Terrestrial Biomass and the Effects of Deforestation on the Global Carbon Cycle

Environmental policymakers, particularly those who consider options for mitigating global warming, require accurate assessments of sources and sinks for atmospheric carbon dioxide. A case in point is the Kyoto Protocol of 1997, the first negotiated agreement for reducing net terrestrial emissions of carbon dioxide on an international scale. Emissions may be reduced by curbing air pollution sources, slowing “slash and burn” deforestation, and promoting regrowth of forest areas that have been logged. The goal of the Kyoto Protocol is to restrict average annual carbon emissions to a percentage of 1990 emissions. Successful implementation of the Kyoto Protocol depends on a reliable accounting of how much carbon is stored and released from the world's forests. Several major uncertainties have hindered the development of a more accurate carbon budget; these uncertainties have to do with global carbon cycling in terrestrial ecosystems, carbon storage in standing plant biomass, and the net effects of forest loss and secondary regrowth (Schimel et al. 1996a). During the 1980s, terrestrial exchange of carbon dioxide with the atmosphere may have accounted for carbon sources of 0.6–2.6 Pg/yr, mainly from land-use change in the tropics (IPCC 1994). The wide range in these source estimates from the Intergovernmental Panel on Climate Change, which exemplifies the difficulty in implementing agreements like the Kyoto Protocol, is due in large part to poorly quantified values for forest biomass and to variability in terrestrial productivity over scales that are relatively small (100–200 km) compared to global forest coverage. Uncertainties in the global carbon budget would be reduced to more acceptable levels with the development of improved techniques for estimating variability in carbon stocks over vast forested areas (IGBP 1998). One such technique is the use of ecosystem modeling of carbon pools and fluxes. Global ecosystem models are valuable tools in situations in which ground-based measurements of carbon pools are not adequate to realistically capture regional variability. A computer model of this type based on satellite measurements has been developed to simulate ecosystem carbon cycling (Potter and Klooster 1997, 1998). This model, the NASA-CASA (National Aeronautics and Space Administration-Carnegie Ames Stanford Approach) model, is designed to estimate daily and seasonal patterns in carbon fixation, plant biomass, nutrient allocation, litter fall, soil nutrient mineralization, and carbon dioxide exchange, including carbon emissions from soils worldwide. Direct input of satellite “greenness” data from the Advanced Very High Resolution Radiometer (AVHRR) sensor into the NASA-CASA model can be used to accurately estimate global monthly net primary production (NPP), biomass accumulation, and litterfall inputs to soil carbon pools at a geographic resolution of 1° latitude and longitude. Soil fertility factors included in the NASA-CASA model control the allocation of new plant growth to aboveground tissues (leaf and wood) versus fine-root tissue allocation for acquisition of soil nutrients. In this article, I examine several different methods for estimating changes in terrestrial biomass sources of atmospheric carbon dioxide using a combination of global satellite observations, ecosystem model (such as NASA-CASA) predictions of aboveground biomass for the late 1980s, and data on country-by-country changes in global forest cover for the years 1990–1995 (FAO 1997). When the NASA-CASA model is used, the analysis suggests that yearly net terrestrial losses of carbon dioxide from changes in the world's forest ecosystems are 1.2–1.3 Pg of carbon for the early 1990s. This estimate, which accounts for forest area regrowth and expansion sinks in temperate and boreal forest zones, is based on the most recent global maps for observed climate, soils, plant cover, and changes in forest areas from natural and human forces. Major carbon fluxes between the atmosphere and terrestrial biosphere are often expressed in terms of net biomass accumulation from annual NPP. Several methods exist for estimating terrestrial NPP over large areas of the globe. One set of methods is based on ecophysiology models, which link carbon metabolism with water, energy, and nutrient cycling in plants (e.g., Kindermann et al. 1996, Schimel et al. 1996b, Churkina and Running 1998). Other methods use remotely sensed data to provide direct time-series data on properties of the vegetation cover, such as changes in surface “greenness,” which is commonly expressed as leaf area index or canopy light absorption (e.g., Maisongrande et al. 1995, Potter et al. 1999) and which represents the status of the leaf canopy of any plant functional type (e.g., forest, savanna, grassland, tundra, or desert). Global NPP of vegetation can be predicted using the relationship between greenness reflectance properties and absorption of photosynthetically active radiation (PAR), assuming that net conversion efficiencies of PAR to plant carbon can be approximated for different ecosystems or are nearly constant across all ecosystems (Goetz and Prince 1998). A successful approach to estimating terrestrial plant production is based on the concept of vegetation greenness in the NASA-CASA formulation (Potter and Klooster 1999). Canopy greenness is measured using a Normalized Difference Vegetation Index (NDVI). NDVI is a unitless parameter (scaled from 0 to 1000) that is computed from the ratio of visible and near-infrared radiation reflected from the canopy as detected by the AVHRR satellite sensor. The AVHRRNDVI of greenness has been closely correlated with vegetation parameters such as the fraction of absorbed PAR (FPAR) and leaf area index (Running and Nemani 1988, Sellers et al. 1994, DeFries et al. 1995). Terrestrial NPP fluxes from the NASA-CASA model have been extensively validated against both seasonal patterns of atmospheric carbon dioxide measurements at sampling stations around the world (Denning 1994) and multi-year estimates of NPP from field stations and tree rings (Malmstrom et al. 1997). The monthly fraction of annual NPP flux, defined as net fixation of carbon dioxide by vegetation, is computed, in the NASA-CASA model, on the basis of light-use efficiency (Monteith 1972). Monthly production of plant bio-mass is estimated in the model as the product of surface solar irradiance, Sr (Bishop and Rossow 1991), FPAR from the AVHRR NDVI, and a light utilization efficiency term (emax) that is multiplied by air temperature (T) and soil moisture balance (W) stress scalars: The ɛmax term is set at 0.56 g/MJ (carbon/PAR), a value that derives from calibration of predicted annual NPP to previous field estimates of NPP (Potter et al. 1993). The T stress term is computed with reference to derivation of optimal temperatures (Topt) for plant production. The Topt setting varies with latitude and longitude, ranging from near 0 °C in the Arctic to the mid-30s (°C) in low-latitude deserts. The W term is estimated from monthly water deficits, which are based on a comparison of moisture supply (precipitation and stored soil water) to evapotranspiration water potential demands using the method of Thornthwaite (1948). Algorithms in the NASA-CASA model allow evapotranspiration to be connected to water content in the soil profile layers (Figure la; Potter 1997). The model design includes heat and moisture content computations for three soil layers: surface organic matter (M1), topsoil (0.3 m; M2), and subsoil to rooting depth (1–10 m; M3). These layers can differ in soil texture, moisture-holding capacity, and carbon-nitrogen dynamics. Water balance for each layer of the soil is modeled as the difference between precipitation plus volumetric percolation inputs, on the one hand, and monthly estimates of potential evapotranspiration plus drainage output, on the other. Inputs from rainfall can recharge the soil layers to field capacity. Excess water percolates through to lower layers and may eventually leave the system as seepage and runoff. In the NASA-CASA model, freeze-thaw dynamics with soil depth operate according to the empirical degree-day accumulation method (Jumikis 1966, as described by Bonan 1989). Structure of the NASA-CASA model. Predictions are based on plant functional types, such as deserts, grasses, and overstory woody plants (shrubs and trees). Plant functional types determine net primary productivity (NPP) and nutrient cycling rates, which in turn control trace gas fluxes, (a) Typical soil water balance is shown as the shaded depth level in soil profile layers (M1M3; see text for details). Soil water balance is a function of both precipitation (PPT) and potential evapotranspiration (PET). Freeze-thaw of the soil profile is a function of the seasonally accumulated heat flux, (b) Climate controls on NPP are defined by the equation NPP = Sr FPAR ɛmax T W, where Sr is solar irradiance (SOLAR), FPAR is fraction of absorbed photosynthetically active radiation (derived from NDVI, the Normalized Difference Vegetation Index), ɛmax is a model constant, T is air temperature (TEMP), and W is soil moisture balance (i.e., a function of PPT and PET). Controls on litter and soil carbon decomposition are a function of soil temperature (Temp), of the water-filled pore space (WFPS) of mineral soil, and of the ratio of litter nitrogen to lignin (Lit q). (c) Biogenic emission fluxes of soil trace gases include heterotrophic respiration (CO2), methane (CH4), and nitrous (N2O) and nitric (NO) oxides. Because it is based on plant production as the primary carbon and nitrogen cycling source, the NASA-CASA model is able to couple daily and seasonal patterns in soil nutrient mineralization plus soil heterotrophic respiration (Rh) of carbon dioxide with net nitrous oxide, nitric oxide, and methane emissions from soils worldwide. Net ecosystem production can be computed as NPP minus Rh fluxes, excluding direct effects of fire and other small-scale disturbances. The trace gas components of the NASA-CASA model (Potter et al. 1996a, 1996b) are summarized in Figures lb and lc. The soil model component of NASA-CASA uses a set of compartmental difference equations with a structure comparable to those of the CENTURY ecosystem model (Parton et al. 1992). First-order equations simulate exchanges of decomposing plant residue (metabolic and structural fractions) at the soil surface. They also simulate surface soil organic matter fractions that presumably vary in age and chemical composition. Active (microbial biomass and labile substrates), slow (chemically protected), and passive (physically protected) fractions of the soil organic matter are represented in the model. Along with moisture availability and litter quality, estimated soil temperature in the Ma layer controls soil organic matter decomposition. Interannual results of global net ecosystem production estimates from the NASA-CASA model (Potter and Klooster 1999, Potter et al. 1999) suggest that land plants have been absorbing more carbon dioxide in the Northern Hemisphere during the late 1980s than previously believed—almost one-third of the annual amount of carbon dioxide released from the burning of fossil fuels. When AHVRR satellite data are used in the NASA-CASA simulation model of net ecosystem production, the results show increasing carbon dioxide accumulation in vegetation over extensive areas of Canada, Europe, and Russia during 1985–1988. This simulation modeling indicates precisely which forest and tundra areas on Earth act as temporary “sinks” for atmospheric carbon dioxide in response to warmer than average spring temperatures or lower than average summer drought stress. Many large-scale disturbances, such as conversion of land to agriculture, may be detected in the AHVRR data used to drive the model. Soil fertility effects on biomass allocation are included in the NASA-CASA model. For global simulations, the 1 ° grid resolution Soil of the is in the NASA-CASA model according to three levels of soil fertility and the by and For soils, a is that increasing biomass from NPP for acquisition of soil and For and soils, a is that increasing and leaf biomass from NPP to in the canopy and et al. et al. 1997). These effects of fertility on allocation for forests. from tree is in the NASA-CASA model, in terms of the in of carbon in the standing woody tissue on the plant functional as percentage of and for leaf and fine-root biomass are expressed in a based on estimates from the These empirical values for and determine the accumulation of plant biomass in plant and soil pools across the 1° global and parameters for major plant functional and parameters for major plant functional AVHRR data for the 1980s have been from and Global These data of and temperatures for cover from the AVHRR the and Monthly NDVI data much of the due to cover that is in the daily AVHRR data of the satellite is to part of the Global and et al. 1994) of Space Sellers et al. developed and solar for AVHRR data to NDVI from global 1° data from for the in previous NDVI data including cover and These NDVI data show with of the (Malmstrom et al. which suggests that have been for and between For surface temperature and precipitation average values from and used with monthly 1° for the and 1993). monthly NDVI and solar irradiance data (Bishop and Rossow from the 1980s data (Potter and Klooster used to ecosystem model results of the For the 1980s, predicted NPP from the NASA-CASA model estimated at of carbon aboveground biomass leaf and estimated at Pg of carbon (Figure carbon storage in aboveground biomass predicted to be in those in more than with lower storage in of temperate carbon stocks in biomass than predicted carbon stocks in leaf biomass for most forest ecosystem areas (Figure carbon estimates for vegetation from the NASA-CASA model and the field data analysis of et al. carbon estimates for vegetation from the NASA-CASA model and the field data analysis of et al. from the NASA-CASA model for aboveground (a) (b) leaf Global patterns are of net primary production (NPP) computed using satellite and inputs to the NASA-CASA ecosystem model for the of the model predictions can be by comparison to ground-based For the NASA-CASA model value for global carbon in aboveground biomass, is than et al. estimate of which based on a data set of biomass are other estimates of standing biomass for predicted aboveground biomass from the NASA-CASA model for of This the lower et al. is in agreement with measured of aboveground biomass from recent field et al. 1995, et al. 1998). to the estimates of global aboveground biomass, average predicted aboveground biomass from the NASA-CASA model for and from the et al. mainly of the relatively estimated annual production in these vegetation based on the monthly NDVI used in the model. to the et al. observations, the lower NASA-CASA model estimate of aboveground biomass for and areas is to the model ratio setting (leaf versus for biomass allocation from NPP. The model ratio results in than aboveground allocation of biomass at the of aboveground biomass in the different methods for estimating aboveground biomass, as compared in may the potential and the variability of biomass storage in terrestrial the values from measurements by et al. the NASA-CASA model estimate is designed to land surface observed by satellite for a recent of than that the late NASA-CASA predictions also include controls on plant production at 1° effects of soil fertility on carbon allocation to different plant and of 1980s global land cover and use patterns and 1994). of the NASA-CASA model estimates for carbon in aboveground biomass is that it to leaf from standing and fine-root accounting for effects of on net carbon dioxide fluxes to the measurements by et al. are for standing bio-mass and are not to a recent of than which is where land use is on the status of forest cover, 1995, and on recent of annual change in forest cover and in forest area or can be as (e.g., through natural of land or regrowth on or (e.g., through deforestation, forest or for The estimates of forest cover change during 1990–1995 for more than a net loss of of (FAO 1997). This value represents a of in that by an of in developed the of in is the to have the 1980s 1997). the 1° resolution NASA-CASA model estimates for standing biomass (i.e., aboveground in (Figure with country-by-country of change in forest area by estimates can be for global carbon losses from and from expansion of forest area or secondary regrowth of other land of carbon to the atmosphere from forest and a biomass of aboveground biomass, which has been from field by et al. used as a for annual over the The estimate for global carbon flux, which based on conversion for suggests that the net terrestrial loss of carbon dioxide from changes in area of the world's forest ecosystems of carbon (Figure This estimate includes of carbon to the atmosphere from sources, in part by of carbon accumulated in the terrestrial biosphere through forest area regrowth and In this estimate, with the losses of carbon from are with Pg/yr, by and with losses of each of carbon accumulation in areas of forest regrowth are in Canada, and the each with annual of more than be that the age structure of can have a on estimated carbon pools from it is not to consider these types of factors in this of analysis of a of data from satellite Global carbon from annual changes in forest Predictions are based on conversion for with NASA-CASA model predictions of standing plant values loss of carbon to the atmosphere from deforestation, values of carbon from the atmosphere from forest regrowth or 1° values are based on the that of forest carbon loss or in to the geographic of standing biomass amount on a estimated change in forest aboveground biomass over the years 1990–1995 for with the carbon estimated change in forest aboveground biomass over the years 1990–1995 for with the carbon by global vegetation the net carbon losses from are predicted to in where Pg/yr, and where 0.56 Net carbon from regrowth and expansion of forest areas over boreal and other forest are predicted to be Pg/yr, predicted carbon in biomass over all temperate forest nearly for the an to this that a reliable estimate for change in forest area between 1990 and not be for the of change in forested areas of Russia are in the forest at other recent estimate that forest in Russia or of the boreal forest area each and This forest loss estimate is an of than that by fire a with major for global carbon dioxide emissions that has not been in other recent assessments of the carbon (e.g., 1998). a loss estimate of for net annual change in forest area for Russia over the 1990–1995 is the net terrestrial loss of carbon dioxide from changes in the world's forest ecosystems would be to of carbon for the early 1990s. A loss estimate of for annual change in forest area would the area by the the source of carbon dioxide in the to global net carbon losses that would from changes in forest ecosystems worldwide. A major from the global modeling analysis in this is that the potential for recent carbon from forest area regrowth or expansion are by the losses from and temperate and boreal may be increasing in area and in biomass this global analysis suggests that the are by losses of carbon to the atmosphere at a ratio of to this to be to the carbon balance equation by mitigating and forest in the the to the conversion of the world's forest biomass to atmospheric carbon dioxide would the amount of forest area from reducing the of and into and to and to vegetation and to The Kyoto Protocol that developed and those with in to for average annual emissions to a percentage of 1990 emissions. These are based on the that all emission sources and sinks can be accurately in the improved on biomass carbon losses from in this article, the Kyoto on and to be one of the more to and of the Kyoto and For soils and biomass stocks of carbon more than the standing biomass that is or during soils with large stocks of in to stored carbon for several forest changes in these soil surface and The of ecosystem which be through a small set of soil sampling measurements is in this For the global NASA-CASA model estimates that the amount of biomass in forested ecosystems represents Pg of which eventually into the atmosphere in the that standing biomass stocks in are or This biomass be to the NASA-CASA model estimate of more than Pg of carbon stored in litter and soil carbon pools pools with of than in global forest ecosystems (Potter and Klooster 1997). of the NASA-CASA ecosystem model can be to estimate forest biomass and production at the or to at the 1 grid for AHVRR greenness This resolution can capture direct effects of on land cover and predicted biomass carbon NASA-CASA model using resolution land cover and NDVI data have regional for NPP and trace gas fluxes over the et al. and for the of (Potter et al. 1998). A for more extensive (e.g., of the model at resolution is the of maps for monthly and soil data Several methods can global ecosystem models by satellite remotely sensed data to in on carbon stocks and the uncertainties in net terrestrial sources of carbon in the the forest sampling of and soil carbon pools can be with geographic for across the These field measurements can be used to ecosystem A of measurements of net ecosystem carbon exchange over several can be using the approach et al. 1996, et al. in which measurements of the component of and allow for of forest moisture and carbon fluxes based on the of these with reliable of forest cover types from or other relatively resolution sources, these measurements of net ecosystem carbon exchange may be with to regional scales and compared to ecosystem model regional to global a of gas sampling stations would provide over for atmospheric carbon dioxide that be used with models of global to terrestrial and fluxes of net ecosystem production. A new method for forest structure is the Vegetation Canopy an active method using and is the first Earth for Earth et al. 1997). have been used extensively for small-scale from The the first for the the extensively over the data for canopy as as for the terrestrial The sensor provide direct estimates of forest areas of and areas of forest structure for changes in In the of human on carbon several sources of in the type of global modeling analysis in this for biomass and effects on terrestrial carbon fluxes. For forest assessments have been as estimates of 1994). an the forest cover data for in are based on assessments that on different and for have been to the reference years of 1990 and This using a model function developed to changes in forest cover area over with including change and forest cover, and of the forest area are with each For new assessments by several to as inputs to the model for the of the forest cover area for and the of the 1990 forest for several including and the improved methods of land-use are and not included in assessments of forest and the of into forests. For in the forest, the biomass of through the and of et al. 1999). This to to the forest where it the organic by The of can the of forest leaf and are to these of forest the effects of and drought can to large-scale forest in and other particularly during which the of and carbon emissions of drought The to accurately and carbon storage over with the of an international system for carbon for carbon pools and fluxes on a global level be by to and all methods of ecosystem and including ground-based ecosystem and geographic from each of the methods be reduced to the by of to Klooster for development of the NASA-CASA model computer on a previous of the This by the Ames