Skip to main content
ORNL DAAC HomeNASA Home
DAAC Home > Get Data > NASA Projects > North American Carbon Program (NACP) > User guide
NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data

Get Data
Revision Date: August 21, 2013

Summary:

This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013).

The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).

The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).

An additional summary file of annual mean NEE (2000-2006) is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided.

The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files.

Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.

This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.

GHG inventory map


Figure 1. North American inventory zones map.

This data set is related to two other processed regional data sets (i.e., NACP Regional: Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs; and NACP Regional: Supplemental Gridded Observations, Biosphere and Inverse Model Outputs); and to the originally-submitted NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs. The data set was also used in the model-inventory comparison activities of the NACP Regional Synthesis.

This data set was compiled by the Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities. The overall objective of MAST-DC was to provide data management support to NACP investigators and agencies performing modeling and synthesis activities. The products were used in the model-inventory comparison activities of the NACP Regional Synthesis.

 

Data and Documentation Access:

Get Data: http:daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1179

Companion Documentation for this Data Set:

Model Characteristics Overview and References: NACP_Model_Characteristics.pdf

Related Data Products:

NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs
NACP Regional: Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs [http:daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1157]
NACP Regional: Supplemental Gridded Observation, Biosphere and Inverse Model Outputs [http:daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1158]

 

Data Citation:

Cite this data set as follows:

Wei, Y., D.J. Hayes, M.M. Thornton, W.M. Post, R.B. Cook, P.E. Thornton, A.R. Jacobson, D.N. Huntzinger, T.O. West, L.S. Heath, B. McConkey, G. Stinson,W. Kurz, B. de Jong, I. Baker, J. Chen, F. Chevallier, F.M. Hoffman, A. Jain, R. Lokupitiya, D.A. McGuire, A. Michalak, G.G. Moisen, R.P. Neilson, P. Peylin, C. Potter, B. Poulter, D. Price, J. Randerson, C. Rodenbeck, H. Tian, E. Tomelleri, G. van der Werf, N. Viovy, J. Xiao, N. Zeng, and M. Zhao. 2013. NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA http://dx.doi.org/10.3334/ORNLDAAC/1179

 

Table of Contents:

 

1. Data Set Overview:

Project: North American Carbon Program (NACP)

The NACP (Denning et al., 2005; Wofsy and Harriss, 2002) is a multidisciplinary research program to obtain scientific understanding of North America's carbon sources and sinks and of changes in carbon stocks needed to meet societal concerns and to provide tools for decision makers. Successful execution of the NACP has required an unprecedented level of coordination among observational, experimental, and modeling efforts regarding terrestrial, oceanic, atmospheric, and human components. The program has relied upon a rich and diverse array of existing observational networks, monitoring sites, and experimental field studies in North America and its adjacent oceans. It is supported by a number of different federal agencies through a variety of intramural and extramural funding mechanisms and award instruments.

NACP and MAST-DC organized several synthesis activities to evaluate and inter-compare biosphere model outputs and observation data at local to continental scales for the time period of 2000 through 2005. The synthesis activities have included three component studies, each conducted on different spatial scales and producing numerous data products: (1) site-level analyses that examined process-based model estimates and observations at over 30 AmeriFlux and Fluxnet-Canada tower sites across North America; (2) a regional, mid-continent intensive study centered in the agricultural regions of the United States and focused on comparing inventory-based estimates of net carbon exchange with those from atmospheric inversions; and (3) a regional and continental synthesis evaluating model estimates against each other and available inventory-based estimates across North America. A number of other NACP syntheses were conducted, including ones focusing on non-CO2 greenhouse gases, the impact of disturbance on carbon exchange, and coastal carbon dynamics. The Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) is the archive for the NACP synthesis data products.

NACP Regional Synthesis

This data set contains part of the third NACP synthesis product described above: regional analyses. The data products were compiled and used in model-inventory comparison activities.

 Table 1. Contributors

Last Name First Name Organization Email Role
Wei Yaxing ORNL weiy@ornl.gov MAST-DC
Hayes Daniel ORNL hayesdj@ornl.gov Synthesis Lead
Thornton Michele M. ORNL thorntonmm@ornl.gov MAST-DC
Post Wilfred M. University of Tennessee wmp@ornl.gov MAST-DC
Cook Robert B. ORNL cookrb@ornl.gov MAST-DC
Thornton Peter E. ORNL thorntonpe@ornl.gov MAST-DC, CLM-CN
Jacobson Andy NOAA andy.jacobson@noaa.gov Synthesis Lead, CarbonTracker
Huntzinger Deborah N. Northern Arizona University deborah.huntzinger@nau.edu Synthesis Lead
Baker Ian Colorado State University baker@atmos.colostate.edu SIB3.1
Chen Jing University of Toronto chenj@geog.utoronto.ca BEPS
Chevallier Frederic LSCE Frederic.Chevallier@lsce.ipsl.fr LSCE no.2
Hoffman Forrest ORNL forrest@climatemodeling.org CLM-CASA
Jain Atul University of Illinois at Urbana-Champaign jain@atmos.uiuc.edu ISAM
Liu Shuguang USGS sliu@usgs.gov EC-LUE
Lokupitiya Ravi Colorado State University ravi@atmos.colostate.edu CSU no.1
McGuire David A. University of Alaska Fairbanks ffadm@uaf.edu TEM6
Michalak Anna Stanford University michalak@stanford.edu Michigan Geostatistical
Moisen Gretchen G. USDA Forest Service gmoisen@fs.fed.us U.S. Forest Biomass
Neilson Ronald P. USDA Forest Service rneilson@fs.fed.us MC1
Peylin Philippe LSCE Philippe.Peylin@lsce.ipsl.fr
LSCE no.1
Potter Chris NASA Ames Research Center chris.potter@nasa.gov NASA-CASA
Poulter Ben Potsdam Institute for Climate Impact Research ben.poulter@pik-
potsdam.de		 LPJmL
Price David Canadian Forest Service dprice@nrcan.gc.ca
Can-IBIS
Randerson Jim University of California Irvine jranders@uci.edu CASA-Transcom
Rödenbeck Christian Max Planck Institute for Biogeochemistry christian.roedenbeck@bgc-jena.mpg.de Jena
Tian Hanqin Auburn University tianhan@auburn.edu DLEM
Tomelleri Enrico Max Planck Institute for Biogeochemistry etomell@bgc-jena.mpg.de
MOD17-plus
van der Werf Guido University Amsterdam guido.van.der.werf@falw.vu.nl CASA-GFEDv2
Viovy Nicolas LSCE viovy@lsce.ipsl.fr
ORCHIDEE
West Tristram O. PNNL tristram.west@pnnl.gov
Crop NPP
Xiao Jingfeng University of New Hampshire j.xiao@unh.edu EC-MOD
Zeng Ning University of Maryland at College Park zeng@atmos.umd.edu VEGAS
Zhao Maosheng University of Montana zhao@ntsg.umt.edu MODIS GPP/NPP


Table 2 lists all of the models and their corresponding output variables that were aggregated to inventory zones, along with their temporal coverage and resolution. For selected models and output variables, other variables were used to derive the data. For example, the aggregated Veg variable of model CLM-CASA was derived from LEAFC+WOODC+FROOTC, which was comparable to variable TOTVEGC of model CLM-CN or variable TotLivBiom of model MC1. A cell marked with “n/a” means a variable is neither directly available nor derivable from other variables for a model.

 

Table 2. List of Model and Variables Aggregated to Inventory Zones

Model Temporal Coverage* Temporal Resolution Variables
NEENPP Veg Rh FE
Terrestrial Biosphere Models CLM-CASA i01.54 2000-2004 monthly NEE NPP (LEAFC + WOODC +  FROOTC) HR n/a
i01.54_q15
i01.55
i01.55_q15
CLM-CN i01.56 2000-2004 monthly NEE NPP TOTVEGC HR n/a
i01.57_q15
ORCHIDEE 2001-2007 monthly CO2FLUX NPP n/a HET_RESP n/a
SiB3 2000-2005 monthly NEE n/a n/a n/a n/a
CASA-Transcom 2002-2003 monthly NEE n/a n/a n/a n/a
CASA GFed2 2000-2005 monthly NEEF n/a n/a n/a CFire (derived from NEEF-NEE)
NASA-CASA 2001-2004 monthly (-1 * NEP) NPP n/a (NPP – NEP) n/a
MC1 2000-2007 monthly NEE + BioCons NPP TotLivBiom RespH BioCons
TEM6 2000-2006 monthly NCE NPP VEGC RH (NCE - NEE)
DLEM 2000-2005 monthly NEE NPP TotLivBiom Rh n/a
VEGAS 2000-2007 monthly NEE NPP cvege Rh cfire
EC-MOD 2001-2006 monthly NEE n/a n/a n/a n/a
MOD17-plus 2000-2004 monthly (GPP – Reco) n/a n/a n/a n/a
ISAM 2000-2007 monthly NEE NPP n/a resp n/a
BEPS 2000-2004 monthly (-1 * NEP) NPP n/a (NPP - NEP) n/a
LPJmL 2000-2006 monthly, yearly NEE (monthly), ANEEF (annually, derived from ANEE+FIRE) NPP VEGC (annually) RH FIRE (annually)
Can-IBIS2000-2005 monthly,yearly NEE (derived from -1*NEP) NPP CBiomass (annually) Rh n/a
 
Inverse Models UToronto 2001-2003 monthly NEE n/an/a n/a n/a
CarbonTracker 2000-2007 monthly NEE n/a n/a n/a n/a
LCSE-no1 (Peylin) 2002-2004 monthly NEE n/a n/a n/a n/a
JENA (Rodenbeck) 2000-2007 monthly NEE n/a n/a n/a n/a
Michigan-Geostatistical 2000-2001 monthly NEE n/a n/a n/a n/a
CSU-no1 (MLEF-PCTM) 2003-2004 monthly NE n/a n/a n/a n/a
LSCE-no2 (Chevalier) 2000-2006 monthly NEE n/a n/a n/a n/a

Note: * Only data in and after year 2000 were included in aggregation.

 

Table 3. Data Product Summary

Data Set Category Sub Category Variables File Formats
Model Outputs Aggregated to Inventory Zones Terrestrial Biosphere Model (TBM) Outputs 5 (NEE, NPP, VegC, Rh, FE) Comma-Separated-Value (.csv)
Inverse Model (IM) Outputs 1 (NEE)
GHG Inventory Data National GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEE) by reporting zone and land sector 9 (forest area, live biomass C, soil organic matter/non-live, non-soil C, fire CO2 emissions, C removal as harvested products, NPP, HR, C emissions in processing of harvested products, NEE)
NEE (2000-2006) Summary Data Annual mean NEE (2000-2006) by sectors and zones in North America 1 (NEE) 

 

2. Data Description:

Aggregated Model Output

This data set contains two major categories of data:

(1) standardized gridded biosphere model and inverse model outputs aggregated to the inventory zones defined for North America (United States, Canada, and Mexico) and further divided into Forest Lands, Crop Lands, and Other Lands sectors for each inventory zone based on the 1-km resolution GLC2000 land cover map; and

 (2) North American national GHG inventories, which contain estimated land-atmosphere exchange of CO2 (NEE) in Forest Lands, Crop Lands, and Other Lands sectors synthesized from inventory-based data on productivity, ecosystem carbon stock change and harvested product stock change.

An additional summary file of annual mean NEE (2000-2006) by both land sectors and reporting zones in North America was created by combining the aggregated model output and the national GHG database and is provided.

The aggregated monthly/yearly model output data and the national GHG inventories data are stored in comma separated value (*.csv) format.

Spatial Data Connection

All the above data files are non-spatial data, but they can be linked to the two auxiliary spatial data files described below for further spatial analysis and mapping purposes. NAZonesFine.shp is a Shapefile that provides detailed inventory zones information, including zone names, boundaries, and associated zone attributes at a spatial scale of 1:15,000,000. NA_Reporting_Zones_Sectors.tif is a 1-km resolution reference zone and sector mask data file in GeoTIFF format that defines to which reporting zone and sector each 1-km grid cell belongs. It was created by merging the North American reporting zones shapefile with land sector masks.

 

2.1. Spatial Coverage

Site: North America

Site boundaries: (All latitude and longitude given in decimal degrees)

Site (Region) Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
North America -170 -50 84 10

2.2. Coordinate Reference System

The Coordinate Reference System (CRS) of both auxiliary spatial data files is WGS 84. Below is its definition in the format of Open Geospatial Consortium Well Know Text (OGC WKT) format:

GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]]

2.3. Spatial Resolution

The spatial scale for the inventory zone shapefile NAZonesFine.shp is 1:15,000,000. The spatial resolution for the reference zone & sector mask data file is 0.0089285714 degree, or about 1-km.

2.4. Temporal Coverage

Varies (range: 2000-2007)

2.5. Temporal Resolution

Monthly or yearly

2.6. Data File Information

2.6.1. Aggregated Model Output

For each TBM and IM model, a compressed file (model.zip) contains the aggregated output data files, organized by variable, with one data file for each of the monthly and yearly time periods in and after 2000. The output variables and temporal range for each model are listed in Table 2.

Table 4. Terrestrial Biosphere Model Output Data Files

TBM Output Data Files IM Output Data Files
TBM_BEPS_InventoryZone.zip IM_CSU-no1_Zone.zip
TBM_CASA-GFEDv2_InventoryZone.zip IM_CarbonTracker_Zone.zip
TBM_CASA-Transcom_InventoryZone.zip IM_JENA_Zone.zip
TBM_CLM-CASA_55_Zonal_Monthly.zip
IM_LCSE-no1_Zone.zip
TBM_CLM-CASA_i54_InventoryZone.zip
IM_LCSE-no2_Zone.zip
TBM_CLM-CASA_i54q15_InventoryZone.zip IM_Michigan-Geostatistical_Zone.zip
TBM_CLM-CASA_i55q15_InventoryZone.zip IM_UToronto_Zone.zip
TBM_CLM-CN_56_Zonal_Monthly.zip  
TBM_CLM-CN_57q15_Zonal_Monthly.zip  
TBM_CLM-CN_Zonal_Monthly.zip  
TBM_Can-IBIS_InventoryZone.zip  
TBM_DLEM_Zonal_Monthly.zip  
TBM_EC-MOD_Zonal_Monthly.zip  
TBM_ISAM_Zonal_Monthly.zip  
TBM_LPJmL_Zonal_Monthly.zip  
TBM_MC1_Zonal_Monthly.zip
TBM_MOD17-plus_Zonal_Monthly.zip  
TBM_NASA-CASA_Zonal_Monthly.zip  
TBM_ORCHIDEE_Zonal_Monthly.zip  
TBM_SiB3-plus_Zonal_Monthly.zip  
TBM_TEM6_Zonal_Monthly.zip  
TBM_VEGAS2_Zonal_Monthly.zip  

When a compressed file (model.zip) is expanded, the data files are organized by variable and named as follows:

Annual time-step data files are named as: model_variable_YYYY.csv

Monthly time-step data files are named as: model_variable_YYYYMM.csv

Example of Expanded Model Output File: TBM_CLM-CN_Zonal_Monthly.zip

TBM_CLM-CN_Zonal_Monthly/ (there is a subdirectory for each output variable)

NEE/

CLMCN_NEE_2000.csv (annual file, YYYY)

…

CLMCN_NEE_2004.csv

CLMCN_NEE_200001.csv (monthly file, YYYYMM)

…

CLMCN_NEE_200412.csv

NPP/

Rh/

Veg or VegC/

FE/

NEEF/

Note that IM model files only contain NEE data files.

Model Output File Contents:

For each variable, the following information is provided at each annual and monthly time step: a sum and area-weighted average for each zone, the sum of each variable by land cover type (forest, crop, other) for each zone, and the area-weighted average of each variable by land cover type (forest, crop, other) for each zone.

Table 5. Annual Data File Contents:

Column NameUnits Description
OBJECTID  Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis. Also, Zone_ID
Zone_  Name of reporting zone. Also, Zone_Name
SUM_variable* kgC/year Sum of variable for forest, crop, and other land cover types for zone. Sum of next three columns.
SUM_variable_F kgC/year Sum of variable for forest land cover type for zone.
SUM_variable_C kgC/year Sum of variable for crop land cover type for zone.
SUM_ variable_O kgC/year Sum of variable for all other land cover types for zone.
AVG_variable kgC/m2/year Area-weighted average of variable for forest, crop, and other land cover types for zone.
AVG_variable_F kgC/m2_of_forest_area/year Area-weighted average of variable for forest land cover type for zone.
AVG_variable_C kgC/m2_of_crop_area/year Area-weighted average of variable for crop land cover type for zone.
AVG_variable_O kgC/m2_of_other_area/year Area-weighted average of variable for all other land cover types for zone.
MODEL_NAME   Model abbreviation/acronym
PERIOD YYYY Year reported.

*variable = NEE, NPP, Rh, VegC**, FE, NEEF

**Note that VegC was reported as a single monthly estimate of biomass -- not the sum of more frequent estimates.


Example Data Records: BEPS_NEE_2000.csv

 

OBJECTID,Zone_,SUM_NEE,SUM_NEE_F,SUM_NEE_C,SUM_NEE_O,AVG_NEE,AVG_NEE_F,
AVG_NEE_C,AVG_NEE_O,MODEL_NAME,PERIOD
1,"Aguascalientes",1348576487.9333,354109096.48465,124316331.60469,870151059.8416,0.241047345,0.241495992,
0.258280493,0.238592579,"BEPS","2000"
2,"Alabama",-3469663161.320003,-52290465.266001,-2545006998.444,-872365697.6271,-0.025819, -0.000519,
-0.102347,-0.099634,"BEPS","2000"
3,"Alaska",1557942057.51001,4226545721.485,-503947616.85851,-2164656047.252998,0.001032567, 0.010325192,
-0.033816,-0.001996,"BEPS","2000"
…
37,"Kentucky",-6286697580.731999,-503608786.5066,-5536245211.075,-246843583.1518,-0.060367, -0.008801,
-0.124193,-0.105449,"BEPS","2000"
56,"New Jersey",-4621521636.3857,-3553303595.4253,-403854192.2452,-664363848.7137,-0.234434, -0.267431,
-0.179307,-0.159149,"BEPS","2000"
72,"San Luis Potosi",23016515438.107,7922971766.1432,10299937853.608,4793605818.3542,0.359743892, 0.493990662,
1.169769434,0.122484361,"BEPS","2000"

Table 6. Monthly Data File Contents:

Column NameUnits Description
OBJECTID  Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis. Also, Zone_ID
Zone_  Name of reporting zone. Also, Zone_Name
SUM_variable* kgC/month Sum of variable for forest, crop, and other land cover types for zone. Sum of next three columns.
SUM_variable_F kgC/month Sum of variable for forest land cover type for zone.
SUM_variable_C kgC/month Sum of variable for crop land cover type for zone.
SUM_variable_O kgC/month Sum of variable for all other land cover types for zone.
AVG_variable kgC/m2/month Area-weighted average of variable for forest, crop, and other land cover types for zone.
AVG_variable_F kgC/m2_of_forest_area/month Area-weighted average of variable for forest land cover type for zone.
AVG_variable_C kgC/m2_of_crop_area/month Area-weighted average of variable for crop land cover type for zone.
AVG_variable_O kgC/m2_of_other_area/month Area-weighted average of variable for all other land cover types for zone.
MODEL_NAME   Model abbreviation/acronym

*variable = NEE, NPP, Rh, VegC**, FE, NEEF

**Note that VegC was reported as a single monthly estimate of biomass -- not the sum of more frequent estimates.


Example Data Records: LPJmL_NPP_200412.csv

 

OBJECTID,Zone_,SUM_NEE,SUM_NEE_F,SUM_NEE_C,SUM_NEE_O,AVG_NEE,AVG_NEE_F,
AVG_NEE_C,AVG_NEE_O,MODEL_NAME,PERIOD
1,"Aguascalientes",-188405989.211,-50187718.2739,-16245868.144,-121972402.793,
-0.033676, -0.034227,-0.033753,-0.033444
2,"Alabama",2700373282.88,2079147028.96,447472522.485,173753731.432,
0.020094261,0.020634001, 0.017995067,0.01984454
3,"Alaska",-2039038892.45,-629978804.87,-58795085.4363,-1350265002.14,
-0.001351,-0.001539, -0.003945,-0.001245
…
95,"Wyoming",-596649117.106,-226724409.814,-6400282.72015,-363524424.572,
-0.002346,-0.003803,-0.002213,-0.001895
96,"Yucatan",-297105626.192,-222049975.169,-59293206.5953,-15762444.4289,
-0.007848, -0.007363,-0.008712,-0.017647
97,"Zacatecas",-1712269243.9,-304987190.751,-29052579.5864,-1378229473.57,
-0.022886,-0.023377,-0.030178,-0.022665

2.6.2. GHG Inventories and NEE

GHG inventories, which contain estimated land-atmosphere exchange of CO2 (NEE) in Forest Lands, Crop Lands, and Other Lands sectors synthesized from inventory-based data on productivity, ecosystem carbon stock change and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico.

For ease of comparison, the descriptions below of the *.csv data files are grouped by Forest Lands, Crop Lands, and Other Lands rather than by country.

 

Table 7. National GHG Inventory Data Files

FILE NAMES
COMPRESSED FILE NAMES UNCOMPRESSED DATA FILE NAMES
Inventory_CAN.zip Inventory_CAN_Crop.csv
Inventory_CAN_Forest.csv
Inventory_CAN_Other.csv
Inventory_MEX.zip Inventory_MEX_Forest.csv
Inventory_MEX_Other.csv
Inventory_USA.zip Inventory_USA_Crop.csv
Inventory_USA_Forest.csv
Inventory_USA_Other.csv

2.6.3. Attributes of Forest Lands GHG Inventories and NEEF

Canada Forest Lands: Table 8 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEF) by Canada reporting zone in forest lands sector.


Table 8. Attributes of Canada Forest Lands GHG Inventories

Attribute Attribute Column NameUnits Description
Zone ID Zone_ID  Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID  Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name  Name of reporting zone.
Forest Area Forest_Area km2 Area of forest in each reporting zone.
ΔLiveC dLiveC TgC yr-1Carbon stock change in live biomass pools
ΔDOM dDOM TgC yr-1Carbon stock changes in soil organic matter and in non-live, non-soil pools (standing dead, litter, coarse woody debris)
Fire (C) FireC TgC yr-1Fire emissions (carbon in all forms)
HR HCRemoved TgC yr-1Total C removed from the stand as harvests in the Managed Forest sector of each reporting zone
NPP NPP TgC yr-1Net Primary Productivity
Rh Rh TgC yr-1Heterotrophic Respiration
Fire (CO2) FireCO2 TgC yr-1Fire emissions (carbon in CO2 only)
HE HCEmitted TgC yr-1The amount of C emitted from the processing of harvested products within the forest sector = -0.3*HR
NEEF NEE TgC yr-1 ΔLiveC + ΔDOM - (Fire (C ) - Fire (CO2)) + HR + HE = NPP + Rh + Fire (CO2) + HE

U.S. Forest Lands: Table 9 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEf) by US States in forest lands sector.

Table 9. Attributes of US Forest Lands GHG Inventories

Attribute Attribute Column NameUnits Description
Zone ID Zone_ID  Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID  Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name  Name of reporting zone.
Forest Area Forest_Area km2 Area of forest in each reporting zone.
ΔLiveC dLiveC TgC yr-1 Carbon stock change in live biomass pools
ΔDOM dDOM TgC yr-1 Carbon stock changes in soil organic matter and in non-live, non-soil pools (standing dead, litter, coarse woody debris)
HR HCRemoved TgC yr-1 Total C removed from the stand as harvested products in each reporting zone
HE HCEmitted TgC yr-1 The amount of C emitted from the processing of harvested products within the forest sector = -0.3*HR
NEEF NEE TgC yr-1 NEEF = ΔLive + ΔDOM + HR + HE

Mexico Forest Lands: Table 10 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEf) by Mexico States in forest lands sector.


Table 10. Attributes of Mexico Forest Lands GHG Inventories

Attribute Attribute Column NameUnits Description
Zone ID Zone_ID  Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID  Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name  Name of reporting zone.
Forest Area Forest_Area km2 Area of forest in each reporting zone.
ΔLiveLUC Biomass_Conversion TgC yr-1 Carbon stock change in live biomass pools due to forest land use conversion
ΔSoilLUC dSoilC TgC yr-1 Carbon stock change in soil organic matter pools due to forest land use conversion
ΔLiveABND Abandonment TgC yr-1 Carbon stock change in live biomass pools due to biomass increment on forest land regenerating after agricultural abandonment
ΔLiveMNGD Fuelwood+Uptake TgC yr-1 Carbon stock change in live biomass pools due to biomass increment and fuelwood harvest in managed forest land
Total NEE Total_NEE TgC yr-1 Total Net Ecosystem Exchange from forest land

2.6.4. Attributes of Crop Lands GHG Inventories and NEEc

Canada Crop Lands: Table 11 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEc) by Canada reporting zone in crop lands sector.


Table 11. Attributes of Canada Crop Lands GHG Inventories

Attribute Attribute Column Name Units Description
Zone ID Zone_ID   Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID   Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name   Name of reporting zone.
Crop Area Crop_Area km2 Area of crop in each reporting zone.
NPP NPP TgC yr-1Crop Net Primary Productivity
HR Harvest TgC yr-1Total C removed as harvested crop products in each reporting zone
ΔSOILC dSoilC TgC yr-1Carbon stock change in soil organic matter pools in agricultural land
NEEC NEE TgC yr-1NEE = HR + ΔSOILC

US Crop Lands: Table 12 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEc) by US States in crop lands sector.

Table 12. Attributes of US Crop Lands GHG Inventories

Attribute Attribute Column Name Units Description
Zone ID Zone_ID   Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID   Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name   Name of reporting zone.
Crop Area Crop_Area km2 Area of crop in each reporting zone.
NPP NPP TgC yr-1 Crop Net Primary Productivity
HR Harvest TgC yr-1 Total C removed as harvested crop products in each reporting zone
ΔSOILC dSoilC TgC yr-1 Carbon stock change in soil organic matter pools in agricultural land
NEEC NEE TgC yr-1 NEE = HR + ΔSOILC

2.6.5. Attributes of Other Lands GHG Inventories and NEEo

Canada Other Lands: Table 13 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEo) by Canada reporting zone in other lands sector.

Table 13. Attributes of Canada Other Lands GHG Inventories

Attribute Attribute Column Name Units Description
Zone ID Zone_ID   Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID   Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name   Name of reporting zone.
AO Other_Area km2 Area represented in the Other Lands sector: the Total Reporting Zone Area minus Inventory Forest Area minus Inventory Crop Area
PopH Population 103 Human population (thousand persons) in each reporting zone estimated by overlaying reporting zone boundaries on census units containing year 2006 population estimates from Statistics Canada (www.statcan.gc.ca)
CH Human_Crop_Consumption TgC yr-1 Human crop consumption and CO2 emissions from human respiration estimated using per capita consumption (61.6 kg C yr-1) and respiration (54.0) rates from the U.S. data ([West et al., 2009])
EH Human_CO2_Respiration TgC yr-1
EL-CH4 Livestock_CH4_Emissions TgC yr-1Livestock (cattle and swine) methane emissions from Statistics Canada 2006 Census of Agriculture
EL Livestock_CO2_Emissions TgC yr-1 Estimated livestock CO2 emissions; the column total is equal to the national total HR adjusted for the net crop harvest export out of the country (27%*), minus national total human crop consumption and total C emitted as CH4 from livestock; the column total is distributed proportional to C emitted as CH4 from livestock in each reporting zone; *national-level crop harvest imports vs. exports is based on cash value, from the Canadian Socio-Economic Information Management System (Statistics Canada)
EF Forest_Product_CO2_Emissions TgC yr-1 Estimated CO2 emissions from decay of forest products; the column total is equal to the national total emissions from HWPE which is distributed proportional to human population in each reporting zone
NEEG NEE_Grasslands TgC yr-1NEE for grasslands estimated by multiplying the average grassland sink per area from the U.S. data (2.1 g C m-2 yr-1) by the "other" land area in each reporting zone
NEES NEE_Settlements TgC yr-1NEE for settled areas estimated by multiplying the average settlements sink per capita from the U.S. data (95.6 kg C per capita yr-1) by the human population in each reporting zone
NEEO Total_NEE_Other_Lands TgC yr-1NEEO = EH + EL + EF + NEEG + NEES

U.S. Other Lands: Table 14 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEo) by US States in other lands sector.

Table 14. Attributes of US Other Lands GHG Inventories

Attribute Attribute Column Name Units Description
Zone ID Zone_ID   Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID   Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name   Name of reporting zone.
AO Other_Area km2 Area represented in the Other Lands sector: the Total Reporting Zone Area minus Inventory Forest Area minus Inventory Crop Area (average area years 2000 - 2006)
PopH Population 103 Human population (thousand persons) is the average state population between 2000 and 2006, estimates from U.S. Census Bureau [2009]
CH Human_Crop_Consumption TgC yr-1For human consumption of crop products , there is a consistent respiration-to-consumption multiplier (1.14) across all age/gender classes in Table 1 of West et al., [2009], which was applied to the data on human respiration.
EH Human_CO2_Respiration TgC yr-1
EL-CH4 Livestock_CH4_Emissions TgC yr-1Livestock methane emissions from enteric fermentation from the USDA Greenhouse Gas Inventory [2008]
EL Livestock_CO2_Emissions TgC yr-1Estimated livestock CO2 emissions; the column total is equal to the national total crop harvest (HR from Table A5), adjusted for the net crop harvest export out of the country (HCPIMP – HCPEXP = -46.7 TgC yr-1between 2000 and 2006*), minus national total human crop consumption and total C emitted as CH4 from livestock; the column total is distributed proportional to C emitted as CH4 from livestock in each reporting zone; *national-level crop harvest imports vs. exports is based on volume converted to dry-weight biomass carbon using data in USDA Economic Research Service (2010)."Foreign Agricultural Trade of the United States (FATUS)" [2010] http://www.ers.usda.gov/data/fatus/
EF Forest_Product_CO2_Emissions TgC yr-1Estimated CO2 emissions from decay of forest products; the column total is equal to the national total emissions from HWP; the column total is distributed proportional to human population in each reporting zone
NEEG NEE_Grasslands TgC yr-1NEE for grasslands estimated by distributing the annual, national-level grassland sink estimates from the U.S. Environmental Protection Agency Greenhouse Gas Inventory [2010] proportional to the area of the Other Land sector in each reporting zone
NEES NEE_Settlements TgC yr-1NEE for settled areas estimated by distributing the annual, national-level settlements / other sink estimates from the U.S. Environmental Protection Agency Greenhouse Gas Inventory [2010] proportional to the human population in each reporting zone
NEEO Total_NEE_Other_Lands TgC yr-1NEEO = EH + EL + EF + NEEG + NEES

Mexico Other Lands: Table 15 lists the major components of GHG inventories and the estimation of land-atmosphere exchange of CO2 (NEEo) by Mexico States in other lands sector.

Table 15. Attributes of Mexico Other Lands GHG Inventories

Attribute Attribute Column Name Units Description
Zone ID Zone_ID   Unique ID of each reporting zone. It can be used to link back to the reporting zone polygons for mapping and spatial analysis.
SectorZone ID SectorZone_ID   Unique ID of each reporting zone and land sector combination. It can be used to link back to reference land sector mask data for mapping and spatial analysis.
Zone Name Zone_Name   Name of reporting zone.
AO Forest_Area km2 Area of forest in each reporting zone.
ΔLiveLUC Biomass_Conversion TgC yr-1Carbon stock change in live biomass pools due to non-forest land use conversion
ΔSoilLUC dSoilC TgC yr-1Carbon stock change in soil organic matter pools due to non-forest land use conversion
ΔLiveABND Abandonment TgC yr-1Carbon stock change in live biomass pools due to biomass increment on non-forest land regenerating after agricultural abandonment
ΔLiveMNGD Fuelwood+Uptake TgC yr-1Carbon stock change in live biomass pools due to biomass increment and fuelwood emissions in managed non-forest land
Total NEE Total_NEE TgC yr-1Total Net Ecosystem Exchange from forest land

2.7. Annual Mean NEE (2000-2006) by Sectors and Zones in North America

Annual mean NEE (2000-2006) summary data by both land sectors and reporting zones in North America (NEE_Zones_Sectors_Mean_2000-2006.csv) was created by combining the aggregated model output and the national GHG database. This NEE summary data can be used for model-inventory comparison purpose directly or be linked with the reference raster data (NA_Reporting_Zones_Sectors.tif) for mapping purpose. Table 16 describes the fields included in the NEE summary data.

Table 16. Attributes of NEE summary data

Attribute Attribute Column Name Units Description
Sector Zone ID Zone_ID  Unique ID for each sector-zone combination in the pattern of “NXY”, see section 4 for details
Sector Sector Sector type: forest, crop, or other
Zone Name Zone_Name Name of reporting zone
Country Country  Country
NEE from Forward Model NEE_Forward g C m-2 yr-1 Annual mean NEE (2000-2006) from mean of multiple forward models
NEE from Inverse Model NEE_Inverseg C m-2 yr-1 Annual mean NEE (2000-2006) from mean of multiple inverse models
NEE from GHG Inventory NEE_Inventoryg C m-2 yr-1 Annual mean NEE (2000-2006) from national GHG database

Example Data Records: NEE_Zones_Sectors_Mean_2000-2006.csv

Sector_Zone_ID,Sector,Zone_Name,Country,NEE_Forward,NEE_Inverse,NEE_Inventory
101,Forest Land,Aguascalientes,Mexico,4.80,16.52,13.78
102,Forest Land,Alabama,United States,-62.36,-114.35,-138.29
103,Forest Land,Alaska,United States,-10.36,-4.85,-59.48
…
395,Other Land,Wisconsin,United States,-71.98,-99.84,313.25
396,Other Land,Wyoming,United States,0.48,-34.85,11.98
397,Other Land,Yucatan,Mexico,-46.67,-1.12,-26.39
398,Other Land,Zacatecas,Mexico,3.67,-5.90,-8.69

2.8. Auxiliary Files

Detailed inventory zones information, including zone names, boundaries, and associated zone attributes are defined in ESRI Shapefile NAZonesFine.shp. File NAZonesFine.shp is described in Section 3 of the companion file, Regional-Standardized Inventory Zones.pdf. The following inventory zones attributes are included in the shapefile:

• Zone (string): Name of the state or reporting zone
• Country (string): The country to which the zone belongs
• SUM_Area (double): Area of the state or reporting zone (in square meters)
• SUM_AreaF (double): Area of forest land cover type within each Zone (in square meters)
• SUM_AreaC (double): Area of crop land cover type within each Zone (in square meters)
• SUM_AreaO (double): Area of “other” land cover type within each Zone (in square meters)

File NA_Reporting_Zones_Sectors.tif is described in Section 4 of the companion file, Regional-Standardized Inventory Zones.pdf. For the purpose of creating spatial maps as shown in Fig. 5 of Hayes et al. (2012), the North American reporting zones shapefile was merged with masks to create a 1km resolution reference raster data file (NA_Reporting_Zones_Sectors.tif) to describe which reporting zone and sector each 1km grid cell belongs to. The values in the reference raster data are 3-digit numbers following the pattern of “NXY”. If “N” is 1, it means the grid cell belongs to the forest land sector. Value “2” and “3” indicate the crop and “other” land sectors, repectively. “XY” is a 2-digit number representing the ID of reporting zones, as listed in Table 19 below. For example, a grid cell with value 141 means that 1km grid cell belongs to the forest sector and Maryland, U.S.

Table 17. Auxiliary Files

COMPRESSED FILE NAME FILE NAME DESCRIPTION
NAZonesFine.zip NAZonesFine.shp Detailed inventory zones information, including zone names, boundaries, and associated zone attributes at a spatial scale of 1:15,000,000
NA_Reporting_Zones_Sectors.zip   North American reporting zones shapefile merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference raster data file to describe to which reporting zone and sector each 1-km grid cell belongs.

 

2.9. Companion File Information

Table 18. Companion Files

FILE NAME DESCRIPTION
Regional-Description_of_Observations_and_Models.pdf Overview of observation measurement data and biosphere and inverse models, including descriptions, sources, contacts, and a comprehensive reference list
NACP_Model_Characteristics.pdf Overview of process descriptions in participating ecosystem or forward models and boundary conditions and driver data used in participating ecosystem or forward models with a comprehensive reference list
NACP_Model_Metadata_Survey_Results.pdf Metadata for Forward (Ecosystem) Model Intercomparison: Site Model Data Comparison and Regional Model Data Comparison: Survey Results
Regional-Standardized_Inventory_Zones.pdfs Provides a description of the aggregation process, inventory zones, and mapping methods.

 

3. Data Application and Derivation:

This data product contributes to a multidisciplinary research program to obtain scientific understanding of North America's carbon sources and sinks and of changes in carbon stocks needed to meet societal concerns and to provide tools for decision makers. The data were generated as part of an NACP regional and continental synthesis to evaluate and inter-compare models and observation measurements across North America.

map of locations

Figure 2. Mean area-weighted average annual NEE (g C m-2 yr-1), 2000–2006 for the Forest Lands, Crop Lands and Other Lands sectors, along with all land (total), in each reporting zone, from inventory-based estimates against mean results from the sets of terrestrial biosphere (forward) models and inverse models. (source: Figure 5 in Hayes, et al., 2012)

4. Quality Assessment:

This study (1) created a set of modeled carbon flux variables (NEE, NPP, VegC, Rh, and Fire Emissions) at the inventory-zone scale by aggregating model results at 1-degree spatial resolution and (2) developed an approach for estimating NEE using inventory-based information over North America for a recent 7-year period (ca. 2000–2006). The approach notably retains information on the spatial distribution of NEE, or the vertical exchange between land and atmosphere of all non-fossil fuel sources and sinks of CO2, while accounting for lateral transfers of forest and crop products as well as eventual emissions.

The GHG inventory-based results were derived from, and so are generally consistent with, recent inventory-based updates of the carbon budgets reported for Canada forests, U.S. forests and agriculture, and the agriculture and forest sector in Mexico. The new information provided in this study comes from the combination of those national- and sector-specific estimates into a continental-scale analysis, while using a novel conceptual model to estimate land-atmosphere exchange of CO2 at the sub-national scale. As a result, the inventory-based data and the methodology used in this study suggest considerable spatial variability in NEE estimates across sectors and reporting zones (Figure 1). The spatial patterns are driven both by the estimated direct, vertical surface fluxes, as well as the lateral transfer of carbon between sectors in the form of harvested products.

The inventory-based estimates of NEE were compared with results from a suite of terrestrial biosphere and atmospheric inversion models. The study used ‘off-the-shelf’ model simulations without a consistent set of driver forcing data or simulation protocols and other recently published studies. These simulations and studies served as a pre-cursor to more formal model inter-comparison activities that will follow this study.

The mean model estimates from both the terrestrial biosphere and inverse models suggest a much stronger overall North American sink than the inventory-based estimate. Yet model estimates generally do follow similar spatial patterns as the inventory-based data where the strongest sinks are found in US forests on the east and west coasts and in croplands of the midcontinent, with a smaller source from the tropical area of southern Mexico. However, the model vs inventory differences are mostly in the magnitude of the estimates.

The wide range in the land surface flux estimates is related to a number of factors, but most generally because of the different methodologies used to develop estimates of carbon stocks and flux, and the uncertainties inherent in each approach:

• Biomass inventories provide valuable constraints on changes in the size of carbon pools over years to decades. However, dynamics and fluxes can be under-sampled or missed altogether (e.g., inventory sampling can produce reliable estimates of biomass, but other carbon pools, such as litter and soil C stocks, may not have been sampled at the same intensity in all areas. Inventory-based modeling can be used to estimate growth and disturbance impacts, but does not yet provide full capability in partitioning the forcing brought about by non-disturbance factors. On the other hand, inventory and commerce data sets can often be used to quantify the storage, emissions and/or lateral movement of carbon in product pools, which are typically not well characterized in modeling approaches.

• TBMs can be used to simulate the dynamics of multiple ecosystem components. However, TBMs contain substantial uncertainty because of the sheer number of often poorly understood underlying processes simulated. They also vary widely in the data used to drive them, in the particular processes simulated, and in their level of detail.

• IM analyses provide constraints on estimates of land-atmosphere carbon exchange at a detailed temporal resolution, relying on the strong diurnal and seasonal cycles in CO2 concentration in the observations. However, these estimates are associated with large uncertainties from the limited density of observation networks, uncertainty in the transport models, and errors in the inversion process.

In summary, this study highlights the differences in three general scaling approaches to NEE (inventory, forward TBMs, and inverse modeling), and by comparing and evaluating their estimates several strengths and weaknesses emerge (Table 18). For additional information, consult Hayes et al. (2012).

Table 19. A comparison of the strengths and weaknesses of alternative NEE scaling approaches (inventory-based, IMs and TBMs (Source: Hayes et al., 2012).

  Inventory-based Atmospheric Inversion Models (IM) Terrestrial Biosphere Models (TBM)
Strengths Employs a large number of repeated biomass measurements Assimilates measurements of atmospheric CO2 concentration Processes are represented so attribution is possible
Allows estimation of product related C sources Employs atmospheric mass balance Sensitive to interannual variation in climate
    Many opportunities for validation
       
Weaknesses Not all C pools are measured Transport model uncertainty Many inputs, each with their own uncertainty
Potential under-sampling Limited number of CO2 measurements Many parameters, each with their own uncertainty
Limited attribution ability Low spatial resolution Spatial resolution may not resolve management scale disturbances
Missing NEE of unmanaged ecosystems Limited attribution ability  
Poorly resolved temporally    

5. Data Acquisition Materials and Methods:

5.1. Inventory Zones

The NACP standardized-gridded model output data were aggregated onto inventory zones in order to be compared with inventory-based national GHG data. The inventory zones for North America are defined as states for the United States and Mexico and ecoregion-based managed forest reporting zones for Canada. The definition of inventory zones for North America is consistent between the aggregated standardized-gridded model output data and the national GHG data. Figure 1 shows the map for all 97 inventory zones in North America and Table 19 lists the zone IDs and names.

Table 20. North American Inventory Zones

No Zone IDZone NameCountry
1 1 Aguascalientes Mexico
2 2 Alabama United States
3 3 Alaska United States
4 4 Arizona United States
5 5 Arkansas United States
6 6 Atlantic Maritime Canada
7 7 Baja California Mexico
8 8 Baja California Sur Mexico
9 9 Boreal Cordillera Canada
10 10 Boreal Plains Canada
11 11 Boreal Shield East Canada
12 12 Boreal Shield West Canada
13 13 California United States
14 14 Campeche Mexico
15 15 Chiapas Mexico
16 16 Chihuahua Mexico
17 17 Coahuila Mexico
18 18 Colima Mexico
19 19 Colorado United States
20 20 Connecticut United States
21 21 Delaware United States
22 22 District of Columbia United States
23 23 Distrito Federal Mexico
24 24 Durango Mexico
25 25 Florida United States
26 26 Georgia United States
27 27 Guanajuato Mexico
28 29 Guerrero Mexico
29 30 Hidalgo Mexico
30 31 Hudson Plains Canada
31 32 Idaho United States
32 33 Illinois United States
33 34 Indiana United States
34 35 Iowa United States
35 36 Jalisco Mexico
36 37 Kansas United States
37 38 Kentucky United States
38 39 Louisiana United States
39 40 Maine United States
40 41 Maryland United States
41 42 Massachusetts United States
42 43 Mexico Mexico
43 44 Michigan United States
44 45 Michoacan Mexico
45 46 Minnesota United States
46 47 Mississippi United States
47 48 Missouri United States
48 49 Mixedwood Plains Canada
49 50 Montana United States
50 51 Montane Cordillera Canada
51 52 Morelos Mexico
52 53 Nayarit Mexico
53 54 Nebraska United States
54 55 Nevada United States
55 56 New Hampshire United States
56 57 New Jersey United States
57 58 New Mexico United States
58 59 New York United States
59 60 North Carolina United States
60 61 North Dakota United States
61 62 Nuevo Leon Mexico
62 63 Oaxaca Mexico
63 64 Ohio United States
64 65 Oklahoma United States
65 66 Oregon United States
66 67 Pacific Maritime Canada
67 68 Pennsylvania United States
68 69 Puebla Mexico
69 70 Queretaro Mexico
70 71 Quintana Roo Mexico
71 72 Rhode Island United States
72 73 San Luis Potosi Mexico
73 74 Semiarid Prairies Canada
74 75 Sinaloa Mexico
75 76 Sonora Mexico
76 77 South Carolina United States
77 78 South Dakota United States
78 79 Subhumid Prairies Canada
79 80 Tabasco Mexico
80 81 Taiga Cordillera Canada
81 82 Taiga Plains Canada
82 83 Taiga Shield East Canada
83 84 Taiga Shield West Canada
84 85 Tamaulipas Mexico
85 86 Tennessee United States
86 87 Texas United States
87 88 Tlaxcala Mexico
88 89 Utah United States
89 90 Veracruz Mexico
90 91 Vermont United States
91 92 Virginia United States
92 93 Washington United States
93 94 West Virginia United States
94 95 Wisconsin United States
95 96 Wyoming United States
96 97 Yucatan Mexico
97 98 Zacatecas Mexico

 

5.2. Forest/Crop/Other Land Sectors

The aggregation of gridded model output into different land sectors for each reporting zone requires a mechanism to calculate the fraction of forest, crop, and other land sectors. In this NACP regional interim synthesis activity, it’s achieved by analyzing the Global Land Cover 2000 (GLC2000) (Bartholome and Belward, 2005) data at 1-km spatial resolution.

GLC2000 data uses a land cover classification scheme containing 22 different types mapped at a pixel resolution of 1 x 1 km. These types were first mapped to 3 sectors for the NACP analyses: forest, crop, and other, as described in Table 20. For detailed information on the mapping process, please see the companion file, Regional-Standardized Inventory Zones.pdf, pp 6-8.

Table 21. Mapping between GLC2000 Land Cover Classes and Group Classes (Forest, Crop, and Other)

Land Cover Classes Group Classes
1 Tree Cover, broadleaved, evergreen Forest
2 Tree Cover, broadleaved, deciduous, clsd
3 Tree Cover, broadleaved, deciduous, open
4 Tree Cover, needle-leaved, evergreen
5 Tree Cover, needle-leaved, deciduous
6 Tree Cover, mixed leaf type
7 Tree Cover, flooded, fresh water
8 Tree Cover, flooded, saline
9 Mosaic: Tree Cover/Other Veg
10 Tree Cover, burnt
11 Shrub Cover, closed-open, evergreen Other
12 Shrub Cover, closed-open, deciduous
13 Herbaceous Cover, closed-open
14 Sparse herbaceous or shrub
15 Regular flooded shrub or herbaceous
16 Cultivated and managed areas Crop
17 Mosaic: Cropland/Tree Cover/Other Veg
18 Mosaic: Cropland/Shrub and/or grass
19 Bare Areas Other
20 Water Bodies
21 Snow and Ice
22 Artificial surfaces
23 No data

5.3. Aggregated Model Output

Selected variables, including NEE, NPP, Veg, Rh, and FE, depending on availability, of the NACP standardized-gridded model output data were aggregated from 1-degree spatial resolution to the North American inventory zones.

5.3.1. Models and Variables

Table 2 lists all the models and their corresponding output variables that were aggregated to inventory zones, along with their temporal coverage and resolution.

5.3.2. Aggregation Procedure

The aggregation process started with the standardized gridded model output data that have been standardized to a common geographic projection and a 1-degree resolution gridded format. The aggregation processing was performed using the commercial GIS software, ESRI ArcGIS. Key input layers were derived separately and used within a processing model built with the ArcGIS Model Builder. The overall processing model is shown in Figure 3.

For detailed information on the aggregation procedure, please see the companion file, Regional-Standardized Inventory Zones.pdf, pp 10-13.

aggregation model diagram

Figure 3. The aggregation model diagram within ArcGIS Model Builder

 

5.4. National GHG Inventories

The national GHG inventories database compiled for the NACP synthesis activities contains inventory-based data on productivity, ecosystem carbon stock change and harvested product stock change to produce estimates of land-atmosphere exchange of CO2 ( NEE) for the 2000 to 2006 time period for the Forest Lands and Crop Lands sectors in Canada and the United States. Additional information from national-level GHG inventories was used to fill in data on carbon balance in the Other Lands sector, including data on human and livestock consumption of harvested products. For Mexico, the national GHG inventories database accounts primarily for carbon flux caused by land use change according to the study by de Jong et al. (2010), which covers the period of 1993 to 2002. Data on carbon exchange for each sector are summarized according to GHG inventory “reporting zones.”

The methodology for producing estimates of NEE for each country/sector during the study period can be found from S4. Supporting Information Tables and Figures in Hayes et al. (2012). For additional information, please see the companion file, Regional-Standardized Inventory Zones.pdf, pp 14-15.

6. Data Access:

This data set is available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Data Archive:

Web Site: http://daac.ornl.gov

Contact for Data Center Access Information:

E-mail: uso@daac.ornl.gov
Telephone: +1 (865) 241-3952


7. References:

Bachelet, D., J.M. Lenihan, C. Daly, R.P. Neilson, D.S. Ojima, and W.J. Parton. 2001. MC1: A dynamic vegetation model for estimating the distribution of vegetation and associated ecosystem fluxes of carbon, nutrients, and water. USDA General Technical Report PNW-GTR-508. 95 pp. [http://www.treesearch.fs.fed.us/pubs/2923]

Bartholome, E., and A.S. Belward. 2005. GLC2000: a new approach to global land cover mapping from earth observation data. International Journal of Remote Sensing 26: 1959–1977. doi: 10.1080/01431160412331291297

de Jong, B., C. Anayab, O. Maserac, M. Olguína, F. Pazd, J. Etcheversd, R.D. Martínezc, G. Guerreroc, and C. Balbontíne. 2010. Greenhouse gas emissions between 1993 and 2002 from land-use change and forestry in Mexico. Forest Ecology and Management 260(10): 1689–1701. doi:10.1016/j.foreco.2010.08.011

Denning, A.S., et al. 2005. Science implementation strategy for the North American Carbon Program: A Report of the NACP Implementation Strategy Group of the U.S. Carbon Cycle Interagency Working Group. U.S. Carbon Cycle Science Program, Washington, DC. 68 pp.

Dickinson, R.E., K.W. Oleson, G. Bonan, F. Hoffman, P. Thornton, M. Vertenstein, et al. 2006. The Community Land Model and its climate statistics as a component of the Community Climate System Model. Journal of Climate 19(11): 2302-2324. doi:10.1175/JCLI3742.1

Environment Canada (2011) National Inventory Report 1990–2009: Greenhouse Gas Sources and Sinks in Canada. The Government of Canada’s Submission to the UN Framework Convention on Climate Change. Environment Canada, Ottawa, ON. Available at: http://www.ec.gc.ca/ges-ghg/(accessed 16 May 2011).

EPA (2011) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009. USEP-A #430-R-11-005. U.S. Environmental Protection Agency, Washington, DC. Avail-able at: http://www.epa.gov/climatechange/emissions/usinventoryreport.html (accessed 15 April 2011).

GLC2000. 2003. Global Land Cover 2000 database. European Commission, Joint Research Centre.

Hayes, D.J., D.P. Turner, G. Stinson, A.D. McGuire, Y. We1, T.O. West, L.S. Heath, B. de Jong, B.G. McConkey, R.A. Birdsey, W.A. Kurz, A.R. Jacobson, D.N. Huntzinger, Y. Pan, W.M. Post, and R.B. Cook. 2012. Reconciling estimates of the contemporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem exchange from inventory-based data. Global Change Biology 18(4): 1282–1299. doi:10.1111/j.1365-2486.2011.02627.x

Thornton, P.E., and N.A. Rosenbloom. 2005. Ecosystem model spin-up: estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecological Modelling 189(1-2): 25-48. doi:10.1016/j.ecolmodel.2005.04.008

USDA Economic Research Service. 2010. Foreign Agricultural Trade of the United States (FATUS). [http://www.ers.usda.gov/data/fatus/]

USDA. 2008. U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2005, Technical Bulletin No. 1921, Global Change Program Office, Office of the Chief Economist, U.S. Department of Agriculture.

Y. Wei, W.M. Post, R.B. Cook, D.N. Huntzinger, P.E. Thornton, A. Jacobson, I. Baker, J. Chen, F. Chevallier, F. Hoffman, A. Jain, S. Liu, R. Lokupitiya, D.A. McGuire, A. Michalak, G.G. Moisen, R.P. Neilson, P. Peylin, C. Potter, B. Poulter, D. Price, J. Randerson, C. Rödenbeck, H. Tian, E. Tomelleri, G. van der Werf, N. Viovy, T.O. West, J. Xiao, N. Zeng, and M. Zhao. 2013. NACP Regional: Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1157

West, T., G. Marland, N. Singh, B. Bhaduri, and A. Roddy. 2009. The human carbon budget: an estimate of the spatial distribution of metabolic carbon consumption and release in the United States. Biogeochemistry 94: 29-41. doi:10.1007/s10533-009-9306-z

Wofsy, S.C., and R.C. Harriss. 2002. The North American Carbon Program (NACP). Report of the NACP Committee of the U.S. Interagency Carbon Cycle Science Program. U.S. Global Change Research Program, Washington, DC. 56 pp.

Additional Sources of Information:

Blackard, J.A. et. al. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of the Environment. 112: 1658-1677. doi:10.1016/j.rse.2007.08.021

Chen, J.M., J. Liu, J. Cihlar, and M.L. Guolden. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling 124: 99-119. doi:10.1016/S0304-3800(99)00156-8

Please also see the Reference section in the NACP Regional Synthesis - Description of Observations and Models document for publications related to the TBMs and IMs used in this study.