Biogeosciences [B]

B23B MCC:3001 Tuesday 1340h

Carbon Cycle Science in North America: Recent Results Relevant to the North American Carbon Program III

Presiding:A E Andrews, NOAA Climate Monitoring and Diagnostics Laboratory; P P Tans, NOAA Climate Monitoring and Diagnostics Laboratory; S Denning, Colorado State University

B23B-01 INVITED 13:40h

Soil Carbon Dynamics in US Agricultural Ecosystems

* Paustian, K (keithp@nrel.colostate.edu) , Natural Resource Ecology Lab, Colorado State University, Ft. Collins, CO 80523
* Paustian, K (keithp@nrel.colostate.edu) , Dept. Soil and Crop Sciences, Colorado State University, Ft. Collins, CO 80523
Ogle, S (ogle@@nrel.colostate.edu) , Natural Resource Ecology Lab, Colorado State University, Ft. Collins, CO 80523
Easter, M (marke@nrel.colostate.edu) , Natural Resource Ecology Lab, Colorado State University, Ft. Collins, CO 80523
Killian, K (kendrick@nrel.colostate.edu) , Natural Resource Ecology Lab, Colorado State University, Ft. Collins, CO 80523
Williams, S (stevewi@nrel.colostate.edu) , Natural Resource Ecology Lab, Colorado State University, Ft. Collins, CO 80523

Historically, a significant C emission source, agricultural soils in the US are now largely in a state of `recovery' with respect to soil C stocks. Estimates from national inventory methods, employing either empirical-based models or dynamic simulation approaches, show soil C accumulations occurring on most agricultural soils, whereas a relatively small area of cultivated histosols($<$ 1 Mha) are a net source of CO2. Net soil C storage is estimated at around 10-20 Tg C /year, where mineral soils are accumulating 15-25 Tg/year and organic soils (histosols) are emitting 5-10 Tg C per year. C sink and source strengths vary considerably across the continental US. Regional differences in land use and management practices and climatic conditions are the main drivers of the spatial heterogeneity in sinks/sources. The availability, quality and limitations of the driving variables needed for national-scale estimates of agricultural C dynamics are discussed along with methods and results of uncertainty analyses.

B23B-02 14:00h

Assessing Carbon Storage and Flux in Agricultural Soils

* Lal, R (Lal.1@osu.edu) , The Ohio State University, 2021 Coffey Rd., Columbus, OH 43210 United States

The heightened importance of assessing carbon (C) storage and flux in agricultural soils is attributed to: (i) being a source of atmospheric CO2 since the dawn of settled agriculture 10,000 years ago, (ii) the large sink capacity for C sequestration vis vis soils of other managed ecosystems, (iii) high risks of agricultural soils being a source of C because of seasonal perturbations associated with farming operations, (iv) hidden C costs of numerous input, (v) numerous ancillary benefits of C sequestration in agricultural soils, (vi) potential of increasing farm income through trading C credits, and (vii) a methodological challenge of assessing C storage and flux across a range of scales from an aggregate scale of a few mm to regional scale of thousands of km2. Agronomists have measured soil C pool as an index of soil fertility since late part of the 19th century. These measurements were usually made for the plow depth (20 cm) and reported in the units of concentration (g/kg, %). The significance of C storage and flux in agricultural soils, as a source or sink for atmospheric CO2 widely recognized since 1980s, has changed the boundary conditions of these measurements. In the context of climate change, soil C storage and flux must now be measured to at least 1-m depth (preferably to 2-m depth) and reported in the units of Mg/ha as pool and Mg/ha/yr as flux. In this regard, the new challenges faced by soil scientists are: (i) obtaining a sufficient number of samples to adequately represent the depth distribution of C, (ii) measuring soil bulk density for each layer, (iii) developing a sampling protocol to assess spatial and temporal variations in C pool and flux in relation to land use and management, (iv) aggregating the point data to soilscape, watershed and regional scales, and (v) relating C pool and fluxes to agronomic productivity on the one hand and to soil vs. climate processes on the other. Rapid progress is being made in addressing these new challenges by adapting traditional methods and developing innovative, cost-effective and time saving techniques.

B23B-03 14:20h

Soil Organic Matter \delta $^{13}$C across the Great Plains grasslands: both rainfall and temperature control C3 vs. C4 productivity

* von Fischer, J C (jcvf@lamar.colostate.edu) , Colorado State University, Dept. of Biology & Natural Resource Ecology Lab, Ft. Collins, CO 80523 United States
Tieszen, L L (tieszen@usgs.gov) , USGS - EROS Data Center, International Program Mundt Federal Building, Sioux Falls, SD 57198

Grass species that dominate the Great Plains grasslands assimilate CO2 by one of two photosynthetic systems, C3 or C4, with the proportions of these types largely controlled by climate. Because C3 and C4 plants differ in their magnitudes of carbon isotope fractionation, regional and global scale biogeochemical analyses (e.g., isotopic inversion studies of CO2) depend on knowing the relative activity of these groups and how they vary with climate. However, regional analyses of C3/C4 proportions, such as Teeri and Stowe (1979), have measured species composition but not relative production. To quantify the relationship between climate and relative production, we have analyzed the carbon isotope composition of soil organic matter (SOM) from 75 native prairie relicts across the Great Plains and compared these values to long-term (30-year mean) climate data. Although temperature has long been recognized as a key determinant of C3 vs. C4 success because of its effects on photorespiration, we find that the timing of rainfall is an additional important predictor; increased rainfall in summer months leads to increased relative production of C4 species. This finding adds complexity to the dominant view that temperature alone controls C3/C4 balance, and suggests that regional scale biogeochemical studies should evaluate the degree to which rainfall is included as a predictor of C3 vs. C4 productivity.

B23B-04 14:35h

Aboveground Live Forest Biomass Map for the US From Satellite Imagery and Inventory Data

* Helmer, E (ehelmer@fs.fed.us) , USFS Intl. Institute of Tropical Forestry, Jardin Botanico Sur 1201 Calle Ceiba, Rio Piedras, PR 00926 Puerto Rico
Blackard, J (jablackard@fs.fed.us) , USFS Rocky Mtn. Research Station, 324 25th St, Ogden, UT 84401 United States
Finco, M (mfinco@fs.fed.us) , USFS Remote Sensing Applications Center, 2200 W 2300 S, Salt Lake City, UT 84119 United States
Holden, G (gholden@fs.fed.us) , USFS North Central Forest Expt. Station, 1992 Folwell Ave, St. Paul, MN 55108 United States
Hoppus, M (mhoppus@fs.fed.us) , USFS Northeastern Research Station, 11 Campus Blvd, Newtown Square, PA 19073 United States
Jacobs, D (djacobs@fs.fed.us) , USFS Southern Research Station, 4700 Old Kingston Pike, Knoxville, TN 37919 United States
Lister, A (alister@fs.fed.us) , USFS Northeastern Research Station, 11 Campus Blvd, Newtown Square, PA 19073 United States
Moisen, G (gmoisen@fs.fed.us) , USFS Rocky Mtn. Research Station, 324 25th St, Ogden, UT 84401 United States
Nelson, M (mdnelson@fs.fed.us) , USFS North Central Forest Expt. Station, 1992 Folwell Ave, St. Paul, MN 55108 United States
Riemann, R (rriemann@fs.fed.us) , USFS Northeastern Research Station, 11 Campus Blvd, Newtown Square, PA 19073 United States
Ruefenacht, B (bruefenacht@fs.fed.us) , USFS Remote Sensing Applications Center, 2200 W 2300 S, Salt Lake City, UT 84119 United States
Salajanu, D (dsalajanu@fs.fed.us) , USFS Southern Research Station, 4700 Old Kingston Pike, Knoxville, TN 37919 United States
Weyermann, D (dweyermann@fs.fed.us) , USFS Pacific Northwest Research Station, 1221 SW Yamhill, Portland, OR 97205 United States
Winterberger, K (kwinterberger@fs.fed.us) , USFS Pacific Northwest Research Station, 3301 C St, Anchorage, AK 99503 United States
Czaplewski, R (rczaplewski@fs.fed.us) , USFS Rocky Mtn. Research Station, 240 W Prospect Rd, Fort Collins, CO 85026 United States
Tymcio, R (rtymcio@fs.fed.us) , USFS Rocky Mtn. Research Station, 324 25th St, Ogden, UT 84401 United States
Brandeis, T (tbrandeis@fs.fed.us) , USFS Southern Research Station, 4700 Old Kingston Pike, Knoxville, TN 37919 United States

A gridded map of aboveground live forest biomass for the conterminous U.S., Alaska and Puerto Rico with a 250-m cell size resulted from integrating plot-level biomass estimates, from USDA Forest Service (USFS) nation-wide forest inventory data, with satellite imagery and ancillary geospatial data. The image and other predictor layers included MOD09 8-Day surface reflectance imagery (1) from the Moderate Resolution Imaging Spectroradiometer, MODIS-derived proportional tree cover (2), Landsat image-derived proportional land cover (3-4), climate averages (5-6) and topographic variables (7). By state or mapping zone (8), plot-based aboveground live forest biomass estimates generally fell within 5 percent of map-based estimates, and the map provided previously unavailable spatial detail. Here we describe the inventory data, the modeling approach, and the error maps. We secondly compare estimates of U.S. forest carbon storage in live woody biomass from this map with other estimates. We also critically evaluate the modeling process and spatial scaling issues. (1)Vermote EF, Vermueulen A. 1999. MOD09 ATBD, Univ. of Maryland, College Park, 107 pp. (2) Hansen M, DeFries R, et al. 2003. GLCF, Univ. of Maryland, College Park (3) Vogelmann JE, Howard S, et al. 2001. Photogramm Eng Rem S 67:650 (4) Helmer E, Ramos O, et al. 2002. Caribbean J Sci 38:165 (5) Daly C, Kittel T, et al. 2000. 12th AMS Conf on Applied Climatology, Amer Meteorol Soc, Asheville (6) Daly C, Helmer E, et al. 2003. Intl J Climatology 23:1359 (7) Gesch D, Oimoen M, et al. 2002. Photogramm Eng Rem S 68:5 (8) Homer C, Huang C, et al. 2004. Photogramm Eng Rem S 70:829

B23B-05 14:50h

Carbon Source and Sink Distribution in Canada's Forests and Wetlands Based on Remote Sensing, Forest Inventory, Large Fire Polygons, Drainage Class, Topography, and Climate

* Chen, J M (chenj@geog.utoronto.ca) , University of Toronto, Department of Geography 100 St. George St., Room 5047, Toronto, ONT M5S 3G3 Canada
Ju, W (juw@geog.utoronto.ca) , University of Toronto, Department of Geography 100 St. George St., Room 5047, Toronto, ONT M5S 3G3 Canada

Based on our previous work (Chen et al., 2003, Tellus B. 55(2): 622-642) on the carbon source and sink distribution in Canada's forests using Canada-wide data from remote sensing, forest inventory, large fire polygons for the last 50 years, soil texture and carbon data, nitrogen deposition measurements in 29 locations, and monthly gridded climate from 1901 to 1998, we here include the carbon balance of wetland areas estimated using additional data on the drainage class and digital terrain model. A simple TOPMODEL is used to model the water balance of wetland areas over last 100 years at 1 km resolution. The inclusion of wetland areas increases the carbon sink by about 25 MtC per year as wetlands have been carbon sinks in most years of the last century when methane is not included. As biomass data in inventory are missing over the northern part of boreal forests and soil carbon data are incomplete in many regions, intensive work is conducted to test model abilities to simulate these carbon pools against available data and then use the model to produce the complete coverages. The RMSE in estimated soil carbon in 2000 polygons with complete data reduced from 40 KgC m$^-2$ to 28 KgC m$^-2$ when wetland carbon accumulation processes are considered.

B23B-06 15:05h

Carbon Sequestration in Southeast US: Ecosystem responses to multiple stresses

* Tian, H (tianhan@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Chen, H (chenhua@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Zhang, C (zhangch@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Pan, S (panshuf@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Chen, G (chengu1@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Chen, S (chensiq@auburn.edu) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Liu, M (liuml@igsnrr.ac.cn) , School of Forestry and Wildlife Sciences, Auburn University, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849 United States
Melillo, J M (jmelillo@mbl.edu) , The Ecosystem Center, Marine Biological Laboratory, The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA 02543 United States
Kicklighter, D (dkick@mbl.edu) , The Ecosystem Center, Marine Biological Laboratory, The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA 02543 United States
Felzer, B (bfelzer@mbl.edu) , The Ecosystem Center, Marine Biological Laboratory, The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA 02543 United States

Land ecosystems in the southeast US are thought to be currently functioning as the largest carbon sink among the six major bioclimatic regions of the conterminous United States. Inverse-and inventory-based estimates on the carbon sink do not identify the mechanisms responsible for the carbon sink. Many of the major factors (and complex interactions) that affect this carbon sink are operating concurrently in the southeastern ecosystems. In this study, we intend to examine how ecosystem carbon storage has changed as a result of multiple stresses and interactions among those stresses including land-cover change, climate variability, atmospheric composition (carbon dioxide and tropospheric ozone), precipitation chemistry (nitrogen composition), and natural disturbances such as fire using estimates of carbon fluxes and storage from factorial simulation experiments with the Terrestrial Ecosystem Model (TEM) in conjunction with remotely sensed and field data. Our analysis suggests that the net carbon exchange of terrestrial ecosystems with the atmosphere in this region varied substantially from a source of 246gC/m2 to a sink of 201 gC/m2. Forest recovery after cropland abandonment and natural disturbances have resulted in an carbon uptake, but rapid urbanization and rising tropospheric ozone pollution have led to a significant reduction in carbon storage in the southeast.

http://www.sfws.auburn.edu/tian/

B23B-07 15:20h

Quantitative Flux Ecoregions for AmeriFlux Using MODIS

* Hoffman, F M (forrest@climate.ornl.gov) , Oak Ridge National Laboratory Climate and Carbon Research Institute, Building 5600, B109, MS 6008 P.O. Box 2008, Oak Ridge, TN 37831-6008 United States
Hargrove, W W (hnw@fire.esd.ornl.gov) , Oak Ridge National Laboratory Climate and Carbon Research Institute, Building 5600, B109, MS 6008 P.O. Box 2008, Oak Ridge, TN 37831-6008 United States

Multivariate Geographic Clustering was used with maps of climate, soils, and physiography and MODIS remotely sensed data products to statistically produce a series of the 90 most-different homogeneous flux-relevant ecoregions in the conterminous United States using a parallel supercomputer. Nine separate sets of flux ecoregions were produced; only two will be discussed here. Both the IB and IIIB maps were quantitatively constructed from subsets of the input data integrated during the local growing season (frost-free period) in every 1 km cell. Each map is shown two ways --- once with the 90 flux ecoregions colored randomly, and once using color combinations derived statistically from the first three Principal Component Axes. Although the underlying flux ecoregion polygons are the same in both cases, the statistically derived colors show the similarity of conditions within each flux ecoregion. Coloring the same map in this way shows the continuous gradient of changing flux environments across the US. The IB map, since it considers only abiotic environmental factors, represents flux-ecoregions based on potential vegetation. The IIIB map, since it contains remotely sensed MODIS information about existing vegetation, includes the effects of natural and anthropogenic disturbance, and represents actual or realized flux ecoregions. Thus, differences between the maps are attributable to human activity and natural disturbances. The addition of information on existing vegetation exerts a unifying effect on abiotic-only flux ecoregions. The Mississippi Valley and Corn Belt areas show large differences between the two maps. Map IIIB shows a mosaic of ``speckles'' in areas of intense human land use, ostensibly from disturbances like agriculture, irrigation, fertilization, and clearing. Such ``speckles'' are absent from areas devoid of intense human land use. Major cities are also evident in the IIIB map. We will use the quantitative similarity of the suite of flux-relevant ecosystem characteristics to modify existing flux measurements and estimate fluxes within unmeasured flux ecoregions. A number of investigators are trying to scale flux tower measurements up to represent larger geographic regions. The flux ecoregion approach is complementary to these bottom-up strategies, since it relies on remotely sensed data to scale flux tower measurements up to continental scales in a top-down way.

http://geobabble.ornl.gov/flux-ecoregions/