitrogen between the atmosphere, vegetation, and soil 

for croplands and grazing lands. Carbon emissions from forests 

are estimated using the FORCARB2 model. The NRI (USDA, 

2009) is an important data source for USDA’s greenhouse-gasemission inventory for the United States.

The annual FIA program conducted by the USFS provides 

the Nation with the most extensive and intensive in situ data 

about forest, species composition, timber volume, aboveground biomass, LULC classes, various ecosystem services (for 

example, water supply and wildlife habitat conditions), and 

other variables (W.B. Smith, 2002; Birdsey, 2004; W.B. Smith 

and others, 2009). The FIA visits between 15,000 and 60,000 

plots annually across the Nation. Soil and forest health data 

also are collected in the FIA database, but are not as extensive 

as some of the other data variables. Using the FIA database as 

the primary data source, the USFS conducts an assessment of 

forest and rangelands resources every 10 years, as required by 

the Forest and Rangeland Renewable Resources Planning Act of 

1974 (RPA) (U.S. Congress, 1974). The two most recent RPAmandated assessments (1990 and 2000) focused on present and 

future resource conditions, productivity (including forest carbon), 

sustainability, and economic demand of forest and rangeland 

ecosystems (Powell and others, 1993; Langner and Flather, 1994; 

Joyce, 1995; Dwyer and others, 2000; Joyce and Birdsey, 2000; 

W.B. Smith and others, 2001, 2009; Alig and others, 2003). 

The upcoming 2010 RPA-mandated assessment will continue 

to assess resources and impacts and pressure on resources from 

climate change, LULC change, and global supply and demand. 

Although reports are not yet available, several distinct characteristics of the 2010 RPA assessment have been provided below:

• Global effects on U.S. forest resources and trends will 

be considered by integrating forest-product models 

within a larger global forest model.

• Effects of climate change will be addressed in resource 

analyses, including projections of forest resources, 

wildlife habitat effects, and water supply.

• Analysis of forest resources will incorporate three future 

IPCC scenarios to address the climate change, LULC 

change, and uncertainty associated with the assessment.

2.5.4. Uncertainty Assessment and Reporting in

Existing National Assessments

An evaluation of uncertainty is a critical component of 

resource assessments and is necessary in order for an assessment to be translated into information that is useful for formulating policy. In addition, when complex models are used 

as a basis for evaluating policy or management alternatives, it 

is important that the models are consistent, accurate, verifiable, and transparent (Prisley and Mortimer, 2004). Therefore, 

model validation and verification approaches, such as sensitivity analysis and expert review, are recommended.

The IPCC (2006) recommended techniques to develop 

estimates of uncertainty for GHG reporting and guidance for 

incorporating these techniques These estimates may be developed from measured data, published information, modeling 

approaches, and expert judgment. One widely used modeling 

approach is Monte Carlo simulation. Here, variables in a model 

are assumed to have probability distributions rather than single 

deterministic values; models are run multiple times and draw 

parameters from distributions of possible values. For example, 

IPCC (2006) identified a range of popular distribution functions that might be used in a simulation. The outcomes from 

multiple runs of the model provide a distribution of results, 

thereby allowing the variability of results to be quantified. In an 

example of this type of evaluation, Heath and Smith (2000) conducted a Monte Carlo simulation of the national forest carbon 

budget and reported that carbon stocks had uncertainty levels of 

±10 percent, although fluxes had confidence intervals of 50 to 

100 percent. Because many biophysical parameters (such as soil 

characteristics and forest growth) are not independent, but rather 

are strongly related, it is important to account for covariability 

among parameters as well. J.E. Smith and Heath (2001) found 

that distributional forms for variables were less important than 

covariability between parameters.

Several recent assessments of different aspects of GHG 

flux serve to illustrate viable techniques for uncertainty assessment and provide the results from similar efforts. Table 2.3 

summarizes some of the carbon-sequestration quantities and 

uncertainties from recent studies.

Some general lessons can be learned from these and other 

assessments. Uncertainties expressed as relative terms (as a 

percentage of a mean estimate) tend to be higher for small 

pools and fluxes (as noted above). Fluxes tend to have higher 

relative uncertainties than stocks, and estimates for detailed 

subcategories (for example, specific pools or components) 

have higher relative uncertainties than broadly aggregated 

categories. Similarly, geographic aggregation serves to lower 

relative uncertainties. As an example, in the spatial aggregation of forest-inventory data, an estimate for a State has a 

lower uncertainty than the collective estimate for the survey 

units (regions that make up the State) (Reams and others, 

2005). For example, Kim and McCarl (2009) described the use 

of the reduction in the coefficient of variation (an uncertainty 

measure related to confidence interval) when conducting an 

assessment, because estimates were aggregated progressively 

from a county to a region to a State.

In the examples described above, uncertainty of carbon 

stocks and flux estimates is characterized by the use of confidence intervals. Spatially explicit modeling approaches also 

can characterize uncertainty in a spatial model or a map. In 

such cases, spatial patterns and relationships in uncertainty can 

be examined, which could lead to insights in model validation 

and improvement. For example, Blackard and others (2008) 

developed percent-error maps to graphically depict the spatial 

distributions of the variability of estimated biomass.
20 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

2.5.5. Economic Analysis and Its Use in Existing

National Assessments

Climate-change mitigation assessments focus on the 

future ability of a system to sequester carbon and reduce GHG 

emission. Climate-change mitigation policy analyses often 

estimate a sector of society’s capacity to abate climate change. 

Some approaches include the specific analyses of proposed 

legislation (for example, EPA, 2009; Larsen, 2009; Ross and 

others, 2009) or approximations of national levels of commitment to incentives, investments, regulatory reforms, and 

urgency for action (Creyts and others, 2007). From a policy 

perspective, carbon sequestration by ecosystems is one of 

many types of climate-change mitigation.

Economic analyses, including those that concern climatechange mitigation assessment, can be differentiated in terms 

of scope. The narrowest and generally more detailed analyses 

are those of a single sector or a single market. Conversely, the 

most comprehensive analyses attempt to capture economywide effects, but this often comes with a loss of detail. The 

following studies exemplify this compromise between scope 

and detail. An econometric model (Lubowski, 2006) operates 

at the unit of private parcel of land. It accounts for land-use 

decisions; for example, the incentive for land conversion to 

forest based on a carbon subsidy for growing trees. The model 

assumes that landowners choose to maximize the present value 

of expected net benefits from the land and base their expectations of future land-use profits on historical and current subsidy levels. Looking at the agricultural sector, Lewandrowski 

and others (2004) adapted the U.S. Mathematical Programming Regional Agricultural Sector Model to analyze the performance of alternative incentive designs (for example, cost 

shares) and payment levels (for example, carbon price) paid to 

farmers for adopting land uses and management practices that 

increase the storage of carbon in soil.

In order to capture sector details, yet retain economywide scope, the EPA applies a set of interactive tools to 

analyze climate-change mitigation strategies and the ensuing 

effects. These models include the Forest and Agriculture Sector Optimization Model with Greenhouse Gases (FASOM–

GHG; Murray and others, 2005), which is a partial equilibrium 

model that can evaluate joint economic and biophysical effects 

of GHG mitigation scenarios in the U.S. forestry and agricultural sectors. The Applied Dynamic Analysis of the Global 

Economy Model (ADAGE; Ross and others, 2009) is a computable general equilibrium model that can estimate policy 

effects while accounting for all interactions between businesses and consumers. Such economy-wide models generally 

seek to explain the behavior of supply, demand, and prices in a 

whole economy that has several or many interconnected markets. As an example, the FASOM–GHG model indicates that 

increasing the quantity of forest acreage dedicated to carbon 

sequestration has implications for current and future industrial 

forest production and prices, and for agricultural production 

and prices. Next, the ADAGE model indicates that these price 

and production changes generate feedback through the broader 

market. Finally, the FASOM–GHG model indicates that this 

feedback affects the forest and agricultural sectors.

The above-mentioned policy models capture (to varying 

degrees) the competing land uses for carbon sequestration, 

food, timber, and biofuel-energy-crop production. Although 

the policy models help decisionmakers understand the economic influences on resource capacity, they currently are not 

adequate for an understanding of the biophysical capacities 

of carbon sequestration in all disturbed ecosystems under a 

range of climate scenarios. Also, the policy models tend to be 

concerned with resources on private lands, although a publiclands policy model for forests recently has been developed 

that can be coupled with FASOM–GHG (Darius Adams, 

Oregon State University, written commun., 2009).

Table 2.3.  Selected estimates and uncertainties reported from recent national-scale assessments of carbon sequestration.

[SOCCR, first State of the Carbon Cycle Report (U.S. Climate Change Science Program (CCSP), 2007); Pg, petagram (1 billon metric tons); CO2, carbon dioxide]

Assessment and source Assessment components Geographic scope Quantity

(stock or flux)

Uncertainty

(95 percent

confidence level)

EPA (2010) Forests and harvested wood Conterminous United States 0.8 Pg CO2

 equivalent (annual sequestration) ±18 percent

Sundquist and others (2009), a 

rapid assessment of carbon 

sequestration

Soil organic carbon Conterminous United States 73.4 Pg carbon (storage) ±30 percent

Sundquist and others (2009), a 

rapid assessment of carbon 

sequestration

Forest biomass carbon Conterminous United States 17.0 Pg carbon (storage) ±20 percent

CCSP (2007), SOCCR Forest carbon United States 0.3 Pg carbon (annual sequestration) ±50 percent

CCSP (2007), SOCCR Wood products United States 0.06 Pg carbon (annual sequestration) ±50 percent

CCSP (2007), SOCCR Wetlands United States 0.02 Pg carbon (annual sequestration) ± >100 percent
21

3. Methodology for the National Assessment

regional scale. The first 10 years of the assessment 

(2001–2010) will provide current carbon storage and 

GHG production conditions, while also enabling calibration, validation, and estimation of uncertainties. The 

next 40 years will be used to project future potential 

changes in carbon storage, carbon sequestration, and 

GHG fluxes. Assessment over the 50 years will permit 

an examination of complex interactions among climate 

change, land cover, land management, and other controlling processes.

• Conduct the assessment at a spatial resolution sufficient to evaluate process-level connections between 

land use, land cover, management, climate, and 

site-specific properties such as soil type, hydrology, 

and topographic setting. Thus, the assessment will be 

spatially explicit for the purpose of capturing the variety of processes that exist in heterogeneous landscapes 

and will provide GHG flux and carbon-storage results 

that are meaningful when aggregated and compared 

over broad areas, such as a region or a State. The 

spatially explicit methods also will provide a greater 

understanding of geographic distributions of ecological 

carbon sequestration by pools and flux types.

• Investigate links between (1) potential land-use and 

land-cover change and land-management activities, 

and (2) future carbon storage and GHG fluxes in ecosystem and ancillary effects (for example, ecosystem 

services and costs). This analysis will permit decisionmakers and other stakeholders to evaluate the effects 

of mitigation strategies on future potential ecological 

capacities for carbon sequestration and GHG flux 

while also considering the potential unintended consequences within or between other ecosystems.

• Identify and collaborate with other existing national 

programs that evaluate carbon storage and GHG fluxes. 

Use common data, assumptions, and scenarios as much 

as possible for this assessment in an effort to minimize 

inconsistent or conflicting results. Portions of existing 

national programs, such as the U.S. Environmental 

Protection Agency (EPA) national emissions inventory 

and the U.S. Forest Service’s RPA-mandated assessment, overlap with parts of this assessment, thus creating an opportunity to enhance consistency between this 

assessment and other, more specific programs.

• Assimilate appropriate in situ and observational data to 

constrain methods and models and to evaluate uncertainty. Efforts will be made to include suitable data or 

models in order to further reduce uncertainty. In situ 

data (for example, the FLUXNET database; Running 

3.1. Introduction

This chapter introduces the scope of the methodology, a 

framework for assessing carbon and other GHG fluxes, and 

specific methods for the assessment. Detailed discussions 

about the framework and specific methods are provided in the 

appendixes.

3.1.1. Design Requirements and Goals for the

Assessment

This section describes the integrated suite of methods 

necessary to conduct an assessment of carbon stocks, carbonsequestration capacity, and fluxes of GHGs (CO2

, CH4, and 

N2O) in the Nation’s ecosystems, as mandated by the EISA 

(U.S. Congress, 2007) (see chap. 1). In assessing these GHGs, 

the EISA requires an examination of the effects of controlling 

processes (land-use and land-cover changes and ecosystem 

disturbances are two major controlling processes for GHG 

fluxes), and the potential for land-mitigation activities to 

increase carbon sequestration and reduce GHG emission over 

time. Carbon sequestration and GHG emissions in natural and 

managed ecosystems are the result of complex interactions 

among land use, land cover, management activities, ecosystem compositions and structure, natural and anthropogenic 

disturbances, and biogeochemical processes. Thus, to perform 

the assessment and meet the requirements of the EISA, an 

integrated multidisciplinary methodology is needed based on 

the following design considerations:

• Assess GHG fluxes and carbon-sequestration capacities comprehensively by considering all major pools, 

stocks, flux types, and controlling processes for all 

national lands and aquatic ecosystems of the 50 

States. Incorporate key processes or factors that affect 

carbon cycling and GHG emissions, such as land-use 

and land-cover changes, ecosystem disturbances (for 

example, fire), lateral fluxes, and management activities. The comprehensive nature of the assessment 

should lead to an improvement in the quality of the 

assessment and a characterization of the uncertainties 

in the assessment results (Loveland and DeFries, 2004; 

Running, 2008).

• Assess both present and future GHG fluxes and 

carbon-sequestration capacities and produce annual 

estimates for 50 years, from 2001 to 2050. An evaluation of future potential ecosystem carbon and GHG 

conditions will be based on a framework of reference 

and alternative enhanced land-cover and management scenarios that are calibrated and reported at the 
22 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

and others, 1999; Baldocchi and others, 2001) are commonly used for resource and GHG-flux assessments. 

To the extent possible, these and any other appropriate 

datasets will be incorporated, such as those containing 

biophysical data (for example, soils, climate) and data 

derived from remote-sensing methods (such as landcover change, wildland fires, or vegetation indices). 

Judgments as to the suitability and use of input data 

will be made on a case-by-case basis as the assessment 

proceeds.

3.1.2. Scope of Assessment

This section describes the scope of the assessment, 

including definitions of pools and flux types, assessment units, 

ecosystems, temporal scales, and spatial scales.

Scale of Assessment and Reporting: Assessment Units.—

Operational logistics require that the assessment be separated 

into several individual units to stratify data collection and 

modeling efforts, plan and prioritize the assessment, and report 

results. The assessment and reporting units will correspond to 

level II EPA ecoregions (Omernik, 1987, 2004). Ecoregions 

are large geographic regions that exhibit relatively unique 

and similar biotic and abiotic characteristics of ecosystems at 

the chosen scale (Omernik, 2004; Bailey, 2009). There are 23 

modified EPA level II ecoregions (fig. 3.1), and assessment 

results will be provided for each ecoregion. Components of 

the aquatic assessment will be stratified using watersheds that 

are aligned, to the extent possible, with the boundaries of the 

ecoregions.

The use of the EPA level II ecoregions as units of the 

assessment defines the scale for reporting the assessment 

results because it is within each of these ecoregions that 

the scenarios will be developed and the results will be analyzed (including validation and uncertainty analysis) and 

reported. Within each level II ecoregion, scenario development, modeling, and analysis of results will be conducted to 

characterize within-region variations in resource conditions. 

The assessment of within-region variations can be made possible by using EPA level III ecoregions nested within level 

II ecoregions. Below this scale, data products may still be 

useful because many data products are geographic information system (GIS) maps that are generated at a pixel size (map 

resolution) of 250 meters (m) using spatially explicit models. 

However, the map resolution does not designate a scale of the 

assessment. The scale of the methodology is set as assessment 

units. Users are encouraged to explore further validation and 

uncertainty measures in order to address scaling and other 

effects when using GIS map data.

Ecosystems.—The EISA requires the assessment of 

carbon storage, carbon sequestration, and GHG fluxes in and 

out of the Nation’s ecosystems. For the purpose of this methodology for the assessment, the ecosystem terms used in this 

report are defined as broad, regionally unique types of forests, 

grasslands/shrublands, croplands, wetlands, and aquatic ecosystems (table 3.1). The use of these broad, regionally unique 

biome types for ecosystem classification follows the intent 

(but not the actual terminology) of the U.S. Climate Change 

Science Program (now U.S. Global Change Research Program) “State of the Carbon Cycle Report,” Part III, Land and 

Water Systems (U.S. Climate Change Science Program, 2007). 

Because this assessment is conducted at the ecoregion scale, 

the ecosystems defined above will be assessed and analyzed 

on the basis of their unique regional characteristics.

Within each assessment unit, ecosystem boundaries will 

be determined by using the 2001 National Land Cover Dataset 

(NLCD, Homer and others, 2004) and other datasets such as 

the National Wetland Inventory (NWI, Cowardin and others, 

1979). These datasets, which have comparable definitions 

for various ecosystems, will be crosschecked and used in the 

assessment to help define the spatial boundaries of the ecosystems (table 3.1). Further discussion about spatial boundaries 

for the ecosystems may be found below in section 3.2, “Methodology Framework.”

Pools and Flux Types.—Production, consumption, and 

transitions of carbon among seven pools (table 3.2) will be 

assessed in order to account for carbon stocks and GHG 

fluxes. The methodology includes the five primary carbon 

pools and a harvested wood pool that are defined and recognized by the IPCC (Penman and others, 2003; IPCC, 2006) 

and are most commonly used for other national GHG inventories (for example, U.S. Climate Change Science Program, 

2007; U.S. Environmental Protection Agency, 2009). Unique 

to the methodology for this assessment is the inclusion of 

an aquatic pool, which accounts for carbon that is exported 

by rivers and streams and is used to evaluate the effects of 

Table 3.1.  Ecosystems, descriptions, and thematic components of source datasets.

[The use of the 2001 National Land Cover Dataset (NLCD) and other datasets helps to define spatial boundaries of the ecosystems at a regional scale. Other abbreviations are as follows: NHD, National Hydrography Dataset; NID, National Inventory of Dams; DLG, digital line graph; NWI, National Wetland Inventory]

Ecosystem Included land-cover type (and dataset source)

Forests Deciduous, evergreen, mixed, and disturbed forests (NLCD).

Grasslands/shrublands Shrub/scrub and grassland/herbaceous classes, as well as Alaska-specific areas mapped as sedge/herbaceous, lichens, and moss 

(NLCD).

Croplands Cultivated cropland, irrigated land, and pasture/hay classes (NLCD).

Wetlands Combinations of NLCD wetland classes and NWI wetland classes (for example, palustrine wetland).

Aquatic ecosystems Lakes, impoundments, estuaries, coastal waters, ponds, rivers, and other inland water bodies (combined use of NLCD, NHD, NID, 

DLG, and NWI).
3. Methodology for the National Assessment 23

Figure 3.1.  Map showing level II ecoregions of the U.S. Environmental Protection Agency. This ecoregion framework will be used

as the basis for the assessment units, and the ecoregions will be used as assessment units for purposes of planning, prioritization,

analysis, and reporting. Ecoregion boundaries within the United States are based on Omernik (1987); within Alaska, they are based on

Commission for Environmental Cooperation Working Group (1997). Hawaii is not shown, although it will be addressed in the assessment.

5.2

5.3

6.2

7.1

8.1

8.2

8.3

8.4

8.5

9.2

9.3

9.4

10.1

10.2

11.1

15.4

8.1

8.1

8.1

8.3

8.4

5.3 5.3

6.2

6.2

6.2

6.2

6.2 8.5 8.3

2.2

2.3

3.1

3.2

6.1

7.1

2.2
24 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table 3.2.  Broad-level definitions of relevant carbon pools to be included for carbon-assessment products.

[Definitions for all but harvested wood and lateral flux are adapted from Intergovernmental Panel on Climate Change (IPCC) (2006). Abbreviations are as follows: mm, millimeter; cm, centimeter; DOC, dissolved organic carbon; DIC, dissolved inorganic carbon; POC, particulate organic carbon]

Pool Description

Living biomass

Aboveground biomass All biomass of living vegetation, both woody and herbaceous, above the soil, including stem, stump, branches, 

bark, seeds, and foliage.

Belowground biomass All biomass of live roots. Fine roots of less than 2-mm diameter often are excluded because often they cannot 

be distinguished empirically from soil organic matter or litter.

Dead organic matter

Dead wood All nonliving woody biomass not contained in the litter, either standing, lying on the ground, or in the soil or 

sediments. Dead wood includes wood lying on the surface, dead roots, and stumps larger than or equal to 10 

cm in diameter.

Soils/sediments1

Litter and dead wood All nonliving biomass with a diameter less than the minimum diameter chosen for dead wood (10 cm), lying 

dead, in various states of decomposition above mineral or organic soil or sediments. Includes the litter layer 

as usually defined by soil typologies. Also includes live fine roots less than 2 mm in diameter as these cannot be distinguished empirically from the litter and dead wood.

Soil organic matter Organic carbon in mineral and organic soils and sediments to a specified depth chosen for the assessment and 

applied consistently through the time series. Includes live fine roots less than 2 mm in diameter where they 

cannot be distinguished from it empirically.

Harvested wood

Wood Harvested wood from forests.

Aquatic environment

Dissolved organic and inorganic carbon; 

particulate organic carbon

DOC, DIC, and POC that are exported by surface waters, and POC that is stored in inland and coastal waters.

1

Inorganic carbon stocks (such as calcium carbonate) in mineral soils and sediments will be estimated using the Soil Survey Geographic (SSURGO) database 

(http://soils.usda.gov/survey/geography/ssurgo/) (see section 3.3.1 of this report); however, given the uncertainty in modeling formative processes in relation to 

land use as well as tracking vertical and lateral leakage processes, future potential changes in inorganic carbon stocks will not be modeled.

or life-cycle GHG fluxes (for example, GHG emissions from 

hauling and processing of timber are not assessed for harvest 

rotation changes); and (4) GHG emissions from livestock.

Assessment Timeframe.—The assessment will be conducted in annual time steps from 2001 to 2050. This timeframe meets the legislative requirements for assessing annual 

present and future ecosystems capacities and addresses 

the following considerations. The 2001 starting year was 

selected because the National Land Cover Database (NCLD) 

2001 (which describes the general land cover of the Nation) 

and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) dataset (which describes 

vegetated ecosystem composition, structure, succession state, 

and wildland fire disturbances) were both available during 

that year. The two datasets will provide the starting point for 

modeling future land changes, disturbances, and GHG fluxes 

into and out of ecosystems. The data for years between 2001 

and 2010 will offer opportunities to assess current ecosystem 

carbon stocks, sequestration rates, and GHG fluxes and will 

be used for the model design and the calibration and validation of results. The selection of 2050 as the endpoint was 

influenced by two considerations: (1) Uncertainties associated with scenarios, data, and methods will increase with 

time. Limiting the assessment to 2050 will help constrain 

such uncertainties. (2) The EISA does not specifically define 

a time horizon for the assessment, but an assessment over a 

50-year time frame should provide adequate information for 

policy and management applications.

terrestrial management on carbon storage and GHG production in inland and coastal waters. The relations between the 

carbon pools and the fluxes of carbon and nitrogen to be 

assessed are illustrated in figure 3.2.

The net ecosystem exchange (NEE) is the net flux or 

uptake of carbon (CO2 and CH4) or nitrogen (N2O) between 

the ecosystem and the atmosphere. The primary processes 

in determining NEE are (1) net primary productivity (NPP, 

which is calculated by subtracting autotrophic respiration 

from photosynthesis), (2) fluxes from heterotrophic respiration 

(HR), (3) fire, and (4) the production of biomass commodities (for example, wood products). The net ecosystem carbon 

balance (NECB) accounts for all physical, biological, and 

anthropogenic sources and sinks (for example, photosynthesis 

and the lateral movement of dissolved organic carbon (DOC), 

inorganic carbon (DIC), and particulate organic carbon (POC)) 

(fig. 3.2).

Mitigation Activities.—Changes in carbon stocks and 

fluxes in the seven pools are affected by mitigation activities 

of two types: land-use change and land-management change. 

Mitigation activities within this scope directly increase carbon 

sequestration in soils, vegetation, wood products, and sediments. The following items are not included when considering 

mitigation activities: (1) indirect effects from the generation 

of energy from biomass; (2) technological actions that can aid 

in ecologically sequestering carbon, but that are not explicitly 

land-use or land-management changes (for example, growing 

algae in industrial fluxes); (3) activities to reduce downstream 
3. Methodology for the National Assessment 25

Figure 3.2.  Diagram showing fluxes of carbon (as carbon

dioxide and methane) and nitrogen (as nitrous oxide) and

exchanges among the primary carbon pools (yellow boxes).

Abbreviations are as follows: CH4, methane; CO2, carbon dioxide;

DIC, dissolved inorganic carbon; DOC, dissolved organic carbon;

3.1.3. Methodology Constraints

Comprehensive national-resource assessments are usually 

limited by many constraints, including the scope of the assessment, data availability, technological developments, established scientific concepts and methods, available project time, 

and resources. These limitations apply to this assessment as 

well. Given that this methodology has been developed to fulfill 

the EISA’s legislative requirements, the limitations inherent in 

this process need to be discussed:

• The scope of the assessment will be limited to the 

requirements set forth by section 712 of the EISA, as 

detailed in chapter 1. The scope also will be limited by 

the availability of resources.

• Consistency at the national level is needed, such that 

quality, inherent variability, and uncertainty of results 

are comparable among regions and contain minimum biases when compared with known reference 

data (such as national inventory programs). Scenario 

construction and methods for assessment also must be 

transparent in order to maintain consistency in interpretation.

• Established and simplified methods and models that 

incorporate datasets of national coverage will be used 

in the assessment. The assessment needs simplified 

dependencies between technical components to permit 

effective coupling of methods and models. Areas 

where established methods or models are limiting will 

be prioritized for research treatment by others.

• Availability of in situ, mapped, and remotely sensed 

data is uneven for the national assessment. The GHG 

flux data are especially uneven. The methodology is 

NEE, carbon (CO2 + CH4)

Product flux NPP Total fire flux Total HR flux

Change in

products

Change in

harvested wood

Change in lateral flux DOC, DIC, POC export

NECB

Fire HR Fire

NEE, nitrogen (N2

O)

Change in

living biomass

Change in

organic matter Change in

soils

POC, particulate organic carbon; HR, heterotrophic respiration;

NECB, net ecosystem carbon exchange; NEE, net ecosystem

exchange; N2O, nitrous oxide; NPP, net primary productivity.

For more information about these terms, see Chapin and others

(2006).
26 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

designed to circumvent, where necessary, issues of 

poor data availability or quality by using surrogate data 

and appropriate available techniques for calculation. 

Ultimately, the quality and availability of input data will 

affect the quality and uncertainty of the assessment.

3.1.4. Collaborations for the Assessment

Many Federal agencies, nongovernmental organizations, 

and international entities already have developed unique 

inventory, assessment, and research programs in support of 

their policy and science needs for understanding the carbon 

cycle and processes and for mitigating GHG fluxes. Where 

appropriate, these ongoing programs will play important 

roles for this assessment, including (1) active collaborations 

for conducting the assessment, and (2) review, feedback, and 

use of results of the assessment. As discussed below in the 

methodology descriptions, a successful implementation of the 

methodology will depend on the extent to which this assessment is developed collaboratively. Important areas of collaboration for the assessment are described below:

• Data sharing—In situ reference data (such as national 

inventory programs, flux-tower data, informal networks, and location- or ecosystem-specific GHG-flux 

data) are critical in order to constrain methods and 

models for estimating current and future potential carbon and GHG fluxes. Data about land management and 

their associated costs are required in order to construct 

potential mitigation actions and to analyze tradeoffs 

between the management of carbon and other ecosystem services.

• Review of methods and results—A rigorous scientific 

review process will set the foundation for the assessment. Throughout the methodology development process and the assessment, the science community (such 

as the North America Carbon Program) and agency 

research programs will be engaged both for the review 

of this method and the assessment, and for the opportunity to compare methods and models.

• Participation—The quality and usefulness of the 

assessment will benefit greatly from participation by 

individual investigators, agency programs, and stakeholders. For various methods and data needs, agencies 

and organizations have roles to play, including that of 

providing assessment components, models, and data. 

In addition, stakeholders, such as land managers in 

various regions, may find that participation in regional 

consultation processes for constructing mitigation 

scenarios will benefit their organization’s missions. 

Encouraging broad participation by stakeholders in 

the use of assessment results is critical to the ultimate 

value of this assessment effort.

• Enhancing consistency—Agencies or organizations 

that play active roles in resource assessment will be 

actively consulted throughout development of the 

methodology and the assessment. Consistency between 

this assessment and other national programs will be 

enhanced by (1) using the same high-quality in situ 

data, and (2) using comparable scenarios or assumptions.

3.1.5. Methodology Organization

As discussed above, the EISA requires the national 

assessment to consider carbon-sequestration capacities and 

GHG fluxes for both terrestrial and aquatic ecosystems. The 

assessment also will address major controlling processes that 

affect present and future carbon storage and GHG fluxes in 

order to support a range of policy and management applications. The organization of this methodology document 

addresses each of these issues in turn:

• An approach for assessing present carbon storage in 

ecosystems and GHG fluxes that is consistent with 

both existing accounting guidelines and the subsequent 

methods presented.—Years 2001 through 2010 are 

considered in order to determine the current carbon 

storage and rates of flux and carbon sequestration.

• An approach for assessing future carbon storage in 

ecosystems and GHG fluxes.—The scenario framework for years 2011 through 2050 will link future 

potential climate and socioeconomic projections with 

the design of future potential mitigation activities (for 

example, potential land-use and land-management 

changes to enhance carbon-sequestration capacity).

• A set of methods that supports the assessment of both 

present and future potential conditions.—The methods 

are (1) mapping and modeling of current and future 

land use and land cover, (2) characterizing and modeling present and future ecosystem disturbances, (3) estimating and modeling carbon storage and GHG fluxes 

from terrestrial and aquatic ecosystems, (4) syntheses 

of mitigation scenarios (including ecosystem services 

and cost), and (5) validation, uncertainty analysis, and 

monitoring.

3.2. Methodology Framework

This methodology is designed for a comprehensive 

assessment of current and future potential carbon stock, 

carbon sequestration, and GHG fluxes. Assessment results 

will be produced for the years 2001 through 2050. The results 

for years 2001 through 2010, which are based on past and 

current input data, will be used to estimate the current carbon 

and GHG conditions. Future potential carbon stocks, carbon 

sequestration, and GHG fluxes will be modeled and estimated 

for 2001 through 2050 for a range of future mitigation scenarios aligned within three IPCC scenarios (discussed later in 

this section).
3. Methodology for the National Assessment 27

Table 3.3.  Time periods, land use and land cover, ecosystem disturbances, and land-management activities used for assessments of

current and future potential carbon stocks, carbon sequestration, and greenhouse-gas fluxes.

[Abbreviations are as follows: LULC, land use and land cover; GHG, greenhouse gas]

Time period of

assessment

LULC, ecosystem disturbances Land management Major input data and uses

Current assessment (2001–

2010)

Current LULC, changes in 

LULC, and ecosystem 

disturbances

Current land managementIn situ data, soil data, current climate data, and other input data 

together with current LULC and disturbances data are used to 

create empirical and process models to estimate current carbon 

stocks, carbon sequestration, and GHG fluxes.

Future potential assessment 

(2011–2050)

Projected future LULC and 

disturbances for each 

future scenario

Projected land management for each 

future scenario

Input data (above) combined with projections of climate, LULC, 

and disturbances to create parameters for simulation models and 

estimate future potential carbon stocks, carbon sequestration, and 

GHG fluxes. 

The framework incorporates recommendations of IPCC’s 

good practice guidelines for the assessment of carbon and 

GHG for land use, agriculture, and forestry (Penman and 

others, 2003). The methods to be used for the assessment are 

based on extensive observational data, as well as on tested 

empirical or process-based models. A common set of input 

data and controlling processes will be analyzed and used in the 

assessment of both current and future potential carbon stocks, 

carbon storage, and GHG fluxes. Table 3.3 specifies the common characteristics and the differences between assessments 

of current and future potential carbon sequestration and GHG 

fluxes. In this section, methods and models supporting the 

assessment are introduced; specific technical information is 

discussed in more detail in the appendixes.

3.2.1. Framework for Assessing Current Carbon

Stocks, Carbon Sequestration, and GreenhouseGas Fluxes

Relation to Existing Inventory and Accounting Methods.—This methodology must be designed to maintain consistency with other existing inventory and assessment guidelines 

and methods for assessing current carbon stocks, carbon 

sequestration, and GHG fluxes. This concern involves both 

U.S. and international efforts developed under the IPCC guidance for land-use change and forestry (Penman and others, 

2003; IPCC, 2006). The primary national-scale efforts in the 

United States include (1) the State of the Carbon Cycle Report 

(U.S. Climate Change Science Program, 2007); (2) the EPA 

Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. 

Environmental Protection Agency, 2009); (3) U.S. Department 

of Agriculture (USDA) Forest Service (2007), and (4) a report 

on the economics of sequestering carbon in the U.S. agricultural sector (Lewandrowski and others, 2004). These national 

assessments follow the three-tiered approach recommended by 

IPCC (2006), as reviewed in chapter 2.

The primary methods and models used in this methodology for regional-scale assessment are a tier 3 effort 

in the IPCC (2006) hierarchy. Where appropriate data are 

unavailable, tier 2 approaches involving simple algorithms 

will be incorporated into the methodology. In addition, by 

assessing similarly defined ecosystems and pools and by using 

the same national-level datasets for land cover, vegetation, 

soils, and ecosystem disturbances that are maintained by the 

USDA, DOI, and other agencies, the methodology should 

yield consistent results at the national level. All of these 

approaches will maintain a relationship and consistency with 

other national efforts.

Carbon stocks, carbon sequestration, and GHG fluxes 

assessed for the period from 2001 through 2010 will be compared with those of other existing inventories. If conflicting 

results are found, efforts will be made to consult with appropriate agency programs, identify the source of discrepancies, 

capture and correct any errors, and notify the climate-change 

community about the differences.

Ecosystems and Current Land Use and Land Cover.—The 

ecosystem terms that have been chosen for this methodology 

and for the assessment of ecosystems are provided in section 

3.1.2 and are described in table 3.1. To better represent carbon 

stocks, carbon storage, and GHG fluxes associated with LULC 

change, the national assessment will use a spatially explicit 

representation of the defined ecosystems and the thematic 

components or classes within each ecosystem, as listed in 

table 3.1. The NLCD 2001 land-cover classes can be easily 

aggregated and keyed to the ecosystems described in table 

3.1; they also contain enough thematic classes that they can be 

aggregated to the six LULC categories used in IPCC (2006) 

for reporting purposes. The LULC classifications initially will 

be based on NLCD 2001 classes and will be modified to meet 

the needs of the project (table 3.4). Specifically, the following 

modifications will be made:

Forests.—The U.S. Forest Service Forest Inventory and 

Analysis (FIA) program defines forested land as “any plot 

that is 10 percent stocked, except woodland, can be forest if it’s 5 percent stocked, with a minimum area of 1 acre 

[0.4 hectare] and width of 120 ft [37 m]” (Smith and others, 

2009). The Multi-Resolution Land Characteristics Consortium (MRLC), which sponsors the development of the NLCD 

datasets, defines a forest class in the NLCD in terms of pixels 
28 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table 3.4. Thematic land-cover classes used to describe current conditions.

[The same classes will be used to parameterize modeling for future land-cover changes. Classes are modified from NLCD 2001 (National Land Cover Database; 

Homer and others, 2004). Abbreviations are as follows: m, meters; cm, centimeters]

Land-cover class Description

Open water All areas of open water.

Perennial ice/snow All areas characterized by a perennial cover of ice and (or) snow

Developed Includes NLCD 2001 developed classes with impervious surfaces accounting for greater than 20 percent of total cover within a 

pixel.

Barren land Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, unconsolidated 

shoreline, and other accumulations of earthen material.

Deciduous forest Areas dominated by trees generally more than 5 m tall, and that represent greater than 20 percent of total vegetative cover. More 

than 75 percent of the tree species shed foliage simultaneously in response to seasonal change.

Evergreen forest Areas dominated by trees generally more than 5 m tall, and that represent greater than 20 percent of total vegetative cover. More 

than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage.

Mixed forest Areas dominated by trees generally more than 5 m tall, and that represent greater than 20 percent of total vegetative cover. Neither deciduous nor evergreen species are more than 75 percent of total tree cover.

Disturbed forest Forest (deciduous, mixed, or evergreen) disturbed by logging. Forest areas that are thinned out, but not cleared, are not included 

in this category, but instead are tracked through management subattributes.

Dwarf scrub Areas only in Alaska, dominated by shrubs less than 20 cm tall and where shrub canopy typically represents more than 20 percent 

of total vegetation. Dwarf scrub is often associated with grasses, sedges, herbs, and nonvascular vegetation.

Shrub/scrub Areas dominated by shrubs less than 5 m tall with shrub canopy typically greater than 20 percent of total vegetation. This class 

includes true shrubs, young trees in an early successional stage, or trees stunted from environmental conditions.

Grassland/herbaceous Areas dominated by grammanoid or herbaceous vegetation, generally greater than 80 percent of total vegetation. These areas are 

not subject to intensive management, such as tilling, but can be used for grazing.

Sedge/herbaceous Areas only in Alaska, dominated by sedges and forbs, generally greater than 80 percent of total vegetation. This sedge/herbaceous type can occur with other grasses or other grasslike plants and includes sedge tundra and sedge tussock tundra.

Lichens Areas only in Alaska, dominated by fruticose or foliose lichens, generally greater than 80 percent of total vegetation.

Moss Areas only in Alaska, dominated by mosses, generally greater than 80 percent of total vegetation.

Pasture/hay Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation.

Cultivated crops Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton; and perennial woody 

crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also 

includes all land that is actively tilled.

Wetland Includes all wetland classes currently mapped by NLCD.

Mining Strip mines, gravel pits, and other surface materials or features resulting from mining extraction methods.

Irrigated land Includes all irrigated cropland.

with tree cover of greater than 20 percent. Differences in 

the definitions of “forest” can result in differences in forest 

biomass, especially in regions where woodland habitats (such 

as pinion-juniper and black spruce) are common. For forested 

areas, a solution by the MRLC, of which the Forest Service is 

a member, will be followed that uses remote-sensing-derived, 

continuous-forest-canopy estimates to interactively adjust spatial boundaries to match FIA in situ data (Huang and others, 

2001; Hansen and others, 2003)

For forest cover in urban areas, the NLCD 2001 forestcanopy dataset that characterizes the percentage of forest 

canopy will be intersected with classes of developed lands in 

the NLCD 2001 land-cover dataset to provide regional (EPA 

level II ecoregion) proportional distributions and averages of 

forest-canopy percentage in urban areas. The resulting urban 

forest cover will allow the biogeochemical model to quantify 

regional impacts of urban forestry on carbon stocks, carbon 

sequestration, and GHG fluxes.

Wetlands and Aquatic Ecosystems.—Wetlands include 

a variety of systems such as prairie potholes, coastal-plain 

woody swamps, boreal peat lands, and salt marshes (for 

example, palustrine habitats consistent with the U.S. Fish 

and Wildlife Service (FWS) definitions by Cowardin and 

others (1979)). Wetlands will be assessed using the same 

methods as for terrestrial ecosystems. Aquatic ecosystems 

in this assessment include coastal waters, estuaries, streams, 

rivers, lakes, impoundments, and other inland water bodies. 

Aquatic ecosystems will be assessed using models developed for this purpose. The boundaries for wetlands will be 

mapped by using NWI data that are supplemented with data 

about two NLCD wetland classes: woody wetland (class 90) 

and emergent herbaceous wetlands (class 95) (Homer and 

others, 2004). Aquatic ecosystems will be mapped by using 

a combination of datasets, including the National Wetland 

Inventory (NWI), the National Water Information System 

(NWIS) of the U.S. Geological Survey (USGS), the National 
3. Methodology for the National Assessment 29

Hydrography Dataset (NHD), the National Inventory of Dams 

(NID), digital line graphs (DLGs), and the NLCD. Open-water 

bodies such as rivers, lakes, and other aquatic systems will be 

similarly mapped. The initial land-cover map will be based 

on the revised NLCD 2001, as discussed above (table 3.4). To 

represent land-cover conditions for 2001 through 2010, the 

LANDFIRE ecosystem-disturbance data (Rollins, 2009) will 

be used along with a 2006 update to the 2001 NLCD by Xian 

and others (2009) to quantify contemporary LULC change. 

These data will inform a land-change model (section 3.3.2, 

“Land-Use and Land-Cover Change”) that will be used to 

produce spatially explicit LULC maps for the period of 2001 

through 2050. Reference datasets (such as LANDFIRE disturbance data) from the period of 2001 through 2010 will be used 

to calibrate and validate results of the LULC-change model for 

the same period of time.

Major current ecosystem disturbances caused by both 

natural and anthropogenic events (for example, wildland fires, 

forest cuts, insect and disease outbreaks, and storm damages) 

for 2001 through 2010 will be summarized by assessment 

units. Technical details for generating present LULC and 

ecosystem disturbances are provided in section 3.3 and in 

appendixes B and C.

3.2.2. Framework for Assessing Future Potential

Carbon Stocks, Carbon Sequestration, and

Greenhouse-Gas Fluxes

Scenario Framework.—Annual carbon stocks, carbon 

sequestration, and GHG fluxes for ecosystems of the United 

States will be analyzed within the context of a range of 

LULC and land-management projections (scenarios). The 

results will generate a rich set of spatial and temporal data 

products that will be used for assessing the effectiveness of 

mitigation activities to sequester carbon. Although scenarios 

will be constructed for assessment units, national-level 

consultation will be needed in order to establish construction guidelines for them that will ensure consistency across 

regional assessments. The use of a scenario framework will 

allow scientists to employ the methodology to provide a 

range of data products and bound overall uncertainties (fig. 

3.3) of carbon stock capacity, carbon-sequestration capacity, 

and GHG fluxes.

Scenarios are neither predictions nor forecasts of the 

future; rather, they are ways of showing how the future may 

unfold under a set of assumptions. Scenarios are a useful tool 

for exploring the uncertainty associated with projecting potential resources in the future. Raskin (2005, p. 134) described 

scenarios as “drawing from the human imagination as well as 

science to provide an account of the flow of events leading to 

a vision of the future...using both words and numbers.” He 

continued by stating (p. 134) that, “the great strength of scenario research lies in its blending of the richness, texture, and 

imaginative qualities of narrative with the structure, replicability, and rigor offered by modeling.”

Scenarios combining both qualitative and quantitative 

elements have been used in several global assessments, including the Global Scenario Group (GSG) (Raskin and others, 

1998), the World Water Commission scenarios (Alcamo and 

others, 2000; Cosgrove and Rijsberman, 2000), the IPCC 

“Special Report on Emission Scenarios” (SRES; Nakicenovic and others, 2000), the United Nations Environment 

Programme’s (UNEP) third Global Environmental Outlook 

(UNEP, 2002), and the Millennium Ecosystem Assessment (Carpenter and others, eds., 2005). Within the global 

change community, an increased emphasis has been placed 

on integrated assessment scenarios that promote scaling from 

regional to global scales. One such effort was that of the 

UNEP third Global Environmental Outlook (UNEP, 2002) 

where, through a collaborative process, four GSG scenarios 

were refined with input from SRES and with an emphasis on 

giving the global scenarios a “regional texture” (Carpenter and 

others, eds., 2005). The use of similar scenarios as a methodology framework is proposed for the national assessment. The 

selection of the scenario framework should meet the following 

criteria:

• Scenarios for the assessment should be based on socioeconomic conditions, such as trends in demographics, 

changes, and patterns of economic growth, and rates 

of energy consumption.—Socioeconomic scenarios, 

as opposed to climate scenarios, provide the means to 

Figure 3.3.  Graph showing hypothetical emission ranges for

each of three scenarios defined by Nakicenovic and others

(2000). The scenario framework will allow assessment of potential

carbon sequestration capacities and associated uncertainties

within each scenario and mitigation potential of GHG fluxes

across the scenarios. Emissions are in petagrams of carbon

dioxide equivalent per year (PgCO2-eq/yr).

180

160

140

120

100

80

60

40

20

2000 2020 2040 2060 2080 2100

0

Year

Greenhouse-gas emissions, 

in petagrams of carbon dioxide equivalent per year
30 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

explore the interaction of LULC change and the primary factors that drive that change, which ultimately 

affect the flux of GHG from ecosystems.

• The scenarios should consist of both qualitative and 

quantitative components.—(1) Qualitative components 

include “storylines,” which describe elements of alternative futures. Storylines, or narratives, are scalable and 

can be interpreted to result in certain conditions based on 

regional landscape characteristics. Qualitative components also are highly useful for communicating scenario 

characteristics to a nonscientific audience, which is an 

important component of this research. (2) Quantitative components include modeling and projections of 

LULC and land management based on scenario assumptions about the interactions among the driving forces of 

change. Within the SRES process, there are 40 quantified 

scenarios based on 4 scenario families produced by 6 

modeling teams (Nakicenovic and others, 2000).

• Links to associated projections of climate conditions.—These data are available from both the IPCC 

third assessment (IPCC, 2001) and fourth assessment 

(IPCC, 2007) reports and are based on the projections 

of emissions and changes in LULC associated with 

SRES scenario assumptions.

• Use of only current mitigation policies in order to provide a reference for the evaluation of potential mitigation activities

• Scalability.—Data must be scalable from global down 

to regional scales. The chosen scenario framework 

must also be portable across regions (that is, the methods can be applied to different regions with consistent 

input parameters).

• Review by the scientific and policymaking communities.—For example, the SRES scenarios were produced 

in an open process with broad scientific participation. 

Where possible, scenarios will incorporate robust projections of LULC drivers (for example, population projections) that are accepted by the scientific community.

• Transparency and easy communication to stakeholders 

and decisionmakers.—Scenarios should avoid depending on “black box” model outputs that cannot be modified or reproduced.

A set of regional LULC scenarios based on the IPCC 

SRES scenario structure (Nakicenovic and others, 2000) will 

satisfy the criteria listed above. A new set of scenarios, however, is being developed for the IPCC’s fifth assessment (AR5) 

(Moss and others, 2010). The new process, representative concentration pathways (RCP), will begin with radiative forcing 

targets (measured in watts/m2), and will allow modeling teams 

to explore various ways to achieve the forcing goal, including 

the imposition of various climate-change mitigation policies. 

Unlike SRES, which begins with a fixed set of socioeconomic 

conditions, the RCP process will provide a framework to allow 

modeling teams the ability to explore different and perhaps 

diverging LULC conditions to reach the same radiative forcing 

target. The first set of RCP scenarios already has been developed and analyzed (Wise and others, 2009); however, reference scenarios (those devoid of any climate-mitigation action) 

are still under development. Although the RCP approach is 

not the ideal framework for the reference scenarios for this 

methodology (see criteria above), they may provide insight for 

understanding the role of specific mitigation activities. As the 

RCP scenarios become more widely available, their use within 

the methodology framework will be considered, specifically to 

explore comprehensive climate-mitigation scenarios.

The IPCC SRES storylines (Nakicenovic and others, 

2000) will serve as the primary basis of the framework for the 

assessment. Reference and mitigation (that is, enhanced for 

carbon sequestration) scenarios will be constructed for each of 

three major storylines found in the SRES. The storylines themselves are broad in scope, focus on global-level driving forces, 

and will need to be downscaled to the national and regional 

level for the United States. For this assessment, three IPCC 

SRES storylines from the IPCC Fourth Assessment (IPCC, 

2007) will be used to guide the development of the specific 

LULC and management scenarios: A1B, A2, and B1 (Nakicenovic and others, 2000). The choice of three SRES storylines 

is dictated by the availability of the downscaled regionalclimate data described in section 3.3 of this report. To date, the 

General Circulation Model (GCM) data for the B2 scenario that 

meet climate downscaling methods adopted for the methodology are not available. Should B2 climate data become available, or should an alternate source of climate data be found, 

then the B2 storyline may be included in the analysis.

For each assessment unit, regional LULC scenarios will 

be constructed based on experiences and results of ongoing 

LULC studies and regional expert knowledge. The SRES narratives and storylines and the existing knowledge of regional 

LULC changes provide a basis for constructing both the reference and mitigation scenarios, which will allow for opportunities to explore a wide range of regional LULC scenarios while 

remaining consistent with overall SRES assumptions.

Within each SRES storyline (Nakicenovic and others, 

2000), there will be an opportunity to assess carbon stocks, 

carbon sequestration, and GHG fluxes under both a “reference” and an “enhanced” LULC and land-management 

scenarios. The framework will be designed to first identify a 

“reference” scenario of carbon stocks, carbon sequestration 

and GHG fluxes under the varied socioeconomic and climate 

conditions represented within the SRES storylines. Because 

the SRES storylines are inherently devoid of specific policies for sequestering carbon or mitigating GHGs, the use of 

reference assessments will provide a baseline against which 

effectiveness of various mitigation activities can be estimated. 

The “enhanced” scenarios will allow for both independent and 

joint evaluation of the LULC and land-management activities 

to enhance carbon sequestration and reduce GHG emissions 

within the assumptions of the IPCC SRES storylines.

The following sections introduce methods of constructing mitigation activities and scenarios that will be prioritized 
3. Methodology for the National Assessment 31

for the national assessment, evaluated within each assessment 

unit, and combined into the LULC and land-management scenarios. A summary of the reference and alternative scenarios 

also is provided. Further details are available in appendix A.

Mitigation Activities.—In the context of this assessment, mitigation activities refer to an ecological means of 

sequestering carbon or mitigating GHG gases (see table 3.5). 

The assessment includes two types of mitigation activities: 

land-management change (such as increased use of prescribed 

burning in the interior Western United States) and LULC 

change (for example, afforestation of agricultural land). See 

table 3.5 for candidate mitigation activities and chapter 2 for 

a more detailed description of current knowledge about these 

activities. Candidate mitigation activities will be presented to 

agencies that have land-management responsibilities and to 

other stakeholders for review and prioritization. The following 

criteria will be important for selecting mitigation activities to 

enhance carbon-sequestration capacity:

• Sequestration capacity per hectare of mitigationactivity change

• Hectares of suitable lands for mitigation-activity 

change to identify applicable upper bounds on mitigation-activity change

• Time-effectiveness of sequestration to address how 

quickly the mitigation activity provides climate-change 

mitigation and duration of the effect of the mitigation 

activity on the sequestration rate (for example, five 

decades for management activities affecting forest 

and soil pools, one decade for cropland management 

changes, and two decades for LULC conversion)

• Permanence of sequestration to address differences in 

how much carbon remains sequestered over time for 

each mitigation activity

Table 3.5.  Candidate mitigation activities to be considered for the assessment.

[Abbreviations are as follows: CH4, methane; N2O, nitrous oxide; GHG, greenhouse gases]

Ecosystem Strategy Potential land-management change Potential land-use or land-cover 

change

Forests Carbon sequestration Lengthen timber harvest-regeneration rotation

Increase forest management intensity (increase in forest 

density, forest fertilization, thinning, reduction in fire 

fuel to reduce severe fires, management of insects and 

diseases)

Reduce logging frequency.

Convert lands to forest (afforestation).

Preserve forest, avoid deforestation.

Mitigation of net GHG 

emissions

Reduce logging impacts Reduce deforestation.

Offsite wood product 

sequestration

Improve mill waste recovery

Increase wood-product production

Extend wood-product life

Increase paper and wood recycling

Croplands Soil carbon sequestration Reduce crop tillage

Change crop mix to high-residue crops and crop rotations

Increase winter cover crops

Increase efficiency of crop fertilization

Reduce summer fallow

Restore agricultural land

Use biochar

Convert to grassland and perennial crops. 

CH4 and N2O emission

mitigation

Improve crop tillage

Improve crop mix

Increase efficiency of crop fertilization

Expand irrigation

Reduce rice acreage.

Grasslands/

shrublands 

Soil carbon sequestration Modify grazing management practices

Improve efficiency of fertilizer

Allow natural succession towards native shrub and forest

Restore degraded rangelands

Mitigation of net GHG 

emissions

Reduce severe rangeland fires Reduce conversion of grassland to energyproducing crops.

Wetlands Carbon sequestration Unknown Restore wetlands.

Mitigation of net GHG 

emissions

Unknown Preserve wetlands.

Aquatic ecosystemsMitigation of net GHG 

emissions

Reduce nutrient export from urban and agricultural lands

Alter withdrawal from deep reservoirs
32 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

In addition to those criteria, the interests of consulting 

agencies and needs of policy makers will be considered in the 

prioritization of mitigation activities for the national assessment, 

but the final selection of activities will be subject to data availability, acceptance of assumptions, and (or) model capabilities.

Mitigation activities will be evaluated for their potential 

(the possible capacity in terms of amount and longevity) to 

sequester carbon and reduce GHG emissions in each assessment unit and to identify the effectiveness of these activities. 

For example, the conversion of grasslands to woodlands may 

not increase carbon-sequestration capacity in all regions. 

These evaluations will be conducted with an awareness of 

the tradeoffs within a management activity. For example, 

although reducing grazing may enhance carbon sequestration 

on rangelands, it also increases wildland fuel availability and 

flammability. The evaluation of mitigation activities for each 

assessment unit will be accomplished by (1) reviewing and 

synthesizing regional studies of carbon-sequestration mitigation activities, (2) estimating areas of land that are ecologically suitable and economically available for the mitigation 

activity, (3) consulting with regional experts (for example, 

participants in the “greenhouse gas reduction through agricultural carbon enhancement” network (GRACEnet) or the U.S. 

Fish and Wildlife Service’s (FWS) Landscape Conservation 

Cooperatives (LCCs)) for likely amounts and intensities of 

mitigation activities, and (4) developing a spreadsheet tool to 

quantitatively evaluate and summarize attributes of candidate 

mitigation activities. An evaluation of mitigation activities 

to enhance carbon sequestration (both the intensities and the 

amounts) will enable a more informed construction of alternative mitigation scenarios, which is pertinent to the limited 

number of scenario simulations that will be run. Refer to 

appendix A for more information on the methodologies used to 

evaluate mitigation activities.

Summary of LULC and Land-Management Scenarios.—

A scenario is a combination of future potential LULC and 

land-management changes (“mitigation activities”) associated 

with vetted climate and socioeconomic conditions. Scenarios 

will be used to help identify possible GHG mitigation activities under various assumptions. Figure 3.4 illustrates how one 

of the scenarios (A1B in Nakicenovic and others, 2000) will 

be used in the assessment framework and will be used to help 

illustrate the sections below. Appendix A provides the details 

of scenario development methods.

Reference Land Use, Land Cover, and Land Management 

(R).—The “reference land use, land cover, and land management” (R) scenario will be designed to provide reference 

LULC and land-management scenarios that are consistent with 

SRES storylines (Nakicenovic and others, 2000). Because of 

the use of SRES storylines in the methodology, the R scenario 

will be devoid of any direct carbon-sequestration or GHG mitigation policies or actions and thus serves as a baseline against 

which to compare alternative ecological carbon-sequestration 

or GHG mitigation activities.

The first step toward creating a set of regional LULC and 

land-management scenarios will be to develop a set of national 

narratives that are consistent with the SRES storylines and 

the related three scenarios (Nakicenovic and others, 2000). 

This step will be done primarily through a national workshop. Using existing LULC projections associated with SRES 

storylines and other supporting data, expert opinions will be 

solicited in order to describe plausible pathways of LULC and 

land management based on the underlying assumptions of the 

SRES storylines. The “downscaled” national storylines will be 

viewed as geographically meaningful sets of SRES storylines with characteristics that are specific to LULC and land 

management in the United States. The primary outcome of 

the national workshop will be expanded LULC narratives and 

national-scale LULC trajectories. Nested within the national 

narratives will be assumptions about the regional variability of 

LULC and land management, where available. For example, 

a national-scale narrative might include assumptions on forest 

use while also highlighting certain regions as likely places for 

changes in forestry activities.

Regional reference scenarios will be based on the 

national scenarios discussed above. The foundations of 

regional scenarios will be LULC and land-management 

Figure 3.4.  Diagram showing the assessment framework for

each IPCC SRES storyline (Nakicenovic and others, 2000), using

storyline A1B as an example. R represents the reference scenario

with conventional (existing) land-management activities and

will be used to generate spatially explicit land-use and landcover forecasts for analyzing reference carbons stocks, carbon

sequestration, and greenhouse-gas (GHG) fluxes. The enhanced

land management (M), enhanced land use and land cover

(L), or enhanced land use, land cover, and land management

(ML) scenarios will be used to represent increases in carbon

sequestration and (or) to mitigate GHG emissions. Future potential

land-use and land-cover projections associated with the M,

L, and ML scenarios will be produced in order to analyze how

land-cover or land-management changes affect carbon stocks,

carbon sequestration, and GHG fluxes. Finally, a potential natural

vegetation (PNV) is introduced as a separate scenario. LULC, land

use and land cover.

Conventional Enhanced

Enhanced

PotentialNaturalVegetation

Reference

Carbon sequestration

Carbon sequestration

(R) References SRES

storyline, with LULC

and management

consistent with basic

SRES assumptions

(M) Reference LULC

remains constant, but

land management is

optimized for carbon

sequestration 

(ML) LULC and land

management both

optimized for carbon

sequestration 

(PNV) LULC returns to potential natural

vegetation form and structure. Assumption

of no anthropogenic alteration of landscape 

(L) LULC optimized for

carbon sequestration,

with conventional land

management 

Management

LULC

IPCC

SRES

A1B
3. Methodology for the National Assessment 33

histories that will be developed through review of existing 

historical data sources and will include the comprehensive 

analysis of recent historical LULC change reported by the 

U.S. Geological Survey (USGS) Land Cover Trends project 

(Loveland and others, 2002). Consultations with regional 

experts will be used to project recent LULC into the future 

based on our understanding of the interactions among the drivers of LULC change. Regional experts will link both the SRES 

storylines and the national storylines with the biophysical and 

socioeconomic characteristics of the assessment units (ecoregions) in order to provide a range of LULC scenarios that will 

be consistent with recent historical observations.

The scenario construction process may incorporate exogenous projections of particular LULC types or management 

that are not covered by the SRES storylines. Examples may 

be projections of population from the U.S. Census Bureau or 

LULC projections from the U.S. Forest Service’s RPA-mandated assessment. Maintaining fidelity to SRES storylines will 

be desirable, however, and the regional expert consultation 

process will determine the degree to which these exogenous 

projections will be used, if at all.

Reference Land Use and Land Cover With Enhanced Land 

Management (M).—The “reference land use and land cover 

with enhanced land management” (M) scenario will examine 

the potential for land-management activities to increase carbon 

sequestration and mitigate GHG emissions with land use unaltered from the reference conditions (that is, land use and land 

cover are unchanged). The M scenario will be constructed to 

enhance carbon sequestration by enhancing land-management 

activities (such as increased timber rotation age) from the reference point of the R scenario. Like the R scenario, the mitigation 

scenarios will be influenced by the national storyline in order to 

encourage regional experts to reflect beyond the current range of 

thinking and to create a diverse set of M scenarios. The national 

storyline will inspire different emphases on mitigation activities, different amounts of change, and different concerns for the 

temporal aspects of carbon sequestration, including timeliness 

and permanence of sequestration. The national consultation 

process will be relied upon to provide guidelines for mitigation 

scenarios, including setting realistic bounds on increases in mitigation activities. The aforementioned evaluations of mitigation 

activities at the ecoregion level are used to regionalize the M 

assessment. The result is an altered land-management prescription for the M assessment.

The modeling of the M scenario will be conducted using 

the same 2011 through 2050 LULC forecast data from the R 

scenario, but it will use the altered regional land-management 

prescription. The enhanced land-management prescription will 

be assessed using the ecosystem-level carbon and GHG modeling methods (discussed below in this chapter) to analyze the 

impacts of land-management change on carbon sequestration 

and mitigation of other GHG emissions.

Enhanced Land Use and Land Cover With Reference 

Land Management (L).—The “enhanced land use and land 

cover with reference land management” (L) scenario will 

examine the potential for land-use change to increase carbon 

sequestration and to mitigate GHG emissions but with landmanagement activities unchanged from the R scenario. The 

approach outlined in the section on the M scenario (above) 

will be used to inform construction of the L scenario. The 

SRES storylines will influence a range of LULC changes. For 

example, the emphasis of storyline A1B on managed solutions may be associated with evergreen plantations, whereas 

the emphasis of storyline B1 on more sustainable forestry 

may favor restoration of natural, unevenly aged forests. In 

addition, national programs for ecological carbon sequestration will be elicited from consulting agencies and applied to 

varying degrees (for example, high, medium, and low levels 

of national commitment) across the storylines. Maintaining 

the integrity of the SRES storylines (Nakicenovic and others, 2000) will become more difficult when land use is being 

altered because of the competing uses of the land for food, 

fiber, and energy; and the potential effects of large regional 

changes in agricultural or forest land use on potential displacement and leakage of carbon credits. Again, “reasonable” 

fluctuations in land-use proportions will maintain a general 

fidelity to the SRES storylines. The result will be an altered 

land-use prescription for the L assessment.

The modeling of the L scenario will be conducted by 

using the altered land-use prescription for a given SRES 

storyline and by using the LULC model to produce a spatially 

explicit LULC projection for 2001 through 2050. Without 

changing the land-management assumptions from the R 

scenario, an ecosystem biogeochemical method (discussed 

in section 3.3.4) will be used to analyze the impacts of the 

land-use changes in the L scenario on carbon sequestration and 

mitigation of other GHG emissions

Enhanced Land Use and Land Cover With Enhanced 

Land Management (ML).—The “enhanced land use and land 

cover with enhanced land management” (ML) scenario will 

examine the potential for both land-use, land-cover, and landmanagement changes to increase carbon sequestration and 

mitigate GHG emissions. Of all of the reference and enhanced 

assessment groups, the ML scenario will be designed to 

maximize carbon sequestration and GHG emissions mitigation, while staying within the context of the SRES storylines 

(Nakicenovic and others, 2000). Land-management activities 

related to enhanced carbon sequestration have been shown to 

be effective in significantly increasing landscape carbon stocks 

(Hudiburg and others, 2009). Methodologies for providing 

land-management and LULC prescriptions will be consistent 

with the M and L scenarios described above. The land-management activities of the M scenario will be distributed across 

the LULC data from the L scenario and used as input to the 

biogeochemical model. The resulting scenario will be used to 

analyze carbon sequestration and GHG-flux mitigation from 

land-use, land-cover, and land-management change.

Potential Natural Vegetation (PNV).—Potential natural 

vegetation is defined as the native vegetation that would grow on 

any given parcel of land given a set of environmental (climate 

and site) conditions, but without land-use or land-management 

practices. The potential natural vegetation (PNV) scenario will 
34 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

be designed to analyze each assessment unit’s carbon-sequestration potential in a scenario where the land is allowed to revert to 

the biophysical potential vegetation type, without anthropogenic 

alteration or management of the landscape, but with an approximation of present disturbance regimes. The use of PNV in the 

methodology will provide a basis for comparison with the other 

four scenarios (R, M, L, and ML). Given the persistence of urban 

and developed lands (that is, hardscapes, or parcels of land that 

rarely revert to another land use once they are developed), the 

current urban and developed lands will be kept constant, but all 

other land-cover types will be allowed to revert to their potential vegetation types (native forests, shrubs, and grasslands). 

Therefore, the use of PNV as a scenario will have no LULC or 

land-management implications. Instead, it will be an exercise in 

modeling potential natural vegetation conservation under overall 

influences of the biophysical environment as maintained by 

natural disturbances (Sundquist and others, 2009). No new PNV 

maps will be developed by this assessment. Instead, the assessment will examine existing sources of PNV datasets such as the 

biophysical settings map produced by LANDFIRE (Rollins, 

2009) and the global potential vegetation maps reported by Daly 

and others (2000) and Gonzalez and others (2010), and make 

necessary modifications or updates, if necessary.

3.2.3. Methodology Framework Summary

A comprehensive set of data products (table 3.6) will 

be produced for both the current and future potential assessment. The results of the assessment will provide opportunities 

to examine the implications of the EISA requirements (U.S. 

Congress, 2007) as described below:

• For a given assessment unit, ecosystem capacities for 

carbon stocks, carbon sequestration, and GHG fluxes 

will be reported by pools and flux types. For estimating 

the current carbon sequestration and GHG conditions, 

results will be averaged values for the years 2001 

through 2010. Results will be constrained by available 

in situ data about carbon stocks, carbon sequestrations, 

and GHG fluxes, supplemented by LULC change data, 

ecosystem disturbance data, and other biophysical data.

• For estimating the future potential carbon sequestration and GHG conditions from 2011 through 2050, 

regionally specific ecosystem capacities for increased 

carbon sequestration and GHG-flux mitigation will be 

estimated within each IPCC SRES scenario. The M, 

L, and ML scenarios (fig. 3.4) will provide information on the effects of specific land-use, land-cover, and 

land-management mitigation actions within a given 

assessment unit. This information also will inform the 

analyses of the most economically feasible regional 

mitigation actions.

• Regionally specific ecosystem capacities for increased 

carbon sequestration and GHG-flux mitigation will be 

estimated in order to compare results across multiple 

SRES storylines (Nakicenovic and others, 2000). The 

variability in results across the SRES storylines will 

frame the uncertainties in carbon sequestration and GHGflux mitigation that result from uncertain future demographic, socioeconomic, energy, and climate projections.

• The regionally significant effects on ecosystem services 

that will result from the potential increased carbon 

sequestration and mitigation activities will be identified. 

An analysis of such ancillary effects on ecosystem services can be conducted across different SRES storylines 

and climate projections, across different mitigation 

scenarios within an SRES storyline, across temporal 

projections, and across geographic landscapes.

3.3. Introduction to Assessment Methods

The scenarios and storylines described in the previous 

section outline an overall framework and describe data products 

that will be generated by the national assessment. Integrated 

assessment methods or models are required to assess current 

and future potential ecosystem conditions for carbon stocks, 

carbon sequestration, and GHG fluxes and produce the desired 

information products. In designing and developing the methods, 

Table 3.6.  Summary of the assessment framework for linking

climate-change mitigation scenarios to changes in ecosystem

capacities for carbon stocks and carbon sequestration and to

changes in greenhouse-gas fluxes.

[Covers current (2001–2010) and future potential (2011–2050) assessments. 

Abbreviations are as follows: SRES, Special Report on Emission Scenarios 

of the Intergovernmental Panel on Climate Change (Nakicenovich and others 

(2000); NA, not applicable; R, “Reference land use, land cover, and land 

management” scenario; M, “Reference land use and land cover with enhanced 

land management” scenario; L, “Enhanced land use and land cover with reference land management” scenario; ML, “Enhanced land use and land cover 

with enhanced land management” scenario]

SRES 

storyline Land use and land cover Landmanagement

Scenario 

code

Current

NA Current Current Current

Future potential

A1B Reference Reference A1B–R

Enhanced A1B–M

Enhanced Reference A1B–L

Enhanced A1B–ML

Potential natural vegetation NA A1B–PNV

A2 Reference Reference A2–R

Enhanced A2–M

Enhanced Reference A2–L

Enhanced A2–ML

B1 Reference Reference B1–R

Enhanced B1–M

Enhanced Reference B2–L

Enhanced B–ML

Potential natural vegetation NA B1–PNV
3. Methodology for the National Assessment 35

choices were made based on technical merits, data availability, 

and the consensus of the underlying science for components 

of the assessment. The methods introduced here represent a 

hybrid methodology involving in situ and remote-sensing data, 

process-based ecosystem models, empirical models, statistical 

methods, and expert knowledge. The overall approach follows 

guidelines by the IPCC for agriculture, forestry, and other land 

uses in designing a combined tier 2 and tier 3 and approach 3 

methodology (IPCC, 2006) to investigate LULC transitions, 

ecosystem disturbances, and changes in carbon stocks and GHG 

fluxes. Figure 3.5 illustrates relations and data flows among the 

major components of the methodology. The methods are briefly 

introduced in this section with detailed descriptions and discussions provided in appendixes A through I.

3.3.1. Technical Plan for Key National Datasets

Data Needs and Sources.—Various types of data will need 

to be assembled in order to complete a national assessment. The 

methodology will rely on existing data sets, promote collaborations to improve data availability, and use remotely sensed data 

to monitor key geospatial processes. Reference and observation 

data (in situ data, mapped biophysical data, remote-sensing data, 

and management- and policy-oriented data) will be used as the 

initial input data for (1) assessing present carbon stocks, carbon 

sequestration, and GHG fluxes, and (2) parameterizing and constraining the methods and models that will be used for estimating future potential conditions. A critical deciding factor for the 

quality of the assessment will be the availability and quality of 

reference input data. Input data used for the national assessment 

will include the following:

• In situ, mapped, or remotely sensed (for example, light 

detection and ranging, or LIDAR) data about carbon 

stocks, rates of sequestration, or GHG fluxes in different pools and flux types

• In situ and remotely sensed data or studies that document the effects of controlling processes, such as ecosystem disturbances and land-use and land-cover change

• Up-to-date mapped biophysical data that has regional 

to national coverage, including current weather and 

climate, future climate projections, soil, permafrost, 

topography, land cover, vegetation types and structure, 

wetlands, and ecosystem disturbances (for example, 

areas affected by insect outbreaks, storms, and fires)

• In situ, mapped, or remotely sensed data that document 

temporally relevant ecological relations, such as information about the intra- and inter-annual variations for 

carbon stocks and GHG fluxes that can be measured in 

different pools and for different flux types, or information about the behavior of vegetation growth along 

different climate trajectories

A general summary of the assessment’s input data needs, 

data sources and time span, essential attributes, and uses in the 

assessment methodology are provided in table 3.7.

Data Gaps and Plans.—The needs of the national assessment will not be met completely by existing data sources. 

Although some data development efforts may be necessary, 

the assessment largely will rely on existing suitable datasets 

for practical reasons. Other data gaps may be filled partially 

with surrogate data (for example, remote-sensing-based 

biomass data for ground biomass measurements); however, 

uncertainty caused by data gaps will be reported as part of the 

overall uncertainty assessment. Major data gaps are summarized below:

• A well-distributed, national spatiotemporal dataset of 

fluxes specifically for CH4 and N2O does not exist to 

support the national assessment and help constrain 

estimates of GHG modeling. Although the assessment 

will rely on all available flux data from sources such 

as FLUXNET, GRACEnet, and other available sources 

for parameterization and calibration purposes, the 

primary method for producing GHG-flux estimates for 

different ecosystems will rely on ecosystem simulation models (discussed in section 3.3.4). Uncertainties 

related to GHG-flux estimates will be provided at the 

regional scale. Data from other researchers or programs will be needed in order to increase the availability of GHG-flux data.

• The availability of many types of data, including data 

on GHG fluxes, is limited for Alaska; however, mapping efforts by the National Land Cover Database and 

the LANDIFRE database have improved the availability of data on land cover and vegetation as well 

as wildland fires. Forest, soil, and stream inventory 

data are undersampled and digital maps of vegetation structure, such as biomass, do not exist. Although 

this methodology will rely on surrogate data (for land 

cover, vegetation types, and fire data) and on limited 

data for soils and permafrost, the strategy for collecting 

data on vegetation, permafrost, and biomass will rely 

primarily on (1) increasing the spatial extent of LIDAR 

data and (2) conducting strategic sampling campaigns 

in areas where carbon-sequestration capacity and (or) 

Figure 3.5.  Diagram showing relations among major methods

that are designed to run scenarios and produce assessment

deliverables. These methods (statistical models, analyses,

process models, or simple algorithms) are introduced in sections

below. Abbreviations are as follows: GIS, geographic information

system; GHG, greenhouse gas.

Scenario

development

Reports by

assessment

units

Ecosystem

disturbances

Aquatic

carbon and

GHG fluxes

Terrestrial

carbon and

GHG fluxes

Analysis of

validation and

uncertainty

Analysis of

management

activities and

ramifications

Land use and

land cover

changes

GIS maps and

data products
36 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table 3.7.  Data needs, sources, variables, spatial and temporal resolution, and uses in the assessment methodology.

[Datasets and sources represent only the major data needs. There are other data needs that are met by miscellaneous individual datasets that are not listed here. 

For explanations of acronyms, please see “Abbreviations, Acronyms, and Chemical Symbols” in the front of this report. Abbreviations are as follows: CH4, 

methane; CO2, carbon dioxide; hr, hour; km, kilometer; m, meter; N2O, nitrous oxide; yr, year]

Datasets and sources Variables Spatial and temporal 

attributes

Use in the assessment 

methodology

PRISM climate grid data, PRISM Climate 

Group, OSU

Precipitation, maximum and minimum 

temperature

4 km, monthly Disturbance, aquatic methods.

NCEP, NOAA Precipitation, temperature, humidity, wind 

speed, wind direction

32 km, 3 hr Disturbance.

Downscaled IPCC GCM data: BCC–BCM2.0, 

CSIRO–Mk3.0, CSIRO–Mk3.5, INM–

CM3.0, MIROC3.2.

Precipitation, maximum and minimum 

temperature

1 km, monthly Disturbance, LULCC, terrestrial BGC methods.

EDNA and NED topographic data, USGS Elevation, slope, aspect, stream networks 

and flows

30 m, static LULCC, disturbance, aquatic 

and terrestrial BGC methods.

Soil databases: STATSGO2, SSURGO, USDA 

NRCS

Soil carbon and texture, crop suitability 250 m/1 km/polygon, static Aquatic and terrestrial BGC 

methods.

Conservation datasets by USDA NRCS: CRP, 

WRP, CEAP, EQIP, CSP, WHIP, GRP, 

FRLPP, and HFRP

Acreage enrolled, locations, cost-sharing 

amounts, length of contract, crop or 

vegetation types

Parcel records, polygons, 1 

to 30 yr

LULCC, terrestrial BGC 

methods, scenario development.

Litter and soil carbon turnover: literature compilations at national scaleLitter and soil carbon pool sizes (capacities) and their turnover times250 m/1 km/polygon, 

dynamic

BGC methods; scenario development.

Agriculture residue management data, USDA 

NRCS

Tillage type and residue level information County, biennial LULCC, terrestrial BGC 

methods.

National Resource Inventory, USDA NRCS Land use, agricultural and rangeland 

production

County, 5-yr cycle Terrestrial BGC methods.

Areas of crop types, production, and management, USDA NASSTillage, crop rotation, crop harvest, grazing, manure applicationCounty and state statistics, 

annual

LULCC, terrestrial BGC 

methods.

PAD-US (CBI) Protected areas and status, level of protection, land ownershipCounty, decades LULCC, disturbance.

FIA, U.S. Forest Service Forest type, age class, biomass, and litter 

by pools, management information, 

disturbance information

Inventory plots, 5-yr cycle LULCC, disturbance, terrestrial BGC methods, 

scenario development.

Urban Forestry Program, U.S. Forest Service Urban forest coverage, production, 

disturbance

Subset of FIA plots LULCC, terrestrial BGC 

methods.

Eddy-covariance flux-tower measurements from 

FLUXNET 

CO2 flux of various pools and ecosystems Point, about 160 sites in the 

United States, hourly

Terrestrial BGC methods.

GRACEnet, USDA ARS Chamber-based CO2

, CH4, N2O flux 

measurements of agricultural soils, 

land-management scenarios

Point, 31 sites in lower 48 

States

Terrestrial BGC methods, 

scenario development.

Carbon Cycle Sampling Network, NOAA Atmosphere measurements of CO2

, CH4, 

N2O

Point data Terrestrial and aquatic BGC 

methods.

National Atmospheric Deposition Program Wet deposition of nitrate Point, about 250 sites in the 

United States, weekly

Aquatic and terrestrial GHG 

methods.

National Water Information System, and National Water-Quality Assessment Program, 

USGS

Calculated constituent loads, POC, DIC, 

and DOC concentrations, other waterquality information

Variable Aquatic and terrestrial BGC 

methods

Digital Coast dataset, Coastal Service Center, 

NOAA

Primary production in coastal waters and 

bathymetric details

About 130 estuaries, 30 m 

and 3-arc-sound

Aquatic methods.

National Estuarine Eutrophication Assessment, 

NOAA

Nitrogen load, other chemical and physical parametersAbout 130 estuaries, periodic 

(1992 to 1997) 

Aquatic methods.

Watershed Boundary Dataset, USDA NRCS Watershed boundaries, HUC 1:24,000 scale, static Aquatic methods.

National Pollutant Discharge Elimination System, U.S. Environmental Protection AgencyPermitted waste discharges 800,000+ point sources, 

annual

Aquatic methods.

Storm data by National Hurricane Center, 

NOAA

Hurricane- and tornado-track archives Line segments Disturbance.
3. Methodology for the National Assessment 37

Datasets and sources Variables Spatial and temporal 

attributes

Use in the assessment 

methodology

Monitoring Trends in Burn Severity, U.S. Forest 

Service, USGS

Fire perimeters and severity classes

(1984–present)

30 m, by fire event Disturbance, terrestrial BGC 

methods.

National Fire Plan Operations and Reporting 

System, DOI

Fuel treatment types and locations Point, yearly Disturbance.

Forest Health Monitoring Program’s Aerial 

Surveys, U.S. Forest Service

Insects and diseases, other disturbances Variably sized, polygons, 

yearly

Disturbance.

LANDFIRE, U.S. Forest Service and USGS Surface and canopy fuel classes, 

vegetation types, succession classes, 

transition pathways

30 m, updated annually Disturbance.

Vegetation change tracker data products, USGS, 

U.S. Forest Service, NASA, UMD

Land-use and land-cover changes, and 

major ecosystem disturbances

30 m, annual products from 

1985 to present

LULCC, disturbances, terrestrial and aquatic BGC 

methods.

National biomass and carbon dataset 2000, 

Woods Hole Research Center

Mapped aboveground biomass for conterminous United States, using 2000 space 

shuttle radar mission data

30 m, static Terrestrial BGC methods.

MODIS, NASA NDVI, FPAR, fire scars and fire perimeters1 km, 8 and 16 days Terrestrial BGC methods.

NLCD, USGS Present and future LULC classes 60 m, 250 m, national maps LULCC, disturbance, terrestrial BGC.

NWI, FWS Geospatial wetlands digital data GIS polygons LULCC.

Distance to roads, National Overview Road 

Metrics, USGS

Distance to roads 60 m LULCC.

U.S. Census Bureau, USDC Population County, decades LULCC, disturbance.

National Irrigation Water Quality Program, DOI 

bureaus

National irrigation maps 1 km, 2001 and 2006 LULCC, terrestrial BGC 

methods.

Agricultural land-use costs, USDA ERS ARMS 

Program

Financial data about land use and commodity productionTabular data Tradeoff analysis of management activities.

Table 3.7.  Data needs, sources, variables, spatial and temporal resolution, and uses in the assessment methodology.—Continued

GHG emissions are deemed most likely to change (see 

chapter 4 for a discussion of science needs).

• Assessment of carbon sequestration and GHG emissions in aquatic ecosystems will be based on existing data on streamflow, water chemistry, suspended 

sediment, coastal production, and sedimentation rates, 

which are stored in national databases such as the 

National Water Information System (NWIS) and the 

Reservoir Sedimentation Database (RESSED). Existing GHG data related to aquatic ecosystems also will 

be used, but the data are scattered (and are not in a central repository) and availability is limited. Additional 

data collection will be needed to improve the accuracy 

of the assessment; the following areas specifically will 

need to be addressed: (1) gaps in the spatial coverage 

of surface-water and groundwater chemistry (carbon 

and nutrient species); (2) a lack of fine-resolution temporal data for stream chemistry; and (3) poor spatial 

and temporal coverage for coastal, estuarine, lake, and 

impoundment sedimentation rates, sediment carbon 

concentrations, and GHG fluxes. It is recommended 

that additional chemical data be collected at sites along 

transects from mountains into coastal waters and at a 

temporal resolution sufficient to accurately estimate 

carbon, nitrogen, and suspended-sediment fluxes. The 

transport of carbon, nitrogen, and suspended sediments during storms can be particularly important, and 

estimating this transport will require a combination of 

manual sampling, automated sampling, and the use of 

in situ sensors. It is also recommended that measurements of sedimentation rate, organic carbon in sediment, and GHG fluxes in these aquatic ecosystems be 

substantially expanded, particularly in small impoundments, estuaries, and nearshore environments, where 

carbon cycling and burial can be quite rapid.

• Although the Soil Survey Geographic (SSURGO) database (http://soils.usda.gov/survey/geography/ssurgo/) 

contains the most spatially detailed soil information 

available for the Nation (1:24,000- or 1:12,000-scale), 

it is not a periodic soil inventory and does not offer 

information on changes in soil carbon stocks. In addition, the SSURGO data are complete for approximately 

86 percent of the land area of the conterminous United 

States and 7 percent of Alaska. Complete coverage for 

the conterminous United States and Alaska is available from the U.S. General Soil Map (STATSGO2, 
38 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

formerly STATSGO, http://soils.usda.gov/survey/

geography/statsgo/), but it has a reduced spatial detail 

(1:250,000-scale for the conterminous United States 

and 1:1,000,000-scale for Alaska). The scale for Alaska 

is a concern because the region is undergoing warming trends (Chapin and others, 2008). Warming trends 

have lead to an increase in wildfires in Alaska that have 

the potential to release more CO2 than all of the terrestrial net primary productivity (NPP) in the United 

States (Wiedinmyer and Neff, 2007; Chapin and others, 

2008). Efforts are underway by the USDA NRCS to fill 

gaps in the SSURGO data, but workshops and studies 

that are targeted to address soil carbon dynamics are 

needed. An enhanced soil survey effort focused on soil 

carbon dynamics will make use of multiple data sources 

(for example, databases with contributions by multiple 

investigators, satellite images, radar, LIDAR, digital elevation models, published soil maps and pedon datasets, 

targeted soil sampling, and opportunistic soil sampling 

in conjunction with trace-gas measurements) in order to 

improve the hydrologic, thermal, and landscape assessments of soil carbon and its potential for GHG release. 

The USGS has initiated the North American Carbon 

Network (Johnson and Harden, 2009), a database for 

Alaska with contributions from multiple investigators, 

and has begun to identify areas that are undersampled 

(Bliss and Maursetter, 2010). Soil carbon dynamics 

will be the topic for one or two targeted workshops 

with other relevant agencies and science programs. The 

results of the workshops can then be used to identify 

large and (or) vulnerable soil carbon stocks. A targeted 

soil-sampling campaign that links soil carbon stocks, 

soil carbon pools, and trace-gas characterization most 

likely will be recommended.

• Future potential climate scenarios associated with 

IPCC SRES storylines (Nakicenovic and others, 2000) 

are needed. Forecasts of future climate conditions have 

been produced using GCMs for each scenario and 

are available from the IPCC’s and various other Web 

sites; however, downscaling the GCM datasets for use 

with the individual IPCC scenarios is a necessary step 

that will provide the spatial resolution required for the 

national assessment. Downscaled datasets exist (for 

example, Maurer and others (2007) and Conservation 

International (2009)). These datasets will be examined 

to ensure that they meet the data criteria of fine temporal resolution (monthly) for simulations, appropriate 

parameters (maximum and minimum temperature and 

precipitation), and fine spatial resolution (4 km) for the 

three IPCC SRES emission scenarios of interest (A1B, 

A2, and B1). If it is necessary to downscale and process the GCM data for use by the assessment, “change 

factors” (percent changes in precipitation and temperature between baseline and projected conditions; Arnell 

and Reynard, 1996; Pilling and Jones, 1999; Hay and 

Sem, 2000; Prudhomme and others, 2002; Arnell, 

2003a,b; Eckhardt and Ulbrich, 2003; Diaz-Nieto and 

Wilby, 2004) will be computed by comparing the output from IPCC’s Scenario 20C3M simulations for the 

20th century (IPCC, 2007), which uses GCM baseline 

conditions, with output for the three IPCC SRES emission scenarios (Nakicenovic and others, 2000). Five 

GCMs (see models in table 3.8) from the World Climate Research Programme’s Coupled Model Intercomparison Project (phase 3) (CMIP3) multimodel dataset 

archive are listed as examples; these GCMs meet the 

criteria listed above for use by the assessment.

3.3.2. Land-Use and Land-Cover Change

Current Land Use and Land Cover.—The examination of 

carbon sequestration and emissions will require an analysis of 

changes in both land use (for example, a conversion of agricultural land to urban land) and land cover (for example, harvesting 

trees on forested land). To analyze both requires techniques that 

will use spatial and nonspatial data. The LULC method (this 

section) and the ecosystem disturbance method (section 3.3.3) 

will provide spatially explicit representations of both land-use 

and land-cover components, and will require spatially explicit 

input data. Given the need for a spatially explicit assessment for 

all areas of the Nation, remote-sensing data will be extensively 

used, from which we will determine land cover and will indirectly infer some land-use information. Data will be included 

on the broad land-use and land-cover categories that are readily 

Table 3.8.  Examples of general circulation models that are suitable for use by the national assessment.

[Output from these GCM models may be downscaled for this assessment. From Nakicenovic and others (2000). GCM, general circulation model]

GCM dataset name and abbreviation Responsible agency

Bergen Climate Model 2.0 (BCC–BCM2.0) Bjerknes Centre for Climate Research, University of Bergen, Norway.

Commonwealth Scientific and Industrial Research Organisation Mark 3 

(CSIRO–Mk3.0)

Commonwealth Scientific and Industrial Research Organisation, Australia.

Commonwealth Scientific and Industrial Research Organisation Mark 3 

(CSIRO–Mk3.5)

Commonwealth Scientific and Industrial Research Organisation, Australia.

Institute for Numerical Mathematics CM3 (INM–CM3.0) Institute for Numerical Mathematics, Russian Academy of Science, Russia.

Model for Interdisciplinary Research on Climate 3.2 (MIROC3.2) National Institute for Environmental Studies, Japan.
3. Methodology for the National Assessment 39

available from remotely sensed data (land-use classes, such as 

“agriculture” and “development,” or land-cover classes, such 

as “deciduous forest” and “barren”). Specific land-management 

activities associated with land use that aren’t available as spatially explicit data will be handled through a statistical scaling 

approach that is related to the biogeochemical modeling framework. The integration of the LULC, ecosystem-disturbance, 

and biogeochemical models will provide the ability to examine 

the effects of both land-use and land-cover change on carbon 

sequestration and GHG emissions.

This section describes the procedures used to model 

spatially explicit LULC change. The NLCD 2001 database 

(Homer and others, 2004) will be used as the primary spatialdata source for land-cover information for the “current” time 

frame (2001), the year in which model simulations begin. The 

NLCD classification scheme has been modified to include not 

only NLCD land-cover classes, but also a limited number of 

land-use classes that could be handled easily by the LULC 

modeling framework (table 3.4). Augmentation of the NLCD 

dataset will be accomplished by incorporating (1) vegetation change tracker (VCT) data products (Huang and others, 

2010) produced from the LANDFIRE program in order to 

map forested areas that have been disturbed by clearcutting, 

and (2) irrigated lands data (U.S. Geological Survey, 2010) to 

distinguish dry land from irrigated land. Given the complexity 

of modeling multiple urban-development classes at a national 

scale, we also have condensed the 2001 NLCD developed 

classes into one comprehensive developed class. See table 3.4 

for the final modified thematic land-cover classes.

Future Potential Land-Use and Land-Cover Changes.—

For each of the scenarios outlined in figure 3.4 and table 3.6, 

an LULC model will be used to provide spatially explicit 

thematic maps that cover each year from 2001 through 2050. 

Between 2001 and 2010, LULC trajectories will be the same 

across all scenarios because they are considered collectively 

to be “current.” The 2001 to 2010 time frame will be based on 

empirically measured LULC change as mapped by the 2006 

NLCD change product (Xian and others, 2009) and the VCT 

data products (Huang and others, 2010) produced from the 

LANDFIRE program. These data will serve as reference data 

to both calibrate the 2001 to 2010 “projections” and to validate 

model results. The LULC model will be used next to project 

LULC from 2011 to 2050 for each scenario.

The spatially explicit simulation model, “forecasting 

scenarios of land cover change” (FORE–SCE) (Sohl and others, 

2007; Sohl and Sayler, 2008) will be used for projected LULC 

change. FORE–SCE uses two distinct, but linked, components 

called “Demand” and “Spatial Allocation,” a structure that will 

allow for both linkages with external models and the inclusion 

of input data on driving-force variables derived from data at 

different scales. The complete LULC modeling framework will 

include an ability to ingest scenario-based assessments (LULC 

demand) to produce spatially explicit LULC maps that are compatible for assessing carbon sequestration and GHG fluxes.

The “Demand” component will provide overall proportions of LULC classes at a regional scale, and will be 

expressed as annual “prescriptions” for future LULC change. 

The annual prescriptions typically will be in the form of a simple table that will provide annual proportions of all mapped 

LULC classes. The “Demand” component will be constructed 

through extrapolation of historical trends, econometric modeling, integrated modeling, or scenarios based on expert knowledge. For this assessment, the LULC “Demand” component 

for the R scenario (see section 3.2.2) will be provided by the 

IPCC SRES scenario construction described in section 3.2.2 

(“Framework for Assessing Future Potential Carbon Stocks, 

Carbon Sequestration, and Greenhouse-Gas Fluxes”) and 

in appendix A (“Reference and Alternative Mitigation Scenarios”). The “Demand” component for the scenarios where 

LULC is enhanced for carbon sequestration (the M, L, and ML 

scenarios defined in section 3.2.2) will be done by combining 

the spreadsheet results (detailed in section 3.2.2 and appendix 

A) for enhancing LULC for carbon sequestration with the 

reference IPCC SRES scenario LULC proportions.

The “Spatial Allocation” component will use the LULC 

prescriptions from the “Demand” component to produce 

spatially explicit thematic LULC maps on an annual basis. The 

“Spatial Allocation” component of FORE–SCE was designed 

to take advantage of both historical and contemporary LULC 

research and data from the USGS. For this methodology, data 

from the USGS Land Cover Trends project (Loveland and 

others, 2002) and the NLCD (Homer and others, 2004) will be 

used to parameterize a unique, patch-based spatial-allocation 

procedure, one which can mimic realistic configurations and 

placement of individual patches of LULC change on an annual 

basis. The placement of patches will be guided by probability 

surfaces for each LULC type that are constructed through the 

analyses of empirical relationships between existing LULC 

patterns and a wide array of spatially explicit biophysical and 

socioeconomic data. The “Spatial Allocation” component 

places patches of LULC “change” on the landscape, one by 

one, until the annual prescription from the “Demand” component is met. The model then proceeds to the next yearly iteration, producing annual LULC maps from 2001 to 2050.

FORE–SCE also tracks the ages of forest stands. The initial (2001) age will be established by using the VCT data products (Huang and others, 2010) produced from the LANDFIRE 

program, which tracks natural and anthropogenic disturbances 

by analyzing historical layers of Landsat Thematic Mapper 

(TM) data and Forest Inventory and Analysis (FIA) sample 

points. A composite image will be constructed from these two 

sources that will identify the initial age of the forest stand for 

each 250-m pixel. The forest-stand age will be increased for 

each annual FORE–SCE scenario run; the age will be reestablished at “0” if forests are clearcut or if previously nonforested 

land is newly established (afforested) (for example, if a new 

pine plantation is established on previously nonforested land). 

Forest-stand age also will be used to more realistically mimic 

typical regional forest-cutting cycles and to inform biogeochemical modeling.

For the national assessment, each of the scenario runs 

outlined in figure 3.4 and table 3.6 will be run for each of 
40 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

the three IPCC SRES storylines (Nakicenovic and others 

2000). The ecosystem-disturbance method (discussed in the 

next section) will be integrated directly with annual ecosystem disturbance data and with LULC data passed between 

FORE–SCE and the disturbance model in order to ensure that 

the projected LULC change results will be integrated with the 

annual ecosystem disturbance results (introduced in the next 

section). The direct integration of FORE–SCE, the disturbance model, and the biogeochemical modeling framework 

(the General Ensemble Modeling System, or GEMS) also will 

allow for the examination of land-use, land-cover, and landmanagement components that cannot be handled by any one 

individual model. Although FORE–SCE models all thematic 

LULC change for all terrestrial ecosystems, the model is not 

well equipped to handle coastal processes that affect thematic 

LULC change along coasts (for example, changes in coastal 

wetlands or other ecosystems due to sea-level rise or other 

coastal processes). An external coastal wetland model (discussed in appendix B, “Mapping and Modeling of Land-Use 

and Land-Cover Changes”) will be used to map thematic 

LULC change for coastal wetland areas for each of the three 

IPCC SRES storylines (Nakicenovic and others, 2000). These 

data will be integrated with the FORE–SCE and disturbance 

model results when modeling is completed for a scenario. The 

final data products will be annual, 250-m-resolution, thematic 

LULC maps and transition statistics from 2001 to 2050 for 

each scenario. A much more detailed description of the LULC 

modeling framework can be found in appendix B.

Test Results Using the Land-Use and Land-Cover 

Model.—A test using the LULC modeling methodology 

was created for two EPA level III ecoregions (modified from 

Omernik, 1987), the Mississippi Alluvial Plain (ecoregion 73) 

and the Mississippi Valley Loess Plains (ecoregion 74). Of the 

scenarios listed in table 3.6, LULC modeling was completed 

for the R and L scenarios (see section 3.2.2). Using a simplified protocol for regional scenario construction, annual prescriptions of LULC change that are consistent with the A1B 

scenario were produced for each ecoregion, thus providing 

the “Demand” component for the R scenario. The “Demand” 

component for A1B was fed to the FORE–SCE “Spatial 

Allocation” component, which was then parameterized 

independently for each ecoregion (using methods described in 

appendix B). Spatially explicit LULC maps from 2001 to 2050 

then were produced for the R scenario.

The L scenario also was modeled. The spreadsheet 

approach for assessing land-use mitigation actions was used 

to independently identify optimal land-use changes that 

would increase carbon sequestration and mitigate other GHG 

fluxes in each ecoregion. Some selected land-use changes that 

resulted from running the L scenario were as follows:

• Restore forested wetlands (bottomland hardwood) 

where previously they have been used for agriculture 

in the Mississippi Alluvial Plain.

• Increase afforestation by converting marginal agricultural 

land to forests in the Mississippi Valley Loess Plains.

• Eliminate deforestation caused by processes other than 

forest harvesting and replanting.

• Eliminate the loss of wetlands (other than coastal wetlands) caused by conversion to other land uses.

• Increase the time between forest harvests from 25 to 45 

years.

• Reduce the rates of clearcutting forests by 50 percent.

Annual LULC change prescriptions were constructed for 

the L scenario on the basis of the spreadsheet analysis and the 

land-use-mitigation actions identified above, thereby providing 

a “Demand” component. This “Demand” component was then 

fed into the FORE–SCE “Spatial Allocation” components, 

which was used to produce spatially explicit LULC maps from 

2001 to 2050 for the L scenario.

Figure 3.6 shows the net LULC change between 2010 

and 2050 for both the R and L scenarios, for the entirety of 

both EPA level III ecoregions 73 and 74. Ecoregion 73 (the 

Mississippi Alluvial Plain) was characterized by very little 

LULC change in the R scenario, but it changed significantly 

in the L scenario (primarily due to restoration of croplands to 

woody wetlands). Ecoregion 74 (the Mississippi Valley Loess 

Plains) showed active LULC change in the R scenario due to 

significant urban development and afforestation (primarily by 

converting agricultural land to pine forests). In the L scenario, 

significantly more afforestation occurred, where more agricultural land was converted to natural forest types rather than 

pine forests. The L scenario also was characterized by much 

less forest cutting (the “anthropogenic” class in figure 3.6).

Figure 3.7 shows the initial 2010 LULC and the projected LULC changes for the period 2010 through 2050 for a 

portion of the two ecoregions. The reference (R) scenario is 

used in parts C and E and the enhanced LULC (L) scenario is 

used in parts D and F. Very significant changes in LULC are 

evident between part D (the result of running the R scenario) 

and part F (the result of running the L scenario); the results 

project lower forest-cutting rates in the Mississippi Valley 

Loess Plains (Claiborne County, Miss.) and large increases in 

forested wetland restoration in the Mississippi Alluvial Plain 

(Tensas Parish, La.). The projected land-cover maps from 

2010 to 2050 for both the R and L scenarios will be used to 

model carbon stocks, carbon sequestration, and GHG fluxes, 

as described in section 3.3.4.

3.3.3. Ecosystem Disturbances

As discussed in chapter 2, ecosystem disturbances are 

defined as episodic events that may affect the composition, 

structure, or function of an ecosystem (Pickett and White, 

1985; E.A. Johnson and Miyanishi, 2001; M.G. Turner and 

others, 2001). Ecosystem disturbances are treated distinctly 

from global environmental change effects, which include 

sustained alterations in climate that may arise from increasing 

CO2 in the atmosphere or nitrogen deposition (B.L. Turner 

and others, 1990). The definition of ecosystem disturbances 
3. Methodology for the National Assessment 41

Figure 3.6.  Graphs showing the net change for the modeled land-use and land-cover types in the two U.S. Environmental Protection

Agency level III ecoregions (modified from Omernik, 1987) used in the test study, using the prototype methodology and running both the

R (reference land use, land cover, and land management) and L (enhanced land use and land cover with reference land management)

scenarios. Ecoregion 73, Mississippi Alluvial Plain; Ecoregion 74, Mississippi Valley Loess Plains.
42 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Figure 3.7.  Maps showing the results of a land-use-modeling test

for the A1B scenario (Nakicenovic and others, 2000). A, Area of

study showing land-cover classes for two EPA level III ecoregions

(modified from Omernik, 1987) as follows: 1, Mississippi Alluvial Plain;

2, Mississippi Valley Loess Plains. Tensas Parish, La., and Claiborne

County, Miss. (outlined in red on the map), were selected to run the

scenarios that are shown in the enlargements (parts B–F). Grey

areas are other level III ecoregions. B, “Current” land cover (2010). C,

Projected land cover in 2050 using the “reference land use, land cover,

and land management” (R) scenario. D, Projected land cover in 2050

using the “enhanced land use and land cover with reference land

management” (L) scenario. E, Land-cover change from 2010 through

2050 using the R scenario. F, Land-cover change using the L scenario.

Obvious differences in land cover are evident in parts E and F.

2010

“Current” land cover

2050

Projected land cover

R scenario

2050

Projected land cover

L scenario

2010–2050

Land cover change

R scenario

2010–2050

Land cover change

L scenario
3. Methodology for the National Assessment 43

is also separate from events related to LULC, such as forest 

converted to cropland. Major ecosystem disturbances are one 

of the primary mechanisms that have potential to reset carbon 

sequestration pathways and change ecosystems from carbon 

sinks to sources (Baldocchi, 2008; Running, 2008).

Disturbances Included in the Assessment.—Ecosystem 

disturbances are discrete events that affect the composition, 

structure, and (or) function of an ecosystem or landscape (Pickett and White, 1985; M.G. Turner and others, 2001; Johnson 

and Miyanishi, 2001). Ecosystem disturbances are important 

because they result in a transfer of carbon between live and dead 

pools; in the case of fires, the disturbance causes the immediate 

release of carbon and GHGs to the atmosphere (Campbell and 

others, 2007; Meigs and others, 2009). Carbon stocks, carbon 

sequestration, and GHG fluxes may be altered further in the 

years immediately following a disturbance because of patterns 

of mortality, regeneration, and productivity (Hicke and others, 

2003; M.G. Turner and others, 2004). Currently, fuel treatments 

and controlled burning are used in many fire-prone ecosystems 

to reduce wildfire hazard and risk (Agee and Skinner, 2005). 

Recent studies also have demonstrated the potential reductions in carbon loss from fires in fire-prone ecosystems through 

the use of fuel treatments and controlled burning (Hurteau 

and North, 2009; Stephens and others, 2009; Wiedinmyer and 

Hurteau, 2010); however, in ecosystems with long fire-return 

intervals, treatments may result in a reduction of long-term carbon-sequestration capacity (Harmon and others, 2009; Mitchell 

and others, 2009). Therefore, both ecosystem disturbances and 

disturbance-management activities must be incorporated in the 

assessment in order to evaluate their potential effects on carbon 

stocks, carbon sequestration, and GHG fluxes.

The following ecosystem disturbance types (both natural 

and anthropogenic) should be considered in the national 

assessment: wildfires, hurricanes, tornados and other damaging winds, insect- and disease-related forest mortality, and 

land-management activities such as fuel treatments and forest 

cuts (table 3.9). The impacts of disturbances and land-management activities on carbon stocks, carbon sequestration, and 

GHG fluxes will be considered for the following ecosystems: 

forests, grasslands/shrublands, and wetlands.

Current Ecosystem Disturbances.—The task of capturing 

current ecosystem disturbances will start by creating annual 

summaries of past disturbances using records of recent wildfires, storms, and insect and disease outbreaks, by ecoregion. 

These annual summaries will include disturbance type, cause, 

number of events, and total area affected. Fire summaries also 

will include ecoregion-level estimates for emissions, which 

will be created by totaling individual estimates for each fire 

using the Consume model (Prichard and others, 2006) and the 

First Order Fire Effects Model (FOFEM) (Reinhardt and others, 1997) with the fuel-loading model (FLM) and fuel-characteristic classification system (FCCS) data produced for the 

LANDFIRE project (Rollins, 2009). The annual disturbance 

summaries will be provided as tables and further summarized 

as probability distributions for each EPA level II ecoregion.

Future Ecosystem Disturbances.—The occurrence and 

spread of disturbances are influenced by a variety of processes 

and patterns operating at different scales (Peters and others, 

2004; Falk and others, 2007; Raffa and others, 2008). Therefore, the methodology to simulate future disturbances incorporates a series of components operating at different spatial and 

temporal scales to characterize and forecast regional patterns 

as well as the footprints and impacts of individual disturbance 

events. Relations between past disturbances (frequencies and 

extents) and climate, vegetation, and socioeconomic drivers 

will be identified at ecoregion scales using empirical relations, 

which also will be used to forecast potential future disturbance 

occurrence patterns. Future disturbance footprints will be 

simulated using a variety of approaches, described in more 

detail below and in appendix C.

Fire-related disturbances and fire-management activities 

will include wildfires, prescribed fires, and fuel treatments. The 

methods for forecasting wildfires will incorporate the four basic 

processes: ignition, spread, effects, and succession (Keane and 

others, 2004). The projections of ecoregion wildfire activity will 

be made using climate-driven predictions of the number of wildfires each year (Westerling and others, 2006; Preisler and Westerling, 2007). The individual ignition locations will be determined 

from empirical probability surfaces using climate, vegetation, 

land cover, and topography as predictor variables (Syphard and 

others, 2008). The predicted probability surfaces will be updated 

each year by incorporating changes made by the LULC (section 

3.3.2) and BCG (section 3.3.4) methods. The spread of individual fires will be simulated each year using the minimum-travel 

Table 3.9.  Major natural and anthropogenic ecosystem disturbances, selected attributes, and data sources.

[MTBS, Monitoring Trends in Burn Severity project; LANDFIRE, Landscape Fire and Resource Management Planning Tools Project; RSLC, remote sensing of 

landscape change activities at U.S. Geological Survey; NFPORS, National Fire Plan Operations and Reporting Systems; USFS, U.S. Forest Service]

Disturbance type Characteristic attributes of data sources Data sources

Wildland fires Fire size, severity, and emissions MTBS, LANDFIRE.

Hurricanes, tornados, and damaging winds Storm tracks, severity, and areas of mortality RSLC.

Insects and diseases Areas of defoliation and mortality USFS Forest Health Monitoring Program’s aerial 

surveys, RSLC.

Forest cuts (clearcuts and thinning) Areas of cuts, cutting types RSLC.

Fuel treatments (including prescribed fires) Areas and types of treatment NFPORS, LANDFIRE.
44 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

time (MTT) algorithm (Finney, 2002), the LANDFIRE fuels and 

topography layers (Rollins, 2009), and National Oceanic and 

Atmospheric Administration (NOAA) North American Regional 

Reanalysis (NARR) weather data. Fuel-treatment projections 

(including prescribed fires) will be made from historical distributions (the number of treatments per year and the size of individual treatments). The treatment locations will be placed randomly within wildland vegetation types (forest, shrub, or grass, 

depending on the type of treatment) in public lands and allowed 

to spread (using the MTT algorithm for prescribed fire and a 

“patch-grow” algorithm for other treatments; Finney, 2002) until 

a predetermined treatment area is reached or an entire patch of 

contiguous wildland vegetation has been treated.

Disturbances that are not related to fire (nonfire disturbances) will include insects and disease outbreaks, hurricanes, tornadoes, and damaging winds. The location and 

spread of insect and disease outbreaks will be based on 

empirical probability surfaces developed using epidemiology 

and species-distribution modeling techniques with vegetation, climate, topography, and previous outbreak locations 

as predictors (Elith and others, 2006; Phillips and others, 

2006; Elith and Leathwick, 2009). Hurricane, tornado, and 

damaging wind activity (number of storms per year) will be 

based on a random selection of data from historical stormoccurrence summaries (for tornados and damaging winds) 

and national summaries (for hurricanes). An empirical stormtrack generator (Vickery and others, 2000) will establish the 

storm path. For tornados and damaging winds, the footprint 

of the storm disturbances will be determined using remote 

sensing of landscape change (RSLC) techniques and historical storm locations. A similar approach will be used for 

hurricanes, but a surface-wind-field and exposure model will 

also be incorporated to determine where damage to vegetation occurs (Boose and others 1994).

The redistribution of biomass among the different pools 

following both fire and nonfire disturbances will be quantified using a look-up table approach containing information on 

changes in biomass pools by ecosystem type, for each type of 

disturbance or management activity. The look-up table (example given in table D2 in appendix D) will be derived from published estimates and field inventories (for example, FIA) and 

will be used by the BGC modeling methods (discussed later in 

this chapter) to distribute biomass among different pools following disturbances. For fires, emissions will be estimated for 

each fire using data layers from the LANDFIRE fuel-loading 

model (FLM) and fuel-characteristic classification system 

(FCCS) with the Consume and First Order Fire Effects Model 

(FOFEM) fuel-consumption and emission models (Reinhardt 

and others, 1997; Prichard and others, 2006). The post-disturbance influence on vegetation productivity will be accounted 

for by the BGC methods.

During the disturbance simulations for the national assessment, a critical step will be to update the LULC and fuels data 

(fire behavior fuel model, canopy height, canopy cover, canopy 

bulk density, canopy base height, FLM, and FCCS) that will be 

used to simulate disturbance locations and spread. During each 

annual time step in the simulation, in places where disturbances 

and management activities occurred, the LULC and fuels layers will be updated by using the existing vegetation state and 

transition models developed for LANDFIRE and look-up tables 

that link vegetation state to fuel layers and NLCD categories. 

Appendix C contains a detailed technical discussion of the data 

sources, the methods that will be used to characterize and model 

the ecosystem disturbances and management activities, and the 

data products that will be produced.

Disturbance modeling components are linked with the 

scenario framework (fig. 3.4). Climate changes associated 

with each of the IPCC SRES scenarios (Nakicenovic and 

others, 2000) will increase or decrease the frequency of the 

disturbances and will influence the spread and severity of 

individual disturbance events. LULC-change projections will 

interact with disturbance modeling by influencing the extent 

and arrangement of land-cover types within ecoregions, 

therefore constraining the spread of individual disturbance 

events. Additionally, the influence of disturbance-related 

land-management activities will be incorporated through 

fuel-treatment and fire-suppression modules. This integrated 

modeling framework will allow for a comparison of how 

changes in land cover, land use, and land management under 

different scenarios might influence disturbances and their 

impacts on carbon storage and GHG emissions in various 

ecosystems. See appendix C for more details on modeling 

major ecosystem disturbances.

Expected Outputs for Ecosystem Disturbances.—For the 

references and mitigation scenarios associated with the IPCC 

storylines (table 3.6), the final data products from the ecosystem disturbance modeling will include regional summaries 

and maps of current and future potential annual disturbances, 

levels of severity, and GHG emissions (carbon monoxide, 

carbon dioxide, methane, and nonmethane hydrocarbons). The 

data products will be presented as probability distributions that 

will summarize the range of results produced across replicated 

simulation runs.

Test Results Using the Ecosystem Disturbance Model.—

The wildfire component of the ecosystem disturbance model 

was tested in the same two EPA level III ecoregions that 

were used in the test that used the land-use and land-cover 

model (section 3.3.2): the Mississippi Alluvial Plain and 

the Mississippi Valley Loess Plains. For the test, wildfire 

histories for the two selected ecoregions were constructed 

by using the Monitoring Trends in Burn Severity (MTBS) 

project database (Eidenshink and others, 2007). In order 

to show the relation between fire occurrences and the land 

cover (which is based on the nominal year 2001) in each 

ecoregion, only the data for wildfires that occurred before 

2001 were used. This search resulted in data on 12 fires that 

occurred in the Mississippi Alluvial Plain ecoregion and 22 

fires that occurred in the Mississippi Valley Loess Plains 

ecoregion. The small sample sizes prevented the construction 

of a predictive model that might demonstrate a statistically 

significant relation between wildfires and climate and LULC. 

Therefore, the number of wildfires simulated for each month 
3. Methodology for the National Assessment 45

was determined by drawing randomly from the historical 

distribution of monthly fire occurrences. A random distribution of ignition locations was used (with ignition points limited to natural vegetation types, such as is found in forests, 

grasslands/shrublands, and wetlands) in order to estimate the 

probability of ignition locations.

Overall, the test showed that wildfires in the two ecoregions burned a small area; between 2001 and 2008, the 

observed (MTBS data) annual number of wildfires and area 

burned were 2 wildfires and 1,471 ha per year in the Mississippi Alluvial Plain and 0.5 wildfire and 166 ha per year in the 

Mississippi Valley Loess Plains. Simulation results for the same 

time period using the IPCC SRES A1B storyline (Nakicenovic 

and others, 2000) produced annual results of 0.6 wildfires and 

2,450 ha burned in the Mississippi Alluvial Plain and 0.8 wildfires and 500 ha burned in the Mississippi Valley Loess Plains 

(figs. 3.8 and 3.9). The simulation results do not exactly match 

the observed results because of the stochastic nature of fire 

occurrence in the model; however, the simulated results were 

within the range of variability of observed values for number 

of fires and area burned each year. Because the wildfire simulation runs did not result in a large area burned each year in the 

test area, the fires’ effects were not incorporated into the BGC 

modeling methods discussed later in this chapter.

The initial results suggest that wildfires will not have a substantial impact on GHG emissions in the test region. The results 

indicate that there would be few fires and most of the fires would 

be small; less than 3,000 ha were burned each year in the simulations. Fuel consumption and emissions were not estimated using 

the FOFEM and Consume models because the input data (FCCS 

and FLM) were not yet available for the Southern United States 

(they are available now for the Western United States). Predicting fire occurrence and spread is an inherently difficult process 

to simulate well. With this in mind, the differences between the 

observed and simulated number of fires and the area burned were 

not large; they were on a similar order of magnitude and reflected 

the inherently random nature of annual fire occurrence patterns 

and the spread of individual fires in the region.

3.3.4. Carbon Stocks, Carbon Sequestration,

and Greenhouse-Gas Fluxes in Terrestrial

Ecosystems

The 2006 IPCC Guidelines for National Greenhouse Gas 

Inventories (IPCC, 2006) recognized two major approaches for 

estimating GHG fluxes in ecosystems: an inventory approach 

and a process-based modeling approach. The inventory 

approach (also referred to as the “bookkeeping” or “spreadsheet” approach) relies on direct measurements of carbon pools 

over a specific time period and applies empirically derived algorithms (such as carbon-response curves and emission factors) to 

estimate net carbon sequestration (Houghton and others, 1999). 

In contrast, the modeling approach uses process-based BGC 

models to estimate carbon stocks, carbon sequestration, and 

GHG fluxes over time in response to controlling factors such as 

climate, LULC change, and ecosystem disturbance. The carbon 

stocks, carbon sequestration, and GHG fluxes are estimated 

at each modeled time step. For this assessment, the current 

(2001–2010) and future (2011–2050) carbon stocks, carbon 

sequestration, and GHG fluxes will be evaluated. For current 

estimates, the approaches will use field observations, published 

data, and other published information to calibrate model inputs 

and to evaluate model results. For future projections, the two 

general approaches will diverge on the basis of various LULCand climate-change scenarios. In addition, the process-based 

modeling approach will incorporate several BGC simulation 

models for various ecosystems, as discussed below.

Accounting approach.—A spreadsheet model will be 

used to simulate carbon dynamics and GHG emissions. The 

spreadsheet approach generally will be limited to nonspatial or 

coarse-spatial-resolution simulations; the number of formulas 

used in a spreadsheet usually will be small, which will prevent 

the inclusion of a simulation of a complex ecosystem, GHG 

fluxes, LULC, or land-management interactions. The Guidelines for National Greenhouse Gas Inventories (IPCC, 2006) 

provide equations and factors for building the spreadsheets. 

For this assessment, the spreadsheet will be developed in a 

parallel manner with the BGC modeling approach in order to 

compare and verify outputs (for example, there will be only 

a cursory check for sizes, distributions, patterns, or trends of 

estimates in order to capture and correct obvious errors).

Process-based modeling approach.—For this assessment, process-based BGC modeling will be conducted using 

the general ensemble modeling system (GEMS) developed by 

the USGS (S. Liu, Loveland, and Kurtz, 2004). The GEMS is 

designed to provide spatially explicit biogeochemical model 

simulations over large areas. The system uses both agent and 

direct implementation approaches to interact with encapsulated biogeochemical models, such as Century (Parton and 

others, 1987), Erosion-Deposition-Carbon Model (EDCM) 

(S. Liu and others, 2003), and Integrated Biosphere Simulator 

(IBIS) (Foley and others, 1996).

The agent implementation model interface is used with 

GEMS to conduct model runs of existing encapsulated BGC 

models. This approach requires minimum modifications to 

encapsulated models and can be useful for reusing models that 

are difficult to modify. Regional-scale BGC models, such as 

Century, EDCM, and IBIS, can be encapsulated or linked in 

GEMS (S. Liu, Kaire, and others, 2004; S. Liu, Loveland, and 

Kurtz, 2004; Tan and others, 2005; J. Liu and others, 2006). 

Because GEMS is designed to encapsulate multiple models 

and to parameterize and implement these models using the 

same data, it provides an ideal platform for using “model 

ensembles” to identify and address issues and uncertainty that 

are related to model structure and to mathematical representations of biophysical processes.

The direct implementation approach is used to merge 

BGC models directly with GEMS to allow more efficient, spatially explicit simulations. Many regional-scale model applications adopt a time-space sequence simulation approach, which 

implements a complete simulation for an individual pixel 
46 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Figure 3.8.  Graphs showing the observed and simulated number

of wildfires per year, and the observed and simulated number

of hectares of area burned by wildfire per year, using the IPCC

SRES A1B storyline from the Intergovernmental Panel on Climate

Change’s Special Report on Emissions Scenarios (Nakicenovic

and others, 2000) for the two EPA level III ecoregions used in the

test. Note that the horizontal axes for graphs showing number of

hectares burned have different scales, and that some years had

no observed or simulated fires. A and B, Mississippi Aluvial Plain;

C and D, Mississippi Valley Loess Plains.
3. Methodology for the National Assessment 47

Figure 3.8.—Continued
48 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

90°

35°

30°

EXPLANATION

Simulated fire scars (2001–2050)

State boundary

County or parish boundary

Boundary of ecoregions used in the test
3. Methodology for the National Assessment 49

from beginning to end before moving to the next pixel. In an 

example linking GEMS with EDCM, a space-time sequence 

is used instead (each time step is simulated for the whole 

region before moving to the next step). This approach can be 

exploited to quantify lateral movements of water, soil, carbon, 

and nitrogen and can interface with other modeling systems 

such as FORE–SCE and ecosystem disturbance maps. BGC 

modeling will be a dynamic process, and national experts will 

be consulted during the assessment to provide comments on 

the overall modeling approach and to consider other potentially suitable BGC models for use on the GEMS platform.

As discussed above, there are two primary reasons to use 

both the spreadsheet approach and the process-based modeling approach for this assessment: First, the different methods 

each have their unique applications. The spreadsheet approach 

is relatively straightforward and transparent, but it is limited in 

spatial deployment and in linking with environmental changes 

and mitigation activities. In contrast, the process-based modeling approach is spatially explicit and dynamic, but it can be too 

complex for users to follow all of the processes considered and 

internal calculations. Second, applying both approaches provides the opportunity to crosscheck a model’s performance and 

results and enhances overall confidence in assessment results.

Different input data will be used for the two different, yet 

complementary approaches because of the varying model structures and data-format requirements. For example, the combination of the GEMS spreadsheet and the EDCM uses joint frequency distribution (JFD) tables and Monte Carlo simulations 

for forest ages in order to generate the initial biomass in a forest; 

however, GEMS combined with Century uses remote-sensing 

data (showing tree-canopy cover types and height) to estimate 

biomass without considering the forest’s age. The data-model 

integration will be improved to allow the same datasets to be 

used by different BGC methods during the assessment.

Table 3.10 lists examples of the methods or models, 

deliverables, technical processes, target ecosystems, and data 

needs or sources that will be used in the assessment. Details 

of the spreadsheet and process-based modeling methods are 

described in appendix D.

Assessment of Carbon Stocks and Carbon Sequestration.—The primary input data for the assessment of carbon 

stocks and carbon sequestration will come from in situ measurements of aboveground biomass (inventory data), in situ 

soil measurements (for example, from GRACEnet), soil maps, 

carbon-flux measurements from eddy-covariance flux towers, 

remote sensing of vegetation, LULC maps, ecosystem-disturbance datasets, and land-management datasets available from 

various sources. See table 3.7 for the datasets and sources 

for this data. The assessment of carbon stocks and carbon 

sequestration will be conducted by using both the spreadsheet 

and the process-based model simulation approaches. The net 

ecosystem carbon change will be calculated as the difference 

in the carbon stock between two time steps. As indicated in 

table 3.10, parameterization for current carbon stocks and 

sequestration will be based on observational data from different sources, as well as on current biophysical data such as soil, 

climate, LULC, and ecosystem disturbances. Parameterization 

for future potential carbon stocks and carbon sequestration 

will require projected future potential climate, LULC changes, 

and disturbances along the scenario trajectories. The spreadsheet approach will compute carbon stocks, carbon sequestration, and GHG fluxes averaged at the level of assessment 

units, using predefined algorithms and the input data. For the 

process-based model simulation approach, carbon fluxes will 

be modeled using the technical processes listed in table 3.10. 

For both approaches, the primary drivers of carbon stocks and 

carbon sequestration will be climate change, LULC changes, 

ecosystem disturbances, and possible changes in land-management practices.

Assessment of Greenhouse-Gas Fluxes.—Modeling and 

assessing methane (CH4) and nitrous oxide (N2O) fluxes will 

be more complicated than modeling and assessing carbon 

dioxide (CO2) fluxes because direct observational data for CH4

and N2O are scarce. Available data include eddy-covariance 

flux-tower data, field measurements from various sources 

and published literature, and soil-flux measurements that are 

compiled in sources such as GRACEnet. A general strategy 

for assessing GHG flux, in light of the shortage of measured 

flux data, will be to focus on ecological conditions such as soil 

moisture and temperature that control GHG fluxes, which are 

more prevalent and available.

The emission of CH4 will be estimated through the simulation of soil biogeochemical processes, including methane 

production by methanogenic bacteria under anaerobic conditions, oxidation by methanotrophic bacteria under aerobic 

conditions, and transport to the atmosphere (Conrad, 1989). 

The principal controls on these processes are soil moisture, 

water-table position, soil temperature, the availability and 

quality of suitable substrates, and physical pathways for CH4

to be released into the atmosphere. Many models have been 

developed to simulate site-scale processes of CH4 generation, 

consumption, and transport (C. Li and others, 1992; Cao and 

others, 1996; Potter, 1997; Walter and others, 2001; Zhuang 

and others, 2006). Some of these models yield a detailed 

representation of the site-scale vertical soil processes; however, the deployment of these models over large areas has been 

challenging because of the difficulties in parameterizing these 

models and in simulating some of the critical driving variables, such as water-table position.

The denitrification-decomposition (DNDC) model (Li 

and others, 1992) has been applied to estimate CH4 and N2O 

Figure 3.9 (facing page). Map of the test area showing locations

of simulated wildfires in two EPA level III ecoregions for 2001

through 2050, using the A1B storyline from the Intergovernmental

Panel on Climate Change’s Special Report on Emissions Scenarios

(IPCC SRES; Nakicenovic and others, 2000). Although the largest

scars are present in the Mississippi River delta area, scars are

present throughout both ecoregions, but are too small to be

shown at the scale of publication. Ecoregions are as follows: 73,

Mississippi Alluvial Plain; 74, Mississippi Valley Loess Plains.
50 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table 3.10.  Preliminary methods or models, quantifying parameters, technical processes, target ecosystems, and data needs or

sources that will be used to assess parameters of carbon stocks, carbon sequestration, and greenhouse-gas flux.

[The methods or models listed have been tested and prototyped, but additional models may be added, depending on unique ecosystem conditions or technical 

needs encountered during the assessment. Input data requirements for each ecosystem also are listed. For an explanation of acronyms, please see “Abbreviations, 

Acronyms, and Chemical Symbols” in the front of this report. Abbreviations are as follows: Cs, carbon stock; Csr, carbon sequestration; GHG, greenhouse gas 

(greenhouse gas is carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4)); Ced, carbon erosion and deposition]

Method or model Quantifying 

parameters Technical process Target ecosystem Data needs or sources

Spreadsheet

(Houghton and others, 

1999)

Cs, Csr, GHG flux Algorithms based on storageage growth curves

Forests

Urban forests

Grasslands/shrublands

Croplands

Wetlands

Growth curve from FIA, crop production data from NRCS, NWI, local 

and IPCC standard GHG emission 

factors, GRACEnet data.

EDCM

(S. Liu and others, 

2003)

Cs, Csr, GHG flux Ced, carbon and nitrogen 

leaching

Maximum potential productivity, monthly time step, spatial 

sampling, and ensemble 

simulation

Parameterizations (Cao and 

others, 1995; S. Liu, 1999; 

Parton and others, 2001)

Forests

Urban forests

Grasslands/shrublands

Croplands

Wetlands

LULCC data, current climate data, 

IPCC GCM projections, USDA agriculture production data, disturbance 

data (fire, drought), hydrological 

model inputs (soil erosion, deposition), land-management data (grazing intensity, fertilizer application), 

SSURGO soil data, GRACEnet data.

Century

(Parton and others, 

1987)

Cs, Csr, GHG flux,

carbon and nitrogen 

leaching

Maximum potential productivity, monthly time step, spatial 

sampling, and ensemble 

simulation

Forests

Urban forests

Grasslands/shrublands

Croplands

LULCC data, topography (DEM), current climate data, IPCC GCM projections, USDA agriculture production 

data, disturbance data (fire, drought), 

hydrological model inputs (soil erosion, deposition), GRACEnet data.

IBIS

(Foley and others, 1996)

Cs, Csr, CO2,

carbon and nitrogen 

leaching

Farquhar-type leaf level model, 

hourly time step, use of 

subpixel information

Forests

Urban forests

Grasslands/shrublands

Croplands

LULCC data, topography (DEM), 

current climate data, IPCC GCM 

projections, USDA census data, 

disturbance data (fire, drought), hydrological model inputs (soil erosion, 

deposition).

USPED

(Mitas and Mitasova, 

1998)

Ced Empirical two-dimensional 

algorithm

Forests

Grasslands/shrublands

Croplands

Link with EDCM

Soil erodibility factor (K) from 

SSURGO, SRTM data, LULCC 

data, precipitation data derived from 

climate data (current and future 

projections).

Zero-dimensional model GHG flux Process-based, simple framework, compatible with large 

scales

Parameterizations (C. Li and 

others, 1992; Cao and others, 

1996; Potter, 1997; Walter 

and others 2001; Hénault and 

others, 2005; Zhuang and 

others, 2006)

Wetlands Link with EDCM

NWI, SSURGO, NCDC, NLCD, 

regional wetland databases, GRACEnet data.

fluxes for a range of ecosystems, including prairie potholes. 

Although DNDC is one option for estimating CH4 and N2O 

in this assessment, finding supporting data will be very difficult. A potential solution is to implement an approach that is 

similar to an empirical approach developed by Cao and others 

(1996), which balances the needs of considering the site-scale 

processes with the feasibility of deploying the site-scale model 

over large areas in order to address spatial heterogeneity.

Other methods also exist for simulating N2O emissions 

(for example, C. Li and others, 1992; S. Liu and others, 1999; 

Parton and others, 2001; Hénault and others, 2005). Methods 

for estimating N2O emissions from ecosystems will parallel 

those used by a study of N2O emissions in the Atlantic zone of 

Costa Rica using GEMS and EDCM (S. Liu and others, 1999; 

Reiners and others, 2002). Nitrification and denitrification are 

the major processes that lead to the emission of N2O from soils. 

Atmospheric and terrestrial deposition, plant uptake, mineralization, soil sorption, and soil leaching act as major controls 

on the nitrogen balance. For the assessment, the GEMS and 

EDCM algorithms will be enhanced in order to simulate the 

N2O flux. The results of the simulation will be compared with 

observational data (for example, from GRACEnet).

Land-Use and Land-Cover Changes.—LULC change 

(such as a conversion of agricultural land to forest) is a significant driver of changes in carbon stocks, carbon sequestration, and GHG fluxes. For this assessment, the BGC modeling 
3. Methodology for the National Assessment 51

process using the GEMS platform will be directly coupled 

with LULC-change modeling results (section 3.2.2) to account 

for the effects of past and (or) current LULC changes and for 

projected future land-use changes on carbon-nitrogen dynamics in ecosystems. LULC-change maps generated by FORE–

SCE model will be used to produce spatial simulation units. 

For an individual simulation unit, a LULC-change file, called 

the “event schedule file,” will be created. This file specifies 

the type and timing of any LULC-change event, as well as the 

type and timing of land-management practices, such as cultivation and fertilization.

Ecosystem Disturbance.—The extent and severity of 

natural or anthropogenic disturbances will be determined 

using combined outputs from LULC-change and ecosystemdisturbance methods. For example, maps showing fire perimeters 

and burn severity (Landscape Succession Model (LANDSUM) 

and MTBS maps (Keane and others, 2007; Eidenshink and 

others, 2007)) will be used in combination with the new fire 

modeling effort in simulation model runs to indicate the timing, 

location, and severity level of fires. The effects of fires will be 

expressed as biomass consumption loss and mortality loss (see 

table D2 in appendix D). On the basis of the loss rates, simulation 

model runs will reallocate the aboveground-biomass and soilcarbon pools for each individual land pixel. Consumption loss 

results in direct carbon emission to the atmosphere, but mortality 

loss converts live biomass carbon to dead carbon pools. The 

disturbed ecosystem will start to regrow (through the vegetation 

recovery process) based on the new soil-nutrient pool and new 

leaf-area index calculated by the models. The calculation of 

other disturbance effects will follow a similar approach, but with 

different carbon transition coefficients among various pools. 

The regrowth processes that follow the disturbances will be 

calculated based on light and water availability, temperature, 

nutrient availability, plant competition, and other environmental 

conditions. Tree planting will be assumed to follow a clearcutting 

or a stand-replacement fire event if a forest plantation is indicated 

in the resulting land-cover map; otherwise, natural vegetation 

recovery will be assumed to occur.

Assessment of Land-Management Activities.—In addition to natural disturbances (for example, geological disasters, 

wildfires), human land-management activities play a critical 

role in carbon stocks, carbon sequestration, and GHG fluxes. 

For example, implementing conservation residue management 

can significantly mitigate carbon emissions from soils and can 

make a bigger difference than conventional tillage management. For the assessment, the following land-management 

activities will be evaluated:

• Conversions between LULC classes and crop rotation

• Land management practices, including—

◦ Logging or forest thinning

◦ Forest fertilization

◦ Fuel treatment of forest and rangeland, including 

thinning, prescribed burns, and so on

◦ Grazing intensity

◦ Tillage practices coupled with residue input (such 

as conventional tillage, reduced tillage, and no-till 

practices)

◦ Fertilization rate for and manure application on 

croplands

◦ Irrigation of croplands and forests

The key algorithms that account for land-management 

activities (such as irrigation, fertilization, and residue return) 

will be embedded in the GEMS. Relevant data and other 

parameter inputs will be compiled from existing databases, as 

noted in table 3.7.

Assessment of Erosion and Deposition.—Soil erosion and deposition affect soil-profile evolution, the spatial 

redistribution of carbon and nutrients, and the dynamics of 

carbon and nitrogen in ecosystems (S. Liu and others, 2003; 

Lal and others, 2004). Soil erosion and deposition will be 

assessed using the Unit Stream Power Erosion and Deposition (USPED) model (Mitas and Mitasova, 1998) to quantify 

the impacts of soil erosion and deposition on soil-carbon loss, 

soil-profile evolution, onsite dynamics of carbon and nitrogen, 

and offsite transport of carbon and nitrogen on the landscape 

and into wetland environments and aquatic systems. Quantitative estimates of soil carbon erosion and deposition estimates 

will be compared with assessments of aquatic carbon stocks 

and carbon sequestration described in following sections.

Wood-Product Carbon Pool.—Carbon sequestration in 

wood products, landfills, and other offsite storage areas can be 

significant in the accounting of terrestrial carbon-sequestration 

capacity (Skog and Nicholson, 1998). The fate of harvested 

wood can be tracked by using a simple offline spreadsheetaccounting approach (for example, S. Liu and others, 2003), 

but it requires forest-based assessments of harvest rates 

(Manies and others, 2005). The USFS maintains accurate data 

and methods for tracking and estimating carbon in wood products (for example, see W.B. Smith and others, 2009). For the 

assessment, the USFS, the EPA, and others will be consulted 

to develop the appropriate algorithms to estimate wood-product carbon.

Data Assimilation.—A major source of uncertainty in the 

assessment is the scarcity of in situ and other observational 

data obtained at appropriate spatial and temporal scales. Data 

assimilation refers to techniques that constrain simulations 

with reference conditions using limited observational data. For 

example, the Markov Chain Monte Carlo (MCMC) method 

(an algorithm used to simulate probability distributions) relies 

heavily on computation and is, therefore, difficult to apply over 

a region where the number of simulation units is large; however, 

the method can be an effective and ideal way to derive representative values and their uncertainties for the model parameters 

from limited point observations, such as data from FLUXNET. 

Other data-assimilation techniques include model inversion; for 

example, PEST (EPA’s model-independent parameter estimation 

application; http://www.epa.gov/ceampubl/tools/pest/) (S. Liu 

and others, 2008), Ensemble Kalman Filter (EnKF) (Evensen, 

1994, 2003), and Smoothed Ensemble Kalman Filter (SEnKF) 
52 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

(Chen and others, 2006, 2008). These methods have been implemented in the GEMS to derive parameter information for the 

model from plot measurements of carbon and nitrogen stocks 

(for example, S. Liu and others, 2008) and from eddy-covariance flux-tower observations (for example, Chen and others, 

2008). For the assessment, a combination of data-assimilation 

techniques will be used to ensure that the model simulations 

agree well with various observations from different sources and 

scales.

Integration With Other Methods or Models for the 

Assessment.—Model integration will be a critical step for the 

assessment because of the time- and space-dependent relations 

among the different technical components. For example, modeling LULC requires information about site-scale soil fertility or soil organic carbon from BGC modeling to inform the 

allocations of crops in space and time. On the other hand, the 

ecosystem-disturbance information will affect land-use behaviors, such as timber harvesting. Without stepwise coupling 

between FORE–SCE and the ecosystem-disturbance model, 

timber-harvesting activities might still be prescribed in areas 

where biomass will have been completely consumed by fire in 

the ecosystem-disturbances model. Carbon or biomass stock 

(fuel load) will strongly affect the probability of fire occurrence and the level of severity of those fires, which requires 

coupling the ecosystem-disturbance model with carbon-stock 

information from the GEMS.

One goal of the GEMS modeling is to link the terrestrial 

and aquatic components of both the biogeochemical cycling 

and the transport of carbon. This linkage will constrain terrestrial simulations of carbon loss with calculations of lateral 

carbon flux, aquatic carbon stocks, and aquatic GHG emissions determined from water flow, water chemistry, and lakeand reservoir-sedimentation data, as described in section 3.3.5.

Uncertainty in the Assessment.—All models are simplified representations of the real world; therefore, biases and 

uncertainties in model results are common phenomena. The 

overall approach for estimating uncertainty for the assessment 

is discussed in section 3.3.8. To reduce biases in modeling, the 

BGC models will be calibrated with in situ data. Uncertainties 

(random errors) related to assessment results, parameters, and 

model structure will be handled following the general IPCC 

(2006) guidance. Influencing factors considered in uncertainty evaluation (such as forest age and soil-carbon content) 

should have an uncertainty range, expressed as a probability 

distribution function (PDF) curve or stated in a probability 

look-up table, so that the IPCC error propagation equations 

can be applied to evaluate regional level uncertainty. In modeling carbon stocks, carbon sequestration, and GHG fluxes, 

uncertainty factors also may include forest and crop species, 

soil type, canopy density, logging location, burn severity, and 

agricultural management. The PDFs of model parameters will 

be derived by using data-assimilation techniques at eddycovariance flux-tower sites across the country. Opportunities 

for biogeochemical model comparisons will be sought.

Test Using Terrestrial Methods to Assess Carbon Stocks, 

Carbon Sequestration, and Greenhouse-Gas Fluxes.—The test 

area for the LULC modeling effort included Tensas Parish, La., 

and Claiborne County, Miss., in the Mississippi Alluvial Plain 

and the Mississippi Valley Loess Plains (EPA level III ecoregions 73 and 74, modified from Omernik, 1987), respectively 

(fig. 3.10). The reason for selecting these two jurisdictions was 

to cover three major ecosystem types (forests, croplands, and 

wetlands). As of 2001, Claiborne County was dominated by 

forests (73 percent, consisting of 47 percent deciduous, 6 percent evergreen, 9 percent mixed, and 11 percent anthropogenic 

disturbances), followed by wetlands (10 percent) and croplands 

(10 percent, including hay/pasture). Tensas Parish was mainly 

classified into croplands (54 percent), wetlands (33 percent), 

forests (3 percent), and other (10 percent).

Three methods (GEMS-spreadsheet, GEMS-Century, and 

GEMS–EDCM) were used for the test. As noted above, different input data were used by the methods. GEMS-Century used 

STATSGO and GEMS–EDCM used SSURGO for the initial 

soil data. To initialize the biomass carbon data and to show the 

general relation between vegetation height and biomass carbon, 

GEMS-Century used vegetation-height maps from the interagency LANDFIRE database, whereas GEMS–EDCM used 

forest-age maps from FORE–SCE (which were derived from 

the FIA) and a correlation between age and biomass (that is, 

forest growth curves). The percentage of area of specific crop 

types (found by running a Monte Carlo simulation) was initialized as follows: corn, 34 percent; cotton, 30 percent; soybeans, 

12 percent; wheat, 10 percent; and others, 14 percent. GHG 

fluxes in wetlands were estimated by using the GEMS–EDCM 

method, based on the technical processes described in table 

3.10. USPED was used to estimate soil erosion and deposition. 

Monte Carlo simulation was applied to the initial forest ages, 

types of crops, and soil organic carbon. A complete ecoregion 

simulation was performed at a 250-m spatial resolution; every 

pixel was simulated only once instead of selecting sample pixels 

and performing ensemble simulations.

All simulations for the test were performed under the R 

and L scenarios (see definition above in section 3.2.2) generated by FORE–SCE for the period from 2001 through 2050 

(see appendix B, “Mapping and Modeling of Land-Use and 

Land-Cover Changes” for a detailed discussion on mapping 

and modeling LULC changes). The model simulations were 

constrained by grain yields for crops and forest age growth 

curves. The major output variables included biomass carbon 

stock, total ecosystems carbon stock, carbon sequestration, 

and N2O and CH4 emissions. Additional output data, such as 

carbon stock and carbon sequestration by pools, also were 

produced. No validation or uncertainty assessments (for both 

input data and data products) were performed for the test 

because of time constraints and because the validation and 

uncertainty assessments were designed to be conducted using 

the EPA level II ecoregions (discussed below in this chapter), 

not the level III regions used for the test.

Table 3.11 shows that estimates of the total carbon 

stocks at the beginning of the model simulations (2001) 

were 40.91, 34.22, and 43.30 Tg, and estimates for the end 

(2050) were 49.36, 51.89, and 48.07 Tg, respectively, for the 
3. Methodology for the National Assessment 53

GEMS-spreadsheet, GEMS-Century, and GEMS–EDCM 

methods. For the initial carbon stock in 2001, the GEMSspreadsheet and GEMS–EDCM methods showed little difference, but the GEMS-Century method began with a much 

lower estimate (data not shown). The difference was caused 

by their different initialization approaches in biomass and soil 

organic carbon (SOC). Although the GEMS-Century method 

began with a lower carbon stock value in 2001, it reached a 

higher carbon stock value in 2050 than the other because of 

the higher carbon-sequestration rate during the study period.

Although there were differences in how the biomass carbon was initialized among these methods, some conclusions 

may be drawn from the test results (table 3.11, fig. 3.11). First, 

the annual rates of carbon sequestration were consistent, varying only within a range of 0.2 TgC/yr. Second, the GEMSCentury and GEMS–EDCM method runs demonstrated a 

synchronized temporal-change pattern, and the pattern was 

different from that of GEMS-spreadsheet method. This difference in temporal patterns may suggest that the two biogeochemical methods (GEMS-Century and GEMS–EDCM) captured the impacts of climate variability and change on carbon 

dynamics and the GEMS-spreadsheet method did not. Third, 

carbon sequestration in biomass decreased over time primarily 

because of the aging of forests in the region.

Figure 3.10.  Map showing study area for a test using terrestrial methods to assess carbon stocks, carbon sequestration, and

greenhouse-gas fluxes. A, Location of test area. B, Distribution of land-cover classes in 2001. The test area includes Tensas Parish, La.,

and Claiborne County, Miss., in the Mississippi Alluvial Plain and the Mississippi Valley Loess Plains (EPA level III ecoregions 73 and 74

modified from Omernik (1987)), respectively.

93° 90°

36°

33°

30°

La.

Miss.

Ark.

Tenn.

Mo. Ky.

Tex.

0 50 100 MILES

0 50 100 KILOMETERS

0 1 5 0

10 15

15

5

20 KILOMETERS

0 20 MILES

A B

EXPLANATION

EXPLANATION

Figure 3.11.  Graph showing comparisons of annual carbonsequestration rates of biomass carbon stock among the three

methods (GEMS-spreadsheet, GEMS-Century, and GEMS–EDCM)

using the “enhanced land use and land cover with reference land

management” (L) scenario for the whole test area from 2001 to

2050. The same comparison made using the same methods and

the “reference land use, land cover, and land management” (R)

scenario yielded similar results. Abbreviations and acronyms

are as follows: EDCM, Erosion-Deposition-Carbon Model, GEMS,

general ensemble modeling system.

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Annualrate of change in biomass carbon,

in teragrams of carbon per year
54 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

The total carbon sequestration (the change in carbon 

stocks) using the GEMS-Century method was 17.67 Tg from 

2001 to 2050, which was much higher than that calculated by 

using the GEMS-spreadsheet model (8.45 Tg) or the GEMS–

EDCM method (4.76 Tg) (table 3.11). The corresponding 

annual carbon-sequestration rates for the test area were 

0.17, 0.35, and 0.14 Tg C/yr from the GEMS-spreadsheet, 

GEMS-Century, and GEMS–EDCM methods, respectively. 

The differences shown here might be attributed to the differences in the input data sources, initial parameter values, and 

simulation algorithms of each model, especially between the 

GEMS-Century and GEMS–EDCM methods. For example, 

higher rate of carbon sequestration from the GEMS-Century 

method might have been caused by the lower initial biomass 

carbon values, faster biomass accumulation (compared to the 

GEMS-spreadsheet method’s result), and SOC accumulation. 

In contrast, the lower carbon-sequestration estimate from the 

GEMS–EDCM method can be attributed to lower biomass 

accumulation (compared to the GEMS-spreadsheet method’s 

result) and SOC loss.

All three methods estimated significantly higher ecosystem carbon stocks (table 3.11) for the L scenario, indicating 

additional carbon sequestration of 1.64, 1.75, and 1.08 Tg 

from the GEMS-spreadsheet, GEMS-Century, and GEMS–

EDCM methods, respectively, relative to the R scenario. 

These amounts represented about an additional 20 percent, 

10 percent, and 23 percent increase, respectively, above the 

carbon-sequestration values calculated using the R scenario. 

The result suggests that these models, rather consistently, are 

capable of quantifying additional carbon sequestration from 

enhanced changes in land-use and land-cover activities such as 

the Wetland Reserve Program.

Table 3.12 lists major differences in CH4 and N2O 

emissions between the GEMS-spreadsheet and GEMS–

EDCM methods (no results are currently available from 

the GEMS-Century method). The major conclusions of this 

comparison of methods were as follows: (1) the GEMSspreadsheet method estimated an annual CH4

-emission rate on 

wetlands that is more than double that of the GEMS–EDCM 

method; (2) estimates of N2O emissions demonstrated opposite temporal trends, although both methods produced similar 

N2O-emission rates; and (3) the GEMS-spreadsheet method 

showed small increases in annual emission rates of CH4 and 

N2O, whereas the GEMS–EDCM method showed decreasing 

trends. Both CH4 and N2O emission rates were greatly affected 

by soil moisture, temperature, and substrate availability, and 

thus varied considerably depending on site conditions. For 

example, CH4 emissions from rice paddies ranged from 2 to 

1,642 kgC/ha/yr (Lindau and others, 1990). After reviewing many field studies, we found that the uncertainty of the 

CH4 and N2O emission factors using the GEMS-spreadsheet 

method was very high. The predicted emission rates of CH4

and N2O from the GEMS–EDCM method were within the 

uncertainty range of local field observations. Using the L scenario, the GEMS-spreadsheet method resulted in greater CH4

and N2O emission rates than the GEMS–EDCM method (fig. 

3.12) relative to the R scenario.

The preliminary results from the test highlighted several 

issues. First, the differences between the models (specifically 

the biases and errors in the individual models) were a major 

contribution to the overall uncertainty. Using model ensembles 

within the GEMS framework, some of the model uncertainty 

can be reduced and the model’s structure errors can be corrected. Second, the input data process (for example, using 

different forest biomass initialization data and processes in 

the models) might significantly affect the model’s output and, 

therefore, the assessment of carbon stocks, carbon sequestration, and GHG fluxes. As new and improved data and modeling results become available, they will be incorporated into 

this methodology. Third, future efforts should emphasize 

Table 3.11.  Total carbon stocks and cumulative and additional carbon sequestration within the test area (Tensas Parish, La., and

Claiborne County, Miss.), calculated using the specified method, and using the “reference land use, land cover, and land management”

(R) and “enhanced land use and land cover with reference land management” (L) scenarios.

[Values represent the amount at the end of the given year. Abbreviations and acronyms are as follows: EDCM, Erosion-Deposition-Carbon Model; GEMS, 

general ensemble modeling system; Tg, teragrams]

Year

Total carbon stocks, by method, in Tg1 Cumulative carbon sequestration, by 

method, in Tg1

Additional carbon sequestration, by 

method, in Tg2

GEMSspreadsheetGEMSCenturyGEMS–

EDCM

GEMSspreadsheetGEMSCenturyGEMS–

EDCM

GEMSspreadsheetGEMSCenturyGEMS–

EDCM

2001 40.91 34.22 43.30

2010 43.45 38.37 42.56 2.54 4.15 -0.74 0.30 0.47 0.02

2020 45.57 42.11 43.71 4.67 7.90 0.41 0.52 0.54 0.15

2030 47.32 45.88 45.24 6.41 11.66 1.94 0.90 0.95 0.39

2040 48.48 49.14 46.70 7.58 14.92 3.39 1.27 1.43 0.82

2050 49.36 51.89 48.07 8.45 17.67 4.76 1.64 1.75 1.08

1

Values were calculated using the “enhanced land use and land cover with reference land management” (L) scenario. 

2

Values represent the difference between the L scenario and the “reference land use, land cover, and land management” (R) scenario.
3. Methodology for the National Assessment 55

Table 3.12.  Annual emission rates of methane and nitrous oxide and their total differences (between 2001 and 2050), for the “reference land

use, land cover, and land management” (R) and the “enhanced land use and land cover with reference land management” (L) scenarios.

[Abbreviations and acronyms are as follows: CH4, methane; EDCM, Erosion-Deposition-Carbon Model; GEMS, general ensemble modeling system; L, “enhanced 

land use and land cover with reference land management” scenario; N2O, nitrous oxide; R, “reference land use, land cover, and land management” scenario]

Year

CH4 from wetlands (billion grams of carbon per year) N2O from all land (billion grams of carbon per year)

GEMS-spreadsheet method GEMS–EDCM method GEMS-spreadsheet method GEMS–EDCM method

L R L R L R L R

2001 28.47 28.42 15.50 15.47 2.74 2.74 2.77 2.76

2010 28.88 28.53 13.32 13.20 2.78 2.77 1.98 1.99

2020 29.26 28.36 12.66 12.45 2.82 2.76 1.91 1.92

2030 29.80 28.24 13.57 13.24 2.87 2.77 1.86 1.89

2040 30.43 28.10 13.04 13.65 2.92 2.77 1.74 1.77

2050 31.01 27.94 12.92 12.42 2.96 2.76 1.73 1.77

Difference between 2050 and 

2001

2.54 -0.48 -2.59 -3.05 0.22 0.02 -1.04 -1.00

Average 29.64 28.27 13.50 13.41 2.85 2.76 2.00 2.02

Standard deviation 0.96 0.23 1.03 1.12 0.08 0.01 0.39 0.37

coastal aquatic ecosystems are important because they 

receive, sequester, and biogeochemically process riverine and 

groundwater inputs of terrestrial carbon and nutrients. Coastal 

primary production is enhanced by inputs of terrestrially 

derived nutrients and coastal sequestration is enhanced by the 

co-transported sediments.

Aquatic ecosystems are not fully integrated into current terrestrial ecosystem models; therefore, their role in a 

national assessment of carbon sequestration and GHG fluxes 

must be quantified independently, relying more on empirical and statistically based assessment methods instead of the 

BGC modeling used for terrestrial ecosystems. This quantification requires the tracking of the carbon’s sources and 

sinks from headwater areas, along stream and river courses, 

to and through inland water bodies, to and through estuaries, to its delivery and fate in coastal waters. Water is the 

principal carrier of dissolved and particulate carbon, and 

aquatic carbon flux is dependent on streamflow; therefore, a 

literature review and metadata analysis in order to quantify the 

uncertainty of field observations at the regional scale.

3.3.5. Carbon Sequestration and GreenhouseGas Fluxes of Aquatic Ecosystems

Aquatic ecosystems in this assessment are defined to 

include streams, rivers, estuaries, coastal waters, and perennial ponds, lakes, and impoundments. Coastal and freshwater 

wetlands and ephemeral wet depressions that temporarily retain water following precipitation or flooding events 

will be assessed using the methods described for terrestrial 

ecosystems.

Inland aquatic ecosystems are important components 

of terrestrial landscapes and commonly are locations of 

intense carbon sequestration, biogeochemical cycling, and 

greenhouse-gas emissions (Cole and others, 2007). Similarly, 

Figure 3.12.  Graphs showing comparisons of emissions between

the GEMS-spreadsheet model and the GEMS–EDCM model,

showing the difference between the “reference land use, land

cover, and land management” (R) scenario and the “enhanced

land use and land cover with reference land management” (L)

scenario, from 2001 to 2050. The emission rate from the GEMS–

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2000 2010 2020 2030 2040 2050

Year

Difference in CH4

,

in gigagrams of carbon 

GEMS-spreadsheet method

GEMS–EDCM method

EXPLANATION

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

2000 2010 2020 2030 2040 2050

Year

Difference in N O,

2

in gigagrams of nitrogen

GEMS-spreadsheet method

GEMS–EDCM method

EXPLANATION

EDCM model is the 10-year average. A, Annual methane emission

from wetlands. B, Total annual nitrous-oxide emission from all

land. Abbreviations and acronyms are as follows: CH4, methane;

EDCM, Erosion-Deposition-Carbon Model; GEMS, general

ensemble modeling system; N2O, nitrous oxide.
56 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

quantitative understanding of the relation between precipitation and runoff for ecoregions, and an accurate accounting 

of stream and river flow will be required. The assessment 

of the BGC cycling of aquatic carbon (including the production, consumption, and emission of GHGs) also will 

require additional knowledge of water chemistry and water’s 

physical conditions, such as temperature, light penetration, 

and water-level fluctuations. The assessment of aquatic 

ecosystems will rely on empirical methods that use available 

national and regional datasets (current and forecasted) of 

streamflow, water chemistry, size and distribution of water 

bodies, watershed characteristics, sediment transport and 

deposition, and other environmental variables to estimate and 

predict amounts and rates of carbon sequestration and GHG 

fluxes. Appendix E provides detailed discussion of methods 

for assessing aquatic ecosystems.

Lateral Fluxes.—The initial assessment of lateral 

fluxes of dissolved and particulate carbon will be based on 

available streamflow and water-chemistry data, including 

data from the National Water Information System (NWIS; 

http://waterdata.usgs.gov/nwis). Long-term changes in lateral 

fluxes may be more closely linked to a change in water 

quantity than to a change in the relation between water and 

carbon yield (Striegl and others, 2007). Additionally, streamflow data are much more prevalent than water-chemistry data 

that specifically characterizes carbon yield, and predicting 

streamflow is much more reliable than predicting the change 

in carbon yield; therefore, the primary emphasis will be 

placed on developing an ecoregion-level understanding of 

the relation between water and carbon yield in water using 

existing data. Projecting the changes in water discharge based 

on climate-change and land-use change scenarios can then be 

accomplished using the USGS Precipitation-Runoff Modeling 

System1 or similar programs.

These projections will be used together with empirically 

derived relations between water and carbon yield to project 

changes in lateral carbon export. Alternative methods for 

estimating lateral flux using data that characterize LULC in 

watersheds also will be explored, including the application 

of a carbon module (currently under development) of the 

“spatially referenced regressions on watershed attributes” 

(SPARROW) water-quality model.2 Existing SPARROW 

modules will be used to model nutrient and sediment fluxes 

(Alexander and others, 2008; Schwarz, 2008). A related 

goal of the assessment is to move towards fully coupling the 

GEMS and other terrestrial ecosystem models with a lateral 

export model. Additional information on the methodology 

for calculating carbon stocks, carbon sequestration, and 

GHG fluxes from inland waters, estuaries, and coastal waters 

is provided in appendix E.

1

http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/software/oui_and_mms_s/

prms.shtml.

2

http://water.usgs.gov/nawqa/sparrow.

Lakes and Impoundments.—The net storage of carbon 

in lakes and impoundments reflects a balance between carbon 

burial in sediments and GHG emissions from the surfaces 

and outlets of the water bodies. Carbon burial in lakes is 

driven mainly by autochthonous production, which has been 

quantified in a variety of settings (Cole and others, 2007). 

For the assessment, carbon-burial estimates will be compiled 

and analyzed statistically to derive a probability distribution function (PDF) of sedimentation rates in lakes. Carbon 

burial in impoundments (reservoirs and farm ponds) depends 

primarily on sedimentation rates and the concentration of the 

organic carbon in the buried sediments (S.V. Smith and others, 

2005). Sedimentation-rate data are sparse, but include data for 

approximately 1,800 reservoirs in the Reservoir Sedimentation Database (RESSED);3

 these data will be used to develop 

a PDF of sedimentation rates in reservoirs. The concentration of organic carbon in the buried sediments often reflects 

the carbon content of the upland soils from which they were 

eroded (Ritchie 1989; S.V. Smith and others, 2005). For the 

assessment, the concentration of organic carbon in lake and 

impoundment sediments will be approximated on the basis 

of a new map showing soil carbon that was developed by the 

USGS using SSURGO data (Bliss and others, 2009). The surface areas of lakes and impoundments within each assessment 

unit will be mapped using data in the National Land Cover 

Database (NLCD). Carbon for each assessment unit will be 

calculated as the product of sedimentation rates, concentrations of organic carbon in the sediments, and the surface areas 

of lakes and impoundments.

Data on GHG emissions from lakes and impoundments 

are very sparse, particularly for CH4 and N2O; available data 

will be compiled from published literature, and the statistical 

distribution of GHG fluxes will be analyzed. The resulting 

PDFs will be combined with lake and impoundment surfacearea data to estimate GHG fluxes from lakes and impoundments within each EPA level II ecoregion. Region-specific 

data collection on sedimentation rates and GHG fluxes 

from lakes and impoundments will be necessary in order to 

further refine the model estimates; these data will enable the 

development of new regression models that will be used to 

estimate carbon sequestration in inland water bodies, using 

watershed characteristics and nutrient loads as explanatory 

variables.

Coastal Waters.—Coastal and estuarine biogeochemical processes related to fixation and burial of carbon are 

intimately tied to coastal autochthonous production through 

the process of terrestrial riverine transport of nutrients and 

sediments to estuarine and coastal environments. Nutrients 

transported from inland regions may stimulate the primary 

production in coastal waters (da Cunha and others, 2007; 

Seitzinger and Mayorga, 2008), and sediments may act to 

increase the flux of this material to the deep ocean, where 

the carbon would be buried and effectively sequestered from 

3

http://ida.water.usgs.gov/ressed.
3. Methodology for the National Assessment 57

the atmosphere for millions of years (Hedges and Keil, 1995; 

Armstrong and others, 2002; Sarmiento and Gruber, 2002). 

The assessment of carbon sequestration in coastal waters 

thus will include a model of the primary production that is 

sensitive to the changing nutrient content of the water and a 

process model that explicitly incorporates both the primary 

production and the controlling processes in carbon remineralization, such as degradation during sinking, ballasting, 

bioturbation, and burial (Dunne and others, 2005). Because 

the method used here is a sensitivity analysis approach as 

a function of changes in terrestrial GHG transport, there is 

no need to spatially define the seaward boundaries of the 

coastal waters; however, because local conditions such as the 

water-column depth and the depositional environment are 

important controlling factors, the estimates of carbon sequestration and associated BGC processes will be produced on an 

individual basis for coastal waters that have a large terrestrial 

source and on a regional basis for coastal waters that have 

smaller sources. The changes in production and release of 

methane and nitrous oxide in sediments in intertidal, estuarine, and coastal waters will be estimated by using regression 

models to generate projected water-column depths, sediment 

production, and the contribution of groundwater to coastal 

waters, which can be significant (Bange, 2006; Hirota and 

others, 2007).

3.3.6. Analyses of Assessment Results—

Mitigation Activities, Ecosystem Services, Costs,

and Benefits

The primary data products of the assessment will contribute to an understanding of how carbon and GHG move 

in and out of natural and managed ecosystems under current 

and future potential conditions. The potentially broad range of 

users most likely will need data products that are synthesized 

to highlight (1) the potential effects (such as LULC change) 

and effectiveness of mitigation activities (such as landmanagement activities), (2) the direct and ancillary effects 

on ecosystem services, and (3) the associated economic and 

social costs for carbon sequestration and the reduction of GHG 

emissions. Quantifying the direct and ancillary effects on 

ecosystem services will increase the relevance to and impact 

of the assessment results on mitigation strategies and management actions. This section summarizes the proposed methods 

for analyzing the effects of mitigation activities, the effects on 

ecosystem services, and the relevant economic and social costs 

of mitigation activities. See appendixes D and F for details 

about the methods.

Analysis of the Effects of Mitigation Activities.—Converting nonforested land to forested land sequesters more 

carbon per unit of area (expressed as “carbon density”) than 

other land-management activities that are focused only on 

increasing the soil organic carbon (SOC) (Thomson and others, 2008). The actual amount of carbon stored in a forest is a 

function of the forest type (for instance, deciduous or evergreen), its location, and the time required for the trees to grow. 

If the land use remains in agriculture, then increases in SOC 

will vary by management activity and the effects of crop cultivation are usually minimal after 15 to 20 years (West and Post, 

2003). In addition, future climate conditions may dramatically 

alter key controls, such as temperature or moisture availability, 

thus causing the historic rates of carbon accumulation to be 

inaccurate. These variable effects of deliberate LULC changes 

or land-management changes on carbon sequestration can be 

evaluated quantitatively and displayed in formats such as table 

3.13. For the assessment results, it is important to understand 

not only the total amount of potential carbon sequestration, 

but also the relations between the changes in carbon and the 

cost of gaining additional sequestration capacity. The cost 

Table 3.13.  Example of a table format for reporting the effectiveness of mitigation activities for sequestering carbon, by

the duration (years) of the implementation.

[Mitigation activities include land-use and land-cover changes and land-management activities. The values in the cells would be given as MgC/

ha/yr (millions of grams of carbon per hectare per year). LULCC, land-use and land-cover change]

Mitigation activity

Duration, in MgC/ha/yr

0–5

years 

6–10

years 

11–15

years 

16–20

years

21–25

years

26–30

years

31–35

years

36–40

years

LULCC

Conversion of pasture to evergreen forest

Conversion of croplands to woody wetland

Land-management change

Increased harvest rotation

Increased conservation tillage

LULCC and land-management change

Conversion of pasture to managed evergreen forest
58 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

may be expressed in terms of the time taken to reach the goal, 

resources that are spent, acres of lands used, and combinations 

of these. The effects of LULC changes and (or) land-management changes over a period of years can be easily analyzed 

using tools such as statistical software, GIS, or spreadsheets, 

and the results can be summarized using tables such as the 

example shown in table 3.13.

Analysis of the Effects of Mitigation Activities on Ecosystem Services.—Ecosystem services are the benefits that people 

and societies derive from the natural processes that sustain 

ecosystems (Daily, 1997). A mitigation strategy may have ancillary effects on ecosystem goods and services. Ancillary effects 

are defined as those effects that are subordinate to the primary 

goal or intended impact of a strategy, policy, or mitigation 

activity, including unintended consequences. Any change, either 

anthropogenic or naturally occurring, that affects structural 

components (such as the composition of a plant community) 

or processes (such as nutrient cycling) will impact the quality, 

quantity, and types of services produced from that ecosystem. 

Although it is beyond the scope of the assessment to quantify 

all ecosystem services, some of the important services that are 

likely to be affected by mitigation activities for ecological carbon sequestration are listed in table 3.14.

Estimating and forecasting the changes in carbon stocks, 

carbon sequestration, and GHG fluxes resulting from mitigation strategies will be based on the spreadsheet and the GEMS 

modeling approaches described in section 3.3.4 and appendix 

D. Many of these primary assessment data products can also 

be categorized as ecosystem services (table 3.14), including 

carbon stocks in soils and vegetation, carbon sequestration, 

CH4 and N2O emissions, net ecosystem productivity, timber 

production, grain production, and soil erosion. The estimates 

of how changes in carbon stocks, carbon sequestration, and 

GHG fluxes will affect ecosystem services will be produced 

for each ecoregion because they will be based on the primary 

assessment data products.

In addition to data already produced as the result of the 

analysis of mitigation effects for carbon stocks, carbon sequestration, and GHG fluxes, further analysis will be necessary. 

As an example, biophysical production functions and habitat 

suitability indices will need to be constructed based on the 

known relations between the LULC classes in an ecosystem 

(generated by FORE–SCE and GEMS modeling) and the relevant ecosystem services (Nelson and others, 2008; Tirpak and 

others, 2009). For example, suitable habitat for specific wildlife 

species will vary as a function of forest composition and will 

be different for evergreen and deciduous forests. These data 

will be combined with existing models such as SWAT (Soil and 

Water Assessment Tool of the USDA), GEMS-Century, GEMS–

EDCM, and the Landscape Disturbance and Succession model 

(LANDIS–II, a forest landscape model created by a consortium 

of the USFS, University of Wisconsin, and Portland State University). A distributed geospatial model-sharing platform will 

be used to facilitate sharing and integrating these models, which 

will quantify ecosystem services and provide decision support. 

Additional details are provided in appendix F.

Given the need to have regionally specific information 

and our limited understanding of the complex relationships 

among ecosystem processes, land-management actions, climate change, and ecosystem services, this part of the assessment will be limited to case studies within selected ecoregions 

where data and models already have been developed and can 

be readily incorporated into the assessment framework. The 

most likely regions include the Mississippi Alluvial Valley, 

Prairie Pothole Region, southern Florida, and the Chesapeake 

Bay watershed.

Table 3.14.  Ecosystem services that are likely to be affected by mitigation activities and will be analyzed, their functions, and the

assessment data products that will be used to analyze the effects of mitigation activities.

[Abbreviations are as follows: CH4, methane; GHG, greenhouses gas; N2O, nitrous oxide]

Ecosystem service Function of the

ecosystem service

Assessment data products to be used in analyzing 

effects of mitigation activities

Soil formation Supporting Soil organic carbon.

Primary production Supporting Net ecosystem productivity.

GHG mitigation Regulating Soil organic carbon.

Carbon sequestration.

N2O and CH4 emissions.

Water quality Regulating Soil erosion.

Nitrate retention.

Food Provisioning Grain production.

Wildlife habitat Provisioning Species richness.

Occupancy and connectivity models.

Species climate vulnerability.

Metapopulation dynamics.

Fiber Provisioning Timber production.

Recreation Cultural Species richness.

Occupancy models.
3. Methodology for the National Assessment 59

Different ecosystem services have different definitions, 

ranges, and meanings, so an ecosystem services change indicator (ESCI) has been defined in this report in order to compare 

them simultaneously:

, (1)

where ES refers to the output value of a selected 

ecosystem service, and

ES0 is the corresponding baseline value.

A test that compared the ESCI values for selected 

ecosystem services was conducted for Tensas Parish, La. (in 

EPA level III Ecoregion 73, Mississippi Alluvial Plain), and 

Clairborne County, Miss. (Ecoregion 74, Mississippi Valley 

Loess Plains). The changes in selected ecosystem services as 

they relate to carbon sequestration were considered by using 

the IPCC SRES A1B storyline (Nakicenovic and others, 

2000), the land-cover classes for the two jurisdictions (see 

figure 3.7), and the “reference land use, land cover, and land 

management” (R) and the “enhanced land use and land cover 

with reference land management” (L) scenarios (see section 

3.2.2). The results in table 3.15 and figure 3.13 are an example 

of the model outputs and one method (using ESCI) of comparing changes over time using the IPCC SRES A1B storyline 

and the R and L scenarios. In practice, ecosystem services will 

be quantified using multiple models for the assessment and 

for providing uncertainty estimates. In the table, the modeled timber production as an ecosystem service for the 2041 

through 2050 time period for the R and L scenarios is 9.70 

and 3.61 grams of carbon per square meter per year (gC/m2/

yr), respectively, although the baseline value for 2001 through 

2010 is 4.89 gC/m2/yr. An ESCI value of greater than zero 

indicates a positive increase in ecosystem service change 

compared to baseline (2001–2010); an ESCI value of less than 

Figure 3.13.  Chart showing a comparison of ecosystem

service changes using the ecosystem service change indicator

(ESCI). Baseline data for 2001 through 2010 are shown along

with projected changes from 2041 through 2050 using the

“reference land use, land cover, and land management” (R) and

the “enhanced land use and land cover with reference land

management” (L) scenarios. Values shown apply to the whole

chart.

Ecosystem

productivity

Soil organic carbon

Biomass carbon

Timber production

Grain production

Carbon storage

Carbon sequestration

N2

O emission

CH4

 emission

Erosion

-1.0

-0.5

0.0

0.5

1.0

R scenario from 2041 through 2050

L scenario from 2041 through 2050

Baseline data from 2001 through 2010

Table 3.15.  Preliminary ecosystem service estimates for a test in Tensas Parish, La., and Claiborne County, Miss., using the A1B storyline.

[Abbreviations and acronyms are as follows: CH4, methane; ESCI, ecosystem services change indicator; L, “enhanced land use and land cover with reference 

land management” scenario; N2O, nitrous oxide; R, “reference land use, land cover, and land management” scenario]

Assessment data products Unit of measurement

Baseline 

value

(2001–2010)

R (2041–2050) L (2041–2050)

Output 

value ESCI Output value ESCI

Net ecosystem productivity Grams of carbon per square meter per year 651 571 -0.123 575 -0.117

Soil organic carbon Grams of carbon per square meter 5,433 6,153 0.133 6,155 0.133

Living biomass carbon Grams of carbon per square meter 6,193 9,872 0.594 10,207 0.648

Timber production Grams of carbon per square meter per year 4.89 9.70 0.985 3.61 -0.260

Grain production Grams of carbon per square meter per year 70 57 -0.185 52 -0.252

Carbon storage Grams of carbon per square meter 12,377 16,810 0.358 17,146 0.385

Carbon sequestration Grams of carbon per square meter 148 91 -0.384 105 -0.292

N2O emission Gigagrams of nitrogen 24.3 21.6 0.112 21.7 0.110

CH4 emission Teragrams of carbon 0.163 0.133 0.183 0.143 0.125

Erosion Tons per hectare per year -0.062 -0.059 0.049 -0.061 0.008
60 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

zero indicates a negative change. The ESCI absolute value 

reflects the magnitude of the ecosystem change.

Analysis of Mitigation Costs and Benefits.—The implementation of mitigation activities to enhance carbon sequestration can result in societal benefits associated with reducing 

impacts of climate change and can also provide benefits from 

marketable commodities, such as harvested timber and other 

ecosystem services. Depending on the activity, the net societal 

values for carbon sequestration can be positive (societal benefits) or negative (societal costs). Carbon-sequestration activities also have costs, such as the opportunity cost of the land on 

which to enact a mitigation activity, as well as any associated 

capital or maintenance costs. This section will explain a simple 

accounting approach that can be used to estimate the potential 

benefits and costs of a management activity so that a user will 

get a “first-cut” approximation of an activity’s possible payoff. 

Both current and potential market and societal benefits and 

management-activity costs will be included in a discounted cash 

flow (DCF) analysis (Schaltegger and Burritt, 2000; Wrisberg 

and de Haes, 2002). A DCF analysis is a widely used valuation tool that will (1) account for both the benefits (the societal 

benefits of carbon sequestration and any other benefits) and the 

capital and operating costs of a potential project and (2) assume 

the value of money changes over time (discount rate). All future 

flows of benefits and costs will be estimated and discounted to 

yield a present value. Assuming that all other relevant market 

conditions are constant, if the value of the investment is greater 

than the cost of the investment, the activity will have a positive 

net present value. An example of the application of this method 

in Tensas Parish, La., using one potential mitigation activity, is 

given later in this section.

The assessment methodology will use the DCF analysis 

to estimate (1) the carbon sequestration and other ecosystemservice benefits as well as any income from a marketed 

commodity (for example, sawtimber), if applicable; and (2) 

the economic costs of acquiring the land and implementing a 

mitigation activity. The benefits and costs will be estimated 

in terms of the present value of the benefits (PVB) and the 

present value of the costs (PVC) of a mitigation activity. In 

addition, the DCF method will be used to associate the benefit 

and cost information of mitigation activities in one ecoregion 

with others. Furthermore, the analysis of the benefits and costs 

of a management activity will not be quantified as constraints 

for the scenario construction, which are ramifications of landmanagement activities.

Two types of ecosystem services benefits will be included 

in the methodology (Jenkins and others, 2010). The first 

benefit is the market value of a commodity that is sold in 

traditional markets. The second benefit is the economic value 

to society in terms of the flow of ecosystem services. Both 

should be used in societal-benefit and cost analyses of public 

policies or programs. The present value of the market benefits 

will be entered into the numerator of equation F1 in appendix 

F to calculate the present value of benefits (PVB).

Market benefits.—The market value for services provided 

by a particular ecosystem is based on the commodities that 

are currently bought and sold in traditional markets. Market 

values for the economically valuable outputs of certain ecosystems, such as timber (stumpage value), will be estimated using 

a market price of the output harvested in the year it is sold, 

which will be assumed to be the final year in which the assessment was conducted; the estimated market value is entered 

into the DCF. These direct-use services are typically consumptive (for example, commercial fishing and pharmaceuticals).

Nonmarket benefits.—Some ecosystem services, such 

as recreational fishing and birdwatching, are not valued in 

traditional markets (nonmarket values). Although the price of 

a marketable commodity is determined by willing buyers and 

sellers in the marketplace, ecosystem services that currently 

are not traded in a market require alternative ways to estimate 

their value to society (Merlo and Croitoru, 2005; Richardson 

and Loomis, 2009).

The measurement and estimation of societal values in the 

assessment will link ecosystem services to existing valuation 

methods in order to facilitate the analysis of these externalities 

by users. For example, economic studies that elicit the willingness to pay either by using economic surveys (Hanemann and 

others, 1991; Stevens and others, 2000) or by market-based 

factors (Bernknopf and others, in press) can provide monetary 

benefit estimates of ecosystem services (Loomis and Helfand, 

2001). These types of analyses will be used to estimate the 

willingness of society to pay for environmental improvements. Brookshire and others (2010) used the stated preference approach to estimate the value of vegetation composition, 

water availability, bird breeding, and migratory bird abundance in a watershed in the southwestern United States; however, when the resources for conducting an economic analysis 

in certain places like this one are limited, one approach to 

economic valuation is to use benefit transfer studies. Benefit 

transfer studies are a means to adapt a study from one location or region to another. This approach is a way to harness 

the benefits of existing economic studies while minimizing 

the need for costly new site-specific analyses (Brookshire and 

Neill, 1992; Devosouges and others, 1998; Brookshire and 

Chermak, 2007; Brookshire and others, 2007). The benefit 

transfer method will be applied in the test described below 

and will apply the specific results from a preexisting study for 

valuing several ecosystem services for the Mississippi Alluvial 

Valley to Tensas Parish, La. (Jenkins and others, 2010). For 

example, ecosystem societal value estimates will be based 

on the number of hectares converted from agricultural use to 

managed forest plantations. The nonmarket benefit estimates 

will be entered into equation F1 in appendix F to calculate the 

PVB of these ecosystem services.

Because few markets exist for ecosystem services, the 

assessment methodology will incorporate the possibility of 

potential markets for specific ecosystem services such as 

nitrogen mitigation (Jenkins and others, 2010). Potential 

markets will be included because of the possibility that, 

while the assessment is being planned, the markets for 

ecosystem services will expand and new policies associated 

with those markets will be implemented. Potential market 
3. Methodology for the National Assessment 61

values can be entered into the numerator of equation F1 in 

appendix F.

Economic costs.—Land and the cost to implement a 

mitigation activity will vary over time and space because of 

the type, size, and design criteria of the mitigation activity; its 

geographic location; the cost of labor and materials for it; the 

alternative uses of the targeted land; and the biophysical site 

characteristics. In the methodology, economic costs will be 

estimated as a present value (PVC equation F2 in appendix F) 

and an equivalent annual cost (EAC, which is calculated using 

equation F3 in appendix F and is derived from the PVC).

The components of cost are (1) the cost of obtaining 

the land and (2) the direct engineering costs4 involved in the 

ecological carbon-mitigation activity that has been chosen. The 

second component includes the following factors: (1) up-front 

or one-time capital-investment costs for establishment and 

installation of the mitigation activity, including site preparation, 

planting, and any initial chemical treatments (and documentation of the environmental impacts of all of the preceding); (2) 

recurring capital expenses of the activity, such as the expenses 

related to boundary maintenance; and (3) annual operating, 

maintenance, and management costs (including performance 

monitoring, administration, insurance, and other transaction 

costs). See appendix F for details on these cost categories.

For the test, the economic costs were estimated using 

methods found in Huang and others (2004), Atkinson and others 

(2004), and Brown and Kadyszewski (2005). They are shown 

in table F1 and are computed using equations F2 and F3 in 

appendix F. These cost estimates were used in the test below 

for Tensas Parish, La. Other estimates have been developed to 

assess the engineering costs for afforestation projects (Adams 

and others, 1996); reviews and summaries of the studies that 

employ them are found in Stavins and Richards (2005).

Test for Estimating the Costs and Benefits of a Mitigation 

Activity.—In this test, the benefits and costs of a mitigation 

activity were calculated. The theoretical mitigation activity for 

the test was the conversion of 10,475 ha of agricultural land to 

forest in Tensas Parish, La. Specifically, this study compares the 

cost and benefits of the mitigation activity (foresting the land) 

with the costs and benefits of the current (or reference) unmitigated agricultural land. The benefits of the marketable sawtimber (timber suitable for sawing) products mentioned below were 

estimated using equations F4 through F9 in appendix F.

Benefits.—In 2009, if the intent of the mitigation activity 

included harvesting the timber, the market value for the timber 

was based on stumpage values of $31.01 per ton for sawtimber 

(Louisiana Department of Agriculture and Forestry, 2010). By 

applying equation F1 in appendix F, the present value of the 

benefits for harvesting the timber for a 40-year period, discounted at 4 percent, for the mitigation activity was calculated 

to be $303,700 (assuming that all the harvested timber was of 

sawtimber quality). The timber would be harvested in year 40 

of the mitigation activity, which is 2050 for this test.

4

Investment and operating costs are incurred for economic production and 

its environmental impacts and improvements.

The test links biophysical outcomes with economic values. The benefits estimate of the ecosystem services is based 

on the benefit transfer method for the Mississippi Alluvial Valley (EPA level III ecoregion, modified from Omernik (1987)) 

in Tensas Parish, as estimated by Jenkins and others (2010). 

The benefits estimate is entered into the numerator of equation 

F2 in appendix F. The estimates for the mitigation activity are 

calculated using the same 4 percent discount rate as for the 

costs and market values calculation described above. The current ($1 per hectare per year) and potential ($396 per hectare 

per year) market values for carbon sequestration are less than 

$1 million per year and $4.1 million per year, respectively, 

although the societal value could range from $1.8 million to 

$2.3 million per year (a societal value range of $171 to $222 

per hectare per year). Two examples of societal benefits would 

be (1) avoiding loss of wildlife habitat caused by rising temperatures by sequestrating carbon dioxide and other GHG and 

(2) wetland preservation to improve water quality. The potential economic value of this service that could be realized is as 

high as $6.4 million per year; the present value benefits at a 4 

percent discount rate would be $61.1 million (calculated using 

equation F1 in appendix F). Nitrogen mitigation could have 

potential market ($624 per hectare per year) and social ($1,248 

per hectare per year) values of $6.5 million per year and $13.1 

million per year, respectively. The potential economic value 

of this ecosystem service could be as great as $19.6 million 

per year; the present value benefits at a 4 percent discount 

rate would be $387.9 million (calculated using equation F1 in 

appendix F).

Costs.—The PVC and EAC were estimated using equations F2 and F3 in appendix F with cost data indexed to 2009 

dollars (Council of Economic Advisers, 2008, table B–101) 

(Huang and others, 2004; Brown and Kadyszewski, 2005). 

The cost estimates are for the analysis of the mitigation 

activity that converts 10,475 hectares of agricultural land 

to woody wetlands over a 40-year period in Tensas Parish, 

under the “enhanced land use and land cover with reference land management” (L) scenario; the assumption is that 

converting the land from agricultural use to woody wetlands 

improves the carbon-sequestration capacity of that acreage. The potential land and implementation costs would be 

about $18.5 million (PVC using a cost of $1,766 per hectare 

discounted at a rate of 4 percent) and $1.4 million per year 

(EAC at $130 per hectare per year) using the data in table F1 

in appendix F.

Net benefits.—The net present value of the mitigation 

activity (the difference between present values of benefits 

and costs) ranges between -$18.2 million (assuming marketable timber value only) and $436.9 million (assuming that 

all potential and societal values for the ecosystem services 

are realized). Although the mitigation-activity costs may be 

significant at $18.5 million, the values of the marketable commodities along with the potential values of ecosystem services 

could be even greater. Thus, depending on the assumptions 

of the benefits to be included, the return on investment in the 

mitigation activity could be significant.
62 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

3.3.7. Validation Methods

The validation strategy for the national assessment is 

designed to achieve two overarching objectives: (1) identify, 

quantify, and document sources of error that underlie the 

assessment results; and (2) guide efforts to increase accuracy through improvements in data collection, model design, 

sampling design, and other elements of the methodology. The 

validation effort will focus primarily on the assessment data 

products; the quality of the input data will be documented by 

reference to existing reports. (The methodology for validation 

is found in appendix G.)

Because the assessment deliverables will be produced and 

reported at the scale of assessment units (EPA level II ecoregions of Omernik (1987)), validation exercises also will be 

conducted at that scale. Validation will be conducted for assessment results of the “current” (2001–2010) carbon stocks, carbon 

sequestration, and GHG fluxes in ecosystem. Because the same 

methods and model runs will be used to produce results for 

2001 through 2050, the validation results for target data products can be considered indicative of future potential assessment.

A set of output data products (estimates) from the assessment of terrestrial and aquatic systems will be the initial targets 

for validation (table 3.16). The target data products will be 

selected based on their relative importance to the assessment 

results and the availability of suitable, existing reference data. 

As a general rule, the validation approach will compare the data 

products to the best available (most suitable) reference data 

sets that were produced independently of the national assessment. The validation strategy is adaptable to changes in data 

availability and information requirements. Depending on the 

assessment data products and the availability of reference data, 

probability sampling will be considered as a statistical framework for validation (Stehman and others, 2003). Individual 

assessment data products may be added or removed from the 

list of validation targets in response to model performance or 

specific issues that may arise. Additional or improved reference datasets will be incorporated as they become available and 

when deemed effective in support of validation objectives.

3.3.8. Methods for Assessing and Reporting

Uncertainty

Gaps in data, current modeling capabilities, interactions 

between ecological phenomena, and scientific understanding of 

the mechanics of these complex interactions can produce large 

uncertainties in the assessment. The treatment of uncertainties is 

related to the validation assessment, discussed above. Although 

validation methods will be used for assessing of current carbon 

stocks, carbon sequestration, and GHG fluxes, uncertainty in the 

assessment will be focused mainly on scenarios, data, and methods related to future potential conditions. There are two general 

sources of uncertainty for the assessment: uncertainty related to 

the IPCC storyline framework, which is unquantifiable (unpredictable); and uncertainty related to data and methods, which 

may be quantified and reported. The methods for treating uncertainties in the assessment are designed on the basis of IPCC 

guidelines on uncertainties (IPCC, 2006). Appendix G provides 

more detailed discussion.

Table 3.16.  Partial list of deliverable and intermediate data products targeted for validation, and the corresponding reference data

sources and needs.

[For explanations of acronyms, please see “Abbreviations, Acronyms, and Chemical Symbols” in the front of the report. Abbreviations are as follows: GHG, 

greenhouse gas; km, kilometers; m, meter]

Data products for validation Reference data sources Reference data needs

Land-cover and land-use change LANDFIRE VCT None.

Wildland fires, and carbon emissions by fires LANDFIRE MTBS, Consume outputs, NOAA 

Carbon Tracker

Field plots of changes in aboveground biomass.

Delivery of water to coastal area USGS streamgage network None.

Net ecosystem productivity (NEP) by pools and 

ecosystems

AmeriFlux, NEON, NOAA Carbon Tracker Additional flux data points.

Net biome productivity (NBP) AmeriFlux, NEON, NOAA Carbon Tracker Additional flux data points.

Modeled Leaf Area Index (LAI) MODIS LAI (1-km resolution) LAI at less than or equal to 250-m resolution (30-

m from Landsat).

Grain yields USDA NASS and ARS 30-m from Landsat.

Carbon stocks by pools and ecosystems FIA, LTER, NEON, ARS, GRACEnet Aboveground biomass data from LIDAR.

Carbon removal by forest harvesting USFS FIA None.

Carbon pool size in lake or reservoir sediments RESIS–II, ad-hoc reports None.

Methane emission by ecosystems AmeriFlux, NEON, NOAA Carbon Tracker data, 

ad-hoc reports

GHG flux data for aquatic and wetland systems.

Nitrous-oxide emissions by ecosystems AmeriFlux, NEON, NOAA Carbon Tracker, adhoc reportsGHG flux data for aquatic and wetland systems.

Carbon delivery by rivers to coastal areas NWIS, SPARROW, NEWS None.
3. Methodology for the National Assessment 63

Uncertainty From the Use of the Storylines and Scenarios.—As noted in the previous discussion of storylines (sections 2.5.1 and 3.2.2), the use of three IPCC SRES storyline 

(Nakicenovic and others, 2000) will effectively bound the size 

of the overall uncertainty. In addition, the scenarios for alternative land-use and land-management options (section 3.2.2) 

will be assessed for their potential effects on enhancing carbon 

sequestration and reducing GHG fluxes. These scenarios 

also will produce uncertainty in assessment results. For this 

methodology, the strategy for treating storyline- or scenariorelated uncertainties will involve the following steps:

• Communicating the sources of uncertainty.—Potential 

sources of uncertainty (see appendix G) include choices 

of storylines or scenarios and the downscaling process.

• Reducing the unknown uncertainties.—Measures to 

reduce unknown uncertainties will include (1) downscaling the IPCC SRES storylines based on data and 

studies rather than on global-scale model outputs; (2) 

standardizing the downscaling methods, which will 

be accomplished through consultation sessions with 

regional experts; and (3) increasing the consistency of 

the scenario framework by using the same design criteria for each alternative scenario and aligning it with the 

IPCC SRES storylines.

Uncertainty Related to Data and Assessment Methods.—

Although uncertainties related to input data and methods are 

bound by the storyline and scenario uncertainties discussed 

above, it is still important to assess and quantify uncertainties 

related to the data and assessment methods under each of the 

storylines used. Providing information on quantifiable uncertainties will allow users to evaluate assessment results and the 

methodology for a given scenario. The sources of uncertainty 

related to the assessment data and methods include the following: input data; the scarcity of data (such as GHG flux data for 

different ecosystems) that pertains to the assessment methods 

and deliverables; the process-model structure and associated 

parameters that are used to estimate carbon stocks, carbon 

sequestration, and GHG fluxes; and the interactions between 

components of the assessment (for example, projecting LULC 

change and evaluating the effects).

The basic approach for estimating uncertainties related to 

the data and assessment methods will follow IPCC (2006) recommendations. The input data (including the derived intermediate data products that are produced during the assessment) will 

be processed to produce joint frequency distributions, which in 

turn are used in Monte Carlo resampling and simulation runs 

to estimate uncertainty in the resulting output products. For 

uncertainties introduced by using different methods or models, 

multiple model runs and statistical analysis will be used to summarize the relative contributions of the technical components to 

the final uncertainties. All of the data resulting from the assessment also will be evaluated by experts in consultation sessions. 

Expert opinions then can be used to assess uncertainties.

The focus of assessing and communicating uncertainties is on quantifying the variability of end results, which will 

be carbon storage, carbon sequestration, and GHG fluxes; 

therefore, some uncertainties that may arise during the many 

parts of the modeling process may have very little effect on 

the final outcome, which will be summarized and delivered at 

the scale of assessment units. For example, specific locations 

of land-cover changes across a homogeneous landscape may 

be highly uncertain, but they may make very little difference 

in the overall long-term carbon-sequestration measurement at 

the scale of the assessment units.

3.3.9. Requirements of Section 712 of the Energy

Independence and Security Act for Measuring

and Monitoring

As discussed in chapter 1, section 712 of the EISA (U.S. 

Congress, 2007) requires that the methodology address the 

measuring, quantifying, and monitoring of carbon stocks, carbon sequestration, and GHG fluxes across the Nation, including coastal waters and estuaries. These three required tasks are 

closely related: measurements collected directly or remotely 

provide the necessary data for quantifying the carbon stocks, 

carbon sequestration, and GHG fluxes, and the continual, systematic repetition of such measurements constitutes monitoring. 

The methods that are designed to fulfill the EISA requirement 

for measuring and monitoring carbon stocks, carbon sequestration, and GHG fluxes include the identification of objectives and 

data sources, and a plan for filling data gaps are summarized 

below. Detailed information on the method is in appendix H.

Objectives for Measuring and Monitoring.—The principal objectives for measuring and monitoring carbon stocks, 

carbon sequestration, and GHG fluxes are as follows:

• Periodically quantify carbon stocks, carbon sequestration, GHG fluxes, and related ecosystem properties 

and processes in the United States for the purpose of 

evaluating their status and trends.

• Aggregate and update observational monitoring data 

for the purpose of validation; that is, for assessing the 

accuracy of model results.

• Provide a basis for evaluating the effectiveness of 

applied mitigation activities and strategies undertaken 

to reduce GHG emissions from ecosystems and promote carbon sequestration.

The methodology for measuring and monitoring is 

designed to support the scope of the national assessment and to 

be adaptive to changing data resources, improved methodologies, and evolving requirements for data and information, while 

maintaining consistency, scientific credibility, and transparency.

Methodology for Measuring and Monitoring.—Achieving 

the above objectives requires the continual coordination and 

implementation of two major activities:

• Quantification of the relevant data products through the 

spatial aggregation of measurements and (or) model 

results
64 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

• Provision of the data and information that is required 

for such quantification and for validation and evaluation of mitigation effectiveness

The methodology for measuring and monitoring for 

the assessment (appendix H) focuses on the provision of the 

required data and information. The methodology builds on 

existing data resources that are created, managed, or supported by various agencies and programs across the Federal 

Government (such as DOI, USDA, NASA, NOAA, and 

the National Science Foundation (NSF)) and is designed 

to be adaptive to changing data resources, improved techniques, and evolving requirements for data and information, 

while maintaining consistency, scientific credibility, and 

transparency.

Availablity of Data .—The effectiveness of the 

methodology for measuring and monitoring will be constrained by the availability of required in situ and other 

observational data, which often are not uniformly distributed 

in space or among major ecosystems and pools. Known data 

gaps or deficiencies are identified in appendix H, along with 

a strategy for developing new or enhanced measurement 

capabilities. The strategy for ensuring that adequate data is 

available for measuring, quantifying, and monitoring focuses 

on critical data shortages and monitoring needs and includes 

the following:

• Expanded airborne and ground-based measurements of 

GHG fluxes in terrestrial and aquatic ecosystems

• Expanded measurements of dissolved and particulate 

forms of carbon (DOC, DIC, and POC) and nutrients 

(nitrogen and phosphorus), sedimentation rates, and 

concentration of organic carbon in sediments of aquatic 

ecosystems

• Improved remote-sensing capabilities for quantifying 

and mapping terrestrial biomass and small inland water 

bodies by developing and applying high-resolution 

satellite imagery and such promising technologies as 

small- and large-footprint LIDAR

The successful implementation of this strategy requires partnerships and coordination among government agencies and 

other organizations.

3.4. Data Products, Deliverables, and Reports

The assessment will generate a large quantity of data products in tabular and map formats; for example, carbon stocks, 

carbon sequestration, and GHG-flux parameters by ecosystem, 

pool, scenario, and time period will result from the assessment, 

as will the associated validation and uncertainty estimates 

(where appropriate). Assessment results will be reported as the 

final deliverables. In this section, various data products that will 

be generated by the methodology are introduced, followed by a 

discussion of assessment reporting mechanisms.

3.4.1. Data Products

The methodology uses a set of integrated methods to assess 

carbon stocks, carbon sequestration, and GHG fluxes in relation 

to major controlling processes (such as LULC change and ecosystem disturbances) and potential mitigation strategies (such as 

LULC change and land-management change). As a result, both 

the intermediate data products (such as LULC, wildland fire, and 

river discharge datasets) and final data products (such as carbon 

stocks, net biome productivity and ecosystem carbon balance, or 

CH4 and N2O flux derived either as digital maps or tabular data) 

will be generated by various methods. Table 3.17 lists examples 

of the data products. For the maps, a common spatial resolution of 250 m is listed as a pixel size used by spatially explicit 

models; however, the map resolution does not designate the 

scale of the methodology. The scale of the methodology is set as 

assessment units, as discussed in section 3.1.2.

3.4.2. Assessment Reporting

The methods and format for reporting the results of 

the assessment will follow the guidelines in IPCC (2006) 

for reporting carbon stocks, carbon sequestration, and GHG 

fluxes. For each assessment unit, the types of deliverables to 

be reported are listed below:

• Estimates of present and future carbon stocks and carbon sequestration by pool, ecosystem, and assessment 

scenario, and by 10-year intervals

• Estimates of present and future GHG fluxes by pool, 

ecosystem, and assessment scenario, and by 10-year 

intervals

• Analyses of biophysical effects (for example, climate, 

land-cover patterns, or ecosystem disturbances, such as 

fire) on carbon stocks, carbon sequestration, and GHG 

fluxes

• Analyses of the effectiveness of potential LULC 

changes and land-management activities for enhanced 

carbon sequestration and reduced net GHG emissions

• Analyses of the potential ramifications of mitigation strategies, including analyses of the effects and 

effectiveness of potential mitigation activities and their 

effects on other ecosystem services

• Validation and uncertainty estimates and associated 

analyses for appropriate deliverables and data products

Examples that illustrate the methods by which these 

assessment results will be reported are presented here for a 

subset of results and associated estimated uncertainties (table 

3.18), for reporting emissions and effects of wildfires and 

manmade fires (table 3.19), and for validation results (table 

3.20). The method, timing, format, and content of reporting 

the assessment results will be determined early in the assessment process and will be based on actual results.
3. Methodology for the National Assessment 65

Table 3.17.  A subset of primary deliverable or intermediate products for the national assessment.

[For an explanation of acronyms, please see “Abbreviations, Acronyms, and Chemical Symbols” in the front of this report. Abbreviations are as follows: CH4, 

methane; kg, kilogram; kgC/ha/yr, kilograms of carbon per hectare per year; kgN/ha/yr, kilograms of nitrogen per hectare per year; MgC, megagrams of carbon; 

MgC/ha, megagrams of carbon per hectare; MgC/ha/yr, megagrams of carbon per hectare per year; MgCH4/km2, megagrams of methane per square kilometer; 

MgCO2-eq/ha/yr, megagrams of carbon dioxide equivalent per hectare per year; MgDOC/km2, megagrams of dissolved organic carbon per square kilometer; 

N2O, nitrous oxide; TgC, teragrams of carbon; TgC/yr, teragrams of carbon per year]

Product name Data type Unit of measurement Time interval

Net primary productivity (NPP) Map series and statistics MgC/ha/yr Annual for 2001–2050.

Net ecosystem productivity (NEP) Map series and statistics MgC/ha/yr Annual for 2001–2050.

Net biome productivity (NBP) Map series and statistics MgC/ha/yr Annual for 2001–2050.

Net ecosystem carbon balance (NECB) Map series and statistics MgC/ha/yr Annual for 2001–2050.

Soil carbon stock Map series and statistics MgC/ha Annual for 2001–2050.

Fire-induced carbon emission Map series and statistics MgC/ha/yr Annual for 2001–2050.

Tree biomass removal Map series and statistics MgC/ha/yr Annual for 2001–2050.

Grain yields Map series and statistics MgC/ha/yr Annual for 2001–2050.

Carbon stock/flux trends Statistics TgC/yr, TgC Annual for 2001–2050.

Carbon accumulation in lake and reservoir sediments Statistics MgC Annual for 2001–2050.

Carbon accumulation in coastal waters Statistics MgC Annual for 2001–2050

CH4 efflux Map series and statistics kgC/ha/yr Annual for 2001–2050.

N2O efflux Map series and statistics kgN/ha/yr Annual for 2001–2050.

Methane flux from lakes, reservoirs Statistics MgCH4

/km2 Annual for 2001–2050.

Delivery of organic carbon by rivers to coastal areas Statistics MgDOC/km2 Annual.

Delivery of inorganic carbon by rivers to coastal areas Statistics MgDOC/km2 Annual.

CH4 and N2O flux from estuaries and coastal waters Statistics MgCO2-eq/ha/yr Annual.

Land suitability for REDD by NPP, fire disturbance categories, and 

scenario storylines

Map series and statistics Thematic classes Annualized average.

Future soil erosion and surface runoff potential by major ecosystem 

types and management scenarios

Map series and statistics Thematic classes Annualized average.

Greenhouse-gas (N2O, CH4) reduction by ecosystem type and LULC 

and land-management scenario

Map series and statistics MgC/ha/yr,

MgCO2-eq/ha/yr

Annualized average.

Effects of management activities on carbon sequestration Tabular data MgC/ha sequestered Annualized average.

Ancillary effects of mitigation activities on ecosystem services Tabular data Units will vary by 

service type

Annualized average.

Updated and modified NLCD land-use and land-cover data Map series Thematic classes Annual for 2001–2010.

Projected modified NLCD land-use and land-cover data Map series Thematic classes Annual for 2011–2050.

Updated and modified wildland fire perimeters and severity Map series Thematic classes Annual for 2001–2010.

Projected modified wildland fire perimeters and severity Map series Thematic classes Annual for 2011–2050.

Sediment and nutrient flux to estuaries and coastal waters Statistics kg Monthly and annual.
66 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table 3.18.  Example of a table format for reporting the results of the assessment of carbon stocks, carbon sequestration, and

greenhouse gases, including uncertainties, for current or future scenarios depicted in table 3.6.

[The table will be used as part of assessment-unit reports to present results for years encompassed by the assessment (2001–2050). Acronyms are as follows: 

DIC, dissolved inorganic carbon; DOC, dissolved organic carbon; GHG, greenhouse gases; NBP, net biome productivity; NECB, net ecosystem carbon balance; 

POC, particulate organic carbon]

Carbon and GHG measurements

Ecosystems

Forest Cropland Grasslands/shrublands Wetlands Aquatic ecosystems

Carbon stocks

NBP/NECB

Carbon flux

N2O flux

CH4 flux

Lateral carbon flux (DOC, DIC, POC)

Global warming potential (GWP)

Table 3.19.  Example of a table format for reporting the effects of wildfires and manmade fires on carbon stocks for a given assessment

unit, for all ecosystems.

[kgC/m2/yr, kilograms of carbon per square meter per year]

Wildfire types

Years of the assessment

2001–2010,

kgC/m2/yr

2011–2020,

kgC/m2/yr

2021–2030,

kgC/m2/yr

2031–2040,

kgC/m2/yr

2041–2050,

kgC/m2/yr

Prescribed surface fire

Low-severity wildland fire

High-severity wildland fire

Table 3.20.  Example of a table format for reporting validation results of comparing the reference data with the 2010 assessment

estimates (present conditions) for selected assessment parameters, for a given assessment unit.

Validation target Measurement units Estimated value Mean deviation Mean absolute 

deviation

Root mean 

square error

Forest carbon stock

Forest carbon emission by fire

Forest net ecosystem productivity

Carbon export to coastal waters
67

4. Conducting the National Assessment

Prioritization and scheduling assessment activities.—The 

methodology uses U.S. Environmental Protection Agency 

(EPA) level II ecoregions (Omernik, 1987) as the primary 

assessment unit so that carbon stocks, carbon sequestration, 

and GHG fluxes can be assessed one ecoregion at a time, in 

the context of mitigation scenarios that will be developed 

specifically for that ecoregion. Federal agencies and other 

stakeholders will be consulted to prioritize the order in which 

ecoregions will be assessed. For instance, ecoregions with the 

greatest potential for increased carbon sequestration, regions 

that are predicted to experience the most profound impacts 

from climate-change or land-use and land-cover changes, and 

regions where established collaborative opportunities already 

exist may receive the highest priorities. Prioritization also 

should be based on data quality and availability. For example, 

Alaska may be assessed at a later time to allow for additional 

data collection (in situ and remote sensing) and processing.

Active project management.—A well-defined, structured, 

and scalable project-management process should be established and followed for the assessment. The project-management plan should be developed, organized in structured 

phases and tasks, and submitted for review by the interagency 

assessment oversight team. The plan should establish metrics 

and include all linked dependencies. The project plan will 

ensure that activities are executed effectively and efficiently, 

with progress measured against established metrics in order to 

complete the assessment within the allotted time frame.

4.2. Major Scientific Research and Development 

Needs

The methodology is based on balanced considerations of 

the established scientific knowledge, the operational efficiency 

of methods and models, and the availability of datasets that 

meet the assessment needs. The gaps in required input data are 

addressed in chapter 3, which also contains plans for reducing the effects of the data gaps; however, as noted throughout 

chapter 3 and in the various appendixes, it is crucial to address 

scientific needs and data gaps to further improve and enhance 

the ability to accurately assess carbon stocks, carbon sequestration, and GHG fluxes of the Nation’s ecosystems. Key areas 

of research and developemnt are as follows:

Permafrost.—Assessments of permafrost and GHG 

responses to changes in permafrost would benefit greatly from 

targeted studies linking permafrost degradation to changes in 

surface water and GHG fluxes. Although some such studies 

are underway and their results may be available for the assessment, in situ measurements and model development should 

be designed to establish probability assessments for hotspots 

of GHG release; such assessments should be based on field 

With the proposed methodology framework and specific 

methods and models outlined in chapter 3, the focus of this 

chapter is on implementing the national assessment. This 

chapter also includes a discussion of science needs, as well as 

a brief examination of potential applications.

4.1. Operational Issues

In order to implement the EISA-mandated national 

assessment (U.S. Congress, 2007), several operational and 

logistical issues, including interagency cooperation, access to 

required data, assessment prioritization and scheduling, and 

project management, will need to be addressed. These issues 

are outlined below.

Interagency cooperation and coordination.—The 

methodology is the result of a multidisciplinary approach 

that requires cooperation and collaborations with more than 

one organization. Shared activities include development of 

the mitigation scenarios, remote sensing, in situ data access, 

and field validation of assessment results. For the assessment, 

close coordination with agencies and organizations that conduct relevant resource assessments and research will continue 

to be necessary. Close cooperation and coordination can be 

facilitated by organizing an interagency assessment team 

established for this purpose, with scientists from appropriate 

organizations coordinating technical exchanges, developing 

interagency agreements about data sharing, overseeing production of data products, and forming an executive oversight 

committee to provide high-level support to the assessment.

Engagement of the national and international science 

community.—The active engagement of the national and international science community throughout the assessment will be 

necessary to ensure that the results are timely, useful, accessible, and relevant. This engagement will facilitate internal 

benefits (such as possible advances in scientific areas such as 

climate change, biogeochemical modeling, or ecosystem disturbances) and external benefits (such as assistance in comparing various models, synthesis workshops, and comparing the 

assessment results derived using the various models).

Enhancement of data access and management.—The 

national assessment will require access to numerous datasets 

from a variety of sources to ensure the quality of the assessment 

and to minimize the uncertainty of assessment results. Access to 

some types of data may present varying degrees of difficulty. For 

example, some datasets are proprietary, some must be obtained 

through formal acquisition processes (for example, remotely 

sensed wildfire perimeters and severities), and some will require 

formal agreements that precisely dictate how to acknowledge 

credit for providing the data. There also may be difficulty in 

organizing and managing the data (including the metadata). 
68 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

studies in stratified sampling designs by landscape (based on 

slope and permafrost information), ecosystem (vegetation 

type, disturbance age), and geohydrologic unit (surficial and 

bedrock geology).

Ecosystem disturbances.—Spatially explicit mapping and 

modeling of ecosystem disturbances are challenging. Mapping 

and modeling of wildland fires and anthropogenic disturbances (such as forest cuts) are technically more advanced 

than modeling other ecosystem disturbances (such as storm 

damage, and forest defoliation and mortality caused by insect 

outbreaks). There are national programs that produce spatially 

explicit datasets of various major ecosystem disturbances, but 

there is a lack of consensus about their technical standards and 

readiness for operational applications. Although this methodology documents an approach for spatially mapping, characterizing, and forecasting wildland fires and other disturbances, 

there is a strong need for continued vetting of the proposed 

methods by comparing results with other methods and models 

and by conducting validation exercises using in situ and other 

fire data.

Wetlands.—Accurate mapping of wetlands and aquatic 

ecosystems is a key step in the assessment of different ecosystems; however, despite the availability of national datasets, 

such as the National Land Cover Database and the National 

Wetland Inventory, determining the spatial boundaries of 

wetlands and aquatic ecosystems will be an early research and 

development priority in the assessment. Practical methods 

will be devised to spatially separate upland systems, inlandfreshwater systems, inland-wetland systems, coastal saltmarsh systems, and coastal aquatic systems. Certain satellites 

that collect high-resolution data (for example, GeoEye1 and 

Worldview2) could provide extensive coverage that would 

aid in mapping these systems. An eight-band sensor on the 

Worldview2 satellite provides imagery with a bathymetric 

wavelength that could be used for measuring and monitoring 

terrestrial vegetation with the additional benefit of detecting 

sediment beds of reservoirs, impoundments, and coastal estuaries. These mapping efforts would provide data enabling a 

better understanding of patch- and landscape-scale controls on 

carbon stocks, carbon sequestration, and GHG fluxes, including the potential effects of sea-level rise, and would enhance 

the assessment results for wetlands.

Inland basins, reservoirs, estuaries, and coastal areas.—

Few data are available to construct models for determining 

GHG fluxes and accumulation rates of carbon in sediments in 

inland basins, reservoirs, estuaries, and coastal areas. Additional data are needed to accurately model fluxes and carbon 

sequestration as a function of surface-water and groundwater 

flow of nutrients and sediment into these systems.

Biogeochemical models.—Biogeochemical modeling 

of carbon stocks, carbon sequestration, and GHG fluxes will 

be a core capability of the assessment and will incorporate 

both bookkeeping and process-based modeling methods in 

order to improve consistency in and enhance the transparency 

of the overall methodology. The crosscheck and the use of 

data assimilation techniques, as discussed above, are helpful, 

but more research and development needs exist, such as the 

identification and use of other appropriate BGC models based 

on their unique suitability for different ecosystems, pools, and 

flux types. Techniques need to be improved for model comparison, result validation or accuracy of the assessment, and 

implementation of uncertainty assessment.

Integration of the assessments for terrestrial and aquatic 

ecosystems.—Terrestrial and aquatic ecosystems have different 

ecological processes that determine GHG fluxes. The assessment of aquatic ecosystems should be dynamically integrated 

with that of the terrestrial systems so that the relevant terrestrial estimates (for example, rates of surface runoff and erosion) may be used as input data for estimating carbon sequestration in and GHG fluxes from aquatic ecosystems. Research 

needs to be conducted to link and integrate methods for assessing the interplay between these two types of ecosystems. An 

application of the research would include the consideration of 

the tradeoffs between decisionmaking related to the management of water resources versus carbon sequestration and GHG 

fluxes, as well as inland and coastal sediment management and 

supply.

Sequestration planning.—One approach to evaluating 

potential mitigation strategies may be a constrained optimization analysis that combines biophysical feasibility with 

economic, ecosystem-service, political, and other constraints. 

To develop a constrained optimization approach, research is 

needed to develop a mechanism that would provide feedback 

on the interactions between applying the mitigation scenarios 

(including the evaluation of costs and the impact on ecosystem 

services) and modeling the future land-use transitions that 

might affect carbon sequestration and GHG fluxes. Currently 

there is no feedback between the models and the mitigation 

scenarios, which means that the effects and effectiveness of 

mitigation scenarios are not interactively modeled. The oneway flow from mitigation scenarios to LULC transitions and 

to changes in carbon sequestration and GHG fluxes limits the 

range of outcomes for decisions. Sequestration planning will 

be most useful when a constrained optimization framework 

is adopted and the sensitivity of planning to the constraints is 

incorporated to achieve the most sequestration per dollar of 

cost.

Ecosystem services.—Carbon sequestration and GHG 

mitigation are just two of the many services provided by ecosystems. Additional research is needed in the following broad 

areas to improve the ability to evaluate the direct and ancillary effects of carbon-management activities and mitigation 

strategies on the suite of ecosystem services that are relevant 

to programs in the U.S. Department of the Interior and other 

agencies:

• Empirical data and models (statistical, mechanical, 

driver-stressor-response) that quantify how changes in 

ecosystem structure and processes affect the quality 

and quantity of ecosystem services

• Effects of spatial and temporal scales on ecosystem 

service measurements
4. Conducting the National Assessment 69

• Development of a nationally consistent carbon suitability index for implementing prospective management 

actions, mitigation strategies, and scenario development

• Integration of socioeconomic, ecological, and naturalscience components for measuring and evaluating 

ecosystem services including valuation, decisionmaking, stakeholders, ecological endpoints, resilience, and 

sustainability

• Spatially explicit decision-support tools to simultaneously evaluate ecological tradeoffs of multiple services 

Uncertainty.—Consistent methods need to be developed 

and applied for assessing all major sources of uncertainty. The 

identification of major factors that contribute to uncertainty 

in estimates of carbon stocks, carbon sequestration, and GHG 

fluxes will result from a series of sensitivity analyses. These 

results will help guide the focus of future monitoring efforts. 

Documentation of levels of uncertainty must be completed, 

and recommendations for approaches to reducing uncertainty 

will be developed, where feasible. A comprehensive review 

of input data uncertainty (for example, variability in forestinventory data, classification accuracy of land-cover data, and 

assessment of spatial autocorrelation in input layers) will be 

conducted to derive distribution functions that can be used in 

the simulation modeling process. Experiments will be conducted to determine the impact of uncertainty on certain modeling assumptions and decisions (for example, aggregating 

land-cover categories, choosing the spatial resolution at which 

modeling is conducted, and comparing results with relevant 

published literature).

4.3. Intended Applications

Given the legislative requirements of the EISA (discussed 

in chapter 1), the assessment results are intended to assist in 

the development of carbon- and GHG-mitigation opportunities and strategies, promote understanding of adaptation 

needs under different climate-change scenarios, and estimate 

potential ancillary effects of mitigation actions on other ecosystem services, as well as many other activities. Users of the 

assessment results are likely to include public policymakers 

and analysts, Federal, State, and local government officials, 

nongovernmental organizations, individuals and community 

stakeholders, and the scientific community.

The methodology is designed to conduct an assessment 

and improve the understanding of the spatiotemporal distribution of GHG fluxes and carbon-sequestration capacities in 

ecosystems, as well as effects and effectiveness of a range of 

future potential climate-change and mitigation scenarios. The 

assessment will provide information regarding the benefits and 

possible tradeoffs between policies and land-use activities that 

enhance carbon sequestration and reduce net GHG emissions. 

To help inform these choices and permit comparison, the 

assessment will proceed by ecoregion, providing maps, statistics, and tabular data of existing and potential carbon stocks, 

carbon-sequestration capacity, and GHG reduction. Specific 

applications include the following:

1. Estimation of the economic payoffs of mitigation activities and the impacts to the landscape and other ecosystem 

services caused by mitigation activities. The datasets 

and maps will be compatible for analysis by others who 

employ econometric models and economic sector models 

for benefit and cost studies of policies and regulations

2. Measurement of ecosystem-service flows in terms of 

physical and economic production and impacts that reflect 

physical, economic, and institutional constraints (for 

example, services provided by protected lands versus 

potential carbon-sequestration actions in surrounding 

lands)

3. Monitoring for resource management by landowners, 

developers, verifiers, and regulators. To track and forecast 

changing conditions, the methodology uses remote sensing to assess land-area changes at the resolution of 250 m 

that can be aggregated first to EPA level II ecoregions and 

then to a national scale

4. Identification of potential disturbance regimes (for example, wildfires) and the effects of land-management actions 

(such as fuel treatments) to help inform decisions about 

the risks and opportunities of land-management activities 

related to natural and human hazards

These applications are consistent with other evaluation 

measurements and decision frameworks used by resource 

managers to achieve the maximum increase in carbon-sequestration capacity and GHG reduction. The results of the assessment should be a complement to economic policy models 

already in use by the EPA and the USDA to analyze the impact 

of policies related to climate change. This assessment does 

not, however, include macroeconomic policy analysis with the 

objective to allocate resources among economic sectors, nor 

will it contain a microeconomic model of individual investment opportunities and behavior. Rather, the assessment will 

be an estimate of carbon-sequestration capacity and mitigation 

costs in ecosystems, as determined by land cover, land use, 

land management, and climate projections, but not determined 

by the influence of the market economy and individual behavioral decisions.
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71

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87

Glossary

crop rotation Sequentially growing different crops in the same 

field or area to (1) avoid the buildup of pests such as insects and 

pathogens, and (2) replenish nutrients and soil structure.

denitrification The process of converting nitrate or nitrite 

to nitrogen containing gases like nitrous oxide by microbial 

processes.

deforestation The process of removing or clearing trees 

from forested land.

ecophysiology An area of plant ecology that investigates the 

relation between an organism’s function and its surrounding 

environment.

ecosystem A natural system that is formed by the interaction 

of a group of organisms with their environment. 

ecosystem disturbance An episodic event that may affect 

the composition, structure, and (or) function of an ecosystem.

ecosystem service The benefits that people and societies 

derive from the natural processes that sustain ecosystems.

emission A discharge or release, such as discharging 

greenhouse gases into the atmosphere through natural 

processes and human activity. 

evapotranspiration A compound term used to describe the 

process of evaporation and plant transpiration. Evaporation 

accounts for the movement of water to the atmosphere from 

surfaces such as soils, plant canopies, and water bodies. 

Transpiration refers to the evaporation of water from plant 

leaves.

externality The economic impact on a party that is not 

directly involved in a transaction. In such a case, prices do not 

reflect the full costs or benefits in production or consumption 

of a product or service.

flux A flow of an entity, such as the flow of carbon from one 

pool to another.

flux tower A tower with instruments (for example, an 

anemometer that measures windspeed) that gives estimates of 

heat, water, and gas flux in the atmosphere. 

gross primary productivity The sum of carbon fixation 

by plants. Photosynthesis is the process by which plants fix 

atmospheric carbon and assimilate it within the plant biomass.

inventory A sampling-based data collection and quantitative 

evaluation of recent natural resource conditions.

land cover General vegetative landscape or other types of 

surface cover, such as forests, grasslands, wetlands, or barren 

areas that can be mapped using remote-sensing techniques.

land mangement In the context of this methodology and the 

subsequent assessment, refers to management decisions that 

afforestation The process of establishing trees on land that 

is not a forest, or has not been a forest for a long time, by 

planting trees or their seeds.

allochthonous From the outside, such as energy or nutrients 

that come from outside an ecosystem.

anaerobic An environment where atmospheric oxygen is 

absent, or an organism that doesn’t require oxygen to function. 

ancillary effect A positive or negative effect that is 

subordinate to the primary goal or the intended impact of a 

strategy, policy, or management action, including unintended 

consequences. For example, planting more trees to increase 

carbon sequestration may have the ancillary effect of 

increasing bird habitat.

assessment A quantitative evaluation of present and future. 

For this report, it is specifically an evaluation of carbon 

stocks, carbon sequestration, and greenhouse-gas fluxes in 

ecosystems.

assessment units Synonymous with the U.S. Environmental 

Protection Agency (EPA) level II ecoregions (Omernik, 

1987) and watersheds that are aligned, to the extent possible, 

with boundaries of the ecoregions. Coastal areas also are 

considered to be assessment units.

autochthonous From within, such as energy or nutrients that 

come from within an ecosystem.

baseline The reference for a measurable quantity against 

which an alternative outcome can be measured. A baseline 

can be static and can serve as an initial or starting condition. A 

baseline can also be dynamic and serve as a reference line for 

a defined set of conditions through time.

biome A general ecosystem classification, including forests, 

grasslands, and wetlands.

carbon burial In this report, refers to deposition of organic 

carbon and subsequent burial by inorganic sediments in lake, 

impoundment, stream, estuarine, and marine systems. Carbon 

also may be sequestered (sometime referred to as “buried”) 

by injection of CO2 into suitable underground geologic 

formations.

carbon sequestration The removal of carbon dioxide from 

the atmosphere and its storage in ecological sinks (components 

of terrestrial or aquatic ecosystems). 

conservation tillage Reduced tillage that is defined, in part, 

by limited cultivation and retention of plant residues on the 

soil surface. 

contingent valuation A survey-based technique to collect 

information to determine the value of a nonmarket resource, 

such as protecting the environment or an ecosystem service.
88 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

are applied to the use of land, such as frequency of forest cuts 

or amount of fertilization used in agriculture.

land use In the context of this methodology and the 

subsequent assessment, refers to the use of land by humans, 

particularly the type or intensity of economic use of the land 

that changes attributes of the land cover, such as forest cuts or 

agricultural crop types, which can be mapped using remotesensing techniques.

lateral flux The transport of particulate inorganic and 

organic carbon and dissolved organic and inorganic carbon by 

rivers. A portion of this carbon is deposited in inland basins, 

waterways, coastal areas, and oceans.

methanogenesis A form of anaerobic respiration by 

microbes that produces methane. 

mitigation Human actions to reduce the sources of or 

enhance the sinks of greenhouse gases. 

monitoring The systematic collection and analysis of 

repeated measurements or observations through time.

net ecosystem exchange A value that reflects the net 

exchange of carbon between the land or ocean and the 

atmosphere, and equals the net ecosystem production minus 

the transport of carbon to groundwater or to deep ocean water.

net ecosystem carbon balance A value that reflects the 

overall carbon balance from all sources and sinks in an 

ecosystem, whether the sources are physical, biological, and 

human (including runoff and lateral transport by rivers). 

net ecosystem production The net annual carbon 

accumulation by an ecosystem, which is calculated by 

subtracting ecosystem respiration from the gross primary 

productivity, and refers to the amount of organic carbon fixed 

in an ecosystem that is not respired there and is therefore 

available for accumulation, export, or oxidation.

net primary productivity The amount of new plant material 

produced annually, which is calculated by subtracting plant 

respiration from the gross primary productivity.

nitrification The process of converting ammonium to nitrate 

by microbial processes.

opportunity cost What must be given up in terms of the 

next best alternative in making a decision. Any decision 

that involves a choice between two or more options has an 

opportunity cost. It does not have to be measured in dollars.

pool A natural region or artificial holding area containing an 

accumulation of carbon or having the potential to accumulate 

carbon.

primary productivity The process of converting carbon 

dioxide, water, and solar energy into plant biomass.

reforestation The process of establishing a new forest 

by planting or seeding trees in an area where trees have 

previously grown.

reporting units Equivalent to assessment units. Synonymous 

with the U.S. Environmental Protection Agency (EPA) level II 

ecoregions (Omernik, 1987) and watersheds that are aligned, 

to the extent possible, with boundaries of the ecoregions.

risk A chance for injury or loss. In this report, it refers to 

the range of potential values of certain carbon-sequestration 

capacities or greenhouse-gas fluxes given certain 

environmental, economic, and policy conditions. It also refers 

to the potential harm or benefit to the environment because 

of a particular mitigation action implemented to maximize 

carbon sequestration.

scenario A plausible description of how the future may 

develop based on a coherent and internally consistent set of 

assumptions about key driving forces (for example, land-use 

and land-cover changes) and relations. 

sink A natural region or artificial holding area in which the 

amount of carbon is accumulating.

soil organic carbon The amount of organic carbon held in 

the soil.

source A natural region or artificial holding area in which 

the amount of carbon is decreasing.

stock The amount or quantity of carbon contained in a 

natural region or artificial holding area.

storyline Narratives developed by the Intergovernmental 

Panel on Climate Change (IPCC) to describe consistent 

relations between the driving forces that cause greenhousegas emissions and provide context for scenario quantification. 

Each storyline represents a different set of demographic, 

technological, and economic developments.

uncertainty The inability to precisely know properties (such 

as the magnitude or position) of a quantifiable parameter for 

estimating and projecting carbon-sequestration capacities and 

greenhouse-gas fluxes.

validation Quantitative evaluation of the quality of the input 

and (or) output data products of the assessment.
89

Appendix A. Reference and Alternative Mitigation Scenarios

For each ecoregion, mitigation activities will be evaluated for 

the potential to maximize carbon sequestration and minimize GHG emissions; evaluations will use published studies, 

regional expertise, and spreadsheet calculations. Mitigation 

scenarios are portfolios of management activities and LULC 

changes described in terms of amounts of change and timing 

of implementation. In accordance with national guidelines, 

alternative mitigation scenarios will be constructed to enhance 

carbon sequestration. Further elaboration of mitigation-activity 

selection, interpretation of the IPCC storylines for the reference and mitigation scenarios, and scenario construction follows. The last section provides a summary of the national and 

regional consultation process.

A.1. National Selection of Mitigation Activities

The selection of mitigation activities for the national 

assessment involves prioritization of mitigation activities and 

identification of the relevant national datasets. The assessment 

addresses two types of mitigation activities that ecologically 

sequester carbon or mitigate GHG emissions in ecosystems: 

LULC change and land-management change. LULC change 

is described in terms of changes between thematic LULC 

classes (section 3.2 of the main document), and land management occurs within the confines of a LULC class. LULC 

changes for increasing carbon sequestration include afforestation (conversions from pasture and croplands into forested 

land classes), mine-land reclamation, and wetland restoration 

or construction (transitions into the wetland classes). LULC 

changes to reduce GHG emissions may include the reduction 

of deforestation and the reduction of rice cultivation. Management activities on croplands may include conservation tillage, 

more efficient fertilization application, and crop rotations. On 

forest lands, management pertains to forest management and 

timber harvesting. All these mitigation activities affect GHG 

fluxes from aquatic systems via effects on erosion and nutrient 

loads. 

Candidate mitigation activities have been compiled 

from the literature (for example, Lewandrowski and others, 2004; Murray and others, 2005; Eggleston and others, 

2006) and underwent preliminary preparation and review at 

a policy workshop that was conducted by the Center for Climate Strategies and convened at the U.S. Geological Survey 

(USGS) headquarters in Reston, Va., December 1–2, 2009. A 

catalog of mitigation activities was distributed in advance and 

participants provided feedback. Criteria to prioritize mitigation activities for national assessment may include the primary 

considerations for ecological carbon-sequestration capacity:

• Sequestration capacity per hectare of mitigation 

activity

The reference and alternative mitigation scenarios will 

be designed to deliver results on the assessment components. 

(See chapter 1 for interpretation of the components and key 

concepts.) In essence, the components of the assessment 

required by section 712 of the Energy Independence and Security Act of 2007 (EISA) (U.S. Congress, 2007) will be satisfied 

by an evaluation of current carbon inventories, as well as 

potential carbon-sequestration capacity and greenhouse-gas 

(GHG) emission reductions from mitigation activities and 

strategies in ecosystems under a range of climate scenarios. 

The three overarching scenarios will be from the Intergovernmental Panel on Climate Change (IPCC) “Special Report on 

Emission Scenarios” (SRES)—A1B, A2, and B1 (Nakicenovic 

and others, 2000). The three IPCC scenarios are the socioeconomic storylines that will be used to guide the development 

of specific mitigation scenarios for this assessment. They are 

broad in scope and consistent with the IPCC SRES used in the 

U.S. Department of Agriculture (USDA) U.S. Forest Service’s 

(USFS) 2010 Forest and Rangeland Renewable Resources 

Planning Act (RPA) assessment (U.S. Department of Agriculture, U.S. Forest Service, 2010b). The IPCC SRES storylines 

are derived from assumptions on global-level driving forces 

and need to be downscaled to the national and regional level. 

The role of the storylines is to push experts to think beyond 

present conditions and the current range of thinking.

For this assessment, reference and alternative scenarios 

for mitigation will be developed within each of the IPCC 

storylines. Reference scenario (R) and alternative mitigation 

scenarios in each assessment unit are developed for various 

socioeconomic storylines and climate scenarios. Alternative 

mitigation scenarios are designed to enhance carbon sequestration and reduce GHG emissions from land-management 

change (scenario M), land-use and land-cover change (scenario L), or both (scenario ML).

This appendix provides details on the national and 

regional processes to construct reference and alternative 

scenarios. At the national level, mitigation activities—such as 

land-use and land-cover (LULC) change and land-management changes—are selected for national assessment from a 

compilation of candidate mitigation activities. They are prioritized for national importance based on the existing knowledge 

of carbon-sequestration capacity, quantity of land suitable for 

the activity, and consulting agency priority. Ultimate inclusion 

of a mitigation activity in the assessment is determined by data 

availability and modeling capabilities. Also, national-level 

consultation will be used to develop a national set of storylines 

consistent with IPCC SRES and to establish guidelines for 

scenario construction to ensure consistency across ecoregion 

assessments. At the ecoregion level, reference scenarios (R) 

of LULC and land management are projected for each of the 

storylines within the confines of the national interpretation. 
90 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

• Hectares of suitable lands for mitigation activity 

change to identify applicable upper bounds on mitigation activity change

• Time-effectiveness of sequestration to address how 

quickly the mitigation activity provides climate-change 

mitigation and duration of the effect of the mitigation activity on sequestration rate (for example, five 

decades for management activities affecting forest 

and soil pools, one decade for cropland management 

changes, and two decades for LULC conversion)

• Permanence of sequestration to address differences in 

how much carbon remains sequestered over time for 

each mitigation activity

The product of the first two measures recognizes activities that may result in smaller changes in rates of sequestration, but that are applicable to a vast quantity of lands (for 

example, rangelands). In addition, the priorities of consulting 

agencies will be considered in the selection of mitigation 

activities for the assessment.

Candidate mitigation activities listed in table 3.5 of this 

report are aligned with those incorporated into the Forest and 

Agriculture Sector Optimization Model with Greenhouse 

Gases (FASOM–GHG) (Murray and others, 2005) to provide 

perspective on the scope of the assessment and to indicate 

common areas where FASOM–GHG methods, data sources, 

and results for mitigation activities will be informative for the 

methodology (table A1). Not all FASOM–GHG mitigation 

strategies are within the scope of the assessment; for example, 

the category of fossil-fuel mitigation from crop and livestock 

production will not be used. Conversely, some mitigation 

activities not represented in FASOM–GHG are included as 

candidate mitigation activities in the assessment.

The mitigation activities will be researched before presentation to policymakers and the consulting agencies. The 

legislation requires the Secretary of the Interior to consult 

with the Secretary of Energy, the Secretary of Agriculture, the 

Administrator of the Environmental Protection Agency, the 

Secretary of Commerce (acting through the Under Secretary 

for Oceans and Atmosphere), and the heads of other relevant 

agencies. The candidate mitigation activities priorities will 

be discussed at a national workshop, but final selection will 

be subject to data availability, acceptance of assumptions, 

and model capabilities. Mitigation activities that currently are 

operational for the assessment are listed in table A2.

Land-use change is measured as the number of hectares 

of LULC class change. Within LULC classes, land-management activities are measured as the number of hectares 

of land-management change. Land management is complex 

because any one type of land management can be implemented 

with varying intensity. By way of example, Adams and others 

(1996, 2005) defined and used regional forest-managementintensity classes, ranging from passive to high-intensity management, for their analyses of carbon-sequestration supply. 

For the assessment, land management “carbon-sequestration 

intensity” relates to carbon-sequestration rate. For example, 

carbon optimal harvests that maximize sequestration in the 

forest ecosystem and wood products are of greater “carbonsequestration intensity” than economically optimal harvest 

rotations (Huang and Kronrad, 2001). Analogies for croplands 

include crop rotation (versus monocultures) and tillage intensity (Choi and Sohngen, 2009). Carbon intensities of grazingland management will be framed by recent (2010) rangeland 

and grassland research (Follett and others, 2001; Bremer and 

Ham, 2010; Brown and others, 2010).

Also specified at the national level are guidelines to 

construct alternative mitigation scenarios that are portfolios 

of mitigation activities, with details on the amounts of change 

and the timing of implementation. At the regional level, three 

alternative mitigation scenarios are constructed relative to a 

reference scenario. The storylines behind each set of reference 

and alternative scenarios need to be interpreted at the national 

level to ensure consistency across ecoregions.

A.2. National Interpretation of Storylines

The next step toward creating a set of regional LULC 

and land-management scenarios is to develop a set of national 

storylines consistent with the IPCC SRES. This step will be 

done primarily through a national workshop. Using existing 

LULC projections associated with the IPCC SRES scenarios 

and other supporting data as a guide, expert opinions will be 

solicited to describe plausible reference scenarios of LULC 

and land management based on the underlying assumptions of 

the SRES storylines. National storylines can be viewed as thematically enriched sets of SRES storylines with characteristics 

specific to United States land use and land management. Nakicenovic and others (2000) provided the following narratives 

and assumptions for each of the SRES scenario families (table 

A3). For more complete descriptions of SRES storylines, see 

Nakicenovic and others (2000).

The A1B storyline and scenario family describes a future 

world of rapid economic growth, minimal or decreasing population growth, and the introduction of new and more efficient 

technologies. The primary underlying themes are convergence 

among regions, capacity building, and increased cultural and 

social interactions, with a substantial reduction in regional 

differences in per-capita income. The A1 scenario family 

develops into four groups that describe alternative technological changes in the energy system.

The A2 storyline and scenario family describes a heterogeneous world. The underlying theme is self-reliance 

and preservation of local identities. Fertility patterns across 

regions converge slowly, which results in population growth. 

Economic development primarily is regionally oriented, and 

per-capita economic growth and technological changes are 

more fragmented and slower than in other storylines.

The B1 storyline and scenario family describes a convergent world with the same low population growth as in the 

A1 storyline, but with rapid changes in economic structures 

toward a service and information economy, with reductions in material intensity, and the introduction of clean and 
Appendix A 91

Table A1.  Candidate mitigation activities compared with strategies associated with the Forest and Agriculture Sector Optimization

Model with Greenhouse Gases.

[Modified from Murray and others (2005). FASOM–GHG, Forest and Agriculture Sector Optimization Model with Greenhouse Gases; CH4, methane; N2O, 

nitrous oxide; GHG, greenhouse gas]

Strategy/activity Candidate mitigation activities Mitigation activities tracked in 

FASOM–GHG

Afforestation carbon sequestration Convert lands to forest1 Convert agricultural lands to forest.

Forest management carbon sequestration Lengthen timber harvest-regeneration rotation

Increase forest management intensity (increasing forest density, thinning, 

fire-fuel reduction, insect and disease management)

Preserve forests1

Lengthen timber harvest-regeneration 

rotation.

Increase forest management intensity.

Preserve forests.

Forest management CH4 and N2O mitigation Reduce deforestation1 Reduce deforestation.

Agricultural soil carbon sequestration Reduce cropland tillage

Crop mix change to high-residue crops

Increase efficiency of crop fertilization

Convert grasslands and perennial crops1

Increase winter cover crops

Reduce summer fallow

Restore agricultural land

Increase irrigation efficiency

Change crop tillage.

Change crop mix.

Change crop fertilization.

Convert grassland.

Fossil-fuel mitigation from crop production Change crop tillage.

Change crop mix.

Change crop input.

Change irrigated/dry land mix.

Agricultural CH4 and N2O mitigation Reduce cropland tillage

Improve crop mix

Increase efficiency of crop fertilization

Reduce rice acreage1

Change crop tillage.

Change crop mix.

Change crop input.

Change rice acreage.

Change irrigated/dry land mix.

Control enteric fermentation.

Change livestock herd size.

Change livestock system.

Manage manure.

Grassland/rangeland soil carbon 

sequestration

Improve grazing management practices

Restore degraded rangelands

Improve use of fertilizer

Allow natural succession towards native shrub and forest

Not applicable.2

Rangeland GHG mitigation Reduce severe rangeland fire Not applicable.

Grassland GHG mitigation Avoid conversion to energy crops1 Not applicable.

Wetland carbon sequestration (negative 

impact on CH4)

Preserve, construct, and restore wetlands1 Not applicable.

Wetland carbon mitigation Preserve wetlands1 Not applicable.

Urban land carbon sequestration Increase urban forests Not applicable.

Barren land carbon sequestration Reclaim mined lands1 Not applicable.

Wood product sequestration Improve mill waste recovery

Extend life of wood products

Extend storage in landfills

Change wood product mix.

Wood product GHG mitigation Improve logging Not applicable.

Fossil-fuel mitigation Not applicable Produce crops for biofuel use.

Technological potential for carbon sequestrationUse biochar

Restore degraded rangelands

Use biochar (under investigation).

Aquatic GHG mitigation Reduce nutrient export from urban and agricultural lands

Alter withdrawal from deep reservoirs

Not applicable.

1

Indicates a land-use change in the assessment (as distinct from a land-management change).

2

FASOM–GHG 2010 has expanded rangeland and grassland categories and therefore can track LULC changes in these categories, but no mitigation activities 

are currently implemented.
92 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

resource-efficient technologies. The emphasis is on global 

solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate 

initiatives.

A primary deliverable of the national workshop will be 

expanded LULC narratives and national-scale LULC trajectories. Nested within the national narratives will be assumptions 

on the regional variability of LULC and management where 

available (fig. A1). For example, a national-scale narrative 

might include assumptions on forest use while highlighting 

certain regions as likely places for changes in forestry.

In figure A1, the primary graph begins with the solid 

black line which denotes measured LULC change based on 

the USGS Land Cover Trends project (Loveland and others, 2002). In this figure, the trends data represent national 

estimates for a hypothetical LULC class from 1970 to 2000. 

The subsequent pink line represents LULC composition 

as modeled using the “forecasting scenarios of land cover 

change” (FORE–SCE) model based on actual LULC measurements from the 2006 National Land Cover Database (NLCD) 

change product and LANDFIRE vegetation change tracker 

(VCT) products as inputs for 2001 through 2010. The set of 

three blue lines that follows represents hypothetical trajectories of the same LULC class for each of the scenarios in the 

IPCC SRES for the United States. The inset graph represents 

the spatial and temporal variability of LULC composition for 

the A1B SRES scenario across assessment units (U.S. Environmental Protection Agency’s level II ecoregions, modified 

from Omernik (1987). The different colors represent discrete 

geographic regions.

Table A3. Qualitative characteristics associated with three

scenario families from the Intergovernmental Panel on Climate

Change Special Report on Emission Scenarios.

[Modified from Nakicenovic and others (2000). GDP, gross domestic product]

Scenario

characteristics

Special Report on Emission Scenarios 

storyline

A1B A2 B1

Population growth Low High Low.

GDP growth Very high Medium High.

Energy use Very high High Low.

Land-use changes Low Medium 

high

High.

Resource availability Medium Low Low.

Pace and direction of 

technological change

Rapid Slow Medium.

Technological change 

favoring

Balanced Regional Efficiency and dematerialization.

Figure A1.  Diagram showing

the national and regional

scenario scaling concept,

showing past, present, and

projected land-use and landcover change. LULC, land use

and land cover; FORE-SCE,

Forecasting Scenarios for

Future Land Cover model; NLCD,

National Land Cover Database;

LANDFIRE, Landscape Fire

and Resource Management

Planning Tools; VCT, vegetation

change tracker; IPCC,

Intergovernmental Panel on

Climate Change; SRES, Special

Report on Emission Scenarios.

1970 2000 2010 2050

LULC composition (percentage of United States)

2020 2030 2040

Historical LULC data

Projection using

current LULC Scenario-based LULC projections

USGS Land Cover Trends

FORE–SCE

(NLCD 2006

LANDFIRE VCT)

National scenario constructions

based on interpretation of IPCC SRES storylines

LULC model

validation

B1

A1B

A2

High

Year

Low

Table A2.  Mitigation activities included in dataset of land-use

and land-cover starting points.

[These mitigation activities have been implemented in simulation models proposed for use in this methodology. A discussion can be found in section 3.2 of 

this report. USGS EROS, U.S. Geological Survey Center for Earth Resources 

Observation and Science; USDA NASS, U.S. Department of Agriculture 

National Agricultural Statistics Service; USDA FIA, U.S. Department of Agriculture Forest Service Forest Inventory and Analysis Program; LANDFIRE, 

Landscape Fire and Resource Management Planning Tools]

Mitigation activity Data source

Irrigation USGS EROS National Irrigation Map.

Drainage USGS EROS National Irrigation Map.

Fertilization USDA NASS.

Residue management Residue management county statistics.

Tillage USDA NASS.

Crop rotation USDA NASS.

Grazing USDA NASS.

Manure application USDA NASS.

Forest cutting USDA FIA.

Forest thinning USDA FIA.

Fuel treatments LANDFIRE.
Appendix A 93

A.3. Ecoregion Reference Scenario Construction

Regional reference scenarios build from the national 

storylines discussed above. The foundations of regional scenarios are LULC and land-management histories developed 

through review of existing historical data sources, including 

the comprehensive analysis of recent historical LULC change 

from the USGS Land Cover Trends research project (Loveland 

and others, 2002). Regional expert consultations will be used 

to project recent historical LULC into the future based on the 

current (2010) understanding of the interaction of drivers of 

LULC change. Regional experts will link SRES and national 

LULC and management storylines with the biophysical and 

socioeconomic characteristics of ecoregions to provide a range 

of LULC and management futures consistent with historical 

observations.

The scenario construction process also may incorporate 

exogenous projections of particular LULC types or management. Examples include projections of population from the 

U.S. Census Bureau or the USFS’s 2010 RPA land-use projections; however, maintaining fidelity with the SRES storylines 

is required, and the regional expert consultation process will 

determine the degree to which these exogenous projections 

are used, if at all. Other coarse-scale LULC modeling efforts, 

such as those provided by IPCC SRES modeling teams, can be 

empirically downscaled and combined with existing expertise in LULC science, LULC histories reported in existing 

studies, and regional expert knowledge to construct regional 

LULC scenarios for each ecoregion. Constraints on scenario 

construction primarily are the SRES storylines and existing 

knowledge of regional LULC change. This flexible framework creates opportunities to explore a wide range of regional 

LULC scenarios, and remain consistent with overall SRES 

assumptions and characteristics.

A.4. National Interpretation of Storylines for 

Alternative Mitigation Scenarios

The interpretation of each national storyline is expanded 

to support the construction of the alternative mitigation 

scenarios. A mitigation scenario is a combination of future 

potential LULC and land-management changes (activities) 

associated with vetted climate and socioeconomic conditions 

to illustrate possible GHG mitigation capacities with various 

assumptions. There is no precedent for constructing alternative mitigation scenarios for the IPCC SRES. The USFS RPA 

assessment has not embarked on this endeavor. Alteration 

of each reference scenario to explore mitigation activities to 

enhance carbon sequestration and reduce GHG emissions 

compromises the internal consistency of the reference scenario 

that is devoid of carbon-sequestration policy. Notably, carbonsequestration policy that leads to reduced GHG emissions 

and increased carbon sequestration affects climate, but this 

effect cannot be captured without modeling policy changes 

at the global scale. Although the more recent representative 

concentration pathways (RCP) effort (Moss and others, 2010) 

avoids this problem by finding alternative scenarios for each 

radiative forcing target, it requires scenarios to meet carbon 

sequestration and GHG-emission reduction targets, rather than 

explore ecosystem capacity. The methodology accepts climate 

incongruity for alternative scenarios. Conceptually, it is easier 

to guard each storyline for changes in land management (M), 

although changes in land management have implications for 

yields and consumption patterns. Departure from the storyline 

is exaggerated by LULC changes to enhance carbon sequestration (L and ML) because of competing uses for land to 

produce food, fiber, and energy for the population. The IPCC 

SRES storylines are used to motivate a diverse set of alternative mitigation scenarios emphasizing mitigation activities, 

different change amounts, and different concerns for the 

temporal aspects of carbon sequestration, including timeeffectiveness and permanence of sequestration.

The three storylines are differentiated by assumptions 

that will be related to emphases on mitigation activities, 

amounts of change, and importance of timing and permanence 

of carbon sequestration. Examples are described below:

• For IPCC SRES storyline A1B—

◦ The freeing of natural resources could enable afforestation

◦ The emphasis on management versus conservation 

is more likely to promote plantations rather than 

natural forest

◦ The pervasiveness of urban sprawl may restrict 

reductions in the rates of urban development

◦ The technological progressiveness of A1B may 

justify technological feasibility of biochar and rangeland restoration

• For IPCC SRES storyline A2—

◦ The increased or large population growth and focus 

on sustainable food production may limit conversion 

of agricultural lands to forest lands, but it may be 

compatible with agricultural practices that enhance 

carbon sequestration through conservation tillage, 

efficient fertilizer usage, crop rotations, and cover 

crops

◦ The concern for water quality for sustainable agriculture may prompt wetland restoration

• For IPCC SRES storyline B1—

◦ The focus on sustainable development may embrace 

sustainable food production and wetland protection within A2, but it also may include sustainable 

forestry practices including the following:

▪ Afforestation and avoidance of deforestation

▪ Expansion of urban forests

▪ Forest management for carbon sequestration

▪ Native species
94 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

◦ Resource efficiency may promote slower rates of 

development and increased urban density

◦ The sustainable development focus may value longand short-term carbon-sequestration activities

Alternatively, proposals for national initiatives, similar to 

Conservation Reserve Program (CRP), for ecological carbon 

sequestration will be invited from consulting agencies and 

applied to varying degrees (for example, high, medium, and 

low levels of national commitment) across the storylines. 

Either way, the national consultation process will be relied 

upon to provide national bounds on increases in mitigation 

activities. Similarly, bounds on mitigation activities in ecoregions also need to be evaluated.

A.5. Ecoregion Evaluation of Mitigation 

Activities

For each ecoregion, mitigation activities will be examined 

for relevance (for example, grazing-land management is more 

prevalent in some ecoregions than others) and evaluated for 

their potential to sequester carbon and reduce GHG emissions. 

Evaluating LULC conversions and land-management changes 

in the ecoregion will identify activities that maximize carbon 

sequestration to provide an efficient means to construct alternative mitigation scenarios that “optimize” sequestration in the 

ecoregion given a limited number of scenario simulation runs 

and no iteration. Furthermore, a transparent evaluation of mitigation activities complements the complex simulations of LULC 

change and biogeochemical modeling. Evaluating mitigation 

activities is accomplished by using results from regional studies 

of carbon-sequestration-mitigation activities, consulting with 

regional experts (for example, GRACEnet participants), and 

developing a spreadsheet tool. Two objectives of GRACEnet 

are to identify an agricultural system that most likely maximizes 

soil carbon sequestration and to identify an agricultural system 

that minimizes net global warming potential (Jawson and others, 

2005; U.S. Department of Agriculture, Agricultural Research 

Service, 2010). As a result of carbon-sequestration research, 

some regional studies of mitigation activities are readily available. For example, relevant studies about mitigation activities 

in the Mississippi Alluvial Plain and Mississippi Valley Loess 

Plains ecoregions include the following:

• Loblolly pine harvest rotations (Huang and Kronrad, 

2006; Sohngen and Brown, 2008)

• Fertilization of loblolly pine plantations (Fox and others, 2007)

• Sequestration from softwood (evergreen) forest versus 

hardwood (deciduous) forest (Sohngen and Brown, 2006)

• Reforestation of poorly stocked pine plantation (Huang 

and others, 2004)

• Conservation tillage (Franzluebbers, 2005)

• Cotton rotations including pasture (versus monoculture) 

and with cover crops (Causarano and others, 2006)

• Restoration of bottomland forests (Wainger and King, 

2001; Jenkins and others, 2009)

These studies provide details on potential land-management and LULC change for the ecoregion. State climate-change 

studies provide further indications of the potential to enhance 

sequestration. For example, the Arkansas Governor’s Commission on Global Warming plan sets a target of 4 percent increase 

in urban trees by 2025 (Arkansas Governor’s Commission on 

Global Warming, 2008). Consulting with regional experts will 

further develop a knowledge base for mitigation-activity potential to enhance sequestration capacity in the ecoregion.

A spreadsheet tool will be used to synthesize available study 

results and fill in details on carbon sequestration. The tool will 

present aggregated planning-level estimates of how annual rates 

of carbon sequestration and cumulative carbon sequestration will 

change with time as a result of changes in LULC and management from time of implementation. (This is in contrast to the 

reference and alternative scenarios that stage the implementation 

of the mitigation activities with time.) The spreadsheet tool will 

enable exploration of the sensitivity of carbon sequestration to 

forest management (thinning, harvest rotation, and sequestration 

in wood products) and agricultural crop rotations of importance to 

the ecoregion, as needed. A similar spreadsheet tool was developed for the U.S. Environmental Protection Agency (EPA) in the 

late 1990s and implemented for Omernik’s (1987) ecoregions 

(Dennis King, University of Maryland, written commun., 2010). 

The Natural Capital Research Group at the University of Maryland Center for Environmental Science (UMCES) developed a 

spreadsheet approach during 1996 and 1997 for the EPA Office 

of Policy Analysis, in preparation for the 1997 Kyoto Conference. When the United States pulled out of the Kyoto negotiations in 1998, the project was suspended; the spreadsheet results 

for 22 management activities were not published, although they 

were used in 1999 and 2000 in a project for the EPA Office of 

Air to show how one might “score” domestic carbon trading 

that involved land-based carbon sequestration. Although this 

tool needs updating, further development, and refinement for the 

assessment, the effort provides a proof of concept.

The mitigation-activity spreadsheet tool will be developed 

to report results for each of the assessment units with calculations 

made at the regional and subregional levels. Each spreadsheet 

will show the expected change (gains, losses, and net) in carbon 

sequestration resulting from a specific LULC or management 

activity to another during three periods: a transitional term of 15 

years, the medium term of 16 to 40 years, and the longer term 

of 41 to 100 years, or to an endpoint deemed appropriate for the 

mitigation activity (as the capacity of some forest ecosystems to 

sequester carbon is not captured within the 40-year horizon of 

the assessment). The results will be backed up by detailed tables 

showing how aboveground, belowground, and on-ground carbon 

is expected to increase and decrease with time. The spreadsheets 

will refer to the models that generated them (for example, the 

Century model from Parton and others (1993)), so that users 

interested in moving beyond planning-level estimates to consider 

specific policies in specific jurisdictional boundaries will have 

access to more detailed and precise numbers.
Appendix A 95

A column for hectares of suitable or available land for 

each mitigation activity will be inserted to complete estimates 

of total carbon potential for the activity and to provide an 

upper bound on change amount. Designating and bounding 

suitability is not a well-defined task. The FASOM–GHG conversion (for example, pasture to forest) suitability was derived 

from Natural Resources Conservation Service (NRCS) land 

surveys and expert opinion (Adams and others, 1996). The 

FORE–SCE probability surfaces indicate relative suitability of 

land for LULC. There are limits on land-management suitability, as well. For example, the benefit of no-till methods in 

heavy clay soils is questionable (Rochette and others, 2008).

A concept related to suitability is availability, which is the 

supply of hectares of mitigation activities for a price (Brown 

and Kadyszewski, 2005); therefore, suitability also is indicated by the hectares of mitigation activities supplied at a high 

carbon price. In short, various data sources and model results 

provide indications of hectares of suitability and availability; 

for the prototype study region, the availability of bottomland 

forest was taken from the marginal land analysis of Wainger 

and King (2001). The percentage change in bottomland forest 

was noted to fall within the percentage change of national 

afforestation estimated by FASOM–GHG for the $50-pertonne (megagram) price (Murray and others, 2005) and the 

USDA (Lewandrowski, 2004). Similarly, the national results 

from the latter two studies were used to indicate conservation 

tillage change bounds, but regional results from those studies 

could be accessed for the assessment. The increase in urban 

forests was adopted from the State climate-change plans. 

Regional consultation also will be used to indicate bounds on 

the suitability and availability of hectares for mitigation activities in the ecoregion.

The assessment unit spreadsheets for mitigation activities 

will be populated as follows:

1. Available studies and regional subject expertise will be 

used to specify the mitigation activities for each LULC 

class in the assessment unit.

2. The Carbon Online Estimator (COLE; U.S. Department 

of Agriculture, U.S. Forest Service, 2010a), the Voluntary 

Reporting of Greenhouse Gases-Carbon Management 

Evaluation Tool (COMET–VR; U.S. Department of Agriculture, Natural Resources Conservation Service, 2010), 

and the Century carbon model (which has a user-friendly 

interface) will be used to estimate annual rates of carbon 

sequestration for each LULC and management activity within each EPA level II ecoregion from year 1 (for 

example, year of transition to the management activity 

intensity) to year 40 and beyond as appropriate.

3. The results of step 2 will be compared with results of earlier 

nationwide research (for example, Sperow and others, 2003).

4. The number of hectares in each LULC class and landmanagement carbon-intensity class for the starting conditions for each EPA level II ecoregion (section 3.2 of this 

report) will be obtained.

5. The hectares of LULC class suitability will be estimated 

using FORE–SCE probability surfaces, estimates from 

other studies (for example, FASOM–GHG suitability 

estimates based on data from the National Resources 

Inventory (NRI) of the NRCS), and regional consultation, 

as will land-management suitability limits.

6. A spreadsheet tool for each EPA level II ecoregion will be 

prepared (see table A4 for a sample report).

7. Instructions and caveats about using the spreadsheet data, 

providing explanations about how spreadsheet numbers 

were generated, and links back to underlying simulations 

and models will be documented.

Table A4.  Sample spreadsheet format adapted from earlier spreadsheets for the U.S. Environmental Protection Agency.

[GHG, greenhouse gas; LULC, land use and land cover; Mg/ha/yr, megagram (metric ton) per hectare per year]

Ecoregion 1

From agriculture, croplands, 

harvested, conventional tillage,

no cover crops

Hectares of

mitigation activity

Carbon sequestration rate from “no 

change” (Mg/ha/yr)

Other

GHG

emissions

Starting Suitable Years 

1–15

Years 

16–40

Years 

41–100

Years 

1–40

LULC class To

Agriculture, croplands, harvested Conventional tillage, no cover crops

Conventional tillage, cover crops

Conservation tillage, no cover crops

Conservation tillage, cover crops

Agriculture, croplands, idle Agriculture, permanent grass

Agriculture, wetlands

Deciduous forest Deciduous, 0–15 years

Evergreen forest Evergreen, 0–15 years

Pasture/rangeland Pasture/rangeland, herbaceous 
96 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

low), and aggressiveness of carbon sequestration (for example, 

urgent, constant, delayed). The evaluation of the mitigation activities, described above, provides the means by which to specify 

mitigation activities that maximize carbon sequestration, to bound 

mitigation activity change within the ecoregion and storyline, and 

to indicate carbon sequestration with time.

In the absence of national and regional consultation, a 

preliminary example of an alternative mitigation scenario 

for the sample region is provided for illustrative purposes in 

table A5. The features of an alternative enhanced scenario 

(for example, ML scenario for the A1B storyline (denoted as 

A1B_ML)) are as follows:

• Net change from 2010 to 2050 of the alternative scenario 

reported against the net change of the reference case

• LULC change implemented at an annualized rate of the 

net change (constant implementation)

For users outside of the assessment, additional steps can 

be implemented to refine a spreadsheet tool to organize information for various spatial scales, such as counties and States, 

and to package spreadsheet tools in various ways to support 

the needs of users.

A.6. Alternative Mitigation Scenario 

Construction for Ecoregions

For an ecoregion, three alternative mitigation scenarios will 

be constructed to enhance carbon sequestration and reduce GHG 

emissions through land management (M), LULC change (L), and 

both (ML) for each storyline. Each set of storyline alternative 

mitigation scenarios will be built from national guidelines regarding mitigation activity emphases (for example, managed versus 

natural), commitments to change (for example, high, medium, 

Table A5a.  Reference A1B_R and alternative scenario A1B_ML for Mississippi Alluvial Plain.

[km2, square kilometers; lbs/acre, pounds per acre (multiply by 1.12 to obtain kilograms/hectare); LULC, land use and land cover; N, nitrogen; P, phosphorus]

LULC class

2010

LULC

(km2)

A1B_R net 

change

2010–2050

(percent 

LULC class)

A1B_L net 

change 

2010–2050

(percent 

LULC class)

2050: 

A1B_L-R 

(km2)

LULC change comments/

directions Land management change 

comments/directions

Water 13,396 10.8 10.8 0 No reduction in coastal wetlands

Developed 9,855 12.9 12.9 0 No reduction in development (urban 

sprawl), no conversion of wetland 

to developed

Increase urban forestry: five trees per 

capita over next 15 years.

Barren 251 -1.6 -1.6 0 Mostly sand, not suitable for sequestration activities

Mining 6 -3.9 -3.9 0 Mine lands ignored because of small 

size of area 

Mechanically disturbed 140 0.5 -48.8 -69 Reduced by doubling harvest rotation 

(with thinning)

Fire disturbed 4 0.0 0.0 0 Forest-fire management maintains 

disturbance despite afforestation

Deciduous forest 2,826 -16.5 -16.5 0 Reduce deforestation

Evergreen forest 2,272 0.2 32.2 728 Favor plantations Increase stock on understocked land.

Fertilize at planting: 200 lbs/acre N and 

25 lbs/acre P.

Mixed forest 1,505 -17.6 -17.6 0 Reduce deforestation

Shrub/scrub 0 0.0 0.0 0

Grassland 0 0.0 0.0 0

Hay/pasture 4,352 12.1 6.1 -264 Reduce the R scenario increase in 

pasture for conversions to bottomland forest restoration

Increase pasture/crop rotations by 10 

percent

Row crops 62,620 -2.4 -16.4 -8,767 Conversions of marginal croplands to 

bottomland forest restoration

Increase pasture/crop rotations by 10 

percent

Implement conservation tillage up to 50 

percent of cropland.

Implement wheat cover crop on 50 

percent of cotton acreage.

Increase cotton-soybean crop rotations 

(to reduce monocultures).

Reduce rice acreage by 50 percent.

Woody wetlands 27,048 -1.9 29.1 8,372 Restore 2 million acres of bottomland 

forest 

Herbaceous wetlands 8,191 -6.1 -6.1 0 Avoid conversion of wetland to 

developed

Total 132,468 no change no change 0
Appendix A 97

• Reasons for and amounts of LULC changes, and constraints on conversions

• Reasons for and amounts of land-management changes 

within LULC classes

Supporting references for each of the two types of mitigation activities in these ecoregions were noted in section A.2. 

Except for bottomland forest restoration in the Mississippi 

Alluvial Plain (where a study proposed acres of conversion), 

land-use percentages and management change are fabricated 

in the absence of a functioning regional consultation process.

There are many other factors that affect realized sequestration capacity including cost, technological feasibility, 

effects on ecosystem functions and services, energy usage, and 

policy implementation. The primary purpose of the scenario 

construction is to assess the ecological sequestration capacity of ecosystems. Demonstrating relations between carbon 

sequestration and other factors are provided in chapter 3 of 

this report and appendix F.

A.7. National and Regional Consultation 

Processes

A.7.1. Framework for Engaging Expert Opinion to

Calibrate Models

An important conceptual component of the modeling 

process is consultation. The goal of this consultation process 

is to ensure that the constituents of the reference and alternative mitigation scenarios being modeled in the assessment 

are relevant at the scale of the assessment unit. Further, it 

is desirable, and indeed necessary, that the processes and 

assumptions used to construct scenarios (and constituents) 

Table A5b.  Alternative scenario A1B_ML for Mississippi Valley Loess Plains.

[km2, square kilometers; lbs/acre, pounds per acre (multiply by 1.12 to obtain kilograms/hectare); LULC, land use and land cover; N, nitrogen; P, phosphorus]

LULC class

2010

LULC

(km2)

A1B_R net 

change 

2010–2050

(percent 

LULC class)

A1B_L net 

change 

2010–2050

(percent

LULC class)

2050: 

A1B_L-R 

(km2)

LULC change comments/

directions

Land management change 

comments/directions

Water 562 26.3 26.3 0 No reduction in coastal wetlands

Developed 4,998 38.8 38.8 0 No reduction in development (urban sprawl), no 

conversion of wetland to developed

Increase urban forestry: five trees 

per capita over next 15 years.

Barren 72 0.0 0.0 0 Mostly sand, not suitable for sequestration activities

Mining 16 -11.1 -11.1 0 Mine lands ignored because of small size of area 

Mechanically disturbed 424 0.5 -49.6 -213 Reduced from doubling harvest rotations (with 

thinning)

Fire disturbed 3 0.0 0.0 0 Forest-fire management maintains disturbance 

despite afforestation 

Deciduous forest 11,379 2.0 2.0 0 Reduce deforestation

Evergreen forest 4,831 20.6 33.9 641 Favor plantations Increase stock on understocked 

land.

Fertilize at planting: 200 lbs/acre N 

and 25 lbs/acre P.

Mixed forest 4,681 3.4 3.4 0 Reduce deforestation

Shrub/scrub 0 0.0 0.0 0

Grassland 0 0.0 0.0 0

Hay/pasture 5,008 -35.5 -35.5 0 Increase pasture/crop rotations by 10 percent

Row crops 9,536 -15.2 -22.1 -661 Increase pasture/crop rotations by 10 percent Implement conservation tillage up 

to 50 percent of cropland.

Implement wheat cover crop on 50 

percent of cotton acreage.

Increase cotton-soybean-crop rotations (to reduce monocultures).

Reduce rice acreage by 50 percent.

Woody wetlands 4,065 -5.7 0.0 233 Prevent further loss of woody wetlands, no study 

found to support restoration of woody wetlands

Herbaceous wetlands 328 -1.4 -1.4 0 Avoid conversion of wetland to developed

Total 45,903 no change no change 0
98 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Figure A2.  Diagram showing the national and regional

consultation process to obtain expert knowledge to calibrate

scenarios for the assessment.

are consistent across ecoregions so that comparisons are 

valid. The assessment will engage regional experts to ensure 

regional relevance operating within a national framework to 

ensure consistency.

The assessment requires the integration of somewhat 

disparate models and project components, and consequent 

fields of expertise, such as LULC change, land-management 

change, biogeochemical modeling, policy, and economics. 

As such, the approach to calibrating modeling components 

will be split into two branches (fig. A2). One branch of the 

calibration process will focus on reference scenario development; the other will focus on mitigation scenario development. The two calibration branches are not independent, but 

can proceed in parallel with careful scheduling. Each of the 

two branches will be led by a subgroup in the assessment; 

the subgroup members will maintain contact with the other 

subgroup through the project leadership group. To improve 

the efficiency and outcomes of the calibration process, external organizations with significant experience in working with 

experts may be retained and used to help develop protocols, 

materials, and tools where needed.

An important aspect to all scales of scenario construction 

is the use of historical LULC, rates of LULC change, landmanagement dynamics, and other related data, both spatial 

and nonspatial. Examples of such data are found in table A6. 

These data will first be used during national reference scenario construction and consultation to document the historical trajectories of variables affecting U.S. LULC change and 

management, inform experts about spatial variability of U.S. 

LULC change, and provide a historical baseline from which to 

project changes in LULC and management under alternative 

scenarios.

A.7.2. National Reference Scenario Development

(Box A in Figure A2)

The foundation of the assessment process is a suite of 

SRES-based reference scenarios. The three SRES scenarios 

will be interpreted, and a narrative description will be developed. These three scenarios form the basis for the national reference modeling scenarios. These national, initial “strawman” 

reference scenarios will be reviewed and refined by a wide 

array of relevant experts at a national workshop. Comments 

and suggestions from a geographically diverse set of workshop participants will be reviewed and incorporated where 

needed. The outcome of this process will be a set of nationallevel, reference storylines and scenarios. At this scale, focus 

Table A6.  Data sources and characteristics used to inform and develop reference and alternative scenarios.

[USDA, U.S. Department of Agriculture; USGS, U.S. Geological Survey; LULC, land use and land cover; EPA, U.S. Environmental Protection Agency; NRCS, 

Natural Resources Conservation Service; m, meter]

Data source Description Spatial resolution Temporal resolution

U.S. Population Census (U.S. Census 

Bureau)

Decadal population estimates, U.S. 

population projections

Subcounty (block, block group), 

county, State

10 years, with annual population 

projections.

USDA Census of Agriculture Estimates of crop production County, State Every 5 years.

USDA Cropland Data Layer State maps of crop type 30–60 m, States Annual; 2000 to present 

(not national coverage).

USGS Land Cover Trends Sample-based estimates of LULC 

change for conterminous United 

States; reports documenting LULC 

dynamics 

EPA ecoregions (levels I, II, and III) 

and States

Every 6 to 8 years between 

1973 and 2000.

National Land Cover Dataset and Database (NLCD)Land-cover map of the conterminous 

United States

30 m 1992, 2000, 2006.

Forest Inventory and Analysis (FIA) 

Program of the U.S. Forest Service

Sample-based point locations; characterizations of U.S. forest dynamicsPoint-based observations on U.S. 

forest lands

Annual.

Protected Areas Database of the United 

States (PAD-US)

Polygons of protected areas of the 

United States

Feature-based; individual protected 

areas

Current; each feature has attributed 

data for establishment of area.

National Resources Inventory (NRI) of 

the NRCS

Sample-based estimates of land use and 

other changes (non-Federal lands)

Point-based estimates for States (some 

local and regional data/estimates 

also available)

1977, 1982, 1987, 1992, 1997.

National Wetlands Inventory (NWI) Characterization of wetlands Feature-based; local to national Based on source data from 1980s 

and 1990s.

National

Reference

scenarios

Mitigation

scenarios

Time

Regional

Box A

Box B Box D

Box C

National reference

scenario development

(National workshop)

(National consultations)

(Regional consultations)

scenario development scenario development

scenario development

(Regional consultations)

Regional reference

National mitigation Regional mitigation
Appendix A 99

is on developing qualitative storylines rather than quantitative 

scenarios. The storylines will be the primary product used to 

develop regional land-use scenarios.

A.7.3. National Mitigation Scenario Development

(Box B in Figure A2)

One of the key questions to be addressed for the assessment is which mitigation activities have the greatest potential to 

increase carbon sequestration and GHG fluxes. Because landchange modeling and biogeochemical assessment models are 

a central part of the methodology, the list of candidate mitigation activities must be compared against the capabilities of the 

models and data availability to ensure that they are compatible. 

A national consultation with a diverse set of experts will be 

used to obtain additional external review of the list of mitigation 

activities, prioritize mitigation activities for the assessment, set 

national guidelines for plausible maximum mitigation activity 

change, and provide guidelines on the assignment of mitigation activities to the storylines. Supplemental small meetings 

with specific relevant experts also may be held to ensure that 

a variety of stakeholder interest groups are represented. The 

outcome of this process will be a national set of guidelines for 

implementing mitigation scenarios to increase carbon sequestration and reduce GHG emissions at the regional level.

A.7.4. Regional Reference Scenario Development

(Box C in Figure A2)

To improve the relevance of assessment results at 

regional levels, the national reference scenarios will be 

downscaled to EPA level II ecoregions (assessment units; 

modified from Omernik, 1987). To accomplish this important 

goal, a series of regional consultations will be conducted. 

Relevant assessment staff will identify and engage key 

experts in each ecoregion. Because many of the ecoregions 

are quite large, the team will explore using model input 

ranges based upon regional expert suggestions (for example, 

various data scales from the U.S. Census Bureau) with qualitative (for example, types of specific LULC transitions) and 

quantitative (for example, overall national rates of LULC 

change) constraints developed during the national scenario 

development stage to guide regional scenario construction. 

The results of this development process are a critical input 

to the next step in the process—developing regional-level 

mitigation scenarios.

A.7.5. Regional Mitigation Scenario Development

(Box D in Figure A2)

Results from the national mitigation scenario workshop 

and regional reference scenario consultations are combined 

in this final step aimed at regionalizing mitigation scenarios. 

Regional experts will be engaged to work from a set of 

activities from the national list in the context of the reference 

scenarios developed for that region. Regional engagement 

may be organized through regional entities such as the U.S. 

Fish and Wildlife Service Landscape Conservation Cooperatives (LCC) and the U.S. Department of Interior initiative 

for Climate Science Centers. Regional experts will suggest 

changes to the selected mitigation activities to improve local 

and regional relevance, will review the synthesis of relevant research and spreadsheet results, and will review three 

regional mitigation scenarios for each storyline. It is important 

to note that process consistency will be maintained throughout these steps by requiring that each successive scenario 

construction build upon previous stakeholder’s or expert’s 

calibration steps.

A.7.6. Schedule Considerations

The two branches of the calibration process interrelate, and 

careful scheduling of the workshops and consultations will be 

required to meet overall deadlines. It is important to remember 

that this calibration process is an early component of the overall 

production scheme, and delays in the calibration process will 

cascade down through the schedule and ultimately create delays 

in the final products. Adequate time for planning workshops and 

consultations, as well as compiling and interpreting the results, 

must be explicitly included in the calibration schedule.

The general process is as follows: national reference 

scenario development (box A in figure A2); national mitigation 

scenario development (box B in figure A2); regional reference 

scenario development (box C in figure A2); and regional mitigation scenario development (box D in figure A2); however, 

these steps are not necessarily sequential. Regional scenario 

calibrations must precede the corresponding regional mitigation strategy calibration, but the regional scenario calibrations 

can take place significantly earlier and do not need to proceed on a pace that matches the regional mitigation strategy 

calibrations.

A.7.7. Approximate Schedule

The approximate schedule is summarized below.

• The national reference scenario workshop likely will 

occur in fall 2010.

• National mitigation scenario workshop and consultations will follow as soon as the final results are available.

• Regional reference scenario consultations require products from the mitigation scenario workshop, therefore 

they cannot commence until the products are complete 

and the final results are available.

• Regional mitigation scenario consultations must follow 

the associated scenario consultation, and they should 

be completed for each ecoregion well before the products are needed by the modelers.
100 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

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103

Appendix B. Land-Use and Land-Cover Modeling

others, 2007; Sohl and Sayler, 2008) and exploratory scenarios 

constructed through the use of expert knowledge (Sohl and 

others, 2007). CLUE modeling applications also have used 

trend extrapolations for demand (Verburg and others, 1999; 

Verburg, Overmars, and others, 2006), scenarios constructed 

through the use of empirical data and expert knowledge (Kok 

and Winograd, 2002), and complex modeling of demand 

through the use of a global economic model and an integrated 

assessment model (Verburg and others, 2008).

Demand for this application is directly linked to the use 

of storylines from the Intergovernmental Panel on Climate 

Change (IPCC) “Special Report on Emission Scenarios” 

(SRES) (Nakicenovic and others, 2000). Reference scenario 

demand will be provided by the scenario downscaling processes discussed in section 3.2 of this report (“Methodology 

Framework”) and appendix A of this report (“Reference and 

Alternative Mitigation Scenarios”). Demand for alternative 

management scenarios associated with policy and mitigation 

actions will be provided by the methodologies discussed in 

section 3.2 of this report. Scenario-specific demand for reference and alternative scenarios will be provided as regionally 

specific prescriptions for annual LULC change from 2001 to 

2050, with annual net change in individual LULC types. This 

information will be passed to the spatial allocation component 

of FORE–SCE, which will spatially distribute annual demand 

for change.

B.1.2. Spatial Allocation Component

The spatial allocation component of FORE–SCE ingests 

“demand” for a given region and spatially allocates prescribed LULC change on the landscape. The core drivers for 

identifying locations of LULC change are probability surfaces, constructed through the analysis of empirical relations 

between existing LULC patterns and a wide array of spatially 

explicit biophysical and socioeconomic data. Although the use 

of probability surfaces follows the primary methodology used 

by the CLUE series of models, the actual allocation of change 

is markedly different, with FORE–SCE utilizing a patch-based 

allocation methodology. The following provides a summary of 

the primary elements of the spatial allocation methodology.

B.1.2.1. Construction of Probability Surfaces

The spatial allocation component requires probability 

surfaces for each LULC class being modeled. Empirical 

analyses of the relation between spatial datasets representing 

drivers of LULC change and existing LULC patterns are used 

to construct the probability surfaces, using a stepwise logistic 

regression. The most stable and robust explanation for regional 

LULC patterns is obtained by analysis of endpoint (the most 

This appendix describes details of the spatially explicit 

land-use and land-cover (LULC) modeling component of this 

methodology. The simulation model FORE–SCE (forecasting scenarios of land cover change) will be used, which is a 

spatially explicit modeling framework that produces scenariobased, thematic LULC maps at annual time steps. The model 

begins with a LULC map representing conditions at the 

beginning of the simulation period and places realistic patches 

of LULC “change” for each subsequent yearly iteration. The 

proportion and type of LULC change are determined by the 

scenario being simulated, whereas the location of change 

is driven by site-specific biophysical characteristics. The 

modeling framework is capable of producing scenario-based 

simulations of future LULC change at a variety of spatial and 

thematic resolutions.

FORE–SCE originally was developed in support of a 

sensitivity analysis of the effects of LULC change on climate 

variability (Sohl and others, 2007; Sohl and Sayler, 2008). 

Although the initial application had specific requirements that 

helped define the initial model structure, the U.S. Geological 

Survey (USGS) modeling team designing FORE–SCE wanted 

to develop a flexible modeling system that could be adapted 

for future applications covering a range of research interests. 

FORE–SCE development began by adopting some of the 

key characteristics of the Conversion of Land Use and its 

Effects (CLUE) series of models (Veldkamp and Fresco, 1996; 

Verburg, Overmars, and others, 2006; Verburg, Rounsevell, 

and Veldkamp, 2006; Verburg and others, 2008). One of the 

primary components adopted from CLUE is the modular 

framework, with distinct but linked “Demand” and “Spatial 

Allocation” modules. This structure allows for both linkages 

with exogenous models, but also for direct or indirect incorporation of driving force factors operating at multiple scales. 

The flexibility offered by this framework greatly increases 

model utility for a variety of applications. What follows is an 

explanation of model design, potential data gaps, and primary 

outputs.

B.1. FORE–SCE Structure

B.1.1. Demand Component

The “Demand” component of FORE–SCE provides overall, regional proportions of LULC annual change (an annual 

regional “prescription” of LULC change). A wide variety of 

methodologies potentially can be used to construct demand, 

as long as the final products are simple tables of annual LULC 

change for each LULC class being mapped. Approaches used 

for construction of demand for past FORE–SCE applications 

consisted of extrapolations of historical trends (Sohl and 
104 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

current) LULC (de Koning and others, 1998); therefore, the 

dependent variable for use in the logistic regression analysis 

is the presence or absence of a given LULC type as mapped 

by the 2001 starting land-cover product (section 3.2 of this 

report). Independent variables used in the logistic regression 

include any spatially explicit datasets representing LULC driving forces.

Drivers of LULC change are unique and are based 

on geographic setting (Sohl and others, 2007). Given the 

unique characteristics of each region, probability surfaces 

will be independently modeled and constructed for each U.S. 

Environmental Protection Agency (EPA) level II ecoregion 

(modified from Omernik, 1987). For each ecoregion, drivingforce variables linked to LULC change for that region will be 

identified, acquired, and formatted. Spatially and thematically 

stratified sample points are drawn from within an ecoregion, 

and values for land cover (dependent variable) and all ancillary datasets (independent variables) are extracted. Probability 

surfaces are then constructed for every thematic LULC class 

being modeled. For each LULC class, driving-force variables 

linked with the LULC class are identified, and an initial logistic regression run is performed. Initial results are examined 

for the presence of correlated independent variables. In cases 

where two highly correlated variables are selected by the stepwise regression, one of the two paired variables is discarded to 

mitigate the effects of multicollinearity. The regression is run 

again with the remaining variables. Output from the stepwise 

logistic regression is then used in the construction of probability-of-occurrence surfaces for that LULC class, as:

, (B1)

where θh is the probability for pixel h being a member of 

the class (values range from 0 to 1);

α is the intercept parameter;

b is the regression coefficient for LULC class k; 

and

x is an explanatory variable.

The probability surfaces constructed from the logistic 

regression process are referred to as “baseline probability” and 

are a primary component of the remaining spatial allocation 

procedure as described below.

B.1.2.2. Model Parameterization

The FORE–SCE model relies on historical LULC data 

for parameterization of the spatial allocation component. Several key parameters governing FORE–SCE’s patch-placement 

procedure use information from the USGS Land Cover Trends 

project (Loveland and others, 2002). As with the probability 

surface construction, model parameterization is done on an 

ecoregion-by-ecoregion basis. Before running the spatial 

allocation module, the following parameters are populated as 

follows: patch size, “clumpiness,” probability modifier, and 

patch library.

Patch size.—Typical patches of LULC change differ 

in size and configuration, depending upon LULC type and 

region. For the patch-based spatial allocation procedure, 

patch-size distribution for every LULC type is required. 

Empirically measured patch sizes from the USGS Land Cover 

Trends project are analyzed for each LULC type. Mean patch 

size and standard deviation for every LULC type is used to 

populate tables for each ecoregion in the analysis area.

“Clumpiness.”—Some forms of LULC change tend to 

occur as tightly collocated clumps, whereas other forms of 

LULC change tend to be more dispersed. A “clumpiness” 

parameter is used to control dispersion of LULC-change 

patches in the spatial allocation procedure. “Clumpiness” 

refers to the parts of the probability surface where change 

patches are allowed to be placed, and is expressed as a threshold value on the probability-surface histogram. For typically 

“clumped” LULC types such as urban and developed lands, 

the greatest probability values get preference for selection and 

placement of a change patch. For more dispersed LULC types, 

restrictions on the part of the probability histogram that can 

be used are more relaxed, resulting in more dispersed change 

patches. The “clumpiness” parameter for each LULC type is 

established through examining LULC change characteristics 

as mapped by the USGS Land Cover Trends project.

Probability modifier.—A probability modifier for a given 

LULC transition is based on scenario specifications and the 

likelihood of a given transition based on empirical historical 

data. Contingency tables from the USGS Land Cover Trends 

project provide a complete descriptive matrix of historical 

land-cover change for a given level II ecoregion, and thus 

provide historical context for the likelihood of a given LULC 

transition in that region. Scenario specifications also may have 

a strong effect on the potential likelihood of a given LULC 

transition. The USGS Land Cover Trends contingency tables 

and a scenario’s unique specifications are used to construct 

probability-modifier tables for each ecoregion. Probabilitymodifier values range from 0 to 1 at 0.1 increments and simply 

are multipliers affecting the baseline probability surfaces 

(those constructed through the logistic regression procedure). For example, a probability modifier of “0” typically is 

assigned to all possible transitions of urban or developed land 

to another LULC type because these transitions are extremely 

unlikely, given the relative permanence of development on 

the landscape once it has occurred. As a multiplier to baseline 

probability, existing urban lands are thus excluded from potential change to another LULC type. A similar application of 

probability-modifier values can be used to alter baseline probability surfaces, reducing probabilities for specific forms of 

transition. Using the probability modifier is a powerful methodology for controlling specific scenario-defined storylines.

Patch library.—Patch size and distribution (through 

patch size and “clumpiness” parameters) are only two components affecting aggregate landscape pattern. Patch configuration and shape are another component. FORE–SCE mimics 

actual historical patches of landscape change to better represent landscape pattern. For each ecoregion, patch “libraries” 
Appendix B 105

are populated for every LULC type by copying actual patch configurations (patches of a specific size, 

shape, and orientation) from the USGS Land Cover Trends database. The populated patch libraries are 

sorted by size, with multiple configurations for each patch size. The patch libraries are then used for the 

patch-by-patch spatial allocation procedure as discussed below.

B.1.2.3. Establishing Protected Areas

Although the probability surfaces define the suitability of a location to support a given LULC type, 

they do not account for the protected status of each parcel of land. The Protected Areas Database of the 

United States (PAD-US Partnership, 2009; for this methodology, the version of the database maintained by 

the Conservation Biology Institute of Oregon is used (PAD-US (CBI)) provides attributed polygons of protected lands in the United States. The PAD-US Partnership is a public and private collaboration to provide a 

database of public and private protected lands and includes Federal, State, and local protected lands, as well 

as information from national nonprofit organizations, such as The Nature Conservancy and Ducks Unlimited. Although it does not cover some protected lands, such as private conservation easements, the database 

does cover most of the protected lands in the United States. These data are used to better represent LULC 

change that may occur on these lands, with decision rules used to either alter or eliminate probabilities of 

LULC change occurring, dependent on the type of protection identified with each polygon.

B.1.2.4. Tracking Forest-Stand Age

FORE–SCE utilizes a forest-stand-age layer to establish and track the age of a stand of forest. This 

layer is used to mimic actual forest-cutting cycles and to inform the biogeochemical modeling on not only 

LULC type, but also the age structure of forested lands. Two sources of information are being used to 

construct an initial forest-stand-age layer. The vegetation change tracker (VCT) product (Huang and others, 

2010) tracks disturbance using stacks of Landsat Thematic Mapper (TM) data. These data are being used to 

populate a database that identifies forest pixels disturbed between 1984 and 2001 and the date of last disturbance. In areas that have not been disturbed since 1984, an interpolated stand age surface is constructed 

from Forest Inventory and Analysis (FIA) sampling points. The “composite stand age” image constructed 

from these two sources will be used to track forest age as the model iterates through 2050.

B.1.2.5. Running the Spatial Allocation Component

The core of the spatial allocation module consists of the placement of individual patches of LULC 

change, guided by the aforementioned model parameterization and the regional probability surfaces. The 

process begins with the baseline probability surface for one of the LULC types being modeled. Patch placement is dependent on the combined characteristics of baseline probability, LULC type in the current iteration, the probability modifier parameter, decision rules on protected areas, and in the case of forest pixels, a 

function of current stand age. A “total probability” value is calculated for each pixel in the study area:

TPROBij = PROBij × PROBABILITYMODIFIERij × Function(PROTECTED) × Function(HISTORY) (B2)

where TPROBij is the total probability for LULC type i in ecoregion j;

PROBij is the baseline probability for LULC type i in ecoregion j from 

the regression results;

PROBABILITYMODIFIERij is the scenario-prescribed probability modifier for LULC type i

in ecoregion j;

PROTECTED are decision rules specific to the type of protected land; and

HISTORY is the age since the last change in thematic LULC type.

The probability modifier is applied independently for every possible transition type in a given ecoregion. 

Probabilities within protected lands are altered according to decision rules specific for each form of protection. The HISTORY component is used to alter baseline probability for forest pixels, depending on when a 

pixel was last harvested.

Once total probability is calculated for a given LULC type, the “clumpiness” parameter is used to 

segment the probability-surface histogram into an “allowable” part for patch placement. To begin the patchplacement procedure, a stochastic methodology is used to place a “seed” pixel on the probability surface. 

A patch size then is assigned to the seed pixel. In past applications, patch-size distributions are represented 
106 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

as Gaussian, an assumption that greatly simplifies the patch 

development process. A number generator capable of producing a random value within the desired Gaussian distribution 

is used to select a patch size based on the mean and standard 

deviation of patch sizes measured by the USGS Land Cover 

Trends project for that LULC type. The patch library for that 

LULC type is then consulted, and a random patch configuration for the assigned patch size is selected. The patch then is 

placed on the landscape.

The process is repeated for each LULC type, with the 

requisite number of patches placed on the landscape to meet 

areal “demand” for each LULC type. When demand is met, an 

LULC map is produced for that yearly iteration. Forest-standage maps are updated, with all undisturbed pixels iterated 

upwards by “1,” and all disturbed (cut) forest pixels assigned a 

stand age of “0.” The process then iterates forward to the next 

yearly iteration. At the start of each iteration, new probability 

surfaces are recalculated from updated, dynamic independent 

variables. For example, precipitation and temperature data will 

be used as independent variables in the logistic regressions, 

and if selected as predictor variables for a given LULC type, 

coefficients for the regression equation will be established. For 

future years, downscaled General Circulation Model (GCM) 

projections of precipitation and temperature will be used to 

update LULC probabilities, based on those changes in climate 

variables. Whereas some variables will remain static throughout the simulation period (for example, topographic variables), 

other variables associated with the changing landscape (for 

example, changes in urban development density) will be 

dynamic and will affect future probability surfaces.

Upon the completion of a modeling run, annual, thematic 

LULC maps from 2001 to 2050 will have been produced, 

consistent with the scenario-defined assumptions and resultant 

“demand” for each LULC type. Past applications of FORE–

SCE have produced one set of maps for a scenario. For the 

national assessment, many model runs for each scenario will 

be produced. Given the stochastic components related to patch 

placement and configuration, using Monte Carlo simulations 

will allow for the examination of uncertainties associated with 

location of LULC change.

B.2. Land-Use and Land-Cover Modeling 

Components External to FORE–SCE

B.2.1. Coastal Modeling

The existing version of FORE–SCE is not equipped to 

deal with processes affecting coastal LULC change, especially 

coastal-wetland change in response to natural processes such 

as sea-level rise or erosion and deposition. Given the difficulties in specifically modeling all processes affecting LULC 

change, it is important that regional LULC models be able 

to use existing research and modeling activities where possible (Sohl and others, 2010). Rather than utilize FORE–SCE 

to loosely mimic coastal-change processes, an exogenous 

coastal-process model will be used with modeling results separately integrated with FORE–SCE results.

Assumptions of static landscapes inspire predictions that 

about one-half of the world’s coastal wetlands will disappear 

in response to acceleration in the rate of sea-level rise during 

this century. These estimates, however, incorrectly rely on 

models where bed surfaces accrete at historical rates, where 

inundation occurs across static landscapes, or on comparisons 

between historical accretion and future sea-level rise (Kirwan 

and Guntenspergen, 2009).

Coastal ecosystems are dynamic environments that have 

significant capacity to adjust to changes in rates of sea-level 

rise through nonlinear feedback mechanisms. These types of 

ecogeomorphic feedbacks likely explain the persistence of 

wetlands within the intertidal zone for thousands of years, 

as indicated by the stratigraphic record, and observations of 

accretion rates that are highest in regions with historically high 

rates of sea-level rise.

An ecogeomorphic model that incorporates nonlinear 

feedback among inundation, plant growth, and substrate accretion (Kirwan and Guntenspergen, 2010) will be used to project 

coastal-wetland change for the United States under different sea-level-rise scenarios. In this model, the marsh surface 

accretes at a rate determined by its elevation relative to sea 

level. Increasing inundation leads to higher rates of sediment 

deposition, which helps coastal wetlands keep pace with sealevel rise. Vegetation also responds to increasing inundation 

and vegetation growth increases at low elevations, enhancing 

sediment trapping and organic matter accretion and limiting 

erosion; however, the model also recognizes that there are limits to the conditions under which feedback between inundation 

and sediment accretion can maintain a stable intertidal system.

This model has been used in the first comprehensive 

attempt to model coastal-wetland resilience to accelerating 

sea-level rise. Our experiments indicate that a threshold rate of 

sea-level rise exists above which inundation leads to rapid and 

irreversible conversion of intertidal marshland into unvegetated subtidal surfaces. The specific site conditions (tidal range 

and suspended-sediment concentration) that respond to maximum rates of sea-level rise also were identified. The results 

indicate that the amount of sediment available for accretion 

strongly affects the maximum rate of sea-level rise that coastal 

wetlands can survive, a positive relation exists between the 

threshold rate of sea-level rise and tidal range, and interactions occur between tidal range and suspended sediment in the 

water column.

The predictions of threshold sea-level-rise rates for a 

large range of sediment concentrations and tidal ranges agree 

with observations from estuaries worldwide that were not used 

to design or parameterize the model. The results indicate that 

regions with low tide ranges or suspended-sediment concentrations will submerge in the near future, even for conservative 

projections of sea-level rise, and that marshes in high-tiderange environments with abundant sediment are likely to 

remain stable under more rapid projections of sea-level rise.
Appendix B 107

B.2.2. Integration With Disturbance Modeling

To better represent processes related to fire disturbance, 

insect damage, and other natural disturbances, an exogenous 

disturbance modeling effort will be used (section 3.3.3 in this 

report). Given the “competition” for land between the primarily anthropogenic change modeled by FORE–SCE and the primarily natural change modeled by the disturbance modeling, 

annual communication between the models is essential for the 

national assessment. At the start of a yearly iteration, the disturbance model will produce polygons of disturbance and pass 

those data to the FORE–SCE modeling environment. Those 

natural disturbance polygons will be directly used in that 

year’s final LULC map and will be excluded for consideration 

for LULC change within the FORE–SCE modeling environment. The relation between natural disturbance and potential 

effects on probabilities for the anthropogenic LULC change 

tracked by the FORE–SCE model also will be examined.

B.3. Potential Data Gaps

LULC modelers must try to establish causality between 

LULC change and the biophysical and socioeconomic drivers of change; however, a primary difficulty in establishing 

those linkages is the availability of representative spatial data 

for those driving-force variables (Parker and others, 2002). 

Spatially explicit statistical models have been criticized for 

overreliance on datasets that happen to be available and underrepresentation of significant drivers of LULC change without 

easily obtained spatial data (Briassoulis, 2000). This uneven 

availability of the data remains an issue for spatial models that 

rely on logistic regression and the use of probability surfaces. 

Simply put, adequate data to represent all pertinent driving 

forces of LULC change often are not available. Although the 

outlined methodology and available data should successfully 

meet the goals of the assessment, LULC modeling potentially 

could be improved if land-ownership information, water-availability information, updated wetlands information, FIA data 

access, national VCT data, local zoning and regultory data, 

and data about dynamic independent variables were available. 

These data needs are summarized below.

Land-ownership information.—Individual land owners and resource managers make land-use decisions based on 

the constraints or opportunities afforded to them within their 

unique geographic and ecological setting (Sohl and others, 

2010). Detailed land-ownership information at the national 

level would undoubtedly improve the ability to represent 

differences in land-use decisions between primary ownership groups. For example, shifts in ownership patterns in the 

Southeastern United States have the potential to dramatically 

alter forest structure in the region (Sohl and others, 2010). 

Both private industrial forestry and private nonindustrial 

forestry are altering the landscape significantly in the Southeastern United States, but there are major differences between 

the groups in land use and management. The capability is 

lacking to explicitly map and track land-management changes, 

as spatially explicit data on ownership at that level of thematic 

detail also are lacking. Because of the lack of ownership data, 

regional assumptions regarding land management across all 

ownership types are made.

Water-availability information.—Availability of groundwater or surface water has a tremendous effect on agricultural 

land use. Downscaled, projected climate data consistent with 

IPCC SRES storylines will be available for use by the landcover modeling team, and projected precipitation changes 

will affect characteristics of probability surfaces used in the 

spatial allocation module; however, projected changes in 

surface water or groundwater that can be used as irrigation 

sources will not be used. Ideally, FORE–SCE would link with 

a comprehensive hydrologic model that is tied to water use 

and projected climate change for each IPCC scenario; however, the complexity and site-specific nature of hydrologic 

models that potentially could provide information on groundwater or surface-water changes prohibits their utilization at 

the national scale. There is no mechanism, therefore, by which 

to model changes in irrigated agriculture as a direct response 

to changes in water availability. The primary option in lieu of 

this information is to make informed estimates of projected 

future effects of future water availability at the regional scale, 

and to handle changes in irrigated agriculture through the topdown, “Demand” component of the LULC modeling (define 

future proportions of irrigated agriculture through the scenario 

construction process).

Updated wetlands information.—Wetlands are difficult 

to map through standard mapping methodologies relying on 

remote-sensing data. Dedicated, intensive interpretation efforts 

such as the National Wetlands Inventory (NWI) program are 

extremely valuable for providing consistent, accurate, and 

thematically detailed wetlands mapping. Two issues that 

potentially affect the ability to represent wetland extent are 

digital availability of products such as NWI for the entire 

Nation, and the date of wetland information and the lack of 

updating. A national wetlands layer for the National Spatial 

Data Infrastructure (NSDI) is being constructed, but as of late 

2007, coverage was limited to 60 percent of the conterminous 

United States (U.S. Fish and Wildlife Service, 2007). The U.S. 

Fish and Wildlife stated two primary goals for the NSDI: (1) 

complete wetlands mapping for the Nation, and (2) explore 

ways to keep the national wetlands database populated with 

updated (current) information, while simultaneously acknowledging the practical considerations with regard to funding. A 

consistently updated wetlands layer for the NSDI likely would 

satisfy current and future needs of the national assessment.

Forest Inventory and Analysis (FIA) data access.—As 

discussed above, FIA data currently are used in conjunction 

with VCT data to produce a starting “forest-stand-age” surface. FIA data are used much more extensively by the biogeochemical modeling team. Given the privacy and dissemination 

issues associated with FIA data, data access remains a primary 

challenge.
108 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

National vegetation change tracker (VCT) data.—VCT 

data are used to populate an initial “forest cutting” class in the 

2001 baseline land cover (section 3.2.1 of this report), as well 

as to produce an initial forest-stand-age surface. As of 2010, 

VCT data are not available at a national scale, as past VCT 

research has focused on prototype development, but plans 

are underway to produce these data at that scale. If timeline 

or other issues delay availability of VCT data for the national 

assessment, other data and methodologies will have to be used 

for mapping the initial forest-cutting class and the foreststand-age surface.

Local zoning and regulatory data.—Urban development 

is a relatively small land use at the national scale, but LULC 

transitions to urban development typically are “one-way” transitions, with the land permanently removed from the pool of 

pixels available for LULC change (and also subsequently limited in the potential options for carbon sequestration and mitigation). The basic FORE–SCE design should provide realistic 

regional patterns of urban change, but because local zoning or 

regulatory information that may restrict or encourage urban 

development is not being used (apart from the PAD-US data 

discussed in the previous section), local accuracy may suffer. 

Availability and incorporation of nationally consistent zoning 

and regulatory information at the local scale would improve 

local accuracy of urban development; however, this is a minor 

issue at the national scale in terms of carbon-sequestration 

potential. It could potentially affect the ability to accurately 

portray local effects of LULC change on carbon and other 

ecosystem services.

Dynamic independent variables.—As mentioned previously, future climate projections are consistent with IPCC 

reference scenarios, and as the model iterates, neighborhood 

variables (for example, urban density) also will be updated 

to be used as independent variables for the logistic regressions. Future projected changes in many independent variables 

cannot be tracked or modeled. Some independent variables 

are relatively static and likely would not require updating (for 

example, topographic variables), but there are independent 

variables that are inherently dynamic and for which projected 

values through 2050 are not readily available. This limited 

availability limits the ability to examine LULC response to 

changes in these driving-force variables; however, trying to 

model processes governing all input independent variables is 

difficult.

B.4. FORE–SCE and Modeling Deliverables

LULC modeling deliverables include information and 

data related to scenario-based LULC forecasts and the scenario framework and assumptions themselves. In summary, 

primary deliverables provided by the LULC modeling team 

will include the following:

•	 Initial (2001) land cover

•	 Initial (2001) land-use and land-management characteristics

•	 Narrative storylines for each of the “baseline” IPCC 

scenarios. Constructing national and regionally specific 

scenarios will include techniques for incorporating 

exogenous modeling results, historical LULC data, 

and the primary assumptions associated with each 

IPCC scenario. These data will be used to construct 

regionally specific scenarios consistent with IPCC 

assumptions. Narrative storylines will illustrate general 

expected effects of IPCC storylines on regional LULC 

change and can be used to communicate regionally 

specific driving forces of change

•	 Quantified scenarios (“Demand”), including LULC 

trends with time, land-management characteristics, and 

land-use histories

•	 Annual LULC for each “baseline” IPCC scenario 

through 2050, including maps of LULC and spatially 

explicit probability distributions resulting from Monte 

Carlo runs of the spatial allocation module

•	 Annual LULC for each “alternative” policy or mitigation scenario, including maps of LULC and spatially 

explicit probability distributions

•	 Land-use history information, including annual 

forest-stand age, for each “baseline” and “alternative” 

scenario

B.5. References Cited

[Reports that are only available online may require a subscription for 

access.]

Briassoulis, Helen, 2000, Analysis of land use change—

Theoretical and modeling approaches, in Loveridge, Scott, 

ed., The Web book of regional science: Morgantown, W. 

Va., West Virginia University, Regional Research Institute, accessed March 31, 2010, at http://www.rri.wvu.edu/

regscweb.htm.

de Koning, G.H.J., Veldkamp, A., and Fresco, L.O., 1998, 

Land use in Ecuador—A statistical analysis at different 

aggregation levels: Agriculture, Ecosystems, and Environment, v. 70, p. 231–247.

Huang, Chengquan, Goward, S.N., Masek, J.G., Thomas, 

Nancy, Zhu, Zhiliang, and Vogelmann, J.E., 2010, An automated approach for reconstructing recent forest disturbance 

history using dense Landsat time series stacks: Remote 

Sensing of Environment, v. 114, p. 183–198.

Kirwan, M.L., and Guntenspergen, G.R., 2009, Accelerated sea-level rise—A response to Craft et al.: Frontiers 

in Ecology and the Environment, v. 7, no. 3, p. 126–127, 

doi:10.1890/09.WB.005.

Kirwan, M.L., and Guntenspergen, G.R., 2010, The influence of tidal range on the stability of coastal marsh-
Appendix B 109

land: Journal of Geophysical Research, v. 115, F02009, 

doi:10.1029/2009JF001400.

Kok, K., and Winograd, M., 2002, Modeling land-use change 

for Central America, with reference to the impact of Hurricane Mitch: Ecological Modelling, v. 149, p. 53–69.

Loveland, T.R., Sohl, T.L., Stehman, S.V., Gallant, A.L., Sayler, K.L., and Napton, D.E., 2002, A strategy for estimating the rates of recent United States land-cover changes: 

Photogrammetric Engineering and Remote Sensing, v. 68, 

p. 1091–1099.

Nakicenovic, Nebojsa, Alcamo, Joseph, Davis, Gerald, de 

Vries, Bert, Fenhann, Joergen, Gaffin, Stuart, Gregory, Kenneth, Grübler, Arnulf, Jung, T.Y., Kram, Tom, La Rovere, 

E.L., Michaelis, Laurie, Mori, Shunsuke, Morita, Tsuneyuki, 

Pepper, William, Pitcher, Hugh, Price, Lynn, Riahi, Keywan, Roehrl, Alexander, Rogner, H.-H., Sankovski, Alexei, 

Schlesinger, Michael, Shukla, Priyararshi, Smith, Steven, 

Swart, Robert, van Rooijen, Sascha, Victor, Nadejda, and 

Dadi, Zhou, 2000, Special report on emissions scenarios; A 

special report of Working Group III of the Intergovernmental Panel on Climate Change [IPCC]: Cambridge, UK, Cambridge University Press, 599 p., accessed June 14, 2010, at 

http://www.grida.no/publications/other/ipcc%5Fsr/?src=/

climate/ipcc/emission/index.htm.

Omernik, J.M., 1987, Ecoregions of the conterminous United 

States: Annals of the Association of American Geographers, 

v. 77, no. 1, p. 118–125.

Protected Areas Database of the United States (PADUS) Partnership, 2009, A map for the future—Creating the next generation of protected area inventories 

in the United States: Protected Areas Database of 

the United States Partnership, 20 p., accessed June 

18, 2010, at http://www.protectedlands.net/images/

PADUS_FinalJuly2009LowRes.pdf.

Parker, D.C., Berger, Thomas, and Manson, S.M., eds., 2002, 

Agent-based models of land-use/land-cover change—

Report and review of an International Workshop, October 

4–7, 2001, Irvine, Calif.: Indiana University, LUCC Focus 1 

Publication 6.

Sohl, T.L., Loveland, T.R., Sleeter, B.M., Sayler, K.L., and 

Barnes, C.A., 2010, Addressing foundational elements of 

regional land-use change forecasting: Landscape Ecology, 

v. 25, no. 2, p. 233–247, doi:10.1007/s10980-009-9391-3.

Sohl, T.L., and Sayler, K.L., 2008, Using the FORE–SCE 

model to project land-cover change in the southeastern 

United States: Ecological Modelling, v. 219, p. 49–65.

Sohl, T.L., Sayler, K.L., Drummond, M.A., and Loveland, 

T.R., 2007, The FORE–SCE model—A practical approach 

for projecting land-use change using scenario-based modeling: Journal of Land Use Science, v. 2, no. 2, p. 103–126.

U.S. Fish and Wildlife Service, 2007, Wetlands layer—

National spatial data infrastructure—A phased approach 

to completion and modernization: U.S. Fish and Wildlife 

Service, 9 p., accessed June 18, 2010, at http://www.fws.

gov/wetlands/_documents/gNSDI/WetlandsLayerNSDIPhasedApproachtoCompletionModernization.pdf.

Veldkamp, A., and Fresco, L.O., 1996, CLUE–CR—An 

integrated multi-scale model to simulate land use change 

scenarios in Costa Rica: Ecological Modelling, v. 91, no. 1, 

p. 231–248, doi:10.1016/0304-3800(95)00158-1.

Verburg, P.H., Eickhout, Bas, and van Meijl, Hans, 2008, 

A multi-scale, multi-model approach for analyzing 

the future dynamics of European land use: Annals of 

Regional Science, v. 42, no. 1, p. 57–77, doi:10.1007/

s00168-007-0136-4.

Verburg, P.H., Overmars, K.P., Huigen, M.G.A., de Groot, 

W.T., and Veldkamp, A., 2006, Analysis of the effects of 

land-use change on protected areas in the Philippines: 

Applied Geography, v. 26, no. 2, p. 153–173.

Verburg, P.H., Rounsevell, M.D.A., and Veldkamp, A., 2006, 

Scenario-based studies of future land use in Europe: Agriculture, Ecosystems and Environment, v. 114, no. 1, p. 1–6.

Verburg, P.H., Veldkamp, A., and Fresco, L.O., 1999, Simulation of changes in the spatial pattern of land use in China: 

Applied Geography, v. 19, p. 211–233
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111

Appendix C. Characterization and Modeling of Major Ecosystem Disturbances

The characterization of recent trends and statistical relations will capture broad-scale patterns, but additional methods 

are needed when projecting to locate and simulate the effects 

of each individual disturbance event; therefore, the national 

assessment will incorporate a second suite of methods to 

simulate the spread or placement of individual disturbance 

events at the pixel level when possible. These components of 

the disturbance model will include fire spread, empirical windfields, and habitat-suitability models for insects and disease. 

Some disturbances, especially insects and diseases, will lack 

the data or ecological understanding needed to build predictive 

relations at the 30-meter (m) pixel scale. In these cases, the 

summaries of recent disturbances and projections will be used 

to provide ecoregion-level disturbance probability distributions to the biogeochemical model (appendix D of this report), 

which will incorporate the spatial uncertainty inherent to the 

ecoregion-scale probabilities. The disturbance modeling will 

be adaptive and will incorporate new scientific understanding, data, and methods as they become available during the 

national assessment.

The assessment of the Nation’s ecosystems for biological carbon sequestration will explicitly address disturbances 

such as wildfires (resulting from natural causes and human 

activity), as required by section 712 of the Energy Independence and Security Act (EISA; U.S. Congress, 2007). 

The national assessment disturbance methodology also will 

include tornadoes, damaging winds, hurricanes, and insect 

and disease outbreaks (table C1). Additionally, management 

activities, such as fuel treatments, designed to affect disturbances also will be considered by the disturbance methodology. For each disturbance type or management activity, the 

national assessment will follow a similar series of steps (fig. 

C1). First, recent disturbance patterns will be characterized as the number of events and area affected each year by 

ecoregion. The characterizations of recent trends will then 

be used to identify relations with climate, biophysical, and 

anthropogenic variables using statistical methods. When the 

resulting relations are statistically significant and ecologically relevant, they will be used to project future disturbance 

patterns.

Figure C1.  Generalized process and data flowchart showing disturbance modeling component

tasks. Solid lines represent processes linking input datasets, models, and output datasets. Dashed

lines indicate processes that update data sources required for disturbance modeling. Because each

disturbance type differs in terms of the driving forces and the scales over which they operate, this

streamlined modeling approach will be modified for each disturbance type. These distinctions will

necessitate that the disturbance methodology be adaptive and include components that operate at

different spatial and temporal scales.

Disturbance histories

Climate and weather histories

Land use and land cover

Land-use and land-cover forecasts

Downscaled climate forecasts

and weather data

Vegetation, fuels, and topography data layers 

(LANDFIRE)

Land use, management, 

and fuel treatments

Vegetation dynamics and 

succession models

Historic disturbance frequencies

(regional scale)

Forecasted disturbance frequencies

(regional scale)

Spatially explicit disturbance footprints

(fine scale or pixel level)

Annual area 

affected

Validation with 

remote sensing
112 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table C1. Summary of major ecosystem disturbances resulting from both natural causes and human activities, including disturbancerelated management activities, to be assessed for effects on carbon sequestration and greenhouse-gas fluxes, spatial resolution of

disturbance forecasts, disturbance projections reported, input data sources, explanatory variables, and potential management options

implemented under the reference and enhanced scenarios.

[m, meter; MTBS, Monitoring Trends in Burn Severity; LANDFIRE, Landscape Fire and Resource Management Planning Tools Project; NFPORS, National 

Fire Plan Operations and Reporting System; NOAA, National Oceanic and Atmospheric Administration; RSLC, remote sensing of landscape change; USFS, 

U.S. Forest Service]

Disturbance type Spatial 

scale Attributes Data sources

Broad-scale 

explanatory 

variables

Fine-scale 

explanatory variables

Scenario and management options

Wildland fires

(human caused 

and natural)

250-m pixels Fire size, severity, 

and emissions

MTBS

LANDFIRE

Climate

Land use

Population

Weather

Vegetation

Topography

Distance from urban area

Active fire suppression.

Fuel treatments

(including 

prescribed fires)

250-m pixels Areas and types of 

treatment

NFPORS

LANDFIRE

Vegetation

Land ownership

Increased or decreased 

number and area of 

treatments.

Hurricanes, tornadoes, 

damaging winds

250-m pixels Storm tracks and 

areas of mortality

NOAA

RSLC

Vegetation

Topography

Insects and diseases 250-m pixels Areas of defoliation 

and mortality

USFS Forest Health 

Monitoring Aerial Surveys

RSLC

Climate

Past

outbreaks

Climate

Vegetation

Topography

Distance from previously 

affected areas

storm locations; additional information is needed to characterize the area affected. Remote sensing of landscape change can 

help fill these information gaps. Dramatic vegetation changes, 

such as stand-replacing fires and forest clearcuts, are easily 

identified in imagery; however, less severe types of disturbances, such as insect outbreaks and storm damage, are more 

difficult to distinguish (Ahren, 1988; Franklin and others, 

2003; Skakun and others, 2003; Kennedy and others, 2007; 

Vogelmann and others, 2009). Therefore, disturbed areas will 

be identified by using vegetation change-detection algorithms 

that (1) take advantage of the rich temporal information in 

Landsat and Moderate Resolution Imaging Spectroradiometer 

(MODIS) time-series stacks, and (2) search for anomalies in 

spectral reflectance and vegetation indices trends across all 

image dates (Huang and others, 2009, 2010).

The results of the vegetation change analysis will be 

used to augment the events database and provide additional 

data about the locations of fuel treatments, insect and disease 

outbreaks, and storm damage to vegetation; for instance, the 

FHM aerial-survey data are provided as polygons indicating 

disturbance cause, such as the mountain pine beetle. There is 

heterogeneity in disturbance severity within the FHM polygons—healthy trees are interspersed among unhealthy trees. 

This heterogeneity will be captured by assigning attribute 

information about the disturbance cause provided by the FHM 

polygons to disturbed areas identified by the vegetation change 

analysis. Similarly, disturbed patches in the imagery that are 

spatially coincident with storm locations would be attributed 

as storm damage.

C.1. Characterizing Past and Current Ecosystem 

Disturbances

C.1.1. Events Database

The national assessment will leverage the fire-disturbance 

data compiled and used to maintain the Landscape Fire and 

Resource Management Planning Tools Project (LANDFIRE) 

data products. Additionally, several key datasets, not currently (2010) utilized by LANDFIRE, will be incorporated to 

characterize past and current nonfire disturbances. An events 

database will be constructed to hold data describing major 

ecosystem disturbances from the LANDFIRE refresh data call; 

burn perimeter and severity data from Monitoring Trends in 

Burn Severity (MTBS) (Eidenshink and others, 2007); data 

describing insects and diseases from the Forest Health Monitoring (FHM) program of the U.S. Forest Service (USFS); data 

from the National Oceanic and Atmospheric Administration 

(NOAA) National Hurricane Center’s hurricane archive; and 

data from NOAA’s National Weather Service Storm Prediction 

Center tornado track and damaging wind event archives (table 

C1). Data from the National Fire Plan Operations and Reporting System (NFPORS) also will be incorporated into the 

events database to characterize fuel treatments (table C1).

For many disturbance types, the existing data incorporated into the events database will contain only point and line 

vector information. For example, hurricane, tornado, and damaging wind data consist of lines and points of the approximate 
Appendix C 113

C.1.2. Field-Reference Database

The field-reference database will be a compilation of 

all existing georeferenced field data available for the United 

States in the Fire Effects Monitoring and Inventory Protocol 

(FIREMON) database structure compiled for LANDFIRE. It 

includes Forest Inventory and Analysis (FIA) data from the 

USFS, fire monitoring data from the National Park Service, 

and data from the U.S. Geological Survey’s Gap Analysis 

Program (GAP). Data will be acquired and compiled as the 

assessment progresses geographically across the Nation. Data 

in the field-reference database will be used to validate the disturbance and management information in the events database 

(section C.1.1 of this report).

C.1.3. Annual Summaries of Past and Current

Disturbances

Using the disturbance data described above, participants 

in the national assessment will start by characterizing past 

disturbances into annual summaries using records of recent 

wildfires, storms, and insect and disease outbreaks by ecoregion. These annual summaries will include disturbance type, 

cause, number of events, and total area affected. Fire summaries will include additional ecoregion-level estimates for emissions, and individual estimates for each fire calculated will be 

totaled using the Consume model (Prichard and others, 2006) 

and the First Order Fire Effects Model (FOFEM) (Reinhardt 

and others, 1997) with the fuel-loading model (FLM) and 

fuel-characteristic classification system (FCCS) data produced for the LANDFIRE project (Rollins, 2009). The annual 

disturbance summaries will be provided as tables and further 

summarized as probability distributions for each U.S. Environmental Protection Agency (EPA) level II ecoregion (modified 

from Omernik, 1987) (table C2).

C.2. Identifying Drivers of Ecosystem 

Disturbances

To project future disturbance events, the critical driving variables determining occurrence patterns need to be 

identified. Within the disturbance-modeling framework, these 

relations will be identified for ecoregions and for individual 

disturbance events. This two-scale approach will allow incorporation of broad-scale climatic drivers as well as fine-scale 

land-use, vegetation, and topographic patterns that affect individual disturbance events.

C.2.1. Ecoregion-Level Relations

The ecoregion annual disturbance summaries will be 

used to identify relations between disturbances and broadscale climate, biophysical, and anthropogenic drivers using 

empirical methods. For example, interannual variability in fire 

occurrence has clear relations to extreme weather (Bessie and 

Johnson, 1995) and climate variables capturing drought and 

moisture availability and vegetation productivity in the preceding year (Swetnam and Betancourt, 1990; Westerling and 

others, 2006; Falk and others, 2007). Similarly, severe insect 

outbreaks often are related in part to climate conditions (Gan, 

2004; Aukema and others, 2008). Broad-scale patterns of land 

use and land cover (LULC), topography, and population also 

may play significant roles in explaining disturbance patterns, 

especially for human-caused wildfire ignitions (Cardille and 

others, 2001; Syphard and others, 2007). By using previously 

identified relations, researchers will use statistical methods to 

test ecoregion-scale relations among recent disturbance occurrence, weather, climate, LULC trends, and population trends. 

General linear models will be used with negative binomial 

and Poisson responses for the number of disturbances and 

Gaussian responses for area affected; however, other statistical 

techniques may be used where appropriate. For certain disturbance types, such as hurricanes, where there is little evidence 

Table C2.  Example of an output table showing recent

disturbance summary data for the Mississippi Valley Loess Plains

ecoregion.

[n, number of wildfires; MTBS, Monitoring Trends in Burn Severity]

Disturbance type Year n Hectares Source

Wildfire, human 1984 0 0 MTBS

Wildfire, human 1985 0 0 MTBS

Wildfire, human 1986 1 239 MTBS

Wildfire, human 1987 0 0 MTBS

Wildfire, human 1988 2 518 MTBS

Wildfire, human 1989 0 0 MTBS

Wildfire, human 1990 2 529 MTBS

Wildfire, human 1991 0 0 MTBS

Wildfire, human 1992 1 223 MTBS

Wildfire, human 1993 3 705 MTBS

Wildfire, human 1994 4 2,666 MTBS

Wildfire, human 1995 0 0 MTBS

Wildfire, human 1996 6 4,367 MTBS

Wildfire, human 1997 0 0 MTBS

Wildfire, human 1998 2 383 MTBS

Wildfire, human 1999 1 202 MTBS

Wildfire, human 2000 2 731 MTBS

Wildfire, human 2001 0 0 MTBS

Wildfire, human 2002 0 0 MTBS

Wildfire, human 2003 0 0 MTBS

Wildfire, human 2004 0 0 MTBS

Wildfire, human 2005 0 0 MTBS

Wildfire, human 2006 0 0 MTBS

Wildfire, human 2007 0 0 MTBS

Wildfire, human 2008 2 599 MTBS
114 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

of a long-term trend, relations with climate and other broadscale predictors may not be identified. In such cases, the recent 

annual disturbance summaries will be used to simulate future 

disturbance occurrence patterns.

C.2.2. Fine-Scale Relations

Ecoregion-level relations will explain broad-scale patterns in 

terms of the number of disturbance events and total area disturbed 

each year; however, they provide minimal information about 

exactly where within ecoregions the individual disturbance events 

are most likely to occur. These patterns will be explained by using 

a second suite of empirical methods that predict the probability 

of disturbance at fine spatial resolutions (250-m pixels) and will 

incorporate fine-scale relations between disturbance-occurrence 

patterns and vegetation types, topography, and land use—especially from human pressures. For instance, abiotic and anthropogenic variables have been shown to be effective predictors 

of human-caused wildfire ignitions (Cardille and others, 2001; 

Syphard and others, 2008). Similarly, many of these same variables also have been shown to affect insects and disease because 

of preferential selection for certain hosts or vegetation types 

and transport by humans to previously unaffected areas (Prasad 

and others, 2010) in addition to topographic position, climate 

conditions, and previous outbreak locations (Dodds and others, 

2006; Aukema and others, 2008; Santos and Whitham, 2010). In 

contrast, the likelihood of hurricane, tornado, and wind damage 

is largely dependent on vegetation type and topographic position 

(Boose and others, 2001; Kramer and others, 2001; Ramsey and 

others, 2001; Schulte and others, 2005). For each disturbance 

type, potential predictors will be identified from existing studies, 

and the relation between disturbance locations and predictors will 

be tested and quantified using statistical methods.

C.3. Future Ecosystem Disturbance

For the national assessment, projections of future disturbance events will be made for each of the reference and 

enhanced carbon-sequestration scenarios. The number of 

events and area affected by each disturbance will be projected 

using the previously identified ecoregion-scale and fine-scale 

methods (sections C.2.1 and C.2.2 above). To incorporate 

management activities, model parameters, probabilities, and 

predictions may be altered. For example, an increase in a 

prescribed fire-use scenario may simply double the number 

of prescribed fires simulated in any given year. Ecosystem 

disturbance modeling will be conducted and reported for EPA 

level II ecoregions. The details of the modeling steps for each 

disturbance type and incorporation of mitigation and management actions are provided in the following sections.

C.3.1. Wildfire (Human Caused and Natural)

Simulations for wildfires will be made for each EPA 

level III ecoregion for each reference and enhanced scenario 

and reported by EPA level II ecoregion. Predictions of annual 

ecoregion fire activity (n wildfires per year) will be based on 

previously developed empirical relations with broad-scale 

climate and LULC variables (section C.2.1 above). Ignition 

locations of individual fire events will be based on an additional set of probability surfaces based on empirical relations 

with weather, climate, vegetation, topography, and LULC 

(section C.2.2 above). Once ignition locations are determined, 

individual fire spread will be simulated using the minimumtravel-time (MTT) algorithm (Finney, 2002), the LANDFIRE 

fuels and topography layers (Rollins, 2009), and the fireweather climatology derived from the NOAA North American 

Regional Reanalysis (NARR) weather data. Following fire 

spread, emission estimates will be summarized for each fire 

using the Consume and FOFEM models and the FCCS and 

FLM data produced by LANDFIRE (Rollins, 2009).

C.3.2. Fuel Treatment (Including Prescribed Fire)

Simulations for fuel treatments will be run for EPA level 

III ecoregions for each reference and enhanced scenario. 

Predictions of annual ecoregion treatment activities will 

be based on a random selection from the recent probability 

distribution of fuel-treatment activity (n treatments and area 

treated per year), but may be modified in terms of the number 

of treatments per year or area treated per year under the different enhanced scenarios. Individual fuel treatments will be 

restricted to public lands and randomly placed within wildland vegetation types depending on the type of treatments; 

for instance, forest-fuel thinning cannot occur in grasslands. 

Nonfire treatments will expand using a patch-grow algorithm 

until a final predicted treatment size is reached, or an entire 

contiguous wildland vegetation patch is treated. For prescribed 

fire-fuel treatments, the MTT fire-spread algorithm used by 

the wildfire modeling will be used (section C.3.1 above). The 

LANDFIRE fuel data layers will be updated after placement 

of fuel treatments, to account for treatment effects on fire 

behavior and spread.

C.3.3. Insect and Disease Activity

Simulations will be run for EPA level III ecoregions for 

each reference and enhanced scenario. Predictions of annual 

ecoregion-level insect and disease activity (area affected per 

year) will be based on ecoregion empirical relations derived 

from epidemiological and species distribution modeling techniques (section C.2.1 above; Elith and others, 2006; Phillips 

and others, 2006; Elith and Leathwick, 2009) using climate, 

vegetation, topography, and previous outbreaks as predictors. Because of the potentially large number of unique insect 

species and diseases that could be simulated, spatially explicit 

species-occurrence modeling will be prioritized on the basis of 

the amount of area currently (2010) affected and the effect on 

standing biomass; example insects include the mountain pine 

beetle (Dendroctonous ponderoseae), the southern pine beetle 
Appendix C 115

(Dendroctonus frontalis), and the gypsy moth (Lymantria dispar) (Krist and others, 2007). In cases where it is not possible 

to generate statistically and ecologically significant, spatially 

explicit probability surfaces, ecoregional probability distributions will be used instead.

C.3.4. Tornado and Damaging Wind Events

Tornado and damaging wind simulations also will be 

made for EPA level III ecoregions under each reference and 

enhanced scenario. Ecoregion-level predictions of tornado 

activity (n storms per year) will be based on a random selection from regional summaries of recent occurrences. Then for 

each simulated tornado, an empirical storm-track generator 

(Vickery and others, 2000) will establish the tornado path. The 

width of the tornado-disturbance footprint will be determined 

from the recent distribution of storm track widths measured 

using remote sensing of landscape change (section C.1.1 

above). If wind damage to vegetation can be effectively monitored from remote sensing of landscape change, then damaging wind models will be made similarly to tornado methods. 

Historic frequencies of the number of damaging wind events 

and area affected will be used to simulate future occurrence 

patterns.

C.3.5. Hurricane Events

Hurricane effects often occur over areas larger than the 

EPA level III ecoregions used for other disturbance types; 

therefore, hurricane simulations will run annually, but for the 

entire United States for each reference and enhanced scenario. 

Predictions of hurricane activity (n storms per year) will be 

based on a random selection from regional summaries of 

recent occurrences. As with tornadoes, an empirical stormtrack generator will create a storm path and wind speeds along 

the path for each hurricane (Vickery and others, 2000). A 

surface wind-field and exposure probability surface based on 

topography and vegetation, calibrated with remote sensing of 

landscape change data, will determine areas where vegetation 

damage will occur (Boose and others, 1994).

C.4. Disturbance Model Outputs

Disturbance model outputs are listed in table C1 and will 

include tabular annual summaries of the number of events and 

area affected for each disturbance type or management activity for each assessment unit. Additionally, tabular summaries 

of annual greenhouse-gas emissions (methane, carbon monoxide, carbon dioxide, and nonmethane hydrocarbons) will be 

produced for wild and prescribed fires. Disturbance maps with 

250-m spatial resolution also will be generated annually, with 

unique labeling for specific disturbance types. These outputs 

will be provided for recent disturbances (1984–2010) and future 

disturbances (2011–2050). Models will be run using a Monte 

Carlo framework to quantify uncertainties, and as a result, 

projection outputs will be presented as probability distributions 

representing the range of variability in potential outcomes.

C.5. Vegetation Dynamics

At the end of each year in the LULC-change model 

and disturbance-model simulations, updates will be made to 

vegetation-type and fuel data layers to incorporate the effects 

of disturbances, management actions, LULC, and vegetation 

succession. Initial vegetation conditions will be established 

from the existing vegetation-type and succession-class data 

layers in LANDFIRE (Rollins and Frame, 2006; Rollins, 

2009). Each vegetation type has an existing vegetation-dynamics model that defines transitions among a number of succession classes. Transitions will be initiated using disturbance 

type, severity, and time since last disturbance. These succession trajectories are defined from historic disturbance regimes, 

and the vegetation dynamics are being updated to incorporate 

modern disturbance types; for instance, forest harvesting and 

invasive species. Furthermore, LANDFIRE fuel-model layers 

are defined using the vegetation types and succession classes, 

thereby allowing updates to account for vegetation changes 

(Keane and others, 2001; Rollins and Frame, 2006). Thus, 

future vegetation-type, succession-class, and fuel models will 

be updated using the existing vegetation type and succession 

class and the outputs from the simulated disturbances and 

LULC changes.

C.6. Scenarios and Management Options

Management activities affecting fuel and ignition patterns 

potentially can have effects on carbon storage and greenhousegas emissions. The disturbance task will allow different management activities in future scenarios that incorporate a range 

of fire-management strategies. Specifically, fuel treatments 

(including fuel reduction and prescribed fire) will allow for 

increases or decreases in the area of different fuel treatments 

to be specified under alternative scenarios. The disturbance 

model also will incorporate fire suppression and its effect on 

limiting the size of wildfires using a wildfire containment 

probability algorithm developed by Finney and others (2009). 

Finally, the disturbance model’s probability surfaces are 

sensitive to LULC changes and, therefore, may demonstrate 

unintended effects of land-management policies on disturbance regimes.

Management activities and the extent to which they affect 

disturbances will be simulated for each of the IPPC reference 

and enhanced scenarios. See section 3.3.1 of this report for 

details of the scenario development. Specifically, information will be gathered using questions such as “Within the A2 

storyline, would it be feasible to double the area treated using 

prescribed fire in your region?” The results will be compiled 

into a management portfolio for each scenario and will be 

used to assess how different mitigation strategies might affect 

biological carbon storage and greenhouse-gas emissions.
116 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

C.7. Relations to Existing Disturbance Models

The modeling approach for future potential fires parallels other fire-modeling efforts in the United States, but there 

are some important differences. Desktop applications such as 

Fire Area Simulator (FARSITE) and FlamMap (a USFS firebehavior mapping and analysis model) are used to examine 

individual fire events or landscape-level fire behavior. FARSITE simulates individual fire growth given an ignition point 

(Finney, 2004) and is considered to be “state of the art” in 

terms of fire-spread simulation, but the computation requirements are expensive and that prohibits its use for nationalscale assessments. The MTT algorithm, which is used for the 

national assessment, produces similar results with less of a 

computational burden (Finney, 2002). The MTT algorithm 

is integrated into FlamMap and relies on landscape-level 

fire behavior outputs produced by FlamMap to simulate fire 

growth in addition to other fire-behavior indices across a 

landscape (Finney, 2006). The Fire Spread Probability model 

(FSPro) simulates fire spread using the MTT algorithm from 

thousands of randomly placed fire ignitions and stacks the 

results to produce burn probabilities. FSPro is integral to 

the wildland fire decision-support system (WFDSS) and fire 

program analysis (FPA). This approach is similar to FSPro in 

many ways because the same fire-spread algorithm is used; 

however, instead of generating burn probabilities like FSPro, 

this method generates individual burn perimeters and interacts 

with the LULC change model and the biogeochemical cycling 

model.

C.8. Integrating Land-Use- and Land-CoverChange Modeling and Biogeochemical 

Modeling

There are reciprocal feedbacks among the primary 

modeling components, with the disturbance model, LULCchange model (appendix B of this report), and biogeochemical model (appendix E of this report) sharing data before and 

after each year in the simulations. At the end of each annual 

disturbance-model simulation, the results will be communicated to the LULC-change model and the biogeochemical 

model. The biogeochemical and LULC-change models do 

not require the level of thematic and spatial detail provided 

by the LANDFIRE vegetation types; therefore, the LANDFIRE vegetation-type layer will be aggregated to 250-m 

pixels and reclassified to National Land Cover Database 

(NLCD) categories using a look-up table at the end of each 

year in the disturbance-model simulations. The updated 

layer will then be transferred to the LULC-change model, 

and eventually to the biogeochemical model for calculating carbon stocks and greenhouse-gas fluxes for the current 

model-simulation year. In turn, the LULC-change modeling 

component will provide an updated land-cover layer to the 

disturbance-model component so that disturbance probability 

surfaces can be updated to reflect any changes that occurred. 

Additionally, the biogeochemical-model component will 

provide information on biomass-pool changes because of 

growth, mortality, and decomposition to recalibrate fuel-load 

data.

C.9. Ecosystem-Disturbance Data Needs

Representing the range of disturbances affecting ecosystem carbon stocks and greenhouse-gas fluxes depends largely 

on the availability of input data needed to parameterize and 

execute the various disturbance components. Fires and fuel 

treatments have the most complete existing datasets; however, even these datasets have limitations. Many fires are not 

mapped by the MTBS project, especially small fires and unreported fires occurring on public and private lands (Eidenshink 

and others, 2007). The NFPORS fuel-treatment database lacks 

the spatial detail and treatment-effects information needed 

for more sophisticated modeling. Other disturbance types, 

especially insect outbreaks and storms, lack data documenting 

the extent and effects of these disturbances with enough detail 

to use for the modeling efforts. Even though the capability to 

use remote-sensing data or aerial surveys to track storm and 

insect damage has been demonstrated, nationally consistent 

datasets currently (2010) are lacking. Consequently, these 

data gaps will limit the ability to account for the effects of all 

disturbances on ecosystem carbon storage and greenhouse-gas 

fluxes. Future research is needed to identify the most suitable algorithms and approach to generate a comprehensive 

land-disturbance and severity inventory for the Nation for use 

in carbon and greenhouse-gas assessments, and to develop 

models sensitive to climate and land change to project future 

disturbance-occurrence patterns.

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119

Appendix D. Methods for Assessing Carbon Stocks, Carbon Sequestration, and 

Greenhouse-Gas Fluxes of Terrestrial Ecosystems

spatial and temporal trends of carbon and nitrogen dynamics, 

but it also determines uncertainty estimates of the predicted 

variables. GEMS previously has been applied in this way to 

simulate carbon dynamics for large areas in Africa (Liu, Kaire, 

and others, 2004) and the United States (Liu, Loveland, and 

Kurtz, 2004; Tan and others, 2005; Liu and others, 2006).

The spreadsheet and biogeochemical modeling approaches 

that will be used to quantify biological carbon sequestration and 

greenhouse-gas (GHG) emissions for the national assessment 

are described in detail in the following sections. In addition, 

model uncertainty, model integration with other model systems, 

and ecosystem-services modeling are described.

D.1. Accounting and Modeling Simulations of 

Carbon Sequestration and Greenhouse-Gas 

Fluxes

D.1.1. GEMS Accounting Using the Spreadsheet

Approach

Spreadsheet approaches use a computer spreadsheet tool 

to simulate carbon dynamics and GHG emissions. The primary 

advantages of the spreadsheet approach are ease in model 

development and model transparency. The disadvantages of 

the spreadsheet approach include nonspatial or coarse spatial 

resolution of simulations and the relatively small number of formulas used in spreadsheet calculations. Nevertheless, although 

many processes have to be simplified or ignored, the spreadsheet approach provides reference results that are useful to compare with those from more process-based modeling systems.

In general, carbon accounting for almost all terrestrial 

sectors can be conducted using the spreadsheet approach. 

The 2006 Intergovernmental Panel on Climate Change

(IPCC) Guidelines for National Greenhouse Gas Inventories 

(Intergovernmental Panel on Climate Change, 2006) provides equations and factors for building GHG spreadsheets. 

A spreadsheet approach will be implemented in parallel to 

GEMS to compare and verify GEMS outputs; this method is 

called “GEMS-spreadsheet.”

The GEMS-spreadsheet method requires the following 

input data at the ecoregion level (or any geographic region):

• Land-cover transition tables during two periods (for 

example, 2001–2010 and 2011–2050)

• Vegetation-age distribution by land-cover type

• Carbon density by age and land-cover type

• GHG fluxes by vegetation age and land-cover type

Quantifying terrestrial carbon dynamics for large regions 

is a challenging task for scientists (Potter and others, 1993; 

Intergovernmental Panel on Climate Change, 1997; Houghton 

and others, 1999; McGuire and others, 2002; Liu, Loveland, and 

Kurtz, 2004; Parton and others, 2005; Sierra and others, 2009). 

Generally, two approaches are used to quantify terrestrial carbon 

dynamics for large regions. The first of these is the spreadsheet 

or bookkeeping approach (Intergovernmental Panel on Climate 

Change, 1997; Houghton and others, 1999) that relies on a set 

of predefined carbon-response curves (for example, tree-growth 

curves) and uses regression equations or look-up tables; however, 

most carbon-response curves are created locally on the basis of 

limited categories of site conditions. They may be insufficient for 

capturing the effects of the spatial and temporal variability of land 

use, soils, and climate on carbon dynamics. The second approach 

depends on process-based biogeochemical models (Schimel and 

others, 1994; Melillo and others, 1995; McGuire and others, 

2002; Chen and others, 2003; Potter and others, 2005; Tan and 

others, 2005; Liu and others, 2006). Instead of predefining 

the carbon-response curves under typical conditions, as in the 

bookkeeping approach, this process-based approach simulates 

carbon dynamics under specific and changing environmental 

and management conditions. Although it is capable of capturing 

detailed responses to changes in the driving variables, it usually 

requires more complicated input data and parameters.

Many site-scale process-based biogeochemical models 

were developed during the past 20 years (Parton and others, 

1987; Running and Coughlan, 1988; Li and others, 1992). They 

benefitted from an improved understanding of biogeochemical 

processes resulting from controlled experiments and field 

observations. For regional studies, however, these models 

usually were directly applied to grid cells (for example, 0.5 × 

0.5 degrees longitude and latitude) that were larger than the 

site scale (Melillo and others, 1995; Pan and others, 1998; 

McGuire and others, 2001; Potter and others, 2005) without 

incorporating information on field-scale heterogeneities. This 

can result in significant biases in the estimations of important 

biogeochemical and biophysical processes (Avissar, 1992; 

Pierce and Running, 1995; Turner and others, 2000; Reiners 

and others, 2002). Therefore, deploying field-scale ecosystem 

models to generate regional carbon-sequestration estimates with 

measures of uncertainty is a challenge.

The General Ensemble Modeling System (GEMS) was 

designed to facilitate the application of classic site-scale 

models on a regional scale and to better integrate well-established ecosystem biogeochemical models by using a Monte 

Carlo-based ensemble approach to incorporate the probable 

occurrence of parameter values in simulations. Consequently, 

GEMS not only drives biogeochemical models to simulate the 
120 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

• The severity of disturbances or management activities 

on live biomass carbon, expressed as the fraction of 

biomass killed or harvested

• Carbon transfer coefficients among different pools, 

including the atmospheric carbon dioxide (CO2) pool

• Carbon decomposition rates in various pools

The GEMS-spreadsheet method tracks the carbon stock 

of unchanged land units (that is, no land-cover transitions) in 

carbon pool p1 in a given region using the following accounting procedure:

, (D1)

where n and m are the number of land-cover classes 

and age classes, respectively,

At,i is the total unchanged area of landcover class i at time t,

at,i,j, and ct,i,j,p1are, respectively, area fraction and 

carbon density of land-cover class i, 

at time t, and in age class j.

Carbon-density values will be derived from the U.S. 

Forest Service’s (USFS) Forest Inventory and Analysis (FIA) 

program data. Land-cover transitions and age-distribution 

information will be from the “forecasting scenarios of landcover change” (FORE–SCE) model.

For those land units that experienced land-cover transitions, the following procedures are used to track carbon flow 

among different pools:

, (D2)

where At,i,j is the area changed from land-cover class i

to j at time t;

ct,i,p1is the average carbon density in pool p1, and

αi,j,p1

→p2

is the fraction of carbon density in pool 

p1 that is transferred to p2 because of 

land-cover transition from i to j.

In the GEMS-spreadsheet method, carbon is transferred 

among the live and dead, aboveground and belowground biomass pools and the wood-products pool (harvested materials). 

Carbon-transfer coefficients will be developed based on expert 

knowledge, remotely sensed data (for example, fire severity), 

and output from disturbances modeling.

The decomposition of carbon in a given pool (except the 

live biomass pool) is calculated as follows:

, (D3)

where βp

1

→CO2

is the decomposition rate of carbon in pool p1, 

defined as a fraction of the pool size.

In summary, the carbon stocks in live biomass, 

aboveground and belowground dead biomass, and wood products in a region at time t are calculated as follows:

, (D4)

where k is the number of carbon pools.

The total regional nitrous-oxide (N2O) and methane 

(CH4) fluxes are calculated as follows using the GEMSspreadsheet method:

, (D5)

where λt,i,j is the flux of N2O or CH4 per area on land-cover 

class i, at time t, and in age class j.

Region-specific GHG fluxes for different ecosystems under 

various management practices will be compiled from extensive literature review and metadata analysis.

D.1.2. GEMS Biogeochemical Modeling

GEMS provides spatially explicit biogeochemical-model 

simulations for large areas. The overall GEMS input-data 

requirements and model functions are shown in figure D1, 

Figure D1.  Diagram showing

functionality and major types

of input data for the General

Ensemble Modeling System

(GEMS). FIA, Forest Inventory

and Analysis Program (U.S.

Forest Service); USDA, U.S.

Department of Agriculture;

EDCM, Erosion-DepositionCarbon Model; BIOME–BGC,

biome biogeochemical cycles;

IPCC, Intergovernmental Panel

on Climate Change; FORE–SCE,

“forecasting scenarios of landcover change” model; CLUE,

Conversion of Land Use and its

Effects model.

GEMS

Data assimilation

Biogeochemical models

(EDCM, CENTURY, BIOME–BGC)

Land-cover- and 

land-use-change model

(FORE–SCE, CLUE)

Spatially distributed 

characterization of ecosystem 

states and changes (carbon stocks 

and fluxes) with measure of 

uncertainty under various land use, 

management, and climate-change 

scenarios

Assessment (carbon sequestration, 

risk, cost benefit)

Reporting by land-management

units, regions, and nation

In-situ 

and 

census 

data

Land 

surface 

dynamics

Climatechange 

scenarios

Land-use 

scenarios

Vegetation data 

(I–IA, USDA, and so on)

Soil data

FLUXNET data

Land cover and 

disturbances

Ecosystem performance 

(productivity)

IPCC, and so on

Economic and 

management 

scenarios
Appendix D 121

which indicates that GEMS, as an expandable framework, can 

process various land-use and disturbance data and link with 

existing models and tools. GEMS uses two approaches to 

interact with encapsulated biogeochemical models: agent and 

direct implementation.

D.1.2.1. Ensemble Models or Agent Implementation

A special model interface (that is, the agent) controls 

diverse plot- and regional-scale models in GEMS. This 

approach requires minimum or no modifications to the underlying biogeochemical models and can be useful for reusing 

models that are difficult to modify. Under the “agent implementation” mode, GEMS uses plot-scale ecosystem biogeochemical models to simulate carbon and nitrogen dynamics at the 

plot scale. It controls these site-scale models by automatically 

parameterizing them according to the biophysical conditions of 

any land parcel and deploying them across space without considering the interactions among land pixels. Plot- and regionalscale biogeochemical models, such as the Century model 

(Parton and others, 1987), the Erosion-Deposition-Carbon 

Model (EDCM; Liu and others, 2003), and the Integrated 

Biosphere Simulator (IBIS; Foley and others, 1996), can serve 

as encapsulated ecosystem biogeochemical models in GEMS 

(Tan and others, 2005; Liu and others, 2006). Because GEMS is 

designed to encapsulate multiple models, and parameterize and 

execute these models using the same data, it provides an ideal 

platform to conduct “model ensemble” simulations to identify 

and address issues and uncertainty related to model structure 

and mathematical representations of biophysical processes.

To ensure a nationally consistent approach for selecting 

biogeochemical models for the assessment, a modeling workshop 

will be held in summer of 2010, and national ecosystem modeling 

experts will be invited to help identify additional suitable models. 

Model selection will address the ability to consider the effects of 

land-use and land-cover change, major disturbances, and climate 

change on carbon sequestration and GHG emissions. Predefined 

criteria are listed in table D1; this list does not mean that a single 

biogeochemical model must meet all these criteria.

D.1.2.2. Direct Implementation

Biogeochemical models, such as EDCM and Century, are 

merged directly with GEMS to allow more efficient, spatially 

explicit simulations. Many regional model applications adopt 

a time-space simulation paradigm, which runs a simulation 

for an individual pixel from beginning to end in time before 

moving to the next pixel. In the direct implementation (for 

example, GEMS–EDCM), the space-time sequence paradigm 

will be used instead (thus, GEMS simulates the whole region 

for a given time step first, then moves to the next time step). 

The space-time sequence paradigm provides easy ways to integrate with other modeling systems such as FORE–SCE (“forecasting scenarios of land-cover change” model), USPED (Unit 

Stream Power-Based Erosion Deposition), and the disturbance 

models in a parallel computation fashion; lateral movements 

of carbon and nitrogen can be effectively quantified as well. 

Detailed descriptions of GEMS–EDCM, including its theoretical basis, general structure, simulation capability, and unique 

approach are provided in the following sections.

D.1.2.3. GEMS Data Flow and Linkages With Other

Modeling Products

The overall GEMS flow chart of data and processes, including the spatial simulation unit setup, the Monte Carlo process, 

biogeochemical-model simulation, data assimilation, network 

Common Data Form (NetCDF) data processing and visualization, the post-simulation process, and uncertainty assessment are 

shown in figure D2. The model also is capable of parallel simulations to estimate lateral carbon-nitrogen movements. These processes and data are described in detail in the following sections.

D.2. GEMS Modeling

D.2.1. Major Processes Affecting Carbon

Sequestration and Greenhouse-Gas Fluxes in

GEMS

The overall processes of land-atmosphere interactions 

(for example, vertical fluxes of carbon and nitrogen), lateral 

fluxes of carbon and nutrients, and the pertinent controlling 

mechanisms in GEMS are shown in figure D3. The simplified carbon cycle, which is the main biogeochemical cycle 

modeled with GEMS, includes gross primary productivity (GPP), net primary productivity (NPP), photosynthesis 

allocations (to leaf, root, stem), litter fall, mortality, debris 

accumulation, and decomposition of soil carbon. The carbon 

cycle is tightly coupled with nitrogen and water cycles. The 

water cycle includes algorithms to estimate rain interception, 

Table D1. Tentative selection criteria and checklist for

biogeochemical models to be included in the General Ensemble

Modeling System (GEMS).

[CO2, carbon dioxide]

Criteria Questionnaire checklist

Ecosystem processesInclude ecosystem carbon, nitrogen, and water cycles?

Include ecophysiological processes (for example, photosynthesis)?

Consider major ecosystem disturbances (fire, logging)?

Consider major ecosystem management activities?

Ecosystem types 

and carbon 

pools

Include all major natural forest/shrub/grassland systems?

Include agricultural ecosystems?

Include wetland ecosystems?

Include major vegetation and soil carbon/nitrogen pools?

Model structure 

and reuse

Allow for parallel model simulation?

Well modularized and easy to be incorporated into 

GEMS?

Coded in familiar programming language (C/C++, 

Fortran)?

Scientific rigor Model is well accepted and published?

The team has some experience with the model?

Allow sensitivity testing on key driving variables (for 

example, climate, CO2)?
122 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

evaporation, transpiration, runoff, and soil water content. The 

water cycle is also linked with soil organic carbon decomposition and plant growth through soil water availability. 

The nitrogen cycle is coupled with the carbon cycle through 

nitrogen availability that controls plant growth and soil 

carbon decomposition. External driving forces are climate 

variation and change, human land-management activities, 

and natural disturbances. These forces and their effects are 

discussed in subsections below in this appendix. CH4 and 

N2O emissions will be quantified using available equations 

within biogeochemical models. If unavailable, other empirically derived approaches will be adopted (for example, the 

model of Cao and others (1996)).

D.2.1.1. Ecosystem Production

Quantification of ecosystem production starts with 

vegetation photosynthesis, which will be modeled using three 

different approaches in GEMS to overcome the disadvantages 

of any single algorithm. The three approaches include a lightuse-efficiency approach (Yuan and others, 2007), a biochemical-modeling approach (IBIS; Foley and others, 1996), and a 

scalar approach (Century; Parton and others, 1993). For example, the algorithm for leaf photosynthesis in IBIS is a modified 

Farquhar-type model (Farquhar and others, 1980). The gross 

photosynthesis rate through light-limited, Rubisco-limited, and 

triosephosphate-utilization-limited mechanisms (Foley and 

others, 1996, equations 2, 4, and 5) is partly determined by 

intercellular CO2 concentration within the leaf, which in turn 

determines the water conductance and CO2 concentration at 

the leaf surface (Foley and others, 1996, equations 13, 14, and 

15). The gross photosynthesis rate also is modified by the leaf 

nitrogen level, which is determined by the soil nitrogen pool 

(Liu and others, 2005, equations 1, 8, and 9). At the canopy 

level, IBIS allows the leaf area index (LAI) to change dynamically depending on living leaf biomass.

Figure D2.  Flow chart of the General Ensemble Modeling System (GEMS) for biogeochemical simulations. Abbreviations are found in

“Abbreviations, Acronyms, and Chemical Symbols” in the front of this report.

NLCD 

land-cover

data

Various climate 

data

DEM and 

hydrological

data

STATSGO2 and 

SSURGO data

Ecoregion and 

administrative

boundaries

Wetland 

data (NWI)

Atmospheric data 

(atmospheric CO2

concentration,

nitrogen deposition)

Other applicable 

data

Land-cover 

trends

LANDFIRE 

VCT

LANDFIRE 

MTBS

FIA stand age 

and biomass

Soil texture, soil 

carbon, soil 

erodability factor

Crop species, 

yield, irrigation, 

fertilization

Disturbance effect look-up table 

(cutting, burn, and insect damage)

Upland erosion 

(USPED)

(Field observations) 

NASS, NAWQA, 

MODIS data, crop 

yield, FLUXNET

Monte Carlo downscaling

Start—independent variable

Spatial simulation 

units (JFD)

Initial site condition 

and disturbance 

effects

Biogeochemical 

models (EDCM, 

DNDC, IBIS, and 

so on)

JFD Builder and 

Monte Carlo 

downscaling

Use IPCC and 

FORE–SCE 

scenarios 

(NetCDF data)

EnKF data 

assimilation

Proceed to 

next year

Proceed to 

next JFD/pixel

Proceed to 

next run

Field data 

exist?

All ensemble 

runs finished?

All JFD/pixels 

finished?

All years

finished?

FORE–SCE 

model

River and 

coast model

Final reporting 

items

Biomass carbon, 

litter and soil 

carbon, carbon 

fluxes, sediment, 

and so on

Post-processing,

uncertainty

Yes

Yes

Yes

No No Yes

No

No

No

Yes

LOOP–4 LOOP–3 LOOP–2 LOOP–1

Historical?
Appendix D 123

and IBIS (Foley and others, 1996). The soil nitrogen pools 

and fluxes in an agricultural system as simulated by Century 

and EDCM are shown in figure D5. All the nitrogen pools are 

tightly coupled with the carbon cycle.

Owing to its inheritance from its antecedent model 

(Century), EDCM is an advanced biogeochemical model that 

simulates the effects of various natural processes (for example, 

fires, hurricanes, atmospheric nitrogen deposition, atmospheric 

CO2 “fertilization,” climate change and variability, and erosion 

and deposition) and management practices (for example, grain 

harvesting, timber harvesting, fertilization, land-cover and 

land-use change, cultivation, fertilization, manure addition) on 

carbon and nitrogen cycles at the ecosystem scale. EDCM can 

simulate the effect of soil erosion and deposition on carbon 

Figure D3.  Diagram showing the interactions of the

biogeochemical processes in the General Ensemble Modeling

System (GEMS). Black arrows indicate mass flow and red arrows

indicate control modifiers. CO2, carbon dioxide; GHG, greenhouse

gas; C, carbon; N, nitrogen; GPP, gross primary productivity; NPP,

net primary productivity. Figure D4.  Diagram showing carbon-nitrogen cycles and nitrogen

controls in the Integrated Biosphere Simulator (IBIS). Abbreviations

and variables are as follows: GPPc, Canopy-level gross primary

productivity; Growth_resp, growth respiration ratio; KP, Modifier

of plant biomass construction; KM, Modifier of soil organic matter

(SOM) mineralization; KI, Modifier of soil nitrogen immobilization;

KCN(P), Modifier of plant (leaf) carbon-to-nitrogen ratio; KCN(S),

Modifier of SOM carbon-to-nitrogen ratio; LAI, leaf area index;

Leaf_C:N, leaf carbon-to-nitrogen ratio; Maint_resp, maintenance

respiration N_dep, nitrogen deposition; N_fix, nitrogen fixation;

N_storage, nitrogen storage; N_uptake, nitrogen uptake; N_volat,

nitrogen volatilization; NM, soil mineral nitrogen; NPPb, Net primary

productivity as new biomass; NPPc, Canopy-level net primary

productivity (pure carbohydrate); p, stimulation factor of SOM

decomposition because of the priming effect; RWTC, Radiation,

water, temperature, and carbon dioxide; Vm, Maximum Rubiscolimited carboxylation rate adjusted by leaf carbon-to-nitrogen ratio.

Modified from Liu and others (2005), used with permission.

Vm

NPPb

N_uptake

Leaf_C:N

NM

Litterfall

N_dep N_fix

K N_storage P

KM KI

K Immobilization CN(S) Mineralization

RWTC

NPPc Disturbance

N_volat

KCN(P)

Growth_resp

Atmosphere

Leaching

LAI

GPPc

Maint_resp

p

Allocation

Control

Nitrogen mass flow

Neighboring landscape

Atmospheric carbon, nitrogen, moisture

Erosion

CO2, GHG emission C–N fixation

Sedimentation Moisture

Evaporation,transpiration

Soil water Soil C

Debris

Available N

 for growth

Available water

 for growth GPP, NPP

 Leaf, root

stem biomass

Soil N

Driving force

Climate and site condition

Human and natural disturbance

Driving force Removal

The diagram of the carbon-nitrogen flow in IBIS is shown 

in figure D4. Foliar nitrogen concentration is represented by 

the leaf carbon-to-nitrogen ratio (and is denoted as leaf_C:N), 

which is dynamically adjusted by a carbon-to-nitrogen 

modifier (KCN), which is determined by soil mineral nitrogen 

content (NM). The environmental conditions (radiation, water 

availability, temperature, and CO2 concentration), the LAI, 

and the maximum Rubisco activity (Vm) as limited by available leaf nitrogen determine the canopy-level gross primary 

productivity (GPPc). After deducting maintenance respiration 

(using the factor Maint_resp), GPPc gives canopy-level NPP 

(NPPc). At this point, NPPc represents the production of pure 

carbohydrate, rather than of new biomass carbon. A fraction of 

NPPc is consumed in growth respiration, with the remainder 

being converted to “stabilized” biomass (NPPb). The remaining biogeochemical processes, especially soil decomposition, 

are similar to those of the Century model.

D.2.1.2. Soil Organic Carbon Cycle

EDCM is an embedded ecosystem biogeochemical 

model in GEMS. It is based on the well-established ecosystem model Century (version IV) (Parton and others, 1993; 

Liu and others, 2003). Both models use empirical maximum 

potential vegetation productivity, together with limitations 

from temperature, water, and nutrients, to calculate production of trees and crops. The established algorithms of soil 

organic carbon (SOC) dynamics in Century form the basis of 

several other biogeochemical models, such as the CarnegieAmes-Stanford Approach (CASA; Potter and others, 1993), 

the Integrated Terrestrial Ecosystem Carbon (InTEC) model 

(Chen and others, 2000), TRIPLEX (Peng and others, 2002), 
124 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

and nitrogen dynamics. More than 100 output variables are 

provided by EDCM, including NPP, net ecosystem productivity (NEP), carbon and nitrogen stocks in aboveground 

and belowground biomass, soil carbon dynamics, and so on. 

Century has a one-soil-layer structure for carbon and nutrients 

(nitrogen, phosphorus, and sulfur). In contrast, EDCM adopts 

a multiple-soil-layer structure to account for the stratification 

of the soil profile and SOC in each soil layer. It dynamically 

tracks the evolution of the soil profile (up to 10 soil layers) and 

carbon storage as affected by soil erosion and deposition.

D.2.1.3. Effects of Disturbances

Natural and anthropogenic disturbances (for example, 

fires, hurricanes, tornadoes, and forest cutting) are accounted 

for in the land-use- and land-cover-change (LULCC) model. 

Ecosystem disturbances will be parameterized in the model 

separately because the biogeochemical consequences of these 

types of disturbances can be vastly different; however, the 

basic procedures are similar.

Historical fire perimeters and burn-severity maps are 

used in GEMS to indicate the timing, location, and severity 

level of burns. The extent and severity of a disturbance event 

are usually captured by remote sensing or field monitoring 

and also can be estimated by models, such as the First Order 

Fire Effects Model (FOFEM) by Reinhardt and others (1997) 

and the Landscape Successional (LANDSUM) model by 

Keane and others (2006). The effects of burns are expressed 

as biomass consumption loss and mortality loss (table D2). 

Based on the loss rates, GEMS reallocates biomass and soil 

carbon pools for each individual land pixel. Consumption 

loss is a direct carbon emission to the atmosphere, whereas 

motility loss converts live biomass carbon to dead carbon 

pools. The disturbed ecosystem will start regrowth with a 

new soil nutrient pool and a new LAI calculated in the model. 

Calculation of other disturbance effects will follow a similar 

approach to that used for fire effects, but with different carbon 

transition coefficients among various pools. The regrowth 

processes following disturbances are calculated based on light 

and water availability, temperature, nutrient availability, and 

Figure D5. Diagram showing nitrogen cycling in a terrestrial ecosystem as simulated by the Century model and the ErosionDeposition-Carbon Model (EDCM). Modified from Metherell and others (1993), used with permission. The nitrogen cycle is tightly

coupled with the carbon cycle. C:N, carbon-to-nitrogen ratio; N, nitrogen. Abbreviations are found in “Abbreviations, Acronyms, and

Chemical Symbols” in the front of this report.
Appendix D 125

other factors. GEMS assumes tree planting will follow the 

clearcutting event if a plantation is prescribed in the landcover map, otherwise natural vegetation recovery will occur.

For the national assessment, simulated future-firedisturbance maps will be produced along with (or embedded 

in) the future land-use and land-cover (LULC) maps. These 

disturbance maps (including simulated severity levels) will 

be linked with GEMS the same way as the LULCC maps are 

linked. Annual fluxes of disturbance-induced carbon loss and 

the legacy multiyear cumulative effects will be reported.

D.2.1.4. Effects of Management Activities

In addition to natural disturbances (for example, climate 

variation, geological disasters, wildfires), human management 

activities also play a critical role in annual ecosystem carbon 

fluxes and soil carbon budgets. For example, implementing 

conservation residue management can significantly mitigate 

carbon emissions from soils in comparison to conventional 

tillage management. The conceptual carbon-change scenarios 

based on explicit simulations of management effects and feedback are shown in figure D6.

Management activities considered in the current GEMS 

include (but are not limited to) the following:

• Land-use changes, including conversions between 

land-use classes and crop rotation

• Land-management practices, consisting of —

◦ Logging event

◦ Forest fertilization

Figure D6.  Conceptual model of soil organic carbon (SOC) dynamics

under a paired treatment of conventional tillage (CT) and no-till (NT)

after initialization of cultivation from natural status. A, SOC gain upon

converting from CT to NT following CT for a period of time (T1–T0). B,

SOC loss caused by general cultivation practices associated with CT

since T0. Difference (D) in SOC stock between NT and CT varies with

time. SOC reaches a new equilibrium at T3 under CT and T4 under NT.

The rates of SOC gain and SOC loss do not coincide but are a function

of the initial SOC stock level and time scale.

Table D2.  Fuel-consumption effects under different burnseverity levels, based on comparison of remotely sensed burnseverity and field observations.

[This table also is used in the fire-disturbance modeling tasks to calibrate 

fire-emission estimates. Source: Carl Key, U.S. Geological Survey, written 

commun., May 28, 2009]

Components Consumption (percent) Mortality (percent)

Low Moderate High Low Moderate High

Forest floor and soil

Litter/fine fuel 15–60 61–90 91–100 – – –

Duff 5–30 31–70 71–100 – – –

Medium fuel 10–30 31–50 51–100 – – –

Heavy 5–15 16–40 41–100 – – –

Soil 5–20 21–50 51–100 – – –

Understory layer

Herb 16–60 61–85 86–100 – – –

Shrub-leaf-wood 10–40 41–80 81–100 – – –

Shrub-leaf-wood – – – 1–20 21–70 71–100

Premature trees

Leaf 1–20 21–70 71–100 – – –

Fine branch 1–20 21–70 71–100 – – –

Wood – – – 1–20 21–75 76–100

Mature trees

Leaf 1–20 21–70 71–100 – – –

Branch 1–20 21–70 71–100 – – –

Wood – – – 1–20 21–70 71–100

◦ Fire-fuel management, including prescribed burns

◦ Grazing (specified into various intensity classes)

◦ Tillage practices coupled with residue input

◦ Fertilization rate and manure application

◦ Irrigation

Key algorithms, such as irrigation, fertilization, and residue 

return, are embedded in GEMS. Data and parameter sets will be 

collected and compiled from existing databases and literature.

D.2.1.5. Effects of Erosion and Deposition

Soil erosion and deposition affect soil profile evolution, 

spatial redistribution of carbon and nutrients, and ecosystem 

carbon-nitrogen dynamics (Liu and others, 2003; Lal and 

others, 2004). Soil erosion and deposition will be simulated 

by using the USPED model (Mitas and Mitasova, 1998). The 

effects of soil erosion and deposition on soil carbon erosion will be quantified; the processes to be modeled include 

soil-profile evolution, onsite ecosystem-carbon dynamics, and 

offsite transport of carbon and nitrogen onto the landscape and 

into wetland environments and aquatic systems.

USPED is a simple two-dimensional hydrological model 

that is comparable to the more broadly used Universal Soil 

Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE); however, unlike the USLE/RUSLE models 

that can predict only soil erosion, USPED also can simulate 

deposition on landscape and requires only four major inputs:

• Rainfall intensity, which is to be adjusted by the actual 

rainfall each year

Starting

point

of CT

Steady-state Steady-state

Loss Steady-state

Time

Baseline

Accumulation

(A) NT

(B) CT

Initial CP0

Initial CP1

upon

changing

to NT

D2

T0 T1 T2 T3 T4

D3 D4

Soil organic carbon pool (SOCP)
126 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

• Soil erodibility factor (K factor), which is available 

in the U.S. General Soil Map (also called the State 

Soil Geographic (STATSGO2) database) and the Soil 

Survey Geographic (SSURGO) databases

• Field carbon factor, which is directly converted from 

land-cover type

• Digital elevation model (DEM) data

Most of the input requirements are the same as those of USLE/

RUSLE.

USPED is suitable for GEMS because of its appropriate 

time step, level of complexity, capability of simulating erosion 

and deposition, and robustness. For linking soil carbon with 

erosion and deposition, EDCM adopts a multiple-soil-layer 

structure to account for the stratification of the soil profile and 

SOC in each soil layer. EDCM dynamically keeps track of the 

evolution of the soil profile (up to 10 soil layers) and carbon 

storage as affected by soil erosion and deposition.

In EDCM, each soil-carbon pool in the top layer will lose 

a certain amount of carbon, if erosion happens. The carbon 

eroded is calculated as the product of the fraction of the top 

soil layer experiencing erosion, the total amount of SOC in the 

top 20 centimeters of the layer, and an enrichment factor for 

the eroded SOC to account for the uneven vertical distribution 

of SOC in the top layer. EDCM can dynamically update the 

soil layers affected by erosion and deposition.

One approach for linking USPED with GEMS is shown 

in figure D7. Simulated erosion and deposition are grouped into 

discrete classes, which will be included in the GEMS spatial 

simulation unit (joint frequency distribution (JFD) cases; see 

later explanations), to represent the land and water surfaces of the 

study area. Losses of carbon and nitrogen during lateral sediment 

transportation are accounted for using an oxidation factor.

D.2.1.6. Fate of Wood Products

Carbon in wood products, landfills, and other offsite 

storage can be significant in the accounting of terrestrial 

carbon-sequestration capacity (Skog and Nicholson, 1998). 

Currently (2010), GEMS does not track the fate of carbon 

in wood products. Because GEMS is linked directly to the 

data-management system for the purposes of reporting and 

dissemination of assessment results, a spreadsheet summarizing sequestration and GHG fluxes across ecosystems and 

carbon pools will be created. Most of the carbon pools will be 

simulated at a pixel level. For wood products, average values 

will be provided. The U.S. Environmental Protection Agency 

(EPA) and the USFS will be consulted about the proper way 

to estimate forest-product carbon and potential collaboration 

opportunities. Existing factors and equations about harvestedwood-product carbon pools (Smith and others, 2006; Skog, 

2008) will be adopted and modified to link with GEMS to 

track the fate of harvested wood.

D.2.1.7. Methane and Nitrous-Oxide Fluxes

The emission of CH4 at wetland sites will be simulated 

in terms of soil biogeochemical processes, including CH4

production by methanogenic bacteria under anaerobic conditions, oxidation by methanotrophic bacteria under aerobic 

conditions, and transport to the atmosphere (Conrad, 1989). 

The principal controls of these processes are soil moisture, 

water-table position, soil temperature, availability and quality 

of suitable substrates, and pathways of CH4 transport to the 

atmosphere. A wide range of models have been developed to 

simulate the plot-scale processes of CH4 generation, consumption, and transport (Li and others, 1992; Cao and others, 1996; 

Potter, 1997; Walter and others, 2001; Zhuang and others, 

2006). A simple compartmental (zero-dimensional) model 

was developed by Cao and others (1996) to simulate wetland 

carbon dynamics for large areas. Another model by Potter 

(1997) simulated CH4 production rates from a microbial production ratio of CO2

 and CH4, which changed as a function of 

the water-table depth. Slightly more complex one-dimensional 

models (Walter and others, 2001; Zhuang and others, 2006) 

also are available to tailor more detailed process descriptions. Some of these models have a detailed representation 

of plot-scale vertical soil processes. The deployment of these 

models for large areas, however, has been challenging because 

of the difficulties in defining parameters for these models and 

in simulating some of the critical driving variables, such as 

water-table position in individual wetlands for large areas.

The GEMS modeling team has applied the denitrification-decomposition (DNDC) model to simulate CH4

 and N2O 

fluxes in the Prairie Pothole Region. A process-based model 

for CH4 that is similar to the model in Cao and others (1996) 

and to DNDC approaches has been implemented in GEMS 

that will balance the needs of considering the plot-scale processes and the feasibility of deploying the plot-scale model for 

large areas to address spatial heterogeneity. Estimates of CH4

production by the model depend on the substrate availability 

Figure D7.  Diagram linking the erosion-deposition model

(USPED; Unit Stream Power-Based Erosion Deposition) with the

terrestrial biogeochemical model (EDCM; Erosion-DepositionCarbon Model) in GEMS (General Ensemble Modeling System).

A JFD (joint frequency distribution) case indicates one or more

pixels with the same site condition. DEM, digital elevation model.

Erosion JFD

Deposition JFD

Land-cover

change

Climate

Fixation

Oxidation

Decomposition

and disturbance

Fixation

Decomposition

and disturbance

Transport

out of system

Disturbance

Vegetation Soil physical

property
Appendix D 127

(soil carbon and vegetation root carbon) and soil condition 

(soil temperature, redox), whereas CH4 oxidation is calculated 

based on the soil redox condition or water table.

In a zero-dimensional modeling approach, the CH4

emission from wetlands to the atmosphere is calculated as the 

difference between the CH4 production and oxidation:

, (D6)

where MERt is the emission mass of CH4 per unit surface 

area of a wetland at time t,

MPRt is the production mass of CH4 per unit surface 

area of a wetland at time t, and

MORt is the oxidation mass of CH4 per unit surface 

area of a wetland at time t.

MPRt and MORt are estimated on the basis of such controlling 

factors as decomposed organic carbon, water-table position, 

soil temperature, and primary production of existing plants. 

These controlling factors are parameterized by applying or 

synthesizing techniques described in Cao and others (1996), 

Potter (1997), Walter and others (2001), and Zhuang and others (2006). A reasonably accurate prediction of the water-table 

position, in particular, is a challenging aspect. Further details 

are given below in section D.3.2 as part of a discussion on 

modeling lateral fluxes in and out of wetland systems.

Various models exist for simulating N2O emissions (for 

example, Li and others, 1992; Liu and others, 1999; Parton 

and others, 2001; Hénault and others, 2005). Procedures for 

estimating N2O emissions from ecosystems were developed 

in the prototype of the GEMS–EDCM method and applied to 

simulate and project N2O emissions in the Atlantic zone of 

Costa Rica (Liu and others, 1999; Reiners and others, 2002). 

Nitrification and denitrification processes are the primary 

processes that lead to the emission of N2O from soils. Atmospheric and terrestrial (for example, fertilizer, litter) depositions of nitrogen, plant uptake, mineralization, and leaching 

can act as the major controls. The existing GEMS algorithms 

for N2O flux simulations will be used to compare simulation results with observations (for example, GRACEnet) and 

to improve the model when necessary. A zero-dimensional 

model is also applicable for estimating N2O emissions from 

wetlands:

, (D7)

where NOEt is the N2O emission mass per unit surface 

area of a wetland at time t,

NOEdenit,t is the production mass by denitrification per 

unit surface area of a wetland at time t,

and

NOEnit,t is the production mass by nitrification per 

unit surface area of a wetland at time t.

NOEdenit,t and NOEnit,t are quantified by applying or synthesizing techniques described in Li and others (1992), Liu and others (1999), Parton and others (2001), and Hénault and others 

(2005).

Subject to the availability of observation data, empirical 

regression models also can be developed for emissions of CO2, 

CH4, and N2O from wetlands with different land-cover types, 

as well as hydrologic and meteorological regimes. Much of 

the variation in CO2 and CH4 may be explained by considering 

wetland soil temperature and water-table elevation as predictor variables. Variations of N2

O flux also could be captured by 

regressing with soil temperature and water-filled pore space 

as the predictor variables; however, such regression models 

probably are highly site-specific and require large datasets 

given their purely statistical nature. Because such datasets 

rarely exist in current literature, deployment of such models in 

large spatial (as well as temporal) scales can hardly be justified as reliable given the uncertainty of estimated regression 

coefficients.

The IPCC tier 1 approach (Intergovernmental Panel on 

Climate Change, 2006) is a simple way of obtaining crude 

estimations of CO2, CH4, and N2O emissions from wetlands. The approach is based on some aggregate measures 

of emissions of specific GHG per unit of time and wetland 

area. Although IPCC (2006) provided global estimates of 

these emission factors based on existing literature, regional 

estimates for the wetlands in the United States also may be 

obtained from a comprehensive literature survey. Emission 

estimates obtained through the tier 1 approach would complement the evaluations of results of the simple biogeochemical 

models described previously.

D.2.2. GEMS Spatial Simulation Unit

The spatial heterogeneities of the biophysical variables (such as land cover, soil texture, and DEM) often are 

represented on thematic maps and stored in georeferenced 

geographic information system (GIS) databases. The simulation unit in GEMS is a cluster of land pixels sharing a unique 

combination of values of environmental driving variables. 

Combining multiple input raster layers (maps) on a cell-bycell basis in a GIS, a JFD table can be created to list all unique 

combinations of the values of the overlay variables and their 

associated frequencies (areas or number of pixels). Each 

unique combination forms a GEMS simulation unit. The geographic locations of all the JFD cases are uniquely determined 

by the JFD map, thereby providing the spatial framework 

to visualize and analyze the spatial and temporal patterns of 

biogeochemical properties and processes.

Two examples of the JFD map are shown in figure D8. 

The first example (fig. D8A) shows the land pixel sampling 

at certain spatial intervals (for example, 5 kilometers) on 

a stack of relatively higher resolution (for example, 30- to 

250-m) maps. This sampling approach is used when there are 

too many land pixels and map layers. It also creates a JFD 

table where each JFD case contains only one land pixel. The 

second example (fig. D8B) overlays the soil and land-cover 

maps; the resulting JFD map shows the unique combinations 

of soil and land-cover conditions. An important feature of this 

JFD approach is the elimination of the need to perform model 

simulations pixel by pixel. One pixel represents all the pixels 

of a JFD case.
128 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Figure D8.  Conceptualized diagram showing two approaches to producing a joint-frequency distribution map. A, Sampling at certain

spatial intervals. B, Overlaying the soil map with the land-cover map series produces the joint-frequency-distribution map; each image

is 10 kilometers wide. (Note that the scales of parts A and B are exaggerated relative to the location map.)
Appendix D 129

D.2.3. Using Ensemble Simulations to Reconcile Nonlinearity and

Heterogeneity

Studies indicate that averaging across the spatial and temporal heterogeneity of the input 

data could have significant effects on the carbon simulations (Avissar, 1992; Pierce and Running, 1995; Turner and others, 1996; Kimball and others, 1999). This indicates that incorporating ecosystem heterogeneity is necessary to accurately upscale carbon dynamics from site to 

regional scales. The direct approach of incorporating variance and covariance of input variables 

in the simulation process can be expressed as the following:

, (D8)

where E is the operator of expectation,

p is the nonlinear model,

X is a vector of model variables,

n is the number of strata or total JFD, and

F is the frequency of cells or the total area of strata i as defined by the vector of Xi.

Any difference between the model scale and the spatial resolution of the data may introduce biases caused by model nonlinearity. An ensemble approach can assimilate the fine-scale 

heterogeneities in the databases to reduce potential biases. The mean value of a variable (for 

example, carbon stock and flux) of simulation unit i in equation D8 can be estimated by using 

multiple stochastic model simulations:

, (D9)

where m is the number of stochastic fine-scale model runs for simulation unit i, and

Xij is the vector of model input values at the fine scale generated using a Monte Carlo 

approach within the space defined by Xi.

As a result, input values for each stochastic model run are sampled from their corresponding 

potential value domains (Xi) that usually are described by their statistical information, such as 

moments and distribution types. The variance of the model simulations on regional scale can be 

quantified as follows:

, (D10)

where the variance and covariance of the model simulations on unit i can be expressed as follows:

 and (D11)

. (D12)

Other descriptive statistics, such as skewness, also can be calculated from the ensemble 

simulations. These moments characterize not only the spatial and temporal trends and patterns 

of simulated variables, but also their uncertainties in space and time.

Solving equation D10 will require excessive computational effort if the number of strata n is 

quite large; however, if p(Xi) and p(Xj) are independent among a great number of strata, computations 

will be dramatically reduced because covariance defined in equation D12 will be zero. Hence, actual 

applications should sufficiently identify the independence among strata. For example, suppose p(Xi)

and p(Xj)represent soil organic carbon within strata i and strata j, respectively, and their random 

properties result from the randomness of soil texture and precipitation. If there is no lateral flow 

between strata i and strata j, then p(Xi) and p(Xj) can be regarded as independent.

D.2.4. Automated Model Parameterization (Monte Carlo Downscaling)

Models developed for site-scale applications need linkages with georeferenced data to be 

deployed across a region. Most information in spatial databases is aggregated to the map-unit 
130 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

level as the mean or median values, making the direct injection 

of georeferenced data into the modeling processes problematic 

and potentially biased (Pierce and Running, 1995; Kimball 

and others, 1999; Reiners and others, 2002). Consequently, an 

automated model parameterization process usually is needed 

to incorporate field-scale spatial heterogeneities of state and 

driving variables into simulations. A Monte Carlo approach is 

built into GEMS to downscale aggregated information from 

map-unit level to field scale. Examples of data variables to be 

downscaled for parameterization include soil property, tree 

age, crop rotation, and forest cutting. The following describes 

the automated stochastic soil and forest-age initializations.

Soil polygons on the STATSGO2 and SSURGO maps are 

represented by map units; each has a unique map-unit identifier (ID), size, and location. Each map unit contains from 1 to 

20 soil components, representing distinct soils types. Each soil 

component has a soil attributes table; however, the locations 

of the soil components within a map unit are not known. In 

GEMS, for any specific stochastic simulation, a soil component was randomly picked from all components within a soil 

map unit according to the probability defined by the areal fractions of the components. Once the component was determined, 

soil characteristics were retrieved from the corresponding soil 

component and layer attribute databases. For the variables 

with increased (Vhigh) and decreased (Vlow) values, the following equation was used to assign a value (V) to minimize 

potential biases from model nonlinearity (Pierce and Running, 

1995; Reiners and others, 2002):

(D13)

where p is a random value that follows standard normal 

distribution N(0,1).

The above equation assumes that the possible values of the 

soil characteristics follow a normal distribution with 95 percent of the values varying between Vhigh and Vlow.

The Monte Carlo approach also is used to downscale 

regional initial forest age. The currently available forest-age 

data come from State- or county-level forest-inventory statistics. The forest-age class distribution (area weight) is a feature 

on the regional scale. To assign a forest age for a specific 

location, a cumulative probability curve must be created on 

the basis of the forest-age class distribution (fig. D9). The next 

step is to generate a random p value between 0 and 1. The p

value will point to a specific level on the cumulative probability curve and match it to a corresponding age class. GEMS 

then uses a look-up table to retrieve initial forest biomass 

based on the age (Liu, Liu, and others, 2008).

D.2.5. Data Assimilation

Data assimilation techniques can be activated to constrain 

GEMS simulations with various observations at different spatial 

and temporal scales. Different data-assimilation techniques are 

implemented in GEMS to leverage the advantages and disadvantages of each method. For example, the Markov Chain Monte 

Carlo (MCMC) method is computation intensive and, therefore, 

difficult to apply to a region where the number of simulation units 

is large. It can be effective and ideal, however, to derive representative values and their uncertainties of model parameters from 

limited point observations, such as flux-tower measurements.

Other data assimilation techniques used by GEMS 

include model inversion using PEST (EPA’s model-independent parameter estimation application; http://www.epa.gov/

ceampubl/tools/pest/) (Liu, Anderson, and others, 2008), 

Ensemble Kalman Filter (EnKF) (Evensen, 1994, 2003), 

and Smoothed Ensemble Kalman Filter (SEnKF) (Chen and 

others, 2006, 2008). Model inversion with the PEST package 

is based on optimal theory and thus requires that the model 

have a smooth response to model parameters. Both EnKF 

and SEnKF are based on statistical Bayesian theory and joint 

technology of Monte Carlo sampling with a Kalman filter. 

EnKF has many successful applications in weather forecasting and hydrology through incorporating various data into the 

model simulation process to improve estimation of model state 

variables. The GEMS team has used some of the approaches 

to derive model parameter information from plot measurements of carbon and nitrogen stocks (Liu, Anderson, and others, 2008) and from eddy-covariance flux-tower observations 

(Chen and others, 2008). A combination of data-assimilation 

techniques will be used to ensure that model simulations agree 

well with observations from different sources and scales.

Plot-scale.—FLUXNET (the flux network) and FIA data 

(plot-scale repetitive measurements of biomass stocks and vegetation dynamics) will be used to derive information on modelparameter values and their uncertainty. The derived modelparameter information at the plot scale will then be extrapolated 

to regional and national scales (Liu and others, 2008).

Regional to national scales.—EnKF, SEnKF, or other 

data-assimilation techniques will be used to assimilate remotely 

Figure D9.  Monte Carlo downscaling of State- and county-level

forest-age data to pixel level. From Liu, Liu, and others (2008), used

with permission.

1.0

0.8

p

0.6

0.4

5 15 25 35 45 55 65 75 85 95 105

0.2

0.0

Probability (0–1)

Forest age
Appendix D 131

by SEnKF with data assimilation matched observations substantially better than predictions made without data assimilation (fig. D10). Additionally, this approach also is efficient in 

finding the optimum parameters (fig. D11).

D.2.6. Input and Output Processor and NetCDF

Interface

A GIS program (JFD Builder) was developed for 

generating a JFD table from primary input data layers. A 

NetCDF program (called NCWin) for processing and visualizing 

NetCDF data also was developed. All mapped data (for example, climate, soil, vegetation cover, disturbance events) are saved 

in NetCDF format in GEMS. The NCWin graphical user interface (GUI) provides the capability to convert and visualize input 

and output maps as well as temporal data trends (fig. D12).

D.3. Integrating With Other Models

D.3.1. Linkages With Land-Use- and Land-CoverChange Data and Projections

For the national assessment, GEMS will be directly 

coupled with the land-use-change model FORE–SCE 

(appendix B of this report) to account for the effects of past 

land-cover and land-use changes and simulated future landuse changes on ecosystem carbon-nitrogen dynamics. LULCC 

maps generated by the model will be used to produce spatial 

simulation units either by the JFD approach or a land pixel 

sampling approach. For an individual plot, an LULCC file, 

called the “event schedule file,” will be created. This file 

specifies the type and timing of any LULCC events, as well 

sensed and ground-based observations. For example, Zhao 

and others (2010) successfully assimilated the gross primary 

productivity (GPP) data of the Moderate Resolution Imaging 

Spectroradiometer (MODIS) products to support regional model 

simulations of carbon sequestration in a southeastern region.

The SEnKF (Chen and others, 2006, 2008) will be used at 

the plot and regional scales. By combining EnKF with a kernel 

smoothing technique, SEnKF has the following characteristics:

• Simultaneously estimates the model states and parameters through concatenating unknown parameters and 

state variables into a joint state vector

• Mitigates dramatic, sudden changes of parameter values 

in the parameter-sampling and parameter-evolution process, and controls the narrowing of the parameter variance

• Recursively assimilates data into the model, and thus 

detects the possible time variations of parameters

• Properly addresses various sources of uncertainty stemming from input, output, and parameter uncertainties

In GEMS, the SEnKF procedure becomes regular Monte Carlo 

analysis at the time steps when no observation data are available for assimilation.

The SEnKF method was tested by assimilating observed 

fluxes of CO2 and environmental driving-factor data from an 

AmeriFlux forest station (located near Howland, Me.) into a 

model for partitioning eddy-covariance fluxes (Chen and others, 2008). Analysis demonstrated that model parameters, such 

as light-use efficiency, respiration coefficients, and the minimum and optimum temperatures for photosynthetic activity, 

are greatly constrained by eddy-covariance flux data at daily to 

seasonal time scales.

The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Predictions made 

Figure D10.  Graphs showing an example of Smoothed Ensemble Kalman Filter (SEnKF) data assimilation on state variables. The

“GEMS” curve represents the GEMS model without data assimilation. The “Data Assimilation” curve represents the GEMS model with

data assimilation. Field observations (red squares) are from the online data archive of American Flux Network (AmeriFlux) sites. NEE, in grams of carbon per square meter per month

-200

-100

0

100

200

300

400

January

2001

July

2001

January

2002

July

2002

January

2003

July

2003

January

2004

January

2005

July

2004

Time

Corn Soybean Corn Soybean

-20

0

20

40

60

80

120

100

January

2002

July

2002

January

2003

July

2003

January

2004

January

2005

July

2004

Time

NEE, in grams of carbon

per square meter per month
132 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Figure D12.  Screen capture showing an example of the NCWin map and data trends graphical user interface.

Time

Potential production rate,

in grams of carbon per square meter per month

Time

Potential production rate,

in grams of carbon per square meter per month

Figure D11.  Graphs showing the results of parameter estimation for the plant-production submodel using the Smoothed Ensemble

Kalman Filter (SEnKF) in the biogeochemical General Ensemble Modeling System (GEMS). The graphs show seasonal variations of the

potential plant-production rate in croplands (left) and in forests (right). The seasonal variations imply that the structure of the plantproduction model might not be adequate to represent the seasonality of crop growth.
Appendix D 133

ferent crops have different biological characteristics and 

management practices, likely resulting in different effects 

on carbon dynamics in vegetation and soils. Disaggregation of the agricultural land data is done stochastically in 

GEMS based on crop composition statistics at a district 

or county level. For example, in the U.S. Carbon Trends 

Project (Liu, Loveland, and Kurtz, 2004; Tan and others, 2005; Liu and others, 2006), schedules of cropping 

practices, including shares of various crops and rotation probabilities, were derived from the NRI database 

developed by the USDA Natural Resources Conservation 

Service. The NRI database is a statistically based sample 

of land-use and natural-resource conditions and trends 

on non-Federal lands in the United States. The inventory, 

covering about 800,000 sample points across the country, 

is done once every 5 years. Management practices, such 

as cultivation and fertilization, are incorporated into the 

LULCC sequences generated for the site according to 

crop or forest types and geographic region.

D.3.2. Linkages With Aquatic and Wetland Systems

The carbon and nitrogen fluxes within the aquatic ecosystems of wetlands, lakes, rivers, and streams, as well as their 

lateral interactions with the terrestrial ecosystems and vertical exchanges with the atmosphere, will be quantified within 

the integrated framework of GEMS through an encapsulated 

aquatic biogeochemical model. A general framework for the 

aquatic model, which is primarily developed at the site scale, 

is presented in figure D13.

as the type and timing of management practices, such as 

cultivation and fertilization.

The LULCC information from the land-change model 

and other information (for example, the USDA Natural 

Resources Inventory (NRI) database) will be assimilated using 

the following procedures:

• Events such as forest clearcutting, deforestation, urbanization, and reforestation will be directly incorporated. A 

biomass removal or restoration algorithm will be applied 

to land pixels with these land-use-change events.

• Although annual clearcutting events will be provided 

by the land-change model, selective cutting events (for 

example, group-selection harvesting and fuel treatment) 

are not available. The selective cutting activities can be 

scheduled based on selective cutting rates derived from 

other sources, such as FIA databases, the new vegetation 

change tracker (VCT) product derived from LANDFIRE 

(Huang and others, 2009), and forest fuel-treatment data. 

GEMS can aggregate the total selective cutting area to 

an equivalent amount of clearcutting area and randomly 

assign the derived clearcutting to the forest landscape. 

GEMS also will calculate specific thinning effects on 

biomass and soil carbon change when related publications and field data are synthesized.

• Mapping crop species distribution and rotations for large 

areas is still a primary challenge for national land-cover 

database development. Crops are aggregated into broad 

categories (for example, row crops, and other agricultural 

land). It is necessary to downscale aggregated classes into 

specific crops for biogeochemical modeling because difFigure D13.  Diagram showing

a simplified conceptualization

of carbon (C) and nitrogen

(N) fluxes, as well as their

controls and driving forces,

for an encapsulated aquatic

biogeochemical model in

GEMS. Solid arrows indicate

mass flow and dashed arrows

indicate controls or driving

forces. Abbreviations are found

in “Abbreviations, Acronyms,

and Chemical Symbols” in the

front of this report.
134 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

The primary methodology related to aquatic and wetland 

systems is described in section D.2.1.7 and appendix E of this 

report. This subsection focuses on the geospatial aspects and 

heterogeneity of wetland conditions and processes for large areas. 

Wetlands are important systems that likely play a pivotal role in 

the sequestration or release of GHG gases. Physical processes 

such as the hydrology of flooding (which often is intermittent) 

and associated soil saturation can be considered as some of the 

common, principal drivers of wetland biogeochemistry. A functional wetland ecosystem can be conceptualized by interactions 

among the four major components of water, nutrients, habitat 

(plants and soils), and animals. A schematic diagram of these 

functional components and interactions is shown in figure D14.

Given the wide variety of coastal and inland wetlands and 

the wide range of biophysical and climate conditions across the 

country, it is very difficult to simulate the hydrological dynamics 

(for example, water-table position) for individual wetlands over 

large areas using a purely process-based approach. The major 

challenges for testing and implementing these models include the 

limited availability of reliable datasets and proper parameterizations of important driving forces and boundary conditions. To 

address this challenge, a hybrid modeling approach, combining 

the process modeling and empirical modeling, is being developed 

to simulate water storage and water-table dynamics in wetlands. 

Model simulations will be used to derive relatively robust representations of water storage and water-table dynamics for different types of wetlands, such as permanent to semipermanent and 

ephemeral to transitional. A frame-based state-transition approach 

will then be used along with prior knowledge to describe hydrological regimes for different wetlands under various meteorological conditions across the country.

The wetland approach (described in section D.2.1.7 of this 

report, as well as the river-stream-lake-impoundment methodology in appendix E of this report, will ingest the upland-erosion 

and organic-carbon data from GEMS as inputs of lateral fluxes 

from the terrestrial systems. Statistical analyses and findings of 

the aquatic team can contribute to the calibration, validation, 

and improvements of the wetland models for realistically simulating the greenhouse-gas fluxes to the atmosphere and evaluating the carbon sequestration of wetland ecosystems.

D.3.3. Feedback Among the Models

Model integration is a critical step in the project because 

there are time- and space-dependent feedbacks among the 

different modeling components. For example, FORE–SCE 

requires information about the site fertility or the SOC level 

from GEMS to optimize the allocations of crops in space and 

time. On the other hand, land-disturbance information will 

affect the land-use behaviors, such as timber harvesting. Without stepwise coupling between FORE–SCE and the disturbances model, timber harvesting activities might still be prescribed in areas where biomass has been completely consumed 

by fire in the disturbances model. Carbon or biomass stock 

(fuel load) will strongly affect the probability of fire occurrence and the severity of fires, which requires the coupling 

between the disturbances model and GEMS, with the latter 

Figure D15.  Diagram showing

the system structure of the

Geospatial Model Sharing

Platform. Modified from

Feng and others (2009), used

with permission. GeoMPI,

Geospatial Model Programing

Interface; GeoMSI, Geospatial

Model Sharing Interface.

Data 

translation

Process A

Process B

Model service

Model clients

Process A

Process B

... ...

Controller

Model A

Model B

GeoMSI GeoMPI

Model integration

Process 

management

Geospatial 

model sharing 

platform

Figure D14.  Diagram of conceptual wetland ecosystems and

interactions among the functional groups. Modified from Fitz and

Hughes (2008), used with permission.
Appendix D 135

providing carbon-stock information. Model integration will be 

accomplished on a parallel-processing computer system.

D.4. Relations With Evaluation of Ecosystem 

Services

Mitigation opportunities that are considered as management scenarios are evaluated with a spreadsheet approach. These 

opportunities will be modeled using GEMS. Examples of GEMS 

data products supporting mitigation opportunities (including ecosystem-services evaluation) are carbon stocks, CH4

, N2O fluxes, 

soil erosion, NPP, wood harvests, surface runoff, and crop yields. 

In addition, linkages to ecosystem-service evaluation methods 

(section 3.3.6) will be built based on GEMS output.

The Energy Independence and Security Act of 2007 

(EISA) (U.S. Congress, 2007) requires that “short- and longterm mitigation or adaptation strategies” be developed as an 

outcome of the assessment. In chapter 1 of this report, this was 

interpreted as a requirement to develop relevant data products 

and information packages that can be used conveniently by 

land managers and other stakeholders to develop specific strategies; however, what is “good” from the perspective of one 

user may be “bad” to another. Land-use change and climate 

change affect a myriad of ecosystem services simultaneously; 

some identified specific ecosystem services may be misleading 

because the overall effect on the ecosystem is not evaluated. 

Hence, a broader perspective and context is needed to evaluate and understand concurrent effects on multiple ecosystem 

services. To solve this problem, a platform will be established 

to project changes in ecosystem services to support adaptive 

land-management practices. This provides a spatially explicit 

platform that can accommodate a diversity of land uses and 

climate change for simultaneous evaluations to better understand biophysical response and tradeoff analyses, highlighting 

relative effectiveness and efficiency of management activities.

A distributed geospatial model-sharing platform (fig. D15) 

will be used to model ecosystem services and provide decision 

support. This platform is necessary to facilitate sharing and integrating geospatial disciplinary models. A platform based on Java 

Platform Enterprise Edition (J2EE) and open-source geospatial 

libraries (Feng and others, 2009) is in development. Shared 

models on the platform are accessible to applications through 

the Internet using the Open Geospatial Consortium (OGC) Web 

Processing Service (WPS) standard (fig. D16).

Assessment results related to the evaluation of ecosystem services, such as soil erosion and deposition, biomass 

production, CO2 emission, and GHG flux, will be evaluated 

Figure D16.  Conceptual flow

diagram illustrating access to

the shared geospatial model.

Modified from Feng and others

(2009), used with permission.

Figure D17.  Typical

probability distribution

(density) function (PDF)

curves. A, Uniform; B, Triangle;

C, Fractile; D, Normal; E,

Lognormal. Modified from

Intergovernmental Panel on

Climate Change (2006, p. 3.25),

used with permission.
136 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Table D3.  Preliminary methods or models to be used to assess parameters of carbon stocks, carbon sequestration, and greenhousegas fluxes by ecosystems defined for the assessment.

[The methods or models listed have been tested and prototyped, but additional models may be added depending on unique ecosystem conditions or technical 

needs encountered in the assessment. Input data requirements for each ecosystem are also listed. An explanation of abbreviations and acronyms is found in 

“Abbreviations, Acronyms, and Chemical Symbols” in the front of this report]

Methods Deliverables Technical processes Target ecosystems Data needs or sources

Spreadsheet Cs, Csr, CO2, N2O, 

CH4

Algorithms based on storage-age growth 

curves

Forests

Urban forests

Grasslands/shrublands

Croplands

Wetlands

Growth curve from FIA, crop production from 

NRCIS, NWI, local and IPCC standard GHG 

emission factors, GRACEnet data.

EDCM Cs, Csr, CO2, N2O, 

CH4,

Carbon and nitrogen 

leaching, erosion, 

and deposition

Maximum potential productivity, monthly 

time step, spatial sampling, and ensemble simulation

Parameterizations based on Cao and 

others (1996), Liu and others (1999), 

Parton and others (2001) 

Forests

Urban forests

Grasslands/shrublands

Croplands

Wetlands

LULCC, current climate, IPCC GCM projections, USDA census data, disturbance (fire, 

drought, and so on), hydrological model 

inputs (soil erosion, deposition), management 

data (grazing intensity, fertilizer application), 

SSURGO soil data, GRACEnet data.

Century Cs, Csr, CO2, N2O, 

CH4,

Carbon and nitrogen 

leaching

Maximum potential productivity, monthly 

time step, spatial sampling, and ensemble simulation

Forests

Urban forests

Grasslands/shrublands

Croplands

LULCC, topography (DEM), current climate, 

IPCC GCM projections, USDA census 

data, disturbance (fire, drought), hydrological model inputs (soil erosion, deposition), 

GRACEnet data.

IBIS Cs, Csr, CO2,

Carbon and nitrogen 

leaching

Farquhar-type leaf-level model, hourly 

time step, use of subpixel information

Forests

Urban forests

Grasslands/shrublands

Croplands

LULCC, topography (DEM), current climate, 

IPCC GCM projections, USDA census data, 

disturbance (fire, drought), hydrological 

model inputs (soil erosion, deposition).

USPED Ced Empirical two-dimensional algorithm Forests

Grasslands/shrublands

Croplands

Link with EDCM,

SSURGO K factor, SRT DEM data, LULCC, 

precipitation from climate data (current and 

future projections).

Zerodimensional 

model

CH4, CO2

N2O,

Process-based, simple framework, compatible in large-scales

Parameterizations using Cao and others 

(1996), Li and others (1992), Potter 

(1997), Walter and others (2001), 

Zhuang and others (2006), and Hénault 

and others (2005)

Wetlands Link with EDCM,

NWI, SSURGO, NCDC, NLCD, regional wetland database, GRACEnet data.

and distributed using the model-sharing platform (fig. D15). 

For a specific region and specific interest, however, numerous submodels can be added to reflect the relative effectiveness and efficiency of management activities. For example, 

water quantity and water quality, which are important indices 

of ecosystem services, are increasingly affected by natural 

and anthropogenic activities. The widely used Soil and Water 

Assessment Tool (SWAT) can be used to estimate the landphase processes (for example, surface runoff, soil erosion, 

nonpoint-source nutrient loss, groundwater recharge, and 

baseflow) and water-phase processes (for example, water routing, sediment transport, and nutrient transport and its fate in 

the aquatic systems). GEMS will link with SWAT to assess the 

climate-change effects on water availability, and sediment and 

nutrient transport for the landscape. A pilot platform, named 

EcoServ (ecosystems services model), was developed in the 

Prairie Pothole region (PPR) to simulate the diversity of ecosystem services simultaneously at landscape scale.

D.5. Estimating Uncertainties

Uncertainty estimates can be in the form of estimated 

percent errors, standard deviations, confidence intervals, or 

any other relevant coefficient (Larocque and others, in press). 

For the assessment, an overall approach to assessing uncertainties is presented in appendix G of this report. Here, a brief 

discussion is presented about how uncertainties related to 

GEMS data, parameters, and model structure will be handled.

Following the IPCC (2006) guidance, uncertainty analysis mainly focuses on random errors. Model bias removal will 

be based on model calibration with in situ data.

The factors to be considered in the uncertainty evaluation 

should have an uncertainty range, either expressed as a probability distribution function (PDF) curve or a probability lookup table. Typical PDFs described by IPCC (2006) are shown in 

figure D17. In GEMS, uncertainty factors may include forest 

age, crop species, soil type, canopy density, logging location, 

burn severity, and agricultural management.

If a model parameter has a PDF, it can be evaluated using 

error propagation. When a parameter PDF is not available, it is 

possible to derive the PDF using a data-assimilation technique. 

Some parameters may be obtained from expert judgment, 

which also has uncertainties.

The IPCC error propagation equation(s) will be used to 

aggregate the uncertainty from different vegetation types (such 

as forest or crop) to the JFD level, and aggregate uncertainty 

from the JFD level to a region:
Appendix D 137

, (D14)

where x is area weight, and

U is uncertainty.

Beyond error propagation, another effective approach to 

quantify modeling uncertainty is model comparison. Because 

GEMS can encapsulate multiple models, and parameterize 

and drive these models with the same data, it provides an ideal 

environment or platform to identify and address issues and 

uncertainty related to model structure and mathematical representations of biophysical processes. GEMS eventually will 

include 5 to 10 BGC models in the national assessment.

D.6. Biogeochemical Deliverables

Major GEMS deliverables generated from various models 

or approaches are listed in table D3. Most of the outputs can 

be summarized in tables and displayed in map series.

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141

Appendix E. Methods for Assessing Carbon Stocks, Carbon Sequestration, and 

Greenhouse-Gas Fluxes in Aquatic Ecosystems

that water body are likely to be smaller than global or regional 

averages. For these reasons, accurate regional modeling of carbon sequestration and gas exchange currently (2010) requires 

(1) independent assessment of the lateral flux of carbon to and 

from those regions, (2) an accurate accounting of the areal 

extent and size distribution of the water bodies within those 

regions, and (3) the assignment of regionally explicit biogeochemical rates of carbon sequestration and gas exchange 

that encompass the size distribution of streams, rivers, ponds, 

lakes, and reservoirs within those regions.

The carbon mass balance of estuaries and coastal areas 

of the Nation also is poorly quantified. Estuaries and coastal 

areas are some of the most biologically productive areas of 

the world, and the delivery of terrestrial carbon and nutrients to them by lateral flux and coastal erosion substantially 

enhances that production. In addition, local currents, temperature, bottom slope, and biogeochemical reactions all affect the 

quantity and form of particulate and dissolved carbon that will 

be sequestered in coastal areas, passed through to oceans, or 

converted to greenhouse gases and emitted to the atmosphere. 

These and many other physical, chemical and biological 

factors controlling carbon cycling in near-shore areas vary 

substantially with space and time, complicating a national 

assessment of carbon sequestration and flux. Processes in 

coastal areas often are overlooked or underestimated in ocean 

carbon-cycling models because ocean models normally are 

operated at relatively coarse spatial resolution, and inclusion 

of coastal pixels confounds remotely sensed data and model 

execution (Dunne and others, 2005, 2007). Because coastal 

areas represent the confluence of terrestrial and oceanic processes, most ocean carbon sequestration occurs in the coastal 

zone, and terrestrial processes may dramatically alter coastal 

and estuarine processes; therefore, the impacts of terrestrial 

management actions and carbon processes in the coastal 

ocean should be carefully examined (Hedges and Keil, 1995; 

Seitzinger and others, 2005). Because of coastal groundwater 

discharge, carbon fluxes also have received relatively little 

attention, but Cole and others (2007) estimated that, globally, 

groundwater conveys dissolved organic carbon (DOC) and 

dissolved inorganic carbon (DIC) loads making up about 25 

percent of the total carbon flux from land to sea.

The sections that follow present a methodology for 

a national assessment of the lateral flux of carbon, carbon 

sequestration, and GHG exchange associated with inland 

and coastal waters. Methods are proposed for projecting the 

assessment into the future to account for ongoing and anticipated land-cover and climate change. In this report, aquatic 

carbon is grouped into four general categories:

E.1. Introduction

Inland and coastal waters are globally important locations of biogeochemical carbon cycling, carbon sequestration, 

and the exchange of carbon dioxide (CO2), methane (CH4), 

and nitrous oxide (N2O) with the atmosphere. Although inland 

aquatic ecosystems represent less than 3 percent of the total 

land area of the United States, they have greatly accelerated 

areal rates of carbon cycling relative to terrestrial ecosystems 

and may dominate greenhouse-gas (GHG) fluxes and carbon 

sequestration locally and regionally. Globally, the mass of 

carbon exported by inland waters to oceans annually rivals the 

terrestrial net ecosystem exchange (NEE; Sarmiento and Gruber, 2006), and the annual amount of carbon buried in inland 

water sediments is comparable to that of the annual amount 

of carbon buried in coastal ocean sediments (Cole and others, 

2007). When evaluating the importance of coastal, estuarine, 

and inland waters in the carbon cycle, three major factors 

should be considered:

• Stream and river delivery of inorganic carbon 

(IC) and organic carbon (OC) from terrestrial 

uplands; through lowlands, ponds, lakes, and 

reservoirs; and to coastal areas and oceans 

(termed “lateral transport”)

• Biogeochemical production, consumption, 

sequestration, and pass-through of dissolved, 

particulate, and gaseous carbon by ponds, lakes, 

and reservoirs

• Biogeochemical production, consumption, 

sequestration, and pass-through of dissolved, 

particulate, and gaseous carbon by coastal 

waters and estuaries

The importance of inland waters in the carbon cycle 

tends to be overlooked in terrestrial ecosystem models and 

global climate models, partly because of their size. Most water 

bodies are much smaller than the individual grid cells used 

as accounting units for regional-scale models. This problem 

is compounded by the fact that the numbers of water bodies 

increase exponentially as water-body size decreases (Downing and others, 2006) and that rates of carbon sequestration (Downing and others, 2008) and carbon-gas exchange, 

particularly methane emission (Michmerhuizen and others, 

1996), are thought to increase as water-body size decreases. 

Consequently, if a water body is large enough to be detected at 

the pixel scale for terrestrial ecosystem modeling, the rates of 

carbon sequestration and carbon-gas exchange associated with 
142 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

• DOC, which is composed of all the dissolved fraction of organic carbon molecules that result from 

the production and decomposition of living matter 

(dissolved is operationally defined as the fraction 

that passes a 0.45 or 0.2 micrometer filter)

• DIC, which is composed of the aqueous carbon 

anions bicarbonate ( ) and carbonate ( ), 

carbonic acid, and dissolved CO2

• Particulate organic carbon (POC), which is 

composed mostly of plant and animal debris, but 

also includes organic colloids, precipitates, and 

DOC adsorbed to particle surfaces

• Particulate inorganic carbon (PIC), which is composed of mechanically eroded sediments derived 

from carbonate rocks and carbonate precipitates

DOC and DIC represent most of the total carbon mass in 

lateral flux, whereas POC and PIC represent most of the carbon 

stored in inland waters and coastal sediments. Except in areas 

where old DOC is released from the terrestrial environment 

(such as from petroleum hydrocarbons, glacial melt, or permafrost thaw), most DOC tends to be modern in age and represents 

recently produced photosynthate that is leached from decomposing plant debris and soil organic matter. DIC is produced 

primarily by weathering of carbonate and silicate rocks and 

fine particles, where one-half of the carbon in DIC produced 

by carbonate weathering and all of the carbon in DIC produced 

by silicate weathering is derived from ecosystem respiration or 

atmospheric CO2, and is therefore modern. The DOC and DIC 

exported from terrestrial landscapes are largely unaccounted for 

in terrestrial NEE or net ecosystem production (NEP) measurements, but regionally they may represent more than 5 percent 

of total ecosystem production (Striegl and others, 2007). POC 

includes recently produced plant and animal debris and some 

older organic carbon debris that has eroded from landscapes 

and is carried by water. POC that settles to streambeds or lake 

bottoms may serve as a food source for grazing organisms and 

microbes or be sequestered. Except for lakes and ponds having 

recent precipitation of carbonates, PIC plays a relatively unimportant role in the sequestration of modern carbon, as it mostly 

includes carbon from old marine carbonates.

E.2. Transport of Carbon by Streams and Rivers

E.2.1. Lateral Flux

Lateral (or hydrologic) flux of carbon includes the delivery of dissolved and particulate carbon by streams and rivers 

from terrestrial landscapes to inland water bodies, coastal 

waters, and oceans. Flux also includes delivery of dissolved 

carbon by groundwater discharge to inland water bodies and 

coasts. Water is the carrier of all lateral carbon flux; therefore, 

the direct calculation of lateral carbon flux requires a quantitative understanding of water discharge and of the seasonal 

relations between water discharge and the concentrations of 

the aqueous carbon species (DOC, DIC, POC, PIC) that are 

transported. Inferential methods for estimating carbon flux 

based on land-cover characteristics and hydrologic-systems 

modeling of flow based on geomorphic and climatic conditions are promising, but currently (2010) are not fully coupled 

with carbon chemistry. The most accurate way to assess lateral 

flux, therefore, is to develop statistical relations between 

historical flow and chemistry data, and then empirically derive 

daily loads (mass carbon per time) for each carbon species 

(Striegl and others, 2007). There are multivariate statistical 

programs, such as the U.S. Geological Survey (USGS) Load 

Estimator (LOADEST) program, which are designed to do this 

for streams and rivers (Runkel and others, 2004). Estimates 

of groundwater flux are less accurate because groundwaterflow rates and chemistry generally are not measured; however, 

groundwater contributions to total carbon flux for large areas, 

such as the coastal United States, can be assumed to be small 

relative to surface-water flux.

E.2.2. Estimation of Lateral Flux

The LOADEST program (U.S. Geological Survey, 2010b) 

associates daily water-discharge values with constituent concentrations measured for a range of discharge conditions and 

develops statistical concentration-to-discharge relations for the 

constituent of concern. Based on these relations, LOADEST 

simulates concentrations for days without measurements, and 

then integrates discharge and concentration to estimate the total 

constituent load for the analyzed flow period. The accuracy of 

the estimates depends on the accuracy of the water discharge 

and constituent concentration measurements, adequate coverage 

of concentration measurements for a full range of flow conditions and seasons, and stability in concentration-to-discharge 

relations. Generally, at least 13 concentration measurements are 

required for LOADEST to produce accurate estimates; more are 

better, especially where concentration discharge relations vary 

seasonally. Sample collection during storm events is particularly important because most suspended sediment is transported 

during storms (Cohn and others, 1989; Hicks and others, 2000). 

If the LOADEST program is applied for estimating lateral 

flux from streams and rivers that have not been measured by 

streamgages or sampled for concentrations of carbon species, 

the accuracy will be degraded based on additional uncertainties associated with the regression techniques for estimating 

discharge and carbon concentration in those rivers.

The “spatially referenced regressions on watershed 

attributes” (SPARROW) model (U.S. Geological Survey, 

2010e) uses these same techniques to estimate constituent 

loads (Schwarz and others, 2006, 2008). This model has the 

additional advantage of generating flow and concentrations 

based on land-use and land-cover characteristics and climatic 

data. SPARROW has been used extensively for estimating 

nitrogen and phosphorus loads and currently (2010) is under 

development for estimating carbon species, particularly DOC. 

Additional details on the SPARROW modeling approach are 

described in section E.4.3 of this report.
Appendix E 143

E.2.3. Data Needs and Availability

Estimating carbon loads requires both flow and waterchemistry data collected at identical or close locations 

during identical periods. These data are archived in the 

National Water Information System (NWIS; U.S. Geological Survey, 2010f). Nationally, the USGS has collected data 

on daily streamflow at more than 25,000 sites and periodic 

flow information at more than 45,000 sites. Water-chemistry data are sparser—only about 200 stations have more 

than 10 years of records for flow, DIC, and DOC concentration data. There are many more sites having shorter or 

partial records.

A first step in the assessment of lateral carbon flux is to 

extract data from NWIS at locations where streamflow, and 

DIC, DOC, POC, or PIC data have been collected. These data 

will be assembled into a working database for further analysis. 

Carbon-concentration data are most prevalent for carbonate alkalinity, from which DIC can be calculated, followed 

by DOC, POC, and PIC. For organic carbon, older datasets 

commonly have only total organic carbon concentration 

(TOC), which by definition is DOC + POC, but operationally 

is commonly closer to DOC. Where concentration data are 

missing for a particular carbon species, it will be necessary 

to statistically estimate concentrations from other available 

water-chemistry data.

E.2.4. An Approach for the Nationwide

Assessment of Lateral Flux

A key step in the data analysis will be to identify key 

streamgaging stations from throughout the United States where 

carbon loads can be calculated and carbon concentration-todischarge relations can be established. These key stations will 

represent large aggregated basins that drain directly to coastal 

areas, such as the Mississippi River and Columbia River Basins 

(fig. E1), and smaller basins that represent specific land-use 

or land-cover types and (or) ecoregions. Basins of the conterminous United States are mapped in four orders of hydrologic 

units, including 18 regions, 204 subregions, 324 accounting 

units, and 2,111 cataloging units (U.S. Geological Survey, 

2010a). Alaska, Hawaii, and Puerto Rico are similarly divided. 

Approximately 308 hydrologic units drain directly to coastal 

areas of the United States, ranging from a few tens of square 

kilometers to the Mississippi River Basin. The assessment eventually will assign carbon lateral-flux values to all of these units.

Seasonal and annual loads (mass of carbon over time) 

and yields (mass of carbon over basin area over time) will be 

determined for the key streamgaging stations using LOADEST 

and SPARROW. The mass flux of carbon is primarily determined by water discharge, so concentration-to-discharge and 

carbon yield-to-water yield relations also will be determined. 

Figure E1.  Map showing the

water-resource regions of the

United States that will be used

as units for aquatic assessment

(rivers, lakes, coastal regions).

From Seaber and others, 1987,

figure 2.
144 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

These relations will be used for gap analysis to estimate fluxes 

from adjacent basins that are missing carbon-concentration 

data. Regression techniques will be used to estimate discharge 

for ungaged basins that are likely to transport substantial carbon to the coast, as discussed in Hirsch and others (1982).

E.2.5. Coupling of Lateral Flux With Terrestrial

Models

Currently (2010), the General Ensemble Modeling System (GEMS) and other terrestrial ecosystem models solve the 

carbon mass balance in one-dimensional grid cells; they do not 

solve for the lateral flux of water and carbon between cells. 

One goal of this assessment is to couple hydrologic and terrestrial models so that water discharge and carbon flux can be 

estimated on the basis of land use, land cover, physiography, 

and climate. The NWIS, LOADEST, and SPARROW analysis 

of lateral carbon flux will provide empirical validation for the 

development of these model attributes.

E.2.6. Projections of Lateral Flux

Water discharge is the primary determinant of lateral carbon flux from basins, and therefore needs to be accurately projected for estimating future carbon later flux. Estimating future 

lateral flux will require the projection of water discharge from 

downscaled climate predictions and the application of flowgeneration models, such as the Precipitation-Runoff Modeling 

System (PRMS) (Leavesley and others, 1983; U.S. Geological 

Survey, 2010d). Developing relations between lateral carbon 

flux and land use and land cover (LULC) and coupling these 

relations with GEMS or models such as SPARROW will 

further refine these projections. Developing such modeling 

capabilities should be a goal for future assessments.

E.2.7. Greenhouse-Gas Fluxes From Rivers

In addition to the downstream transport of dissolved and 

particulate carbon, streams and rivers commonly are supersaturated with CO2

 and CH4 relative to the atmosphere and 

emit GHGs to the atmosphere along their entire courses (Cole 

and others, 2007). The high concentrations of CO2 and CH4

can be attributed to within-stream biological cycling of carbon 

(Ritchie, 1989) and to supersaturated groundwater and surface 

runoff that contributes to streams and rivers.

E.3. Lakes and Impoundments

Inland waters are an important component of the global 

carbon cycle but often are ignored in global climate models 

because they make up only a small part of the surface of continents (about 3 percent ; Downing and others, 2006). Recent 

studies have shown, however, that fluxes of carbon from terrestrial to aquatic systems are substantial; for comparison, they 

are similar in magnitude to the NEP of the terrestrial biosphere 

(about 2 petagrams of carbon per year (PgC/yr)), and thus 

should not be ignored in global carbon budgets (Randerson and 

others, 2002; Cole and others, 2007). Although inland waters 

make up only a small fraction of the total continental area, 

they are extremely active in the transport and storage of carbon 

received from the terrestrial environment (Cole and others, 

2007; Tranvik and others, 2009). Pools of carbon stored in 

freshwater sediments also are large; approximately 820 petagrams of carbon (PgC) were stored in lake sediments during 

the Holocene (Einsele and others, 2001), which is comparable 

to the amount of carbon currently (2010) stored in the surface 

meter of soils (approximately 1,395 to 1,576 PgC) and terrestrial biomass (approximately 460 PgC) (Post and others, 1982; 

Raich and Schlesinger, 1992; Eswaran and others, 1993).

Humans have had a profound effect on hydrologic systems 

and sediment transport during the last several hundred years. 

Two of the main human activities that have affected the lateral 

flux of carbon are (1) deforestation in order to clear the land 

for tilled agriculture (causing increased erosion) and (2) the 

construction of dams to form impoundments (Mann, 1985, 

1986; Davidson and others, 1993; Paul and others, 1997; Renwick and others, 2005). Impoundments (such as reservoirs and 

ponds) serve many functions; reservoirs commonly are used 

for hydroelectric power generation, recreation, flood control, 

and storing water for drinking and irrigation; ponds are smaller 

impoundments (usually less than or equal to 1 square kilometer) 

that typically are used for sediment retention, urban stormwater 

control, or providing water for livestock. Impoundments alter 

the hydrologic system by capturing sediment that previously 

was carried in suspension by streams and rivers and storing it in 

pools of slow-moving water, where it settles out and accumulates (Meade, 1982; Stallard, 1998). This alteration represents a 

substantial diversion of sediment that previously was exported 

to the ocean; it is estimated that impoundments can store about 

50 percent of erosion products (Smith and others, 2005).

Organic matter makes up a small but important fraction of material that is eroded from upland areas and redeposited in colluvium or alluvium, or downstream in lakes or 

impoundments; in the Mississippi River Basin, for example, 

the average soil organic carbon content is 1.5 percent in erosional and depositional areas (Smith and others, 2005). Most 

organic carbon that is deposited in impoundments remains 

there for the life of the impoundment (tens to hundreds of 

years) because impoundment sediments usually are anoxic, 

which prevents oxidation of the organic matter (Tranvik and 

others, 2009); thus, burial of organic carbon in impoundment 

sediments can represent an important mechanism for carbon 

sequestration. Lakes can sequester organic carbon by burial 

as well; however, most of the carbon that is buried in lakes is 

autochthonous material that is produced by phytoplankton and 

aquatic macrophytes in the lake (Dean and Gorham, 1998). 

Tranvik and others (2009) estimated that global burial of 

organic carbon in lakes and impoundments may account for 

0.6 PgC/yr. This estimate compares well with organic carbon 

burial rates in oceans of 0.1 PgC/yr (Dean and Gorham, 1998) 
Appendix E 145

and with the net uptake of carbon by the terrestrial biosphere 

of 1 to 3 PgC/yr (Sundquist, 1993).

Burial rates for organic carbon in lakes and impoundments are inversely related to water-body size (Smith and 

others, 2002; Downing and others, 2008) because of the 

increased productivity in shallow eutrophic ponds and the high 

rates of erosion and sedimentation in agricultural areas, where 

small farm ponds are common. Although ponds are small, 

their global extent is sizeable, so their cumulative effect on the 

global carbon budget could be substantial. Renwick and others 

(2005) estimated that there may be up to 8 to 9 million ponds 

in the conterminous United States alone, and their number has 

been increasing by 1 to 2 percent annually in agricultural parts 

of the United States (Downing and others, 2006).

Lakes and impoundments emit substantial amounts of CO2

and CH4, and small amounts of N2O to the atmosphere. Global 

CO2 and CH4 fluxes from reservoirs account for 4 percent of 

total anthropogenic CO2 emissions and 20 percent of total 

anthropogenic CH4 emissions, respectively (St. Louis and others, 2000). The balance between carbon burial and GHG emissions determines whether or not lakes and impoundments are 

net sinks or net sources of carbon (Hanson and others, 2004). 

Global emissions of CO2 from lakes and reservoirs have been 

estimated at approximately 0.8 PgC/yr (Tranvik and others, 

2009); for comparison, deforestation releases 1.6 to 2 PgC/yr 

(Sundquist, 1993; DeFries and others, 2002; Houghton, 2003; 

Sundquist and others, 2008). Methane emissions from lakes 

and impoundments could be even more important than CO2 in 

terms of GHG potential. Methane is a powerful GHG that has 

25 times the warming potential of CO2, and accounts for 20 

percent of the anthropogenic GHG effect (Cicerone and Oremland, 1988; Wuebbles and Hayhoe, 2002). Global emissions of 

methane from reservoirs have been estimated to be 70 teragrams of methane per year (TgCH4

/yr), which accounts for 7 

percent of anthropogenic warming (St. Louis and others, 2000). 

Methane emissions from lakes add another 8 to 48 TgCH4/yr to 

the atmosphere (Bastviken and others, 2004). Together, methane 

emissions from lakes and reservoirs are similar in magnitude to 

those from other anthropogenic sources, including fossil-fuel 

combustion (100 TgCH4/yr), waste management (90 TgCH4/yr), 

enteric fermentation (85 TgCH4/yr), rice paddy cultivation (60 

TgCH4/yr), and biomass burning (40 TgCH4/yr) (St. Louis and 

others, 2000, and references therein).

The following section describes the methodology for 

assessing carbon sequestration in and GHG fluxes from lakes 

and impoundments in the United States.

E.3.1. Estimating the Amount of Carbon Buried in

Lakes and Impoundments

The net storage of carbon in lakes and impoundments 

reflects a balance between the amount of carbon buried in 

sediments and GHG emissions from the surfaces and outlets 

of the water bodies. Estimating the amount of carbon buried 

in lakes and impoundments requires several steps that use a 

combination of geographic information systems (GIS), remote 

sensing, and statistical analyses (fig. E2). To determine the 

amount of carbon buried in lakes and impoundments, it is 

necessary to quantify the total area of lakes and impoundments 

within specified size classes, the sedimentation rates, and the 

organic carbon concentrations in sediments.

The statistical distribution of water bodies within the 

assessment units (U.S. Environmental Protection Agency (EPA) 

Level II ecoregions modified from Omernik (1987)) will be 

analyzed in a GIS framework in order to quantify their number 

and cumulative area within each of ten size classes (fig. E2). 

Input datasets will include the National Land Cover Dataset 

(NLCD) and the National Hydrography Dataset (NHD+). 

The NLCD is a nationally consistent land-cover classification 

Figure E2. Schematic

diagram showing key

components of the

methodology for assessing

carbon sequestration in and

greenhouse-gas fluxes from

lakes and impoundments.

Examples of key dependencies

are given. GHG, greenhouse

gas; IKONOS, Earth-observing

satellite; LULC, land use and

land cover; NHD+, National

Hydrography Dataset Plus;

NLCD, National Land Cover

Database; WB, water body.

Data need

Approach

Dependencies

Product

Area by size 

class and type

Sedimentation 

rate

Organic 

carbon 

concentration

GHG fluxes

GIS analysis 

using NHD+, 

NLCD, IKONOS

Multiple 

regression

Multiple 

regression

Estimates from

literature

Function of 

runoff

Function of 

size, WB 

type, LULC

Function of dry 

bulk density, 

sedimentation 

rate, WB type

Scaled to 

surface area

Carbon 

burial

GHG 

emissions
146 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

scheme derived from Landsat Thematic Mapper satellite data 

and is one of the primary datasets used by other components of 

the assessment; therefore, it will be the primary dataset used to 

determine the statistical distribution and surface area of water 

bodies. The NHD+ is a GIS dataset developed by the USGS that 

depicts the Nation’s interconnected network of rivers, streams, 

lakes, impoundments, and canals. The NHD+ will be used to 

validate the information about water bodies in the NLCD. The 

NLCD and NHD+ datasets display information at 30-meter (m) 

resolution, which is useful for identifying water bodies larger 

than approximately 0.001 square kilometers (km2). Because 

of the potential importance of carbon cycling in smaller water 

bodies, the feasibility of mapping those as small as 0.0001 km2

will be investigated using a variety of techniques and datasets. It 

may be possible to map small water bodies using a combination 

of 10-m-resolution digital elevation models (DEMs) and 15-m 

panchromatic Landsat images, which cover most of the Nation. 

GeoEye and IKONOS are Earth-observing satellites that 

provide multispectral images at 2- to 4-m resolution, which can 

be used to identify water bodies, but the images are not available for the entire United States. Light detection and ranging 

(LIDAR) data collected using airborne surveys and synthetic 

aperture radar data collected using the Japanese Earth Resources 

Satellite 1 (JERS-1) have shown substantial promise in the identification and mapping of small water bodies (Telmer and Costa, 

2007), but coverage of the United States is sparse.

The ratio of lakes to impoundments in each assessment 

unit will be determined by manually classifying a randomly 

selected subset of 200 water bodies on the basis of a visual 

inspection of the high-resolution satellite images overlain on 

the NLCD dataset layers. The lake-to-impoundment ratio will 

be combined with water-body-area information to estimate 

the cumulative area of lakes and impoundments within each 

assessment unit. This information is needed because lakes and 

impoundments tend to have different sedimentation rates and 

sediment organic carbon concentrations, which in turn reflect 

differences in land use, autochthonous production, and other 

processes.

An important task for estimating the amount of carbon 

buried in lakes and impoundments is to quantify the sedimentation rates (fig. E2). Relatively few direct measurements of 

sedimentation rates in lakes and impoundments are available; 

this is an important data gap that will limit the accuracy of the 

estimates of buried carbon. Initial estimates of sedimentation 

rates will be derived from data compiled from published sources 

and databases; these data will be used to estimate probability 

distribution functions (PDFs) representing the statistical distribution of measured sedimentation rates, which will be scaled to 

lake and impoundment surface areas. Most sedimentation-rate 

data for lakes are from dated lake-sediment cores (Dean and 

Gorham, 1998; Cole and others, 2007); most sedimentation-rate 

data for impoundments are from repeated bathymetric surveys. 

Although there is no central repository for sedimentation-rate 

data for lakes, sediment-rate data for impoundments are stored 

in the national Reservoir Sedimentation (RESSED) database 

(Advisory Committee on Water Information, Subcommittee 

on Sedimentation, 2010). The RESSED database includes data 

from approximately 1,800 reservoirs; however, this is less than 

0.2 percent of the total number of impoundments in the United 

States (Ackerman and others, 2009). Additional data collection 

on sedimentation rates in lakes and reservoirs could improve the 

accuracy of this assessment.

It may be possible to improve on the initial sedimentation-rate estimates by using statistical relations between 

sedimentation rates and water-body size, water-body type 

(lake versus impoundment), and land use (Wetzel, 1990, 2001; 

Smith and others, 2001, 2002). Smith and others (2001, 2002), 

for example, reported that sedimentation rates vary inversely 

with water-body size and tend to be greater in impoundments than in lakes. Lakes and impoundments in basins with 

substantial tilled agriculture may be expected to have greater 

sedimentation rates than those in basins that are largely 

undisturbed (McIntyre, 1993). Correlations between sedimentation rates, water-body characteristics, and land use will be 

analyzed for lakes and impoundments in each assessment unit, 

and if significant relations are identified, multiple regression 

models will be developed to estimate sedimentation rates for 

unsampled water bodies throughout the assessment unit. The 

development of statistical relations in some assessment units 

may be limited by the scarcity of available sedimentation-rate 

data. Additional data collection may improve the reliability of 

the statistical models that are used to estimate sedimentation 

and carbon-burial rates in unsampled water bodies.

OC concentrations in lake and impoundment sediments 

reflect the OC concentrations in the upland sediments from 

which they were derived plus the particulate carbon derived 

from primary production in the water bodies (Smith and 

others, 2005). OC concentrations tend to be greater in lake 

sediments (where autochthonous production is relatively 

important) than in impoundment sediments (Mulholland 

and Elwood, 1982; Ritchie, 1989; Dean and Gorham, 1998). 

OC concentrations in lake sediments will be estimated from 

data in the literature. If sufficient data exist, then a PDF will 

be developed for OC in lake sediments; otherwise, a simple 

median concentration will be used. OC concentrations in 

impoundment sediments will be approximated by estimating 

the median OC concentrations in soil in areas that are upslope 

and within a specified distance from each water body. Soil 

organic carbon (SOC) concentrations will be derived from a 

GIS dataset of soil carbon provided by USGS (Bliss and others, 2009). Although it would be preferable to define a basin 

boundary and estimate SOC concentrations within that area, 

identifying and mapping basin boundaries for the large number of water bodies in the United States (on the order of 2.6 

to 9 million; Renwick and others, 2005) is not operationally 

feasible. Simplifying the representation of the upslope areas 

that provide sediment to the downstream lakes and impoundments is likely to reduce the explanatory power of the predictive equations.

The amount of OC buried in lakes and impoundments 

(OC buried) will be calculated for each size class and type of 

water body using the following equation:
Appendix E 147

OC buried = Total water body area × sedimentation rate × OC concentration (E1)

The sedimentation rates and OC concentrations will be represented by PDFs in the initial 

analysis. If valid statistical models can be developed for estimating sedimentation rates and OC 

concentrations on the basis of water-body size and type, they will be used as input to equation 

E1. The results will be aggregated by EPA level II ecoregion for consistency with other components of the assessment.

E.3.2. Alternate Method for Calculating the Amount of Carbon Buried in

Freshwater Aquatic Systems

As a check of the estimates of carbon buried lakes and impoundments (outlined above), 

the amount of carbon also will be estimated using an independent mass-balance method, as 

in Smith and others (2005). The method begins with calculating a sediment budget for a river 

basin and solving for sediment storage (S):

E – T = S (E2)

where E is the amount of sediment that is eroded, determined for the basin in GEMS 

from the two-dimensional Unit Stream Power Erosion and Deposition 

(USPED) model (Mitas and Mitasova, 1998);

T is the amount of sediment that is transported to the ocean, calculated in 

LOADEST from river discharge and suspended-sediment concentrations 

(Runkel and others, 2004); and

S is the amount of sediment (including redeposited eroded sediments) that is 

stored, primarily in alluvium, colluvium, and impoundments.

The amount of sediment exported to oceans is a relatively small component of the equation; 

Smith and others (2005) estimated that about 90 percent of eroded sediment is redeposited in 

depositional environments, and about 10 percent is exported to the ocean.

The equation for eroded, transported, and redeposited OC is similar, but requires multiplying each of the terms in the sediment budget equation by the OC concentration (OC percent) 

of each sediment pool and includes a residual term to account for the oxidation of OC during 

transport and storage and replacement of eroded SOC in soil:

E × OC percent – T × OC percent = S × OC percent ± residual (E3)

The right side of equation E3 represents total carbon buried in inland water sediments ± 

residual. The percentage of OC in the eroded sediment is assumed to be the same as the percentage 

of OC percent in the redeposited sediment, whereas the percentage of OC in sediment transported to 

the ocean is approximately twice as high, based on analyses by Ritchie (1989) and Smith and others 

(2005). The oxidation of OC usually is relatively minor, accounting for about 5 percent of the eroded 

OC budget (Smith and others, 2005). The replacement of eroded organic matter accounts for approximately 10 percent of the OC budget for the Mississippi River Basin (Smith and others, 2005). These 

mass-balance calculations provide bounds on the amount of OC that may be stored in inland water 

sediments, which include lakes and impoundments, as well as fluvial and colluvial systems.

E.3.3. Greenhouse-Gas Fluxes From Lakes and Impoundments

Fluxes of greenhouse gases (CO2, CH4, N2O) from lakes and impoundments will be 

estimated based on GHG fluxes reported in the literature. Because of the paucity of available 

data on CH4 and N2O fluxes from lakes and impoundments, a PDF approach will be used and 

emissions will be scaled to lake and impoundment surface areas. This necessarily simplistic 

approach will have large uncertainties associated with results, but could be refined in the future 

if sufficient data become available to build empirical models of GHG fluxes, as in St. Louis 

and others (2000) and Bastviken and others (2004). These studies indicate that GHG emissions from lakes and impoundments are positively related to the area of the lake, which is used 

as a surrogate for lake depth (Michmerhuizen and others, 1996; St. Louis and others, 2000; 

Bastviken and others, 2004). GHG emissions also appear to vary with temperature, based on 
148 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

observations of GHG emissions in the tropics, which were an 

order of magnitude greater than GHG emissions from temperate reservoirs (St. Louis and others, 2000).

E.3.4. Error Estimation

Uncertainties in estimates of buried carbon in and GHG 

fluxes from lakes and impoundments will be large because of 

spatial variability in processes and rates controlling carbon 

cycling; accounting for this variability using a variety of 

explanatory variables (for example, basin characteristics or 

nutrient loads) is difficult in empirical models built on sparse 

data. In some cases, two independent approaches will be used 

to estimate fluxes, which can serve as a check of results. The 

mass-balance calculations, for example, will provide an upper 

bound on OC buried in lakes and impoundments.

PDFs will be used to represent the statistical distribution 

of the input data, such as sedimentation rates, OC concentrations in sediments, and GHG fluxes from lakes and impoundments. The spread, or variability, of the input data affects the 

range of possible outcomes; this range is quantifiable using the 

PDF approach and will provide information about the uncertainty of estimated carbon burial and GHG emissions rates.

Uncertainty in the empirical models will be evaluated 

based on the standard errors of the model slopes and intercepts. Bootstrapping or Monte Carlo approaches could be used 

to evaluate the importance of variations in input datasets to the 

model results; however, these approaches require a minimum 

number of observations (for example, 20) to provide meaningful results (Efron, 1981; Efron and Tibshirani, 1993) and it is 

anticipated that relatively few assessment units will have sufficient data. Additional data collection could allow the use of 

bootstrapping or Monte Carlo approaches in the future, which 

would improve the uncertainty analysis.

Uncertainty in sediment and OC mass-balance calculations 

stems from errors in calculating the sediment and OC fluxes in 

rivers and in erosion rates (Smith and others, 2005). To accurately estimate the fluxes of sediment and OC in rivers, samples 

must be collected for a range of hydrologic conditions using 

appropriate sampling methods. Collecting some samples during 

storm events is particularly important because most suspended 

sediment is transported during storms (Cohn and others, 1989; 

Hicks and others, 2000). Errors in estimated erosion rates are 

difficult to quantify, but it is assumed the mean standard error of 

the estimates is near zero (Smith and others, 2001).

E.3.5. Data Needs, Availability, and Gaps

Sedimentation rates and OC concentrations in sediments 

are key variables for calculating carbon-burial rates in lakes 

and impoundments, but data are sparse. Measurements of 

sedimentation rates in lakes are not coordinated at the national 

level; measurements of sedimentation rates in impoundments 

are stored in the national RESSED database, but are not collected at a sufficient temporal and spatial resolution to support 

an accurate estimate of buried OC for the assessment. It is 

recommended that these measurements be expanded, a routine 

monitoring plan be developed, and the RESSED database be 

used by the U.S. Department of Interior (DOI) and other government agencies as the primary repository for these data.

Despite the importance of GHG fluxes from lakes 

and impoundments in the global carbon and GHG budgets, 

measurements are sparse and uncoordinated, and there is no 

centralized database for these data. Methane emissions from 

reservoirs may be particularly important, but cannot be quantified at either a regional or national scale with current (2010) 

information. It is recommended that a Federal monitoring 

program be initiated to coordinate and conduct GHG flux measurements from lakes and impoundments in support of future 

carbon assessments.

E.3.6. Projections of Future Fluxes To and From

Lakes and Impoundments

Future changes in streamflow, land use, and other 

management actions have the potential to alter carbon burial 

in or GHG fluxes from aquatic systems, including lakes and 

impoundments. Streamflow is a major driver of carbon and 

nutrient fluxes in rivers, as discussed in section E.2.6 in this 

report, and efforts are underway to develop models for projecting streamflow and carbon fluxes under various climatechange scenarios.

Management actions can have complex effects, sometimes creating offsetting benefits in terms of carbon sequestration. Land-use conversion from tilled agriculture to no-till or 

forest, for example, is likely to cause an increase in carbon 

sequestration on land, but will reduce the amount of carbon 

buried in lakes and impoundments because of decreased erosion (table E1). Reducing the nutrient runoff from agricultural 

lands through best management practices (BMPs) will reduce 

eutrophication and CH4 and N2O emissions from inland and 

coastal waters, but also might cause a decrease in carbon 

burial because of reduced POC loads in rivers. Understanding 

the complex effects of these management actions is an area of 

active research by USGS and others. One goal of the assessment is to add the capability of simulating the effects of these 

management actions to existing models.

E.4. Coastal and Estuarine Systems

E.4.1. Carbon Sequestration in Coastal and

Estuarine Systems

Coastal and estuarine systems are sites where the fluxes 

of nutrients from terrestrial and deep-ocean sources to the 

surface of the ocean combine to fuel intense primary productivity. More than 90 percent of global algal productivity occurs 

in coastal zones (including estuaries), where enough algal 

carbon is sequestered to make coastal areas important sinks 
Appendix E 149

in the global carbon cycle (Dunne and others, 2007). Coastal 

areas also are sites that receive riverborne terrestrial exports of 

particulate and dissolved organic material, a fraction of which 

is also preserved in coastal-marine sediments or is transported into the deep ocean. Globally, the magnitude of carbon 

sequestration in coastal oceans is on the same scale as the net 

terrestrial ecosystem exchange and lateral flux of carbon to 

the oceans (fig. E3). Because only a small fraction of algal 

production and terrestrial inputs are preserved, and because 

coastal upwelling contributes CO2 from the deep oceans, carbon-preservation processes in coastal oceans may be obscured 

and difficult to quantify (Hales and others, 2006).

There are two major processes acting to sequester carbon 

in coastal and estuarine sediments and coastal-ocean waters: 

direct burial of OC in sediments and particulate transport of 

OC from the surface to deep oceans (Sarmiento and Gruber, 

2002). The latter commonly is referred to as the biological 

pump. Both processes are strongly related to phytoplankton 

productivity in the coastal surface oceans, both are coupled to 

sediment supply from terrestrial systems, and both result in 

the sequestration of CO2 from the atmosphere on a decadal to 

millennial time scale.

Carbon preserved by coastal ocean processes is from 

autochthonous primary production and from terrestrial inputs. 

Coastal primary production is fueled by nutrients supplied 

during terrestrial export, regeneration of nutrients in sediments 

and the water column, and upwelling of nutrient-rich deep 

waters. The plankton production supported by externally supplied nutrients—the “new” production—represents potential 

net removal of CO2 from the atmosphere. The export of this 

production into the deep ocean and into coastal sediments is 

strongly tied to the total production, with higher productivity increasing export (Wassman, 1990). Other factors such as 

water depth and phytoplankton size also have been linked with 

export (Dunne and others, 2005), with larger exports observed 

for populations of large phytoplankton, such as those produced 

in nutrient-rich coastal areas.

The primary production export from the surface ocean 

to below the mixed layer—the biological pump—is a major 

mechanism for carbon sequestration in coastal oceans 

(Sarmiento and Gruber, 2002; Hales and others, 2006). Primary production in the surface ocean is transported from the 

mixed layer into the deep ocean as settling particles, with the 

transported carbon sequestered from free exchange with the 

atmosphere for periods of decades to centuries, depending on 

ocean circulation (Sarmiento and Gruber, 2002; Gnanadesikan 

and Marinov, 2008). Particle flux (POC) has been estimated to 

range from 0.7 to 1.5 PgC/yr (Dunne and others, 2007; MullerKarger and others, 2005). Coastal carbon preservation in large 

measure is, therefore, directly and immediately affected by 

changes in the export of nutrients from terrestrial systems.

Historical changes in the supply of nutrients to coastal 

oceans that are related to changes in land use and land cover—

such as fertilizer use, increased urbanization, and wetland 

restoration—likely have altered coastal carbon preservation 

and will continue to affect coastal carbon preservation to a 

greater degree in the future (Billen and Garnier, 2007; Seitzinger and Mayorga, 2008). There also is abundant evidence 

suggesting that submarine groundwater discharge contributes 

significant amounts of nutrients to coastal systems (Slomp 

and Van Cappellen, 2004), which is comparable to the nutrient loads delivered by surface water in some watersheds with 

large nitrogen-loading rates and permeable soils (for example, 

Valiela and others, 1997, 2000; Kroeger, Swarzenski, Crusius, 

and others, 2007; Kroeger, Swarzenski, Greenwood, and others, 2007). It is important to note that much of the submarine 

groundwater is quite young (less than 20 years old) and may 

represent source areas that are amenable to management.

Figure E3.  Chart showing the

comparison of coastal carbonsequestration processes to

other important sources and

sinks. Data from Hedges and

Keil (1995), Muller-Karger and

others (2005), Dunne and others

(2007), Intergovernmental Panel

on Climate Change (2007), and

Tranvik and others (2009).
150 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Changes in sediment supply also can affect carbon 

preservation in coastal and estuarine systems. Rivers in the 

conterminous United States export an estimated 5 to 7 TgC/

yr to the oceans in the form of POC (Pacala and others, 2001), 

some of which is directly preserved through carbon burial 

(Hedges and others, 1997; Blair and others, 2004). More 

importantly, however, is the flux of carbon in sediment, which 

is estimated to be more than 1 PgC/yr (Aulenbach and others, 

2007). Sediment supply is a significant control on estuarine 

and coastal carbon sequestration because the lithogenic minerals in sediments increase the particle floc densities and settling 

rates, thereby increasing the efficiency of the biological pump 

(Armstrong and others, 2002). Benthic carbon preservation 

also is affected because higher rates of burial result in the preservation of a greater fraction of the associated organic material (Boudreau and Ruddick, 1991; Hedges and Keil, 1995; 

Dagg and others, 2004). Significant changes in the delivery 

sediment to the coastal oceans have occurred during the past 

several decades, which has altered the patterns of burial (for 

example, Vorosmarty and others, 2003; Leithold and others, 

2005; Syvitski and others, 2005). Pressures from an increasing population, changes in land use, and changes in patterns of 

precipitation also will result in changes in sediment discharge 

(Wilkinson and McElroy, 2007).

Burial of POC—largely derived from phytoplankton—in 

marine sediments is a major sink in the global carbon budget. 

Historically, the estimate for the rate of carbon burial in ocean 

sediments has been approximately 0.15 PgC/yr (Hedges and 

Keil, 1995; Sarmiento and Gruber, 2002; Muller-Karger and 

others, 2005). More recent estimates, which explicitly include 

biogeochemical processes occurring in coastal systems, suggest that this rate may be twice as great (0.32 PgC/yr) (Dunne 

and others, 2007). Coastal sedimentation accounts for 85 

percent of the burial of POC worldwide (Hedges and Keil, 

1995; Dunne and others, 2007), with two-thirds of this burial 

(one-half of the carbon buried in the global oceans) occurring 

in deltaic sediments of large rivers with high productivity and 

rapid sediment-accumulation rates (Blair and others, 2004). 

Much of the remaining burial of carbon in coastal oceans 

occurs as the result of episodic inputs of sediment from small, 

mountainous river systems that often occur in tectonically 

active zones where rates of geologic uplift are high (Milliman and Syvitski, 1992; Blair and others, 2003; Leithold and 

others, 2005; Wheatcroft and others, 2010); for example, the 

Eel River in California accounts for 15 percent of the sediment 

flux from the conterminous United States.

In summary, carbon burial in coastal sediments or its 

accumulation in the deep ocean is directly related to the 

riverborne flux of nutrients (which increases coastal primary 

production) and sediment (which increases the efficiency 

of benthic burial and the biological pump of carbon into the 

deep ocean). The coastal carbon-sequestration methodology 

thus includes a terrestrial-flux component related to land use, 

a model of coastal primary production that is sensitive to 

changing nutrient inputs, and a process model that explicitly 

accounts for controlling processes in carbon remineralization 

such as degradation during sinking, ballasting, bioturbation, 

and burial (Dunne and others, 2005). The modeling approach 

used here is similar to a sensitivity analysis responding to 

changes in terrestrial inputs, and thus no seaward boundary is 

defined; however, because local conditions such as the watercolumn depth and depositional environment are important 

elements that control sequestration, the estimates will be 

conducted individually for large terrestrial inputs, and regionally for smaller ones.

E.4.2. Methane and Nitrous-Oxide Fluxes in

Coastal and Estuarine Systems

Changes in production, uptake, and release of methane 

and nitrous oxide in the sediments of intertidal, estuarine, or 

coastal waters also can be substantially affected by changes 

in nutrient fluxes from the terrestrial system (Seitzinger and 

Nixon; 1985; Seitzinger and Kroeze, 1998) and from groundwater inputs (Bange, 2006; Hirota and others, 2007).

Surface waters of estuaries and coastal waters are 

typically supersaturated with GHG that are emitted into the 

atmosphere. The rates of GHG fluxes and their controlling 

processes, however, are understudied on a national scale, and 

are likely underestimated. At present (2010), there is insufficient knowledge of CH4

 and N2O fluxes in estuaries and 

coastal waters in the United States to either make satisfactory 

estimates or develop mechanistic models of the fluxes. There 

is ample evidence, however, that the fluxes are likely to be of 

substantial size in terms of global warming potential (GWP) 

relative to carbon and GHG fluxes in other ecosystems, and 

the fluxes are likely to change in response to human actions, 

including intentional management to reduce GHG fluxes, and 

unintended environmental changes that may alter the rates of 

flux, such as changes in nitrogen loads, sediment-carbon loads, 

wetland coverage, and the occurrence of hypoxia associated 

with eutrophication and climate change.

Nitrous oxide.—Because of an increase in nitrogen loading from fertilizer use in watersheds, wastewater disposal, 

and atmospheric deposition, estuaries are among the most 

intensely fertilized ecosystems on Earth. Typical fertilizer 

application rates to turf (about 110 kilograms of nitrogen per 

hectare per year; Valiela and others, 1997) and to crops (a 

wide range, but a reasonable average is about 140 kilograms 

of nitrogen per hectare per year; Valiela and others, 1997) are 

commonly exceeded by the rates of nitrogen loading to estuaries (for example, Chesapeake Bay main stem, 141 kilograms 

of nitrogen per hectare per year; Hudson River and Raritan 

Bay, 900 kilograms of nitrogen per hectare per year; Connecticut River, 3,705 kilograms of nitrogen per hectare per 

year; Bricker and others, 2007). Because of nearby terrestrial 

nitrogen sources, global estuarine N2O fluxes are estimated to 

be about 7 to 61 percent of total global marine fluxes (Capone, 

1991; Bange, 1996; Seitzinger and Kroeze, 1998; Nevison, 

2004). At the same time, recent studies suggest that N2O 

fluxes from agricultural uses or in soil may be overestimated 
Appendix E 151

(Bange, 2006), in part because consumption of N2O by the soil 

has not been appropriately considered (Chapuis-Lardy and 

others, 2007; Neftel and others, 2007). Thus, values reported 

by Bange (2006) indicate that estuarine N2O fluxes may be 

in the range of 4 to 25 percent of the total global flux from 

all sources. Furthermore, as argued by Nevison (2000), the 

Intergovernmental Panel on Climate Change (IPCC) methodology for N2

O fluxes from agriculture has identified measurements of N2

O fluxes from estuaries and from groundwater as 

critical needs and areas of data collection that need improvement. Recent literature (Nevison, 2000, and references therein) 

indicates that that the estimates of N2O fluxes in groundwater that are due to the leaching of nitrogen that results from 

agricultural practices may need to be revised downward and 

the estimates of N2O fluxes in estuaries may need to be revised 

upward by perhaps 25 percent because of the loss of agricultural nitrogen that is due to leaching.

In addition to fluxes from estuaries, coastal waters outside 

of estuaries likely contribute significantly to fluxes; furthermore, 

those fluxes will change in response to changes in anthropogenic nitrogen pollution and possibly to climate change. Part 

of the nitrogen load on the landscape ultimately transits to 

continental-shelf waters in discharges from rivers, groundwater, 

and estuaries, where it fuels denitrification and the production 

of new organic matter (Sietzinger and Giblin, 1996; Seitzinger 

and Kroeze, 1998; Seitzinger and others, 2006). The increasing 

anthropogenic nitrogen and associated new organic matter can 

be expected to fuel the production of N2O (Bange, 2006). In an 

increasing number of locations (fewer than 400 documented 

worldwide), eutrophication resulting from increasing nitrogen 

loads is severe enough to produce low-oxygen “dead zones” 

in estuaries and on continental shelves (Diaz and Rosenberg, 

2008), and that process may substantially enhance N2O fluxes 

on continental shelves (Naqvi and others, 2000). Bange (2006) 

compiled published data on N2O fluxes to the atmosphere from 

European estuaries and coastal waters and found that much 

greater flux densities existed within estuaries, but that the larger 

surface areas of the non-estuary coastal waters contained about 

40 percent of the total coastal flux.

Methane.—A global estimate of estuarine CH4 flux has 

been attempted (Middelburg and others, 2002). The conclusions, based on a compilation of existing data, suggested that 

estuaries are consistently a source of CH4 emissions to the 

atmosphere and account for about 9 percent of the total global 

marine source; however, CH4 fluxes in estuaries are likely to 

be severely underestimated and are significant to global fluxes 

(Bange, 2006). Important sources of CH4 fluxes from estuaries 

are direct inputs from rivers (Middelburg and others, 2002) 

and groundwater (Bugna and others, 1996; Crusius and others, 2008; Santos and others, 2009); tidal exchanges with salt 

marshes, mangroves, and intertidal sand and mud flats (Middelburg and others, 2002; Savvichev and others, 2004; Barnes 

and others, 2006; Ferron and others, 2007); and production 

in estuarine sediments (Abril and Iversen, 2002; Kitidis and 

others, 2007). There are two reasons to conclude that estuarine 

CH4 fluxes are underestimated:

• The majority of research attempts to measure 

dissolved CH4 concentrations in estuarine surface water focused primarily on the open waters 

of estuaries, farthest from the nearshore sources 

listed above, and likely after much of the flux to 

the atmosphere has already occurred.

• Most of the studies considered in the global flux 

estimate did not include fluxes from sediments 

to the atmosphere caused by ebullition of biogenic gas bubbles commonly composed primarily of CH4

.

The neglect of accounting for fluxes caused by ebullitions (bubbles) is likely to dramatically underestimate fluxes 

because, in the few cases where such fluxes have been measured, they typically included 50 to more than 90 percent of 

fluxes from sediment (Hammond and others, 1975; Martens 

and Klump, 1980; Chanton and others, 1989; Hovland, 1993; 

Shalini and others, 2006; Rajkumar and others, 2008). Further, 

the importance of ebullitive fluxes is magnified by the fact that 

such fluxes largely escape oxidation in the sediment and water 

column (for instance, Martens and Klump (1980) estimated 

that 85 percent of CH4 in bubbles survived transit through 7.5 

m of water), whereas much of the diffusive flux from sediments is consumed by oxidation before flux to the atmosphere. 

Fluxes resulting from the release of bubbles are likely to be 

particularly important in shallow (less than 5 m) waters (Joyce 

and Jewell, 2003), and their releases are episodic and occur 

on tidal and seasonal time scales (Chanton and others, 1989), 

making them difficult to measure and likely to be missed by 

oceanographic cruises (Hovland, 1993).

Rajkumar and others (2008) provide an example of a 

study where diffusive and ebullitive fluxes were measured and 

scaled to an entire estuary. In a 42-km-long, mangrove-fringed 

estuary with a water-surface area of 690 hectares (ha), ebullitive fluxes accounted for more than 90 percent of the CH4

 flux. 

In terms of GWP, the CH4 flux was 453 moles CO2-equivalents 

per square meter per year. Comparing this to the rates of carbon sequestration in U.S. forests and non-permafrost peatlands, which are -2.9 and -1.0 moles carbon per square meter 

per year, respectively (U.S. Climate Change Science Program, 

2007), this single estuary has a CH4 flux equivalent to the 

GWP of carbon sequestration in about 107,000 ha of forest, or 

313,000 ha of peatland.

As discussed with regard to N2O fluxes in coastal waters, 

coastal waters outside of estuaries likely contribute significantly to CH4

 fluxes as well. For example, Bange (2006) 

also compiled published data on CH4 fluxes from European 

estuaries and coastal waters, and similarly found much higher 

flux densities within estuaries. Still, fluxes from non-estuarine 

coastal waters once again were responsible for about 40 percent of the total coastal flux.

Potential for change.—GHG fluxes from estuaries 

and coasts are likely evolving in response to environmental 

changes and human actions, including changes in nitrogen and 

phosphorus loading; inputs of GHG from rivers, groundwater, 
152 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

and wetlands; delivery of sediment; wetland coverage because 

of removal, restoration, or sea-level rise; and sediment loading 

because of dredging. Climate change can be expected to alter 

estuarine and coastal GHG fluxes in as yet unquantified ways 

because of sea-level rise and temperature increases. In recent 

decades, the areal coverage and intensity of coastal dead zones 

(hypoxic and anoxic zones) has increased dramatically in the 

United States and worldwide because of increasing nutrient 

loading (Diaz and Rosenburg, 2008), and climate change is 

expected to exacerbate that process (Justic and others, 2003; 

Boesch and others, 2007). Both eutrophication and associated increases in OC production and low dissolved oxygen 

conditions are likely to increase CH4 production (Giani and 

Ahrensfeld, 2002) and decrease CH4 oxidation (consumption) 

in estuaries. N2O fluxes are expected to increase in response 

to increasing nitrogen supply: Seitzinger and Kroeze (1998) 

suggest that the proportion of the nitrogen released to aquatic 

environments that is converted to atmospheric N2O depends 

on the nitrogen loading rate—from about 0.3 percent under 

conditions of low nitrogen loading, to 3 to 6 percent with 

higher nitrogen loading. Hypoxic and anoxic zones may be 

particularly important sources of N2O (Naqvi and others, 

2000). Because CH4 fluxes from some estuaries and coasts 

may be large, and the assessment is focused on changes during 

the next four decades, it is worth noting that, over a 20-year 

period, the GWP of CH4 is estimated at 72 CO2 equivalents 

rather than the 25 CO2 equivalents typically considered over a 

100-year period (Intergovernmental Panel on Climate Change, 

2007).

Coastal groundwater as a source.—GHG fluxes from 

coastal groundwater also have received little attention, but 

data do exist and indicate that concentrations of N2O and 

CH4 are at times quite elevated in coastal groundwater (Butler and others, 1987; LaMontagne and others, 2003; Santos 

and others, 2009). Further, the global inventory of N2O in 

groundwater is a large, accounting for perhaps 10 to 20 

percent of all biogenic N2O (Ronen and others, 1988; Haag 

and Kaupenjohann, 2001). Correlations between radon activity (which acts as a tracer of groundwater discharge) and 

concentrations of dissolved CH4 (Bugna and others, 1996; 

Santos and others, 2009) and N2O (Crusius and others, 2008) 

suggest that groundwater is a dominant source for those 

dissolved gases in some estuaries. Finally, coastal groundwater may interact in important ways with CH4

 production in 

estuarine sediments. Several authors have noted associations 

between the presence of discharging fresh groundwater in 

pore waters and elevated CH4 concentrations in shallow sediments, which suggests either (1) the reduced sulfate inhibition of methanogenesis in low-salinity water or (2) in some 

cases, the delivery to the estuary of terrestrial groundwater 

that is enriched in dissolved methane, which is the result of 

onshore aquifer properties and the influence of freshwater 

wetlands and hydric soils (Hill and others, 1992; Bratton and 

others, 2004; Kogan and Paull, 2005). The assessment will, 

therefore, explicitly consider groundwater as a source for 

coastal GHG.

E.4.3. Methodology for Assessing and Projecting

Carbon Sequestration and Greenhouse-Gas

Fluxes From Coastal and Estuarine Systems

There are three subsections to the coastal and estuarine 

assessment methodology. The first subsection describes the 

methods used to assess the terrestrial supply of dissolved and 

particulate organic carbon, nutrients, and sediments to the 

coastal oceans. The second subsection describes the methods 

used to assess the role of estuaries and coastal processes in 

carbon storage. The third subsection describes a method for 

estimating the GHG fluxes from coastal and estuarine waters, 

intertidal sediments, and tidal systems not covered in the wetland assessment.

Wetlands restoration or destruction and other changes 

caused by sea-level rise will substantially affect carbon storage, carbon sequestration, and GHG production along coastal 

margins in the terrestrial to aquatic transition zone. Because 

the methods used to assess wetlands and sea-level rise are contained within the models used to assess terrestrial processes 

(as discussed in appendixes C and D in this report), these 

methods are not described here. Nevertheless, the effects of 

changes to coastal and estuarine wetlands and changes because 

of sea-level rise are represented in the coastal and estuarine 

methodology because those changes affect nutrient, carbon, 

and sediment fluxes.

The goal of the coastal and estuarine assessment is to 

assess the magnitude of linkages between terrestrial land use, 

coastal carbon sequestration, and GHG production, as driven 

by changes in the flux of water, nutrients, sediment, and carbon from the continent. The coastal and estuarine assessment 

focuses exclusively on the carbon-sequestration and GHGproduction functions of estuaries and coastal systems that 

are presently (2010) affected or may be affected by changes 

in terrestrial processes in response to changing management, 

land use, population, or climate. Given that the modeled processes differ because of variations in coastal geomorphology 

and continental flux, the assessment will be segmented into 

physiographic regions, but the seaward spatial boundary will 

remain undefined.

The methodology for assessing and projecting carbon 

sequestration and GHG fluxes for coastal and estuarine 

systems has four parts: (1) surface-water fluxes of carbon, 

nutrients and sediments to estuaries and coasts; (2) groundwater fluxes to estuaries and coasts; (3) carbon preservation 

in coastal waters; and, (4) release or uptake of methane and 

nitrous oxide.

E.4.3.1. Surface-Water Fluxes of Carbon, Nutrients, and

Sediments

The methods used to assess the carbon sequestration and 

GHG productions in coastal and estuarine systems require 

the determination of the terrestrial fluxes that affect these 

processes. Current (2010) fluxes and future potential fluxes 

under different climate and land-use scenarios will be assessed 
Appendix E 153

using a hybrid modeling approach (the SPARROW model) 

that combines process-based and statistical models to calculate constituent fluxes from rivers (head of tide) to estuaries 

and the coastal zone across the United States. The datasets 

that drive the SPARROW model will include the LULC data 

generated as part of the assessment effort (described in chapter 

3 and appendix B of this report). Modeled data will be produced for all coastal and inland hydrologic units that produce 

runoff to estuaries in the United States and will be developed 

for DOC, POC, total suspended sediments (TSS), nitrogen 

(organic and inorganic) and phosphorus (organic and inorganic). The assessment also will incorporate estimates of the 

submarine groundwater flux to estuaries from coastal basins 

using values derived either from the existing literature or from 

models. This broader methodology will not consider inorganic 

carbon (dissolved CO2 or particulate) or micronutrients (silica, 

iron) at this time.

The goals of this part of the assessment are (1) to estimate the mean annual flux of POC, TSS, and nutrients from 

hydrologic basins across the United States to the head of tide; 

(2) estimate the mean annual flux of particulate carbon, TSS, 

and nutrients from coastal landscapes below the head of tide; 

and (3) develop new SPARROW models for key constituents 

that affect coastal carbon cycling, including organic nutrients, POC, and carbon degradation. The lateral-flux estimates 

described earlier will be conducted in coordination with these 

assessments.

A variety of modeling approaches have been used to 

estimate constituent contaminant sources and loads in basins 

including process-based and statistical models with a range 

of complexities (Alexander, Elliot, and others, 2002; Schwarz 

and others, 2006). Process-based (mechanistic) models, such 

as the Hydrological Simulation Program-Fortran (HSPF) 

(Bicknell and others, 2001) and the Soil and Water Assessment 

Tool (SWAT) (Srinivasan and others, 1993), use a detailed 

set of equations that attempt to describe relevant processes 

affecting water and constituent transport. These models typically have a complex mass-balance structure that requires a 

large number of input parameters and assumptions about the 

dominant processes and reactions rates (Schwarz and others, 

2006). In addition, these models often lack robust measures 

of uncertainty in model coefficients and predictions and suffer 

from challenges in extrapolating the results of small catchment 

models and field-scale measurements to larger spatial scales; 

however, these models are based on physical processes occurring in basins and drainage networks and theoretically would 

be applicable across a broad range of sites with detailed input 

data.

In comparison, statistical models have a simple correlative mathematical structure and use empirical relations, such 

as linear regression between stream measurements (load, 

concentration) and sources or landscape drivers. Examples 

of purely statistical models include regressions of nitrogen 

export from large basins on population density (Peierls and 

others, 1991), net anthropogenic sources (Howarth and others, 

1996), and atmospheric deposition (Howarth and others, 1996; 

Jaworski and others, 1997). Although these models can be 

applied in basins of various sizes and can incorporate uncertainty estimates, they typically use a “black box” approach 

that lacks a mechanistic explanation of the processes affecting 

contaminant transport (Schwartz and others, 2006). In addition, these models also lack spatial details about the distribution of sources and sinks within basins and do not allow for 

an assessment of the relative importance of terrestrial versus 

aquatic processes.

Hybrid modeling approaches (SPARROW, Smith and 

others, 1997; Global NEWS, Seitzinger and others, 2005; 

PolFlow, de Wit, 2001) expand on simple statistical models 

by adding process-based model structures to develop relations 

with spatially referenced properties. For example, SPARROW 

has process-based mass-transport components for water-flow 

paths, in-stream processing, and mass-balance constraints on 

model inputs, losses, and outputs (Schwarz and others, 2006). 

Parameters are estimated for monitoring stations that have 

sufficient records for discharge and water-quality measurements (including capturing the dynamic range) by spatially 

correlating stream data with georeferenced data that describes 

constituent sources (atmospheric deposition, fertilizers, human 

and animal wastes) and delivery factors (precipitation, topography, vegetation, soils, water routing). Parameter estimation 

ensures that the calibrated model will not be more complex 

than can be supported by the data.

SPARROW has been included in several model comparison studies with process-based and statistical models. These 

include comparisons of total nitrogen (TN) loading with the 

models at the national scale and for the Chesapeake Bay Basin 

(Alexander and others, 2001). Alexander, Johnes, and others 

(2002) and Seitzinger and others (2002) also compared statistical and hybrid models for the northeastern United States, 

with results typically showing a general agreement between 

the models and the literature estimates, but lower estimates of 

uncertainty with SPARROW when it was compared to many 

other models (Alexander, Johnes, and others, 2002).

In this part of the assessment, SPARROW will be used to 

model the delivery of terrestrial carbon and other constituents 

in rivers to the coastal zone. Understanding carbon transport 

requires models that cover the conterminous United States and 

that estimate the loading of key constituents to estuaries and 

coastal systems. A number of previous studies have used the 

SPARROW model for national-scale assessments of TN and 

total phosphorus (TP) sources and loads throughout the conterminous United States (Alexander and others, 2001; Smith 

and others, 2003). Separate regional studies of TN and TP 

loads also have been conducted in the Chesapeake Bay Basin 

(Preston and Brakebill, 1999; Brakebill and Preston, 2003; 

Roberts and Prince, 2010), the Mississippi River and tributaries (Alexander and others, 2008; Robertson and others, 2009), 

and New England (Moore and others, 2004). Although most 

studies to date have focused on TN and TP, SPARROW models also have also been developed for a range of parameters 

including Escherichia coli (E. coli) (Puri and others, 2009), 

suspended sediment (Schwarz and others, 2006), and national 
154 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

estimates of total organic carbon, and dissolved solids (Anning 

and others, 2007).

SPARROW model parameters are estimated using nonlinear regression techniques by spatially correlating constituent 

flux estimates at monitoring stations with geospatial data about 

constituent sources and factors affecting constituent fate and 

transport. The calibrated models are used to predict flux and to 

estimate the source contributions for stream reaches throughout a river network (Schwarz and others, 2006). Data for 

in-stream nutrient loads at monitoring sites, nutrient sources, 

and land-surface characteristics are assigned to each stream 

reach in a digital stream-reach network, which provides continuity between upstream and downstream loads. The model 

uses mean annual loads typically from a large number of sites 

according to site-selection criteria, including the minimum 

number of observations at each station, a maximum prediction accuracy of plus or minus 50 percent of the mean annual 

load, and sufficient coverage of basin-attribute data (Schwarz 

and others, 2006; Alexander and others, 2008). Statistical 

approaches such as LOADEST (Runkel and others, 2004) 

and Fluxmaster (Schwarz and others, 2006) then are used to 

develop a time-series flow model and estimate the water-quality model (Schwarz and others, 2006).

The SPARROW model relates water-quality and streamflow measurements to spatially referenced characteristics of 

watersheds as described in Schwarz and others (2006). The 

stream reach (and its incremental contributing drainage basin) 

is the basic spatial unit that is used in the SPARROW models. 

Data collected at different spatial scales (for example, by census block or county) are converted to the stream-reach scale 

using GIS techniques (Schwarz and others, 2006).

To accomplish this assessment, the monitoring and 

geospatial data will be collected and evaluated for use in 

SPARROW models of constituent transport to the head of tide. 

Potential data sources are included in tables 3.7 and 3.10 in 

this report. The assessment will (1) use land-use and landcover data that is generated from the “forecasting scenarios 

of future land-cover” (FORE–SCE) model (described in 

appendix B of this report) to project to 2050 at 250-m grid-cell 

resolution; and (2) evaluate existing data, test assumptions, 

and develop correlations between parameters such as fertilizerapplication rates and atmospheric deposition over time. The 

assessment also will include uncertainty estimates for parameters that have significant assumptions associated with them or 

are difficult to evaluate. Given that most SPARROW models 

are developed for studies of TN and TP, additional data types 

or sources may be required for models of other constituents 

that affect the terrestrial-carbon contributions to estuaries and 

coastal systems.

The SPARROW model’s output contains prediction 

results paired with measures of accuracy in stream reaches. 

Constituent transport is predicted as fluxes (mass over time) 

at both the reach and incremental basin scales, and statistical analyses are performed on the prediction results (standard 

errors, prediction intervals). Fluxes then are used to derive 

constituent yields (mass over area over time), flow-weighted 

concentrations (mass over volume), and contributions by 

source (Schwarz and others, 2006). The SPARROW model 

provides estimates of the long-term mean annual or mean 

season flux (mass per unit time) of various constituents

In the assessment, the transport of constituents to the 

head of tide will focus on an annual time scale and will be 

referenced to a specific year as described in Schwarz and others (2006); however, the development of parameter-estimation 

methods for applying the SPARROW model at shorter time 

steps (for example, seasonal or monthly; Schwarz and others, 

2006) will be evaluated early in the study using existing TN 

and TP in order to develop a more mechanistic understanding of processes, fluxes, and temporal variability that will 

inform future model development and interpretation of the 

annual-scale results. A number of terrestrially derived constituents affect carbon cycling in estuaries, and therefore need 

to be included in model output. Critical constituents currently 

(2010) modeled by SPARROW or that are in development 

include DOC, TSS, TN, and TP. The current (2010) methodology also will use SPARROW modeling to predict the form of 

dissolved nitrogen (inorganic versus organic) and POC transport because both are critical to carbon cycling in estuaries and 

coastal systems.

E.4.3.2. Groundwater Fluxes of Carbon and Nutrients

The SPARROW model and streamgage data do not 

estimate groundwater fluxes of carbon and nutrients to coastal 

waters. To adequately estimate these loads, the assessment 

must explicitly estimate and model groundwater loads using 

a land use and geological typology approach. Nitrogen loads 

to estuaries and coasts from groundwater discharge will be 

estimated using existing information about discharges and 

loads, and a typological approach will scale the site-specific 

rates to larger sections of the United States coast. A number 

of published USGS reports include estimates of groundwater 

discharge rates from specific basins that are based on hydrological modeling, commonly using various versions of the 

USGS’s groundwater-flow model (MODFLOW) or related 

groundwater models (U.S. Geological Survey, 2010c); for 

example, see Scorca and Monti (2001) for the north shore of 

Long Island; Monti and Scorca (2003) for the south shore of 

Long Island; Sanford and others (2008) for the Chesapeake 

Bay; and Masterson and others (2006) for Rhode Island. The 

groundwater-discharge estimate from those studies will be 

applied, as a proportion of total rainfall to the catchment, to 

neighboring locations of similar climate and geology. Where 

available, results will be compared to estimates of groundwater discharge at particular locations that have been published 

in journal articles. The concentrations of nitrogen, carbon, and 

GHGs in discharging groundwater will be estimated on the 

basis of published data about selected sites on the east coast 

of the United States, including Kroeger and others (1999), 

Bratton and others (2004, 2009), Crusius and others (2005), 

Cole and others (2006), Kroeger, Cole, and Valiela (2006), 

Kroeger, Cole, York, and Valiela (2006), Kroeger, Swarzenski, 
Appendix E 155

Crusius, and others (2007), Swarzenski and others (2007), 

and Kroeger and Charette (2008); and USGS monitoring data 

for several thousand wells, which are available through the 

USGS’s National Water Information System (NWIS). The 

NWIS data will be selected based on well depth and proximity 

to the coast. The suitability of the data for indicating concentrations of carbon and nutrients in discharging groundwater 

will be assessed by comparing the NWIS data to other USGS 

data. Nitrogen concentrations and loads in groundwater will 

be further estimated based on the application of a modified 

version of Nitrogen Loading Model (NLM) (Valiela and others, 1997, 2000). NLM is an empirical, land-use-based model 

of groundwater nitrogen loads to estuaries. The model will be 

applied to the nearshore parts of basins that are not accounted 

for in SPARROW.

E.4.3.3. Carbon Preservation in Coastal Systems

The assessment of carbon preservation in coastal systems 

also will use a hybrid modeling approach and will be conducted in two tiers. The goals of this part of the assessment 

are (1) to quantify the mean annual flux of carbon into the 

sediments in coastal and estuarine environments, where it is 

buried; (2) to estimate the mean annual net flux of terrestrially 

supported, phytoplankton-derived, “new” carbon production 

into the sediments, across the thermocline, and into the deep 

ocean; and (3) to develop a new modeling structure for coastal 

carbon cycling that incorporates variations in terrestrial inputs, 

POC degradation, burial, and transport to the deep ocean.

The conceptual modeling structure for this effort (fig. E4) 

begins by measuring the effects of changing nutrient fluxes 

from terrestrial systems on productivity (and hypoxia) in 

coastal waters. Inputs of POC from sediments in the rivers will 

contribute to the flux of carbon to the coastal sediments, but will 

also act to ballast algal production, which will increase transport through the mixed layer. Depending on the water depth 

and local currents, the particles are transported to the sediment 

surface or to a layer below the thermocline, where the carbon 

is essentially sequestered (Hales and others, 2006). Particles 

arriving at the sediment surface are subject to continued degradation and resuspension until they are buried deeply enough 

so that penetration of oxygen into the sediment is not possible 

and therefore where the carbon is presumed to be sequestered 

(Hedges and Keil, 1995; Hartnett and others, 1998).

The first major process to be modeled is carbon accumulation in coastal sediments, which, as discussed above, is 

a function of coastal productivity and sediment-accumulation 

rate. The vast majority of carbon preserved in the ocean is 

derived from marine sources and occurs in coastal sediments, 

mainly in deltas (Hedges and Keil, 1995). The initial assessment of carbon sequestration occurring in these systems will 

be based on the sediment-flux values provided by the methods 

described in section E.2 (“Transport of Carbon by Streams 

and Rivers”) of this report, using values for carbon contents 

provided in Hedges and Keil (1995) and other relevant publications that contain data on carbon content and grain-size distribution in major deltas of the United States. The previously 

published estimates of carbon accumulation are based on an 

assumed partitioning between deltaic and coastal sediments, 

and the carbon contents are reported separately for each of the 

two environments.

The same assumptions that are presented in Hedges and 

Keil (1995) initially will be used until a diffusional model is 

implemented in conjunction with a sedimentation model (discussed below). It is anticipated that under some IPCC SRES 

scenarios (Nakicenovic and others, 2000), ocean warming will 

increase stratification of the sediments and induce additional 

Figure E4.  Diagram showing

the modeling structure for

coastal carbon-sequestration

processes. Acronyms are

as follows: NOAA, National

Oceanic and Atmospheric

Administration; SPARROW,

“spatially referenced

regression on watershed

attributes” model; USGS, U.S.

Geological Survey.
156 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

hypoxia (Levin and others, 2009). Our estimates will be 

adjusted based on the anticipated extent of hypoxic areas in 

many major river deltas (Howarth, 2008; Rabouille and others, 

2008), whereby sediments underlying these suboxic zones 

will exhibit elevated levels of carbon preservation because of 

matrix protection and reduced microbial activity (Bergamaschi 

and others, 1997).

The second process to be modeled is the flux of the phytoplankton biomass across the thermocline, which is based on 

models developed by the National Oceanic and Atmospheric 

Administration (NOAA) Geophysical Fluid Dynamics Laboratory (Dunne and others, 2005) and conducted in collaboration with the NOAA team. They have developed a combined 

statistical-mechanistic model using global data that estimates 

the phytoplankton’s size distribution, water temperature, and a 

variety of other ecosystem variables. Ballasting, sinking rate, 

remineralization rate, and other relevant mechanistic variables also are included to assess the export of carbon from the 

photic zone. The model will be adapted for estimating the flux 

of phytoplankton biomass in coastal areas and for determining 

carbon flux to the sediments in shallow zones and below the 

thermocline in deeper zones.

This modeling approach divides the processes into 

productivity based on sediment preservation and productivity 

from the biological pump. The first model component assesses 

the amount of coastal productivity supported by nutrient 

supply from rivers. Changes in nutrient supply can be caused 

by changes in population, discharge, agricultural practice, 

reforestation, and many other similar land-use- or climaterelated variables (Billen and Garnier, 2007) and will affect the 

primary productivity in adjacent coastal areas.

At present (2010), the assessment will calculate nutrient 

loads and sediment and POC flux rates to coastal segments 

at monthly time increments. A discharge intensity factor will 

be used to assess dispersion, and an energetic factor to assess 

resuspension cycles—estimated as a function of bathymetry 

and wind energy—that affect the processing of organic carbon 

associated with particles (Burdige, 2005; Thunell and others, 

2007). Resuspension of particles that results from large events 

such as hurricanes (Chen and others, 2009) is not taken into 

account.

The model will use monthly time increments to incorporate seasonal as well as temporal changes in the river-ocean 

interface, which can have a large effect on the fate of POC in 

coastal ocean systems (Wheatcroft and others, 2010). Dunne 

and others (2005, 2007) include a complete model description, 

model parameters, and equations. Data needs and sources are 

listed in table E1.

E.4.3.4. Net Production of Methane and Nitrous Oxide in

Estuaries and Coastal Waters

Fluxes of N2O and CH4 from estuaries and coastal waters 

will be assessed based on empirical data about flux rates and 

will involve a geospatial approach to quantify the coverage of 

key sources including salt marshes, mangroves, and intertidal 

areas. Where data are available, a regression approach will 

be used to estimate flux rates based on spatial or temporal 

variations in controlling variables. In cases where insufficient 

data are available for a simple regression approach, information gaps initially will be filled with estimated unit values. For 

example, all fringing salt marshes initially will be estimated to 

contribute CH4 to adjacent estuaries at the same (albeit poorly 

constrained) rate.

Terrestrial inputs of N2O and CH4 will be estimated 

through linkages to measured and modeled discharges and 

chemical compositions of rivers and groundwater. Methanogenic aquifers will be identified on the basis of proposed 

geological controls. The N2O and CH4 content of coastal 

groundwater and rivers will be estimated based on LULC 

regressions (appendix B of this report), geological setting, 

biogeochemical conditions, chemical data synthesized from 

published literature, USGS monitoring data for groundwater 

and rivers available through NWIS, and other data sources, 

as available. Future changes in terrestrial N2O fluxes will be 

estimated based on modeled changes in nitrogen loads (SPARROW; NLM; Valiela and others, 1997, 2000), projected landuse changes, and an assumed proportion exported as N2

O (for 

example, Seitzinger and Kroeze, 1998). Where data are available, the variables to be considered will include eutrophication 

status, nitrogen load, hypoxia and anoxia, wetland coverage 

and type, latitude, climate, terrestrial-sediment load, carbon 

content of the sediment, water depth, salinity, and temperature 

or season. Given the limited data availability on N2O and CH4

in rivers and groundwater, the assessment will require limited 

new data collections and monitoring to fill knowledge and data 

gaps, and will require uncertainty estimates.

The N2O flux to the atmosphere from the water column 

will be calculated as the sum of nitrification and denitrification rates multiplied by the emission factor (EF), which is 

the proportion of the production rate of N2O versus other 

products (nitrate or N2) in those transformation processes 

(Seitzinger and Nixon, 1985; Seitzinger and Kroeze, 1998). 

The EF is calculated based on experiments showing that N2O /

N2 as a product of sediment denitrification increased linearly 

with nitrogen load (r2 = 0.97) and the EF for nitrification has 

been observed to vary from 0.3 to 30 percent (Goreau and 

others, 1980; Priscu and others, 1996). Thus, in the methodology, the EF will be calculated as a fraction of the total N 

transformation by denitrification and nitrification, and that 

fraction will increase with the N load per unit area of estuary. The denitrification rate is assumed to be 50 percent of 

the nitrogen-loading rate, supported by a regression between 

those variables (r2 = 0.81 versus inorganic nitrogen load; r2 = 

0.7 versus total nitrogen load) (Seitzinger and Kroeze, 1998). 

The modeled pelagic nitrification rate is less constrained and is 

assumed to be 1.2 times the denitrification rate, which is based 

on observations in Narragansett Bay (Seitzinger and others, 

1984). Benthic nitrification is not included, although the rate 

has been observed to be approximately equivalent to pelagic 

nitrification (Berounski and Nixon, 1993). For the methodology, the total dissolved nitrogen (TDN) loads will be estimated 
Appendix E 157

Table E1.  Examples of data requirements and sources for coastal and estuarine assessment.

[Partial list. Final sources will be selected in consultation with stakeholders and assessment team members. Abbreviations and acronyms are as follows: DEM, 

digital elevation model; DOC, dissolved organic carbon; GIS, geographic information system; km, kilometer; lbs/yr, pounds per year; LOADEST, Load Estimate; LULC, land use and land cover; m, meter; N, nitrogen; NADP, National Atmospheric Deposition Program; NCDC, National Climatic Data Center; NED, 

National Elevation Dataset; NHD, National Hydrography Dataset; NOAA, National Oceanic and Atmospheric Administration; NPCDI, National Coastal Pollutant Discharge Inventory; NPDES, National Pollutant Discharge Elimination System; NWIS, National Water Information System; POC, particulate organic carbon; ppt, parts per trillion; PRISM, “parameter-elevation regressions on independent slopes” model; SSURGO, Soil Survey Geographic Database; STATSGO, 

U.S. General Soil Map; STORET, Storage and Retrieval Data Warehouse; USDA, U.S. Department of Agriculture]

Type File Description Source

Atmospheric nitrogen 

deposition

Wet deposition of nitrate-N from NADP (http://nadp.sws.uiuc.edu/).

Bathymetry GIS NOAA estuary bathymetry maps—includes 

30-m DEM, Google Earth files, detailed 

metadata, images (soundings and nautical 

charts), estuary-footprint shape files, images

http://estuarinebathymetry.noaa.gov/finddata.html.

http://coastalgeospatial.noaa.gov/data_gis.html.

Calculated constituent loads Site 

specific

Data: NWIS (http://waterdata.usgs.gov/nwis), STORET 

(http://www.epa.gov/storet/), other sources.

Calculations: LOADEST, Runkel and others, 2004 

(http://water.usgs.gov/software/loadest/).

Fluxmaster, Schwarz and others, 2006.

Climate (precipitation, air 

temperature)

PRISM (http://www.prism.oregonstate.edu/index.phtml).

NCDC (http://lwf.ncdc.noaa.gov/oa/ncdc.html).

Daymet (http://www.daymet.org/.

Coastal primary productivity GIS Primary productivity calculated from satellite 

chlorophyll data

http://www.csc.noaa.gov/digitalcoast/data/index.html.

Fertilizer use Ruddy and others, 2006 (http://pubs.usgs.gov/sir/2006/5012/).

http://coastalgeospatial.noaa.gov/data_table.html.

Population density Provided by LULC modeling team. Derived 200-m U.S. Census 

Bureau datasets from 1990 and 2000 (http://www.census.gov/).

In-stream and water-column 

losses

First-order decay functions Schwarz and others, 2006; Dunne and others, 2007; other primary 

literature sources.

Land use GIS Provided by LULC modeling team.

National estuarine 

eutrophication assessment

Website Data on individual watersheds from other 

datasets

http://ian.umces.edu/neea/.

Nutrient, POC, DOC 

concentrations in rivers

Site 

specific

http://waterdata.usgs.gov/nwis.

Permitted waste discharges NPDES (http://www.epa-echo.gov/echo/

compliance_report_water_icp.html).

Physical and hydrologic 

characteristics

Data tables Watershed areas (including land versus water), 

mean streamflow, mean monthly streamflow, 

tidal information

http://coastalgeospatial.noaa.gov/data_table.html.

Physical features GIS NOAA GIS maps of shorelines, rivers, lakes, 

LULC by (1) coastal (estuarine) watershed, 

(2) upstream (fluvial) watersheds, 

(3) interior watersheds

http://coastalgeospatial.noaa.gov/data_gis.html.

http://www.csc.noaa.gov/digitalcoast/data/ccapregional/.

Pollution sources Tables Data on non-point and point source loading 

(lbs/yr) for a wide range of nutrients and 

metals, as well as by land use and upstream 

sources

NPCDI; http://coastalgeospatial.noaa.gov/data_table.html.

Salinity zones GIS NOAA GIS data with average annual salinity 

zones by region—mixing zone (5–25 ppt), 

seawater zone (25 ppt ), tidal fresh zone 

(0–5 ppt)

http://coastalgeospatial.noaa.gov/data_gis.html.

Soils (permeability, 

porosity)

1-km resolution data on soil permeability and porosity from USDA

STATSGO and SSURGO database.

STATSGO (http://soils.usda.gov/survey/geography/statsgo/).

SSURGO (http://soils.usda.gov/survey/geography/ssurgo/).

Stream reaches GIS Enhanced River Reach File (ERF1) (Alexander and others, 1999) 

(http://water.usgs.gov/lookup/getspatial?erf1).

NHD, http://nhd.usgs.gov/index.html.

NED, http://gisdata.usgs.net/ned/.

Topography GIS Nolan and others, 2002 (http://water.usgs.gov/GIS/metadata/

usgswrd/XML/erf1_2.xml).

Topography (slope, 

topographic indices)

Nolan and others, 2002 (http://water.usgs.gov/GIS/metadata/

usgswrd/XML/erf1_2.xml).

Watershed boundary GIS Watershed Boundary Dataset (http://www.ncgc.nrcs.usda.gov/

products/datasets/watershed/).

Temperature Satellite-based surface http://coastalgeospatial.noaa.gov/data_gis.html.

Wind speed and direction http://coastalgeospatial.noaa.gov/data_gis.html.
158 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

on the basis of the sum of the loads from rivers (derived from 

the SPARROW model), direct groundwater discharge (derived 

from the NLM groundwater nitrogen model), and atmospheric 

deposition (derived from National Atmospheric Deposition 

Program (NADP) data). Modeled N2O flux rates from estuaries will be calculated as follows:

N2O = EF × (NIT + DENIT) (E4)

calculated as NIT = 1.2 × DENIT

DENIT = 0.5 × TDNinput

N2O = EF × 1.1 × TDNinput

where N2O is the N2O flux (in grams of nitrogen per 

year)

EF is the emission factor and equals N2O /N2

or (1.12 × 10-7) × kilograms of nitrogen 

per square kilometer of estuary per year

NIT is the pelagic nitrification rate (in grams of 

nitrogen per year)

DENIT is the sediment denitrification rate (in 

grams of nitrogen per year)

TDNinput is the external total dissolved nitrogen 

load from rivers plus groundwater plus 

atmospheric deposition (in grams of 

nitrogen per year)

This simple empirical model is intended to produce coarse 

estimates of fluxes because the availability of data to construct 

and validate the model is extremely limited; therefore, the model 

will be updated continuously as additional validation data become 

available. Field research and monitoring will be required to further develop, test, validate, and calibrate the model.

E.4.4. Validation and Error Estimation

Bootstrap methods will be used in the SPARROW model 

to address uncertainties in parameters and correct for potential 

bias (Schwarz and others, 2006; Robertson and others, 2009). 

The output from SPARROW provides statistics that can be 

used to evaluate the results, such as an analysis of spatial bias 

and measures of model fit (Schwartz and others, 2006). Monte 

Carlo methods will be used to estimate uncertainty in models of coastal productivity and carbon accumulation (Dunne 

and others, 2005). Models will be validated using continuous 

monitoring data such as that produced by the USGS National 

Stream Quality Accounting Network (NASQAN) and other 

programs. Parameter data will be validated by comparing it to 

existing scientific literature. Modeled accumulation rates will 

be validated using existing and proposed core data.

E.5. References Cited

[Reports that are only available online may require a subscription for 

access.]

Abril, Gwenaël, and Iversen, Niels, 2002, Methane dynamics 

in a shallow non-tidal estuary (Randers Fjord, Denmark): 

Marine Ecology, Progress Series, v. 230, p. 171–181.

Ackerman, K.V., Mixon, D.M., Sundquist, E.T., Stallard, R.F., 

Schwarz, G.E., and Stewart, D.W., 2009, RESIS–II—An 

updated version of the original reservoir sedimentation survey information system (RESIS) database: U.S. Geological 

Survey Data Series 434, available only at http://pubs.usgs.

gov/ds/ds434/.

Advisory Committee on Water Information, Subcommittee on Sedimentation, 2010, The Reservoir Sedimentation 

Database (RESSED): U.S. Geological Survey Web page, 

accessed November 16, 2010, at http://ida.water.usgs.gov/

ressed/.

Alexander, R.B., Brakebill, J.W., Brew, R.E., and Smith, R.A., 

1999, ERF1—Enhanced river reach file 1.2: U.S. Geological Survey, accessed June 10, 2010, at http://water.usgs.gov/

GIS/metadata/usgswrd/XML/erf1.xml.

Alexander, R.B., Elliott, A.H., Shankar, Ude, and McBride, 

G.B., 2002, Estimating the sources and transport 

of nutrients in the Waikato River Basin, New Zealand: Water Resources Research, v. 38, p. 1268–1290, 

doi:10.1029/2001WR000878.

Alexander, R.B., Johnes, P.J., Boyer, E.W., and Smith, R.A., 

2002, A comparison of models for estimating the riverine 

export of nitrogen from large watersheds: Biogeochemistry, 

v. 57/58, p. 295–339.

Alexander, R.B., Smith, R.A., Schwarz, G.E., Boyer, E.W., 

Nolan, J.V., and Brakebill, J.W., 2008, Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the 

Mississippi River Basin: Environmental Science and Technology, v. 42, no. 3, p. 822–830, doi:10.1021/es0716103.

Alexander, R.B., Smith, R.A., Schwarz, G.E., Preston, S.D., 

Brakebill, J.W., Srinivasan, Raghavan, and Pacheco, P.A., 

2001, Atmospheric nitrogen flux from the watersheds of 

major estuaries of the United States—An application of the 

SPARROW watershed model, in Valigura, Richard, Alexander, Richard, Castro, Mark, Meyers, Tilden, Paerl, Hans, 

Stacey, Paul, and Turner, R.E., eds., Nitrogen loading in 

coastal water bodies—An atmospheric perspective: American Geophysical Union Monograph 57, p. 119–170.

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S.N., Moore, S.J., Schaefer, D.H., Anderholm, S.K., and 

Spangler, L.E., 2007, Dissolved solids in basin-fill aquifers 

and streams in the southwestern United States: U.S. Geological Survey Scientific Investigations Report 2006–5315, 

168 p. (Superseded in 2010 by version 1.1; 187-p. text, 11 

appendixes, and interactive map available at http://pubs.

usgs.gov/sir/2006/5315/.)

Armstrong, R.A., Lee, Cindy, Hedges, J.I., Honjo, Susumu, and 

Wakeham, S.G., 2002, A new, mechanistic model for organic 

carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals: Deep-Sea Research, Part 

2. Topical Studies in Oceanography, v. 49, p. 219–236.
Appendix E 159

Aulenbach, B.T., Buxton, H.T., Battaglin, W.A., and Coupe, 

R.H., 2007, Streamflow and nutrient fluxes of the Mississippi-Atchafalaya River Basin and subbasins for the period 

of record through 2005: U.S. Geological Survey Open-File 

Report 2007–1080, available only at http://pubs.usgs.gov/

of/2007/1080/.

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coastal waters: Estuarine Coastal and Shelf Science, v. 70, 

no. 3, p. 361–374, doi:10.1016/j.ecss.2006.05.042.

Bange, H.W., Rapsomanikis, S., and Andreae, M.O., 1996, 

The Aegean Sea as a source of atmospheric nitrous oxide 

and methane: Marine Chemistry, v. 53, p. 41–49.

Barnes, J., Ramesh, R., Purvaja, R., Nirmal Rajkumar, A., 

Senthil Kumar, B., Krithika, K., Ravichandran, K., Uher, 

G., and Upstill-Goddard, R., 2006, Tidal dynamics and 

rainfall control N2O and CH4 emissions from a pristine mangrove creek: Geophysical Research Letters, v. 33, L15405, 

doi:10.1029/2006GL026829.

Bastviken, David, Cole, Jonathan, Pace, Michael, and Tranvik, 

Lars, 2004, Methane emissions from lakes—Dependence of 

lake characteristics, two regional assessments, and a global 

estimate: Global Biogeochemical Cycles, v. 18, GB4009, 

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167

Appendix F. Methods for Analyzing Ecosystem Services and Benefits and Costs 

of Mitigation Activities

yield models such as the Forest Vegetation Simulator (FVS) 

(Crookston and Dixon 2005, Chivoiu and others, 2006) and 

the Forest Landscape Disturbance and Succession Model 

(LANDIS–II) model (He and others, 2005) are two of the 

models that may be used. Site-occupancy modeling (MacKenzie and others, 2006) accounts for variation in detection 

probability and produces robust estimates of the proportion 

of area occupied (PAO) for specific species. For a service like 

waterfowl habitat, values can be computed as a function of the 

energy values of different crops (mapped according to U.S. 

Department of Agriculture’s (USDA) National Agricultural 

Statistical Service (NASS) program) and vegetation types and 

converted into duck energy days (DEDs, the amount of energy 

required by one mallard-size duck for one day) (Kross and 

others, 2006).

Other services, such as sediment and nutrient retention, 

require data on relevant driver-stressor relations, which can be 

derived from current research, primary scientific literature, and 

expert workshops. The widely used Soil and Water Assessment Tool (SWAT) can be used to estimate the land-phase 

processes (for example, surface runoff, soil erosion, nonpointsource nutrient loss, groundwater recharge, and base flow) and 

water-phase processes (for example, water routing, sediment 

transport, and nutrient transport and its fate in the aquatic 

systems). GEMS will link with SWAT to assess the climatechange effects on water availability, and sediment and nutrient 

transport over landscape.

A distributed geospatial-model-sharing platform will 

be used to model ecosystem services and provide decision 

support (fig. F1). This platform is necessary to facilitate 

sharing and integrating geospatial disciplinary models. A 

platform based on Java Platform Enterprise Edition (J2EE) 

and open-source geospatial libraries (Feng and others, 2009) 

is in development. Shared models on the platform can be 

accessible to applications through the Internet using the 

Open Geospatial Consortium (OGC) Web Processing Service 

(WPS) standard (fig. F2). A pilot platform, EcoServ, was 

developed in the Prairie Pothole Region (PPR) to simulate 

diverse ecosystem services simultaneously at the landscape 

scale.

Given the need to have regionally specific information and the limited understanding of the complex relations 

among ecosystem processes, management actions, climate 

change, and ecosystem services, this part of the assessment 

will be limited to case studies within selected ecoregional 

assessment units where data and models have been developed and can be readily incorporated into the assessment 

framework. Likely areas include the Mississippi Alluvial 

The assessment results will be important to a broad range 

of users to help quantify potential effects and effectiveness 

of mitigation strategies (land-use and land-cover change, 

land-management activities), ancillary effects on ecosystem 

services, and associated economic and social costs for carbon 

sequestration and reduction of greenhouse-gas emissions. This 

appendix includes additional material on quantifying ecosystem services and estimating the present value of the benefits of 

those services and the costs of a management activity.

F.1. Ecosystem Services

Quantifying and projecting changes in carbon stocks 

and greenhouse-gas (GHG) emissions resulting from climate 

change, management actions, and mitigation strategies will be 

based on the accounting approach (using a spreadsheet) and 

the process-based probability approach (using the General 

Ensemble Modeling System (GEMS)) as described in section 

3.3.4 and appendix D of this report. Many of these primary 

assessment data products also can be categorized as ecosystem services, including carbon stocks in soils and vegetation, 

carbon sequestration, methane and nitrous-oxide emissions, 

net ecosystem production (NEP), timber production, grain 

production, and soil erosion. Estimates of value for these 

ecoregion services can be produced for each ecoregion reporting unit because they will be based on the primary assessment 

data products.

For other services not produced within the GEMS biogeochemical models or spreadsheets, the assessment will use 

an integrated ecosystem modeling approach (Starfield and 

Chapin, 1996) to quantify the ancillary effects of management 

activities and mitigation strategies on important ecosystem 

services. This approach builds on the state-transition model 

concept by linking conceptual, statistical, and mechanistic 

models in a spatially explicit framework. The modeling and 

data components contained within the framework of the 

integrated ecosystem services model use “forcasting scenarios 

of land cover change” (FORE–SCE) outputs as the initial 

basis for the spatial distribution of land use and land cover 

(LULC) in a given landscape. Biophysical production functions are constructed from known relations between the LULC 

class ecosystem attributes and the relevant ecosystem services 

(Nelson and others, 2008, 2009). For wildlife habitat, habitatsuitability index methods will be used wherein the composition and structure at the site and landscape scales control the 

amount and quality of suitable habitat for a given species or 

guild (Wakely and Roberts, 1996; Villard and others, 1998; 

Tirpak and others, 2009). Because forest-stand composition 

and structure correlate with habitat suitability, growth and 
168 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Valley, Prairie Pothole Region, southern Florida, and the 

Chesapeake Bay Basin.

F.2. Costs and Benefits

The present value of the benefits (PVB) of a management activity are entered into the formula for calculating the 

present value of benefits in equation F1. Carbon sequestration 

is assumed to start accruing in the 10th year of the activity. All 

other benefits begin in year 1 of the activity or 1 year after the 

initial investment in establishing the activity.

, (F1)

where t is time,

T is the terminal year for the mitigation activity,

r is a discount rate, and

B is the gross ecosystem service benefits 

associated with a management activity.

Present value cost (PVC, equation F2) of management 

activities is the discounted sum of land value, variable 

costs of production (McKenney and others, 2004), and the 

equivalent annual cost (EAC, equation F3) values for the 

time horizon of the analysis (Stavins and Richards, 2005). 

Estimation of the PVC and EAC use the following equations. PVC in equation F2 represents the formulation of 

the present value of the economic costs of a sequestration 

mitigation activity:

, (F2)

where PC is ,

t is time,

T is the terminal year for the mitigation activity,

r is a discount rate,

CEst are periodically recurring capital costs, 

including annual land rental payments and 

other initial investment costs required in 

establishing the management activity,

Figure F1.  Diagram showing

the system structure of the

geospatial-model-sharing

platform. GeoMSI, geospatial

model service interface;

GeoMPI, geospatial model

processing interface. Modified

from Feng and others (2009),

used with permission.

Model service

Process A

Process B

Controller

Data

translation 

Process

management

Model integration

Process A

Process B

Model A

Model B

... ... ... ...

GeoMSI GeoMPI

Model clients

Geospatial 

model sharing 

platform

Figure F2.  Conceptual flow

diagram of accessing shared

geospatial model. Modified

from Feng and others (2009),

used with permission.
Appendix F 169

CO&M are annual operating and maintenance costs of 

the activity with time, and

CMGMT are annual and periodic management costs 

including administration, insurance, and other 

transaction costs.

Possible local combinations of soil quality and other land 

characteristics (nutrients, moisture, composition), access from 

roads, slope, aspect, water availability (precipitation, irrigation), plants (type, density, composition) can affect the costs 

of specific management activities. The data used in estimating 

the present value of costs are listed in table F1. These establishment and continuing costs occur at different times during 

the lifetime of a management activity.

The EAC in equation F3 is a conversion of the PVC into 

an annual value (Stavins and Richards, 2005):

, (F3)

The stumpage value is estimated as follows:

, (F4)

, (F5)

, (F6)

, (F7)

, and (F8)

, (F9)

where WC/ha is the weight of carbon in megagrams 

per hectare,

WMg(timber)/ha is the weight of timber in megagrams 

per hectare,

is the weight of timber in megagrams 

per hectare for the reference case,

is the weight of timber in megagrams 

per hectare for the enhanced case,

is the change in weight of timber in 

megagrams per hectare from the 

reference case to the enhanced case,

is the total weight change for the areal 

unit,

is the total tons of the change for the 

areal unit,

$/ton is the price of the timber commodity, 

and

is the economic value of the timber.

Table F1.  Establishment and other capital and operating costs for management activities in U.S. Environmental Protection Agency

level III ecoregions 73 (Mississippi Alluvial Plain) and 74 (Mississippi Valley Loess Plains) (modified from Omernik, 1987; Omernik, 2004).

[Source: Huang and others, 2004; Brown and others, 2005; U.S. Department of Agriculture, 2010. ha, hectares; ha/yr, hectares per year]

Mitigation activity Establishment (one-time costs) Periodic Annual

Afforestation, hardwood Landa (fee simple if purchased)=$/ha

Saplings and planting = $209/ha

Site preparation = $145/ha

Supervision = $109/ha

Herbicide application one time per year for 

 5 years = $72/ha

Management update every 10 years = $38/ha

Boundary maintenance every 10 years = $8/ha

Land rental price = $77 per ha/yr.

Measuring, monitoring, and administration (for example, insurance) 

 = $4 per ha/yr.

Afforestation, evergreen Seedlings and planting = $281/ha

Site preparation = $412/ha

Herbicide site preparation = $731/ha

Management (initial) = $18/ha

Management update every 10 years = $38/ha

Boundary maintenance every 10 years = $8/ha

Burning every 10 years = $150/ha

Land rental price = $77 per ha/yr.

Measuring and monitoring administration 

(for example, insurance).

Grazing Land rental price (or mortgage payments) 

= $77 per ha/yr.

Management and administration 

 = $37 per ha/yr.

Reforestation Thinning every 15 years; fertilization every

 15 years; and herbicide application 5th and 

 15th years = $912/ha

Management and administration.

No-till agriculture Annuity payment = $22 per ha/yr to 

$48 per ha/yr.

a

Price of rent per hectare at the time of the real-estate transaction. Land could be purchased through annual mortgage payments.
170 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

F.3. References Cited

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171

Appendix G. Methods for Validation and Uncertainty Assessment

of these existing data products. Instead, the validation strategy 

is focused on new data products generated from assessment 

models.

G.1.1. General Approach

The methods employed in the national assessment 

involve numerous input and output variables, each of which 

represents a potential target for validation. The validation 

strategy recognizes that individual variables are not equally 

effective as validation targets and that each target must be 

selected with consideration to its relative importance for the 

assessment results and the availability and quality of reference 

data. These considerations led to the selection of 14 variables 

as both appropriate and feasible targets for validation. The 

selected target variables and their characteristics (measurement units, spatial and temporal attributes) are listed in table 

G1. The set consists predominately of end-point data products 

G.1. Validation for the National Assessment

Within the context of the national assessment methodology, validation is defined as a quantitative evaluation of 

the quality of the input and (or) output data products upon 

which the assessment will be based. The validation strategy is 

designed to achieve two principal objectives: to identify, quantify, and document sources of error that underlie the assessment results; and to guide efforts to increase accuracy through 

improvements in data collection, model design, sampling 

design, and other elements of the methodology.

In terms of conducting validation for the assessment, 

potential errors underlying the assessment results can be attributed either to the input data products that are independent of 

the models or to model performance. The known accuracy of 

independent input data products, such as the National LandCover Datasets (NLCD), will be documented by referencing 

published reports—no new efforts are planned for validation 

Table G1.  Major deliverable data products that will be targeted for validation and corresponding reference data sources for each

deliverable.

[Abbreviations used in this table can be found in “Abbreviations, Acronyms, and Chemical Symbols” in the front of this report]

Deliverable product Measurement units Temporal

validation targets

Reference data source

Existing Prospective

Modeled land-cover and land-use change1 Thematic 2001–2010 estimates LANDFIRE VCT

Ecosystem disturbance (fire)1 Thematic 2001–2010 estimates LANDFIRE MTBS, Consume 

outputs, NOAA CarbonTracker

Delivery of water to coastal areas1 Cubic meters per month; 

cubic meters per year

2001–2010 estimates USGS streamgage network

Net ecosystem productivity by pools, 

ecosystems

Grams of carbon per square 

meter per year

2001–2010 estimates AmeriFlux, NEON, NOAA 

CarbonTracker

Net biome productivity Grams of carbon per square 

meter per year

2001–2010 estimates AmeriFlux, NEON, NOAA 

CarbonTracker

Grain yields Grams of carbon per square 

meter per year

2001–2010 estimates USDA NASS, ARS

Carbon stock by pools and ecosystems Grams of carbon per square 

meter

2001–2010 estimates FIA/RPA, LTER, NEON, ARS, 

GRACEnet

Aboveground biomass 

maps from LIDAR, 

SAR.

Carbon emission by wildland fire1 Grams of carbon per square 

meter per year

2001–2010 estimates LANDFIRE MTBS with 

Consume

Carbon removal by forest harvesting Grams of carbon per square 

meter per year

2001–2010 estimates Forest Service FIA

Carbon pool size in lake and reservoir 

sediments

Grams of carbon per square 

meter

2010 RESIS–II, ad hoc reports Impoundment maps from 

LIDAR, SAR, aerial 

imaging.

Carbon accumulation rate in lake and 

reservoir sediments

Grams of carbon per square 

meter per year

2010 RESIS-II

Methane emission by ecosystems Tons of CO2

e per square 

meter per year

2001–2010 AmeriFlux, NEON, NOAA 

CarbonTracker, ad hoc reports

Nitrous-oxide emissions by ecosystems Tons of CO2

e per square 

meter per year

2001–2010 AmeriFlux, ad hoc reports

Carbon delivery by rivers to coastal areas Grams of carbon per month; 

grams of carbon per year

2010 NWIS, SPARROW, NEWS

1

Intermediate product.
172 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

or impoundments) may be sampled with greater probability to 

increase the sample of validation data appropriate to address 

key questions. Potential validation data from any carbon pool 

then would be collected within the selected sample counties. 

In some cases, the validation data will have originated from 

a probability sampling design (for example, FIA and NRI 

reference data), and the desired probability-sampling feature 

of these validation data will be maintained. In other cases, the 

validation data will not have a rigorous sampling basis (for 

example, existing light detection and ranging (LIDAR) coverage), and the representation of the sample will be limited to 

the area of existing coverage.

The validation sampling design will be constructed to 

allow continuous augmentation of the sample to build the validation database as resources become available; however, the 

sampling design will ensure that defensible estimates can be 

obtained from the sample at any stage during the procurement 

of the validation database.

G.1.3. Quality Control and Quality Assurance

Validation is the cornerstone for quality control and quality assurance (QC/QA) in the national assessment and subsequent monitoring. The validation and modeling teams will 

coordinate a continual review of information from the validation activity to identify problems or deficiencies in model 

results. Inconsistencies in model results and reference data 

that are deemed significant or anomalous in space or time will 

be flagged for further investigation. The modeling teams will 

seek to identify and understand the factors that underlie such 

inconsistencies, define explicit strategies to reduce or resolve 

them, and whenever feasible, promptly implement those strategies. Thus, the QC/QA process is realized through a dynamic 

feedback loop in which the validation leads to improved 

understanding of the methodology performance, which in turn 

leads to improvements in the methodology design and implementation (data, models, sampling).

G.1.4. Relation Between Validation and Monitoring

The strategies for validation and monitoring in the 

national assessment methodology are closely coupled. The 

data requirements addressed by the monitoring strategy 

encompass those for validation. Thus, validation and QC/QA 

will be sustained in parallel with monitoring subsequent to the 

initial national assessment.

G.1.5. Adaptability of Validation Strategy

The validation strategy will be adaptable to changes in 

data availability and information requirements. Individual data 

products from the assessment (including intermediate ones) 

may be added or removed from the list of validation targets 

in response to changes in model performance or specific 

issues that may arise. Additional or improved datasets will 

from the modeling of terrestrial and aquatic systems, but also 

includes key intermediate data products (land-use and landcover change, ecosystem disturbance by fire).

Opportunities for validation fundamentally are constrained by the availability of suitable, existing reference 

datasets and resources to support new dataset development 

and implementation of validation tasks. The key factors that 

affect the suitability of a reference dataset are its inherent data 

quality and its correspondence with the spatial and temporal 

attributes of the target variable. As a general rule, the validation will draw upon the best available (most suitable) existing 

datasets produced independently of the national assessment 

activity and upon additional monitoring data as they become 

available. The reference datasets to be employed for initial 

validation are listed in table G1. The reference data sources 

are identified as existing or prospective. The prospective data 

sources represent data that are currently unavailable but could 

be developed in time to meet the needs of the assessment.

G.1.2. Sampling Strategy for Validation

A sampling approach to validation will be constructed to 

create a practical alternative to the time-consuming and expensive option of a full-coverage national validation using all 

potentially available validation data. The sampling approach 

will focus on a much smaller total area within which validation data can be selected, evaluated for quality, and processed 

for analyses. The probability sampling design underlying the 

validation will allow for rigorous inference to validate the full 

national assessment.

The rationale of the sampling approach is to spatially 

constrain the collection and processing of validation data. The 

candidate validation data from all carbon pools will be collected and the spatial co-location of these data will allow for 

analysis of associations among pools as well as within pools. 

The collection of validation data will not be restricted to the 

sample locations. For example, extremely valuable but sparse 

datasets, such as those available from FLUXNET, will be 

used in their entirety. Data of known quality that are available 

across a broad spatial extent (for example, Forest Inventory 

and Analysis (FIA) and National Resources Inventory (NRI)) 

also may be used in their entirety for certain validation analyses. The sampling approach primarily is targeted for potential 

validation data that require thorough scrutiny to establish 

fitness for use.

The sampling design for validation will be stratified 

with each of the assessment units serving as a stratum. This 

will allow validation results to be reported by these assessment units (U.S. Environmental Protection Agency level II 

ecoregions, modified from Omernik, 1987). The sampling 

unit within each ecoregion will be a county, and a sample of 

counties will be selected within an ecoregion. A further stratification within each region will be constructed using criteria 

defining a priority of interest. That is, counties exhibiting large 

model uncertainties, high quantities of land-cover change, or 

containing rare conditions (for example, estuaries, wetlands, 
Appendix G 173

be incorporated as they become available and when deemed 

effective in support of validation objectives. The potential 

data sources identified in table G1 are recognized to have 

particularly strong potential for improving the reliability of the 

assessment results. In particular, implementation of LIDARbased techniques for estimating aboveground biomass can be 

readily achieved through coordination of the growing set of 

planned and potential LIDAR-related activities of the U.S. 

Geological Survey (USGS) and other governmental or private 

organizations. The benefits and opportunities for incorporating 

LIDAR-derived biomass data in the national assessment are 

addressed below.

G.1.6. Addressing Data Gaps and Deficiencies:

The Case of LIDAR

LIDAR technology of various configurations has been 

well demonstrated in the literature, beginning as early as 

the 1980s, to be effective in quantifying forest and nonforest structure (Lefsky and others, 2002; Lim and others, 

2003). Current (2010) systems collect extremely accurate 

three-dimensional information at the meter level from airborne systems and at the centimeter level from ground-based 

systems. The wealth of commercial and research sensors are 

providing three-dimensional data of vegetation structure at an 

unprecedented rate; however, there is not yet a capability that 

can provide the spatial and temporal coverage that space-based 

optical and radar systems offer. Whereas LIDAR has been 

used successfully on disparate projects across the country to 

quantify vegetation structure and biomass (Nelson and others, 

2003), there is not a coordinated, concerted effort to collect 

systematic, standardized LIDAR-derived structural information for a national-scale biomass estimation, validation, and 

quantification of change.

G.2. Uncertainty Assessment

Although the validation process evaluates the quality 

of output products based on comparison with existing data, 

uncertainty assessment builds on this by estimating confidence 

bounds on estimates that cannot be validated; for example, 

projections into the future or estimates for which there are 

no existing validation data. Assessing complex socioenvironmental systems generally contains some uncertainty resulting 

from data gaps, modeling capabilities, interactions between 

ecological phenomena, and our scientific understanding of the 

mechanics of these complex systems. It is essential for users 

of the national assessment to be aware of the many uncertainties inherent in methods and assumptions used. It is useful to 

distinguish between quantifiable uncertainties where some 

form of statistical information is available and nonquantifiable uncertainties where such information is not available. 

Because the latter are more difficult to analyze explicitly, 

the basic strategy will be to treat these uncertainties separately in terms of a two-level approach, based roughly on the 

Intergovernmental Panel on Climate Change (IPCC) guidelines (Intergovernmental Panel on Climate Change, 2006).

G.2.1. Communication About Unquantifiable

Uncertainties

The first-level uncertainty assessment is designed to 

incorporate unquantifiable uncertainties (designated as “unpredictabilities” by the IPCC) in terms of a representative set of 

scenarios to be modeled. These scenarios will be constructed 

to capture the relevant variability range in those factors 

deemed most important for carbon sequestration (including 

climate changes and population growth). There is no attempt 

to assign probabilities to different scenarios or storylines; 

they simply serve as examples of potential future conditions 

that might reasonably be expected to occur under different 

sets of assumptions about future environments and behavior; 

however, by adopting these different sets of assumptions, it 

is possible to model the carbon and greenhouse-gas (GHG) 

outcomes with measurable levels of uncertainty. Thus, these 

scenarios represent a set of uncertainty bounds on our assumptions about future conditions.

For this assessment, three of the major storylines proposed by the IPCC (Nakicenovic and others, 2000) will be 

followed, within which alternative management and mitigation 

scenarios are proposed (section 3.2 of this report). Uncertainty 

related to scenarios is considered unpredictable; hence, a 

strategy for communicating such uncertainty is needed. The 

communication effort will focus on sources of uncertainties 

and their potential effect.

G.2.2. Uncertainty Sources of Reference

Scenarios and Potential Reduction Measures

Scenarios are useful tools to provide a range of potential 

future alternatives. This assessment will develop national and 

regional reference scenarios (section 3.2 of this report) that 

are consistent with IPCC storylines. The Special Report on 

Emission Scenarios (SRES) identified six primary sources of 

uncertainty within the scenarios framework (Nakicenovic and 

others, 2000). The same sources of uncertainty also are contained in the use of reference as well as alternative management scenarios developed for the assessment. The six sources 

are listed below.

Choice of storylines.—This category describes the uncertainty associated with the characteristics of the storylines and 

mostly is related to the combination of quantitative assumptions, such as increased population growth and decreased 

economic growth, used for each storyline.

Authors’ interpretation of storylines.—Differences in the 

translation of qualitative storylines into quantitative drivers 

can introduce uncertainty into the storylines. Uncertainty may 

be reduced for harmonized drivers (population, gross domestic 

product) if parameters for drivers are chosen consistently with 

the storylines.
174 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Translation of knowledge about driving forces into quantitative inputs for scenario analysis.—Understanding the links 

between the drivers and the quantifiable input parameters for 

models is limited and often results in an inconsistent application across modeling efforts.

Methodological differences.—These uncertainties arise 

from the modeling structure as well as the underlying uncertainties between drivers and their resulting effects.

Different sources of data.—Source data, such as land-use 

histories and baseline conditions, often are inconsistent in their 

availability (both spatially and temporally).

Inherent uncertainties.—Events considered “rare” are 

not included in modeling efforts because of their inherent 

unpredictability. Nonetheless, rare events have the ability to 

affect future trajectories and produce considerably different 

outcomes.

G.2.3. Uncertainties of Alternative Mitigation

Scenarios and Potential Reduction Measures

As noted above, uncertainties around the IPCC SRES 

storylines and interpretation of them also apply to the alternative management scenarios for the same storylines. Here, 

a key uncertainty involves the design and implementation 

of future policies. Policies will affect eligibility for incentives, and policy instruments will motivate change to various degrees. Because of these concerns, direct prediction of 

future potential policies is not considered for the methodology 

in order to avoid increasing uncertainty. Instead, alternative 

management scenarios are linked with interpretation of plausible land-management activities. Sources of uncertainty and 

potential reduction measures relevant to alternative scenario 

development are given below.

Estimates of rates, suitable lands, and timing.—Uncertainties in the mitigation estimates can be improved by broadly 

incorporating expert knowledge at the regional and subregional level. Improving the spatial footprint by incorporating 

region-specific expert knowledge in the scenarios should (at 

least in theory) help improve uncertainties. Additionally, the 

estimates also can be improved by increasing the thematic 

precision of the land-use, land-cover, and land-management 

information (for example, managed loblolly pine forest versus 

softwood forest in the Southeastern United States). Increased 

thematic precision has ramifications for data requirements and 

availability and increases the cost of the analysis.

The evaluation of management activities for cost, ecosystem effects, energy usage, and technological progress.—The 

uncertainties of a management activity’s performance are constrained by the use of relative, rather than absolute, estimates. 

Again, extensive consultation at the regional and subregional 

level will help.

Behavioral responses assumed in developing the scenarios.—Such uncertainties may be captured or reduced by 

comparing the results of a scenario development with the 

results of a biogeochemical simulation and, more effectively, 

by monitoring or repeating assessments that revisit behavioral 

responses.

G.2.4. Estimation for Quantifiable Uncertainties

The second type of uncertainty treatment involves the 

explicit modeling of potential carbon sequestration for different scenarios. From a spatial perspective, the key objective 

of these models will be to downscale the parameters of each 

scenario (such as overall climatic conditions and population 

pressures) to grid cells that are small enough to allow explicit 

carbon-sequestration modeling. At this level of modeling, it is 

deemed that all uncertainties should be treated in a quantifiable 

way. It is important to distinguish between “value” uncertainty 

of input data and model parameters, uncertainty of model 

structure and mathematical processes, and uncertainty affected 

by other technical components of the methodology such as 

land-use and land-cover change and disturbance modeling. All 

value uncertainties will be treated as probability distributions 

that can serve as inputs to model simulations. Where statistical data are available, such distributions will be estimated by 

standard statistical procedures based on IPCC recommendations (Intergovernmental Panel on Climate Change, 2006). 

Otherwise, such distributions will be elicited through expert 

judgments (typically triangular distributions based on elicited 

value ranges and most likely values).

G.2.5. Uncertainty of Input Data and Model

Parameters

Following the IPCC (2006) guidance, uncertainty analysis will focus on random errors associated with input data and 

model parameters. The following steps will be followed during 

the assessment to evaluate and report this type of uncertainty.

Input data used for modeling—such as biophysical 

data (climate, soil), modeled data (wildland fire, land-use 

change), and expert-knowledge-related data (mitigation 

activities)—will be assigned an uncertainty range, either 

expressed as a probability distribution function (PDF) curve 

or a probability look-up table. Example approaches include 

the following.

Land-cover data.—Uncertainty in initial land-use and 

land-cover data may be expressed as a contingency table, 

which can be used to develop empirical distributions of possible land-cover types for individual pixels, based on misclassification rates (Prisley and Smith, 1987; Fang and others, 

2006). These empirical distributions can be translated into 

initial carbon-density distributions (Quaife and others, 2008).

Forest age and biomass.—Parameters used for initializing the biogeochemical modeling are based on the FIA 

program. Plot-level data can be aggregated based on location 

to the level of the Joint Frequency Distribution (JFD) unit 

used in biogeochemical methods. Aggregation can provide distributions for parameters such as forest age, biomass, species 

groups, site quality, canopy density, and so on.
Appendix G 175

Soil parameters.—Using the tables associated with the 

Soil Survey Geographic (SSURGO) database, it is possible to 

obtain ranges and distributions for important soil parameters 

based on the present soil components and their relative 

frequency.

Using approaches such as those outlined above or an 

empirical distribution (probability look-up table), similar to 

the fractile distribution shown in figure G1, input data distributions can be fit to mathematically defined statistical distributions, such as those described by IPCC (2006).

If a model parameter has a PDF, it can be evaluated using 

error propagation. When a parameter PDF is not available, it 

is possible to derive one using data-assimilation techniques. 

Some parameters may be obtained from expert judgment. For 

example, PDF parameters for remote-sensing-based fire-severity modeling may be obtained from table D2 in appendix D.

As input data are processed by models, additional opportunities arise to evaluate uncertainty. For example, the “forecasting scenarios of land-cover change” model (FORE–SCE) 

(appendix B of this report) uses logistic regression to predict 

probabilities of individual types of land-cover transitions. The 

result is a suite of probability surfaces representing the most 

likely locations for different types of changes to occur. Landcover changes are then allocated across a landscape. During this 

process, information on uncertainty is available from fit statistics for the regression models (Hosmer and Lemeshow, 2000), 

as well as from the probability surfaces (Dendoncker and others, 

2008). For example, comparing the probabilities for different 

types of change at a given pixel can indicate how much more 

likely one type of change is than another. When probabilities for 

several types of change are similar, there is greater uncertainty 

and ambiguity as to the type of change that will occur.

It should be noted, however, that the focus of the 

uncertainty analysis will be on quantifying the variability of 

end results, which will be carbon storage and GHG fluxes. 

Uncertainties may arise during many parts of the modeling 

process that may have little effect on the final outcome. For 

example, the specific location of land-cover changes across a 

homogeneous landscape may be extremely uncertain, but also 

may make a minimal difference in overall long-term carbon 

sequestration at the reporting-unit level.

G.2.6. Uncertainty of Model Structure and

Component Interactions

Structural and other conceptual uncertainties will be 

treated using model-run evaluation and expert judgment. One 

issue that the assessment should address is which biogeochemical models to use for which ecosystems, and which key 

carbon-, nutrient-, and water-cycle elements will be treated 

by the models. This will be determined on the basis of a 

criteria evaluation process outlined in chapter 3 of this report. 

Although it is possible to use alternative modeling forms in 

principle, it is deemed most practical from an operational perspective to consistently use the biogeochemical (BGC) models 

most recommended by experts. In doing so, it is vital that all 

assumptions be made explicit to model users. In addition, 

model sensitivities to key assumptions will be evaluated by 

simulations, and results made available to users. Both of these 

analyses can be tracked using a spreadsheet, which will be 

adopted for the entire assessment to track results and enhance 

user transparency.

For other conceptual uncertainties—such as the effects of 

climate change on disturbances and land-use and land-cover 

changes, or the interactions between the carbon, nutrient, and 

water cycles—statistical techniques such as the use of the 

IPCC-recommended PDF and Monte Carlo resampling methods may be used to understand the size of their uncertainties 

and relations between different ecosystem processes.

Because the General Ensemble Modeling System (GEMS) 

can encapsulate multiple models, and parameterize and drive 

these models with the same data, it provides an ideal environment or platform to identify and address issues of uncertainty 

related to model structure and mathematical representations of 

biophysical processes. For this assessment, model comparisons 

Figure G1.  Typical probability

distribution (density) function

(PDF) curves. A, Uniform; B,

Triangle; C, Fractile; D, Normal;

E, Lognormal. Modified from

Intergovernmental Panel on

Climate Change (2006, p. 3.25),

used with permission.
176 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

will be used within the GEMS structure and with other modeling groups via a national workshop. Additionally, to reduce 

biases in modeling, the models will be calibrated with in situ 

data (for example, flux-tower data, FIA data).

G.2.7. Uncertainty Related to Specific Methods

The assessment is required by the Energy Independence 

and Security Act (EISA) (U.S. Congress, 2007) to consider 

ecosystem-controlling processes, such as wildland fire, landuse change, lateral transport, and agricultural practices. The 

use of explicitly mapped and modeled ecosystem-controlling 

processes on a national basis for carbon sequestration and 

GHG fluxes should improve upon uncertainties in assessment 

results (Running, 2008), but it also is possible that the incorporation of such information could introduce new uncertainties 

into the methodology. Uncertainties related to the ecosystemcontrolling processes will be quantified and reported. The 

basic approach for assessing such uncertainties is related to 

developing synthesis information and data products in support 

of formulating mitigation strategies. This approach is discussed in chapter 3 and appendix F of this report.

G.2.8. Increasing User Confidence by Delivering

and Comparing Results

Analyzing uncertainties associated with model inputs 

and model construction provides useful information, but 

from the user’s perspective, uncertainties generally are most 

easily communicated in terms of model outputs. The validation process described previously, when communicated 

together with uncertainty analysis, will help enhance user 

confidence about the input scenarios and data or model 

uncertainties. An important aspect of communicating the 

results and their uncertainties will be the ability to draw 

comparisons between the results of this assessment and other 

published projections of terrestrial carbon sequestration. For 

example, the validation plan includes a comparison of assessment results with the spatial and temporal distribution of 

terrestrial sources and sinks of atmospheric carbon estimated 

by the biosphere and fire modules of the National Oceanic 

and Atmospheric Administration’s CarbonTracker system 

(table G1; Peters and others, 2007; National Oceanic and 

Atmospheric Administration, 2010). Highlighting differences 

between approaches, assumptions, data sources, and modeling techniques used in this assessment and other published 

works will help place the assessment results in perspective.

An additional means for communicating results to users 

is the delivery of assessment products in digital map format. 

The distribution of the maps through an online user interface (described in appendix I of this report) will allow users 

to obtain frequency distributions of the deliverables and an 

opportunity to explore these uncertainties in more depth 

(albeit, at different scales). It is also possible to provide summary measures of uncertainty based on all scenarios, such 

as overall value ranges (Intergovernmental Panel on Climate 

Change, 2006; fig. G2).

Figure G2.  Diagram

illustrating the recommended

process for combining

uncertainty from various

sources in a carbonsequestration assessment.

PDF, probability distribution

function.

Bias assessment 

and bias removal 

Error propagation 

for symmetric

uncertainty

Input data

uncertainty

Select random

input from PDFs

Model calculation

and aggregation

Sensitivity 

analysis

Uncertainty 

in trend 

Uncertainty 

at multiple scales

Monte Carlo method 

for large asymmetric

uncertainty

Model 

improvement
Appendix G 177

G.3. References Cited

[Reports that are only available online may require a subscription for 

access.]

Dendoncker, N., Schmit, C., and Rounsevell, M., 2008, 

Exploring spatial data uncertainties in land-use change 

scenarios: International Journal of Geographic Information 

Science, v. 22, no. 9, p. 1013–1030.

Fang, Shoufan, Gertner, George, Wang, Guangxing, and 

Anderson, Alan, 2006, The impact of misclassification in 

land use maps in the prediction of landscape dynamics: 

Landscape Ecology, v. 21, no. 2, p. 233–242, doi:10.1007/

s10980-005-1051-7.

Hosmer, D.W., and Lemeshow, Stanley, 2000, Applied logistic 

regression (2d ed.): New York, Wiley, 375 p.

Intergovernmental Panel on Climate Change, 2006, 2006 

IPCC guidelines for national greenhouse gas inventories: 

Hayama, Kanagawa, Japan, Institute for Global Environmental Strategies, 5 v., accessed June 22, 2010, at 

http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html.

Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J., 

2002, Lidar remote sensing for ecosystem studies: BioScience, v. 52, no. 1, p. 19–30.

Lim, Kevin, Treitz, Paul, Wulder, Michael, St-Onge, Benoît, 

and Flood, Martin, 2003, LiDAR remote sensing of forest 

structure: Progress in Physical Geography, v. 27, no. 1, 

p. 88–106, doi:10.1191/0309133303pp360ra.

Nakicenovic, Nebojsa, Alcamo, Joseph, Davis, Gerald, de 

Vries, Bert, Fenhann, Joergen, Gaffin, Stuart, Gregory, Kenneth, Grübler, Arnulf, Jung, T.Y., Kram, Tom, La Rovere, 

E.L., Michaelis, Laurie, Mori, Shunsuke, Morita, Tsuneyuki, 

Pepper, William, Pitcher, Hugh, Price, Lynn, Riahl, Keywan, Roehrl, Alexander, Rogner, H.-H., Sankovski, Alexei, 

Schlesinger, Michael, Shukla, Priyadarshi, Smith, Steven, 

Swart, Robert, van Rooyen, Sascha, Victor, Nadejda, and 

Dadi, Zhou, 2000, Special report on emissions scenarios: 

Cambridge, Cambridge University Press, Intergovernmental 

Panel on Climate Change Special Report, 599 p., accessed 

June 22, 2010, at http://www.grida.no/publications/other/

ipcc_sr/?src=/climate/ipcc/emission/.

National Oceanic and Atmospheric Administration, 2010, 

CarbonTracker: National Oceanic and Atmospheric 

Administration Web page, accessed November 12, 2010, at 

http://www.esrl.noaa.gov/gmd/ccgg/carbontracker/.

Nelson, Ross, Valenti, M.A., Short, Austin, and Keller, Cherry, 

2003, A multiple resource inventory of Delaware using airborne laser data: Bioscience, v. 53, no. 10, p. 981–992, doi:

10.1641/0006-3568(2003)053[0981:AMRIOD]2.0.CO;2.

Omernik, J.M., 1987, Ecoregions of the conterminous United 

States: Annals of the Association of American Geographers, 

v. 77, no. 1, p. 118–125.

Peters, W., Jacobson, A.R., Sweeney, C., Andrews, A.E., Conway, T.J., Masarie, K., Miller, J.B., Bruhwiler, L.M., Pétron, 

G., Hirsch, A.I., Worthy, D.E., van der Werf, G.R., Randerson, J.T., Wennberg, P.O., Krol, M.C., and Tans, P.P., 2007, 

An atmospheric perspective on North American carbon dioxide exchange—CarbonTracker: Proceedings of the National 

Academy of Sciences, v. 104, no. 48, p. 18,925–18,930.

Prisley, S.P., and Smith, J.L., 1987, Using classification error 

matrices to improve the accuracy of weighted land-cover 

models: Photogrammetric Engineering and Remote Sensing, v. 53, no. 9, p. 1259–1263.

Quaife, T., Quegan, S., Disney, M., Lewis, P., Lomas, M., and 

Woodward, F.I., 2008, Impact of land cover uncertainties on 

estimates of biospheric carbon fluxes: Global Biogeochemical Cycles, v. 22, GB 4016, doi:10.1029/2007GB003097.

Running, S.W., 2008, Ecosystem disturbance, carbon, and 

climate: Science, v. 321, p. 652–653, doi:10.1126/

science.1158736.

U.S. Congress, 2007, Energy Independence and Security Act—

Public Law 110–140: U.S. Congress, 311 p., available at 

http://frwebgate.access.gpo.gov/cgi-bin/getdoc.cgi?

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179

Appendix H. Methods for Energy Independence and Security Act Measuring and 

Monitoring Requirements

defined here as the application of effective tools and techniques for collecting primary data that address data requirements of the national assessment. Two types of measurements 

are recognized: direct observations (for example, flux towers) 

and remotely sensed observations (for example, Landsat).

Quantification is defined here as the determination 

of numerical values for the data products addressed in the 

national assessment, including current and projected carbon 

stocks, carbon sequestration, GHG emissions, and reductions 

in those emissions because of mitigation actions. Quantification in the national assessment is achieved primarily through 

the spatial aggregation of measurements and model results 

described in the preceding sections.

Monitoring is defined here as periodic measurement, 

which enables quantification and validation of GHG fluxes, 

carbon sequestration, and related ecosystem properties and 

processes. Another purpose of monitoring is for evaluating the 

effectiveness of applied mitigation strategies or management 

actions for increasing carbon sequestration, reducing GHG 

emission, and related goals.

H.3. Types of Resource Monitoring

Successful large-scale monitoring programs typically 

incorporate data collected at several spatiotemporal scales, each 

providing a unique and valuable contribution to the monitoring 

effort (fig. H1). Plot- and local-scale research and monitoring 

provide detailed information not observable at larger scales. 

Long-term monitoring provides trends information not observable by other means. Spatially extensive surveys provide a 

means to assess variability across ecosystems and provide estimates of population parameters for regions of interest. Remotely 

Figure H1.  Diagram illustrating types of monitoring needed for

assessing carbon sequestration and greenhouse-gas fluxes.

The Energy Independence and Security Act (EISA) (U.S. 

Congress, 2007) prescribes that the national assessment methodology include a comprehensive strategy for “measuring, monitoring, and quantifying covered greenhouse gas emissions and 

reductions” from ecosystems—a monitoring plan. Appropriately, 

the EISA also indicates that the assessment methodology should 

be used to carry out this mandate. In the context of the EISA and 

the methodology, therefore, monitoring has two distinct functions. The first is to comprehensively monitor changes in carbon 

sequestration in and greenhouse-gas (GHG) flux from ecosystems 

on a national scale. Monitoring at the national scale involves spatial and temporal extrapolation of data collected at specific locations over broad areas using complex biogeochemical models. 

The second function of monitoring is more classical: measuring 

change with time at specific locations. This type of monitoring is 

used to develop and validate the models used for extrapolation.

H.1. Monitoring Objectives and Scope

To fulfill the EISA requirement for monitoring, the principal objectives and their respective scopes are as listed below:

• Provide ongoing, systematic quantification of carbon 

stocks, sequestration, GHG emissions, and related ecosystem properties and processes in the United States 

for the purpose of evaluating their status and trends.

• Aggregate and update observational monitoring data 

for the purpose of validation; for example, assessing 

the accuracy of model results.

• Provide a basis for evaluating the effectiveness of 

applied mitigation activities and strategies undertaken 

to reduce GHG emissions from ecosystems and promote carbon sequestration.

It is intended that the monitoring plan for the national 

assessment be adaptive to changing data resources, improved 

methodologies, and evolving requirements for data and information, while maintaining consistency, scientific credibility, 

and transparency. The monitoring plan also is designed to be 

closely coordinated with the science-implementation strategy 

of the North American Carbon Program’s (Denning, 2005) 

other U.S. carbon-cycle research activities.

H.2. Definitions

It is useful to clearly define and differentiate among the 

three closely related tasks of measuring, quantifying, and 

monitoring that are prescribed in the EISA. Measurement is 
180 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

sensed data permit observation and assessment at regional to 

global scales. These data must be synthesized into a form that 

permits quantification over the spatial extent of the monitored 

area and analysis of change with time.

Although these four types of monitoring represent different spatial scales, they practically and logistically overlap. 

At the plot and local scale, intensive data collection provides 

information that is essential for developing a better understanding of carbon-cycling processes. This understanding 

enables the continued improvement of ecosystem models used 

to calculate carbon sequestration and GHG fluxes.

Long-term monitoring performed at fixed, georeferenced 

locations is needed to assess temporal trends in direct measurements of GHG flux and carbon sequestration, as well as to 

quantify important variables used in flux calculations, such as 

streamflow, water quality, soil chemistry, and biomass. These 

types of data provide the ability to assess and distinguish among 

short-term, seasonal, annual, interannual, and long-term trends. 

Some examples of programs that could provide key data for the 

national assessment include the U.S. Geological Survey’s (USGS) 

streamflow and water-quality monitoring programs, the National 

Oceanic and Atmospheric Administration (NOAA) climatemonitoring program, and the U.S. Forest Service’s (USFS) Forest 

Inventory and Analysis (FIA) Program; additional examples of 

datasets and programs that could provide data for the assessment 

are listed in the individual appendixes of this report. 

Spatially extensive surveys provide data at regional to 

national scales that can be used to evaluate how variables 

change in response to natural or human-related stressors. A key 

benefit of spatially extensive surveys is that the data can be 

merged with spatially continuous national land-use and landcover data, and statistical relations can be developed that permit 

model estimates of GHG fluxes at sites not directly measured. 

Surveys of dissolved and particulate carbon in rivers from 

headwaters to oceans, for example, can be used to examine how 

concentrations and fluxes of carbon vary in relation to basin 

size, elevation, land cover, soil properties, and geology. Multiple-regression models then can be created to estimate carbon 

concentrations and fluxes at unsampled sites. Survey data also 

can be used to evaluate GHG sources and carbon sinks; spatial 

patterns in GHG concentrations in the atmosphere and oceans, 

for example, can be used to identify site characteristics that are 

useful for flux or carbon sequestration.

Data collected through remote sensing are essential for 

regional efforts such as mapping and tracking changes in 

land cover and land use, assessing biomass, and evaluating 

ecosystem disturbances caused by storms, insects, or fire. For 

example, multispectral estimating data from the Landsat satellite program are used by the USGS and the U.S. Department 

of Agriculture (USDA) to create maps of land cover and land 

use, such the National Land Cover Database (NLCD); these 

data are updated periodically and are key inputs to modeling 

changes in carbon storage and GHG flux with time.

H.4. Existing Monitoring Data Sources

The bulk of the data needed to comprehensively monitor carbon sequestration and GHG fluxes are available from 

existing programs and efforts (U.S. Environmental Protection 

Agency (EPA), 2010). Monitoring data currently are collected 

by a wide variety of Federal agencies, including the USGS, 

NOAA, the National Aeronautics and Space Administration (NASA), the USDA, and the EPA, as well as State and 

local governments and academic and private monitoring and 

research efforts. Monitoring data produced by others and 

needed by the national assessment are derived from a broad 

range of disciplines, including climate, hydrology, biology, 

and soil science (table H1). The methods described in other 

appendixes to this report describe in detail the diverse datasets 

needed for a national assessment, as well as their identification, assimilation, and evaluation.

There is a strong need for coordination among the existing 

monitoring programs and data aggregation efforts to support the 

needs of the national assessment while avoiding a duplication of 

efforts. Several efforts are proposed or underway to aggregate 

some of the many types of data needed for regional- and nationalscale monitoring into a consistent format, and to make it available 

Table H1. Examples of monitoring needs with key parameters and primary areas of application in the assessment.

[Abbreviations and acronyms are as follows: GHG, greenhouse gas; CO2, carbon dioxide; CH4, methane; N2O, nitrous oxide; LULC, land use and land cover; 

LAI, leaf area index; DOC, dissolved organic carbon; POC, particulate organic carbon; DIC, dissolved inorganic carbon]

Monitoring categories 

(examples) Key parameters Primary application

Climate Precipitation, air temperature, radiation, wind speed Estimate GHG flux and aquatic flux quantification.

GHG fluxes CO2

, CH4, N2O Direct GHG flux quantification and validation.

Land use and land cover Percentage change in LULC classes Estimate carbon inventory.

Disturbances Fire, insect and disease, storms Estimate carbon inventory and GHG flux quantification.

Vegetation properties Biomass, LAI, fuels Carbon inventory.

Soil properties Organic and inorganic carbon, soil moisture, permafrost Carbon inventory and GHG flux quantification.

Water quality Sediment, nutrients, DOC, POC, DIC Aquatic GHG flux.

Hydrology Streamflow, groundwater levels GHG flux from terrestrial and aquatic systems.

Coastal primary production Chlorophyll Carbon burial in coastal systems.

Ecosystem services Timber production, habitat condition Ecosystem impacts.
Appendix H 181

for general use, including the science-implementation strategy 

developed for the North American Carbon Program (Denning, 

2005). In the absence of a robust and comprehensive data aggregation program housed within another Federal program, this role 

should be incorporated into the national assessment.

Further, it is suggested that the national assessment 

evaluate new and existing data sources and data aggregation 

programs for incorporation into the framework in conjunction 

with each monitoring cycle. This periodic evaluation would 

ensure that redundant data-collection and data-aggregation 

efforts are avoided in fulfilling the EISA requirement for 

monitoring. It also will ensure that all appropriate available 

data are assimilated, regardless of source.

H.5. Major Monitoring Needs

H.5.1. Land Use and Land Cover

Land use and land cover (LULC) continually evolves in 

response to changes in biophysical and socioeconomic driving forces. Providing updated LULC information through an 

active and sound LULC monitoring system would allow the 

evaluation of the effects of a changing landscape on carbon 

sequestration and GHG fluxes.

Land-use monitoring will focus on updating current 

(2000–2010) LULC information from the perspective of 

LULC and land management, using updated data on LULC, 

socioeconomic drivers, and climate to inform and revise 

scenarios used in periodically updated LULC forecasts and to 

provide updated 50-year LULC scenario-based forecasts.

The monitoring protocol will leverage existing USGS and 

agency LULC initiatives, relying on the Multi-Resolution Land 

Characteristics Consortium (MRLC), specifically, the NLCD and 

the proposed MRLC monitoring program (Yang, 2008). The goal 

of the MRLC monitoring strategy is continual updating and augmentation of a multitemporal (annual to 5-year) and multispatial 

resolution (1 to 250 meters) NLCD to support national-scale environmental and land-monitoring needs. This information, along 

with updated socioeconomic trends and climate data, will be used 

to update potential future scenarios and forecasting LULC.

The task of monitoring carbon sequestration and GHG 

fluxes from vegetated surfaces can be achieved by using a combination of remote sensing and georeferenced plot data. Relative to 

other ecosystems, forests have far more extensive data-monitoring programs that are suitable for evaluating carbon sequestration 

and GHG fluxes. The USFS’s FIA Program provides a rich database; new programs tasked with improving flux-tower networks 

and the characterization of vegetation structure, composition, 

and biomass are critically needed for urban forests, rangelands, 

and other nonforested systems, which occupy nearly 325 million 

hectares in the United States. The National Resources Inventory (NRI), administered by the Natural Resources Conservation 

Service (NRCS), collects such information, but the resulting data 

have been practically inaccessible; however, they could represent 

a valuable asset to the monitoring endeavor. Some systems, such 

as the USDA’s GRACEnet (“greenhouse gas reduction through 

agricultural carbon enhancement” network) and the American 

flux network (AmeriFlux), offer significant promise for monitoring nonforest landscapes and calibrating biogeochemical models, 

yet they are hardly extensive enough to provide estimates of 

carbon-sequestration estimates independently for entire landscapes. Likewise, a paucity of data exists describing belowground 

carbon dynamics, and this deficiency is a clear impediment to 

reliable estimates of belowground carbon sequestration through 

time. A more extensive network focused on belowground carbon 

dynamics is critical. In addition to field-based networks, more 

remote-sensing data suitable for characterizing vegetation attributes in nonforest landscapes are needed. Some high-resolution 

LIDAR sensors are available, which will undoubtedly be used to 

monitor vegetation conditions such as biomass and carbon stocks. 

The success of the monitoring of vegetated surfaces depends 

upon leveraging existing networks while developing new datacollection programs, especially in areas lacking sufficient data or 

exhibiting uncertainty.

Much of the existing knowledge regarding carbon dynamics on vegetated surfaces, especially in nonforested landscapes, 

describes carbon flux but not sequestration. Thus, long-term 

studies focused on carbon sequestration, especially soil organic 

carbon, are needed. More fundamentally, basic research is needed 

to enable determination of carbon stocks in shrublands. Tens of 

millions of hectares of these lands exist in the United States alone 

and have not yet been sufficiently studied in this capacity. To this 

end, simple equations linking stand structure and cover to standing biomass (and thus standing carbon), which are suitable for 

regionally scaling standing carbon estimates when coupled with 

remote-sensing data (such as LIDAR for stand structure), need to 

be reformulated. Additionally, relatively simple variables, such 

as above-ground biomass, remain largely uncharacterized in a 

regional, operational manner. Finally, interagency data sharing is 

critical for determining other data gaps. Comprehensive evaluation of data sources and their temporal and spatial coverage and 

suitability for evaluating GHG and carbon-sequestration dynamics is needed to determine true gaps in data.

H.5.2. Soil Carbon Stocks and Fluxes

It is suggested that the assessment will include the quantification of soil carbon stocks and fluxes from organic sources (for 

example, soil organic carbon (SOC), whereas only stock estimates will be provided from soil inorganic carbon (SIC) pools). 

Relative to other pools, soil-carbon observations are spatially 

and temporally sparse. SOC-flux estimates for grasslands and 

agricultural systems can be obtained from efforts such as those by 

Ogle and others (2007), which provides quantification of SOC in 

support of the EPA’s official GHG estimates (U.S. Environmental 

Protection Agency, 2010). Additionally, the GRACEnet program 

(Jawson and others, 2005) offers a limited number of soil-carbon 

measurements in primarily agricultural landscapes. In the forest 

sector, the FIA program provides the most comprehensive forest 

soil-carbon-monitoring database available (O’Neill and others, 2005). Agency programs and those at research institutions 
182 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

provide a suitable starting point for assimilating and aggregating 

SOC measures for monitoring purposes. Despite the paucity of 

programs offering measures of organic carbon components, those 

aimed at measuring inorganic components are even less numerous. Globally, SIC storage in arid and semiarid soils is approximately 2 to 10 times larger than SOC storage (Schlesinger, 

1982; Eswaran and others, 2000). Annual fluxes from inorganic 

sources, however, are at least an order of magnitude smaller 

(U.S. Climate Change Science Program, 2007) than fluxes from 

organic sources. In addition, estimating fluxes from SIC are more 

difficult than estimating fluxes from SOC (Emmerich, 2003; 

Svejcar and others, 2008). Therefore, although SIC stocks will be 

estimated by using the Soil Survey Geographic database of the 

NRCS (SSURGO) and the State Soil Geographic Database of the 

NRCS (STATSGO; replaced in 2006 by the U.S. General Soil 

Map (STATSGO2)), no estimates of annual flux from SIC will be 

considered.

Data describing belowground nutrient cycling and carbon 

sequestration are sparse. More scientific studies aimed at 

evaluating biophysical processes occurring in soils are necessary to produce more reliable estimates of GHG flux for the 

United States. To this end, Follett and Reed (2010) identified 

the need for a national soil-carbon measurement and modeling 

network. Such a system would improve the understanding of 

soil processes and enable better GHG and carbon-sequestration estimates. In addition, more data are needed that describe 

the annual flux of carbon from inorganic sources, particularly 

in arid regions where little is known about the primary drivers 

and magnitude of this phenomenon.

H.5.3. Aquatic Data

Monitoring the aquatic processes related to GHG emission and carbon sequestration presents a set of unique challenges. Inland waters store and transport considerable carbon; 

thus, quantification of inland processes is critical to the understanding of carbon and GHG processes (U.S. Climate Change 

Science Program, 2007). Rivers in the conterminous United 

States export an estimated 30 to 40 million metric tons of 

carbon per year to the oceans in the form of dissolved and particulate organic carbon and inorganic carbon derived from the 

atmosphere (Pacala and others, 2001). The fate and magnitude 

of riverine carbon exported to the coast are critical to accurately quantifying regional and national carbon sequestration 

(Liu and others, 2000; U.S. Climate Change Science Program, 

2007). GHG fluxes also may be significant in estuaries and in 

the coastal ocean (Blair and others, 2004; Dagg and others, 

2004; Punshon and Moore, 2004; Biswas and others, 2007). 

Given the importance of carbon-sequestration processes and 

GHG production, it is vital to accurately monitor the fluxes 

and alterations of carbon and GHG in aquatic systems. It is 

envisioned that the national assessment will use data primarily 

from existing USGS streamgaging networks and water-quality 

programs for monitoring in the terrestrial domain (Seitzinger 

and Mayorga, 2008) and existing NOAA productivitymonitoring efforts for monitoring in the coastal oceans.

To reduce the uncertainty in modeling carbon fluxes 

to lakes, impoundments, estuaries, and coastal zones, it is 

essential to continue and expand existing hydrologic monitoring of the Nation’s rivers (streamflow and water quality). Sites 

should include a continuum from headwaters to the ocean, and 

better temporal coverage is needed for a range of hydrologic 

conditions. A large fraction of carbon transport occurs during 

short, intense events, many of which are driven by storms; 

thus, a combination of automated samplers and continuous, in-stream monitors are required to obtain improved flux 

estimates. A comprehensive set of constituents should be 

measured, including carbon, nitrogen, and phosphorus in all 

their major forms, as well as turbidity, suspended sediment, 

chlorophyll, temperature, and conductance. These data should 

be collected at a sufficient number of sites to allow regression 

model development within the USGS’s “spatially referenced 

regressions on watershed” water-quality model (SPARROW) 

and Load Estimator (LOADEST) modeling frameworks; this 

approach will provide the best available estimates of carbon 

and nutrient fluxes in rivers and to estuaries.

Despite the importance of GHG fluxes from lakes and 

impoundments in global carbon and GHG budgets, measurements are sparse and uncoordinated, and there is no 

centralized database. Methane emissions from the outlets of 

reservoirs may be particularly important, but they cannot be 

quantified at regional or national scales with currently available (2010) information. A monitoring program is needed to 

estimate regional and national GHG fluxes from the surfaces 

of lakes and impoundments and from the outlets of reservoirs.

Estimates of carbon burial in lakes and impoundments 

have uncertainty because of the sparseness of sedimentationrate and carbon-content data used to parameterize statistical 

models. Existing reservoir-monitoring programs should be 

expanded to include lakes and small farm ponds.

H.5.4. Priorities for New Data Collection

The national assessment will rely on existing interagency 

programs for input data for the models that will be used to 

predict changes in carbon storage and GHG fluxes; however, the accuracy of some model predictions will be limited 

by sparse (or in some cases, nonexistent) datasets that are 

needed to parameterize model equations. New data-collection 

programs are needed to accurately quantify GHG fluxes and 

sequestration in various ecosystems, especially in nonforest and nonagricultural, terrestrial environments and aquatic 

ecosystems. The most critical data gaps in the availability of 

monitoring data are described in table H2. It is envisioned that 

the national assessment will coordinate with existing programs 

to ensure that these gaps are filled. It should be noted that two 

types of gaps are identified: gaps where ongoing monitoring is 

necessary to adequately constrain and calculate fluxes that will 

likely change under future climate regimes, and gaps where 

data should be collected for a limited time because insufficient 

data exist to accurately predict fluxes using parameters collected in current (2010) monitoring programs.
Appendix H 183

H.6. References Cited

[Reports that are only available online may require a subscription for 

access.]

Biswas, H., Mukhopadhyay, S.K., Sen, S., and Jana, T.K., 

2007, Spatial and temporal patterns of methane dynamics in 

the tropical mangrove dominated estuary, NE coast of Bay 

of Bengal, India: Journal of Marine Systems, v. 68, no. 1–2, 

p. 55–64, doiL10.1016/j.marsys.2006.11.001.

Blair, N.E., Leithold, E.L., and Aller, R.C., 2004, From 

bedrock to burial—The evolution of particulate organic 

carbon across coupled watershed-continental margin 

systems: Marine Chemistry, v. 92, no. 1–4, p. 141–156, 

doi:10.1016/j.marchem.2004.06.023.

Dagg, M., Benner, R., Lohrenz, S.E., and Lawrence, D., 2004, 

Transformation of dissolved and particulate materials on 

continental shelves influenced by large rivers—Plume processes: Continental Shelf Research, v. 24, p. 833–858.

Denning, A.S., ed., 2005, Science implementation strategy for the North American carbon program: U.S. Carbon Cycle Science Program, 67 p., accessed June 22, 

2010, at http://www.carboncyclescience.gov/documents/

nacp_sis_2005.pdf.

Downing, B.D., Bergamaschi, B.A., Evans, D.G., and Boss, 

Emmanuel, 2008, Assessing contribution of DOC from sediments to a drinking-water reservoir using optical profiling: 

Lake and Reservoir Management, v. 24, no. 4, p. 381–391.

Emmerich, W.E., 2003, Carbon dioxide fluxes in a semiarid 

environment with high carbonate soils: Agricultural and 

Forest Meteorology, v. 116, no. 1–2, p. 91–102.

Eswaran, H., Reich, P.F., Kimble, J.M., Beinroth, F.H., 

Padmanabhan, E., and Moncharoen, P., 2000, Global 

carbon stocks, in Lal, R., Kimble, J.M., Stewart, B.A., and 

Eswaran, H., eds., Global climate change and pedogenic 

carbonates: Boca Raton, Fla., Lewis Publishers, p. 15–25.

Table H2.  Critical data gaps in the monitoring effort and recommended solutions.

[Abbreviations and acronyms are as follows: DOC, dissolved organic carbon; DIC, dissolved inorganic carbon; NO3, nitrate; DON, dissolved organic nitrogen; 

USGS, U.S. Geological Survey; SPARROW, “spatially referenced regressions on watershed attributes” water-quality model; GHG, greenhouse gas; CH4, methane; N2

O, nitrous oxide; FLUXNET, flux network; m, meter; LIDAR, light detection and ranging]

Monitoring target Data gap and possible solutions

Continuous DOC, DIC, NO3, DON, chlorophyll, 

suspended sediment concentrations 

New sensors can be used to monitor these constituents’ concentrations inexpensively (Downing and 

others, 2008; Saraceno and others, 2009). Existing data are exclusively from discrete sampling. 

Recommend installation at key locations, and paired at the river inflow and estuary mouth.

Temperature, conductivity at USGS surface-water 

gaging stations

Existing USGS gaging network is largely lacking these data, which are critical for models used to 

predict dissolved and particulate fluxes of carbon and nutrients in rivers, such as SPARROW.

Small water bodies (primarily farm ponds) Sparsely available data suggest that GHG exchange between small water bodies and the atmosphere 

may dominate flux of CH4 and N2O from many landscapes. The distribution of small water bodies 

needs to be mapped at high resolution (less than 5 m), and GHG fluxes need to be measured as 

a subset of them. This will allow creation of statistical models that can be used to estimate GHG 

fluxes from small water bodies in a given area. 

Carbon-burial rates in lakes and impoundments There are insufficient data to create accurate statistical models for carbon burial in lakes and 

impoundments across the Nation. Collection and analysis of dated sediment cores from a small 

subset of lakes and impoundments would greatly improve national carbon-burial-rate estimates.

Groundwater levels and chemistry Fluctuations in groundwater levels drive carbon storage and GHG production in soils, and groundwater chemistry can influence nutrient fluxes in surface water and coastal systems. A national 

program for monitoring groundwater levels and chemistry is needed in order to accurately model 

GHG fluxes from soils and to estimate nutrient fluxes to surface water and coastal oceans. 

GHG flux Additional flux data are critically needed from a variety of domains, in particular for CH4

 and N2O. 

Data are especially needed for impoundments, grasslands, and wetlands nationwide. Existing 

FLUXNET tower sites should be expanded to include more sites and constituents (for example, 

CH4 and N2O). Airborne programs should be implemented to characterize spatiotemporal variability in point fluxes. 

Biomass (aboveground and belowground) Biomass monitoring should be expanded, especially in nonforested habitats, where long-term monitoring data are sparse. A combination of LIDAR, radar, and multispectral data might be suitable 

for this need, but additional research is needed to make these processes operational. 

Changes in boreal vegetation and soil in Alaska Because of the rapid rate of change in the Alaskan climate, it is critical to quickly develop an 

interagency, multidisciplinary monitoring program that would include establishment of long-term 

monitoring, spatial surveys, and remote-sensing capabilities. 

Ecosystem disturbances New research is needed to enhance national capabilities to detect, map, model, and project defoliation and mortality of forests caused by insect outbreaks and storm damages. A first step toward 

such national capabilities is a healthy long-term Landsat program, and availability of all Landsat 

scenes acquired and processed at the highest processing level.
184 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes

Follett, R.F., and Reed, D.A., 2010, Soil carbon sequestration 

in grazing lands—Societal benefits and policy implications: 

Rangeland Ecology and Management, v. 63, no. 1, p. 4–15, 

doi:10.2111/08-225.1.

Jawson, M.D., Shafer, S.R., Franzluebbers, A.J., Parkin, T.B., 

and Follett, R.F., 2005, GRACEnet—Greenhouse gas reduction through agricultural carbon enhancement network: 

International Journal of Soil and Tillage Research, v. 83, 

p. 167–172, doi:10.1016/j.still.2005.02.015.

Liu, J.T., Huang, J.S., Hsu, R.T., and Chyan, J.M., 2000, The 

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185

Appendix I. Data Management Technical Plan

programs such as the Forest Inventory and Analysis 

Program (FIA) of the U.S. Forest Service and the 

National Resources Inventory (NRI) of the Natural 

Resources Conservation Service)

• Climate, soil, and other biophysical data compilation 

required for the assessment

• Derived data storage, including geospatial data

• Table, chart, and report production

• Metadata production automation to the extent possible

This data-management system will consist of three subsystems: a data-storage subsystem; a data-conversion and integration subsystem; and a management, mapping, and reporting 

subsystem (fig. I1).

The national assessment of ecological carbon sequestration is based on a number of national capabilities including 

remote sensing, expert knowledge consultation, national 

inventory programs, land-use and land-cover maps, and simulation models. A data-processing, -management, and -serving 

system will be needed to provide the national assessment with 

data input, data output, information query and dissemination, 

and data-archive functionalities. The basic functions of the 

system should include the following:

• Remote-sensing data access

• Database building of expert knowledge of ecosystem 

processes and mitigation scenarios

• Database building of inventory and other in situ data 

(not the proprietary data held by national inventory 

Figure I1.  Diagram showing the overview of the proposed data-storage system for the national assessment of ecological carbon

sequestration. OGC, Open Geospatial Consortium, Inc.; WCS, Web Coverage Service; WFS, Web Feature Service.
186 Assessment Methodology for Carbon Stocks and Sequestration and Greenhouse Gas Fluxes—Public Review Draft

designed following the standards of the International Organization for Standardization (ISO) and the Federal Geographic 

Data Committee (FGDC). This subsystem will be designed to 

synchronize metadata with associated data when new data are 

added or existing data are updated.

The Oracle database engine, coupled with ESRI’s 

ArcSDE, will be able to store effectively all of the datasets 

and data types specified in this document. This data-storage 

solution is expandable to petabytes to accommodate the 

assessment requirements; a data-flow diagram is shown in 

figure I2.

I.2. Data Conversion and Integration Subsystem

The data-conversion and processing subsystem will be 

designed to provide key functions necessary for seamless 

integration of various datasets, including data and file-format 

conversion, reprojection, resampling, and other necessary geospatial transformations. These functions also will enable effective dissemination of results and data products derived through 

the national assessment. This subsystem will provide these 

functions via Web services for data access, exchange, and 

processing using standards-based interfaces. Open Geospatial 

Consortium, Inc. (OGC) and ISO data-service standards, such 

as Web Feature Service (WFS) and Web Coverage Service 

(WCS), will be adopted in the interface design. The interactive 

interfaces will allow other systems or clients to access the data 

through the Internet. In addition, this subsystem will provide 

capabilities for ingesting data from other remote data services 

and processing services. An overview of the processing subsystem is shown in figure I3.

For the prototype, OGC services will be provided by 

using ESRI’s ArcGIS Server. The ESRI software for OGC 

services integrates seamlessly with Oracle and ArcSDE, and 

allows for minimal development efforts.

I.1. Data Storage Subsystem

The data-storage subsystem will be designed to support storage of data types with different spatial and temporal characteristics. Database software and hardware will 

be selected to provide the capability of handling large data 

volumes. An existing Sun Fire 4800 server running Oracle’s 

Enterprise Edition Relational Database Management System 

(RDBMS) and the Environmental Systems Research Institute’s (ESRI) Spatial Database Engine (ArcSDE) will be 

used for the prototype.

Storage of geospatial data and maps will be achieved 

by using geographic information system (GIS) database 

techniques. Spatial and nonspatial indexing will be used to 

enhance the performance of data searching and loading. The 

data-storage subsystem must be able to hold raster geospatial 

data, vector geospatial data, tabular data with no geospatial 

component, and text data.

The data-storage subsystem will be designed to handle 

the following datasets that will be collected from existing 

Federal programs:

• Remotely sensed images

• Vegetation, land-cover, land-use, and change maps

• Flux-tower data

• Climate, soil, and biophysical data

Other datasets will be added to the data-storage subsystem as they are identified as necessary components during the 

national carbon development assessment. Small to moderate 

data volumes will be ingested to demonstrate the abilities 

of this subsystem to handle complex ecosystem data. This 

subsystem also will provide storage for derived data from the 

model simulations, as well as maps and reports.

Metadata will be collected and stored in this subsystem. To ensure compatibility, the metadata structure will be 

Figure I2.  Data-flow diagram

for the data-storage subsystem

to be used for the national

assessment of ecological

carbon sequestration.
Appendix I 187

Figure I3.  Diagram showing

the data conversion and

integration subsystem to

be used for the national

assessment of ecological

carbon sequestration. API,

application programming

interface; ArcSDE, ESRI’s

Spatial Database Engine;

CSW, Catalogue Service for

Web; OGC, Open Geospatial

Consortium, Inc.; WCS, Web

Coverage Service; WFS, Web

Feature Service; WMS, Web

Map Service.

I.3. Management, Mapping, and Reporting 

Subsystem

The managing, mapping, and reporting subsystem 

will provide Web-based tools for users to view, search, and 

update their databases remotely. User authentication and 

access control will be implemented to enable secure data 

access and preserve data integrity. This subsystem also will 

provide functionalities for producing maps and reports using 

datasets collected by other Federal partners or produced 

through the national carbon assessment, or for extracting 

subsets of these data and saving them into a database or other 

desired formats. Web-based techniques will be used in developing this subsystem. The ESRI ArcGIS Server software 

product allows for Web-based delivery and custom tools to 

be developed.

I.4. System Technology Components

The planned system architecture is built on technology 

already in use at the Earth Resources Observation and Science 

(EROS) Center of the U.S. Geological Survey (USGS). The 

system architecture will use Oracle’s Real Application Clusters (RAC) as its database, running ESRI’s ArcSDE management software to add capacity for geospatial data. By leveraging Oracle RAC, the database subsystem can be spread across 

multiple systems and will provide increased availability and 

performance while using inexpensive commodity hardware.
Manuscript approved on November 17, 2010.

Prepared by the Reston Publishing Service Center.

Edited by Elizabeth D. Koozmin (lead editor), Marilyn A. Billone,

Bonnie S. Fink, Elizabeth E. Good, Jeanette C. Ishee, Katherine L. Laub,

Kim L. Otto, Katharine S. Schindler, and Beth A. Vairin.

Graphics by Anna N. Glover (lead), Jeannette M. Foltz, Linda M. Masonic,

and Gary A. Nobles.

Design and typography by Anna N. Glover (lead) and Cathy Y. Knutson.

Web support by Patricia A. Campbell.

Printing support by Cathy Y. Knutson and Gary A. Nobles.

For more information concerning the research in this report, contact

Zhiliang Zhu

U.S. Geological Survey

MS 519 National Center

12201 Sunrise Valley Drive

Reston, VA 20192

Zhu (ed.) and others—Assessing Carbon and Greenhouse-Gas Fluxes in Ecosystems—Scientific Investigations Report 2010–5233