Randal D. Koster NASA/GSFC, Norman B. Bliss Principal Scientist Science and Applications Branch, Saud A. Amer ECS DAAC Scientist EROS Data Center, Soroosh Sorooshian Professor and Head Department of Hydrology and Water Resources University of Arizona
_________________________________________________
| Contact 1 |
______________|__________________________________|
2.3.1 Name |Dr. Randal D. Koster |
2.3.2 Address |Hydrological Sciences Branch |
|Code 974, NASA/GSFC |
City/St.|Greenbelt, MD |
Zip Code|20771 |
2.3.3 Tel. |(301) 286-7061 |
2.3.4 Email |[email protected] |
______________|__________________________________|
2.4 Requested Form of Acknowledgment.
The soil texture data set was constructed by Zobler (1986), and
the soil profile depth data set was constructed by Webb et al.
(1993). Slope data were originally derived from the FAO Soil Map
of the World in a 1 degree grid (GLOBTEX), version 1.0, by the
Science and Applications Branch, EROS Data Center, Sioux Falls,
South Dakota. Dr. R.D. Koster performed the analyses necessary to
assign parameter values to the soil map texture classes.
CREDIT AND DISCLAIMER
Hughes STX Corporation work performed under USGS contract
1434-92-C-40004. Any use of trade, product, or firm names is for
descriptive purposes only and does not imply endorsement by the U.S.
Government.
3. Introduction
3.1 Objective/Purpose.
Climate modelers need information on the water holding capacity of
global soils. The best source of this information is the Soil Map
of the World, which was produced by the Food and Agriculture
Organization (FAO) of the United Nations Educational, Scientific,
and Cultural Organization (UNESCO) in 10 volumes between 1970 and
1978. It provides the most detailed, globally consistent soil
data.
Because water holding capacity is not an explicit attribute of the
FAO soil map, the data on texture, slope, and depth that the soil
map does provide may be used as surrogates. The three data sets
described herein were derived, by various researchers, from
the FAO soil data. For climate modelers, a 1 degree by 1 degree
grid of latitude and longitude has been deemed adequate.
3.2 Summary of Parameters.
Soil texture is characterized here as either coarse,
medium/coarse, medium, fine/medium, fine, ice or organic. Soil
profile depth is an estimate of the depth from the soil surface to
bedrock or other impermeable layer. Slope is the surface slope,
as defined by the topography.
3.3 Discussion.
A) SOIL TEXTURE. The soil texture data file is based on the work
of Zobler (1986) and uses the indices listed in the table below to
identify the texture of the dominant soil type within each 1
degree X 1 degree grid square. The original FAO data provided,
for the dominant soil type in a soil unit, the designation
"coarse", "medium", "fine", or a combination of these based on the
relative amounts of clay, silt, and sand present in the top 30 cm
of soil. Zobler converted this data into a 1 degree X 1 degree
array.
Also listed in the table are some suggested, arbitrarily chosen
values (see caveat) for associated soil moisture transport
properties.
index soil texture n psi_s K_s b comments
----- ------------ ----- ----- ----- ----- --------
1 coarse 0.421 .0363 1.41E-5 4.26 Loamy sand values*
2 medium/coarse 0.434 .1413 5.23E-6 4.74 Sandy loam values*
3 medium 0.439 .3548 3.38E-6 5.25 Loam values*
4 fine/medium 0.404 .1349 4.45E-6 6.77 Sandy clay loam
values*
5 fine 0.465 .2630 2.45E-6 8.17 Clay loam values*
6 ice -- -- -- --
7 organic 0.439 .3548 3.38E-6 5.25 Loam values*
0 (ocean) -- -- -- --
where
n is the porosity (dimensionless),
psi_s is the matric potential at saturation (in m)
K_s is the saturated hydraulic conductivity (in m/s), and
b (dimensionless) is the slope of the retention curve on a logar-
ithmic graph, used to compute transport properties of subsaturated
soils.
* CAUTION: The assignment of loamy sand transport parameter values
to coarse soils does NOT imply that the "coarse" designation
implies a loamy sand in the USDA soil texture triangle (see
below). Similarly, a "medium/coarse" designation does not imply a
sandy loam, a "medium" designation does not imply a loam, and so
on. The mapping of transport parameter values to soil texture in
the table is highly arbitrary and technically incorrect. It is
provided solely as a suggestion for the typical large scale (GCM)
modeler, who could easily run into trouble if the "technically
correct" numbers were used.
The suggested reclassification in the table reflects the
inappropriateness of assigning hydraulic properties of soils as
measured in the laboratory to GCM soil columns that represent
extensive areas -- they tend to produce unrealistic resistance to
soil moisture diffusion. This is almost certainly due to the
inadequacy of current land surface models, which have very limited
treatments of subgrid soil moisture variability, and to the fact
that properties measured in the laboratory often do not describe
soil behavior in the field, which is strongly influenced by
spatial variability in texture, the presence of decayed root
systems, wormholes, etc. As a makeshift response to this problem,
a given soil type in the table above is arbitrarily assigned
transport parameter values for a coarser textured soil.
Determining the optimal parameter values for each type, which are
probably very different from those listed above, would require
much further research.
The values for the four transport parameters were obtained from
the study of Cosby et al. (1984), who analyzed an extensive and
diverse collection of soil samples.
B) SOIL PROFILE DEPTH. The soil profile thickness file was
derived by Webb et al. (1991, 1993) from information contained in
Volumes 2-10 of the FAO/UNESCO Soil Map of the World. First, the
Earth was divided into nine continental regions: North America,
Mexico/Central America, South America, Europe, Africa,
South-Central Asia, North Central Asia, Southeast Asia, and
Australia/South Asia. For each of these regions, the FAO records
were examined to determine the profile thickness for a
representative sample of every component soil type. When a
thickness was undefined for a soil type, an arbitrary thickness of
3.6 meters was assigned; presumably the bedrock is at a greater
depth than this. All soil elements of a given type within a given
continental region were then assumed to have the same profile
thickness. The thicknesses stored in the file's 1 degree X 1
degree array are the thicknesses for the dominant soil types
within the grid squares, as determined by Zobler (1986).
C) AVERAGE SLOPE. The average topographical slope for each 1
degree X 1 degree square was derived from data sets constructed by
the Science and Applications Branch of the EROS Data Center in
Sioux Falls, South Dakota. Unlike the soil texture and soil
profile thickness data, the average slope data reflects all of the
soil regimes in a square, not just the dominant one. The slope
estimates are crude, however, given the qualitative nature of the
original data. See Section 9.2.1 for details on the construction
of the data set.
4. Theory of Measurements
Textural classes reflect the relative proportions of clay (fraction less than
2 micrometers), silt (2-50 micrometers), and sand (50-2,000 micrometers) in
the soil. The texture of a soil horizon is one of its most permanent
characteristics. It is also a very important one because, in combination with
other properties, it influences soil structure, consistence, porosity, cation
exchange capacity, permeability and water holding capacity.
Three textural classes are recognized by the FAO Soil Map of the World:
1. Coarse textured: sands, loamy sands, and sandy loams with less
than 18 percent clay and more than 65 percent sand.
2. Medium textured: sandy loams, loams, sandy clay loams, silt loams,
silt, silty clay loams, and clay loams with less than 35 percent
clay and less than 65 percent sand; the sand fraction may be as
high as 82 percent if a minimum of 18 percent clay is present.
3. Fine textured: clays, silty clays, sandy clays, clay loams, and
silty clay loams with more than 35 percent clay.
The textural class refers to the texture of the upper 30 centimeters of
the soil, which is important for tillage and water retention. The maps
often state that a dominant soil type is composed of combinations of
these textural classes (e.g., coarse AND medium for a given soil).
100/\
/ \
90/ \10
/ \
80/ \20
/ \
/ \ 70/ \30
| / \
| 60/ \40
| / FINE \
Percent clay 50/ \50 Percent silt
/ \ |
40/ \60 |
/--------------------------\ |
30/ \70 \ /
/ \
20/ \80
/-------- MEDIUM \
10/ \ \10
/ COARSE \ \
----------------------------------------\
100 90 80 70 60 50 40 30 20 10
<--------------
Percent sand
To obtain the soil moisture transport parameters listed in the table in
Section 3.3, points corresponding to these textures or texture
combinations were located on the U.S. Dept. of Agriculture (1951, p.
209) textural triangle, a rough reproduction of which is shown below:
100/\
/ \
90/ \10
/ \
80/ \20
/ \
/ \ 70/ \30
| / \
| 60/ C \40
| / /\
Percent clay 50/\ / \50 Percent silt
/ \ / SiC\ |
40/ SC \____________/______\60 |
/______\ CL \ SiCL \ |
30/ SCL \___________\_______\70 \ /
/_________/ / \
20/_ \ L / SiL \80
/ \_ SL \ / \
10/\_ \_ \_____/ ______\90
/ S \ LS \_ / / Si \
/_____\_____\________/_________/_________\
100 90 80 70 60 50 40 30 20 10
<--------------
Percent sand
The soil textures identified in the figure are:
C: Clay
SC: Sandy clay
SiC: Silty clay
SCL: Sandy clay loam
CL: Clay loam
SiCL: Silty clay loam
S: Sand
LS: Loamy sand
SL: Sandy loam
L: Loam
SiL: Silt loam
Si: Silt
The points were then arbitrarily shifted toward coarser soils (see
Section 3.3), and transport parameters for the coarser soils were taken
from Cosby et al. (1984), who used the same triangle to differentiate
soil types.
Refer to the text published with the Soil Map of the World (FAO,
1970-78) for additional information on the methods of measurement. See
also Zobler (1986) and Webb et al. (1991, 1993) for more background on
the data used to determine soil texture and soil profile depth.
5. EQUIPMENT
5.1 Instrument Description.
Not applicable.
5.1.1 Platform.
Not applicable.
5.1.2 Mission Objectives.
Not applicable.
5.1.3 Key Variables.
Not applicable.
5.1.4 Principles of Operation.
Not applicable.
5.1.5 Instrument Measurement Geometry.
Not applicable.
5.1.6 Manufacturer of Instrument.
Not applicable.
5.2 Calibration.
Not applicable.
5.2.1 Specifications.
Not applicable.
5.2.1.1 Tolerance.
Not applicable.
5.2.2 Frequency of Calibration.
Not applicable.
5.2.3 Other Calibration Information.
Not applicable.
6. PROCEDURE
6.1 Data Acquisition Methods.
The original source maps are the FAO Soil Map of the World. The ESRI
digitized the data under contract to the United Nations Environment
Program (UNEP) and the FAO in 1984. The EROS Data Center obtained
the digital data from the ESRI in 1986 and constructed the data
sets that were later used to derive the global array of average
slope (see Section 9.2.1).
6.2 Spatial Characteristics.
The original source map had a scale of 1:5,000,000 (1 millimeter on the
map = 5 kilometers).
6.2.1 Spatial coverage.
The coverage is global. Data in each file are ordered from North
to South and from West to East beginning at 180 degrees West and
90 degrees North. Point (1,1) represents the grid cell centered
at 89.5 N and 179.5 W (see section 8.4).
6.2.2 Spatial Resolution.
The data are given in an equal-angle lat/long grid that has a
spatial resolution of 1 x 1 degree lat/long.
6.3 Temporal Characteristics.
6.3.1 Temporal coverage.
Soil survey and correlation work, primarily in the 1960's and
1970's.
6.3.2 Temporal resolution.
The soil map typically portrays time-invariant features.
7. OBSERVATIONS
7.1 Field Notes.
Not applicable. Field notes were used by the soil surveyors in
developing the original FAO Soil Map of the World and are reflected in
8. DATA DESCRIPTION
8.1 Table Definition.
Not applicable.
8.2 Type of Data.
-------------------------------------------------------------------------------
| 8.2.1 | | | |
|Parameter/Variable Name | | | |
-------------------------------------------------------------------------------
| 8.2.2 | 8.2.3 | 8.2.4 | 8.2.5 |
| Parameter/Variable Description | Range | Units | Source |
-------------------------------------------------------------------------------
|DOMTEX | | | |
| | Dominant soil texture index | min=0 |[Unit- | Zobler et |
| | | max=7 |less]* | al. (1986) |
| | | | | |
-------------------------------------------------------------------------------
|PROFDEP | | | |
| |Soil profile depth | min=0 | [cm] | Webb et |
| | | max=800 | | al. (1993) |
| | | | | |
-------------------------------------------------------------------------------
|AVGSLOPE | | | |
| |Average slope | min=10 | [%] | Manipulation|
| | | max=40 | | of EROS DATA|
| | | | | CENTER data |
| | | | | files (see |
| | | | | sect. 9.2.1)|
-------------------------------------------------------------------------------
* The values in the soil texture map are an index. See table in section 3.3
for description of each index value.
8.3 Sample Data Record.
See Section 8.4, Data Format.
8.4 Data Format.
The CD-ROM file format is ASCII, and consists of numerical fields of
varying length, which are space delimited and arranged in columns and
rows. Each column contains 180 numerical values and each row contain 360
numerical values.
Grid arrangement
ARRAY(I,J)
I = 1 IS CENTERED AT 179.5W
I INCREASES EASTWARD BY 1 DEGREE
J = 1 IS CENTERED AT 89.5N
J INCREASES SOUTHWARD BY 1 DEGREE
90N - | - - - | - - - | - - - | - -
| (1,1) | (2,1) | (3,1) |
89N - | - - - | - - - | - - - | - -
| (1,2) | (2,2) | (3,2) |
88N - | - - - | - - - | - - - | - -
| (1,3) | (2,3) | (3,3) |
87N - | - - - | - - - | - - - |
180W 179W 178W 177W
ARRAY(360,180)
8.5 Related Data Sets.
Digital data for the FAO Soil Map of the World are available from the
Land and Water Development Division, FAO, in Rome, Italy. The data set
does not include information on the components of the soil map units or
the slope and texture within components.
The EROS Data Center in Sioux Falls, South Dakota, has constructed
global arrays containing information on the subgrid distributions
of important soil properties. They first divided soil texture
into three categories (coarse, medium, and fine), soil depth into
three categories (shallow, limited and other), and slope into
three categories (0-8%, 8-30%, and >30%). A given soil element is
then described by one of 27 different combinations of texture,
depth, and slope. The EROS Data Center produced an array for each
of the 27 combinations, giving the percentage of each 1 degree X 1
degree square covered by the particular combination.
9. DATA MANIPULATIONS
9.1 Formulas.
9.1.1 Derivation techniques and algorithms.
Arc/Info software was used for most processing steps in the
construction of the slope data files generated by the EROS
Data Center (which were then used to construct the average
slopes), including projection from the bipolar oblique
conformal projection to geographic (latitude-longitude)
coordinates for the Americas. The remainder of the world
was projected from the Miller oblated stereographic
projection using software provided by Sprinsky (1992).
9.2 Data Processing Sequence.
9.2.1 Processing steps and data sets.
Data processing for the soil texture and profile depth
files are described by Zobler (1986) and Webb et al. (1991,
1993), respectively. The average slopes were generated by
some simple processing of data sets produced by the EROS
Data Center. These data sets provide, for each 1 degree X
1 degree square, the fractions f1, f2, and f3 of area
covered by "level to gently undulating" (0-8%), "rolling to
hilly" (8-30%) and "steeply dissected to mountainous"
(>30%) slopes, respectively. For the calculation of the
average slope, the 0-8% slope category was assigned a
typical slope of 4%, the 8-30% slope category was assigned
an average slope of 19%, and the >30% slope category was
assigned the arbitrary slope of 40%. The average slope was
then taken to be:
f1*4% + f2*19% + f3*40%
average slope = -------------------------
f1+f2+f3
9.2.2 Processing change.
Not applicable.
9.3 Calculations.
9.3.1 Special corrections and adjustments.
See Webb et al. (1991, 1993) for a discussion of the
decision rules they used to account for missing or
inadequate data in the construction of the soil profile
depth data set.
A few of the 1 degree X 1 degree squares that were assumed
by Zobler (1986), Webb et al. (1991, 1993), and/or the EROS
data center to be ocean squares are in fact listed as land
squares in the vegetation data sets provided on the CD-ROM,
and vice-versa. To correct this inconsistency, the soil
data files were modified to use the same land/sea mask as
the vegetation data files. Missing soil data for the "new
land squares" were estimated subjectively from neighboring
squares. Some of the "ice" squares in the original soil
data sets are considered "tundra" in the vegetation data
set; soil properties in these squares were similarly
redefined.
9.4 Graphs and Plots.
Not applicable.
10. ERRORS
10.1 Sources of Error.
The original FAO data represent a generalization of more detailed data,
which may be available in various countries, and which are in
turn a generalized representation of reality. As stated by
Zobler (1986), "about 11,000 maps were reviewed [to construct the
FAO Soil Map of the World]; they varied widely in reliability,
detail, precision, scales, methodologies, etc." As with any soil
map, some of the variability in the actual soils is not shown on
the map. Errors may have been introduced in the digitizing and
map projection process.
The soil texture and profile depth files contain data for the
dominant soil type in each 1 degree X 1 degree square and thus
ignore contributions from potentially significant secondary
components. The profile depths are generally based on depths
measured for an equivalent soil elsewhere on the continent;
depths are not actually measured at each square. For further
discussion of the limitations of these data sets, see Zobler
(1986) and Webb et al. (1991, 1993). (The latter
note, for example, that "in many cases, the soil profile
thicknesses represent minimum possible values because profile
descriptions do not always extend to subsurface bedrock".) An
obvious source of error in the average slope file is the
arbitrary choice of 40% to represent all steep slopes, when all
that is known is that they exceed 30%. Also, for the files used
to compute the average slopes, assumptions were made on the
percentage composition of the components. The vector data sets
were gridded as separate data sets, and the data sets were merged
in grid form. Some overlaps between data sets were removed
manually.
10.2 Quality Assessment.
10.2.1 Data validation by source.
Not applicable.
10.2.2 Confidence level and accuracy judgment.
Some measure of reliability was provided for the original
FAO source maps, but these measures were not considered
when constructing the soil texture, depth, and slope
files, and corresponding arrays of reliability estimates
are not available. The accuracy of the data is, of
course, severely limited by the errors outlined in
Section 10.1.
10.2.3 Measurement error for parameters and variables.
The published FAO Soil Map of the World contains inset
maps showing three categories of reliability for the
source data used to make the map. Those interested in
the reliability at a specific site should consult this
source; again, digitized global reliability estimates are
not available. Detailed soil surveys were performed only
over selected areas of each continent.
10.2.4 Additional quality assessment applied.
Not applicable.
11. NOTES
11.1 Known Problems with the Data.
The FAO Soil Map of the World is becoming out-of-date because of recent
soil surveys and new techniques for measurement and data handling. An
international effort to develop a replacement, the Soil and Terrain
(SOTER) digital data base of the world, is under development by the
International Society of Soil Science, the International Soil Reference
and Information Center, the FAO, and the UNEP.
11.2 Usage Guidance.
The three soil data files are provided mainly for use in defining
land surface properties for general circulation model (GCM)
applications. Many land surface models coupled to GCMs require
estimates of soil profile depth, surface slope, and soil moisture
transport properties (as obtained from soil texture) for their
runoff, soil moisture storage, and drainage parameterizations.
Inherent in the data are large-scale spatial variations in the
soil properties, which presumably are realistic even if values at
various grid squares are inaccurate. This large-scale structure
can be important for defining GCM climate.
Given that climate modelers are the expected users of the data,
the danger of using the data for other applications must be
stressed. Extracting a soil texture, slope, or soil profile
depth from the files for a specific small-scale region (even a
region composed of numerous 1 degree X 1 degree squares) is
foolhardy without further research into the reliability of the
data in the region, as determined, for example, from the original
FAO Soil Map of the World. At some squares, the data is
undoubtedly unreliable. Even if the reliability were high, soil
texture and profile depth are provided only for the dominant soil
component of the 1 degree X 1 degree square, and thus the
appropriate values in a subgrid region of interest can easily be
missed. The moisture transport parameter values listed in the
table in Section 3.3 are undoubtedly inaccurate and are provided
ONLY to give climate modelers a consistent basis for performing
intercomparison studies.
The data can be spatially aggregated by averaging the values in
adjacent grid cells to create, for example, a 2x3 degree grid or a 3x5
degree grid. Although the grid cells are not equal area, and large
errors would be introduced if a cell at the equator were averaged with
a cell at the north pole, the errors from averaging adjacent cells will
be within the accuracy limits for the data set.
11.3 Other Relevant Information.
Not applicable.
12. REFERENCES
12.1 Data Processing Documentation.
Not applicable.
12.2 Journal Articles and Study Reports.
Cosby, B.J., G.M. Hornberger, R.B. Clapp, and T.R. Ginn, 1984. A
statistical exploration of the relationships of soil moisture
characteristics to the physical properties of soils, Water
Resources Research, 20:682-690.
Food and Agriculture Organization (FAO) of the United Nations, 1970-78,
Soil map of the world, scale 1:5,000,000, volumes I- X: United
Nations Educational, Scientific, and Cultural Organization, Paris.
Sprinsky, William H., 1992. The inverse solution for the Miller oblated
stereographic projection: Presented at the 27th International
Geographical Congress, Washington, D.C.
U.S. Dept. of Agriculture, 1951. Soil Survey Manual. U.S. Dept. of
Agriculture Agricultural Handbook, 18, 503pp.
Webb, R.S., C.E. Rosenzweig, and E.R. Levine, 1991. A global
data set of soil particle size properties, NASA Tech. Memo. 4286,
NASA, 34pp.
Webb, R.S., C.E. Rosenzweig, and E.R. Levine, 1993. Specifying
land surface characteristics in general circulation models: soil
profile data set and derived water-holding capacities, Global
Biogeochemical Cycles, 7:97-108.
Zobler, L., 1986. A world soil file for global climate modeling. NASA
Tech. Memo. 87802, NASA, 33pp.
Zobler, Leonard, 1987. A world soil hydrology file for global climate
modeling: International Geographic Information Systems Symposium:
The Research Agenda, November 15-18, 1987, Arlington, Virginia,
Proceedings. 1:229-244.
12.3 Archive/DBMS Usage Documentation.
Contact the EOS Distributed Active Archive Center (DAAC) at NASA Goddard
Space Flight Center (GSFC), Greenbelt Maryland (see Section 13 below).
Documentation about using the archive or information about access to the
on-line information system is available through the GSFC DAAC User
Services Office.
13. DATA ACCESS
13.1 Contacts for Archive/Data Access Information.
GSFC DAAC User Services
NASA/Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Phone: (301) 286-3209
Fax: (301) 286-1775
Internet: [email protected]
13.2 Archive Identification.
Goddard Distributed Active Archive Center
NASA Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Telephone: (301) 286-3209
FAX: (301) 286-1775
Internet: [email protected]
13.3 Procedures for Obtaining Data.
Users may place requests by accessing the on-line system, by sending
letters, electronic mail, FAX, telephone, or personal visit.
Accessing the GSFC DAAC Online System:
The GSFC DAAC Information Management System (IMS) allows users to
ordering data sets stored on-line. The system is open to the public.
Access Instructions:
Node name: daac.gsfc.nasa.gov
Node number: 192.107.190.139
Login example: telnet daac.gsfc.nasa.gov
Username: daacims
password: gsfcdaac
You will be asked to register your name and address during your first
session.
Ordering CD-ROMs:
To order CD-ROMs (available through the Goddard DAAC) users should
contact the Goddard DAAC User Support Office (see section 13.2).
13.4 GSFC DAAC Status/Plans.
The ISLSCP Initiative I CD-ROM is available from the Goddard DAAC.
14. OUTPUT PRODUCTS AND AVAILABILITY
14.1 Tape Products.
None.
14.2 Film Products.
None
14.3 Other Products.
There are several ways in which these data can be made available to
users. One alternative assigns a unique identifier to each one degree
cell and uses a relational data base management system to keep track of
all of the attribute information. This format is appropriate for
researchers with a geographic information system that is linked to a
relational data base management system, especially if additional
interpretations are needed of the 106 FAO soil types.
15. GLOSSARY OF ACRONYMS
CD-ROM Compact Disk--Read Only Memory
DAAC Distributed Active Archive Center
DBMS Data Base Management System
ECS EOS-DIS Core System
EOS Earth Observing System
EOS-DIS EOS Data and Information System
EROS Earth Resources Observation Systems
ESRI Environmental Systems Research Institute, Inc.
FAO Food and Agriculture Organization of the United Nations
FTS Data set name prefix: Fao soil type, Texture, and Slope
GCM General Circulation Model of the atmosphere
GLOBTEX GLOBal soil TEXture and slope data set
GSFC Goddard Space Flight Center
IDS Inter disciplinary Science
ISLSCP International Satellite Land Surface Climotology Project
NASA National Aeronautics and Space Administration
SOTER SOil and TERrain digital data base of the world
UNEP United Nations Environment Program
UNESCO United Nations Educational, Scientific, and Cultural
Organization