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APPENDIX 5. PRESENTATION SUMMARIES


Review of global data on atmospheric greenhouse gases
Terrestrial Carbon Observation Data Resources
Terrestrial Data Accessible Through the ORNL DAAC
Global Soil Resources Information
Terrestrial Carbon Observation: Global Grid Data for Model Input
Carbon Modeling Datasets - New Zealand

Review of global data on atmospheric greenhouse gases

R. J. Francey
Commonwealth Scientific Industrial Research Organisation (CSIRO)
Atmospheric Research

Recent 1000-year, high precision, high time resolution records of atmospheric CO2, d13C and CH4 from Antarctic ice cores overlap modern records for the first time and are closely consistent with them (Etheridge et al. 1996, Etheridge et al. 1998, and Francey et al. 1999). They illustrate a dramatic departure from chemical equilibrium of the atmosphere that is unprecedented over the last 420,000 years and closely mimics the continuing near-exponential growth of human numbers and industrial activity. The CO2 and d13C records are attributable primarily to combustion of fossil fuels.

A double deconvolution (inversion) of the records, using historical records of the mining and use of fossil fuels, provides a time-history of increasing terrestrial and oceanic sinks for atmospheric CO2. However, the inferred terrestrial exchange initially reflects increasing deforestation/land-use release which is consistent with independent estimates. Only after 1930-1940 does the terrestrial biosphere switch to a steadily increasing sink (Trudinger et al., 1999). The nature of this sink and its ability to persist, or to be manipulated, remains a critical research topic.

The capacity for a single CO2 record to represent the global atmosphere is demonstrated for modern CSIRO records over the last decade. Flask data from Mauna Loa (21N), Cape Grim (41S) and South Pole (90S) show closely similar CO2 growth rate variations after application of a low pass filter (removing variations with periods less than 650 days). The ice core CO2 records that are filtered by firn-diffusion for periods less than ~10-15 years, should therefore easily meet this criterion. Assuming each is a global signal and converting the ppm/year growth rate variations to Pg C/year confirms closely matching interannual variations from each site or from a global average (>50 sites) measured by the National Oceanic and Atmospheric Administration (NOAA) flask-sampling network (GLOBALVIEW-CO2). It is concluded that:

The challenge is to understand the processes that most influence atmospheric CO2 levels on interannual (and longer) time scales, and explore the potential to ameliorate human influences. This is being undertaken by: Routine measurements of the stable carbon isotope ration d13C, were instituted more than two decades ago for use as a tool to partition the uptake of industrial CO2 between oceanic and terrestrial reservoirs. Results from these programmes have been somewhat less than satisfactory in the past.

The second longest (20-year) coherent d13C record, from Cape Grim, has been assessed against Southern Hemisphere records. It is clear that differences between various records generally coincide with documented major instrumental changes in one or other of the programmes. Many of the major shifts between past records can now be attributed to a previously neglected cross-contamination of sample and reference CO2 in the typical dual inlet isotope ratio mass spectrometry. In the 1990s several groups introduced whole air reference gases that experience both CO2 extraction and mass spectrometry in a similar manner to air samples (each measured against the pure CO2 reference). While this method is far more robust and insensitive to cross-contamination effects, the assignment of isotopic values to the reference air is critical, and not well determined in the set-up stages of an air-reference programme. At least in the Cape Grim programme, the original air reference high-pressure cylinder standards are still available and their assigned values have been continually refined on the basis of regular analysis. At Cape Grim, CSIRO employs two independent sampling and CO2 extraction methods, and both air and CO2 referencing. The redundancy has aided diagnoses and permitted a largely independent reassignment of calibration factors over the last 20 years.

Under the assumption that the long-term CO2 and re-evaluated d13C records are globally representative on the interannual (650 day filter) timescale, and allowing for the different responses for net uptake of 12C and 13C atmospheric perturbations, a new estimate of the terrestrial versus oceanic contribution to the CO2 interannual variability is obtained. It suggests that the IAV in CO2 is predominantly terrestrial, close anti-correlation of CO2 and d13C signals over the last 20 years with a coefficient ~-0.04 ‰ ppm-1. The implied IAV in oceanic uptake is small (<1 Pg C/yr peak-to-peak), in good agreement with recent Oceanic General Circulation Model (OGCM) and data from the El Niño zones in eastern equatorial Pacific. There also appears to be an emerging agreement with atmospheric O2/N2 records.

Bottom-up, climate driven photosynthesis and respiration in global ecological models have also produced IAV in terrestrial fluxes of a sign and magnitude to explain the global CO2 IAV (but with considerable uncertainties). For example, a CSIRO study that looks at IAV over the last decade in high-precision global records of not only CO2 and d13C, but also CO, CH2, and H2, provides empirical evidence that perhaps half of the CO2 IAV is the result of biomass burning?

The continuing inability to link the well-described global atmospheric greenhouse levels to surface processes switches the focus back onto the global atmospheric composition networks. The main justification now for extensive monitoring is to provide information on the magnitudes and nature of significant regional fluxes and track/confirm regional changes in these fluxes. Current inversions of global network CO2 data provide regional flux estimates on continental/ocean basin scales, but with quoted uncertainties of order 0.5 to 1 Pg C/yr. With these scales and uncertainties, it is still not possible to unambiguously identify surface processes responsible for global composition changes. The long-term vision, is to have climate driven surface process models which can be used in a bottom-up process to predict changes in global radiative forcing These models would be coupled with sufficient global atmospheric composition monitoring to confirm these trace gas prognoses both globally, and regionally via inversion modelling.

In this context, a number of measures have been identified to improve the atmospheric composition measurements currently employed, including the multi-species approach illustrated above. Two other examples are given here, GLOBALHUBS and the LOFLO CO2 analyser.

Target precision for merging CO2 data from different global sampling networks has been set by the WMO Experts group at 0.05 ppm and 0.10 ppm for the Southern and Northern Hemispheres, respectively. CO2 data are used to define CO2 differences in space or time which in turn provide air-surface fluxes of CO2. Generally spatial or short-term differences can be monitored more precisely than an individual measurement can be linked to a primary standard. A consequence has been a historic tendency to only use network data from one laboratory, under the assumption that inter-laboratory calibration is the limiting factor. Three round-robin laboratory calibrations, involving circulations of high-pressure cylinders of air through ~20 laboratories have been conducted since 1990 by NOAA under the auspices of WMO. Although there have been improvements, at best only around 50 percent cylinder air measurements meet target precisions, and there is little consistency from one circulation to the next. Stricter adherence to WMO guidelines, by way of maintaining regular calibration of high-pressure cylinder air in the WMO-endorsed central calibration laboratory, would make a significant improvement. However, it is now clear that this is not a complete answer - within one laboratory there are examples of systematic differences between methods larger than target precisions for inter-laboratory data merging; in less advanced laboratories the effectiveness and cost of maintaining and propagating the link to primary standards has proven to be a limiting factor.

The GLOBALHUBS concept was developed to address this issue. In essence it provides more powerful diagnostic tools to the wider community. These tools quickly diagnose and rectify systematic error throughout the network; monitor with much higher frequency and at lower costs; and verify the on-going effectiveness of independent calibrations. It involves four regionally distributed laboratories (North America, Europe, Eurasia, Southern Hemisphere) that provide several times a year well-characterized low-pressure air spanning atmospheric values, to all participating laboratories in their region. The four hub laboratories stay tightly linked by annual circulation of high-pressure cylinders and high frequency "oscillations" of low-pressure containers. All analyses on GLOBALHUBS air are promptly reported to a Internet site providing transparency. Redundancy, at least initially, and relevance for a range of trace gases are key elements of the concept. GLOBALHUBS concepts are currently being used as part of the CARBOEUROPE programme (with CSIRO and NOAA as sub-contractors) at a cost of around US$0.5m. Eventually international focussed funding may be required to ensure uniform quality across the four HUBS and their customer laboratories. The International Atomic Energy Agency (IAEA) and WMO measurement expert groups have endorsed GLOBALHUBS.

CSIRO have developed a new CO2 analyser system (LOFLO), based on a LI-COR NDIR, which has achieved substantial breakthroughs in a) the sample size requirement, and b) precision. Sample flow rates are ~20-50 times less in the LOFLO compared to a conventional system. For field operations, there are very substantial logistic implications. Precision and long-term stability (repeat measurements over 16 months on an unknown cylinder of air yield a std. dev. in CO2 of ~0.003 ppm (the LI-COR precision is quoted at 0.5 ppm), results in an instrument with excellent diagnostic abilities.

A diagnostic LOFLO analyser is envisaged at each HUB of GLOBALHUBS. Seven high-pressure air standards (e.g. with CO2 spanning 340- 400 ppm), calibrated on the WMO Mole Fraction Scale by the Central CO2 Calibration Laboratory in NOAA, are an integral part of the current CSIRO LOFLO system. With recommended recalibration, the lifetime of these standards is estimated at 10-15 years or more, even with a continuous operation. In a conventional system there is high gas usage and WMO recommendations are calibration strategies involving up to 5 levels of scale propagation, each involving 2-10 high-pressure cylinders. The potential for error propagation is high and the cost of maintaining and effectively utilizing the calibration hierarchy is prohibitive for some of the smaller laboratories. Already, the CSIRO LOFLO has diagnosed several peripheral components in widespread use in the CO2 monitoring community (e.g. most high-pressure regulators) as a source of systematic bias.

As a field instrument, the LOFLO system offers the possibility of unattended operation in remote sites. The prototype system at Cape Grim requires operator intervention once every five months, to change a reference gas cylinder and recharge the drying system. (note: as an interim measure, cylinder valves are opened and closed once a month). There is a realistic expectation of improving this aspect of performance. The logistic advantages come with a dramatic improvement in accuracy, both by virtue of the instrument stability and the much more direct links to primary standards.

Preliminary inversion modelling studies have explored the impact deploying continuous (LOFLO) analysers at the current Australian flask sampling sites. Significant reductions in uncertainty result in the estimated regional fluxes.

References

Etheridge, D.M., Steele, L.P., Langfenfelds, R.L., Francey, R.J., Barnola, J-M. & Morgan, V.I. 1996. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res., 101(D2): 4115 - 4128.

Etheridge, D. M., Steele, L. P., Francey, R. J. & Langenfelds, R. L. 1998. Atmospheric methane between 1000 A.D. and present: evidence of anthropogenic emissions and climatic variability. J. Geophys. Res., 103(D13): 15979 - 15993.

Francey, R.J., Allison, C.E., Etheridge, D.M., Trudinger, C.M., Enting, I.G., Leuenberger, M., Langenfelds, R.L., Michel, E. & Steele, L.P. 1999. A 1000 year high precision record of d13C in atmospheric CO2. Tellus, 51(B): 170 - 193.

GLOBALVIEW-CO2. 2000. Cooperative atmospheric data integration project - carbon dioxide. CD-ROM, Boulder, Colorado, USA, NOAA CMDL. Also available via anonymous FTP: ftp.cmdl.noaa.gov, Path: ccg/co2/GLOBALVIEW.

Keeling, C.D., Whorf, T.P., Wahlen, M. & van der Plicht, J. 1995. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature, 375: 666 - 670.

Masarie, K. A., Langenfelds, R. L., Allison, C. E., Conway, T. J., Dlugokencky, E. J., Francey, R. J., Novelli, P. C., Steele, L. P., Tans, P. P., Vaughn, B. & White, J. W. C. 2001. NOAA/CSIRO flask air intercomparison experiment: A strategy for directly assessing consistency among atmospheric measurements made by independent laboratories, J. Geophys. Res., 106(D17): 20,445 (2000JD000023)

Trudinger C.M., Enting, I.G., Francey, R.J. & Etheridge, D.M. 1999. Long term variability in the global carbon cycle inferred from a high precision CO2 and d13C ice core record. Tellus, 51(B): 233 - 248.

Terrestrial Carbon Observation Data Resources

R.J. Olson and R.B. Cook

Oak Ridge National Laboratory

The following is a synopsis of activities associated with "point" measurements of carbon dynamics prepared as background information for the GTOS Terrestrial Carbon Observation workshop, held in Frascati, Italy, 5-8 June 2001. Table A7 lists the key carbon observations that were identified during a series of GTOS and IGBP carbon meetings held in 2000. The summary emphasizes information (primarily for in situ field measurements) associated the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics and other ongoing work in the ORNL Environmental Sciences Division (ESD). Table A8 lists networks relevant to TCO activities and Table A14 lists the variable measured by these networks.

GTOS tiers

Much work has been done to develop a framework to bring together carbon flux measurements that can be used in developing the TCO plan. GTOS has described a Global Hierarchical Observing Strategy (GHOST, Table A9) consisting of five tiers. The list of data described in this document can be related to the GHOST scheme. The core of GTOS will be a permanent observing system for the world's key managed and natural ecosystems. The system is based on a data sampling strategy involving large-scale studies of the Earth's major environmental gradients, agricultural and ecological research centres, field stations and a gridded series of some 10,000 sampling sites. For tiers 1 to 3, much of the required infrastructure is already in place. But there is a need to upgrade some of the existing ecosystem monitoring sites and fill significant gaps in spatial coverage, especially in developing countries. Table A10 lists categories of measurements to be made at Tier 1-3 stations. Ideally, all TCO/IGBP/GCTE measurement stations should be strongly encouraged to report at least the measurements identified as priority 1 and 2, which require minimal resources, labour and time. Categories 3 and 4 require increasing amounts of resources but should be reported if possible (see also Scurlock et al., 1999).

Syntheses of carbon cycle components

Several major synthesis studies have compiled measurements of components of the carbon cycle as published for research sites worldwide. The status of these compilations of in situ data is summarized in Table A11 with references provided at the end of the report.

Ongoing work in the ORNL Environmental Sciences Division includes:

1. ORNL DAAC - http://daac.ornl.gov/

The NASA Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC) contains over 560 data sets from a variety of field studies and global compilations. It provides free and open access to fully documented data. Table A12 provides a list of the regional and global data distributed by the ORNL DAAC and an overall summary of the DAAC holdings is attached to the end of this report.

2. MERCURY - http://mercury.ornl.gov/ornldaac/

The ORNL DAAC has developed a Internet-based, metadata distribution system (Mercury) to allow individuals to register and provide access to their data, with the control to data access maintained by the individual. Mercury provides an efficient way to identify and access sources of TCO data. The ORNL DAAC has registered over 80 regional and global datasets associated with carbon and related observations (Table A13).

3. CDIAC - http://cdiac.esd.ornl.gov/index.html

The Carbon Dioxide Information Analysis Center (CDIAC), which includes the World Data Center for Atmospheric Trace Gases, is the primary global-change data and information analysis center of the U.S. Department of Energy (DOE). CDIAC's data holdings include records of the concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea level.

4. Network and maps of sites - ftp://www.daac.ornl.gov/data/raid2/rjo/global

The descriptions of data sets, such as those described above, often provide just the overall extent of the spatial coverage. ORNL has compiled a list of over 40 networks or collections of monitoring and research sites (Table A8) containing over 30 000 points. By entering the coordinates of all the points included in each data set into a Geographic Information System (GIS) database, maps and GIS search capabilities can be used to locate combinations of data in specific geographic areas. Locations of sites for over 30 of these networks have been entered into an ArcView GIS database. A series of maps have been produced to show the spatial extent of the various data collections. The maps that have been generated are from a variety of sources and many require additional checking for accuracy and more complete information. There may or may not be data readily available for individual sites; usually only the site coordinates are available. See ftp://www.daac.ornl.gov/data/raid2/rjo/global for sources of site and network information and low resolution images of the maps.

5. GPPDI and EMDI - www.daac.ornl.gov/NPP/npp_home.html

Understanding global carbon dynamics is based on the scientific literature; often the synthesis of the literature is based on the compilation of findings published over many years by many researchers in various countries. Recently, several groups have undertaken thematic compilations of NPP, LAI, Litter, soil rooting depth, etc. These compilations often provide new insights to overall patterns and dynamics; in addition, they provide a valuable resource for model development and validation. Global ecosystem model predictions were compared to field measurements of Net Primary Productivity (NPP) at the Ecosystem Model-Data Intercomparison (EMDI) Workshops held in 1999 in Durham, New Hampshire and 2001 in Santa Barbara, California, USA. The workshops were organized by the International Geosphere-Biosphere Programme (IGBP) project on Global Analysis, Interpretation and Modeling (GAIM) and followed a model intercomparison that was conducted at the Potsdam Institute flor Climate Impact Research (PIK) in the mid-1990s. The EMDI database of basic model drivers for the sites and regions was compiled to match the collection of NPP field measurements that have been compiled under the auspices of the Global Primary Production Data Initiative (GPPDI). Much of the NPP data compilation was performed at ORNL and the University of Maryland with funding provided by the NASA Terrestrial Ecosystems Program. In addition, three international working groups were held as part of the Development of a Consistent worldwide Net Primary Production Database that was sponsored by the National Center for Ecological Analysis and Synthesis (NCEAS).

The EMDI database represents the single largest collection of global in situ data available for model development and validation. However, despite the enormous amount of work that went into preparing the database (assembling the NPP data plus locating and making available the site and region driver data) there is a realization that model-data intercomparison is an extraordinarily complex task, especially the data compilation. A review and outlier analysis process resulted in 81 Class A sites, 933 Class B sites, and 6339 Class C cells derived from the original synthesis of 7648 NPP measurements. The sites were categorized as either Class A, representing intensively-studied or well-documented study sites (e.g. with site-specific climate, soils information, etc.), Class B, representing more numerous "extensive" sites with less documentation and site-specific information available, or Class C, representing regional collections of 0.5 latitude-longitude grid cells.

6. ISLSCP II - The intent of the International Satellite Land Surface Climatology Project (ISLSCP II) is to produce a consistent collection of high priority global data sets using existing data sources and algorithms, designed to satisfy the needs of modellers. The Global Energy and Water Cycle Experiment (GEWEX) is the element of the World Climate Research Program (WCRP) charged with promoting the scientific investigation of the "fast" component of the Earth's climate system. The International Satellite Land Surface Climatology Project (ISLSCP) is one of several projects of the GEWEX and has the lead role in addressing land-atmosphere interactions; Specifically, process modelling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

The ISLSCP II data sets are based on the need to develop a comprehensive data set at consistent spatial and temporal resolutions and are compiled in four key areas: land cover, hydrometeorology, radiation, and soils. The data sets span the 10-year period, 1986-1995, and are mapped to consistent grids (0.5 deg. × 0.5 deg. for topography and land cover, 1 deg × 1 deg for meteorological parameters). The temporal resolution for most data sets is monthly; however, a few are at a finer resolution (e.g. 3-hourly). The ISLSCP II collection includes data sets associated with the carbon cycle (Table A15). All ISLSCP II data sets will become publicly available on line athttp://islscp2.gsfc.nasa.gov/.

For more information contact Dick Olson, 1-865-574-7819 or olsonrj@ornl.gov. Credit: compiled by the ORNL DAAC for Biogeochemical Dynamics http://daac.ornl.gov/, funded by the U.S. National Aeronautics and Space Administration Earth Science Enterprise program at Oak Ridge National Laboratory, which is operated by UT-Battelle for the U.S. Department of Energy.

Table A7. Observation requirements for bottom-up approach - calibration/validation of variables required at selected sites (based on FAO, 2002b)

Variable

Flux Tower

Topical Network

Synthesis collection





ATMOSPHERE




Air temperature

Yes

Weather Stations

Global Historic Climate Network

Precipitation

Yes

Yes


Solar radiation

Yes

Yes


Relative humidity

Yes

Yes


Wind speed

Yes

Yes


Net radiation

Yes



CO2 concentration profile

Yes

FLASKNET


Integrated atmospheric water vapour




Snow water equivalent




Aerosols








SITE








Natural disturbance history

Most



Management history

Most



Topography

DEM



Spatial pattern

Remote Sensing







VEGETATION








Vegetation cover class

Yes



Root carbon

Some


Jackson et al. 2000

Above-ground biomass

Most

Country Forest/Crop inventory


Leaf area index

Most

LAINET

ORNL LAI

Foliage N

Some







SOIL








Biota C, N

Most



Biota biomass

Some



Temperature profile

Yes



Maximum thaw depth

Rare



Thermal conductance

Rare



Thermal diffusivity

Rare



Soil moisture

Most



Hydraulic properties

Some



Ground water table depth

Rare



Soil Carbon content (org. & inorg.)

Some

PEDON database


Soil Carbon age

Rare



Soil N, P content

Some



Soil Bulk density

Some



Percentage of sand and clay fraction

Some

Soil Survey

IGBP Soils

Soil pH

Some



Soil macro & micro nutrients

Some

Soil Survey


Microbial biomass

Rare







PHYSIOLOGY








Foliage N

Most



Foliage lignin

Most



Chlorophyll

Most



Rubisco

Most







FLUXES








Carbon fluxes (above & near ground)

All



Above-ground NPP

Some

GTNET

GPPDI

Below-ground NPP

Rare


GPPDI

Litterfall N, P, C

Some



H, ET (above stand)

All



CH4

Some

TRAGNET


VOC

Some

TRAGNET


DOC

Some

TRAGNET


Heterotrophic respiration rate

Some

LIDET



Table A8. Parameters, networks and collections of sites associated with global change

Acronym

Source

Status/Location*

Data?

No. of sites

Field Campaigns and Focused Studies

ARM

Atmospheric Radiation Monitoring Program (NSA, AAO, SGP, TWP)

www.arm.gov/docs/index.html

Yes

5

BigFoot

BigFoot

www.fsl.orst.edu/larse/bigfoot/index.html

Yes

5

IFC

Field Campaigns: FIFE, BOREAS (NSA,SSA), LBA, S2K, SNF, HAPEX-Sahel, OTTER, Miombo

Mostly in ORNL DAAC

Yes

13

FACE

Elevated CO2 Impacts and Free Air Carbon Dioxide Enrichment

http://cdiac.esd.ornl.gov/programs/FACE/whereisface.html

No

40

NEWS

Network of Ecosystem Warming Studies

http://gcte-focus1.org/activities/activity_11/task_112/warming %20network/overview

No

32

BASIN

Biosphere-Atmosphere Stable Isotope Network

http://gcte-focus1.org/basin.html

Yes

46

LAINET

LAI-FPAR Validation Sites

http://cybele.bu.edu/modismisr/validation/sitespis.html

No

24

LIDET

LTER International Decomposition sites

http://lternet.edu/research/collaborations/syn_04.html

Yes

28

Long Term Research Sites

GTNET

Global Terrestrial Observing Systems

http://sql.lternet.edu/scripts/gtnet/siteview.pl

No

478

TEMS

Terrestrial Ecosystem Monitoring Sites (GTOS)

http://sql.lternet.edu/scripts/gtnet/allsiteview.pl

No

1321

LTER

Long Term Ecological Research Sites (US)

LTER

Yes

22

OBFS

Organization of Biological Field Stations

In process ~200 sites. www.obfs.org/Table_1_Links/stat_all.html

No

0

IOBFS

International Organization of Biological Field Stations

In process ~120 sites

No

0

MABnet

MABnet

Not available

No

0

Monitoring Networks

FLUXNET

FLUXNET

ORNL DAAC

Yes

140

FLASK-NET

Cooperative Air Sampling Network

www.cmdl.noaa.gov/ccgg/flask/ccgnetwork.dat

Yes

80

AERONET

Aerosol Robotic Network

http://aeronet.gsfc.nasa.gov:8080/

Yes

230

BSRN

Baseline Surface Radiation Network

http://bsrn.ethz.ch/wrmc/bsrn_mainframeset.html

Yes

33

BRDF

BRDF

In process

Yes

0

ISIS

Integrated Surface Irradiance Study

In process. http://zepher.atdd.noaa.gov/isis/isis.htm

?

0

NADP

National Atmospheric Deposition Program/National Trends Network

http://nadp.sws.uiuc.edu/

Yes

266

TRAGNET

Trace Gas Network

www.nrel.colostate.edu/PROGRAMS/ATMOSPHERE/TRAGNET/TRAGNET.html

Yes

25

Global Historical Climatology Networks

Temperature

Global Historical Climatology Network: Temperature

CDIAC

Yes

6039

Precipitation

Global Historical Climatology Network: Precipitation

CDIAC

Yes

7533

Pressure

Global Historical Climatology Network: Pressure

CDIAC

Yes

1883

Hydrology Networks

HCDN

Hydro-Climatic Data Network: Streamflow Dataset (US)

Mercury

Yes

1659

RIVDIS

River Discharge

ORNL DAAC

Yes

1019

SOILH2O

Global Soil Moisture Data Bank

Mercury

Yes

354

Vegetation Productivity Measurement Sites

LAI

ORNL

ORNL DAAC

Yes

600

NPP TREEGRASS

Tree/Grass NPP sites - SCOPE (Scholes/Scurlock)

In process

Soon

169

FINE-ROOT

Root Turnover and BNPP (Gill & Jackson)

In process

Soon

190

NPPClassA

EMDI Class A Intensive NPP

ORNL DAAC

Yes

81

NPPClassB

EMDI Class B Extensive NPP

ORNL DAAC

Yes

933

NPPClassC

EMDI Class C 0.5o Grid Cell NPP

ORNL DAAC

Yes

5146

Soil Characteristics Measurement Sites

LITTER

Litter (Holland, Mathews, Post)

In process

Soon

589

PEDONS

Pedons ISRIC, Netherlands:

Mercury

Yes

1125

ROOT-DEPTH

Rooting Depth (Jackson & Schenk)

In process

Soon

330

SOILC

Organic Soil Carbon and Nitrogen (Zinke and Post)

Mercury

Yes

4118

Soil Respiration

Soil Respiration Raich and Schlesinger (1992).

Mercury

Yes

123

Global-Scale Demonstration Networks

TRANSECTS

IGBP Transects


No

13

GTNET-NPP

GTNET NPP Demonstration Project


Soon

25

VALCORE

EOS Land Validation Core Test Sites

Mercury

Soon

24

GLCTS

Global Land Cover Test Sites

Mercury

Yes

20

VCC/VCF

MODLAND Land Cover Change: Vegetation Cover Conversion (VCC) and Vegetation Continuous Fields (VCF)

In process. http://modarch.gsfc.nasa.gov/MODIS/LAND/VAL/products/vcc_vcf.html#ref

?

0

Total points




34 761

* Status/location: Data? (this is an indication of data availability for a network or collection of sites):
Table A9. GTOS Global Hierarchical Observing Strategy (GHOST)*

Tier

~sample numbers

Sample area (km2)

Example variables

1. Large area experiments and gradient studies

10

1,000

Boundary layer gas exchange, biome shifts

2. Long-term research centres

100

10

Energy, water, carbon and nutrient cycling

3. Field stations

1,000

1

Crop yield, ecosystem productivity, land use

4. Periodic, unstaffed sample sites

10,000

0.01-0.1

Land cover, soil state

5. Frequent low-resolution remote sensing

100,000,000

0.001-1

Leaf area dynamics, land cover

* Applies to terrestrial unmanaged ecosystems, agro-ecosystems, lakes, rivers and estuaries, details at: www.fao.org/gtos/GHOST.html
Table A10. Measurement priorities for desirable carbon cycle attributes to be collected at study sites/network sites/ground stations

Priority

Parameters to be measured

1. Basic site characterization "no-brainers"

· latitude/longitude/elevation using GPS, details and position of nearest weather station with long-term monthly precipitation and temperature considered representative of the study site
· vegetation type at site, surrounding vegetation (is site representative of its surroundings?)
· slope, surrounding topography
· basic description of soil type and texture

2. A little more resources needed

· non-woody/foliar biomass increment, preferably estimated monthly
· woody biomass increment, generally estimated yearly
· below-ground standing crop, preferably with above-ground live and dead matter separated

3. Partial characterization of carbon cycling

· estimate of total standing crop, preferably with above-ground live and dead matter separated (report the peak value if this varies greatly during the year)
· soil coring to determine soil layers, structure and texture of different layers, total soil carbon
· simple components of NPP/carbon cycling, e.g. monthly tree litter fall for 24 months

4. More difficult and complete measurements

· estimates of coarse and fine below-ground increment/productivity
· other components of NPP, e.g. herbivory, fire losses, mortality, large woody litter fall (over several years), volatile organic carbon, root exudates
· other ecosystem/stand characterization, e.g. stand age, tree density, age classes, successional status, management history


Table A11. Carbon cycle components and status of in situ data compilations

Component

Number of Unique Observations, Sites

Active Data Compilers

Selected Publications

NPP

>2500 obs, >1500 sites

Scurlock, Olson, Gower

Gower et al. 2001, Scurlock et al. 1999

LAI

>900 obs, >800 sites

Scurlock, Gower

N/A

Litter (stocks, flux, litter nutrients)

~800 obs, ~600 sites

Post, Matthews, Holland, Sulzman

Sulzman et al. 2000

CO2 Flux

>100 active towers

Baldocchi, Olson data for 35 sites

Baldocchi et al. 2000, Valentini et al. 2000

Forest biomass and production

>100,000 plots in USA, ~1000 plots in Latin America

Jenkins, Brown, Iverson

Brown 1992

Fine root turnover

>800 obs.

Jackson, Gill

Gill and Jackson 2000

Soil rooting depth

520 obs, ~300 sites

Jackson, Schenck

Jackson et al. 2000

Soil carbon

~1200 pedons with global mapping method

Scholes, Post

IGBP Global Soil Data Task, 2001

Decomposition rates

108 CWD sites28 LIDET sites

Krankina, Harmon

Harmon et al. 2000

Soil Respiration

183 sites

Davidson

Davidson et al. 2000 and Raich and Schlesinger 1992


Table A12. Regional and Global Data Sets in the Oak Ridge National Laboratory Distributed Active Archive Center (21 May 2001)

Topic/Title

HYDROLOGY

1

GLOBAL DISTRIBUTION OF INUNDATED AREAS, 1971-1982 (Matthews)

2

GLOBAL WETLAND AREAS, 1971-1982 (Matthews)

3

HYDROCLIMATOLOGY CD-ROMS

4

U.S. HYDROLOGY AND CLIMATOLOGY, 1947-1987 (Wallis, Lettenmaier, and Wood)

5

USGS HYDRO-CLIMATIC DATA NETWORK (HCDN) STREAMFLOW DATA, 1874-1988

6

GLOBAL RIVER DISCHARGE, 1807-1991, V. 1.1 (RivDIS)

NPP

7

NPP BOREAL FOREST: 8 data sets for individual sites

8

NPP GRASSLAND: 31 data sets for individual sites

9

NPP MULTI-BIOME: BAZILEVICH/PIK DATA, 1940-1988

10

NPP MULTI-BIOME: CHINESE FORESTS DATA, 1989-1994

11

NPP MULTI-BIOME: GLOBAL IBP WOODLANDS DATA, 1955-1975

12

NPP MULTI-BIOME: GLOBAL OSNABRUCK DATA, 1937-1981

13

NPP MULTI-BIOME: TEM CALIBRATION DATA, 1992

14

NPP MULTI-BIOME: VAST CALIBRATION DATA, 1965-1998

15

NPP TEMPERATE FOREST: 1 data set for individual sites

16

NPP TROPICAL FOREST: 17 data sets for individual sites

17

NPP TUNDRA: 2 data sets for individual sites

SOIL

18

GLOBAL DATA SET OF DERIVED SOIL PROPERTIES, 0.5-DEGREE GRID (ISRIC-WISE)

19

GLOBAL DISTRIBUTION OF PLANT-EXTRACTABLE WATER CAPACITY OF SOIL (DUNNE)

20

GLOBAL GRIDDED SURFACES OF SELECTED SOIL CHARACTERISTICS (IGBP-DIS)

21

GLOBAL ORGANIC SOIL CARBON AND NITROGEN (Zinke et al.)

22

GLOBAL SOIL DATA PRODUCTS CD-ROM (IGBP-DIS)

23

GLOBAL SOIL PROFILE DATA (ISRIC-WISE)

24

GLOBAL SOIL TEXTURE AND DERIVED WATER-HOLDING CAPACITIES (WEBB ETAL.)

25

GLOBAL SOIL TYPES, 0.5-DEGREE GRID (modified Zobler)

26

GLOBAL SOIL TYPES, 1-DEGREE GRID (modified Zobler)

VEGETATION

27

GLOBAL VEGETATION TYPES, 1971-1982 (Matthews)

28

VEMAP 1: GEOREFERENCING

29

VEMAP 1: MODEL INPUT DATABASE CD-ROM

30

VEMAP 1: U.S. CLIMATE CHANGE SCENARIOS BASED ON MODELS WITH INCREASED CO2

31

VEMAP 1: U.S. CLIMATE, 1961-1990

32

VEMAP 1: U.S. POTENTIAL NATURAL VEGETATION

33

VEMAP 1: U.S. SITE FILES

34

VEMAP 1: U.S. SOIL

35

VEMAP 2: U.S. ANNUAL CLIMATE CHANGE SCENARIOS

36

VEMAP 2: U.S. ANNUAL CLIMATE, 1895-1993

37

VEMAP 2: U.S. MONTHLY CLIMATE CHANGE SCENARIOS, VERSION 2

38

VEMAP 2: U.S. MONTHLY CLIMATE, 1895-1993, VERSION 2


Table A13. Regional and Global Data Sets Registered in the Oak Ridge National Laboratory Mercury System (21 May 2001)

Topic/Title

AGRICULTURE

1

China County Data Collection, Agricultural Management Dataset

2

China County Data Collection, Crops Dataset

3

China County Data Collection, Livestock Dataset

4

China County Data Collection, Soil Properties Dataset

ATMOSPHERE

5

A Spatial Model of Atmospheric Deposition For the Northeastern United States

6

AErosol RObotic NETwork (AERONET) Database

7

AmeriFlux: Long-term Flux Measurement Network of the Americas

8

Biosphere Atmosphere Stable Isotope Network Database (BASIN)

9

China County Data Collection, Nitrogen Deposition Rate

10

Climate Datasets from Seven Observation Sites in Brazil (ABRACOS)

11

Cooperative Air Sampling Network Database

12

EDGAR - Emission Database for Global Atmospheric Research V2.0

13

GHCN - Global Historical Climatology Network, 1753-1990

14

Global Hydrological Archive and Analysis System (GHAAS) Water Balance Model, USA Data

15

Global Patterns of Carbon Dioxide Emissions from Soils on a 0.5 Degree Grid Cell Basis

16

Gridded Annual Historical and Predicted Climate Data (VEMAP 2)

17

Gridded Monthly Historical Climate Data (VEMAP 2)

18

Gridded Monthly Predicted Climate Data (VEMAP 2)

19

HAPEX-Sahel: the Hydrology-Atmosphere Pilot Experiment in the Sahel, 1990-1992

20

Hydrologic & Meteorological Data for Valdai water-balance research station, Russia

21

Integrated worldwide CO2 Flux Measurements (FLUXNET)

22

Long-term Carbon Dioxide and Water Vapour Fluxes of European Forests and Interactions with the Climate System (EUROFLUX)

23

Meteorological Forcing and Validation Data Sets for Cabauw, The Netherlands: Experimental Results from PILPS Phase 2(A)

24

Modelled Global Climate Data from the CCCma CGCMI

25

Modelled Global Climate Data from the Hadley Centre HadCM2

26

Modelled Global Climate Data from the Hadley Centre HadCM3

27

NCAR Parallel Climate Model (PCM) Future Scenarios Experiments

28

National Atmospheric Deposition Program/National Trends Network (NADP/NTN): Precipitation Chemistry Database

29

WRMC Baseline Surface Radiation Network (BSRN) Database

BIOSPHERE

30

An Ecosystem Model of Carbon, Nitrogen and Water Balances in Forests

31

Carbon Flux to the Atmosphere From Land-use Changes: 1850 to 1990

32

China County Data Collection, Land Use Dataset

33

Forest Cover Datasets for Pan-Amazon and Central Africa Regions (Landsat Pathfinder Deforestation Data)

34

Global Soil Respiration Data (Raich and Schlesinger 1992)

35

Historic Land Use and Carbon Estimates for South and Southeast Asia: 1880-1980

36

IGBP DISCover Land Cover Data Set V1.2

37

Leaf Area Index (historical, 1932-2000)

38

Modeling Nitrogen Saturation (from PnET Model)

39

Northeastern U.S. Regional Estimate of Net Primary Production (NPP) Using PnET

40

Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database

41

Total Carbon Flux in the Northeastern United States

42

Vegetation/Ecosystem Mapping and Analysis Project (VEMAP), Phase 2, Monthly Historical and Future Scenarios Data

HUMAN DIMENSION

43

China County Data Collection, Geography and Population Dataset

HYDROSPHERE

44

Climate Change Effects on Net Primary Production (NPP) and Water Yield in the Northeastern United States

45

GGHYDRO - Global Hydrographic Data, Release 2.2

46

Global Air Temperature & Precipitation: Regridded Monthly and Annual Climatologies

47

Global Composite Runoff Data Set 1.0

48

Global Distribution of Freshwater Wetlands

49

Global Distribution of Inundated Areal Fraction of 1 x 1 Degree Cells

50

Global Distribution of Wetland Ecosystems at 1 x 1 Degree Resolution

51

Global River Discharge, 1807-1991, V. 1.1 (RIVDIS)

52

Global Composite Runoff Data Set 1.0

53

HYDRO 1K Elevation Derivative Database

54

HYDrologic Routing Algorithm (HYDRA 1.2)

55

Hydro-Climatic Data Network (HCDN):Streamflow Data Set, 1874 - 1988

56

Northeast U.S. Regional Estimate of Water Yield Using PnET

57

R-ArcticNET 2.0 - Pan-Arctic River Discharge Database

58

Regional Hydrometeorological Data Network for South America, Central America, and the Caribbean (R-HydroNET 1.0)

59

SAGE Global River Discharge Data

60

Simulated Topological Networks (STN-30)

LAND SURFACE

61

1 Degree Global Land Cover Data Set Derived from AVHRR

62

1 km Global Land Cover Data Set Derived from AVHRR

63

8 km Global Land Cover Data Set Derived from AVHRR

64

AVHRR Derived Northeastern U.S. Landcover

65

CONUS-SOIL Multi-layer Soil Characteristics Dataset

66

Global 1km Data Set of Percent Tree Cover Derived from Remote Sensing

67

Global 30 Arc-Second Elevation Data Set (GTOPO30)

68

Global Land One-km Base Elevation (GLOBE) 30-second DEM

69

Global Model Reference Data Collection, 0.5 x 0.5 Degree Grid-cell

70

Global Organic Soil Carbon and Nitrogen (Zinke et al.)

71

Global Plant-extractable Water Capacity of Soil

72

Global Potential Vegetation Data

73

Global Soil Types by 0.5-Degree Grid (modified Zobler)

74

Historical Croplands Dataset, 1700-1992

75

History Database of the Global Environment: HYDE 2.0 Database

76

ISRIC-WISE Global Data Set of Derived Soil Properties on a 0.5 x 0.5 Degree Grid (Version 1.0)

77

ISRIC-WISE International soil profile data set

78

NOAA/NASA Pathfinder Amazon 0.1 Degree NDVI Data Set

79

NOAA/NASA Pathfinder Global 0.5 Degree NDVI Data Set

80

National Soil Characterization Database

81

National Soil Data Base (NSDB) of Canada

82

Soil Moisture Observations for Eurasia & Midwestern U.S.

83

Soil Particle Size Properties Global Data Set

84

State Soil Geographic Database (STATSGO) Database

85

TerrainBase 5-minute Global Terrain Model

86

Wilson, Henderson-Sellers' Global Vegetation & Soils, 1-Degr

RADIANCE

87

10 day NDVI Composite Data from the Global Land 1-KM AVHRR Project (1992-1996)

88

NOAA/NASA NDVI and AVHRR channel radiance data sets, 1981-present


Table A14. Summary of variables measured within key networks providing carbon observations

Variable

Flux Tower

ROSELT

GTNET NPP

Topical Network

Synthesis collection

ATMOSPHERE

Air temperature

Yes

Yes

Yes

Weather Stations

Global Historic Climate Network

Precipitation

Yes

Yes

Yes

Yes


Solar radiation

Yes

Yes

Yes

Yes


Relative humidity

Yes

Yes

Yes

Yes


Wind speed

Yes

Yes

Yes

Yes


Net radiation

Yes

Yes

Some



CO2 concentration profile

Some, low calibration

No

Generally no, except overlap with Fluxnet sites

FLASKNET


Integrated atmospheric water vapour

No

No

Generally no, except overlap with AERONET and Fluxnet sites



Snow water equivalent

No

No

no



Aerosols

No

No










Ecosystem

SITE

Natural disturbance history

Most

Yes

Some



Management history

Most

Yes, adding 1,2,3 to method

Some



Topography

DEMs

Yes

Some



Spatial pattern

Remote Sensing

Yes




VEGETATION

Vegetation cover class

Yes

Yes, Adding 4 to method

Yes



Root carbon

Some

No



Jackson et al. 2000

Above-ground biomass

Most

Yes

Some



Leaf area index

Most

No

Yes

LAINET

ORNL LAI

Foliage N

Some

No

Some



SOIL

Biota C, N

Most

Yes




Biota biomass

Some





Temperature profile

Yes

Yes




Maximum thaw depth

Rare

No




Thermal conductance

Rare

No




Thermal diffusivity

Rare

No




Soil moisture

Most

Yes




Hydraulic properties

Some

Yes




Ground water table depth

Rare

Yes




Soil Carbon content (org. & inorg.)

Some

No



PEDON database

Soil Carbon age

Rare

No




Soil N, P content

Some

Yes




Soil Bulk density

Some

Yes




Sand and clay fraction (%)

Some

Yes



IGBP Global Soil Products

Soil pH

Some

Yes




Soil macro & micro nutrients

Some

No




Microbial biomass

Rare





PHYSIOLOGY

Foliage N

Most

No




Foliage lignin

Most

No




Chlorophyll

Most

No




Rubisco

Most

No




FLUXES

Carbon fluxes (above & near ground)

All

No




Above-ground NPP

Some

No


GTNET

GPPDI

Below-ground NPP

Rare

No



GPPDI

Litterfall N, P, C

Some

No



Holland et al.

H, ET (above stand)

All

No




CH4

Some

No


TRAGNET


VOC

Some

No


TRAGNET


DOC

Some

No


TRAGNET


Heterotrophic respiration rate

Some

No


LIDET

Raich et al. Erickson et al.


Table A15. Global, gridded data sets compiled by the ISLSCP II Initiative

Parameters

Originating Institution/ Project/Author

Temporal Resolution

Temporal Extent

Original Spatial Resolution

ISLSCP Spatial Resolution

MODIS Albedo Products

Boston University

Monthly

2000-2001

0.25 deg.

1.0, 0.5, 0.25 deg.

MODIS Land Cover Product

Boston University

Fixed

2000-2001

0.25 deg.

1.0, 0.5, 0.25 deg.

Historical cropland cover with potential vegetation layer

Ramankutty and Foley (Univ. of Wisconsin)

10 Years

1700-1992

0.5 deg.

1.0, 0.5, 0.25 deg.

Historical land cover with potential vegetation layer

HYDE/RIVM

50 Year Interval

1700-1990

0.5 deg.

1.0, 0.5, 0.25 deg.







BRDF Type Classification and Parameters

Boston University

Two Months

1995

1, 10 km

1.0, 0.5, 0.25 deg.

C3/C4 Land Cover

GSFC

Yearly

1986-1995

0.5 deg.

0.5 deg.

Vegetation classification (IGBP-DIScover)

EROS Data Center

Fixed

1992-1993

1 km

1.0, 0.5, 0.25 deg.

Vegetation Classification (UMD)

Univ. of Maryland

Fixed

1995-1996

1 km

1.0, 0.5, 0.25 deg.

Vegetation Continuous Fields (% tree cover, % woody material, etc.)

Univ. of Maryland

Fixed

1995-1996

1 km

1.0, 0.5, 0.25 deg.

Soil moisture and temperature, air temperature dewpoint, surface pressure, wind speed and direction, radiation, sensible and latent heat flux, precipitation, snowfall, runoff (mean, stdev, min, max)

ECMWF Re-Analysis Monthly Fields(ERA-40)

Monthly (Four Soil Layers)

1986-1995

1 deg.

1 deg.,

Precipitation (Gauge Only)

GPCC

Monthly

1986-1995

1 deg.

1 deg.

Precipitation (Satellite& Gauge)

GSFC, GPCP Precipitation

Monthly

1986-1995

2.5 deg.

1 deg.

Elevation (mean, min, max, variance, mean slop, mean aspect, topographic index)

Hydro1k (EDC)

Fixed

Fixed

1 km

0.5 deg.

Estimated Soil Moisture

Axel Kleidon

Fixed

Fixed

1 deg.

1.0, 0.5, 0.25 deg.

Depletion

(Stanford U.)





Soil carbon, nitrogen, texture (sand, silt, clay, organic contents), bulk density, heat capacity, thermal capacity

IGBP-DIS

Fixed

Fixed

0.1 deg.

0.5 deg.

Rooting depth

Rob Jackson (Duke Univ.)

Fixed

Fixed

Point data

1 deg.


References

Amthor, J. S. & members of the Ecosystems Working Group. 1998. Terrestrial ecosystem responses to global change: a research strategy. ORNL Technical Memorandum 1998/27, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

Baldocchi, D. D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, Ch., Davis, K., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, J. W., Oechel, W., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson K. & Wofsy S. 2000. FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor and Energy Flux Densities. Bulletin of the American Meteorological Society, 82: 2415 - 2435

Birdsey, R. A. 1992. Carbon storage and accumulation in United States forest ecosystems. USDA Forest Service, General Technical Report WO-59, Washington, DC.

Brown, S., Gillespie, A. J. R. & Lugo, A. E. 1991. Biomass of tropical forests of South and South-East Asia. Canadian Journal of Forest Research, 21: 111 - 117.

Brown, S. & Lugo A. E. 1992. Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia, 17: 8-18.

Brown, S. L. & Schroeder, P.E. 1999. Spatial patterns of aboveground production and mortality of woody biomass for eastern U.S. forests, Ecological Applications. 9(3):968 - 980.

Brown, S. L., Schroeder, P.E. & Kern, J.S. 1999. Spatial distribution of biomass in forests of the eastern USA. Forest Ecology and Management, 123: 81 - 90.

Clark, D. A., Brown, S., Kicklighter, D. W., Chambers, J. Q., Thomlinson, J. R., Jian Ni & Holland, E. A. 2001. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecological Applications, 11: 371 - 384.

Davidson, E. A., Verchot, L. V., Cattânio, J. H., Ackerman, I. L. & Carvalho, J. E. M. 2000. Effects of soil water content on soil respiration in forests and cattle pastures of eastern Amazonia. Biogeochemistry, 48: 53 - 69.

Esser, G., Lieth, H. F. H., Scurlock, J. M. O. & Olson, R. J. 1997. Worldwide estimates and bibliography of net primary productivity derived from pre-1982 publications, ORNL Technical Memorandum TM-13485, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. Available on-line from the ORNL Distributed Active Archive Center: www-eosdis.ornl.gov.

FAO. 1997. Estimating biomass and biomass change of tropical forests: a primer, by Brown, S. FAO Forestry Paper No. 134. Rome.

FAO. 2002b. Terrestrial Carbon Observation: The Ottawa assessment of requirements, status and next steps, by Cihlar, J., Denning, A.S. & Gosz, J., Eds. FAO Environment and Natural Resources Series No.2. Rome.

Gill, R. A. & Jackson, R. B. 2000. Global patterns of root turnover for terrestrial ecosystems. New Phytol., 147: 13 - 31.

Gower, S. T., Krankina, O., Olson, R. J., Apps, M., Linder, S. & Wang, C. 2001. Net primary production and carbon allocation patterns of boreal forest ecosystems. Ecological Applications, 11: 1395 - 1411

Harmon, M. E., Krankina, O. N. & Sexton, J. 2000. Decomposition vectors: a new approach to estimating woody detritus decomposition dynamics. Canadian Journal of Forest Research, 30: 76 - 84.

Jackson, R. B., Schenk, H. J., Jobbágy, E. G., Canadell, J., Colello, G. D., Dickinson, R. E., Field, C. B., Friedlingstein, P., Heimann, M., Hibbard, K., Kicklighter, D. W., Kleidon, A., Neilson, R. P., Parton, W. J., Sala, O. E. & Sykes, M. T. 2000. Belowground consequences of vegetation change and their treatment in models. Ecological Applications, 10: 470 - 483.

Jenkins, J. C., Birdsey, R. A. & Pan, Y. 2001. Biomass and NPP estimation for the mid-Atlantic region (USA) using plot-level forest inventory data, Ecological Applications, 11: 1174 - 1193.

Lieth, H. F. H. 1975. Primary production of the major vegetation units of the world. In Lieth, H. and Whittaker, R. H. eds. Primary Productivity of the Biosphere, Ecological Studies 14, pp. 203 - 215. New York and Berlin, Springer-Verlag.

Leith, H. & Whitaker, R. H. (eds.) 1975. Primary Productivity of the Biosphere, Ecological Studies 14, pp. 203 - 215. New York and Berlin, Springer-Verlag.

Prince, S. D., Haskett, J., Steininger, M., Strand, H. & Wright, R. 2001. Net primary production of U.S. Midwest croplands from agricultural harvest yield data, Ecological Applications, 11: 1194 - 1205.

Raich, J. W. & Nadelhoffer, K. J. 1989. Belowground carbon allocation in forest ecosystems: Global Trends. Ecology, 70: 1346 - 1354.

Raich, J. W. & Potter, C. S. 1995. Global patterns of carbon dioxide emissions from soils. Global Biogeochemical Cycles, 9(1): 23 - 36

Raich, J. W. & Schlesinger, W. H. 1992. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus, 44: 81 - 99.

Schroeder, P., Brown, S., Mo, J, Birdsey, R. & Cieszewski, C. 1997. Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science, 43(3): 424 - 434.

Scurlock, J. M. O., Cramer, W., Olson, R. J., Parton, W. J., Prince, S. D. & members of the Global Primary Production Data Initiative 1999. Terrestrial NPP: towards a consistent data set for global model evaluation. Ecological Applications, 9(3): 913 - 919.

Sulzman, J. M., Staufer, R., Holland, E. A., Post, W. A., Matthews, E., Krankina, O. & Bossdorf, O. 2000. A Global Compilation of Litter Production, Pools, and Nutrients from the Literature. ESA Annual Meeting, Snowbird, Utah. Poster.

Valentini, R., Matteucci, G., Dolman, A. J., Schulze, E.-D., Rebmann, C., Moors, E. J., Granier, A., Gross, P., Jensen, N. O., Pilegaard, K., Lindroth, A., Grelle, A., Bernhofer, C., Gr.Dcnwald, T., Aubinet, M., Ceulemans, R., Kowalski, A. S., Vesala, T., Rannik, Dc., Berbigier, P., Loustau, D., Guethmundsson, J., Thorgeirsson, H., Ibrom, A., Morgenstern, K., Clement, R., Moncrieff, J., Montagnani, L., Minerbi, S. & Jarvis, P. G. 2000. Respiration as the main determinant of carbon balance in European forests. Nature, 404: 861 - 865.

Terrestrial Data Accessible Through the ORNL DAAC

R.J. Olson and R.B. Cook
Oak Ridge National Laboratory

Field Campaign Data

BOREAS

Boreal Ecosystem-Atmosphere Study, 1994 - 1996
Through remote-sensing and ground-based measurements, BOREAS investigated exchanges of energy, water, heat, carbon dioxide, and trace gases between a Canadian boreal forest and the atmosphere.

FIFE

First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment, 1987 and 1989
FIFE data sets characterize exchanges of radiation, moisture, and carbon dioxide between a Kansas prairie site and the atmosphere. FIFE also developed and tested remote-sensing methods for investigating such exchanges.

FIFE Follow-On

First ISLSCP Field Experiment Follow-On, 1987 - 1989
The FIFE Follow-On data sets further analysed FIFE data related to exchanges of radiation, moisture, and carbon dioxide between Kansas prairie land and the atmosphere.

LBA

Large-Scale Biosphere-Atmosphere Experiment in Amazonia, 1999 - 2001
Pre-LBA data are available, including gridded measurements of precipitation in Bolivia, Brazil, and Peru. Three CD-ROMs produced by Centro de Previsão de Tempo e Estudos Climáticos in Brazil are available. These CDs contain a compilation of measurements that were collected before the LBA project began. Additional data are accessible through Beija-flor (http://beija-flor.ornl.gov/lba/), a version of the ORNL DAAC's Mercury search system.

OTTER

Oregon Transect Ecosystem Research, 1990 - 1991
OTTER data sets estimate fluxes of carbon, nitrogen, and water in three Oregon forest ecosystems (coastal, mid-elevation, and inland), using an ecosystem-process model and remote-sensing data.

SAFARI 2000

Southern African Regional Science Initiative, 1999-2001
The SAFARI 2000 project is an international science initiative to study the linkages between land and atmosphere processes in southern Africa. Data from this current investigation are available through the ORNL DAAC's Mercury search system (http://mercury.ornl.gov/ornldaac/).

ORNL DAAC Data Holdings, January 2001

Superior National Forest

Superior National Forest, 1983 - 1984
SNF research investigated the ability of remote-sensing data to estimate biophysical properties (e.g. leaf area index, biomass, net primary productivity) of a boreal forest in Minnesota.

Land Validation Data

Canopy Chemistry (ACCP)

Accelerated Canopy Chemistry Program, 1992 - 1993
ACCP used remote sensing to study the nitrogen and lignin content of the vegetation canopy in ecosystems in the United States.

EOS Land Validation

Earth Observing System (EOS) Land Validation Project, 1999 - present.
This project is coordinating ground-based and aircraft measurements at test sites worldwide to compare with EOS satellite products. Validation data are accessible through the Mercury system (http://mercury.ornl.gov/ornldaac/).

FLUXNET

Global Flux Tower Network, 1990 - present
The FLUXNET programme is compiling measurements of radiation, water vapour, carbondioxide, and trace gas fluxes from flux towers throughout the world. The data will be used to validate EOS products.

Regional and Global Data

Climate

Data sets of monthly climate data, measured at stations or estimated for Collections grid cells of various sizes. Data available:
· Global 10-Year Mean Monthly Climatology, 19011 - 1990;
· Global 30-Year Mean Monthly Climatology, 1901 - 1960;
· Global 30-Year Mean Monthly Climatology, 1930 - 1960, V. 2.1;
· Global 30-Year Mean Monthly Climatology, 1961 - 1990;
· Global Historical Climatology Network, 1753 - 1990;
· Global Monthly Precipitation, 1900 - 1999;
· Global Monthly Climatology for the Twentieth Century.

Soil Collections

Data sets of soil characteristics, measured at sampling sites worldwide or estimated for grids of various sizes. The following data are available:
· Global Data Set of Derived Soil Properties, 0.5-Degree Grid;
· Global Distribution of Plant-Extractable Water Capacity of Soil;
· Global Gridded Surfaces of Selected Soil Characteristics;
· Global Organic Soil Carbon and Nitrogen;
· Global Soil Profile Data;
· Global Soil Texture and Derived Water-Holding Capacities;
· Global Soil Types, 0.5-Degree Grid;
· Global Soil Types, 1-Degree Grid;
· Global Soil Data Products CD-ROM.

ORNL DAAC Data Holdings, January 2001

Hydroclimatology Collections

Data sets of hydroclimatology characteristics, measured at sampling sites in the continental United States. The following data sets are available:
· U.S. Hydrology and Climatology, 1947 - 1987;
· U.S. Streamflow Data, 1874 - 1988.

Net Primary Productivity (NPP)

Net Primary Productivity, 1937 - 1996
A series of NPP data sets containing field measurements of biomass dynamics as well as estimates of net primary productivity in grassland and forest sites worldwide. The measurements represent various ranges of dates between 1937 and 1996.

River Discharge (RIVDIS)

Global River Discharge, 1807 - 1991, V. 1.1 (RIVDIS)
Long-term monthly averaged measurements of river discharge at stations throughout the world for various dates ranging from 1807 to 1991.

Vegetation Collections

Global Vegetation Types, 1971 - 1982 (Matthews)
A data set about the distribution of 32 types of vegetation, presented in grid cells representing 1º latitude by 1º longitude.

Vegetation-Ecosystem Modelling (VEMAP)

Vegetation/Ecosystem Modeling and Analysis Project, 1961 - 1990
VEMAP is studying the global response of biogeography and biogeochemistry to environmental variability in climate and other environmental factors across the United States. VEMAP will compare models of biogeochemistry and models of the distribution of vegetation types, and it will determine their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing.

Other

Additional regional and global data
Accessible through the ORNL DAAC's Mercury search system (http://mercury.ornl.gov/ornldaac/). The following types of data are currently available.
· Land Cover/Vegetation (11 data sets)
· Hydrology (7 data sets)
· Soil (9 data sets)
· Climate (6 data sets)
· Miscellaneous (6 data sets)

Global Soil Resources Information

Freddy O.F. Nachtergaele

Food and Agriculture Organization of the United Nations (FAO)

With contributions from: M. Jamagne (INRA), R. Oldeman (ISRIC), A. Mermut (IUSS), and L. Montanarella (ESB)

There are two fundamental reasons for the increasing demand for low resolution (1 by 1 degree, 30 by 30 arc-minutes), small-scale (1:1 to 1:5 Million), soil data that cover large areas of the globe. First, there is a growing awareness that the generally approved International Conventions, be those that aim to combat desertification (UN-CCD), those that promote and protect biodiversity (UN-CPB), or the Kyoto protocol that aims to sequester carbon to off-set CO2 emissions, are all in dire need of land resources data to implement them. On the other hand, this demand is complemented by the requests of an established, albeit relatively small, group of applied researchers operating under the International Geosphere and Biosphere Programme (IGBP), the Intergovern-mental Panel on Climatic Change (IPCC) and the Consultative Group on International Agricultural Research (CGIAR), who all need broad scale indicators on land-related factors to refine the various models.

At the same time, there is also an enhanced need for very high resolution georeferenced land resources data. This because there is a growing general concern by the public, particularly in industrial countries, on the state of environmental health and its evolution in time, which translates into a need for land resources monitoring at the local level. The latter information, being more expensive to collect, has up till now only been gathered through limited regional inventories.

It is hard to discuss soils and soil data in isolation of the factors that determine their formation and their use. Therefore, whereas earlier efforts concentrated on soils in their own right, nowadays a more integrated and holistic approach is followed which allows for the characterization of the full land resource, including soils, terrain, geology, climate, land cover and land use.

It is also important to distinguish between geographic data-bases which depict soil information of areas as in classical paper maps, or as polygon information and stored in geographical information systems, and georeferenced point data giving information on a specific site in the form of morphologically described and chemically and physically analysed soil profile information.

It is also important to differentiate among:

(i) what is the proneness and resilience of the soil resource to degradation or desertification (degradation risk);

(ii) what status is the soil resource in, at a given point in time, and how far is it degraded (degradation status);

(iii) what is the likely trend in the degradation given the present and anticipated land management interventions (degradation trend);

(iv) what is the present and likely impacts the degradation will have on production or on the environment (degradation impact).

In order to answer the above questions, harmonized soil resource information on a continental or global scale are required. This paper describes the availability of global soil resource information in particular and land resources information in general and the accessibility to such data. The problems and obstacles that are faced for the further development of such databases and the possible solutions to these obstacles are discussed.

Global Soil Resource Inventories

The Soil Map of the World

At the global level, only two relatively large-scale soil maps exist: a 1:10 million scale map prepared by Kovda and co-workers, and the 1:5 million scale FAO-UNESCO Soil Map of the World (FAO, 1971-1981). In addition, a number of simplifications and transformations of the latter map exist, such as those produced by the United States Department of Agriculture (USDA), using Soil Taxonomy (Soil Survey Staff, 1999) as a classification system, the simplified 1:15 million scale world map of landscapes (Moscow State University, 1993) and the FAO 1:25 million World Soil Resources Map (FAO, 1993).

It is generally accepted that the 1:5 million scale FAO-UNESCO map is the most appropriate source of soil information for studies at continental, regional or global level.

A brief history of the Soil Map of the World

The International Society of Soil Science (ISSS), at its Seventh Congress, in Madison, Wisconsin, USA, in 1960, recommended that soil maps of continents and large regions be published. As a follow-up FAO and UNESCO decided to prepare a soil map of the world at 1:5 000 000 scale.

The project started in 1961 and was completed over a span of twenty years. It is the fruit of worldwide collaboration among innumerable soil scientists. Successive drafts of the soil maps and of the legend were prepared from a compilation of existing material, combined with systematic field identification and correlation. The first draft of the Soil Map of the World was presented to the Ninth Congress of the ISSS, in Adelaide, Australia, in 1968. In accordance with the recommendation of the Congress, that the Soil Map of the World should be published at the earliest possible date, the first sheets (covering South America) were issued in 1971. The results of field correlation in different parts of the world and the various drafts of the legend were published as issues of FAO World Soil Resources Reports (FAO, 1961-1971).

With the rapidly advancing computer technology and the expansion of geographical information systems during the 1980s, several attempts were made to digitize the paper Soil Map of the World. The first effort was carried out in 1984 by the Environmental Systems Research Institute (ESRI) in vector format and contained a number of different layers of land resource-related information (vegetation, geology), often incomplete and not fully elaborated. This version was distributed up till 1991 by UNEP-GRID, when it was replaced by the official FAO Digitized Soil Map of the World, in vector format, which focused on soil information only and allowed an analysis by individual country. This version was released on ten diskettes (FAO, 1991).

In 1984 a first rasterized version of the soil map was prepared by Zöbler using the ESRI map as a base and using 1o x 1o grid cells. Only the dominant FAO soil unit in each cell was indicated. This digital product gained some popularity because of its simplicity, particularly in the United States.

In 1993, FAO and the International Soil Reference and Information Centre (ISRIC) combined efforts to produce a raster map with a 30' × 30' cell size in the interest of the WISE (World Inventory of Soil Emissions) project (Batjes et al., 1995). This database contains the distribution of up to ten different soil units and their percentages in each cell. In 1996, FAO produced its own raster version which had the finest resolution with a 5' × 5' cell size (9 km × 9 km at the equator) and which had a full database completely corresponding with the paper map in terms of soil units, topsoil texture, slope class and soil phase. This version is available on CD-ROM (Price US$ 350, with reduction for countries in development; to order, please contact publications-sales@fao.org). In addition to the vector maps discussed above, the CD-ROM contains a large number of databases and digital maps of statistically (or expert estimates of) derived soil properties (pH, organic carbon, C/N, soil moisture storage capacity, soil depth, etc.).The CD-ROM also contains interpretation by country on the extent of specific problem soils, the fertility capability classification results by country and corresponding maps. More information on this product is available at www.fao.org/WAICENT/FAOINFO/AGRICULT/AGL/lwdms.htm

An overview of the publication stages of the paper Soil Map and its digitized version is given in Table A16

Table A16. Important Dates in the Development of the Soil Map of the World

1960

ISSS recommends the preparation of the soil maps of continents

1961

FAO and UNESCO start the Soil Map of the World project

1971

Publication of the first sheet of the paper map (South America)

1981

Publication of the last sheet of the paper map (Europe)

1984

ESRI digitizes the map and other information in vector format

1989

Zöbler produces a 1º × 1º raster version

1991

FAO produces an Arc/Info vector map including country boundaries

1993

ISRIC produces a 30' × 30' raster version under the WISE project

1995

ISRIC produces a 30' × 30' raster version with derived soil properties


FAO produces a CD-ROM raster (5' × 5') and vector map with derived soil properties

1998

FAO-UNESCO re-issues the digital version with derived soil properties, including corrections


SOTER: The World Soil and Terrain Database

SOTER is another initiative of the ISSS and the approach was adopted at the 13th World Congress of Soil Science in 1986. Under a UNEP project, the SOTER methodology was developed in close cooperation with the Land Resources Research Centre of Canada, FAO and ISSS. After initial testing in three areas, involving five countries (Argentina, Brazil, Canada, United States and Uruguay), the methodology was endorsed by the ISSS Working Group on World Soils and Terrain Digital Database (DM), it was further refined, and, in 1993, the Procedures Manual for Global and National Soils and Terrain Digital Databases was jointly published by FAO, ISRIC, ISSS and UNEP, thus obtaining international recognition. The Procedures Manual is available in English, French and Spanish (UNEP/ISRIC/FAO/ISSS, 1995).

The SOTER programme provides an orderly arrangement of natural resource data in such a way that it can be easily accessed, combined and analysed, in relation to food requirements, environmental impact and conservation. Fundamental in the SOTER approach is the mapping of areas with a distinctive, often repetitive pattern of landform, morphology, slope, parent material and soils at 1:1 million scale (SOTER units). Each SOTER unit is linked through a geographic information system with a computerized database containing, in theory, all available attributes on topography, landform and terrain, soils, climate, vegetation and land use. In this way, each type of information or each combination of attributes can be displayed spatially as a separate layer or overlay or in tabular form.

The SOTER concept was originally developed for application at country (national) scale and national SOTER maps have been prepared, with ISRIC's assistance, for Kenya (1:1 M), Uruguay (1:1 M), Hungary (1:500 000), Jordan and Syria (1:500 000). More information is available from ISRIC's website at www.isric.nl/SOTER.html. Data on Kenya is downloadable from www.isric.nl/SOTER/KenSOTER.zip.

The original idea of SOTER was to develop this system worldwide at an equivalent scale of 1:1 M in order to replace the paper Soil Map of the World (Sombroek, 1984). However, it soon became obvious that the resources were lacking to tackle and complete this huge task in a reasonable timeframe. However, this still remains the long-term objective pursued on a country-by-country basis.

In the early 1990s, FAO recognized that a rapid update of the Soil Map of the World would be a feasible option if the original map scale of 1:5 M were retained, and started, together with UNEP, to fund national updates at 1:5 M scale of soil maps in Latin America and Northern Asia. At the same time, FAO tested the physiographic SOTER approach in Asia (Van Lynden, 1994), Africa (Eschweiler, 1993), Latin America (Wen, 1993), the Commonwealth of Independent States (CIS) and Baltic States and Mongolia (Stolbovoy, 1996). The European terrain inventory is currently ongoing.

These parallel programmes of ISRIC, UNEP and FAO merged together in mid-1995, when at a meeting in Rome the three major partners agreed to join all resources and work towards a common World SOTER approach covering the globe at 1:5 M scale by the 17th ISSS Congress of 2002 to be held in Thailand. Since then, other international organizations have shown support and collaborated to develop SOTER databases for specific regions. This is for instance the case for Northern and Central Eurasia where the International Institute for Applied System Analysis (IIASA) joined FAO and the other national institutes involved. The European Soils Bureau (ESB) is involved in the countries of the European Union. The ongoing and planned activities are summarized in Table A17 and Figure A1.

It should be noted that although the information is collected according to the same SOTER methodology, the specific level of information in each region results in a variable scale of the end products presented. The soils and terrain database for northeastern Africa, for instance, contains information at equivalent scales between 1:1 million and 1:2 million, but the soil profile information is not fully georeferenced (reference is done by polygon or mapping unit, not by latitude and longitude). The same will be true for the soil profile information to be provided by ESB for the European Community to be released as a SOTER database at 1:5 Million scale. For north and central Eurasia, profile information contained in the CD-ROM is very limited. Fully comprehensive SOTER information is available for South and Central America and the Caribbean (1:5 million scale) and includes eighteen hundred georeferenced soil profiles. Data are downloadable from www.isric.nl/SOTER/LACData.zip and viewable using a viewer programme at www.isric.nl/SOTER/Viewer 102b.exe) and for Eastern Europe (1:2.5 million scale, with more than 600 georeferenced soil profiles).

These different SOTER puzzle-pieces will be correlated and integrated in a unique 1:5 million scale World SOTER, using whatever best soil profile information is available by 2002. This will result in a significant update of the global soil and terrain information source.

Table A17. Operational Plan for a World SOTER: 1995-2002

Region

Status

Main Agencies Involved

Published Date

Latin America and the Caribbean

Published

ISRIC, UNEP, FAO, CIAT, national soil institutes

1998

Northeastern Africa

Published

FAO-IGAD

1998

South and Central Africa

Ongoing

FAO-ISRIC-national inst.

2000

North and Central Eurasia

Published

IIASA, Dokuchaev Institute, Academia Sinica, FAO

1999

Eastern Europe

Finalized

FAO-ISRIC-Dutch Government- national inst.

2000

Western Europe

Ongoing

ESB-FAO-national inst.

2001

West Africa

Proposal submitted

Awaits funding (ISRIC, IITA)


Southeast Asia

Proposal discussed

Awaits funding


USA and Canada

Own Effort

NRCS

?

Australia

Own Effort

CSIRO

?


Figure A1. Global SOTER status

At 1:5 M scale, the World SOTER database contains several layers of information which are stored in a relational database.

Point information: Soil profile databases

Several soil profile databases exist which contain georeferenced soil profile morphological and/or analytical information. These databases are of major importance for the development of pedotransfer functions which deduce non-measured soil characteristics from measured ones. It should be noted that if used unstratified and outside their natural context, they may cause extremely misleading statements and conclusions.

Many of these databases are national data sets. The National Resources Conservation Service (NRCS) of the United States is one of the most important database containing over 20 000 profiles. Other countries also have important soil data sets, such as Botswana (more than 2 500 profiles), Denmark (850 profiles) and Mali (600 profiles), to name just a few. The ISRIC Soil Information System stores data of the global soil reference collection and contains about 700 soil profile descriptions and analysis from more than seventy countries. Software was developed to store this soil profile information in a standardized way (FAO/ISRIC/CSIC, 1995).

At the global level, the WISE dataset collected by ISRIC is the most comprehensive. It contains more than 4000 soil profiles, a large part of which is thoroughly checked on internal analytical contradiction and soil classification. A statistical analysis of the soil characteristics by FAO soil taxonomic unit was undertaken and results can be linked to the Soil Map of the World (section 1.1), or to regional soil and terrain (SOTER) databases (Fischer et al., 2000). A subset of this database, giving average soil property values by soil unit, is also contained in the CD-ROM release of the digital Soil Map of the World (FAO, 1995).

The Global Pedon Database, is an extract of the WISE database and was created by ISRIC for IGBP on the basis of information received from the National Resources Conservation Service of USDA, from FAO and from ISRIC, has been extensively tested on internal consistency and contains about 1100 profiles. Table A18 gives the composition by region of the WISE and the IGBP data set (IGBP-DIS/CSIRO/USDA/FAO/ISRIC, 1999).

There also two other soil data sets of interest in the fact that they store mainly analytical data rather than soil morphological data. One is built up by the European Union and which is linked with the 1:1 million soil map of Europe (ESB, 1999) and contains polygon-referenced analytical soil information. The other, developed by Zinke et al. (1984), concentrates on georeferenced data of carbon and nitrogen, and is rather thin on other parameters, which makes its use difficult for more general applications.

Table A18. The Global Pedon Database (IGBP-DIS/CSIRO/USDA/FAO/ISRIC, 1999)

Broad Geographic

In WISE

 

In Homogenized Database (Int. set)

Region

FAO1

NRCS2

ISIS3

Total

Africa

1 799

93

204

18

315

S., W. and Northern Asia

522

24

44

0

68

China, India, Indonesia, Philippines

553

45

129

106

280

Australia and Pacific Islands

122

28

27

0

55

Europe

492

5

2

0

7

North America

266

14

144

0

158

Latin America and the Caribbean

599

41

114

86

241

Total

4 353

250

664

210

1 124

1 FAO: Food and Agriculture Organization of the United Nations
2 NRCS: National Resources Conservation Service of the US Department of Agriculture
3 ISIS: ISRIC Soil Information System (ISRIC, Wageningen)
The Global Status of Soil Degradation

In the late 1980s, UNEP and ISRIC undertook a global inventory of the status of human-induced land degradation (Oldeman et al., 1991). The global assessment of soil degradation (GLASOD) approach involved a structured elicitation of national, regional and international experts on the location, degree, severity and nature of land degradation throughout the world. Expert information was integrated into standardized regional topographic base maps that contained only country boundaries, major cities and hydrological features. Results were published in a 1:10 million scale map in which the relative extent and severity of soil degradation are depicted according to type (water erosion, wind erosion, salinization, acidification, pollution, and physical deterioration) and cause (agriculture, deforestation, overgrazing, industrial pollution). The map also indicates the location of stable land and "wastelands" (e.g. deserts and ice caps).

The GLASOD maps have been digitized and are in the public domain. They can be ordered from UNEP/GRID at unep.hq.grid@cgnet.com. Examples include analysis for relationships between the severity (relative extend and degree) of soil degradation and population density, and between the extent of soil degradation due to water erosion and the extent of sloping land (Wood et al. 1998). This publication is available at: ftp://ftp.cgiar.org/ifpritemp/stan/. A country status of land potential and constraints, including the country's land degradation status, has also been developed (Bot et al., 1999) and preliminary results are available from freddy.nachtergaele@fao.org. UNEP illustrated the GLASOD and related findings particularly in relation to desertification (UNEP, 1997).

A more detailed study at scale 1:5 million, on the status of land degradation was undertaken by UNEP, ISRIC and FAO in the South and Southeast Asia region and published as Asian human-induced soil degradation status (ASSOD) that included a SOTER terrain database as the basic unit in which data are inventoried (UNEP/ISRIC/FAO, 1998). The ASSOD dataset is available from ISRIC at soil@isric.nl.

A more detailed approach was undertaken in Eastern Europe at 1:2.5 Million scale, where a SOTER inventory formed the basic units in which degradation status and soil vulnerability to pollution are assessed (SOVEUR project).

The approach to soil degradation inventories has also been complemented by mapping activities which are involved in combating soil degradation. The methodology was developed by a concerted effort of the University of Bern, the German Technical Co-operation (GTZ), UNEP, ISRIC and FAO, and was called World Overview of Soil Conservation Approaches and Technologies. (WOCAT; FAO et al., 1998). Continental and world coverage is, however, not expected soon.

The conclusions are that:

Monitoring the State of the Soil Resource

There are two ways to monitor the state of the soil resource: one is regional and national baseline studies in the spirit of GLASOD and ASSOD in which subjective information on the degradation or yield trends are incorporated. A similar approach is the gathering of land and water resource information at national scale as carried out by FAO in selected countries, storing information on the Internet on hot spots and bright spots concerning soil potential and constraints, land degradation, water resource and plant nutrition information. An example of how this can be tackled is given at: www.fao.org/WAICENT/FAOINFO/AGRICULT/AGL/swlwpnr/swlwpnr.htm

On this site FAO has presented country results for the state of the land resources in Bangladesh, Ghana, Lithuania, Malaysia, Nigeria and Spain.

Another, perhaps more objective and scientific, second approach is the measurement of a number of soil and environmental characteristics at regular intervals over a period of time in order to establish a trend and determine when critical values are exceeded. Relatively few examples exist of regional approaches. A good example is found on Canadian Benchmark sites as long-term monitoring sites, information on which can be found at http://res.agr.ca/CANSIS/PUBLICATIONS/HEALTH/_overview.htm

Another good example is the monitoring forest soils in the European Community which uses a 16 by 16 km grid and which determines the contents of heavy metals in soils (EC-UN/ECE, 1997).

However, the results and methodology used to monitor point observations must be considered with care because:

On the specific issue of monitoring soil degradation, similar reservations as the one given above apply: the numerous pilot studies applying the Wischmeyer equation throughout the world have done very little to elucidate the quantification of plant nutrient and soil losses and their influence on crop yields. Very few examples of upscaling of such plot studies exist.. The economic impact of soil degradation has been estimated by various studies (Stocking 1995, Stoorvogel et al. 1993 and FAO/UNDP/UNEP, 1994). The cost estimates of production losses due to land degradation vary widely and may be as low as 2% or as high as 25% of the total yearly value of the national agricultural production in a given country.

Conclusions are the following:

Major Problems Related to Global Soil Resource Database Refinement

A number of problems have been raised when discussing the status of the global soil resource inventories and the status of human-induced land degradation.

Soil classification issue

Unlike most subjects of other natural sciences, "soil bodies" are not discrete and well defined entities, and their naming and classification is after more than 100 years of soil science, still a matter of dispute. Until very recently, at an international level two different reference classification systems were generally accepted. The first was the USDA Soil Taxonomy (USDA, 1999), a soil classification system developed in the US in the early 1960s (www.nhq.nrcs.usda.gov/WSR/). The second is the FAO Legend for the Soil Map of the World (FAO, 1974, www.fao.org/waicent/FaoInfo/Agricult/AGL/agll/key2soil.htm), a simplification of Soil Taxonomy, developed by soil scientist worldwide. Most countries in Africa and Europe have adapted the FAO legend for soil correlation at an international scale, while the Americas and most countries in the Near East adapted the USDA Soil Taxonomy. The situation in Asia is mixed with Soil Taxonomy prevailing in most countries. Other soil classification systems that have a large impact are for instance the French classification system (CPCS, 1967) adapted by some countries in francophone West Africa, and the Russian soil classification system used to name soils in the former USSR.

After years of debating the problem, the International Union of Soil Science (IUSS) proposed in 1998 a new international soil correlation system: the World reference base for soil resources (IUSS/FAO/ISRIC, 1998 available at: www.fao.org/WAICENT/FAOINFO/AGRICULT/AGL/AGLL/wrb/Default.htm) which draws heavily on the FAO legends. This system was officially endorsed at the last IUSS congress in 1998.

It is hoped that this recent development of this unique international soil correlation system endorsed by all soil scientists, has now solved this particular problem, although it will probably take years for the system to be fully adapted everywhere. For instance the last newsletter of the IUSS published reservations about the WRB system by the Nordic countries.

Soil mapping methodology

A second methodological obstacle is the lack of a generally accepted soil mapping methodology. Mapping soils within the context of landscapes and parent material is a generally accepted cartographic practice in Europe and forms the basis of the SOTER approach, discussed previously.

Traditional cartography and soil mapping are presently considered too static, because they lacked data concerning soil behaviour and how the soils functioned in the landscape they occurred.

Consequently, a more dynamic approach has led to the analysis of the structural organization of the pedological volumes in landscapes: mainly horizons, solums or pedons, and "soil systems". The next step to know where and how the fluxes going through the soil can be monitored. That work has to be made on specific representative areas: on site and/or catchments. The third step is to study the possibility to generalize those detailed observations to larger areas, and this is the problem of scale transfer.

However, alternative mapping techniques do exist, such as a strict pedo-taxonomic approach practised by those surveyors that follow (too strictly) soil classification criteria, or those soil scientist that question the value of any classification and mapping system, and propose to represent thematic maps by using pedo-statistics, an approach rather popular in the UK for instance.

These kind of differences are acerbated by perceived national or organizational interests, which favour that variants of a given mapping methodology are developed in parallel. To give an example: although the SOTER methodology is endorsed by the IUSS and the approach is generally in line with national mapping techniques in Europe, the European Soil Bureau proposed a slightly different approach for mapping at national scales in Europe.

The quantity and quality of soil profile information

As far as soil profile information is concerned, the various international efforts to come to a homogenized soil profile dataset have led to a number of conclusions that may be summarized as follows:

Land degradation mapping constraints

There exists a clear lack of common understanding on the ways to describe, measure and cost the symptoms and the effects of human-induced soil degradation. Unless a systematic effort is undertaken to link land use, land management and land degradation in an eco-regional framework, the provisional methods developed until now will continue to be criticised and results not taken seriously.

The GLASOD approach itself has been questioned, including by its originators, as a subjective expert-opinion at a very small scale, which allows at the most very general conclusions to be drawn on the status and causes of land degradation. The need for a quantified, national supported inventory is underscribed by all. The need to link the status of the land degradation with land productivity or land sustainability decline is widely recognized. In spite of this general consensus very little support is coming forward to support such national or regional inventories.

Remote sensing has certainly an important role to play, in particular for those applications that aim to monitor land cover and land-use changes. Unfortunately, these efforts usually remain at the level of land cover, while the most dramatic effects of degradation are linked to land use.

Data access

The problem of information access is not unique for soil data but applies for many other research data (climate, geology, topography, etc.) as well. The problem appears to be most acute in Europe, but recent developments point to increasing data access problems in Africa and Asia as well.

In Europe, several phenomena have accelerated data access problems. The first event was the privatization process that started in the late 1970s in Europe and resulted in national soil institutes being less subsidized than in the past. The only valuable money source apart from human resources appeared to be the data and maps that had been collected over the years. These were consequently marketed and commercialized and seen as an extra source of income. Strict copyright rules were put in place to avoid miss-use and illegal reproduction of the data.

Another, less well documented phenomena causing data access problems, appears to be the accelerated regionalization within the EU in which the power of some regions within countries (particularly in Germany, but also in Belgium, France and Italy) have become much more expressed and they have legally limited the data access that national institutes are allowed to extend to interested researchers or commercial companies. As international organizations can only discuss data availability with national entities, the problem becomes more difficult to solve.

A third reason why data access has been made more difficult is that producers of soil data express the sincere wish that data should not be mis-used. It is indeed quite common that soil data are scaled up, much beyond their level of reliability, or that derived data are produced which have little to do with pedological reality (a recent report of NASA indicated average global soil depth at 10 meters, while in reality it is probably 150 cm). Although complying with the wish of data-users is another form of costing data, it is felt that strict regulation on data use is impractical and difficult to impose.

The approach of UN agencies and north America in general is that data should be made available at their production and maintenance cost only.

Some Tentative Solutions

Problems of data access are difficult to solve at scientific level. They should preferably be tackled at the political level, by international agreements such as those that can be arbitrated by the World Trade Organization and guarantee intellectual property.

Making the decisions of a single body (most logically IUSS) binding for all organizations involved (FAO, ESB, IGBP, ISO, ISRIC, IUSS, NRCS, UNEP, etc.) would tackle some of the methodological problems, the lack of international coordination, and organizations claiming to speak for the whole global or for regional (soil) scientific communities.

Technological progress for soil science has been tremendous over the last 10-20 years with computerization allowing the development of sophisticated databases and geographical information systems. Enhanced remote sensing technology has allowed the development of digital elevation models and much improved delineation of soil and landscape boundaries. Automated laboratory methods have improved some of the soil analytical results. However, at the same time, the systematic field collection of soil data has collapsed in most of the world and very few new soil surveys are being undertaken, indicating that much of the technological progress is wasted on data that are often "outdated". Therefore it should be realized that if the technological progress is to be exploited at its full capacity, a fraction of its development costs should be invested in field level investigations, which go beyond ground truthing.

Finally, to make significant progress in the assessment of land degradation and its effect, it is required as a matter of urgency that a systematic and global inventory of land use is undertaken, while the link between the severity and type of land degradation and its economic costs should be tackled scientifically and not emotionally.

Conclusions

Recommendations References

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FAO. 1993. World Soil Resources. An explanatory note on the 1:25 000 000 scale world soil resources map. World Soil Resources Report No. 66. Rome.

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Terrestrial Carbon Observation: Global Grid Data for Model Input

Stephen Plummer

(IGBP-European Space Agency)

Land surface/Dynamic Global Vegetation Models (DGVM) are central to the entire TCO concept which envisages that such models are driven by gridded data inputs either directly or through landscape syntheses, with a two-way interaction with the climate/atmosphere (Figure A2).

Indeed DGVMs form the principal link between terrestrial and atmosphere components of TCO, However, such models are strongly dependent on spatially comprehensive and temporally detailed inputs in gridded form or derivation of parameters from detailed but spatially confined process studies. The vast majority of data/observations are, however, derived from in situ measurements such as flux towers or inventories that are extrapolated to provide spatial coverage. Thus, DGVMs are constrained by accurate representation of landscape heterogeneity and its variation over time derived through landscape syntheses, whether derived from direct but point sampling or indirect spatially comprehensive observations.

The current suite of DGVMs comprises a wide range of different schemes but typically a scheme corresponds to formulation of the processes and flows detailed in Figure A3. The 1995 intercomparison of terrestrial biosphere models by Cramer et al. (1999), while focused on net primary production (NPP), provides a reasonable assessment of the common models.

Dynamic Global Vegetation Models incorporate point-based soil-plant models for every cell in a global grid, being driven by global interpolated databases of environmental conditions and/or satellite observations (Cramer et al. 1999). The DGVMs also require these global databases at a variety of temporal resolutions.

Figure A2. Terrestrial and atmospheric components of the global carbon cycle (FAO, 2002b).

Figure A3. Schematic of a Dynamic Global Vegetation Model (www.pik-potsdam.de/~kirsten/dgvm.gif)

Although models have moved on since 1995 the basic data dependencies are still the same. There is a broad set of data areas where global gridded data is required by some if not all models:

Data sets covering the majority of these areas exist, however, to be of value such data must be on a consistent Earth grid, ideally temporally matched and, where more than one source of data is available, show a clear relationship in terms of accuracy and comparability. Data synthesis efforts have been and are being conducted to merge the diverse products into a single widely adopted output. In saying this it is important to note that the models themselves must continue to develop, particularly focusing on how best to incorporate outputs from such coordinated data efforts.

Climate - Data Sources

DGVMs require climatological data in a long time sequence to initialise (where plant functional types [PFT] are concerned) and also to capture the local/regional spatial and temporal variability. Historical data for non-prognostic work come from a relatively limited number of standard sources, which are accepted by the entire DGVM community.

The principal gridded climatological observation data are:

1) ISLSCP-I (1987-88) and II (1982-90) - 1° (and 1/4° and 1/2° in ISLSCP-II)

2) Leemans and Cramer (1931-60 at 1/2°)

3) New et al. 1999, 2000 (1961-1990 mean, 1901-1995 monthly grids at 1/2°) which comprises:

For prognostic work models use outputs from one of the principal general circulation models (GCM). However, these GCM products are generally constrained by observations over a fixed period to avoid model bias (Cramer et al. 2001). Typically GCMs like HadCM2, ECHAM4, CGCM1, GFDL-R15, CSIRO-Mk2, NCAR-DOE or CCSR, are run following internationally accepted standard forcing scenarios e.g. GSa (greenhouse gas and sulphate aerosol) and IS92a. The model choice, however, can affect DGVM performance since the individual GCMs do exhibit differences (Figure A4).

Figure A4. Example of GCM prediction and variability against observed data for global mean precipitation anomalies (1961-90). The scenario is GSa under IS92a standard forcing

The observed data (black line in Figure A4) are from New et al. (1999) which, along the GCM products shown, are available through Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC-DDC) at the Climate Research Unit, University of East Anglia (UK) (http://ipcc-ddc.cru.uea.ac.uk/cru_data/visualisation/visual_index.html).

Topography - Data Sources

Topographic information is vitally important within models, as an underlying product for climate data (e.g. solar radiation, precipitation and temperature) but also for hydrological routing and soil moisture availability. These data are also widely sought to provide some indication of spatial heterogeneity within model grid units. The standard product is GTOPO30 which comprises elevations regularly spaced at 30 arc-seconds with a variable accuracy in the vertical of at best ±18m (http://edcdaac.usgs.gov/gtopo30/gtopo30.html, Figure A5).

Figure A5. GTOPO30 30 arc-second Digital Elevation Model (DEM).

Recently a major international coordination effort under IGBP-DIS has resulted in the development of a unique, global, digital elevation model called Global Land One- kilometre Base Elevation (GLOBE, see Figure A6). GLOBE has a 30-arcsecond resolution and was developed from multiple sources including GTOPO30 and the NASA Digital Terrain Elevation Data (DTED). The absolute vertical accuracy depends on the source data and the spatial location with DTED data rated at ± 18 metres at best and individual vector DEMs varying from 6-500m RMSE. The first version was released in 1999 but development and improvement is ongoing (Hastings and Dunbar 1999, www.ngdc.noaa.gov/seg/topo/globe.shtml).

Figure A6. GLOBE 30 arc-second global DEM

One of the major initiatives stemming from GLOBE activities was the launch of the Shuttle Radar Topography Mission (SRTM) in February 2000. The objective of this mission was to obtain elevation radar data on a near-global scale and generate the most complete high-resolution digital topographic database of the Earth. Virtually all the land surfaces between +/- 60 degrees latitude were mapped by SRTM (Figure A7) with data points spaced every 1 arc-second of latitude and longitude (approximately 30 metres). These data are being processed using radar interferometric techniques and the planned absolute horizontal and vertical accuracy will be 20 metres and 16 metres, respectively (www.jpl.nasa.gov/srtm/). To date, no digital elevation maps have been released. However, uncalibrated "showpiece" derived products are available from very preliminary DEMs (Figure A8). Interferometric coverage using the Canadian Radarsat system has also been acquired to complement the SRTM product for Antarctica for the development of a South Polar DEM.

Figure A7. Coverage map for the Shuttle Radar Topography Mission

Figure A8. Example SRTM product of the southern coast of the Arabian Peninsula. This image contains about 1400 meters (4600 feet) of total relief.

Soil - Data Sources

Knowledge of soil resources is fundamental to model performance, particularly soil organic carbon, soil total nitrogen, water holding capacity and soil thermal properties. While they are fundamental they also represent the most difficult information to obtain. Typically most models use data on carbon based on the work of Zobler (1986) and Zinke et al. (1984, 1986) and the FAO World Soil Map (FAO 1995). The initial developmental effort came through the International Satellite and Land Climatology Program (ISLSCP-I) but more recent efforts through FAO, ISRIC, and ORNL have increased the resolution to 0.5 degree and increased the usefulness of the data (Batjes 1995, Post 2000 and Carter and Scholes, 2000). The current data sources have recently been collated through IGBP onto a CD-ROM available online at www-eosdis.ornl.gov/DAAC/cd_roms.html#soil.

Updates to the current status are ongoing through the ISLSCP-II project and more detailed data will be available through SOTER (but probably not globally until 2006).

ISLSCP-II, May 2001, 0.5° grid of Texture, Sand, Silt, Clay, Organic Contents, Bulk Density, Retention Curve Information, Heat Capacity, Thermal Conductivity, Pedon Characteristics (Type, C, N, Parent Material, etc.). See http://islscp2.gsfc.nasa.gov/or WORLD-SOTER (2002) atwww.fao.org/ag/AGL/agll/soter.htm.

Vegetation Data Sources

i) Land Cover

DGVMs use a variety of data sources (or none at all for potential vegetation versions) to define the type of vegetation present as a constraint on the form of physiological behaviour in the model. While a large number of datasets exist the standard data products are probably the IGBP Land Cover classification at 1km or prescribed vegetation covers e.g. Matthews (1983).

IGBP

The IGBP global land cover characteristics database was developed on a continent-by-continent basis jointly by U.S. Geological Survey (USGS), the University of Nebraska-Lincoln (UNL), and the European Commission's Joint Research Centre (JRC). All continental databases have 1-km nominal spatial resolution, and are based on 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning April 1992 through March 1993. The continental databases were combined to make seven global data sets, each representing a different landscape based on a particular classification legend. The original version of the Global Land Cover Characteristics database was released in November, 1997 and has been subjected to a formal accuracy assessment using high spatial resolution satellite image data (the IGBP DISCover classification, see Figure A9). Based on comments from users a revised version is now available although a formal accuracy assessment has not been conducted (http://edcdaac.usgs.gov/glcc/glcc.html).

Figure A9. Example of the IGBP Land Cover Classification - Eurasia

Matthews

The Matthews Vegetation data set comes from a global map of vegetation types, which was compiled from up to 100 existing map sources at the Goddard Institute of Space Studies (GISS), Columbia University, in New York. It shows the predominant vegetation within each one degree-square latitude/longitude grid cell. The data are available through the United Nations Environment Programme (UNEP-GRID, www.grid.unep.ch/data/grid/gnv2.html)

Most DGVM models now use the plant functional type (PFT) approach rather than prescribed land type but such data form a useful comparison (e.g. Cramer et al. 2001) and also offer a constraint for incorporating Land Use.

Other sources of data will shortly be made available from satellites e.g. MODIS (http://edcdaac.usgs.gov/modis/mod12q1.html) but also GLC2000 based on VEGETATION data (www.gvm.sai.jrc.it/glc2000/defaultGLC2000.htm). However, the complex nature of the classification process and the multitude of different data sources available requires a concentrated effort on data set inter-comparison. A suitable format of such an effort is the FAO Land Cover Classification system (Figure A10 and Di Gregorio and Jansen 2000).

Figure A10. FAO Land Cover Classification System

ii) Land use

One of the major limitations of the PFT approach adopted by most current DGVMs is that it does not consider the role of humans in altering the vegetation distribution. Some possible information is available from Earth Observation since it records the response from actual rather than potential vegetation. This includes information on urban development and the location of cultivated land, for example the Defence Meteorological Satellite Program (DMSP) Nighttime Lights. Other gridded sources also exist but probably the most widely used is the historical crop cover data compiled by Ramankutty and Foley (1999, see Figures A11 and A12). The diversity of approaches and data sources, however, implies a need for concerted efforts in data synthesis.

Figure A11. Distribution of crops in 1700 (Ramankutty and Foley 1999)

Figure A12. Distribution of crops in 1992 (Ramankutty and Foley 1999)

iii) Above-Ground Biomass

Estimates of above-ground (and below-ground) biomass are fundamental to knowledge of the size and changes of the terrestrial carbon pool as a function of land use and land management practice. Such information is vital for constraining the behaviour of sub-modules within DGVMs. While observations of biomass components have been conducted these have generally been inconsistent internationally with different definitions of above- (and below-) ground components used as a function of the motivation for data collection.

In terms of below-ground biomass, no gridded data set exists but the comments made above on progress towards the generation of gridded soil products apply since it is likely that a similar approach will be taken. Above-ground biomass can be quite detailed at the national level, particularly in respect of national forest inventories. However, most of the detailed information is not generally available since it of commercial value. Further, the individual national products are often sampled data generated using a variety of different methodologies. This means the construction of a global gridded product is fraught with difficulty. Remote sensing is a possible route to global gridded observations, however, to date there is no global product and multiple problems exist in accurate estimation, particularly sensitivity to structure and saturation at relatively low levels of biomass. Recently, regional products as scientific proof-of-concept have started to appear, for example that of the SIBERIA project which mapped 1mio km2 at a spatial resolution of 50m based on synthetic aperture radar (SAR) interferometry (http://pipeline.swan.ac.uk/siberia/). However, the radar variability and limited data meant that only 4 classes of biomass were defined (Figure A13). A follow-on project (SIBERIA-II) will expand the area to 200mio Ha and combine satellite and in situ data and modelling.

Promising results have been produced using lidar techniques, however, the prospects for global gridded measurements based on satellite lidar (Earth System Science Pathfinder [ESSP] Vegetation Canopy Lidar [VCL]) data currently look slim. Further development of radar-based techniques await the launch of new generation radar systems but it is likely that the 'saturation' problem will continue since there are no concrete plans to launch a P-band radar system required for sensitivity to high biomass estimation (>100 m3/ha).

Figure A13. Estimated above-ground biomass for 1mio km2 of Siberian boreal forest, centred on Krasnoyarsk, Russia

iv) Seasonal growth cycle (LAI, Growth Season Duration)

An important part of model input and/or constraint is knowledge of the 'real' behaviour of vegetation. This comprises for example leaf area index, the fraction of photosynthetically active radiation absorbed (fAPAR,) and growth cycle behaviour. Such data are required globally, with frequent temporal sampling and ideally over a long temporal sequence. Effectively the only global gridded sources of data are through conversion of Earth Observation data. Of the available data, the AVHRR record (e.g. Los et al. 2000) plus other similar data sources - ATSR-2, SeaWiFS, VEGETATION and then in future MERIS/MODIS are particularly important. However, there is a vital need to comprehensively inter-compare the different algorithms, reconcile sensor-specific idiosyncrasies and ideally to consolidate these products in much the same way as with the GLOBE effort. The primary data sources are listed in Table A19 along with current status, temporal sequence and spatial coverage.

Table A19. Sources and availability of EO data for conversion to global grids of fAPAR, growth cycle behaviour and leaf area index (spatial resolution 1km or coarser, temporal resolution ³ weekly)

Owner

Program/Processor

Method

Sensor

Temporal Sequence

Spatial Availability

NOAA

NASA/ISLSCP-I

FASIR-NDVI

AVHRR

1987-88

Global

NOAA

NASA/ISLSCP-II

FASIR-NDVI

AVHRR

1982-90

Global

NOAA

tbc

tbc

AVHRR

1990-present

Global

NOAA

CESBIO

LASUR

AVHRR

1989-90

Global

ESA

ESA/HiPROGEN

tbc

ATSR-2

1995-present

Global (tbd)

NASA/NASDA

EC JRC

OVNI

SeaWIFS

1997-present

Europe

SPOT/CNES

tbc

tbc

VEGETATION

1998-present

Europe

NASA

NASA

MODIS ATBD

MODIS/MISR

from 2000

Global (tbd)

ESA

ESA

ENVISAT ATBD

MERIS/AATSR

from 2002

Global


These data (or information on them) can be obtained through the following web links:

ISLSCP-II: http://islscp2.gsfc.nasa.gov

LASUR: www.cesbio.ups-tlse.fr/co2.htm

ATSR-2: http://earthnet.esrin.esa.it or
www.atsr.rl.ac.uk

SeaWiFS: www.me.sai.jrc.it/seawifs/

VEGETATION: www.vgt.vito.be

MODIS: http://edcdaac.usgs.gov/main.html

ENVISAT: http://envisat.esa.int/

v) Disturbance

No global gridded disturbance data-set currently exists that satisfies the requirements for model input. Ideally the required data are disturbance type (fire, insect, wind-throw, etc.) occurrence (year, month, day, hour), severity, location and area affected. However, given that in some biomes this kind of information is needed over temporal sequences of approximately 200 years, it is unrealistic. Alternatively it is necessary only to know the probability of disturbance but this still requires sufficient global data to constrain and calibrate disturbance modules. Fire activity, fire burned area, insect and wind-throw data do exist but again these are nationally partitioned, use different methodologies and generally do not contain comprehensive information especially in forest environments where regeneration times are long. Further, such data as tree-ring information is not distributed evenly across the globe (Figure A14). Earth observation data again offer potentially valuable products in terms of fire activity (Figure A15), and current year burned area will soon be available. There are also some indications that older fire scars are reliably detectable at least in the boreal zone from both optical and radar sensors (see Figure A16 and A17).

Various sources of fire activity need collating, synthesising and turning into useable products for example by coding by month, year or by plant functional definition e.g. Koppen (Figure A18). Furthermore, there is a need to reconcile these remote sensing products with existing databases, for example tree-ring observations and historical fire databases (Figures A14 and A18). From these consolidated data it should be possible to obtain generalised statistics of fire return interval at grid scale for use in fire modules. A remaining problem is derivation of disturbance due to other activities such as wind-throw, insect damage and pollution.

Figure A14. Potential gridded databases of disturbance could come from fire information in tree-rings (www.ngdc.noaa.gov/paleo/treering.html) and by consolidation of fire area databases e.g. the Canadian Large Fire Database (www.nofc.forestry.ca/fire/frn/English/frames.htm)

Figure A15. Night-time global fire activity from ATSR for 1998 (http://shark1.esrin.esa.it/FIRE/AF/ATSR/). Comparable systems are available based on AVHRR (http://wfw.gvm.sai.jrc.it/)

Figure A16. Detection of historical fire scars using coarse resolution optical Earth Observation (EO) data, here cumulative probability results from a sequence of 6 VEGETATION images from 1998 over part of Manitoba. The detected burns match well with 1989 fire scars from the Canadian Large Fire Database indicated in blue

Figure A17. Detection of fire scars using European Research Satellite (ERS) radar data from 1995 over part of Saskatchewan. Burnt area polygons from the Large Fire DataBase (LFDB, 1995) are overlaid. Qualitatively, the agreement is excellent for the 1995 burns. For previous years the SAR also detects the burnt areas although the area detected as burned by the radar backscatter decreases as the time difference between image acquisition and burn gets longer (www.sarmap.ch/prod_FOREST.html)

Figure A18. Comparison of the number of fires detected by ATSR between 1995 and 2000 split by biome type as defined using the Köppen classification scheme

vi) Ecosystem Productivity

Ecosystem productivity quantifies carbon uptake by terrestrial ecosystems and is determined by reference to physiological response. Net primary productivity (NPP) is the net biomass increase through photosynthesis, while net ecosystem productivity (NEP) refers to net carbon exchange with the atmosphere after accounting for soil respiration and organic matter decomposition. To date, ecosystem productivity products have been generated by various groups at global and regional levels but not consistently over time. Initially NPP was calculated using simple Radiation Use Efficiency (RUE) formulations, these do not represent the ecophysiological complexity of the real terrestrial primary production processes and increasingly these are being replaced by more sophisticated treatments. The problem is that there is as yet no global product and the products now generally use an ecosystem model for their generation (Figures A19 and A20). This means that their use as a means of validation or constraint on another model is open to question since the accuracy of the product is dependent on the model used to develop it. Therefore there is a need to assess the methods used and particularly the consequences of use of a certain model in the processing chain, as well as inter-comparison against available in situ observations.

Figure A19. Net Primary Production predicted using the Boreal Ecosystem Productivity Simulator (BEPS) model over Canada for 1994 (www.ccrs.nrcan.gc.ca/ccrs/tekrd/rd/apps/em/beps/bepse.html)

Figure A20. Global predicted NPP for 1987 using the BIOME-BioGeochemistry Cycles (BGC) model and NOAA AVHRR observations (www.forestry.umt.edu/ntsg/RemoteSensing/netprimary/)

Pooling Data for Modelling

One of the key requirements for the provision of global gridded data for use in models is the establishment of either a centralized store (effective one-stop shop) of all the required data or a point of reference/meta-database of where such data are located and accessible. Such data must match the spatial and temporal requirements of the models but also be consistent in terms of data format and Earth projection, and carry detailed accuracy evaluations. The original ISLSCP (ISLSCP-I) initiative was the first step in producing such a resource and based upon the success of ISLSCP I and the proven need for an expanded collection both in space and time, NASA are currently in the process of developing ISLSCP-II through in-kind contributions from many data providers. This forms both an update and extension of ISLSCP-I introducing new data sets but also expanding the temporal sequence to 10 years (http://islscp2.gsfc.nasa.gov).

For ISLSCP-II the spatial resolution will be in one of 3 forms: 1/4, 1/2, and 1 degree over the 10-year period from 1986 to 1995. It will contain approximately 230 parameters organized under the following broad categories:

Observations

The need for inter-comparison, synthesis, consolidation rather than scientific 'competition' have been running themes in the above. From a modelling perspective, while the modelling community are likely to be appreciative of available gridded inputs there are currently a large number of generally independent data products, available at different time periods, using a diverse range of methodologies, and spatial resolutions and coverage. Modelling requires a longer temporal sequence to be available but also requires consistency between input data in resolution, accuracy and comparability. The significant efforts underpinning the NASA's EOS Program, are a major step forward but since the first satellite Terra was launched in 2000 there is a need to extend back from it, focusing on other satellite global data sources but also active coordination in the collation and inter-comparison and synthesis of in situ observations. There is also a vital need to develop models so they are not critically dependent on single data sources and to develop algorithms that similarly are capable of operating, within the same processing chain, using the diversity of available (e.g. satellite) input data sources. Activities such as ISLSCP-II are extremely valuable but there is a need to extend these to encompass 'and ideally' synthesise global and regional products, for example, those that will be produced through ESA projects and programmes such as HiProGen and Envisat (http://envisat.esa.int/) with those of NASA (http://edcdaac.usgs.gov/main.html?/) and in situ sources being compiled at ORNL (www-eosdis.ornl.gov/). Further it is equally important to encourage the registration of new production lines/products as they become available.

The suggested strategy is, therefore, to build on the success of the ISLSCP initiatives, by inter-comparing the range of products and focusing on provision of continuity and clarity in data sourcing. A possible mechanism for this is through the frameworks of the Committee on Earth Observing Satellites (CEOS) Land Product Validation initiative, coordinated activities under the IGBP-WCRP-IHDP Joint Carbon banner or directly through GTOS/TCO. The first step in this process is to compile a database of what global/regional gridded products are available for models, over what time period and spatial resolution and their location and produce a preliminary meta-database of this information.

References

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Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., Brovkin, V., Cox, P. M., Fisher, V., Foley, J., Friend, A. D., Kucharik, C., Lomas, M. R., Ramankutty, N., Sitch, S., Smith, B., White, A. & Young-Molling C. 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 7: 357-373.

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FAO 1995. The digital soil map of the world, version 3.5. FAO, Rome.

FAO. 2002a. Terrestrial Carbon Observation: The Rio de Janeiro recommendations for terrestrial and atmospheric measurements, by Cihlar J. & Denning S., eds. FAO Environment and Natural Resources Series No.3. Rome.

Global Soil Data Task. 2000. Global Soil Data Products CD-ROM (IGBP-DIS). International Geosphere-Biosphere Programme - Data and Information Services.

Hastings, D. A. & Dunbar, P. K. 1999. Global Land One-kilometre Base Elevation (GLOBE) Digital Elevation Model, Documentation, Volume 1.0. Key to Geophysical Records Documentation (KGRD) 34. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325 Broadway, Boulder, Colorado 80303, U.S.A.

Leemans, R. & Cramer, W. 1991. The IIASA database for mean monthly values of temperature, precipitation and cloudiness of a global terrestrial grid. International Institute for Applied Systems Analysis (IIASA). RR-91-18.

Los, S. O., Collatz, G. J., Sellers, P. J, Malmstrom, C. M., Pollack, N. H., DeFries, R. S., Bounoua, L., Parris, M. T., Tucker, C. J. & Dazlich, D. A. 2000. A global 9-yr biophysical land surface dataset from NOAA AVHRR data. Journal of Hydrometeorology, 1: 183 -199.

Matthews, E. 1983. Global vegetation and land use: new high resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22: 474-487.

New, M., Hulme M. & Jones, P. 1999. Representing twentieth-century space-time climate variability. Part I: Development of a 1961-90 mean monthly terrestrial climatology. Journal Climate, 12: 829-856.

New, M., Hulme, G. M. & Jones, P. D. 2000: Representing twentieth-century space-time climate variability. Part II: Development of 1901-1996 monthly grids of terrestrial surface climate. Journal Climate, 13: 2217-2238.

Post, W. M. 2000. Global Soil Types by 0.5-Degree Grid (modified Zobler). Available online at www.daac.ornl.gov/from the ORNL Distributed Active Archive Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A.

Ramankutty, N. & Foley, J.A. 1999. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochemical Cycles, 13(4): 997-1027.

Zinke, P.J., Stangenberger, A.G., Post, W.M., Emanuel, W.R. & Olson, J.S. 1984. Worldwide organic soil carbon and nitrogen data. ORNL/TM-8857. Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Zinke, P.J., Stangenberger, A.G., Post, W.M., Emanuel, W.R. & Olson, J.S. 1986. Worldwide Organic Soil Carbon and Nitrogen Data (CDIAC NDP-018).

Zobler, L. 1986. A world soil file for global climate modeling, NASA Technical Memorandum 87802, 32 pp.

Carbon Modeling Datasets - New Zealand

Leonard (Len) Brown

Environment Waikato

The table below summarises the datasets available for New Zealand that can contribute to the specific observation requirements identified by GTOS. Although the 2001 Frascati workshop focused on in situ datasets, to provide a complete overview, the table also describes key datasets derived from remote-sensing and spatial interpolation. Additional information including a brief description of the dataset and the host organization, and key references for carbon monitoring in New Zealand can be found in Table A20.

Table A20. Observation requirements for bottom-up approach

1. Driving variables required at every grid point for model upscaling

Variable Atmosphere

Spatial

Temporal

Method

Note

Air temperature

25m - 1km

One set of monthly surfaces of average, min and max daily

Modelled surface

1

Precipitation

25m - 1km

One set of monthly surface of total monthly rainfall

Modelled surface


PAR





Relative Humidity

25m - 1km

One set of monthly surfaces of mean relative humidity

Modelled surface


Wind Speed





Net radiation

25m - 1km

One set of monthly surfaces of mean daily solar radiation

Modelled surface


Snow water equivalent





Aerosols





Integrated atmospheric water vapour




1a

Ecosystem





Vegetation cover class

20m

Plans for repeated survey every 5 years. Initial mapping in 1996/1997

Remote sensing

5

Biota biomass





Soil moisture

1km × 1km

One modelled estimate of monthly soil water balance

Modelled surface

3

Soil moisture (2)

vary 20m - 1km

One estimate



LAI



Modelled surface

2

Foliage N





Chlorophyll





Natural disturbance history





Management history





Topography

25m - 100m



15

fAPAR

250m - 1km

One years data (98/99) of monthly composites

Modelled surface

2

NPP

1km

One years data (93/94) of monthly composites

Modelled surface

ref 1

Calibration variables required at selected sites

Atmosphere





Air temperature

105 climate stations

hourly since approx 1900's

site measurement

11

Precipitation

800 rainfall stations

hourly since approx 1900's

site measurement

11

Solar radiation

105 climate stations

total hourly, (global, diffuse and direct) since approx 1900's

site measurement

11

Relative Humidity

105 climate stations

Monthly statistics of mean 9am relative humidity

site measurement

11

Wind Speed

105 climate stations

daily wind run as mean speed, direction and speed

site measurement


Net radiation





CO2 conc profile





Integrated atmospheric water vapour





Snow water equivalent





Aerosols





Ecosystem





Site





Natural disturbance history





Management history





Topography





Spatial pattern





Vegetation





Vegetation cover class

10 sites

one time

site measurement

6

Vegetation composition for forest

many

one time and repeated visits

site measurement

14

Root carbon

10 sites

one time

site measurement

7

Above-ground biomass

~1100 plots

every 5 years

site measurement

9

LAI

5 sites

one time - possibility of repeats

site measurement

12

Foliage N





Soil





Biota C, N





Biota biomass





Temperature profile

10 sites

one time

site measurement

6

Maximum Thaw depth





Thermal conductance





Thermal diffusivity





Soil moisture





Hydraulic properties

438 profiles

one time

site measurement

8

Ground water depth





Organic C content

1426 profiles

one time

site measurement

8

Organic C content (CMS)

32 paired sites

one time

site measurement

9

Inorganic C content

1426 profiles

one time

site measurement


C age





N content

1426 profiles

one time

site measurement


P content

1000 profiles

one time

site measurement


Bulk density

438 profiles

one time

site measurement


Bulk density (CMS)

32 paired sites

one time

site measurement

9

Sand & clay fraction

438 profiles

one time

site measurement


pH

1426 profiles

one time

site measurement


Macro nutrients

1426 profiles

one time

site measurement


Micro nutrients

223 profiles

one time

site measurement


Microbial biomass





Physiology





Foliage N

5 species

one time

site measurement

10

Foliage lignin





Chlorophyll

3 species

one time

site measurement

10

Rubisco

1 species

one time

site measurement

10

Vcmax, Jmax

7 species

one time

site measurement

10

Fluxes





C fluxes above and near ground

1 study

continuously for 1998 - 2000

site measurement of eddy covariance

13

Above-ground NPP

10 sites

one time

site measurement

6

Below-ground NPP

10 sites

one time

site measurement

6

Litterfall N, P, C

10 sites?

collected in above study?


6

ET above stand

1 study

continuously for 1998 - 2000

site measurement of eddy covariance

13

Sensible Heat

1 study

continuously for 1998 - 2000

site measurement of eddy covariance

13

CH4





VOC





DOC





Heterotrophic respiration rate (soil respiration rate?)

11 sites

mix of individual studies since 1995

site measurement

6

Soil respiration rate (2)

1 study

continuously for 1998 - 2000

site measurement of eddy covariance

13


Note 1. Climatic surfaces are generated through a thin-plate spline surfaces fitted to climate station data of 30 year climate normal data. It is also possible to generate surfaces using other time steps (daily) but noise associated with the surface would obviously increase. Contact Dr John Leathwick at Landcare Research (LeathwickJ@landcare.cri.nz).

Note 1a. A modelled monthly surface of mean daily vapour pressure deficit is also available.

Note 2. For spatial LAI coverage, an archive of NOAA AVHRR HRPT data is available for further processing. AVHRR HRPT archive dates from 1992 to the present with between 2-5 images per day. There is no atmospheric or BRDF correction on the imagery. Simple Ratio (SR) and NDVI are being generated for April 1998 to March 1999. A charge would apply for the data. Contact David Pairman at Landcare Research (PairmanD@landcare.cri.nz).

There is also processing of the 1998/99 year of monthly VEGETATION composite data through to whole ecosystem fAPAR using BRDF corrected data, and AVHRR data for the same period, again via a BRDF model. Research may also investigate using SeaWIFs data for the same period, using an alternative BRDF modelling approach. Contact is Dr Craig Trotter at Landcare Research. (TrotterC@landcare.cri.nz).

Note 3. Monthly soil water balance is modelled from mean monthly potential evapotranspiration and soil moisture storage capacity between -0.001 and 1.5 MPa, averaged by major soil classes (IPCC). Contact is Dr Aroon Parshotam at Landcare Research (ParshotamA@landcare.cri.nz).

Note 4. The database is derived by linking point data on available water holding capacity to spatial land system attributes. There are also fundamental soil layers available where point sample data has been extrapolated over soil type information to give a vector spatial coverage (1:50 000 mapping scale). The soil attributes available in this dataset are: permeability assessment, potential rooting depth, topsoil gravel content, depth to a slowly permeable layer, min pH, salinity, drainage class, cation exchange capacity, organic carbon (0-20cm), phosphorus retention, temperature class, profile of available water and macroporosity. Contact is Mr Peter Newsome at Landcare Research (NewsomeP@landcare.cri.nz).

Note 5. The land-cover products are derived from remote sensing. It is also intended to create a vegetation cover of New Zealand as at 1990, from existing land-cover data bases and older satellite imagery. Contact for the Land-Cover Database is Terralink International (www.terralink.co.nz).

Note 6. Soil respiration measurements were made repeatedly at 11 sites. Site vegetation cover was indigenous beech forest, indigenous mixed forest, exotic forest (5 sites in different climate and tree age), scrub, improved pasture (2 sites) and tussock grassland. Contact is Dr Kevin Tate at Landcare Research (TateK@landcare.cri.nz).

Note 7. Root allocation percentage is reported for the sites.

Note 8. The National Soils Database (NSD) is a collection of soil sample data collated over many years. There are 1426 soil profiles with chemical analysis, 786 profiles with mineralogical analysis, 788 profiles with particle size and 438 profiles with soil water properties. As part of the Carbon Monitoring System (note 9), there is also the carbon database. The carbon database has 561 profiles, 369 from the NSD and 192 profiles from a gap filling exercise. Additional data in the carbon database is organic carbon within the depth ranges 0-10cm, 10-30cm and 30-100cm. Contact Mr Hugh Wilde at Landcare Research (wildeh@landcare.cri.nz).

Note 9. The Carbon Monitoring System is a Government Funded (Ministry for the Environment) project aimed at monitoring the amount of terrestrial carbon. There are two sections: change soil carbon due to land-use change and vegetation change. The change in soil carbon uses measures of organic carbon and bulk density in a given land use, climate and soil combination. Changes in land-use determined from satellite imagery will be used to show changes in soil carbon. The information comes from the Carbon database (note 8) and additional sites (32 paired sites, 16 in pasture/pine and 16 in pasture/scrub). These combinations reflect the most common land use change in New Zealand. The vegetation component will use approximately 1 100 plots where researchers measure change in diameter at breast height, height, etc. and calculate the volume and tonnes C/ha through allometric equations. Details are available at www.mfe.govt.nz/about/publications/climate/carbon.pdf.

Note 10. Limited measurements. Rubisco activity has been difficult to measure. The approach used has been to measure Vcmax and Jmax from measurements of photosynthesis. Contact Dr David Whitehead is Landcare Research - (WhiteheadD@landcare.cri.nz).

Note 11. The National Climate Database (katipo.niwa.cri.nz/www_cli.htm) is New Zealand's national repository of climate information. It comprises paper-based archives dating back to 1860 and extensive digital information (15 GB). Information on various climate elements is available at time intervals ranging from minutes to years. Data is from a network of 105 climate stations, 800 rainfall stations and other sources.

Note 12. LAI measurements collected by paired LAI2000 sensors and verified by some destructive sampling. Contact is Dr Craig Trotter at Landcare Research (TrotterC@landcare.cri.nz).

Note 13. Carbon flux data reported was above a grassland site. The next carbon flux study will be above a rimu (Dacrydium cupressinum) forest beginning in October 2001. Previous campaign data collected by Dr Kelliher from New Zealand, Siberia and Oregon has been published and hence is in the public domain. Contact is Dr Francis Kelliher at Landcare Research (KelliherF@landcare.cri.nz).

Note 14. An extensive array of forest plot information is available through Forest Research Institute and Landcare Research Ltd. Contact these organizations directly for information on plot composition and history. Refer also to note 9 in the Carbon Monitoring System where approximately 1 100 forest plots are being monitored every five years.

Note 15. New Zealand wide DEM products developed from 20m contour data and spot heights. Contact is Dr James Barringer at Landcare Research.

References

Tate, K.R., Scott, N.A, Parshotam, A., Brown, L., Wilde, R.H., Giltrap, D.J., Trustrum, N.A., Gomez, B & Ross, D.J. 2000. A multi-scale analysis of a terrestrial carbon budget. Is New Zealand a source or sink of carbon. Agriculture, Ecosystems and Environment, 82:229-246.

Whitehead, D., Leathwick., J.R. & Walcroft, A.S. 2000. Modeling Annual Carbon Uptake for the Indigenous Forests of New Zealand. Forest Science, 47(1): 9-19.

FAO Environment and Natural Resources Series

1. Africover: Specifications for geometry and cartography, 2000 (E)

2. Terrestrial Carbon Observation: The Ottawa assessment of requirements, status and next steps, 2002 (E)

3. Terrestrial Carbon Observation: The Rio de Janeiro recommendations for terrestrial and atmospheric measurements, 2002 (E)

4. Organic agriculture: Environment and food security (E)

5. Terrestrial Carbon Observation: The Frascati report on in situ carbon data and information (E)

Availability: August 2002

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English

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In preparation

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French



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Portuguese



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Spanish




The FAO Technical Papers are available through the authorized FAO Sales Agents or directly from Sales and Marketing Group, FAO, Viale delle Terme di Caracalla, 00100 Rome, Italy.


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