A Global Terrestrial Monitoring Network Integrating Tower. Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data

ELSEVIER

S. W. Running, D. D. Baldocchi, f D. P. Turner,t S. T. Gower,§ P. S. Bakwin,11 and K A. Hibbard11

Accurate monitoring of global scale changes in the ter- OVERVIEW OF GLOBAL TERRESTRIAL restrial biosphere has become acutely important as the MONITORING AND VALIDATION scope of human impacts on biological systems and atmo- The dynamics of the terrestrial biosphere are an integral spheric chemistry grows. For example, the Kyoto Proto- part of global change. Society needs to know particularly col of signals some of the dramatic socioeconomic 1997 if the "human habitability" of the biosphere is decreas- and political decisions that may lie ahead concerning ing, especially because more humans are inhabiting the CO, emissions and global carbon cycle impacts. These land each year. One direct way of quantifying human decisions will rely heavily on accurate measures of global habitability is by evaluation of the vegetation cover and biospheric changes (Schimel, 1998; IGBP TCWG, 1998). the primary productivity that provides food, fiber, and An array of national and international programs have in- fuel for human endeavors. The distribution, health, and augurated global satellite observations, critical field mea- productivity of global vegetation is typically evaluated in surements of carbon and water fluxes, and global model the context of the global carbon budget. Much of what development for the purposes of beginning to monitor the is known about the contemporary global carbon budget biosphere. The detection by these programs of interan- has been learned from careful observations of atmo- nual variability of ecosystem fluxes and of longer term spheric CO, concentration trends and 13C/12C isotope ra- trends will permit early indication of fundamental bi- tios (d3C), interpreted with global circulation models. ospheric changes which might otherwise go undetected From these studies we have learned the following impor- until major biome conversion begins. This article de- tant things about the global carbon cycle: scribes a blueprint for more comprehensive coordination of the various flux measurement and modeling activities On average over the last 40 years roughly half of into a global terrestrial monitoring network that will the annual anthropogenic input of CO2 to the at- have direct relevance to the political decision making of mosphere is taken up by the oceans and the ter- global change. ©Elsevier Science Inc., 1999 restrial biosphere (Keeling et al., 1989). Interpretation of the latitudinal gradient of atmospheric CO2, using transport models indicates that a significant portion of the net uptake of CO2 oc- ° Numerical Terradynamic Simulation Group, School of Forestry, University of Montana, Missoula curs at midlatitudes of the Northern Hemisphere ATDD, Oak Ridge National Laboratory, Oak Ridge, Tennessee (Tans et al., 1990; Ciais et al., 1995; Denning et I Forest Science Department, Oregon State University, Corvallis al., 1995). § Department of Forestry, University of Wisconsin, Madison NOAA/CMDL, Boulder, Colorado There are large year-to-year changes in the net Climate Change Research Center, University of New Hamp- uptake of CO2 by the terrestrial biosphere. These shire, Durham changes are associated with climate anomalies Address correspondence to S. W. Running, Numerical Terrady- such as ENSO (Conway et al., 1994; Keeling et namic Simulation Group, School of Forestry, University of Montana, Missoula, MT 59812. E-mail: [email protected] al., 1995; Keeling et al., 1996). Received 10 August 1998; revised 15 April 1999. The seasonality of terrestrial biosphere carbon

REMOTE SENS. ENVIRON. 70:108-127 (1999) ©Elsevier Science Inc., 1999 0034-4257/99/$—see front matter= 655 Avenue of the Americas, New York, NY 10010 PH 50034-4257(99)00061-9 Global Terrestrial Monitoring Network 109

flux appears to be changing as indicated by shifts tions already exists. The current eddy flux network of in the timing and amplitude of the seasonal cycle sites is growing rapidly and becoming increasingly orga- of atmospheric CO, measured at many "back- nized. Third, the flux towers provide a critical infrastruc- ground" sites. In particular, it appears that spring ture of organized personnel and equipment for other is beginning earlier and fall arriving later (Ran- comprehensive measurements, including ecophysiology, derson et al., 1997; Field et al., 1998). This result structure and biomass of the vegetation, fluxes of other is supported by satellite phenology observations greenhouse gases, and micrometeorology. (Myneni et al., 1997a). Monitoring of the spatial and temporal patterns in 2 02, and their isotopic variants These indications of biospheric changes point to the the concentration of CO , can provide the basis for estimates of carbon cycle fluxes need to 1) better understand and monitor the processes at large scales (Tans et al., 1996). The remarkable that regulate uptake and release of CO, by terrestrial achievements from the geochemistry approach, begin- ecosystems, 2) provide verification from more direct ning with the observations at Mona Loa, which first de- ground-based measurements, and 3) employ satellite ob- tected the upward trend in the global atmospheric CO, servations to clarify spatial patterns in ecosystem func- concentration, establish its importance for biospheric tion. There is also new interest in computing sources and monitoring. The limitations in the geochemistry ap- sinks of carbon for individual nations that will challenge proach for terrestrial monitoring are that it is not spa- current data availability. This article suggests how a num- tially explicit, and generally indicates the net effect of ber of current international research activities can be in- multiple, potentially opposing, processes. tegrated into a biospheric monitoring program that efficiently collects and disseminates key data and analyses Spatial Monitoring—Terrestrial Vegetation Products for evaluation of global change in terrestrial ecosystems from EOS (Running, 1998). After launch in mid-1999, the EOS (Earth Observing System) will inaugurate the first regular, global terrestrial Necessary Components of a Biospheric vegetation products, including land cover, spectral vege- Monitoring System tation indices (SVI), leaf area index LAI, fraction ab- Accurate quantification of the trajectory of change and sorbed photosynthetic active radiation (FPAR), and net potential degradation of the terrestrial biosphere is es- primary production (NPP) (Fig. 1; Running et al., 1994; sential because this understanding will influence many Justice et al., 1998). Many labs now calculate global net socioeconomic decisions concerning resource consump- primary production (NPP), evapotranspiration (ET), and/ tion and conservation. Global estimates of biospheric or precursor variables like leaf area index (LAI) or frac- processes will require a permanent network of ground tion absorbed PAR (FPAR) (Melillo et al., 1993; Ruimy monitoring and model validation points, much like the et al., 1994; Field et al., 1995; Hunt et al., 1996; Prince surface weather station network, to quantify seasonal and and Coward, 1995; Randerson et al., 1997; Foley et al., interannual dynamics of ecosystem activity, that is, to 1996; Myneni et al., 1997b). Because these EOS vari- cover the Time domain. Remote sensing must be used ables provide the basis for spatial scaling of all relevant to quantify the heterogeneity of the biosphere, the Space ground-based measurements, their validation is essential. domain. Finally, because these Time and Space measure- The following web site has summaries and links to im- ment regimes cannot provide a complete view of bio- portant EOS Land validation activities: http://www-eosdis. spheric biogeochemical activity, modeling is required to ornl.gov/eos_lancLval/valid.html. isolate unmeasured ecosystem processes, and to provide The field measurements required for this EOS land predictive capacity. In this article, we outline a nested validation are primarily multitemporal sequences of veg- terrestrial measurement network, a regular remote sens- etation structure and biomass accumulation and turn- ing product stream, and an integrating modeling frame- over, accurately georeferenced to provide spatial frac- work to continuously monitor and validate large scale es- tions of vegetation structure across the landscape. LAI timates of key variables in terrestrial carbon and water and NPP, the most directly measured vegetation struc- budgets. tural and functional variables, respectively, range by 2 or- ders of magnitude among the diverse terrestrial biomes Temporal Monitoring—Carbon, and change seasonally with annual plant growth cycles. Water and Energy Fluxes Spectral vegetation indices such as the well known NDVI Eddy-covariance flux towers serve as the core infrastruc- and FPAR are radiometric products that can only be ture for three reasons. First, they measure carbon and measured instantaneously but can be inferred by vegeta- water fluxes and the surface energy budget, processes di- tion structural measurements, most commonly by LAI. rectly related to ecosystem function, continuously and The plans discussed below will measure LAI to provide semiautomatically, representing an area of approximately inferred validation of VI and FPAR, and will measure 1-3 km2. Second, a global representation of over 80 sta- fractional vegetation cover of regional study areas. 110 Running et al.

Landcover

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ENF 111

DNF 111 EBF $ DBF

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0.5 c a 0.0- ---,— ti 111.111.11111.11111pIPP- (kgC/mVyr) ti g Figure 1. An example of global land cover (LC), leaf area index (LAI), and net primary production (NPP) terrestrial variables that will be produced from the Earth Observing System (EOS) every 8 days at 1 km. is These data will be invaluable for scaling of ecological research and land management, but first need glo- is bal field validation [see Running et al. (1994) and Justice et al. (1998) for details]. t( System Processes and Integration—Ecological Modeling ecosystems under all conditions. Models must then be The eddy fluxes and ecophysiological measurements pro- used to interpolate and extrapolate flux measurements in vide process level understanding of ecosystem function time and space. Hence, models are and will be a key tool that can be incorporated into ecosystem models. How- for making regional and global assessments (Waring and ever, there will never be sufficient eddy flux towers or Running, 1998). Mechanistic ecosystem models also have field measures to adequately characterize all terrestrial the potential for predicting how ecosystems will respond

Global Terrestrial Monitoring Network 11 1

to future changes in atmospheric CO2, temperature, land when soil moisture changes and groundwater losses are use change, nitrogen loading, and precipitation. ignored. Also, both of these historical ecosystem measures, NPP and Q, are typically measured on a weekly- Critical Variables in a Global Terrestrial to-monthly basis, so are temporally inconsistent with the Monitoring System continuous flux tower measures of NEE and ET. Addi- We initially focus on one key variable each of the carbon tionally, there are spatial scale mismatches. The tower and water cycles: net primary production (NPP) and fluxes represent a footprint of roughly 1-3 km2, while evapotranspiration (ET). The carbon budget consists of NPP is typically measured on =41.1 ha plot, and water- several major processes that describe the exchange of sheds can drain many hundreds of square kilometers. carbon dioxide between terrestrial ecosystems and the at- Process-based terrestrial ecosystem models, driven by mosphere. Gross primary production (GPP) is the total spatially represented climate and satellite derived vegeta- carbon assimilated by vegetation. A fraction of GPP is tion parameters, are essential for integrating the suite of lost hack to the atmosphere as the result of autotrophic field-based measurements of inconsistent temporal and respiration ( RA). Net primary production (NPP), the bal- spatial scale to provide a complete and consistent view ance between GPP and autotrophic respiration, is allo- of global biospheric function. cated to wood, foliage, roots, reproductive tissues, stor- We now visit each of four components critical to a age, etc. NPP, the direct measure of vegetation comprehensive monitoring scheme and identify associ- productivity, has been measured from field biomass sur- ated on-going research activities. In the temporal dimen- veys for decades and has the largest historical database. sion, it is a global flux tower network and a global flask NPP relates directly to forest, range, and crop productiv- sampling network that are essential. For the spatial di- ity, and so also has high socioeconomic value. NEE, the mension, we discuss the EOS products. SVAT modeling net exchange of CO, between terrestrial ecosystems and is then examined as a means of scaling carbon and water the atmosphere, is measured by flux towers. NEE has flux over space and time. Subsequently, we consider the high scientific relevance for terrestrial carbon budgets nature of the required information flow among these and greenhouse gas production, but less direct socioeco- components and identify international programs con- nomic significance. cerned with integration. Under optimal conditions, NEE is measured continuously and calculated on a half-hourly or hourly basis, GLOBAL FLUX TOWER NETWORK (FLUXNET) whereas the field-based NPP is measured periodically and calculated on an annual basis. The two fluxes are The cornerstone of this global terrestrial vegetation mon- related in that if NEE is summed over a year, the sum itoring is the tower flux network, FLUXNET. This global should be the difference between NPP and heterotro- array of tower sites is currently comprised of regional phic respiration (R11) summed over the year. Thus networks in Europe (EUROFLUX), North America (AmeriFlux), Asia (JapanNet, OzFlux), and Latin Amer- . (1) ica (LBA). The towers provide a continuous and repre- Note that on a daily time step NEE is related to sentative measure of terrestrial carbon cycle dynamics, GPP and RA in Eq. (2): and an important ancillary suite of measurements of en- NEE,iy= GPPdAy— ( RH+ RA )thily (2) ergy and water fluxes for interpreting carbon fluxes (Fig. 2). The role of FLUXNET includes coordinating the re- NPP and NEE are related theoretically, but the two gional networks so that information can be attained at a carbon fluxes are measured at very different temporal global scale, ensuring site to site intercomparability, co- and spatial scales, necessitating an integrated approach ordinating enhancements to current network plans and to provide global coverage for rapid validation and moni- operation of a global archive and distribution center at toring opportunities. The ultimate goal is to validate the Oak Ridge DAAC. The FLUXNET project web ad- global measures of NEE and NPP. dress is http://daacI.ESD.ORNL.Gov/FLUXNET/ . The The other primary variable, evapotranspiration (ET), web sites contain measurement protocols for consistency, is a component of the surface hydrologic balance and an and data on site, vegetation, climate, and soil characteris- integral part of surface energy partitioning. ET is also tics. It provides a route for users to gain access of hourly measured continuously by a flux tower, providing a high meteorological and flux measurements and proper docu- temporal resolution measurement of the partitioning of mentation. precipitation (PPT) in the hydrological budget of an eco- The FLUXNET concept originated at a workshop on system. However, much like with NPP and NEP, the "Strategies for Long Term Studies of CO 2 and Water Va- variable of the hydrologic cycle with the longest history por Fluxes over Terrestrial Ecosystems" held in March and widest distributed data is watershed discharge Q. 1995 in La Thuile, Italy (Baldocchi et al., 1996). The first These variables are generally related as in Eq. (3): organized flux tower network was EUROFLUX, which Q=PPT —ET (3) now involves long-term flux measurements of carbon•di- 112 Running et al.

CIMEL sunphotometer (sky and surface-reflected radiances + direct radiance) Carbon Water albedometer sonic anemometer (near infrared (wind speed) albedo) )C albedometer net radiometer (broadband (net radiative flux) albedo)

anemometer (wind speed) thermometer and hygrometer (air temperature and humidity)

infrared gas analyzer (IRGA; CO2 concentration) and data loggers rain gauge

fPAR and LAI over satellite footprint

Figure 2. A generalized FLUXNET tower configuration diagram, showing instrument deployment and key carbon and water fluxes measured. Atmospheric optical measurements, automated surface spectral measurements, physiological process studies, flask sampling, and stable isotope sampling are all additions that can be accommodated into this framework to provide a more versatile monitoring system. oxide and water vapor over 15 forest sites in the United entering and leaving the vegetation. Vertical flux densi- Kingdom, France, Italy, Belgium, Germany, Sweden, ties of CO, and water vapor between the biosphere and Finland, Denmark, The Netherlands, and Iceland. A the atmosphere are proportional to the mean covariance website is located at http://www.unitus.it/eflux/euro.html . between vertical velocity and scalar fluctuations. This de- In 1996, AmeriFlux was formed under the aegis of the pendency requires the implementation of sensitive, accu- DOE, NIGEC program, with additional support by rate, and fast-responding anemometry, hygrometery, NASA, and NOAA. The website is http://www.esd.ornl. thermometry, and infrared spectrometry to measure the gov/programs/NIGEC. vertical and horizontal wind velocity, humidity, temperature, and CO2 concentration. Eddy Covariance Principles Errors arise from atmospheric, surface, and instru- The eddy covariance method is a well-developed method mental origins, and they may be random, fully systematic for measuring trace gas flux densities between the bio- and/or selective (Goulden et al., 1996). Most random er- sphere and atmosphere (Baldocchi et al., 1988; Len- rors are associated with violations of atmospheric stationar- schow, 1995; Moncrieff et al., 1996). This method is de- ity and the consequences of intermittent turbulence. In- rived from the conservation of mass and is most applica- strument errors are systematic, caused by insufficient ble for steady-state conditions over flat terrain with an time response of a sensor, the spatial separation between extended tract of uniform vegetation. If these conditions a sensor and an anemometer, digital filtering of the time are met, eddy covariance measurements made from a signal, aerodynamic flow distortion, calibration drift, loss tower can be considered to be within the constant flux of frequency via sampling over a finite space, and sensor layer, and flux density measured several meters over the noise (Moore, 1986; Moncrieff et al., 1996). The Ameri-, vegetation canopy is equal to the net amount of material Flux, Euroflux, and FLUXNET programs are attempting Global Terrestrial Monitoring Network 113

to identify and minimize instrumental errors by circulat- over intensive agricultural areas. Future planning should ing a set of reference instruments, to which all sites can identify the climate/biome combinations of highest prior- be compared. Daily-averaged fluxes reduce the sampling ity to improve global representativeness. errors associated with fluxes measured over 30-60 min intervals. Hence, daily integrals of net carbon flux can be accepted with a reasonable degree of confidence. Goul- THE ATMOSPHERIC CO2 FLASK NETWORK den et al. (1996) conclude that the long term precision Inverse Modeling of Carbon Sources and Sinks of eddy covariance flux measurements is -±5-10% and The eddy flux tower studies are designed to aid under- the confidence interval about an annual estimate of net standing of processes that drive NEE at the ecosystem canopy CO, exchange is ±30 g C m -2 y-. level, and for evaluation of ecosystem models used for regional and global integration. However, a top-down ap- Implementation and Operation proach is also needed to test and validate the results of A typical cost for purchasing instruments to make core the model extrapolations to global and regional scales. measurements is on the order of $40-to $50k (US); this Data from the global CO2 mixing ratio and isotope ratio cost can double if spare sensors, data telemetry, and data measurement network (NOAA/CMDL/Cooperative Air archiving hardware are purchased. The cost of site infra- Sampling Network, website at http://www.cmdl.noaa.gov/ structure is extra and will vary according to the remote- ccg/), when interpreted within general circulation mod- ness of the site (the need for a road and line-power), the els, can provide constraints at least at this scale. The ba- height of the vegetation (whether or not a tall tower sic procedure in this approach is referred to as inverse must be built), and the existence of other facilities. Re- modeling. It involves simulating the global 3-D atmo- cent advances in remote power generation and storage spheric transport using a general circulation model and minimize the need and cost of bringing line power to a tracking the movement and interaction of air parcels hav- remote site. Advances in cellular telephone technology ing different CO, concentrations. Information on the also allow access and query of a remote field station from spatial and temporal patterns in measured atmospheric home or the office. The requirement for on-site person- CO, concentrations derived from flask samples is incor- nel is diminishing, as flux systems become more reliable porated such that sources and sinks of carbon from the and automated. At minimum, a team of two individuals Earths surface can be inferred. are required to operate a flux system, and handle the Initially, flask sampling was primarily in well-mixed day-to-day chores of calibration, instrument and com- marine areas. Expansion of the monitoring networks over puter maintenance, data archiving, and periodic site the last decade has improved the spatial resolution with characterization (e.g., soil moisture and leaf area mea- which annual fluxes can be determined, so that currently surements). fluxes are being estimated at continental scales (Fan et Sites in an organized global flux network can also ex- al., 1998). Sampling at continental sites reveals regional pect to attract additional activities. A synergism between contrasts in seasonality of flux dynamics, and sampling at flux and meteorological measurements and an array of various heights on tall towers (500 rn) identifies gradients other terrestrial science projects is likely. Terrestrial bio- of CO2 in the boundary layer dynamics (Fig. 4; Bakwin climatology, remote sensing, atmospheric optical charac- et al., 1998). As sampling density is increased, it will be terization, water resource, and nutritional biogeochemis- possible to use higher resolution transport models, such try studies are examples of science that are being as mesoscale models, to deduce surface fluxes at finer attracted to the flux network sites (Fig. 2). spatial scales. Additional work is needed to develop and refine the transport models, and particular attention is Climate and Biome Distribution Requirements required to develop parametrizations for simulation of Ideally, global terrestrial monitoring/validation sites should the dynamics of the planetary boundary layer. It is im- encompass the complete range of climate and biome type portant that the resulting estimates of NEE are entirely combinations. The current global array of flux towers is independent of the flux tower measurements, and nearly shown in Figure 3, mapped over the annual tempera- independent of the satellite data (satellite data define ture/precipitation climate space of current global vegeta- surface parameters used in the transport models). tion (Churkina and Running, 1998). It is clear that large regions, including several important biomes, remain un- Isotopic Sampling derrepresented including hot desert and cold tundras, The state of development of isotopic measurements (par- which is inevitable with an ad hoc volunteer global net- ticularly in combination with CO 2 flux tower measure- work (Table 1). Also, the correspondence between flux ments), for better understanding the carbon cycle on local, tower locations and permanent ecological field sites is regional, and global scales, was the topic of a workshop low, illustrating the key role for modeling to spatially ex- titled Biosphere-Atmosphere Stable Isotope Network trapolate results amongst sites. More sites are needed (BASIN, Snowbird, Utah, 7-10 December 1997). A sum- 30

20 4

Figure 3. A two-dimensional climate diagram of rt t the distribution of 1° global vegetation cells re- Fluxnet sites lated to annual precipitation and temperature, sug- Biospheric NPP cells gesting the preferred climatic distribution of sites for complete biome sampling [regraphed from -20 data in Churkina and Running (1998)]. The cli- 4000 5000 0 1000 2000 3000 mates of the current FLUXNET sites as of early annual average precipitation (mm/year) 1999 are superimposed. mary can be found on the Web at http://gcte.org/basin . from careful surface layer measurements. This is an ex- html. Isotopic data can be used to disaggregate compo- citing result because flux towers are located in continen- nent fluxes (e.g., photosynthesis and respiration, transpi- tal areas currently underrepresented by the network for ration, and evaporation), and for regional and global scal- monitoring CO, mixing ratios, and could improve the ac- ing. Measurements of cP3C in CO2 can be used to curacy of CO2 mixing ratio measurements at the flux partition net uptake between the oceans and the terres- towers on the order of 0.2 ppm. trial biosphere, or, on a smaller spatial scale, between C, An alternative for regional flux scaling with good fu- and C4 vegetation. Recent advances in understanding the ture potential is aircraft based mobile flux platforms influence of vegetation (Farquhar et al., 1993) and soils (Crawford et al., 1996; Oechel et al., 1998). These light (Tans, 1998) on the 180/160 ratio of CO2 have opened aircraft fly very close to the vegetated surface, often only the possibility to use measurements of d80 to distinguish 5-20 m above the ground, and ingest air samples that between photosynthetic and respiratory fluxes. Also, flow into a fast response infrared gas analyzer through a measurements of 180/160 in H2O could be used to distin- special nosecone sampler. Onboard aircraft velocity and guish between transpiration and evaporation from the micrometerological data are combined to compute fluxes soil. Recent work by Potosnak et al. (1998) indicates that of CO2 and H2O with about a 3 km2 spatial resolution it may be possible to use the flux tower measurements to integrated across a multiple kilometer flight path. assess the influence of local surface exchange, and hence compute regionally representative CO2 mixing ratios VALIDATION OF EOS TERRESTRIAL VEGETATION PRODUCTS Table 1. Current Biome Distribution of the 80 Established FLUXNET Sites Vegetation Measurements in the EOS/MODIS Grid Functional Type Percent EOS will produce regular global vegetation products pri- Temperate conifer forest 22 Temperate broad-leaved forest 21 marily from the MODIS sensor (Moderate Resolution Semiarid woodland 16 Imaging Spectroradiometer) (Running et al., 1994; Jus- Boreal conifer 7 tice et al., 1998). MODIS satellite products will be in a Grassland 7 regular grid of square, 1 km2 cells that do not exactly Crop 6 overlay a tower "footprint." The tower footprints vary in Alpine 6 Arctic 4 size (up to several km 2), shape, and orientation, de- Tropical forest 3 pending on location, height above canopy, wind speed, Mixed forest 3 and direction (Hollinger et al., 1994; Waring et al., Boreal broad-leaved forest 1995). In order to permit comparisons of tower-based 1 Wetland NEE estimates and the satellite-based NPP estimates

Global Terrestrial Monitoring Network 115

North Carolina ... 375 / • , , / • / ‘.— % / 1 •_./ ‘,/ ,, , •‘, s...,/ N__, ,__, 1 I 1 I 365 lI .. % I ki

355 - 51 m 496 m 345

Wisconsin 375 • - 30 m • 396 m 365

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Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan 92 93 93 94 94 95 95 96 96 97 97 98

Figure 4. Multiyear trends in monthly atmospheric CO2 measurements for two tall towers in contrasting cli- mates of North Carolina and Wisconsin from the NOAA/CMDL flask monitoring network. Note the differen- tial activity of CO2 within the forest canopies at 30-50 m height dominated by biological dynamics compared to the midplanetary boundary layer at 400-500 m where atmospheric transport dominates. When coupled with atmospheric transport models, these data can be used to estimate CO2 fluxes at regional scales as a -top-down" constraint on fluxtower data (Bakwin et al., 1998).

from the MODIS grid, certain transformations are For example, much of the U.S. Pacific Northwest region needed (Fig. 5). A SVAT model that resolves component is characterized by patches associated with forest clear- carbon balance processes of Eq. (1), and validated by lo- cuts that are generally much smaller than 1 km on a side cal flux tower measurements over a grid of 10-100 km2 (Cohen et al., 1998). In the Lake States the choice of around the tower, provides this scale transformation. grain size up to 1 km greatly affects estimates of land The most direct measurement of NPP for validation surface occupied by aquatic versus terrestrial systems involves harvesting and weighing biomass production in (Benson and MacKenzie, 1995). The tendency for the a time sequence. The plot size here is considerably scale of human influence on ecosystem carbon flux to fall smaller than a tower footprint, for example, 1 m 2 for clip- below the 1 km resolution was recognized during the de- ping in a grassland or 1 ha plots for tree coring and lit- sign phase of the MODIS instrument (Townshend and terfall traps in forests. Multiple NPP measurements Justice, 1988), and accounted in part for including chan- made in the 100 km2 area surrounding a tower serves to nels at 250 ni and 500 m in the visible and near-IR extend the model validation over the local environmental wavelengths. gradients and variation in land use. The BigFoot project makes the link between purely satellite-based C flux estimates, tower fluxes and direct Quantifying Land Surface Heterogeneity for EOS field measurements. The BigFoot website is at http:// Validation—BigFoot www.fsl.orstedu/larse/bigfoot/ . The goal is to develop Over a site consisting of homogenous vegetation cover three fine-grained surfaces (25 km2) using a combination and small environmental gradients, the scale inconsisten- of Landsat Enhanced Thematic Mapper (ETM+), SVAT cies between EOS/MODIS NPP estimates and ground- models, and field observations. These surfaces include based validation measurements may be minimal. How- the standard EOS products of land cover class, leaf area ever, many important ecosystems are fairly complex in index (LAI), and NPP. BigFoot currently consists of a structure and topography, even over the relatively small set of four sites (all are FLUXNET sites) spanning the area represented by a MODIS cell or a tower footprint. climatic gradient from boreal to warm temperate: en- 116 Running et al.

Earth Observing System Tower Flux Footprint ( = 1km radius) GTOS, BigFoot Field MODIS Validation Samples Footprint ( =10km sq.)

Regional Biome Footprint ( =100km sq.)

Figure 5. Illustration of the three spatial scales that must be considered for ecological scaling and validation. Measures of vegetation parameters (see Table 2) in the atmospheric footprint of the FLUXNET towers are required for SVAT models to simulate the NEE measured by the towers. Second, a larger area of minimum 3 km x3 km must be sampled to provide ground truth of MODIS LAI and NPP vegetation products. Third, the representativeness of the FLUXNET tower and MODIS sampling site to the larger biome/climate complex must be evaluated by cross biome sampling. Aircraft flux transects and atmospheric flask measurements can provide independent validation of regional flux calculations. Only after all of these scales of measurement are covalidated can comprehensive synthesis of ground data, ecosystem models, and satellite data be accomplished. compassing several important biomes, and including a will consist of site-specific cover classes that are locally variety of land-use patterns. Additional sites will be meaningful for ecological function and model parametri- added in the future, with highest priority being tropical zation. forests, deserts, and arctic tundra, and this protocol is LAI has also proved valuable in scaling efforts, and being adopted by the GTOS-NPP project (discussed in is an input to most existing SVAT models. LAI surfaces a later section) globally. will be based on ETM+ imagery combined with field Land cover is an important variable for the purposes sampling (Gower et al., 1999, this issue) and will be de- of the BigFoot scaling effort because physiological char- veloped using methods that minimize errors associated acteristics that influence carbon, nitrogen, and water with the asymptotic relationship of commonly used spec- vapor exchange between terrestrial ecosystems and the tral vegetation indices and LAI (Turner et al., 1999, this atmosphere differ among vegetation cover types (Land- issue). Errors in the land cover and LAI data layers will sberg and Gower, 1997). In addition, Thematic Mapper- be evaluated with independent field-collected data using based classifications are often able to resolve specific the sampling protocols discussed by Thomlinson et al. stages in local successional sequences (e.g., Cohen et al., (1999, this issue). 1995) and thus may indicate information about levels of Following development of land cover and LAI sur- coarse woody debris, an important input to SVAT models faces, NPP grids will be developed for each BigFoot site simulating heterotrophic respiration. Thus, SVAT models using SVAT models (see the next section). These grids use land-cover type as a stratification factor (Reich et al., will be developed using the models in 2-dimensional 1999, this issue). In BigFoot, land-cover classification will mode in conjunction with the site-specific driver sur- be accomplished using methods described by Thomlin- faces, land cover and LAI, and spatially distributed cli- son et al. (1999, this issue). Most importantly, these maps matic drivers based on extrapolations from flux tower Global Terrestrial Monitoring Network 11 7 meteorological observations. Beside the daily time step SVAT Model Requirements for 1-D Flux Modeling validation of GPP and ET at BigFoot sites with flux SVAT models have been designed with a wide array of towers, the BigFoot NPP surfaces will be carefully evalu- system complexities (Fig. 6). For example, some models ated for error by reference to a gridded network of define each age class and branch whorl of leaves, while ground measurements of NPP, collected according to others use only simple LAI. Time resolutions of various methods described by Gower et al. (1999, this issue). As- models range from 1 h to monthly. The land surface suming the errors in these NPP surfaces are acceptable, models such as BATS and SiB in GCMs are effectively the fine-grained gridded surface over a 25 km2 area can SVAT models despite being used at very coarse spatial then be directly compared to NPP estimates derived grids (Dickinson, 1995). SVAT models of highest rele- from MODIS data over the same area. If the MODIS- vance to FLUXNET have time resolutions in the hourly– based estimates do not satisfactorily agree with the Big- daily domain, treat canopy structure fairly explicitly, and Foot estimates, it will be critical to identify causal resolve components of the carbon balance (photosynthe- factors. sis, heterotrophic and autotrophic respiration; and al- BigFoot will isolate and test three key factors— location). Likewise, stand water balance components, spatial resolution, land cover classification scheme, and (canopy interception, snowpack, soil water storage, evap- light use efficiency factors—that may contribute to dif- oration, and transpiration) must be explicitly computed. ferences between EOS-based and BigFoot NPP esti- All of the leading SVAT models incorporate some treat- mates. To evaluate the role of spatial resolution, the Big- ment of nutrient biogeochemistry interactions with car- Foot 25 m grids for input variables will be aggregated to bon and water processes. However, given these require- resolutions of 250 m, 500 m, and 1000 m using a variety ments, there are still many available and appropriate of standard and experimental algorithms. Model runs will SVAT models [see recent books by Landsberg and then be made at each spatial resolution and comparisons Gower (1997) and Waring and Running (1998)]. What is of simulated NPP at the different resolutions (including needed for a coordinated global program are some com- 25 m) will be made with each other and with the EOS/ mon protocols, of variables, units, timesteps, etc. that MODIS 1 km NPP products. Results of these scaling ex- would allow cooperation and intercomparisons among ercises over the range of biomes and land use patterns groups using different SVAT models in their space/time included in BigFoot will test both SVAT models and sat- scaling. The 1-D SVAT models require meteorological ellite-based NPP algorithms. driving variables measured at the tower, the initializing biomass components of the vegetation, and certain soil physical and chemical properties. All SVAT models have SYSTEM INTEGRATION AND SCALING somewhat different specific requirements, but the gen- WITH MODELS eral list of inputs found in Table 2 covers most of them. To transform basic tower flux and flask data and global remote sensing into an effective biospheric monitoring Relating NEE and NPP in the Flux program, three steps are now required. First, SVAT Tower Footprint models must be used at each tower site to compute the Flux towers measure the net gain or loss of carbon over important system processes that cannot be directly mea- hourly to daily time scales (Fan et al., 1995; Baldocchi sured, such as the component carbon fluxes of NEE. In et al., 1996; Goulden et al., 1996; Frolking et al., 1996). order to operate the SVAT model, certain key site and Because SVAT models estimate photosynthesis, autotro- vegetation characteristics must be measured to parame- phic, and heterotrophic respiration separately, they gen- trize the model for the tower area (Table 2). Second, to erate separate estimates of NEE and NPP. Besides com- provide a spatial frame of reference for the tower site, parisons of measured and modeled NEE and NPP, one satellite-derived characterization of the surrounding veg- specific output of these models is daily GPP (gross pri- etation is needed. The EOS/MODIS standard spatial res- mary production or net photosynthesis). This modeled olution is 1 km, so as to provide adequate sample size, GPP can be compared directly to an estimate of GPP approximately a 10 m X 10 km area needs to be efficiently derived from tower data (daytime NEE minus estimated sampled. Third, with validated MODIS vegetation prod- daytime ecosystem respiration) (Fig. 7). Diurnal evapo- ucts of landcover, LAI, and NPP, a larger region can transpiration (ET) can also be directly compared to ET now be evaluated to understand how the flux tower data measured at the tower. Once a SVAT model is parame- represents the broader biome and region. This three-step trized and validated over a daily time step at a tower site, scaling process, evaluating the tower footprint of (1 km2, it can be run to simulate NPP and ET for a full year. then the EOS/MODIS footprint of =WO km 2, and finally Using direct field measurements of NPP made at the the regional biome footprint of thousands of square kilo- tower sites, model estimates of annual NPP can also be meters provide the scaling logic for global monitoring validated. The SVAT models thus provide an essential (Fig. 5). link between NEE measurements by the tower, and 118 Running et al.

Table 2. The Suite of Measurements Collected at FLUXNET Sites and Needed for SVAT Modeling Activity Variable Symbol Unit Frequency Criticality Mass and energy flux densities CO2 F, ,u mol m -2 s - 1-2 h - Core CO2 storage pmol m -2 s-1 1-2 h -1 Core m -2 Latent heat (water vapor) ,IE W 1-2 h - Core 2 Sensible heat H W m - 1-2 h- Soil heat conduction C W ,-2 1-2 h-1 Core Canopy heat storage S - W m -2 1-2 h -1 Core Momentum kg m-1 s-2 1-2 h - Core Dry deposition of N kg ha- y- Annual Desired Meteorology Global radiation 11 W m-s 1-2 h - Core Net radiation R, W m -2 1-2 h-1 Core Photosynthetic photon flux density Q^ pmol m -2 s-1 1-2 h -1 Core Diffuse radiation pmol in -2 s- or W in-21-2 h- Desired Air temperature T,, °C 1-2 h - Core Humidity 1-2 V Core CO2 concentration [CO2] pmol mol - 1-2 h -1 Core Wind speed U m s - 1-2 h - Core Wind direction deg 1-2 h - Core Precipitation Daily Core Pressure P kPa Hourly to daily Desired Canopy wetness Hourly Desired Pollution, _(03, _ NO _ 2, NO, SO2)SO ) ppb Hourly Desired Bole temperature Tb °C 1-2 h- Light transmission pmol m -2 s- 1-2 h - CDesired Soil characteristics Soil temperature profiles T, °C 1-2 h- Core Soil moisture Daily to weekly Core Bulk density Once Core Soil texture Once Core Root depth Once Core CO2 efflux ,uniol r/1 -2 s -t Hourly to seasonally Core/desired Litter decomposition Annually Core/desired Litter chemistry (C, N, Lignin) Annual Desired SoSoil (C, N) Annual Desired Soil thermal conductivityOnce Desired Soil hydraulic conductivityOnce Desired Cation exchange capacity Once Desired Vegetation characteristics Species composition Once Core Above-ground biomass Once Core Leaf area index Seasonal to annualCore Canopy height H m Seasonal to annual Core Albedo Aerodynamic roughness length zo m Once Desired Zero plane displacement D m Once Desired Multispectral image Annual Desired Above-ground growth increment Annual Core Leaf N and C Seasonal Core Specific leaf weight Seasonal Core Ecophysiology Photosynthetic capacity V„,,„, j„,,I, pmol m -2 s- Weekly to seasonally Desired Tissue dark respiration pmol m -2 s-1 Weekly to seasonally Desired Predawn water potential w MPa Weekly to seasonally Desired Stomatal conductance g, mol m -2 s-1 Weekly to seasonally Desired Tissue 3C/12C Atmospheric 13C/12C Sap flow mol ITC s - Hourly Desired Global Terrestrial Monitoring Network 119

NKAGES CENTURY

Years

FOREST-BGC BEX Figure 6. A general evaluation of the vary- Seasons ing time scales and mechanistic complexity inherent in various current soil-vegetation- atmosphere-transfer (SVAT) models. The MODIS global NPP estimate is represented by the E, a model of minimum process com- Days plexity. Models of higher process detail are required to validate and interpret the E models, but cannot be run globally because Hours of lack of data and computing limitations [redrawn from Landsberg and Gower (1997)]. Process detail , number of parameters

NPP, the C-flux variable most relevant to the standard Regional-scale validation may also be directly accom- EOS NPP product. plished hydrologically. A SVAT model implemented regionally, and incorporating hydrologic flow routing, can Scaling to Regional Ecosystem Processes reproduce stream discharge records. Accurate simulation In a large flat agricultural region with homogeneous to- of stream discharge tests the SVAT model calculations of pography, microclimate, soils, and crops, the tower foot- snowmelt dynamics, soil water flow, and evapotranspira- print fluxes of NEE and ET may directly represent the tion (White et al., 1998). entire regional flux activity. However, variable topography propagates variation in microclimate and soils, and Scaling Carbon and Water Fluxes to the human land use activity typically precludes homogeneity Continental and Global Scales of land cover over hundreds of kilometers. Consequently, Continental- to global-scale modeling of carbon and wa- the ecosystem processes computed by a SVAT model ter fluxes has been limited to gridded surfaces of rela- with the local tower flux data must be put in the context tively coarse grain sizes. For example, Hunt et al. (1996) of the region to be a meaningful biospheric monitor. Ef- used grid cells of nearly 104 km to simulate the influ- fectively this means extending the satellite-based analysis ence of terrestrial sources and sinks of carbon on the of the landscape planned by BigFoot methodology to an seasonal oscillation in the atmospheric CO 2 concentra- even broader region, and entails limited additional field tion. Prince and Coward (1995) used a cell size of 64 sampling for validation of vegetation variables (Fig. 5). km2 in another global scale estimate of NPP. Field et al. Mapping ecosystem process rates across thousands of (1998) used 1° xl° data for simulating terrestrial NPP us- square kilometers is then possible by executing the flux ing satellite data. Unfortunately, in areas with topo- tower validated SVAT model across the landscape with graphic and land-use heterogeneity, these resolutions can satellite and ancillary data providing georeferenced rep- lead to significant errors associated with a loss of fine- resentation of the landscape heterogeneity (Fig. 8). scale vegetation patterns (Pierce and Running, 1995; Direct measurement of regional heterogeneity can VEMAP, 1995; Turner et al., 1996). Typically there has now be accomplished with aircraft flux sampling. Oechel not been an effort to validate the global NPP estimates et al. (1998) measured a range of CO 2 flux of 0.1mg m-2 with a sample of site-level data, in part because of the s- , and a range of latent energy flux of 100 W m2 when mismatch in scales between model outputs and on-site flying a 100 km long transect of Alaskan tundra. Aircraft measurements. The availability of MODIS satellite data measured fluxes averaged 0.02 mg CO2 m -2 less than will provide a strong impetus towards improved global tower-based fluxes; however, their measurement scales NPP modeling. are different. Aircraft-based measurements will provide Satellite remote sensing can be used to estimate fluxes independent of SVAT model extrapolations, thus NPP (see Fig. 1), but is not capable of validating model- offering an important alternative for regional model vali- generated surfaces for heterotrophic respiration (R h) and dations. hence NEE. In addition, process-based modeling of, Rh 120 Running et al.

Temperate Deciduous Forest 1997, Oak Ridge, TN Net Annual Carbon Flux = -420 g C rn" 2 year-1

C13 rE

ar z

I I , • • " " • 0 50 100 150 200 250 300 350 a Day

10 co 5 0 O E -5 -10 - -15 - U 7:1 U -20 - ui -25 - Figure 7. a) An example of FLUXNET carbon bal- z ance data, weekly net ecosystem exchange (NEE) -30 for 1997 measured by an eddy covariance fluxtower -30 -25 -20 -15 -10 -5 0 5 10 for a temperate deciduous forest. b) The comparison of SVAT model simulation of NEE to observed b NEE: measured (u.mol m2 s-1) NEE in 7a (Baldocchi, unpublished). generally requires information on the size of the soil car- inverse modeling based on spatial and temporal patterns bon pools. Global scale Rh algorithms often assume an in the atmospheric CO2, 02 and various isotopic variants equilibrium between Rh and NPP within a grid cell, and indicate terrestrial sources and sinks of C (Tans et al., base estimated Rh on only the soil temperature and mois- 1990; Keeling and Shertz, 1992). ture status (e.g., Denning et al., 1996). However, sensi- GPPDI (web site at http://www-eosdis.ornl.gov/npp/ tivity analyses have suggested that accounting for litter, nppigbp.html) was launched following a meeting of eco- soil organic matter, and coarse woody debris pool sizes system modelers organized under the IGBP-DIS (Data will improve simulations of the seasonality of terrestrial and Information System) at the Potsdam Institute for C flux, and will be needed to capture the influence of Climate Impact Research (PIK) in July 1994 (Prince et interannual variability in climate on global Rh. Ap- al., 1995). The Global Primary Production Data Initiative proaches to generating these important input surfaces for (GPPDI) is intended to provide quality data sets for val- Rh models have included "spinning up" a carbon cycle idating global NPP models by identifying existing field model over a several thousand year period to establish data sets of primary production and associated environ- pool sizes which are in rough equilibrium with the local mental data. The program is using data sets for represen- climate and soil texture (VEMAP, see below), and re- tative sites, and extrapolating or regionalizing the better course to maps of soil taxonomic units and related pedon data sets to grid cells sizes of up to 0.5°x0.5° with intelli- databases (e.g., Kern et al., 1998). Flux tower sites, gent scaling logic similar to BigFoot. Emphasis is on vari- where SVAT model development includes the isolation ables needed to parameterize primary production mod- of Rh from GPP and R„, will provide an opportunity for els, including above and below ground NPP, standing validating the estimated pool sizes as well as the Rh flux. crop, LAI, climate data, site data, and landscape variabil- However, the next level of constraint on Rh flux estimates ity. As of early 1999, over 900 published NPP data sets is apparently at the hemispheric or global scale, where worldwide have been compiled, and 47 intensive sites Global Terrestrial Monitoring Network 121

BOREAS SMSA, 1994 Net C Exchange (Mg C ha-1 yr)

BOREAS Region 106 km2

SSA 104 km2 4 0.1 2.0 SMSA 103 km2 Evapotranspiration (cm yr-1)

Flux Tower 1 km2 X

Figure 8. Ecosystem fluxes represented at regional scales by extrapolating FLUXNET data from three flux towers in the BOREAS project (Baldocchi et al., 1997; Black et al., 1996; Jarvis et al., 1997) with a SVAT model. These simulations for the BOREAS Southern Modeling Sub-area (SMSA) of 2000 km used the BIOME-BGC SVAT model, spatially defined stand parameters with satellite data, integrated topography, soils, and microclimate data (Kimball et al., 1999). BIOME-BGC simulations were first validated with FLUXNET data from the three flux towers (Kimball et al., 1997a,b). have been located to develop data sets with comprehen- validated by flux tower ET measurements and extrapo- sive ancillary data in this ongoing effort. lated by EOS vegetation measurements. General circulation models also incorporate SVAT models such as BATS and SiB that represent the same Biospheric Model Intercomparisons canopy process dynamics as ecological SVAT models, but Another approach to assess accuracy of biospheric mod- generalized to =1-2° grid cells. Thus, the climate model- els when direct measurements are not possible is by ing community faces many of the same validation issues global model intercomparisons. Major discrepancies in as the ecological SVAT modelers (Pielke et al., 1993; results among models draw attention to potential prob- Dickinson, 1995), notably the gap between the ET esti- lem areas and data sets or weak process understanding. mate from a climate model grid cell (--.---104 km2 ) and flux Two international biospheric model intercomparison ac- tower estimates of ET over the tower footprint. Method- tivities are currently underway. ologies such as the BigFoot project will be able to pro- The ongoing IGBP sponsored 1995 Potsdam (PIK)- vide estimates of regional ET based on a SVAT model NPP model intercomparison can use FLUXNET derived 122 Running et al.

NPP estimates to test global NPP model estimates at lo- mate (current and projected under doubled CO 2), atmo- cations sampled by the network. The website is at http:// spheric CO2, and mapped and model-generated vegeta- gaim.unh.edu/. The 1995 Potsdam NPP model inter- tion distributions. Maps of climate, climate change comparison project was an international collaboration that scenarios, soil properties, and potential natural vegetation produced single-year global NPP simulations (Cramer et were prepared as common boundary conditions and driv- al., 1999). There were large discrepancies amongst mod- ing variables for the models (Kittel et al., 1995). As a els of NPP in northern boreal forests and seasonally dry consequence, differences in model results arose only tropics. Over much of the global land surface, water from differences among model algorithms and their im- availability most strongly influenced estimates of NPP; plementation rather than from differences in inputs however, the interaction of water with other multiple (VEMAP, 1995). VEMAP is currently in the second phase limiting resources influenced simulated NPP in a non- of model intercomparison and analysis. The objectives of predictable fashion (Churkina and Running, 1998). Phase 2 are to compare time-dependent ecological re- VEMAP (http://www.cgd.ucar.edu:80/vemap/) is an on- sponses of biogeochemical and coupled biogeochemical- going multi-institutional, international effort addressing biogeographical models to historical and projected tran- the response of terrestrial biogeography and biogeo- sient forcings across the conterminous United States. chemistry to environmental variability in climate and Because the VEMAP project has no validation compo- other drivers in both space and time domains. The ob- nent, interaction with FLUXNET and EOS can provide jectives of VEMAP are the intercomparison of bio- direct model validations. (Schimel et al., 1997) geochemistry models and vegetation distribution models (biogeography models) and determination of their sensi- INTERNATIONAL COORDINATION tivity to changing climate, elevated atmospheric carbon AND IMPLEMENTATION dioxide concentrations, and other sources of altered forc- ing. The completed Phase 1 of the project was structured Global validation and monitoring cannot be done without as a sensitivity analysis, with factorial combinations of cli- international cooperation that transcends any national

Figure 9. Potential synergism of international programs for validating terrestrial ecosystem variables at different space/time scales. Sites contributing to multiple programs have the highest synergism and efficiency. The programs depicted are: GPPIDI=Global Primary Production Data Initiative, FLUXNET=the global network of eddy covariance flux towers, Atm FLASK=the global network of atmospheric flask sampling of NOAA/CMDL and C.D. Keeling and others, GTOS-NPP=a special project of the Global Terrestrial Observing System to measure net primary production of field sites worldwide, BigFoot=a study to establish scaling principles for sampling vegetation over large areas, EOS-MODIS=the Moderate Resolution Imaging Spectroradiometer on the Earth Ob- serving System, the primary terrestrial observation sensor, VEMAP=the Vegetation/Ecosystem Modeling and Analysis Project, GAIM-NPP=the International Geosphere—Biosphere project in Global Analysis Integration and Modeling study of global NPP. See text for details of these projects.

102 high synergism

low synergism VEMA modeling projects

1 0 -

0-1 10-1 10° 10 102 103 10 4 1 05 Spatial Scale (km 2 )

Global Terrestrial Monitoring Network 123

Table 3. The IGBP Terrestrial Transects Currently Identified Region Contributing Transects in Initial Set Humid tropics Tropical forest and its agricultural derivatives Amazon Basin/Mexico Central Africa/Miombo Southeast Asia/Thailand Semiarid tropics Forest-woodland-shnibland (the savannas) Savannas in the long term (West Africa) Kalahari (Southern Africa) Northern Australia Tropical Transect Midlatitude semiarid Forest-grassland-shrubland Great Plains (USA) Argentina North East China Transect High latitudes Boreal forest-tundra Alaska Boreal Forest Transect (Canada) Scandinavia Siberia agency (Fig. 9). When planning global networks, it is es- four key regions, with three or four existing, planned or sential to recognize that not all facilities have equal levels proposed transects contributing to the set in each region of scientific activity; however, all are needed to provide (Table 3). adequate global sampling. The Global Terrestrial Ob- The GTOS-NPP project (website at http://www.fao. serving System (GTOS) and terrestrial components of org/GTOS/Home.htm) is being coordinated through the the Global Climate Observing System GCOS have led in international U.S. Long Term Ecological Network designing consistent international measurements for vali- (LTER) office, http://lternetedu/ilter/. The goal of the dation and monitoring work (GCOS, 1997). The strategy GTOS-NPP project is to distribute the lkm EOS NPP for implementing the plan is being developed in con- and LAI products every 8 days to regional networks for junction with the World Meteorological Organization evaluation, and after validation, translation of these stan- (WMO) and the International GeosphereBiosphere Pro- dard products to regionally specific crop, range, and for- gramme (IGBP). The plan will provide the necessary est yield maps for land management applications. The climate requirements for GTOS and the terrestrial re- project will also provide global validation points for land quirements for GCOS. See http://www.wmo.ch/web/ paramatrization in climate and carbon cycle models. gcos/gcoshome.html. Two core projects of IGBP have been instrumental Databases and Archiving in developing coordinated terrestrial systems. BAHC is Establishing data archiving centers is also important for the original project to suggest FLUXNET, and GCTE international distribution and long-term availability of has led in designing the IGBP Terrestrial Transects. data. Several long-term databases are currently being Both GAIM and IGAC now are supporting the continu- compiled at the Oak Ridge, Tennessee, USA, DAAC fa- ing development of a global validation and monitoring cility. This data archive facility is home for data from system. Two internationally coordinated activities appear FLUXNET, BigFoot, and GPPDI projects, all that have ready to implement FLUXNET and biospheric monitor- been summarized here (Olson et al., 1999, this issue). A ing activities, the IGBP Transects, and the GTOS-NPP centralized permanently funded data center such as the project. Oak Ridge DAAC insures continuity, consistency, and The IGBP Terrestrial Transects, website at: http:// availability of data. However, the proliferation of In- gcte.org/LEMA-IGBP/LEMA-IGBP.html, are a set of in- ternet makes a more distributed array of data archive tegrated global change projects consisting of distributed facilities equally useful if permanent commitments of observational studies and manipulative experiments, cou- support are made. pled with modeling and synthesis activities organized along existing gradients of underlying global change pa- CONCLUSIONS rameters, such as temperature, precipitation, and land use. The IGBP Terrestrial Transects consist of a set of Each of the four monitoring scheme components de- study sites arranged along an underlying climatic gradi- scribed here—flux towers, flask sampling, ecosystem ent; of order 1000 km in length and wide enough to en- modeling, and EOS satellite data—are a source of con- compass the dimensions of remote sensing images. The sistency checks and validation to the other components. initial set of IGBP Terrestrial Transects are located in For example, if [as suggested in Fan et al. (1998)l • the 124 Running et al. inverse modeling from the flask network suggested a C Benson, B. J., and MacKenzie, M. D. (1995), Effects of sensor sink on the order of 1-2 Pg C in temperate North spatial resolution on landscape structure parameters. Land- America, then results from a sample of flux tower sites scape Ecol. 10:113-120. in that region could confirm that specific sites were large Black, T. A., den Hartog, G., Neumann, H. H., et al. 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Appendix: List of Acronyms BAHC Biospheric Aspects of the Hydrologic Cycle (IGBP) BOREAS Boreal Ecosystem Atmosphere Study DAAC Distributed Active Archive Center EOS Earth Observing System ET Evapotranspiration ETM+ Enhanced Thematic Mapper FPAR Fraction photosynthetically active radiation GAIM Global Analysis and Modeling Project (IGBP) GCM General circulation model GCOS Global Climate Observing System (WMO) GTOS Global Terrestrial Observing System (WMO) GCTE Global Change Terrestrial Ecosystems (IGBP) GPP Gross Primary Production GPPDI Global Primary Production Data Initiative (IGBP) ICAC International Global Atmospheric Chemistry (IGBP) IGBP International Ceosphere—Biosphere Program IGBP-DIS IGBP Data Information System ILTER International Long Term Ecological Research LAI Leaf area index LTER Long Term Ecological Research MODIS Moderate Resolution Imaging Spectroradiometer NEE Net ecosystem exchange NPP Net primary production SVAT Soil—Vegetation—Atmosphere transfer model TOPC Terrestrial Observing Panel for Climate (WMO) VEMAP Vegetation/Ecosystem Modeling and Analysis Program