WORLD DATA CENTER A 

for 

Solid Earth Geophysics 

Global Change Data Base 

Pilot (Diskette) Project for Africa 

Data Base Documentation 

Version 1.1 

World Data Center A for Solid Earth Geophysics Publication SE-47 

June 1992 

NATIONAL GEOPHYSICAL DATA CENTER 
WORLD DATA CENTER A 

for 

Solid Earth Geophysics 

Global Change Data Base 

Pilot (Diskette) Project for Africa 

Data Base Documentation 

Version 1.1 

JUNE 1992 

UNITED STATES DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration 

NATIONAL ENVIRONMENTAL SATELLITE, DATA, AND INFORMATION SERVICE 

National Geophysical Data Center 

Boulder, Colorado 80303-3328,U.S.A. 
TRADEMARK ACKNOWLEDGEMENTS 

Mention of a commercial company or product in this document does not imply endorsement by 

the U.S. government, any of its agencies, the International Geosphere-Biosphere Program, or any 

sponsor or participant of the Global Change Data Base, for which this document was produced. 

Use for publicity or advertising purposes of information from this publication concerning 

proprietary products or the tests of such products is not authorized. 

In lieu of placing a trademark symbol in every occurrence of a trademarked name, we state that 

we are using the names only in an editorial fashion with no intention of infringement of the 

trademark. Trademark information indicated below was derived from various sources and are 

accurate to the best of our knowledge. 

IBM is a registered trademark and PS/2, VGA, and ATare trademarks of International Business Machines 

Corporation. 

Idrisi is a registered trademark of Clark University. 

PWAK and PKUNPAK are registered trademarks of Pkware, Inc. 

DISCLAIMER 

While every effort has been made to ensure that the data accompanying this documentation, as 

well as the documentation itself, are accurate and reliable within the limits of the current state 

of the art, NOAA cannot assume liability for any damages caused by inaccuracies in the data or 

documentation, or as a result of the failure of the data or software to function on a particular 

system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution 

constitute a warranty. 

The user must be cautious when using these data and computer programs. Because this is an 

experimental database constructed from existing sources, errors and omissions can be expected. 

The user is referred topeerevaluations of this product and subsequent revisions for determination 

of suitability for any given purpose. 

Global Change Data Base Afiica Documentation page ii 
PREFACE 

Version 0.2 of this documentation and associated data base was distributed to beta-test facilities 

for evaluation and further development in July 1990. Version 1 .O of the documentation was a 

special edition for limited use associated with the United Nations Environment Program and 

United Nations Institute of Training and Research, and the International Geosphere-Biosphere 

Programme's workshop held in Dakar, Senegal, in 1990. This Version 1.1 is a slight revision of 

Version 1.0, and intended for a general audience. 

We anticipate that most users of this documentation will be educators, or people learning about 

the global environment with the data and the Training Exercise Manual distributed by NGDC. 

If you do not know about the data or the Training Exercise Manual, please contact NGDC formore 

information. 

It is likely that the documentation for the full Global Change Data Base will be updated more 

frequently than this set of documentation for Africa. If you wish to keep current with the 

development of global environmental (or global change) data, please contact NGDC to be placed 

on the mailing list for the full Global Change Data Base series. 

Any comments, suggestions, or possible contributions of data are welcome. Please contact: 

Global Change Database Program 

World Data Center-A for Solid Earth Geophysics 

NOAA National Geophysical Data Center, Code E/GCl 

325 Broadway 

Boulder, Colorado 80303 

Telephone: 303-497-6125 

Fax: 303-497-65 13 

Telex: 59281 1 NOAA MASC BDR 

Internet: info@mail.ngdc.noaa.gov 

This documentation contains separate chapters of material edited about separate data sets of the 

overall Global Change Data Base, Pilot (Diskette) Project for Africa. Descriptions of data 

formats, authors, and contributors are accompanied by documentation of the developmenthe of 

the data. These materials have been suggested and contributed directly by the authors of the 

individual data seis. To avoid confusion, some information contained in this documentation that 

is not directly relevant to this data base has been either omitted, crossed out, or blanked out. 

This documentation manual contains copies of several copyrighted scientific publications. 

Written permission has been obtained from the authors and publisher for us to distribute these 

Global Change Data Base Afiira Documentation page iii 
materials. In most cases, we have not obtained complete copyright release for USERS of this 

documentation to make further copies. All other parts of the manual (those parts prepared by our 

staff) are in the public domain and may be freely copied. If you have questions about copyright 

releases, please contact us at 303-497-6125. 

Innumerable people have contributed to the development of this documentation. In addition to 

the authors and contributors of data, we would like to especially note the efforts of John Kineman, 

Jeff Colby, Mark Ohrenschall, David Schoolcraft, David Hastings, Susan Boyle, Andra Mealey, 

Silvanna Della Mana, and David Mellon. 

Note on the Quality of Reproduction of This Volume 

This volume is a collection of overview materials prepared at NGDC, and copies of documents 

produced by many publishers, laboratories, and individual scientists. 

Each section begins with a summary in a common format developed at NGDC. After the 

summary, each section contains materials provided (or recommended) by the creator of the data 

set concerned. The materials are considered the most useful, concise discussion of the creation 

and/or use of the data. 

As these materials come from diverse sources, they have a variety of formats. This shows the 

diversity of approaches (and implementations) of documentation currently used. We apologize 

for the lack of uniformity of these materials, and for the imperfect reproduction of some 

material. 

This document should be referenced as: 

WDC-A, 1992. Global Change Data Base Pilot (Diskette) Project for Africa; Data Base 

Documentation, Version I .I. Publication SE-47. World Data Center-A for Solid Earth 

Geophysics, NOAA National Geophysical Data Center, Boulder, Colorado. 337 pp. 

Global ChangeData Base Africa Documentation page iv 
TABLE OF CONTENTS 

... Preface .................................................................................................................................... rn 

Introduction ............................................................................................................................ 1 

Background ........................................................................................................................ 1 

Getting Started .................................................................................................................... 3 

The Database ...................................................................................................................... 5 

Data Set Documentation ..................................................................................................... 9 

Directory of Data Files on the Floppy Diskettes .............................................................. 13 

Integrated Global Environmental (Global Change) Data Activities 

Software Guide: Browse and Visualization Modules ..................................................... 23 

Support Researchers and Educators: Review Paper .................................................. 19 

Appendices 

A . NOAA Monthly 10-Minute Normalized Difference Vegetation 

Index (April 1985 . December 1988) ....................................................................... A-l 

B . Olson World Ecosystems (WE 1.4) ......................................................................... B-1 

C . Holdridge Life Zones ............................................................................................... C-1 

D . Matthews Vegetation. Land Use. and Seasonal Albedo .......................................... D-l 

E . Wilson and Henderson-Sellers Land Cover and Soils 

for Global Circulation Modeling .............................................................................. E- 1 

F . Zobler Soil Type. Soil Texture. Surface Slope. and Other Properties ...................... F-1 

G . FA0 Soil Units ........................................................................................................ G-1 

H . UNEP/FAO GRID Africa GIS Data Base ............................................................... H-1 

I . Legates and Willmott Average Monthly Air Temperature and Precipitation ............ 1-1 

J . Revised FNOC Elevation. Terrain. and Surface Characteristics ............................... J-1 

K . Integrated Elevation and Bathymetry ....................................................................... K-1 

L . Micro World Data Bank 11: Coastlines. Country Boundaries. 

Islands. Lakes. and Rivers ........................................................................................ 1-1 

M . World Data Bank n: Country Regions and Rivers ................................................ M-1 

N . The World Data Center System ............................................................................... N-l 

Global Change Data Base Africa Documentation Page " 
GLOBAL, CHANGE DATA BASE 

Pilot (Diskette) Project for Africa 

Data Set Documentation 

INTRODUCTION 

I. BACKGROUND 

The Data Management & Information Systems Working Group of the International 

Geosphere-Biosphere Program (IGBP) in concert with the ICSU Panel on World Data Centres 

developed an action plan that was approved by the IGBP Scientific Advisory Council at its 

October 1988 meeting in Stockholm, Sweden. A key element of this plan was the creation of a 

Pilot Data Assembly Project for land surface data. The first phase of this project was the 

assembly on floppy diskettes of a monthly vegetation index for a recent three and a half year 

period and other thematic data for the continent of Africa. Vegetation indices, produced by 

NOAA from its Polar Orbiting satellites, have been composited and re-sampled to create monthly 

values on a 10 arc-minute geographic grid (approximately 18km at the equator). Complementary 

thematic data have been included on compatible grids, as available, and all data sets are registered 

to a common origin and projection (latitude-longitude). The database was structured to be 

compatible with an existing Geographic Information System (GIs), so that a complete analytical 

package could be provided for experimentation, evaluation, and further development. 

The World Data Center-A (WDC-A) €or Solid Earth Geophysics, operated by the US. 

National Geophysical Data Center (NGDC) in Boulder, Colorado, produced this database and is 

leading its implementation in cooperation with the IGBP and ICSU Panel on World Data Centers. 

The software (known as Idrisi) was developed and adapted for this project at Clark University. 

The National Center for Geographic Information and Analysis (NCGIA) in Santa Barbara, 

California, has recently joined the project to assist with training and evaluation. The data 

diskettes and GIS package are being used in a series of workshops as part of GIS training sessions 

organized by the United Nations Environment Program (UNEP) Global Resource Information 

Database (GRID) and United Nations Institute for Training and Research (UNITAR). The first 

workshop was held in 1989 in Accra, Ghana, the second in 1990 in Dakar, Senegal. These UN 

training sessions will serve as the means to distribute the database and establish operational 

capabilities within African countries. The database is also being distributed, together with analysis 

Software, to various research and test facilities worldwide to establish a network of scientists that 

will serve to guide applications, enhancements, and communication among users. The data are 

provided by NGDC/WDC-A on ten diskettes in a compressed data format (with extraction 

software included). The GIS package is distributed and maintained separately by Clark 

University. The complete integrated global and regional databases are also available from NGDC, 

although individual archive data sets should be obtained from sources indicated in this 

Global Change Data Base Africa Documentalion Infroduction 1 
documentation. This Pilot Project for Africa is a prototype for other regional and global projects. 

Beta Test and Research Development facilities have been selected to initiate a process of peer 

review and testing for this and subsequent updates. The test network may expand with 

subsequent releases; hopefully stimulating experimentation, new applications, and the 

development of methodological conventions. Meanwhile, selected sites will evaluate the first 

releases of this database for scientific usefulness, technical accuracy, and usability with commonly 

available software. A long-range goal of this effort is to support various kinds of model building, 

including predictive and diagnostic indicies, hypotheses testing, risk models, classifications, and 

interfaces to process modeling. It is extremely important to determine specifications for future 

releases. Equally necessary are preliminary results which can be obtained from the data in their 

present form that can serve for training purposes, and which can be used to test the mechanism 

for establishing local validation studies. 

Selection criteria: 

The choice of data sets was determined mostly by availability, within a range of resolutions 

which seemed suitable for questions of environmental and ecological change at the continental to 

global level; and on time scales from years to decades or centuries. Consideration of spatial and 

temporal scale and associated resolutions is essential because patterns that can be represented at 

one scale may not be directly relatable to another; and furthermore resolution of phenomena and 

patterns of interest must be matched with appropriate sampling (in both time and space). 

The project has been based on the idea that the resolutions afforded by global AVHRR data 

would be well-suited for studying a particular class of phenomena and patterns that falls in a 

"medium resolution" between global modeling (typically on 2-5 degree grids), and resource 

studies (typically at 1-km to 10-meter resolutions). A nominal 10 arc-minute scale was chosen to 

provide compatibility with other scales and because this corresponds closely with the resolution of 

global AVHRR coverage. Other data sets, however, were not available at this resolution, so in 

order to include these additional variables, and to provide some experimentation with combining 

scales, it was decided to incorporate source data from three spatial resolutions--10 arc-minute 

(raster grid), 112 to 1 degree (raster grid), and country units (vector polygons and rasterized 

units). The latter may provide some links to existing country databases and international 

statis tics. 

It is also worth noting that linkage between the resolutions indicated here and other projects 

dealing with higher resolutions is an important research area. Direct comparison of data at 

different scales can be useful for quality control and developing classification hierarchies. 

Furthermore, interaction at the level of analysis or modeling at different scales can have great 

scientific value by allowing intercomparison of predictions and studies of different kinds of 

phenomena. Future developments may allow resolutions down to 1 km to be included, 

depending on the availability of data with global or regional coverage. The volumes of data at 

this scale would naturally imply different distribution media, such as CD-ROM. On the other 

Global Change Data Base Africa Documentation Introduction 2 
hand, grids of up to 5-degree resolution may be desired for global modeling. One of the goals of 

this project is to experiment with linking studies at different scales. 

Normalized vegetation indices (NDVI) were selected as the basic data because they are 

sensitive indicators of green vegetation health and have been related to significant processes of 

climate variability and human activity. The period from April 1985 to December 1988 was 

selected because it corresponds to a consistent period in NOMS image compositing procedures. 

The period also covers a well-documented El Nino event which occurred between 1986 and 1987 

and which produced major global climatic perturbations. By focusing on this time frame, it might 

be possible to identify the nature and extent of the changes which resulted from El Nino. 

The Idrisi GIS/Analytical software package was chosen for this project because of its 

functionality, availability, adaptability, and relative low cost to developing countries. The 

software requires IBM or MS DOS PC machines, which are widely available and have a well 

established user base. 

II. GETTING STARTED 

System compatibility: 

The diskettes which accompany this documentation are written in IBM DOS compatible format, 

as either 5 1/4, 1.2 megabyte, or 3 1/2, 1.44 megabyte high-density diskettes. The data fdes are 

written in a compressed "archive" format (PKZIP) to conserve space. The un-compression 

software, PKUNZIP, is compiled for IBM DOS compatible machines. The database is also 

available un-compressed, on standard 9-track 1600 BPI ASCII tape. 

Data diskettes: 

Ten data diskettes are provided with this documentation. Before doing anything else, use the 

DOS DISKCOPY command (or some other method) to make a backup CODY of each diskette. 

Refer to your DOS manual for usage of the DISKCOPY command. As an extra margin of 

security, write-protect your backup diskettes. 

The entire database, when completely extracted from the archive, occupies 41,390,874 bytes of 

disk space. The vegetation data alone occupies 9,488,571 bytes, the climate data occupies 

21,900,854 bytes, and the other thematic data sets occupy 10,001,449 bytes. A standard IBM 

DOS disk volume of 32 megabytes will, therefore, not contain the entire database. In such cases 

the user will have to select portions of the data to load and work with at any given time. The 

following archives are provided on the floppy disks as indicated: 

Global Change Data Base Africa Documentation Introduction 3 
Disk #1: 

Disk #2: 

Disk #3: 

Disk #4: 

Disk #5: 

Disk #6: 

Disk #7: 

Disk #8: 

Disk #9: 

Disk #lo: 

AFDISKl.ZIP Monthly Vegetation Index; April 1985 through January 1985 

AFDISK2.ZIP Monthly Vegetation Index; February 1985 through October 1986 

AFDISK3.ZIP Monthly Vegetation Index; November 1986 through June 1987 

AFDISK4.ZIP Monthly Vegetation Index; July 1987 through January 1988 

AFDISK5.ZIP Monthly Vegetation Index; February 1988 through September 1988 

AFDISK6.ZIP Monthly Vegetation Index; October 1988 through December 1988 

and Decadal Average Monthly Climate Errors; 12 months 

AFDISK7.ZIP Decadal Average Monthly Temperature; 12 months 

AFDISK8.ZIP Decadal Average Monthly Measured Precipitation; 12 months 

AFDISK9.ZIP Decadal Average Monthly Corrected Precipitation; 12 months 

AFDISKl0.ZIP Thematic and Vector (linework) Reference Data 

Each diskette contains a single data file (two in the case of diskette #6) in an archive (ZIP) 

format. This archive can onlv be a ccessed usin? the PKUNZIP.EXE program provided o n the 

diskette. The PKUNZIP program is self-documenting (simply make the floppy drive your default 

drive, and type 'PKUNZIP' at the DOS prompt to obtain instructions). However, the following 

examples may be helpful: 

To set up the default floppy drive, insert the floppy disk into one of the floppy disk 

drives, close the door, and type 'A:' or 'B:' depending on which disk drive you have 

inserted the floppy. You may type 'DIR to list the archive filenames on your computer 

screen (or simply note the archive name from the diskette label). 

To view the list of files in the archive, type 'PKUNZIP -VI followed by a space, then the 

archive name (with or without the .ZIP extension), for example: 'PKUNZIP -V 

AFDISKI'. 

To extract all files from the archive to a fixed (hard) disk, type 'PKUNZIP' followed 

by a space, then archive name followed by a space, then the destination data path, for 

example: 'PKUNZIP AFDISKl D:Diskette\Africa'. You must first create your 

destination directory using the DOS 'MD' command (refer to your DOS manual). 

To extract a specified data set from the archive to a fixed (hard) disk, type 

'PKUNZIP' followed by a space, then the archive name followed by a space, then the 

destination data path followed by a space, then the data set name followed by a period and 

asterisk; for example: 'PKUNZIP AFDISKl D:\DISKETTE\AFRICA AFAPR85."'. This 

will extract files, the data file and its text (ASCII) documentation file. 

As you become more familiar with PKUNZIP, you will be able to do more sophisticated search 

and/or retrievals. The ability to retrieve less than the entire archive may be important to you if 

your computer does not have a large disk. 

Global Change Data Base Africa Documentation Introduction 4 
JII. THE DATABASE 

Africa Window and Projection: 

The database consists of a collection of raster (cell) and vector (point, line or polygon) data files 

which have been "windowed" from global files specifically for the IGBP Pilot Project for Africa. 

AU but the UNEP/GRID Africa data sets have archive versions that are global in coverage. The 

original UNEP/GRID Africa data are available only for Africa. All data are provided in a 

geographic (LongitudeLatitude) projection (Le., "unprojected"). The geographic window that 

was choosen contains the entire African mainland and has the following edge coordinates: 

Western Edge: 20-deg. West Longitude 

Eastern Edge: 60-deg. East Longitude 

Northern Edge: 38-deg. North Latitude 

Southern Edge: 35-deg. South Latitude 

Raster Grid (Image) Files: 

The raster data are on a uniform and commonly registered grid with 10 arc-minute spacing (i.e., 

1/6 degree grid). Where source data were provided on coarser grids (up to 1 degree) they have 

been expanded to the standard 10 minute grid for uniformity. This allows the database to be used 

h overlay operations without resampling in systemc: that are unable to mix variable grid spacings. 

There is a direct correlation, however, between the source data and the 10 minute "expanded" 

version supplied with this database (the original data set can be reproduced by contracting the 10 

minute data set to its original grid spacing). Where source data were of finer resolution than 10- 

minutes, or in the case of fractional grid dimensions (such as the NDVI, which was supplied on an 

8.6 minute grid), resampling to 10 minutes was performed by simple averaging (considering the 

percentage of overlap of 10 minute cells with the originals), or grid sampling (in the case of 

classed data). 

The geographic window for Africa is exactly fitted with an array of 10 minute cells that has its 

edges coinciding with the window coordinates. The cell sequencing and registration is shown 

below: 

Global Change Data Base Africa Documenfalion lnrroduction 5 
Western Edge: 20-deg. West Longitude 

Northern Edge: 38-deg. North Latitude 

Southern Edge: 35-deg. South Latitude 

Since the common pixel size is 10-minutes in 

latitude and longitude, the resulting Africa grid 

size is: 

Eastern Edge: 60-deg, East Longitude 

480 Columns 

438 Rows 

In the archived global grids, which have their lefthand-edge at the International Date Line (i.e. 180 

Start column: 960 

Startrow: 312 

End column: 1439 

End row: 749 

Pixel sequenceing 

- (480 Columns) - 

__--____---_________--- 

I 0 I 1 I 2 I 3 1...14791 

I I---+---+---+---+--- +--- I 

(438 I480 I481 I482 I . . . I . . . I . . . I 

rows) I ---+---+---+---+--- +--- I 

II I I I I I I 

I---+---+---+---+--- +--- I 

I I I I I I I 

total - 

________-_-____-____--- - 

2 10 I 2 4 0 

All of the Africa grid data files conform to the above registration and grid dimensions. Each 

raster data file is identified with an 8-letter name followed by a ".img" file extension. 

Raster Documentation Files: 

Each data set is accompanied by an ASCII documentation file which contains information 

necessary for use of the data set in a Geographic Information System or other software. The 

following shows an example of a documentation file, with explanations in brackets. 

Global Change Data Base Africa Documentation Introduction 6 
image title : UNESCO/FAO Soil Classes 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : 0.0000000000E+00 

maximum : 1.3300000000E+02 

cell x : O.l666666667E+OO 

cell y : O.l666666667E+OO 

legend : 133 

category 1 :A Acrisols 

category 2 : Af 

category 3 : Ag 

..... etc. 

Ferric Acrisols 

Gleyic Acrisols 

{Name of the data set] 

{byte, integer, or real] 

{ ascii, binary, or packed binary] 

{number of rows} 

{number of columns] 

{minimum data value in the 

{maximum data value in the 

{cell size = 1/6 degree} 

{cell size = 1/6 degree} 

{number of legend categories 1 

{legends .... 

dataset} 

dataset] 

NOTES: 

(1) The grid (image) data and documentation files are compatible with the Idrisi GIS format. 

(2) The current version of the ASCII documentation file does not allow the specification of 

category 0 in the legends. Legend information for category 0 is, however, included in this 

documentation manual. 

Vector Data Files: 

Vector data are provided in the following format: 

ID# #Points - YYYY - YYYY ... etc. 

00 

where ID## is a feature identification number, #Points is the number of coordinate pairs that follow 

to define the feature, xxxx is the longitude (X) coordinate and yyyy is the latitude (Y) coordinate. 

The last line of each vector file consists of two zeros, separated by one or more spaces. 

Coordinates are all referenced to the Greenwich meridian with positive values indicating East and 

Global Change Data Base Africa Documentation Introduction 7 
negative values indicating West; and to the equator with positive values indicating North and 

negative values indicating South. In this format, point data files would have only one coordinate 

pair defining each feature (point), line data files can have up to 999 coordinate pairs for each 

feature (unlimitted number of features), and are open ended, and polygons; and polygon data files 

are similar to line data files except that each feature is closed (i.e., the starting point and ending 

point are identical). The units for coordinate values used in the vector files are defined in their 

corresponding documentation files. 

Vector Documentation Files: 

title : Africa Coastlines 

data type : integer 

file type : ascii 

object type : line 

coord. span : 1.6667000000E-02 

coord. unit : deg 

min X : - 1.2000000000E+03 

max X : 3.6000000000E+03 

min Y : -2.1000000000E+03 

max Y : 2.2800000000E+03 

{Name of the data set} 

{byte, integer, or real} 

{ascii or binary} 

{point, line or polygon} 

{coordinate span = 1/60 degree] 

{units of measure = degrees} 

{minimum x value} 

{maximum x value} 

{minimum y value} 

{maximum y value) 

NOTES: 

(1) The minimum bounding rectangle (MBR, indicated by the last four fields) has been set to 

values which correspond to the Africa Pilot Project window described above (not the 

actual range of values in the file). Therefore, by matching the MBR of the vector files 

with the edges of the raster fdes, a correctly registered overlay can be obtained, regardless 

of the units used in the vector file. 

(2) The vector data and documentation files are compatible with the Idrisi GIs. 

Data storage formats: 

Raster data files are stored using either byte binary or integer binary numbers. Byte data are 

coded in 8 binary bits, thus allowing numbers from 0 to 255 (decimal) to be stored. Integer data 

are coded as 16 binary bits (or two bytes). Since one bit is used for the algebraic sign, numbers 

from -32,768 to +32,768 (decimal) can be stored as integer data. The order of the the two bits 

used for integer data conforms to conventions used by IBM and DEC. (Note that some other 

computer systems may require integers to be "byte-swapped," Le. the order of the two bytes must 

Global Change Data Base Africa Documentation Introduction 8 
be reversed with a specially written program). 

database. The data type for each data file in indicated in its associated documentation file. 

Binary real data types are not used in this 

Vector data files and both vector and raster documentation files are stored using ASCII 

characters, requiring one byte for each character. The ASCII format uses the IBM DOS 

convention for terminating a line with two bytes, a carriage return (hexidecimal OD) followed by a 

line feed (hexidecimal OA). ASCII files may or may not be ended with an end-of-file mark 

(hexidecimal 1F). 

IV. DATA SET DOCUMENTATION 

Summary Information: 

Each data set has its own section in the documentation. These sections include all relevant 

information provided by the contributor or other sources. At the begining of each section there is 

a brief summary of information about the origin of the data set. 

The following information categories refer to the original and complete version of the data set 

6.e. the archived version before creating regional sub-sets or processing into a uniform grid and 

projection). This information may be critical when considering the reliability and potential 

application of the data set. 

NAME of the archive (i.e., referenced) version of the data set (at top of each page). 

CITATION: 

DATE: 

SOURCE: 

CONTRIBUTOR: 

CENTER: 

Recommended way of citing the data set in official references (when 

referring to the present or other regional sub-set of the data, it is 

recommended to use the same citation, with appropriate descriptions of the 

geographic area extracted). 

Date of production or most recent update of the data set content (not 

format) . 

Development and processing history of the data set. 

Person (or institution) who contributed the data for this project. 

Data center(s) with official responsibility for distribution of the individual 

data set. A list (below) provides information for each of the centers 

referenced in this field. 

Global Change Data Base Africa Documentution Introduction 9 
GEOGRAPHIC 

SAMPLING: The original spatial interval or scale of the data and the type of statistic (i.e. 

vector point, line, or polygon: grid sample, average, mode, etc.). Since all 

data sets in this database are provided on uniform 10-minute grids (or in 

vector form), this field indicates whether or not re-gridding was necessary, 

and from what source. 

COVERAGE: The full geographical area covered by the data set. 

PROJECTION: The original geographic projection of the source data set. Since aU data 

sets in this database are provided in geographic (longitude/latitude) 

projection, this field indicates whether or not re-projection was necessary, 

and from which source projection. 

TIME SAMPLING: The original time interval or resolution of data set and the type of statistic 

(Le. "snapshot," running average, typical or average period, etc.). 

REFERENCES: Literature references for any publications that are relevant to the use of the 

data set. 

The following category refers only to the current Africa continental sub-set of the database, and is 

for reference to the diskettes which accompany this documentation: 

FILE NAMES: The actual names of the digital files included within the .ZIP archive files 

provided with this version of the database. 

Data Center Codes: 

In the summary pages described above, abreviations are used to idenQ centers from which 

source data may be obtained. These abreviations are defined below, with their addresses: 

CDIAC Carbon Dioxide Information Analysis Center 

Oak Ridge National Laboratory 

P.O. Box 2008 

Oak Ridge, TN 37831-6335, USA 

EDC EROS Data Center 

Global Change Data Base Africa Documentation Introduction 10 
US Geological Survey 

Sioux Falls, South Dakota 57 198, USA 

GRID/Geneva Global Resource Information Database 

Global Environment Monitoring System 

United Nations Environment Program 

6 Reu del la Gabell 

CH- 1227 Carouge 

Geneva, Switzerland 

GRID/Nairobi Global Resource Information Database 

Global Environment Monitoring System 

United Nations Environment Program 

P.O. Box 30552 

Nairobi, Kenya 

USA 

NCAR 

NCDC 

NGDC 

SDSD 

International Institute for Applied System Analysis 

A-2361 Laxenburg, Austria 

National Center for Atmospheric Research 

P.O. Box 3000 

Boulder, Colorado 80307, USA 

National Climatic Data Center 

NOAA, NESDIS 

Federal Building 

Ashville, NC 28801, USA 

National Geophysical Data Center 

National Oceanic and Atmospheric Administration 

325 Broadway E/GC 

Boulder, Colorado 80303, USA 

Satellite Data Services Division, 

NCDC 

NOAA, NESDIS 

World Weather Building, Room 100 

Washington, DC 20233, USA 

Global Change Data Base Africa Documentufion Introduction 11 
WDC World Data Centers 

International Council of Scientific Unions 

(see associated national data centers) 

For information, contact: 

Chairman, ICSU Panel on World Data Centers 

University Corporation for Atmospheric Research 

P.O. Box 3000 

Boulder, CO 80307-3000, USA 

Other organization acronyms: 

CIA 

DMA 

ESRI 

FA0 

NESDIS 

NOAA 

UNEP 

UNESCO 

USAF 

USN/FNOC 

USNOO 

Central Intelligence Agency, USA 

Defense Mapping Agency, USA 

Environmental Systems Research Institute, Inc., Redlands, California, USA 

United Nations Food and Agriculture Organization 

National Environmental Satellite Data and Information Service, USA 

National Oceanic and Atmospheric Administration, USA 

United Nations Environment Programme 

United Nations Education Scientific and Cultural Organization 

United States Air Force 

United States Navy, Fleet Numeric Oceanographic Center 

United States Naval Oceanographic Office, USA 

Global Change Data Base Africa Docurnenfation Introduction 12 
DIRECTORY OF DATA FILES ON THE FLOPPY DISKETTES 

Inventory of Data Files on Each Diskette 

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Global Change Data Base Africa Documentation Introduction 18 
(This paper is inpress: Proceedings of the European Conference on the 

International Space Year, 1992) 

INTEGRATED GLOBAL ENVIRONMENTAL (GLOBAL CHANGE) DATA ACIlVITIES 

SUPPORT RESEARCIIERS AND EDUCATORS 

David A. Hastings, David M. Clark, and John J. Kineman 

National Oceanic and Atmospheric Administration. National Geophysical Data Center, Boulder, CO 

ABSTRACTlRESUME 

The Global Change Data Base results from a cooperatively 

devrlnrd method of puhlishing peer-reviewed data designed 

for integrated multivariate spatial analysis. The GCDB with 

accompanying documentation and access software lie eiitireiy 

within the public domain. They m designed (I) to funher 

research, education, and overall awareness of the global en- vironment: (2) to provide beneficial feedback to enable the 

authors to enhance their own research and data development, 

and (3) to provide a mechanism for scientists to share and 

receive credit for the intellectual content and expression of 

their experimental designs, analyses, interpretations, and 

models of the global environment. The prime mediuiii for 

distribution is CD-ROM, though educational data sets are 

being prepared for distribution on floppy diskette. accompanied by a manual of exercises. 

Keywords: global change, data, integration, research. education. outreach. 

1. INTRODUCTION 

An increacing numher of laboratories and individual scientists 

have been developing digital spatial data sets to describe various aspects of the global environment. A cooperative effort 

to integrate many of these data has bcen progressing for SCVCral years, mtl is about to reach fruition in the first public 

release of NGDC's Global Change Data Be (GCDB; Ref. I) 

The integration effort has involved careful inspection of data 

for spatial registration, consistency between documentation 

and data content, and appropriateness for multivariate analysis and inodeling in image processing. spatial statistics, arid 

geographic information systems. 

TO reduce the hugeness of the task of data integration, the 

GCDB is starting with only global data sets. However. as it 

advances, interest is increasing for continental- and regionalscale data bases. Selected continental-scale data sets may 

Soon be added to the GCDB. 

This task may appear menial and boring. Indeed. there is 

much tedious inspection of data and documentation. HOWCV- er. the Global change Data Base is also M experiment UI integrating and improving access to individual data sets. When 

combined, these data constitute a fascinating view of (I) the 

Earth as a system, and (2) how we conduct science. 

2. OBJECTIVES 

why the need for an integrated data base? Many people 

currently concentraling on one or two parameters that attempt 

to describe certain aspects of the global envirorunent would 

like access to more data to work with. However. assembling 

such data is tedious. Even without rigorously acsuring cartogrnphic fidelity and scientific accountrhility, siniply fiiiditig 

and acquiring such data CM be time consuming. A single 

reference data set can thus be acquired more eruiiy, c~li be 

used as a focal point for comparisons, etc. In addition. resources needed to improve the geographic fit and scientific 

usefulness of such data can be expended once to a certain 

level, leaving the scientist or educator to move beyond this 

pin1 in whatever way she chooses. 

2.1 lntemation of Data for Multivariate Analysis 

Many existing environmentd data sets arc produced individually by specinlists in a particular field. These data OR often 

designed for a special application, with conipromises that inay 

not be optinial for other wrs. The data may also have clwacteristics (cartographic projection. legend categories, simpiifications) that may make them difficult to use as pan of an 

integrated multivariate study. 

For the past few years, the USA's National Oceanic and 

Atmospheric Administration has placed emphasis on "Eiutli 

Systems" proaches to science. NOAA's National Geophysical Data Xnter, and its co-located World Data Center - A. 

Boulder (Colorado) Centers. is emphasizing the development 

of integrated data sets for studying the Earth as a system. 

Many studies of the global environment emphasize only one 

or two types of data. For example. detailed studies are sometimes made of several weeks or yem of Global Vegetation 

Index (GVI) from NOAA's Advanced Very High Resolution 

Radiometer (AVHRR). perhaps comparing such data with 

past and predicted crop yields in the African Sahel. Such data 

can be internally consistent but slightly misregistercd to the 

globe (as one can stdl identlfy the West African coactline, for 

exnmple. in the imagery) without significantly affecting son= 

studies. However, (I) mmpts to relate the GVI tn vegetation 

type, soils, or topography (Ref. 2). or (2) attempts to "validate" production and processing of satellite imagery hy 

comparisons with ground-based data can be greatly assisted if 

the separate data sets are integrated into an overall data base 

designed for multivariate application. 

The Global Change Data Base is an attempt to address this 

issue of integration and quality assessment. Hastings and 

others (Ref. 3). Clark and others (Ref. 4) and Kiiieiiiaii and 

others (Ref. 5). discuss some aspects of this activity Ui greater 

detail than the present review. 

2.2 Peer-Reviewed "Publication" of Data 

Fundamental analytical scientific data arc often more widely 

Global Change Data Base Africa Documentation Introduction 19 
used. with liutidreds of users. than are typical scienttfic research pnpen, which may typically be cited by a few other 

aitthors. Yet fiiiancialnogistical/moral support for sharing 

data is often low. Why this problem, and how to solve it? 

The design and execution of experiments that collect (lala 

have frequently been recognized as intellc~t~al contributions 

only if the scientists involved produced publishable findings 

from ilie data. As such. sufficient acknowledgement may not 

have previously been made for the developincnt of certaiii 

pioneering attempts to characterize the global environment. 

Faced with such a reception, scientists have sometimes preferred to publish fuidings from data, rather than to thoroughly 

document the data and share them with the public. 

On the other hand. there has been considerahle discussion on 

what constitutes scientifically valid data on the environment. 

How are such data certified? Should conunittees be fonned 

to validate such data? 

One alternative is to adapt certain existing traditions of peer 

review and hardcopy publication to digital data. The Global 

Change Data Base is an attempt to support such thinking. 

In the GCDB. individual data sets arc considered as authored 

chapters in a symposium volume. Each chapter consists of 

data, basic docurnentation on file format and data development. written discussion of scientific applications of the data 

(often with technical writings on research findings). guidelines on characteristics of the data that might affect tiFw 

applications, and a bibliography of other writings on the data. 

?lie data are reviewed in-house at NOAAMGDC for geographic registration, accuracy and appropnateness of categorization. completeness of documentation. and ability to be 

used with other data in the GCDB. Many data sets have initial problems in these areas that may not have affected their 

original applications but which, if not corrected. would cause trouble when used in the multivariate spatial GCDB. Data 

and documentation arc corrected and/or enhanced for sonic 

dara sets, to improve their integration into tlie GCDB. 

llie (lata are then sent to several expens for their comments. 

These conmients are used to funher edit the data and documentation, for increased value to users of the data base. 

The Clohal Change Data Base has had two forms of peer 

review to date: (I ) The first prototype, an integrated collection of data for Africa. was reviewed by scientists on six 

contttients, as pan of the International Geospliere-Biospliere 

Programme‘s (IGBP’s) Global Change Database Project, Pilot 

Project for Africa. (2) A CD-ROM containing about SO0 

megahvtes of integrated global data was reviewed by about 

ISU Beta testen as part of a cooperative Global Ecosystcnis 

Datahasc project with the USA’s Environmental Protection 

Agency. Both of these data sets arc descrilrd in more drtnd 

below. 

Peer-reviewed data should not be considered as perfect. 

especially m these early Frages of describing the global environment. However, they have been determined by reviewers 

to be appropriate for cautious study by scientists who will 

read the tlocutncntaiion and use care antl judgment in their 

use of the (lata. As in hardcopy infonnation. we can expect 

pcoplc to find enhancements. alternatives. or additions to the 

GCDB. We invite anyone to contact us with coinnients. 

suggestions. or data for subsequent releases of the data base. 

2.3 Public-Domam. Accessible 

The Global Cliangc Data Base. as 1s the case for dl data (Itstrihutetl by NOAAMGDC, IS coinpletely in ihe public tlotnam lliere II no restriction on the distribution or use of the 

GCDB. otlier than comtnoti courtesy that the authors dnd 

publisher be referenced in any subsequent use 

NOAA/NGDC distributes its data in all appropriate meilia. 

Magnetic tape, floppy diskette, and Compact Disc Read-Udy 

Memory (CD-ROM) have been used for data contamed iii the 

GCDB. Because of the large volumes of data already in the 

GCDB. CD-ROM is considered the most practical (and most 

econoniical) fonn of distribution for the data. 

On-line tlistrihution of scientific data has been attractive to 

tiiaiiy scientists. With such systetns, a person can collect 

large quantities of data for the cost of a connection to a 

comniunications network. or for telephone hookup via 

modein to tlie source of the data. Currently. there arc no 

plans for such distribution for the Global Change Data Base 

other than during the review process. NOAAMGDC must 

recover its costs of managing the data, which places a nominal charge on data (with numerous exceptions, such as for 

contributors of data, researchers or cooperators in various 

programs, who get the data at no charge). Also, the size of 

the Global Change Data Base (over a gigabyte and growing 

rapidly) makes even high speed network transmission tiontrtvial. Also, networks are not yet fully accountable for data 

quality. which itiakes them inappropriate media for operatioiially transmitting the reviewed GCDB. 

The data m accompanied by software that helps tlie user to 

browse through and visualize the GCDB and select approprtate data for use. The current version of the software IS being 

enhanced and impletnented for multiple platforms. such as 

IBM-compatible computers. Apple Macmtosh computers. aid 

UNIX workstations. The data are immediately compatihle 

with several spatial analysis systems including GRASS-GIS 

(for UNIX; Ref. 6) and IDWSI (for DOS; Ref. 71. llie data 

have been easily imported into several more spatial a~ialysis 

systema. 

2.4 Suppon Research, Education. Awareness 

The previous paragraph notes that one can view several aspects of the glohal environment with the data and accomluiying software. However, the main purposes of tlie data hase 

are better served by using software of the user‘s choice for 

research. education. or puhlic awareness. For this purpose. 

the data are stored in a latitude-lonptiude utilieatlctl grit1 

format (with associated vector data) compatible with ingest 

into many software systems. 

In-house analysis at NOAAMGDC has already begun to 

roduce some research results (Ref. 2). Severid Beta testers 1 ave commented on their research activities with the data. 

The IGBP’s Glohal Cliange Database Project hac been conducted with the United Nations Environmctit Prograintile 

(UNEP) the United Nations Institute for Training and Research (UNITAR). la Sociedad de Espccialistas Latmoaniertcanos en Perccpcion Remota (SELPER, the Society of Liltit1 

American Remote Sensing Specialists) and otlier organizarions to conduct training and awareness workshops in Africa 

antl Latin America. One result nf this activity is a training 

manual designed for self-instruction and exploratioti of the 

global environment. focusing on Africa as an cxaiiiplc. 

NOAAMGDC lias enhanced the IGBP training workhook for 

puhlication in 1992. Tliere arc plans for additional educational materials, such as iiarratctl 35trun slide sets. ant1 perltap il 

vitleo. We invite commercial publishers to prodocc V~~IIKadded products from these materials, after the GCDB iiiaicrtals arc publicly released. 

2.5 Provide Feedback to Authors of Data Sets 

The integration. m-house antl external testing. and devclopnient of educational matends IS clone in conru1t;iiton with t11e 

authors of the data. even d the data have already been placed 

into the public dotnam No data set enters the GCDB without 

explicit approval of its author(s) for its unrestricted dstributton 

Global Change Data Base Africa Documentation Introduction 20 
We want the GCDB to he as useful to the authors of tlie datn 

as IO cus1omc~s of the data base. The authors IVC the ultimate 

authorities on their work. The authors are also pioneers in 

attempting to describe the environment. However, the GCDB 

offers feedback on attempts to integrate and use these with 

other data, and in documenting the data for users outside the 

authors' own specialties. Our analysis of spatial and thematic 

representations. intercomparison. and other research 011 tlic 

integration of the data offers back to tlie original authors 

information that might stimulate improvements in (and new applications of) the data 

2.6 A Is this a Prototype for Future Distribution 

Of Digital Data7 

Arc there elements of the GCDB that can be useful to others 

in the business of distributing daia? 

We hope so. Indeed. interchange of ideas between NOAAINGDC and the Canada Centre for Remote Sensing has been 

fruitful for the GCDB, as well as helping in the developmelit 

of CCRS' Global Change Encyclopedia for the International 

Space Year. 

Just as the hardcopy publishing industry has many producers 

of a wide variety of puhlications. we can predict niany different ~ources of digital data in the future. We hnpc that some of 

these sources will improve the technology of data quality, 

integration, rcview/cenfkation. docunicnration. and accessibility. 

3. CURRENT STATUS OF THE GCDB 

3.1 Current Contents 

The cumnt version of the GCDB includes the following data: 

tnpgraphy (Ref. R) 

distribution of population (Ref. 8) 

surface water coverage (Ref. 8) 

terram chnrnaeristics (Ref. 8) 

topography/bathymetry (Ref. 9) 

vegetation (Ref. 10) 

hd use (Ref. 10) 

seasonal albedo (Ref. 10) 

~OSystcms (Refs. 11. 12) 

soils (Refs. 13. 14) 

life zones (Ref. 15) 

temperature & precipitation (Refs. 16- 18) 

vector base map linework (Ref. 1Y) 

vegetation index from AVHRR (Refs 20-22) 

Mafly other data sets arc at various stages of incorporation 

mto the data base. Such data will be aildcd to the GCDB ils 

!hey proceed through acquisition. discussion with authors, 

West. quality control, integration. Beta Test peer review. and 

dwumentation. Although all of these data sets are globd. we 

plan IO incorporate significant regionill data sets at a later 

date: for exmple, where such data inight help stiinulate 

demand for (and later supply 00 global versions of such data. 

3.2 Availabdiy 

nic net:, Test of the IGBP Pilot (Diskette) Project for Africa was conducted in IYYO-IYYI. The Beta Test of the 

NOWPA Global Ecosystems Database was conducted. in 

conjunction with the USA's Environmental Protection 

Agency, in 1991-1992. 

me revised GCDB Pilot Project for Africa is slated for tlistribution 011 floppy diskette, with documentation and training 

workbook. sometime in 1992. Annotated slide sets inay 

follow larer in 1992. We invite anyone intercsted in producing edwcational materials for schools (or my other audience) 

to contact NGDC UI Boulder or NOM'S Educational Affairs 

Division in Washington, DC. 

The full Global Qiange Data Base IS slated for puhlication OII 

a series of CD-ROMs, also in 19Y2. A new Bees Test of' 

proposed additions to the GCDB will also be conducted iii 

192. 

We invite anyone interested in the data to contact 

NOMGDC for an information packet, and to be placed on 

the inailing list for the dam. 

We also encourage authors of global or regional data sets to 

contact us if they are interested in the concept of peerreviewed publication of such data. 

4. REFERENCES 

I. NOAA, 1992. Global Cliangr Dura Easr. DiRiral Dura 

wrh Dorumcnraflon. Boulder, Colorado, National Oceanic 

and Atmospheric Administration, National Geophysical Data 

Center. 

2. Hastings, David A,, and Liping Di. 1992. Modelirig of 

global churtgr plrriiomcna wirli GIS usrrig rhc Global Change 

Daru Bust. Remote Sensing of Environment, in review. , 

3. Hastings. David A.. Kineman, John J., and Clark, David D., 

1991, Dcvclopmrnr and applicarroii of global dara boscs: 

roitsidrrahle progress. bur morr rollaborurrorr iirrdt-d. International Journal of Geographical Momation Systenls. v. 5.. 

p~. 137-146. 

4. Qxk. David M., Hastings. David A,, and Kineman. John 

J., 1YY I. Global darabasrs arid rlrrir rntplicurioris for. GIS 

IN Maguire. David J., Goodchild. Michael F., and Rhiiid. 

David W.. eds., Geographical Information Systems: Overview, Principles and Ap licatioiis. Bumt Mill, Ersex, Unitrtl 

Kingdom, Longman. 42. pp. 217-231. 

5. Kineman, J. J., Clark, D. M.. and Croze, H., 1990. Dura 

iiirrgratioci uiid mmirlliiig for global rliarigc: Ail irirerriarroria1 exprrimmir. Proceedings of the International Conference 

rid Wcrrksliop on Glohd Natural Resource Monitoring aid 

Assessments: Preparing for the 2lst Century (Venice, Italv. 

24-30 September 1989). Bethesda, Maryland, American 

Society of Photogrammetry and Remote Sensmg. Vol. 2. pp. 

660-6G9 

6. CERL. 1991. Thc Geogruphic Resorrrres Arialys Sirpp~r r 

Svsrrnr (GRASS-GIS). Vrrsion 4.0. U. S. Army Corps of 

Engineers, Constfuctioii Engineering Research Laborarory, 

Champaign, Illinois. 

7. Eastman. J. R.. 1990. Idrrsi. A Grid-Easrd GeoRrophrcAnalvsrs Svstcm: Usrr's Mama1 for Rrlrasr 3.2. Worcester, 

Massachusetts, Clark University Graduate School of Geogra8. Cuming. Michael J.. and Hawkins, Barhara A,. 198 1. 

TERDAT: Tlrc FNOC Sysrcm for Trrrarti Dura E.~rrarrrori 

and Procrssiitg. Technical Repon MI1 Project M-254 (Ziitl 

Edition). Montgomery. California U. S. Navy fleet Numerical Oceanography Center. 

9. Wwnrds. Margmt Helen. 1986. Digiral fniagr Pr-oc.rssrrr,q 

of Local arid Global Barlr.vmetrrr Data. Maten Thesis. Dcpartment of Earth and Planetary Sciences. Wasliingtoii 

University, SI. Louis, Missouri, 106 pp, 

10. Matthews. E., 1984. Vtgrrarion. bird-use. arid Stuso~tnl 

Alhrdo Dora Srrs: Documrrtranon of Archived Dura Tape 

National Aeronautics and Space Administration Technical 

Memorandum 86-107.12 pp. 

11. Olson, J. S.. 1982. Earrh's vrgcrorrorr and ornrosphrrrc 

rarborr dioxrdc. IN Clark. W. C., ed., Carbon Dioxide 

Review: 1Y82. Oxford. Oxford University Press. pp. 388- 

398. 

PhY. 364 PP. 

Global Change Dab Base Africa Documentation Introduction 21 
12. Olson, J. S.. 1990. World Ecosysrrms (WE 1.4): Di~iral 

Dora. Boulder, Colorado, NOAA National Geophysical Data 

Center. 

13. Stnub, Brad and Rosenzweig, Cynthia, 1987. Global 

digirol data sets of soil rvpc. soil rcxrurc. surfacc slopr, and 

otItrr propcnirs. National Aeronautics and Space Administration Technical Memorandurn 100685. 

14. Witt, R.. ed., 1984. Globol FA0 Soil Uriirr (2-mi11 grid): 

Digital Data. Carouge, Switzerland, United Nations Environment Programme, Global Resources Inforniption Database. 

15. Leemans. Rik. 1990. Possiblc Cimngcs in Natural Vcxcration Patterns duc to a Global Warming. Laxenburg, Austria, International Institute for Applied Systems Analysis, 

Working Paper WP-90-08, I3 pp. 

16. Legates. David R., and Willmott. Cort J., 1989. Mcan 

scosonol and spatial variabilin, in gouge-corrcctcd, global 

prccipirarion. International Journal of Climatology, v. 10, pp. 

17. Legates. David R.. and Willmott. Cort I., 1990. Mcan 

scosoiwl and sparial variobilin, in global swfacc air tcrnprromrc. Theoretical and Applied Climatology, v. 41, pp. 11-21. 

18. Leemans. Rik and Cramer. Wolfgang. P., 1990. 77tc IfSA 

Databusc for Mean Monthly Valucs of Tcmpcronuc, prccipitarion. and Cluudinrss on a Global Tcrremial Grid. henburg, Austria. International Institute for Applied Systems 

Analysis. Worlcing Paper SP-9041.27 pp. 

19. Pospeschill. Fred. 1988. Micro World Dora Bnrtk I/: 

Ccmtliitcs. Courtny Bourtdarirs. Islands. Lords. and Rivcrs. 

Micro Doc. 3108 Jackson Street, Bellewe, Nebraska. 

20. Kidwell. Katherine B.. 1990. Global Vtgrrnriorr Irtdt.r 

Usrrs Guide. Nutional Oceanic and Atmospheric Ahninistration. Notional Climatic Data Center, Satellite Data Services 

Division, Washingon DC 20233. 45 pp. 

21. GaJlo. K. P.. 1992. Etpcrimrnral global w~crariorr iiidcx 

front AVHRR irrilizing prc-lourtclt colibrarion. cloud and stinangle srrcertirtg. Digital Dora. Nutional Oceanic and 

Atmospheric Adminictration, National Geophysical Data Center, Boulder. Colorado. 

22. Ohring. C.. Cdlo. K., Gruber, A.. Planet. W., Stowe. L.. 

and Tarplcy, 1. D.. 1989. Climate arid global chortge: 

Clioractcrisrics ofNOAA sarrllirr dora. EOS Transactions of 

the American Geophysical Union, v. 70. pp. 88Y. 891, 894, 

901. 

1 I I- 127. 

Global Change Data Base Africa Documentation Introduction 22 
SOFTWARE GUIDE 

Browse and Visualization Modules for the 

Global Change Data Base, Pilot (Diskette) Project for Africa 

WELCOME! 

The enclosed floppy diskette contains a subset of the IDRISI Geographic Analysis System 

that has been specially prepared by Clark University and NOAALNGDC for use with the 

Global Change Data Base. Almost any 8000-, 80286-, 80386-, 80486 or compatible personal computer running IBM-DOS or compatible operating system, with EGA, VGA, or 

85142 compatible graphics should be able to run these modules. 

In the future, NGDC plans to have browse and visualization software that runs on IBMpC/MS- Windows, Macintosh, and UNIX/Windows environments. This software will be a 

generalized version of NGDC's GeoVu software (which had its debut in NGDC's CDROM on Geophysics of North America). 

The software can be run from the floppy diskette, but more practically should be copied to 

a separate AFRICAVU (or other) program directory. You can then run the program named 

MENU. The MENU lists modules contained on this diskette, which can then be run interactively by typing in the appropriate command name. 

To get the MENU, type MENU or HELP. 

INTRODUCTORY NOTES: 

1. These modules were developed especially for NGDC's Global Change Data Base, and 

for a cooperative project called the "Global Ecosystems Database Project" between 

NOAA/NGDC and the U. S. Environmental Protection Agency. Some parts of the software refer specifically to this cooperative project, which, within NGDC, is a part of the 

overall Global Change Database Program. 

2. This disk contains two versions of the COLOR program -- one simply called COLOR 

for EGA and VGA systems and a second called COLOR85 for 8514/A or XGA systems 

(the EGA/VGA version can also be used with 8514/A and XGA systems). If you are 

working with an 85 14/A or XGA or compatible graphics adaptor, please be sure that your 

applications interface (AI) driver has been loaded. If the module fails to produce a graphic 

image, it indicates that the AI driver is not memory resident or that it is not compatible. 

Global Change Data Base Africa Documentalion Introduction 23 
3. You can run this software from a floppy diskette drive. Your data can also be on floppy 

diskettes. However, you will have an easier time with the software and data installed on a 

hard disk drive. We will assume that your data are in hard disk drive D: If your data are 

on a drive with a different designation, pay attention to installation step 3 below. 

4. If you already own a copy of IDRISI, these modules may be copied to your IDRISI 

directory. However, since many of these modules are revisions that have been specially 

prepared for this project, you should keep these separate from the general-release versions. 

This is why we suggest a separate AFRICAVU subdirectory. 

SOFTWARE INSTALLATION: 

1. The software can be fun from floppy diskette, but is better run from a hard disk. If you 

have a hard disk, copy the entire contents of the floppy diskette containing software to a 

new directory on your hard disk. The software is set up to run in aMFRICAVU directory 

on your C: hard disk. 

2. If you install the software in a different environment, you should run the ENVIRON 

command (just go to the directory containing the software and type ENVIRON and hit the 

ENTER key). Follow the instructions on the screen to interactively change the drive and 

directory settings appropriately. 

3. The programs are set up to run successfully if your data are on your "D:" drive, in your 

UFRICA subdirectory. If your data are stored differently, you will need to perform a 

simple editing of these programs. To do so: 

--Go to the drive and directory that contain the software modules. 

--Type ENVIRON and hit the ENTER key. 

--If your data are in a drive other than D: do the following 

Type 1, and press the ENTER key 

Type the drive letter followed by a colon (such as D:), press ENTER 

--If your data are in a directory other than \AFRICA do the following 

Type 2, and press the ENTER key 

Type the directory path (such as UFRICA), press ENTER 

--Press ENTER again, to exit ENVIRON and save these settings. 

Global Change Data Base Africa Documentation Introduction 24 
FURTHER NOTES: 

1. Note that we are including several .pal files. These are alternate color palettes for 

displaying images with COLOR or COLOR85. You may want to experiment with these, 

especially those with GVI in their names. To use such a palette, note when COLOR or 

COLOR85 ask you for your choice of palette. Answer with the number for "user-defined 

palette." Then, when prompted, give the name of the palette (which you must know beforehand by using DOS commands). 

2. All IDRISI modules belong to Clark University and are protected by International 

Copyright Law. The modules distributed here have been made available to the EPA and 

NOAmGDC through a special Memorandum of Understanding with Clark University. 

They may not be distributed independently of the Global Change Data Base. For further 

information about the Global Change Data Base, please contact : 

NOAAJNGDC 

325 Broadway 

Boulder, CO 80303 

USA 

Tel : (303) 497-6125 

FAX : (303) 497-65 13 

Attn: David Schoolcraft 

For further information about the IDRISI Geographic Analysis System please contact : 

IDRISI Project 

Clark University 

950 Main St. 

Worcester MA 01610 

USA 

Td : (508) 793-7526 

FAX : (508) 793-8881 

Global Change Data Base Africa Documentation lnfroduction 25 
-ANOAA MONTHLY 1O-MIN. NORMALIZED DIFFERENCE VEGETATION INDEX 

(APRIL 1985 - DECEMBER 1988) 

NOWGDC, 1989. Global Monthly lO-min Normalized Difference Vegetation 

Index (April 1985 - December 1988). Digital Data. NOAADESDIS/National 

Geophysical Data Center, Boulder, Colorado. 105 Megabytes. 

Global Change Data Base Africa Documentation Appendix A-1 
Blank page retained for pagination 
A. NOAA MONTHLY 10-MIN NORMALIZED VEGETATION INDEX 

(APRIL 1985 - DECEMBER 1988) 

DATE: 1989 

SOURCE: 

(1) AVHRR (Global Plate Cmee) 

NOAA/NESDIS/SDSD 

NCDC, World Weather Bullding, Rm. 100 

Washington, DC 20233 

(301) 763-81 11 

(2) Reprocessed by: 

John Kineman 

NOAA/NGDC 

Boulder, CO 

CONTRIBUTOR: NOAA/NESDIS (SDSD AND NGDC) 

DATA CENTER: 

(1) SDSD (primary distribution) 

(2) NGDCWC-A (monthly 10-min composites) 

GEOGRAPHIC SAMPLING: 

IO-min grid, resampled from 8.6 min grid, averaged from weekly samples. 

COVERAGE: Global 

PROJECTION: LatAon 

TIME SAMPLING: Monthly averages 

REFERENCES: 

Katherine B. Kidwell (ed.), 1990. "Globid Vegetation Index User's Guide." 

NOAA/NESDIS/SDSD. (Excerpts included.) 

For additional references, see pp. A1-A3 in Kidwell, 19%. (Also included) 

FILE NAMES: AFAPR85 - AFDEC88 NVI (month/year) (45 files) 

Globd Change Data Base Africa Documentation Appendix A- 3 
Blank page retained for pagination 
NOAA MONTHLY 10-MIN NORMALIZED VEGETATION INDEX 

(APRIL 1985 - DECEMBER 1988) 

EXAMPLE DOCUMENTATION FILE 

File Names: AFAPR85.DOC - AFDEC88.DOC (45 files) 

image title : (~~~il 1985 through December 1988) Vegetation Irides 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : O.OOOO000000E+~~ 

masimum : ~.7SOOOOOOOOE+O2 

cell x : 1.66660000OOE-O1 

cell y : 1.66660000OOE-O1 

legend : 15 

category 1 : 11-21 

category 2 : 22-32 

cateqory 3 : 33-43 

category 4 : 44-54 

category 5 : 55-65 

category 6 : 66-76 

category 7 : 77-87 

category 8 : 88-98 

category 9 : 99-109 

category 10 : 110-120 

category 11 : 121-131 

category 12 : 132-142 

category 13 : 143-153 

category 14 : 154-164 

category 15 : 165-175 

{category 0 : 0-10) 

Note: The maximum indicated in the documentation file has been 

Standardized to 175 so that automatic scaling during display will 

show comparable results between monthly images. 

Units are scaled NDVI values from 0 to 255. Note that these values 

are inverted from the NOAA scaled NDVI values (255 - original NOAA 

scaled value), SO that high values indicate greater vegetation 

response. 

Global Change Data Base Mica Documentation Appendix A-5. 
Blank page retained for pagination 
NOM MONTHLY 10-MIN NORMALIZED VEGETATION INDEX 

(APRIL 1985 - DECEMBER 1988) 

PROCESSING 

Advanced Very High Resolution Radiorneter'(AVHRR) data from NOAA Polar Orbiting 

Environmental Satellites were obtained kom the National Environmental Satellite, Data and 

hformation's (NESDIS) Sarellite Data Services Division. The data were acquired in NOM'S 

operational Normalized Difference Vegetaaon Index (NDVI) "Plate Carree" (latitudeflongitude) 

w&ly image format and were subsequently converted at the National Geophysical Data Center 

into IO-minute grids. composited monthly. 

The AVHRR sped bands used for vegetation monitoring are Channel 1. a visible band 

(0.58 to 0.68 m) and Channel 2, a near infrared band (0.73 to 1.10 m). Since the spectral 

reflectance of chlorophyll is more than three times greater in the reflected infrared than in the 

visible pomon of the specnum. the difference between the value for Channel 2 and 

Channel I is an indication of the degree to which the scene viewed includes preen vegetation. 

Various mathematical combinations of Channel I and 2 data have been found to be sensitive 

indicators of the presence of green vegetation and are referred to as vegetation indica. 

The basic index used by NOAA is the Unscaied Normalized Difference Vegetation Index 

(XVT), defined by the equation: 

XVI = (chz - Chl) / (chz + all) 

In images with vegetation, the NDVIs range from 0.1 m to 0.6 m. the higher values being 

associated with greater density and greenness of the plant canopy. Atmospheric effects. such a 

scattering and sub-pixel-sized clouds, ail act to increase the value of Chl with respecr io 02 

and reduce the values of the computed vegetation indices. The normalized index has another 

advantage for global vegetation monitoring, for it partially compensates for changing 

illumination conditions, Surface slope. and viewing &.pea. Clouds. water. and snow have greater 

reflectance in the visible than in the near infrad, so for these features NDVI values are 

negative. Rock and bare soil have similar reflectances in the visible and near infrared and this 

results in vegetation indices near zero. 

The dm provided by NOAA were scaled as integer values fmm 0 to 255 according ro 

the formula NDVI = 24O-(XVI+0.05)*350 (set Global vegetation Index User's Guide). In 

processmg, however. the scale was inverted by subtraction from 255, so that high values in the 

data correspond to high vegetation signals. Thus the values used in composxdng and re-gridding 

are described by the formula: 

NDVI = (XVI+O.O5)*350 + 15 

The satellite images wen re-sampled from weekly to monthiy averages in a series of steps. 

Two procedures were used to control the quality of these composite images. Fmt. regismtion 

accuracy was ensured by alignment of recognuable geographic locations. Second. each weekly 

composite image was visually inspected for artifacts (Le., scan lines, image swaths. and other 

system noise). If an artifact occurred at the same location in two or more images, oniy one of 

those images was used. The remaining artifacts werc removed during the monthly composite 

procedure, which combined all w& which overlapped the calendar month (thus providing up 

to one week overlap between months). In this procedure, the high and low weekly values for 

each cell were eliminated and a root-mean-square average of the remaining weekly values was 

calculated. This technique eliminated random artifacts and biased the result toward higher (and 

presumably more reliable) values, without forcing the rnonthfy value to its maximum. 'Ihe result 

is therefore a statistic that is presumed to be representative of the month's vegetation activity: 

however. as with any such index. it must be calibrated or classified using additional information. 

Also, these values arc unconected for orbital parameters or sensor dnft. 

Global Change Data Base Africu Documenfation Appendix A-7 
PROCEDURE RIA DEVELOPING NVI MONTtlLY COMPOSITE IMAGES FOR AFRICA 

Wf f 111 

CbMPOSI1E 

1 ClOlAL 1.6. WOllHLl 

5.0 

4.0 

RtCRlO 0 CfLL SIZE 
FLOW CHART FOR NDVI PROCESSING 

Weekly composites in global Plane Came projection (NOMSDSD). 

1.0 Process weekly images. 

1.1 Visually inspect images for artifacts and registration errors. 

1.2 Eliminate images with conaicting artifacts (Le. where artifacts overlap the same area). 

1.3 Correct for registration errors. 

2.0 Composite each month using an "Root-mean square (RMS) average of the median values 

(eliminating the high and low values for each pixel) and correcting for registration errors. 

3.0 Re-grid from original 8.6-minute image cell to 10-minute cell size. 

4.0 Produce both global and Africa windows. 

Notes: 

1. Averaging only the median values for a given month eliminates random emors evident in the 

original images. A "clean" image is guaranteed for the African continent by the visual inspection 

and selection process, which insures that no two arcifacts overlap in Africa An RMS average 

was chosen as a more meaningful statistic given the greater likelihood for high NDVI values to 

be representative of the vegetation (due to cloud effects). This was considered superior to 

choosing only the highest values because of the goal of producing a staristic representing a timeaverage of the month's vegetation "signal," rathem than selecting peak times. 

2. The re-gridding procedure employs an averaging process weighted by the percentage of cell 

overlap in converting from the 8.6-minute grid to a 10-minute grid. This fairly robust procedure 

provides a statistic that is as representative as possible for a given 10-minute cell. Since a given 

pixel from each source image, of the original Plate Carrcc grid has an uncertainty in position on 

the order of one pixel, the final average is of a somewhat random sample within a definable 

region, roughly the size of a 10-minute cell. 

3. The greatest remaining bias in the resulting images is due to cloud obscuration, for which no 

correction was attempted other than the NOAA compositing procedurt of choosing the highest 

NDVI when producing the weekly composites. Various cloud corrections may be attempted 

using other data sets in conjunction with this set of monthly NDVI images. 

Global Change Data Base Africa Documentation Appendix A-9 
Blank page retained for pagination 
GLOBAL VEGETATION INDEX 

USER'S GUIDE 

MAY 1990 

Compiled and edited by: 

Katherine B. Kidwell 

U.S. DEPARTMENT OF COMMERCZ 

NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION 

NATIONAL ENVIRONMENTAL SATELLITE DATA AND INFORMATION SSRnCE 

NATIONAL CLIMATIC DATA CENTER 

SATELLITE DATA SERVICES DIVISION 

PRINCETON EXECUTIVE SQUARE 

WASHINGTON, D. C. 20233 

TELEPHONE: COMMERCIAL (301) 763-8400 

FTS 763-8400 

TELEX: 248376 OBSWUR or 

TELEFJUC: (301) 763-8443 

TRT 197683 KWBCUT 

Global Change Data Base Africa Docummrution Appendix A-1 1 
(Only Exce-Tts Included) 

Introduction . 

ConpKttition of Global Vegetation Index 

2.1 Background ........... 2.2 Processing OJerview ....... 

(Experimental) GVI Product ... 2.3 Changes i r. the First Generation 

Second Generation GVI Product .... 3.1 Introduction .......... 

3.3 Dzily Processing ........ 3.2 ?late Carre4 Projection ..... 

... 

( Gt'I ) 

... 

... 

... 

... 

... 

... 

... '3.3.i Processing of AVHRR GAC Data Sets 

3.3.1.1 Processing of GAC Scan Lines 

3.3.1.3 'IR Tenperature Look-up Table 

Updating the Basic Composite Lrays 

3.3.1.2 Scan Angle Prclcessing .... 

Generation ........... 3.3.2 

3.3.3 Weekly Composite Azrays ...... 

1 

3 

3 

4 

6 

7 

7 

9 

9 

10 

10 

11 

11 

12 

13 

Global Change Data Base Africu Documentation Appendix A-12 
1.0 Introduction 

This User‘s Guide describes the Global Vegetation Index (GVI) 

Product that is archived by the Satellite Data Services 

Division (SDSD) of NOMA’S National Climatic Data Center (NCDC) 

under the National Environrental Satellite, Data, and 

Information Service (NESDIS) . It includes the current Second 

Generation versim of the product produced from April 1985 to 

the present, in addition to the earlier First Generation 

version produced from May 1982 through March 1985. 

This guide contains a section describing the vegetation index 

itself, descriptions of the two generations of GVI products, 

and information on how they may be ordered from SDSD. 

Global Change Data Base Afiicu Docunientuh’on Appendix A- 13 
2.0 Coyputaticn cf Global Veaetation Index [GVT) 

This secticn provides techr.ica1 bacLgrcund on the Global 

Vegetation Index (G'JI) and an overview of hcw the product is 

produced and changes that were made in the production of the 

First Generation experimental product. 

2.1 Backaround 

The spectral reflectance cf chlorophyll pigment in the visible 

and near infrared part of the spectrun; provides a means of 

monitoring density and viqor of green vegetation. Grcen leaves have a reflectance of less than 20 percent in the 0.5 

to 0.7 pm s?Eztral interval, but about 60 percent in the 0.7 

to 1.3 pm range. Differential reflectance in these bands has 

been widely used with LANDSAT multispectral data to classify 

land cover, estimate crop acreage, and detect plant stress. 

NOM series operational meteorological satellites provide data 

that can be used for vegetation monitoring. The Advanced Very 

High Resolution Radiometer (AYH3.E) flown currently on NOM-11 

and previously on NOM-7 and :;OM-9 is a 5-channel instrument 

which scaqs continuously at a ground resolution of 1 km. The 

1 km data is sampled and averaged to a nominal 4 kn resoluticn 

and recorded on board thc satellite for subsrquent 

transmission. AVHRR Global Area Coverage (GAC) 5: 4 km 

resolution is thus available. This coverzge is su?plemented 

by 1 km resolution coverage of selected areas using AVHRR LAC 

(Local Area Coverage) data. The sampling done on board the 

satellite to produce the GAC data consists of selecting every 

third 1 kn LAC data line, and selecting four of every five 

saaples along the line and averaging these to yield one GAC 

sacple. Thus the nominal lffootprintrf of a GAC sample is 1 km 

by 4 kx, wiZh (near nadir) gass between GAC lines and sinall 

gaps bet;.reen GAC samples. LAC and GAC samples are 10 bit 

binary 18counCt1 vzlues (diqitizeci voltages) . 

The s;sestril response of the five AVEZ3 channels is 2s 

follows : 

Channel 1 0.58 to 0.68 DR 

Channel 2 0.725 to 1.10 

Channel 3 3.55 to 3.93 pn 

Channel 4 10.30 to 11.30 pm 

Chanel 5 11.50 to 12.50 

Global Change Data Base Africa Documentation Appendix A- 14 
The spectral bands used currently for vegetation monitoring 

are the Channel 1 visible band (0.58 to 0.68 pr,) and Channel 

2 in the near infrared band (0.73 to 1.10 ~LC). The spectral 

response curves for these channels are similar to those of 

bands 5 and 7 on the LANDSAT nultispectral scanner. Various 

mathematical conbinations of Channel 1 and 2 data have been 

found to be sensitive indicators of the presence cf green 

veqetation and are referred to as vegetation indices. This 

effect is due to the differential reflectance of vegetation in 

these bands. Thus, the difference in the Channel 2 and 

Channel 1 data values (which are proportional to the 

reflectance of the scene viewed) computed as CL2-Ck1 is a 

measure of the degree to which the scene viewed includes green 

vegetation. 

The basic index is the Norrr,alized Difference Vegetation Index, 

(NDVI). It is the main GVI product produced between May 1982 

and the present. The XDVi is defined by the equation: 

where Chl and Ch2 are count values from Channel 1 and Channel 

2, respectively of the AVERR instrument. The NDVI is 

preferred for global vegetation nonitoring because it 

partially coxzpensates for changing illuxination conditions, 

surface slope, and viewing aspect. Clouds, wzter, and snow 

have larger reflectances in the visible than in the near 

infrared, so for these features NDVI is negative. Rock and 

bare soil have similar reflectances in these two bands and result in veqetation indices near zero. In scenes with 

vegetation, the XiDVI ranges fro; 0.1 to 0.6; the higher values 

are associzted with greater density and greenness of the plant 

canopy. Atmospheric effects such as scittering by dust and 

aerosols, Sayleigh scattering, and sub?ixel-sized clouds all 

act to increase chl with respect to Ch2 and reduce the 

computed vegetztion indices. 

Research perforzed with the First Generation GVI product, 

which included only chi, chz, Ch2-Chl, ana NDVI values mapped 

on a Polar-Stereographic grid, suggesced that' additional 

information would be of help in mitigating the effects of 

cloud contanination and facilitating year to year comparisons of hTDVI values over the saxe areas. (Appendix A is a 

bibliography of significant research reports selected to be of 

most value to the users of the GVI products.) Thus, the Second 

Generatioz GVI product also inclnzes Channel 4 and Channel 5 

IR data values, which may be of use in cloud screening, and 

Solar Zenith and Scan angles, which shouid assist in 

norzalitinc .YiWI vzlues for year.to year csnpzrisons. 

Global Change Data Base Africa Documenlatiort Appendix A- 15 
2.2 Processina Cverview 

Claily GVI naps are produced by processing AVHRR data orbit by 

orbit and napping it to a standard base projection. Briefly, 

this process ccnsists of truncating the original 10 bit GAC 

data to 8 bit data and scaling. When the IR channels were 

added (fcr the Second Generation product) , they were 

calibrated, tuned to temperature and scaled. The standard 

base projecticn was Polar-Stereographic from May 1982 through 

M-rch 1985 (First Generation version), and is ccrrently Plate 

CLrreQ (or latit*:de/longituae) for the Second Generation 

version of the pr?duct, April 1985 through the present. On a 

weel:ly basis, Polar-Stereographic ccmposites were produced 

f 'CT? May 1962 to the present, with Mercator composites also 

prcduced for data from April 1985 to the present. The NOAA-11 

satellite (and previsusly the NOAA-7 and NOM-4 satellites) is 

in a sun-synchronous near-polar orbit with equator crossings 

at about 0230 and 1430 local time. A single day's map 

ccnsists of a mosaic of the daytime portion of 14 orbital 

svtths. The Polar-Stereograshic maps are on a one-sixteenth 

sub-mesh grid of the standard 65x65 Polar-Stereographic 

projection used by the National Meteorological Center and the 

Air Force Global Weather Center. Resolution in the mapped 

datz ranges from 13 km at the equator to 26 km at the poles. 

The data are sazpled in the mapping process so that the GVI in 

an array location consizzs of a value c3mputed from a single 

AVHRR pixel; there is no averaginq. 

On any single day, about half the Earth is obscured by clouds. 

To rerncve clouds , se;.en-day maximum vegetation index 

composites are prcduced from the dzily arrays. For each 

compcsite period cf seven days, the pixel frcm the daily data 

having the qreztest ch2-C.51 difference (computed from counts, 

i.e., uncalibrated) is retained at each array locarion (i.e. 

the "greenest" of the seven daily values for each array 

location is retained in the composite). This eliminates 

clouds fro? the composite except fcr areas which were cloudy 

at the tize of satellite overpass for all seven dayz. When 

severzl clsci-fre- views of an area are obtzined cver the 

conp-siting ;?erioc, 1:DVI va:.ues computed on days with haze and 

subpixel clouds are eliminated. The values of Chl, C"22, Ch4, 

Ch5, Solar Zenith and Scm Pbqles that are also present on the 

composite Sroducts are for the same day's data 3s the NDVI 

value selected for the composite. 

The weekly cycle for the First Generation GVI product began 

with Monday and ended on the following Sunday. Beginning with 

the Second Generation GVI product in April 1985, the weekly 

cycle WES synchronized by Julian day (i.e., Julien days 1 

throcgh 7 VerE always the first week, Zulizn days 8 thrmgh 14 

we== the second wee):, etc. ) . This scheme causes the Weekly 

cycle to vary frc7 yezr to yeer depending on what day of the 

Global Change Data Base Africa Documentation Appendix A- 16 
week January 1 falls on (e.g., the weekly cycle for 1985 runs 

fron Tuesday through the follouing Nonday, \chile 1986 tuns 

from Wednesday through the following Tuesday). 

When DPSS took over the CVI processing ~n April 11, 1988, the 

weekly cycle was changed again to Monday through the following 

Sunday. This cycle does not change from year to year. 

2.3 Chancres in the First Generation (E xDer imenta 1 J GVX 

Product 

Because the First Generation NDVI was an experimental product, 

users are cautioned that changes in the computztion of the 

daily arrays were made 2s more was learned about the cloud 

contamination problem. This trial and error process went 

through the following steps: 

1. From May 10, 1982 (Julian day 100) through August 7, 1982 

(Julian day 219) , the daily NDVI was computed from the last 

value mapped to each location on the Folar-Stereoqraphic 

output grid. This was regarded as a random sampling. 

2. From August 8, 1982 (Julian day 220) throqh September 12, 

1982 (Julian day 255), the daily XDVI was computed from the 

Channel 2 and ChanEel 2 values with the c'eatest difference 

mapped to each locacicn on the Polar-Stereographic outpct 

grid. This resulted in an apparent carked improvement in the 

removal of cloud contarhation, but was not a random san?ling, 

in that it selected the 1tgreenestf8 pixel for each nap location 

on a daily basis. 

3. Frox SepZezSer 13, 19Gf (Julian day 2SG) to March 27, 1983 

(Julian day 86) , the llgzeenest" pixel fror only every other 

AVH-RX scan line was zapped, to szve computer time and restore 

a degree of "randonness" ta the sampling. 

4. Fron March 26, 1963 (Julian day 67) to the present, the 

sampling wcs changed back to the randon method (using the last 

sample napped to each grid cell in use from May 10 to August 

7, 1982). Data from only every other scan line is being used 

to save conputer time. This method is usEd in the current 

Second Generation NDVI product. 

Global Change Data Base Afiico Docutnenrution Appendix A- 17 
3.0 Second Generation C-VI Products (Aaril 1985 - Present) 

This section describes the enhanced, Second Generation GVI 

product currently being produced operationally by Zhe Data 

Processing and Services Subsystem's (DPSS) IBM 4331 which is 

managed by the Information Processing Division (IPD), Office 

of Satellite Data Processing and Distribution (OSDPD) of 

NoAA/NESDIS. SDSD produced the Second Generation GVI product 

o;zrationally until April 1988 when DPSS took over this 

functicn. 

3.1 Introduction 

The process by which the GVI Froducts are produced takes place 

in two phases. The first phase involves production of a set 

of daily master arrays from each day's AVHRR GAC data sets and 

the updating of the basic set of composite arrays. The second 

phase consists of the production of a set of final weekly 

composite products from the composite arrays. 

The Plate Carred projection, or latitude/longitude, was 

selected as the map projection tc be used for the daily master 

arrays and the basic composite array,, and is discussed in 

section 3.2 below. 

Each AVHRR GAC Level lb data set contains data from cne orbit 

of the satellite (currently NO=-11). The orbital period is 

102 minutes, so there are approximately 14.1 orbits every 24 

hours. Thcs, there will be 14 and occasionally 15 data sets 

to be processed each day for global coveraqe. Each GAC data 

set contains approxixately 6,600 records, each consisting of 

two scan lines with 409 data points (i.e. 409 sets of c?ata 

values from the five AVHRR channels) per scan line. The total 

volume of one day's worth of GAC data (15 orbits) would be 

637.56 megabytes. Only one quarter of this data volume is 

actually processed, since only dayzine data and only data from 

every other scan line are used. 

The six daily master arrays, each 2500 X 904 elements in size, 

consist of the following data: 

1. Channel 1 (Ch 1) 

2. Channel 2 (C.9 2) 

3. Channel 4 (Ch 4) 

4. Channel 5 (Ch 5) 

5. Solar Zenith Angle (SZ) 

Global Change Data Base Africa Docurnenlation Appendix A-1 8 
6. Scan Angle (SA) 

The Channel 1 and Channel 2 values are original count values 

truncated to 8 bit precision. The Chamel 4 and Channel 5 

values are then converted to "GOES counts" (intended 

originally for use with geostaticnzry satellites). The GOES 

counts are 8 bit binary numbers corresponding to tenperature 

values according to a standard look-up table (Appendix B). 

The solar zenith angle and the scan angle values zre scaled 

binary values to the nearest half of a degree. As noted, the 

Chamel 4 and Channel 5 binary counts are converted to 

counts before they are mapped. This involves using the 

channel calibration coefficients to convert the binary counts 

to energy values, then using the inverse of Planck's radiation 

formula, convert the energy values to absolute temperatures, 

and finally scaling the tenperature values to 8 bit binary 

values to be placed in one byte as GOES counts. In practice, 

the conputation is perforned once for each possible data 

value, for each of the two IR channels, to create a set of 

look-up tables which will be used for the .actual data 

processing. 

The basic coaposite arrays, each of which have 2500 by 904 

elements, include the six itens listed above plus a Normalized 

Difference Vegetation index array (NDVI) . 

Each week, these seven bzsic colrpcsite arrays are used to 

produce the Nercator naps and the Polzr-Stereographic arrays. 

The resolution selected for the Plate Carrei! arrays is 16 km 

at the equztcr, which r5sults in the 2500 X 904 element size 

array to riap the region betjteen 75O North and 55O South. 

All seven weekly ccxposite Plate Carzei! 

to the correspcnding Mercator arrays; 

consisting of 2028 x 1038 elener.ts 

resolution of 19.5 kx at the equator. 

All seven weekly coaposite Plate Carrei! 

arrays are converted 

each Mercator array 

corresponding to a 

arrays 2re converted 

to Polzr-Stereographic arriys. Each hemisphere is a 1024 by 

1024 eler;.ent zzray, consistent with the Polar-Stereographic 

First Generation GVI procuc:. The Polar-Stereographic arrays 

have a better resolution of 13 km at the equator coxpared to 

the corresponding resolution of 16 kn for the master Plate 

Carreg zrrays. To avoid "holes" in the Polar-Stereographic 

arrays near the equator (within 15O latitude north or south), 

the mapping process fetches a Fixel fron the Plate Carre6 

arrays for each cell in the Polar-Stereographic array, which 

results in repetition of some pixels (where other-dise holes 

would have occurred). ks a result of the latitude linits (75" 

North to 55O.South) selected to include significant 1ana.areas 

but also to nininize processing, Polar-Stereoqraphic arrays 

Global Change Data Base Africa Docurrrerrturion Appendix A- 19 
produce", fron the Plate Carrre arrays have conspicuous "holes" 

around the poles. 

3.2 Plate Carrei, Projection 

Several map projections were considered for the daily master 

arrays: Mercator, Equal Area, and Plate Carreg. Plate Carree 

was selected because it is :he simplest for data processag, 

and it pravides a reasonable rerresentation of the problematic 

polar regions, where both tZ - Mercator and the Eqcal Area 

projections would be more distorted. 

The Plate Carrei? projection is not conformal, but has the 

following simple form: 

X- - IN x (1.-1,) 360 

where R and 4 are respectively the longitude and the latitude, 

measured in degrees, while Rn is the central longitude. The 

value for Rn is Oo , corres?ofiding to the Greenwich Meridian, 

which will be at the center of the array, and with the edges 

of the array being 180° of longitude East .and West. IN is the 

number of non-duplicated pixels in the array in the East-West 

direction. IN is set to 2500, corresponding to a resolution 

of 16 kn at the equator. The I and J values on the map are 

given by : 

I-x+ 12 5 0 

where I ranges from 1 to 2500, while J ranges from 1 to 904, 

covering the latitude rznge of 75O North to -55O South. 

Longitudes are measured from Oo to i18Oo eestward and from Oo 

to -180O westward, which is the convention used to store the 

values within the GAC data sets. 

3.3 Dailv Processinc 

This section describes thc claily Frocessing of GAC data sets 

2nd updating of the basic conposite arrays. 

Global Change Data Base Africa Documentation Appendix A-20 
A given GkC data set is read record by record, and for each 

record of daytine data one scan line is processed. The Chl, 

Ch2, and Ch4 data are extracted and napped to the 

corresponding master daily arrays and held in computer memory. 

The Ch5, SZ, and SA data are held in a temporary disk file. 

Once the entire master arrays are written to disk, the Ch5, 

SZ, and SA arrays are read in, and updated from the terporary 

file. When this process is completed for a GAC daza set, 

another is processed, until the full day's worth of GAC data 

sets have been processed. 

3.3.1.1 P rocessina of GAC Sc an L ines 

The first of two scan lines packed in each GAC record is 

examined. If the scan line contains night time czta, or if 

ear3 location or calibration inforrition is missing, the scan 

lin.: is skipped. 1,' the latitudes are oucside the range of 

75O !Jorth through 55O South, the szan line is skipped. For a 

good scan line, first latitude anc longitude values for each 

of the 409 data points are interpolated from the 51 benchmark 

latitude/longitude pairs. These earth locations ere converted 

into 409 pairs of (1,J) values on the Plate Czrree array, 

indexing the positions to which each GAC pixel will be mapped. 

Then the Solar Zenith (SZ) angles are interpolated for the 409 

data points from the 51 benchmark values. 

The five channel count 

unpacked fro3 the video 

The Ch4 and Ch5 count 

using a ye-cozquted 1 

This processing yields 

for each GXC data Faint 

values for each data point are then 

data and truncated to 8 bit precision. 

values are converted to GOES counts 

.ock-up table (refer to A?pendix B) . 10 bytes of inforxation to be napped 

a. I, ,7 location for this ciatz point cn the daily riaster 

arrzy, 4 bytes; 

b. c.91, Ch2 count values, 1 byte each; 

c. Ch4, Ch5 GOES count values, 1 byte each; 

d. SZ and SA values, 1 byte each. 

These data are then used to update the daily master arrays as 

outlined above. Since the resolution of the GAC data (4 km) 

is much finer than the resolution of the Plate Carree arrays, 

a number of GAC pixels will be napped to each location in the 

Plate Cazrei? arrzy. Each value will replace the one 

previously written. Thus, a selection of one GAC sample is 

Global Change Data Base Africa Documentation Appendix A-21 
Eade to represent the population of GAC samples to be r,a?ped 

to that location. Because the selection does not depend upon 

the value of the sar.ple, this selection process is regarded as 

random (as opposed to basing the selection on the value of the 

Ch2-Chl difference, as was experimented with for a time while 

the First Generation GVX product was being produced). 

3.3.1.2 Scan Anale Processinq 

The scan angle. for one orbital swath range from -55.4O to 

+55.4 O relative to nadir, a total span of 110.8". Scan angles 

to the nearest half-degree corresponding to the 405 sample 

positions on a scan line are calculated as a binary count of 

half-degree steps from 0, for sample 1; to 222, for sarr,Tle 

409. The nzdir point will therefore correspond to 110.8 halfdeqree steps, rounded to 111. 

The scan angle SA in half-degree steps for the Nth sample 

position is given by: 

SA- 221'6 x(5N-1) 2047 

vhere 1 I N I 409. 

byte as binary integers. 

The scan angle values are stored in one 

3.3.1.3 13 TenDerature Look-uD Table Generation 

Look-up tables are used to convert Ch4 and Ch5 values ta GOES 

counts. The first step is the conversion of the possible 

range of count values. to radiances, using the Channel 4 and 

Chaznel 5 calibration coefficients present in the GAC sczn 

line: 

Z-iYxN+S 

where .Y is the raw channel count; M is the slope parameter far 

the channel; S is the intercept parameter for the channel; azd 

2 is the channel radiance (energy) in milliwatts/n'-steradiancn-' . 

The ra6iances in turn are converted to the absolute 

temperatures, by the following: 

Global Change Data Base Africu Documentation Appendix A-22 
where TA is the absolute temperature in OK; 

cm-OK; B = 1.1910659 X milliwatts/m2-steradian-cm4; v is 

the central wave nLnber where w = 929.46 cm'' for Channel 4 of 

NOM-9, and v = 845.19 cm'' for Channel 5 of NOM-9.. 

A = 1'.438833 

The temperatures are then rescaled to GOES count values (so 

they will fall in the range from 0 to 255) using the following 

equation: 

GOES= Cx TA + D 

where GOES 

temperature 

and D = 419 

242OK; and 

in the rang 

is the GOES count valu-3, and TA the absolute . Constants C and D have the values C = -1.006412 

,05128 for tezperatures in the range 164OK s TA 5 

C = -2.0057142 and D = 661.88571 for temperztures 

The result zre two look-up tables of 255 GOES count values, a 

Channel 4 table and a Channel 5 table. Appendix B ccntains a 

single GOES count to absolute temperature converslon table 

that may be used to recover the absolute tenperature values. 

3.3.2 UDdztinu the Bzsic ConDosite Arravs 

Each day the seven bzsic conposite Plate Carreg arrays are 

updated fron the six ne;.; daily master arrays. U2dating of the 

conposites is performed cell by cell. The difference of the 

Channel 2 ar.d Channel 1 values (Ch2-Chl) for the new data is 

computed, and conpared to the Ch2-Chl value for the 

corresponding elene?t of the basic compcsite array. If the 

new difference is greater (i.e., if the cell is I1greener" or 

clearer) then the A'DVI: value for the new data point is 

computed and reslaces the NDVI value in the basic cDmposite 

array., and the values in the other six basic ccmposite arrays 

for that cell location are replaced by the corresponding six 

values fron the new daily master arrays. Thus, cornpositing is 

bypassed if the new cell is less ttgreentl than the 

corresponding cell in the basic conposite arrays. Compositing is also bypassed if the difference value for the new cell is 

255. If conpositing is bypassed, the cell values in all seven 

basic cozpcsi=e arrays are left unchanged. 

When a new datz point is to be composited (i.e. when its value 

replaces the previocs cell value in the basic comzosite 

Global Change Data Base Africa Documentation Appendix A-23 
arrays), its X3.I value is conputrd fro= the dcta poict's chr 

and Ck2 values from the Rev daily master array, as follows: 

where XVI is an unscaled NDVI value, and Chl and Ch2 aie the 

Channel 1 and Channel 2 count values, respectively. Thus, 

NDVI is computed 8s follows: 

mVI-240- (X'vI+O. 05) ~350 

The values of XVI range from -.OS (no green) to +.60 

(greenest). These values are then scaled to an eight bit 

binary integer, so that the sreenest areas appear the dzrkest 

when an image is produced. Thus, a scaLed NDVI value of 240 

(white) corresponds to an unscaled XVI value of -.OS, and a 

scaled NDVI value of 12 (black) corresponds to an unscaled 

value cf +.60. 

The process described above is performed for every data point 

(cell) in the daily master arrays. 

3.3.3 Weeklv Co~b~site Arrsvs 

Once a week, the seven bzsic composite arrays (which are in 

the Plate Carre5 projection) are used to prosuce a set of 

Polar-Stereographic end Mercator arrays. The mapping is done 

by sequentially filling each cell location in the PolarStereographic or Mercator arrays with a vzlue from a location 

on the Plate Czrrek arrays to ensure that there are no "holes" 

(i..e. cell locations that do not receive values) restlting 

from the difference in resolution of the new arrays compared 

to the Plate Carreg arrays. 

Global Change Data Base Africa Documentation Appendix A-24 
3.4.2 Keeklv Czs~os ite GVT Products 

The weekly co,-.?osite GVI 2roduct tapes contain the seven files 

lhZ~2 above for the daily maszer array tape wit?. =he addition 

of an eighth file containing scaled NDVI data. Three types of 

weekly tapes a,.: produced: a Plete Carre4 ta?e, a PolarStereo5ra2hic tsse, 2nd a Mercator tape. In each czse, the 

contents cf File 1 is a sinqle Doccmentation Record. The 

number of jays worth of data used to produce the composite, 

an2 identizicction of their Julian dates, is also included. 

The Dccumentation Record contains the fellowing information: 

Bvtes vtes Contents 

1 1 Number: of days of compositing; 

c SB 

2 1 Unuserl, blank filled; 

3 - 8 6 First date processed (YYDDDb); 

9 - 14 6 Sacond date procesed (YYDDDb); 

0 . . 

39 - 44 6 Seventh date processed (YYDDDb) ; 

4s - 5000 Blank filled. 

where VYDDDb indicates the date as the last two digits of the 

year (YY) , Julian day (DDD) , ,and one blank charzcter (b) . 

Files 2 through 8 of each type of tape contain dzta arrzys (in 

the saae order zs the daily master array tape with the 

addition of the NCVi file 2s file aO8) fomatted es follows. 

3.4.2.1 Plate CErre4 TaDe 

The fo-xnat 02 files 2 through 8 of the Plate Carreg weekly 

composite product tape is the sane as the daily master arrzy 

ta?e fornat descri%d in Section 3.4.1, excapt for the 

additizn of the NDVI file as file $8. 

Global Change Data Base Africa Documentation Appendix A-25 
APPENDIX A: BiSlioara::?v of GVT Resezrcfi ReDorts 

Deering, D.W. and T. F. Eck, 1987: Atmospheric optical depth 

effects on anguiar arisotropy of plant canopy reflectance. 

International Journal of Remote Sensinq, 8(6): 893-916. 

Dedieu, G., 1990: Lznd surface reflectancGs and vegetation 

index derived from NOAA/AVHRR. Workshop on the "Use of 

satellite-der1 ,,ed vegetation indices in weather afi3 climate 

prediction models", Camp Springs, MD, Feb. 26-27, 1990. 

DIIorio, M., 1990: Corrections and improvements to NDVI 

procedures. Workshop on the gtUse of Satellite-derived 

vegetation indices in weather and climate prediction moaelsIt, 

Camp Springs, ED, Feb. 26-27, 1990. 

Dijik, A., S.L. Callis, C.M. Sakamoto and W.L. Decker, 1987: 

Smoothing vegetation index profiles: an alternati-Je method 

for reducing radiometric disturbance in NOU/AW'?R data. 

Photourammetrv Enuineerinu Remote Sensinq, 53: 1053-1067. 

Duggin, M.J., D. Piwiniski, V. Whitehead and G. Ryland. 1982: 

Evaluation of NOM-AVHRR data for crop assessment. ADDlied 

Omtics, 21(11) : 1873-1575. 

Duggin, M. J. and. R.W. Saunders, 1984: Problems encountered in 

remote sensinq of .land and ocean surface features, Satellite 

Sensinu of a Cloudv Atmosmhere, A. Henderson-Sellers, ed. 

Taylor and Francis Publishers, Philadelphia. 

Goward, S.N., D.J. Dye, W. Dulaney and J. Yang, 1990: 

Critical assessment of NOM Global Vegetative Index data 

product. Internztional Journal of Remote Sensina, in press. 

Goward, S.N., C.J. Tucker, and D.J. Dye, 1985: North American 

Vegetation Patterns Observed with the NO=-7 Advanced Very 

High Resolution Radiometer, Veuetation, 64: 3-14. 

Gutnan, G., 1987: The derivation of vegetation indices from 

AVHRR data. International Journal or Rerote Sensina, 8: 

1235-1242. 

Gutmail, G., 1989: On the relationship between monthly mean 

and mzximum-value composite vegetation indices. International 

Jourzcil of Remote Sersinc;, lO(8) : 1317-1325. 

Gutman, G., 1990: Vegetation indices from AVHRR: An update 

and fucure prospects. Renote Sensincr of Pnvircnnent, in press. 

Global Change Data Base Afiica Documentation Appendix A-26 
Holben, B.N., i986: Characteristics of naxinum-value 

conposite images frc: terr;poral AVHRR data. Jnternacicnal 

Journal of Remote Sensinq, 7: 1417-1434. 

Holben, B.N. and R.S. Fraser, 1984: Red and near-infrared 

sensor response to off-nadir viewing, International Journzl of 

pezote Sensinq, 5: 145-160. 

Hoiben, B.N., D. Kines and R.S. Fraser, 1986: Directional 

reflectance response in AVHFU? Red and near-IR bands for three 

cover types and varying atnospheric conditions. penote Sensinq 

of Enviroment, 19: 215-256. 

Huete, A.R., 1988: A soil-adjcsted vegetation index (SA'I). 

Renote Sensina of Environment, 25: 295-339. 

Justice, C.O., J.R.G. Townshend, B.M. Holben, and C.J. Tucker, 

1985: Analysis of the Phenology of Global Vegetation using 

Meteorological Satellite Data, International Journal of Remote 

Sensinq, 6(8) : 1271-1318. 

Kaufnan, Y.J. and B.lJ. Holben, 1990: Calibration of the AVHRR 

visible and near-IR bands by atnospheric scattering, ocean 

glint and desert reflection. Jcurnal cf AuDlied Keteoroloav, 

in press. 

Kogan, F., 1990: Rencte sensing of weather inpacts on 

vegetation in non-honogeneous areas. International Journal of 

Rezote Sensinc, in press. 

Price, J.C., 19S8: An update on visible and near infrared 

calikatim of satellite instruments. Rezote Sensinc of the 

Environment, 24: 419-422. 

Singh, S.1.1., 198Sa: Simulation of solar zenith angle effect 

on global vegetation index (GVI) data. Interxatiozzl Journal 

of Remote Sensinu, 9: 237-248. 

Singh, S.M., 1988b: Lowest order correction for sol~r zenith 

angle to Global Vegetation Index (GVI) data. International 

Journzl of Rencte Sensinq, 9: 1565-1572. 

Smith, E.A., W.L. Crosson, H.J. Cooper and W. Heng-yi, 1990: 

Heat and moisture flux modeling of the FIFE graszland canopy 

aided by satellite derived canopy variables. Proceedinas cf 

the ?_MS Svntllcsiun on F'IFZ, Anaheim, CX, Feb. 7-9, 1990. 154- 

162. 

Tar?ley, J.D., S.R. Schneider, and R.L. Money, 1984: Global 

Vegetation Indices from the N0PJ.-7 Meteorological Satellite, 

Journal of Clize=a 2nd p,uulied Metecrcloav, 23: 491-49.;. 

Global Change Data Base Ajku Docimterttutiorr Appendix A-21 
Taylor, B.F., P.W. Din1 and J.W. Kidson, 1985: Determination 

of seasonal and interannual variation in New Zealand pasture 

growth from NOAA-7 data. Remote Sersincr of Environment, 18: 

177-192. 

Thomas, G. and A. Henderson-Sellers, 1987: Evaluation of 

satellite derived land cover characterizxics for global 

climate modelling. Climate Chancre, 11: 313 347. 

Tucker, C.J., J.R.G. Townshend, and T.E. Goff, 1985: African 

land cover classification using satellite data, Sciencd, 

227(4685): 363-375. 

Tueller, P.T. and S.G. Oleson, 1989: Di;;rnal radiance and 

shadow fluctuations in a cold desert shrub plant community. 

Remote Stnsincr of Environment, 29: 1-13. 

Global Change Data Base Africa Documentation Appendix A-28 
SELECTED REFERENCES ON THE NORMALIZED 

DIFFERENCE VEGETATION INDEX FROM AVHRR 

Asrar, G., Fuchs, M., Kanemasu, E. T., and Hatfield, J. L., 1984. Estimating 

absorbed photosynthetic radiation and leaf area index from spectral reflections in 

wheat. Agron. J., V. 76, pp. 300-306. 

Bartlett, D. S., Hardisky, M. A., Johnson, R. W., Gross, M. F., Klemes, V., and 

Hartman J. M., 1988. Continental scale variability in vegetation reflectance and its 

relationship to canopy morphology. International Journal of Remote Sensing, 9(7), 

1223- 1241. 

Choudhury, B. J., and Golus, R. E., 1988. Estimating soil wetness using satellite 

data. International Journal of Remote Sensing, 9(7), 1251-1257. 

Cihlar, J., St.-Laurent, L., and Dyer, J. A., 1991. Relation between the normalized 

difference vegetation index and ecological variables. Remote Sensing of Environment, v. 35, pp. 279-298. 

Clark, David M., Hastings, David A., and Kineman, John J., 1991. Global databases and their implications for GIs. IN Maguire, David J., Goodchild, Michael F., 

and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 

2 17-23 1. 

Di, L., 1991. Regional-scale soil moisture monitoring using NOM AVHRR data, 

Ph.D. Dissertation, Department of Geography, University of Nebraska-Lincoln. 

Di, L., Rundquist, D., and Han, L., 1991. A mathematical model for predicting 

NDVI using daily precipitation. Manuscript to be published. 

Gallo, K. P., 1990. Satellite-derived vegetation indices: a new climatic variable? 

Proceedings, Symposium on global change systems, February 5-9,1990, Anaheim, 

California. American Meteorological Society, Boston, Massachusetts. 

Gallo, K. P., and Heddinghaus, T. R., 1989. The use of satellite-derived vegetation 

indices as indicators of climatic variability. Proceedings, Sixth Conference on 

Applied Climatology, March 7-10, 1989, Charleston, SD. American Meteorological Society, Boston, Massachusetts. 

Gallo, K. P., and Brown, J. F., 1990. Satellite-derived indices for monitoring 

global phytoclimatology. Proceedings, 10th International Geoscience and Remote 

Sensing Symposium, May, 1990, Washington, DC. 

Gallo, K. P., and Brown, J. F., 1990. Evaluation of data reduction and cornpositing 

of the NOM Global Vegetation Index product: A cast study. Washington, DC, 

NOAA Technical Report NESDIS 54, 

Global Change Data Base Aft.ic.0 Documentation Appendix A-29 
Gallo, K. P., Daughtry, C. S. T., and Bauer, M. E., 1985. Spectral estimation of 

absorbed photosynthetically active radiation in com canopies. Remote Sensing of 

Environment, V. 17, pp. 221-232. 

Gatlin, J. A., Sullivan R. J., Tucker, C. J., 1984. Consideration of and improvements to large-scale vegetation monitoring. IEEE Trans. on Geoscience and 

Remote Sensing, GE-22(6), 496-502. 

Goward, S. N. 1990. Experiences and perspective in compiling long-term remote 

sensing data sets on landscapes and biospheric processes. GeoJournal, v. 20, pp. 

107-1 14. 

Goward, S. N., Dye, D., Kerber, A., and Kalb, V., 1987. Comparison of North and 

South American biomass from AVHRR observations. Geocatto International, v. 1, 

pp. 27-39. 

Goward, S. N., Markham, B., Dye, D. G., Dulaney, W., andYang, J., 1991. Derivation of quantitative normalized difference vegetation index measurements from 

Advanced Very High Resolution Radiometer observations. Remote Sensing of 

Environment, v. 35, pp. 257-277. 

Gray, T. I., and McCrary, D. G., 1981. The environmental vegetation index, a tool 

potentially useful for arid land management. AgRISTAR Report No. EW-N-1- 

04076, Johnson Space Center, Houston, Texas 17132. 

Gutman, G. Garik, 1991. Vegetation indices from Am: An update and future 

prospects. Remote Sensing of Environment, v. 35, pp. 121-136. 

Gutman, G. Garik, and Liu, William T., 1991. Bio-climates of South America as 

derived from multispectral AVHRR data. 24th International Symposium on 

Remote Sensing of Environment, Rio de Janeiro, May 1991. Ann Arbor, Michigan, Environmental Research Institute of Michigan. Summary pp. 19-20; Proceedings in press. 

Hastings, David A., and Di, Liping, 1992. Modeling Global Change Phenomena 

with GIs using the Global Change Data Base. Remote Sensing of Environment. 

(in press) 

Hastings, David A. Kineman, John J., Davis, W. M., and Clark, David M., 1989. 

Two unsung problems with GIs: Getting a system that does what you need, and 

working with non-ideal data. Proceedings of the 2nd National Conference on 

Geographic Information Systems, U. S. Professional Development Corporation and 

Government Computer News. 

Hastings, David A., Kineman, John J., and Clark, David D., 1991. Development 

and application of global data bases: considerable progress, but more collaboration 

needed. International Journal of Geographical Information Systems, v. 5., pp. 137- 

146. 

Global Change Data Base Afiicu Docurnerrrariort Appendix A-30 
Kidwell, Katherine B ., 1990. Global Vegetation Index Users Guide. National 

Oceanic and Atmospheric Administration, National Climatic Data Center, Satellite 

Data Services Division, Washington DC 20233. 45 pp. 

Kineman, J. J., Clark, D. M., and Croze, H., 1990. Data integration and modelling 

for global change: An international experiment. Proceedings of the International 

Conference and Workshop on Global Natural Resource Monitoring and Assessments: Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). 

Bethesea, Maryland, American Society of Photogrammetry and Remote Sensing, 

Vol. 2, pp. 660-669 

Kogan, F., 1991. Remote sensing of weather impacts on vegetation in nonhomogeneous areas. International Journal of Remote Sensing, in press. 

Koomanoff, V. A., 1989. Analysis of global vegetation patterns: a comparison 

between remotely sensed data and a conventional map. Biogeography Research 

Series, Report #890201, Department of Geography, University of Maryland, 

College Park, 11 1 pp. 

Ohring, G., Gallo, K., Gruber, A., Planet, W., Stowe, L., and Tarpley, J. D., 1989. 

Climate and global change: Characteristics of NOM satellite data. EOS Transactions of the American Geophysical Union, v. 70, pp. 889,891,894,901. 

Peters, A. J., 1989. Coarse Spatial Resolution Satellite Remote Sensing of Drought 

Conditions in Nebraska: 1985-1988. PhD Dissertation, Department of Geography, 

University of Nebraska, Lincoln. 

Rosenthal, W. D., Blanchard, B. J., and Blanchard, A. J., 1985. Visible/ 

infrared/microwave agriculture classification, biomass, and plant height algorithms. 

IEEE Trans. on Geoscience and Remote Sensing, GE-23(2), 84-90. 

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Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of 

Natural Vegetation. National Aeronautics and Space Administration Goddard 

Space Flight Center Final Report. Greenbelt, Maryland. 

Tarpley, J. D., Schneider, S. R., and Money, R. L., 1984. Global vegetation indices 

from the NOM-7 meteorological satellite. Journal of Climate and Applied Meteorology. v. 23, pp. 491-494. 

Townshend, J. R. G., Goff, T. E., and Tucker, C. J., 1985. Multitemporal dimensionality of images of normalized difference vegetation index at continental scales. 

IEEE Transactions, Geoscience and Remote Sensing, v. 23, pp. 888-895. 

Townshend, J. R. G., Justice, C. O., and Kalb, V. T., 1987. Characterization and 

classification of South American land cover types using satellite data. International 

Journal of Remote Sensing. v. 8, pp. 1189-1207. 

Global Change Data Base Africa Docunientution Appendix A-31 
Townshend, J. R. G., Justice, C. O., Choudhury, B. J., Tucker, C. J., Kalb, V. T., 

and Goff, T. E., 1989. A comparison of SMMR and AVHRR data for continental 

land cover characterization. International Journal of Remote Sensing. v. 10, pp. 

1633- 1642. 

Tucker, C. J., and T. A. Gatlin, 1984. Monitoring vegetation in the Nile Delta with 

NOM-6 and NOAA-7 AVHRR imagery. Photogrammetric Engineering and 

Remote Sensing, 50( l), 53-61. 

Tucker, C. J., Hielkema, J. U., and Roffey, j., 1985a. The potential of satellite 

remote sensing of ecological conditiclns for survey and forecasting desert-locust 

activity. International Journal of Remote Sensing, 6(1), 127-138. 

Tucker, C. J., Vanpraet, C. L., Shaman.! M. J., andvanIttersum, G., 1985b. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 

1980-1984. Remote Sensing of Environment, 17,233-249. 

Tucker, C. J., Fung, I. Y., Keeling, C. D., and Gammon, R. H., 1986. Relationship 

between atmospheric C02 variations and a satellite-derived vegetation index. 

Nature, v. 319, pp. 195-199. 

Walsh, S. J., 1987. Comparison of NOM-AVHRR data to meteorological drought 

indices. Photogrammetric Engineering and Remote Sensing, 53(8), 1069-1074. 

Wiegand, C. L., Gerbexmann, A. H., Gallo, K. P., Blad, B. L., and Dusek, D., 1990. 

Multisite analyses of spectral-biophysical data for corn. Remote Sensing of Environment, v. 33, pp. 1-16. 

Global Change Data Base Africa Documentation Appendix A-32 
-BOLSON WORLD ECOSYSTEMS (WE1.4) 

CITATION: Olson. J. S.. 1989. World Ecosystems (WE1.4) Digital Data. 

NOAA/NGDC/WDC-A, Boulder, Colorado. 2.3 MB. 

Global Change Data Base Africa Documentotion Appendix 9-1 
Blank page retained for pagination 
B. OLSON WORLD ECOSYSTEMS (WE1.4) 

DATE: 1989 

SOURCE: 

(1) From available maps and observations. 

Jeny S. Olson 

Global Patterns Company 

Box 361A, Rt. 2 

Lenoir City, TN 37771 

(2) Reprocessed by: 

Jeny Olson, Lee Stanley, Jeff Colby, Mark Ohrenschall 

NOWGDC 

Boulder, CO 

CONTRIBUTOR: 

Jerry S. Olson 

Global Patterns Company 

Lenoir City, TN 37771 

DATA CENTER: NGDC/WDC-A 

GEOGRAPHIC SAMPLING: 

.5 degree grid (with 10-min updates for Africa), characteristic classes 

COVERAGE: Global 

PROJECTION: Latllon 

TIME SAMPLING: Decadal composites 

REFERENCES: 

Olson. J. S., 1982. "Earth's Vegetation and Ahnospheric Carbon Dioxide," 

in Carbon Dio-ride Review: f982. Ed. by W. C. Clark, Oxford University 

Press, New York pp. 388-398. 

(continued on the next page) 

Global Change Data Base Africa Docirmenrution Appendix B-3 
Olson, J. S.. J. A. Watts, and L. J. Allison, 1983. Carbon in Live Vegetation 

of Major World Ecosystems, Report OW-5862, Oak Ridge Laboratory, 

Oak Ridge, Tennessee. 

Olson, J. S. and J. A. Watts, 1982. "Major World Ecosystem Complexes 

Ranked by Carbon in Live Vegetation," Oak Ridge National Laboratory, 

Oak Ridge, Tennessct (map). 

FILE NAME: AFWE 14 Olson Ecosystem Classes Version 1 4 

Global Change Data Base Afi-rcu Documentation Appendix R-4 
OLSON WORLD ECOSYSTEMS (WE 1.4) 

DOCUMENTATION FILE 

File Name: AFWE l<DOC 

image title : Olson Ecosystem Classes Version 1.4 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : 0.0000000000E+00 

maximum : 7.2000000000E+01 

cell x : 1.66666666673-01 

cell y : 1.66666666673-01 

legend : 72 

category 1 : 1 CCX City complexes--being added for MM4 type cat.1 

category 2 : 2 SPV Shortgrass prairie variant of 40 or 41 

category 3 : 3 Not used 

category 4 : 4 Not used 

category 5 : 5 Not used 

category 6 : 6 TMT Temperate to montane tropical (major forest 

category 7 : 7 Not used 

category 8 : 8 DMB Desert, mostly bare 

category 9 : 9 Not used 

category 10 : 10 Not used 

category 11 : 11 Not used 

category 12 : 12 Not used 

category 13 : 13 Not used 

category 14 : 14 Not used 

category 15 : 15 Not used 

category 16 : 16 Not used 

category 17 : 17 ICE Antarctic ice, land or grounded shore ice 

category 18 : 18 Not used 

category 19 : 19 Not used 

category 20 : 20 SRC Snowy, rainy coastal conifer (with alder etc.) 

category 21 : 21 MBC Main Boreal conifers 

category 22 : 22 SNB Snowy non-Boreal conifer forest 

category 23 : 23 CDS Conifer/deciduous, snow persisting in winter 

category 24 : 24 SED (semi) Evergreen deciduous, little/no snow 

category 25 : 25 SDF Similar to 26, snow persisting in winter 

category 26 : 26 TDF Temperate -deciduous forest, little or no snow 

category 27 : 27 NSC Non-snowy conifer forest 

category 28 : 28 TMC Tropical montane complexes (tree ti other) 

category 29 : 29 TSF Tropical seasonal forest (-evergreen ...) 

category 30 : 30 CFS Cool farmland C settlements (“snowy) 

category 31 : 31 MFS Mild/hot farmland C settlements 

category 32 : 32 RGD Rain-green (drought-deciduous) (major forest 

and woodland) 

category 33 : 33 TRM Tropical rainforest (major forest and 

woodland) 

category 34 : 34 Not used 

category 35 : 35 Not used 

and woodland) 

(major forest/woodland) 

Global Change Data Base Africa Docwnenrurion Appendix B-5 
category 36 : 36 

category 37 : 37 

category 38 : 38 

category 39 : 39 

category 40 : 40 

category 41 : 41 

category 42 : 42 

category 43 : 43 

category 44 : 44 

category 45 : 45 

category 46 : 46 

category 47 : 47 

category 48 : 48 

category 49 : 49 

category 50 : 50 

category 51 : 51 

category 52 : 52 

category 53 : 53 

category 54 : 54 

category 55 : 55 

category 56 : 56 

category 57 : 57 

category 58 : 58 

category 59 : 59 

category 60 : 60 

cateqory 61 : 61 

category 62 : 62 

category 63 : 63 

category 64 : 64 

category 65 : 65 

category 66 : 66 

category 67 : 67 

category 68 : 68 

category 69 : 69 

category 70 : 70 

category 71 : 71 

cateqory 72 : 72 

PRA 

wc I 

CCI 

CCP 

CGS 

MGS 

CSM 

STB 

MAG 

MOS 

MET 

OD H 

EAQ 

HVI 

SDB 

ODS 

css 

TUN 

TRC 

SCW 

RWC 

swc 

Gcw 

STW 

SDT 

SLT 

NMT 

WTM 

HMW 

Nw 

NE 

SE 

sw 

PDL 

GLA 

SSF 

MSM 

Paddy rice and associated lands (part 

anaerobic ) 

Warm/hot crops with extensive irrigation 

Cool crops with irrigation (variable extent) 

Cold crops, pasture, irrigation -local 

Cool (snowy). grass/shrub (including much 2) 

Mild/warm/hot grass/shrub 

Cold steppe/meadow +/- larch, scrub 

Savanna, mostly tallgrass + bush fallow/woods 

Mire (acid bog &/or groundwater-fed fen) -cold 

peatland (or muck): sphagnum, grass-like, 

and/or dwarf shrub or mire tree vegetation 

Marsh or other swamp (warm-hot) salty/ 

freshwater marsh, thicket, -flooded woods 

Mediterranean evergreen tree/scrub 

(winter rain) 

Other dry or highland scrub/tree 

(juniper, etc. ) 

Eucalyptus or Acacia, quebracho, saxaul 

Hot-mild volcanic "islands"(variab1e 

vegetation) 

Sand desert, partly blowing 

Other desert and semidesert 

Cool/cold shrub semidesert/steppe 

(sagebrush. . . ) 

Tundra (polar, alpine) 

Temperate rainforest (+/- conifer) (major 

forest and woodland) 

Similar to 58: cool-cold (-persistent snow) 

Regrowing woods + crop/grass 

Similar to 56: cool-cold (-persistent snow) 

Grass/crop + <40% woods: warm, hot 

Succulent and thorn woods 

Southern dry taiga (and other aspen/birch, 

etc. 

Siberian larch taiga [partly other taiga 211 

Northern or maritime taiga/tundra 

-Wooded tundra margin (or mt. scrub, meadow) 

Heath and moorland, wild or artificial 

(-grated) 

NW quadrant near most land (...mainland, large 

island, . . .) 

NE quadrant near most land (...peninsula, 

small islands, ...) 

SE quadrant near most land (...or isthmus) 

SW quadrant near most land 

Polar desert (rock lichens) 

Glaciers (other polar and alpine) 

Salt/soda flats (playas, lake flats rarely 

-wet) 

Mangrove swamp/mudflat [Africa only] 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Global Change Data Base Africa Documcnti~/ion Appendix B-6 
DATA UPDATING PROCEDURES 

Jeny Olson, Lee Stanley, Jeff Colby and 

Mark Ohrenschall 

National Geophysical Data Center 

Boulder, Colorado 

During the summer of 1989 Dr. Jerry Olson, Let Stanley and research assistants 

from the National Geophysical Data Center updated the data set, llMajor World Ecosystem 

Complexes Ranked by Carbon in Live Vegetation: A Database." The results of this 

update have been incorporated into Olson World Ecosystem wE1.4. 

Data were revised at both 30 and 10 minute-resolution. Changes were first made 

for the 30 minute data between 20 degrees West and 70 degrees East. In addition 

modifications were made to limited portions of the United States data. Revisions were 

made in the following ecological classes--tropical forest (type 33). polar deserts (type 69). 

ice (type 70). and a few areas of mangrove/tropical swamp forest (type 72). Updates for 

the United States were quite limited and affected mostly the islands and coastlines of 

Alaska. To a lesser extent changes were made dong the western and eastern coastlines 

of the lower 48 states. 

Modifications at 10 minute-resolution were numerous, but were conhed to the 

African continent and some coastlines. They included changes in tropical montane 

compIexes (type 28), broadleaved evergreen forest types at high altitudes with cool 

climates despite tropical latitudes (type 6) (for example in Cameroon, Ethiopia, other areas 

of East Africa and some in the Atlas Mountains), mangrove tropical swamp forests (type 

72). and coastlines. Salt/soda flats (type 71) were not systematically reviewed but 

received sample editing. 

Global Change Dolo Base Africo Dociitricrriurion Appendix B-7 
Blank page retained for pagination 
(legwe2.0, Aug. 17, Revised DEC. 7, 1989; edit Jan. 21, 1990) FREE copying. 

LEGE3DICAPTIONS FOR WORLD ECOSYSTEM HAPIDATABASE version 2.0 (or 3.0) 

Oucstions: 615/576 2250) 

Jerry S. Olson, Global Patterns Company, Box 361A, Rt. 2, Lenoir City TN 51771 

Broad Groups (and symbols for printer character output) on the left are here 

related to the Biosphere Atmosphere Transfer Scheme (BATS) codes of Oickinson 

et al. (1986) at the National Center for Atmospheric Research (NCAR), box 3000, 

Boulder, Colorado, 80307-3000. The middle list and numbers under the WE2.0 

heading match improved versions of the Nap of ilajor World Ecosystem Complexes 

(Olson and Watts 19821 with half degree (50’) cells. Additional or alternative 

numbers are being tested for 10’ sub-ceil database for the International Council 

of Scientific Unions (ICSU) and World Data Center A, National Geophysical Data 

Center of U.S. National Oceano graphic and Atmospheric Administration (NOAA). 

18aata~ta=l.8~l~aa~l~a~~~~~a~a~~~aa~~~~~8aa~~~aa~~88a~8

Table !. Groupings of World Ecosystem Complexes and Dominant Vegetation 

Printer a provisional 

Group Landscape/Life Form Major k‘orld Ecosystems (WE) colors or 

symbo 1 b ----------------------c--------------- other 

BATS :/GROUP Vegetation/climate WE2.0 variables 

------ .................... ........................... ------ ------------- 

WETLANDS, COASTS, AND WATER 

Waters, including-- 0 Pale 

14 Inland Waters (doninating 30’ cells) (14) 

15 Ocean (151 BLtiE 

Coast, Island --- , shore 8 hinterland complexes 65-68 

65 ...U mean NW, NE, SE, or SW quadrant, 

respectively, near most land (mainland, large BUCK 

island, peninsula. small islands, or isthmus) 

* Hot-mild volcanic ‘islands‘ (variable vegetation) 49 

I [Rivers/small lakes t land, partly wooded 741 (p. blue) 0 [Other small islands/atolls--for 10’ resolution ’731 --- ........................................................... --------- 

! 

a 

Wetlands: ma;)or or intermittent methane sources 

13 Swamp, Harsh, Bog, or Fen 

Hangrove swa~~p/rnudflat [Africa only, Aug. 19891 

“saline forests, thickets, tideland, estuaries 

Harsh or other swamp (warm-hot) 

salty/freshwater marsh, thicket, “flooded woods 

!!ire (acid bog &/or groundwater-fed fen), “cold 

peatland (or muck): sphagnum, grassilike. 

and/or dwarf shrub or mire tree vegetation 

10 Irrigated Crops--ONLY the following grouped here: 

t Paddy rice and associated lands (part anaerobic) 

(See below for other cropland types.) 

72 dark 

4s 

RED 

1 ight 

CYAN 

36 

TUHDRA-LIKE AND!OR DESERT 

Deserts and Semideserts (nonpolar) 

Saltlsoda flats (playas, lake flats rarely “wet) 71 

8 Desert: mostly bare 8 

- 

Sand desert, partly blowing 50 BROWN 

I Other desert and semidesert 51 --- 1 11 Coollcold shruo semidesert/steppe (sagebrush ...I 52 

Global Change Data Base Africa Docunlermrion Appendix B-9 
Polar and Alpine black 

12 Glaciers 

Polar desert ( rock 1 icnens) 69 - 

Antarctic ice, land or grounded shore ice (17--)12) WHITE 

Glaciers (other polar and alpine1 70 ...................................................... - - - 

9 Tundra 8 Marginal Cold A Tundra (polar, alpine1 SJ 

! 16.17 "Woc:-ed tundra margin (or mt. scrub, meadow) 63 GRAY 

+ Cold steppe/meadow +/-larch, scrub 42 black 

(Siberian to Himalayas or maritime No. Pacific) 

GRASSY &/OR CROP, SETTU!fE?IT LAND 

Main Grass- &/or Shrut'and 

Cool (snowy) grasdshrub (including much 2) 40 

2 Shortgrass prairie variant of above or following 2 

Mildluarmlhot grasslshrub 41 

Heath and moorland, wild or artificial ("grazed) 64 

7 Savanna, mostly Tallgrass +bush fallow/ woods 43 

Parts have seasonal uaterlogcing %lor ungulates; 

these parts and termites are methane sourct?s. ........................................................... 

Cropped, Residential, Commercial, Park, Harginal Lands 

10 Irrigated Drylands (besides paddy rice), +/- trees 

Wardhot cmps with extensive irrigation 37 

Cold crops, pasture, irrigation "local 39 

Cool crops with irrigation (variable extent) 38 

........................................................... 1 Other Crop/mixed Farming, Towns, Industrial complex 

?!ild/hot farmland & settlements 31 

[CITY CDMPLEES--being ADDED for MH4 type cat.1) 1 

Cool farmland 8 settlements ("snowy) .30 

pink 

--- 

RED 

----- 

light 

WEE3 

----- 

YELLOW 

'WOODS'--from forests to tall or dense scrub, frequently patchy: 

INTERRUPTED WOODS, artificial or natural mosaics uith other cover 

ti91 Second-growth Woods and Field Mosaics +/- Towns 

I Fieidlwoods 

G Grass/crop + (40% woods: warm, hot 58 

g similar: cool-cold ("persistent snow) 55 light 

SS Forestf field GREEX 

F Regrowing wads + crop/grass 56 

f similar: cool -cold ( "persistent snow 1 SI ....................................................... ----- 

Nartow Conifers; gradients to scrub/meadow 

8 1191 Northern or maritime taiga/tundra 62 Light 

{Subalpine forest J meadow I th icltet 62a CYAN 

Semiarid Woodland or Lou Forast/Scrub + fields ----- 

E ti91 Eucalyptus or Acacia, quebracho, saxaul 48 

........................................................... 

Other Dry Woods Hosaics (+I- fields "locally) BROWN 

? 16/17 Other Dry or Highland ScrubITree (Juniper etc.) 47 

Ip 16 Yediterranean evergreen tretfscrub (winter rain) 46 

e (16 other (mostly) evergreen scrub 16) 

d (17 (mostly) deciduous scrub NEW 171 

Tropical SucculsntJthorn koods or ?lontane 

x 17/16 Succulent and thorn woods 59 

3 6+7 Tropical montane complexes (tree 8 other) 28 

VIOLET 

Global Change Data Base Africa Documentotion Appendix B-10 
S 

6 

T 

R 

S 

M 

N 

n 

J 

i 

0 

P 

C 

B 

A 

L 

W 

YAJOR FOREST ASD k'OOCL'S0, with cutlburn openings 

"Tropical D,ry Uoodland/Forest 

"Broadleaved evergreen 

5 Rain-green (drought-deciduous) 32 

6 Temperate to montane tropical 

Temperate rainforest (+I- conifer) 

6 

54 -- Tropical rain; xest 33 PURPLE 

Tropical seasmal forest ("evergreen-- 29 

OR "semidec;duous, or deciduous in monsoon area) --------------------------------------------------------.-- 

Other Deciduous and Mixed Forest and Woodland 

18 flised deciduous and cuniferlevergreen 

(semi) Evergreen/deciduous, little/oo snow 24 CYAN 

Coniferldeciduous, snow persisring in winter 23 

See also: southern and Siberian taiga below (60-61: 

--- 

Summergreen ("cold-deciduous, temperate) forests 

Temperate "deciduous forest, little or no snow 25 

similar, SI.OW persisting in winter 25 CREE3 

{Low deciduous woods of cold andlor dry areas 51 

Conifer (with some mixed and local broadleavedl Forest 

3 Evergreen needle-leaved forests (See also taiga) 

Xon-snowy conifer forest 27 

Snowy non-Boreal conifer forest 22 BLUE 

Snowy, rainy coastal cmifer (with alder etc.) 20 

Taiga (Boreal Forest) +/- deciduous trees 

3 Main Boreal conifers 21 

18/4 Southern dry taiga land other aspenlbirch, etc-1 60 

.......................................................... - - .. 

CYAN 

4 Deciduous Conifer 

Siberian larch taiga [partly other taiga 211 61 (41 

(Corning additions: Cypress swamp, Hetasequoia 83-84) 

a Numbers in middle columns from Olson et al. (19831 or Numerical Data Set ;7 

. (= WEl.2) of Olson et al. (19851, refined for wetlands and cther methane, 

isoprene or terpene sources. (1 not yet used; (1 unevenly appiied in 1389. 

b Numbers on left are already in wide use for BATS (Biosphere-Atmosphere 

- Transfer Scheme1 of Dickinson et al. 1986. for better defining vegetationsoil boundary conditions of the global clinate system, for global climate 

models (GCYs) at XCdR (rYacional Center fur Atmospheric Research). 

Global Change Data Base Africa Documentation Appendix B-1 1 
Blank page retained for pagination 
[Excerpts fmm original reference] 

NDP-017 

MAJOR WORLD ECOSYSTEM COMPLEXES RANKED BY 

CARBON IN LIVE VEGETATION: A DATABASE 

Contributed by 

Jerry S. Olson 

Julia A. Watts 

Linda J. Allison 

Environmental Sciences Division 

Oak Ridge National Laboratory 

Oak Ridge, Tennessee 

September, 1985 

Prepared by 

R.E. Millemann and T.A. Boden 

Carbon Dioxide Information Center 

Information Resources Organization 

Oak Ridge National Laboratory 

Oak Ridge, Tennessee 37831 

operated by 

Martin Marietta Energy Systems, Inc. 

for the 

U. SI Department of Energy 

under contract No. DE-AC05-840R21400 

Global Change Data Base Africa Documentution Appendix B-13 
CDIC WC DATA PACKAGE-017 

ABSTRACT 

1- 

2. 

3. 

NUMERIC DATA PACKAGE NAME 

Major World Ecosystem Complexes Ranked by Carbon in Live 

Vegetation: A Database 

Jerry S. Olson, Julia A. Watts, and Linda J. Allison 

Environmental Sciences Division 

Oak Ridge National Laboratory 

Oak Ridge, TN 37831 

CImON OF PACXAGE 

The Carbon Dioxide Information Center (CDIC) recommends the 

following citation for those citing or referencing this package: 

Olson, J.S., J.A. Watts, and L.J. Allison. 1985. Major world 

ecosystem complexes ranked by carbon in live vegetation: A 

Database. NDP-017, Carbon Dioxide Information Center, Oak 

Ridge National Laboratory, Oak Ridge, Tennessee. 

Available vegetation maps and resource inventories are not 

sufficiently detailed, accurate, and current to answer major 

questions about the biological @@source or sink" of at?lospneric 

C32. A numbez of carbon estimates were summarized and discussed 

in Bolin et al. (1979) and Bolin (19811, along with applications 

for modeling the global carbon cycle. Other inventories, 

however, use dfffermt methods and classification systems 

estimating terrestzial carbon and are therefore difficult 

for 

to 

ccmpare. Regional studies, which concentrate on one area with 

litzle atzention to the others, can furnish useful but limited 

data on types and trends. Changes in one vegetation group are 

sometimes offset by changes in the opposite direction in other 

areas. Deforestation or other landscape modifications in one 

region may be balanced by reversion to fxests in others. But 

the carbon cycle must be evaluated on a global scale. While 

detailed, localized studies proceed, the map and data base 

documented in this package provide a unifying format for the 

cmtinuing evaluation of changes in estimated carbon in plant 

mass, and eventually other czmponents, of the whole terrestrial 

ecsystem. 

Global Change Data Base Africa Documerrtutiun Appendix B- 14 
5. SOURCX AND SCOPE OF TBE DATA 

me global ecology maF tiAAa* *YC - shows the spatLal dirtributlon of major world ecosystem complexes estimated for 

1980. 

are older some of this information may not be current. 

for more drascic changes caused by humans, it also reflects the 

However, because some information sources documented here 

Except 

file of land-cover types. 

and addinq their areas over lacitude bands gave total area 

estimates for these eccsystem complexes. 

estimates are brought together Fn Sect. 4.1 of Olson et 

al. (1983) and confirm the thesis that some earlier estimates of 

forest area and forest contribution to global carSon inventories 

were apparently overestimated. 

density of carbon per unit area (Table 1) are discussed in 

Sect. 4.2 and the Appendices of Olson et al. 

the low medium, and high density estimates by ecosystem area 

gives corresponding estiroates of the global total carbon by 

ecosystem complexes (Sect. 4.3 of Olson et al., 19831. 

Only the mass of green plants is considered here, since the 

amounts of anha1 biomass are small Ln comparison. 

fungi and bacteria is not necessarily negligible, Dux evaluating 

it was beyond the scope of the 1983 report. 

decomposers varies greatly with time and space. IC is imporzant for cmtrolling flux or recycling rates rather than for ics own 

inventory. The recycling rate of C02 by respiration is usually 

expressed relative to the substrates of standing, fallen, and 

incoqorated soil residues. The range of uncertainty about total 

plant carbon and its component parts reveals where more attentLon 

could reduce the uncertainty. Implications of these data are 

discussed briefly by Olson et al. (1983). 

Estimation of the inventory of carbon in major world 

ecosystems and of the exchanges with the atmosphere and other 

major reservoirs has thus been approached in two ways. In the 

first approach, development of broad global patterns uses 

potential vegetation maps, or associates vegetation types with 

climatic or other environmental factors independent of local 

disturbance. 

(19671, Lietfi (19751, Kucaler (19781, and Bailey (1978) are 

examples of this approach. In the second approach, development 

of modern regional or stand-type estimates is based on analyses 

of current vegetation and land-use practices. This method uses 

updated resource maps of natural vegetation, forest-T suzveys, 

agricultural yields, and human and economic as well as 

Counting the cells of each type in each 0.So latitude band 

Some independent area 

Current estimates of the range in 

(1983). Multiplying 

The mass of 

Thk mass of 

The distributions described by Bazilevich and Rodin 

Global Change Data Base Africa Documentation Appendix B-15 
geopolitical consiceratlons. 30th approaches were applied in the 

development of the ecDsystem map. The personal judgment of 

experts about eccsystem t-nes, their locations and extenc, and 

likely biomass or carbon in landscape complexes representacive of 

different parts of the world is crucial in either approach. 

6. APPLICATZONS OF THE DATA 

The rates of C02 release to the atmospheze and its removal 

are controlled differencly by factors affecting photosynthesis, 

respizatXJn, and burning as well as by shifts in land use and 

climate. Undezstanding these relations togecher with increased 

knowledge of tSe plant pools undergoing change will enhance our 

ability to integraze infomation from biology and geograpny into 

geoFhysica1 modeling of element cycles and climate. 

c-Lzrent refereace base far interpreting the role of vegecation is 

the global cycling of C02 and other gases. It combines 

improvement in available ecological data ana tee-hniques for 

ccmputer generation of maps. 

The data provide a basis for improved estimates of 

vegetarion and soil carbon, of natural exchanges of CO , and of 

net historic shifts of carbon between the biosphere ani the 

amosphere. 

3. Landscape areas inferred from the map and other sources 

and weighted averages of carbon in various kinds of Vegetation 

suggest significantly lower carbon in global vegetation than has 

been somec~es used in recent analyses of global geochenical 

cycles. 

4. 

the plant carbon. Yet decreases in area and mass of closed 

forest have alreaay been so extensive that hundreds of petagrams 

(billion metzic tons) of carbon were probably released over 

centuries or rrtillenia before recent industzialization and human 

population growth. 

cmtriSute a few petagrams of carbon per year to atmospheric C02, 

if ccnversion as well as harvesting of massive tropical forests 

continues (a significant fraction of recent releases of 5 

pecagrams of carSon per year is from burning of fossil fuels). 

TZ.e problem remains, however, to infer how much of that zelease 

is offset by renewed storage in untilled areas of the tropics. 

II? temperate or Boreal zones, even more forests are regrowing 

after earlier harvesting and clearing and because of recent :;-re 

1. The map of Major World Ecosystem Complexes provides a 

2. 

Tabulations still show tree formations holding most of 

5. The rmaining plant pool is still large enough t3 

protection. 

7. RESTXImONS AND LZMITmONS 

=.e broad ranges given on the map legend for the carbon 

cmtent in tbe major groups probably span all the type means in 

each ecosystem group and most of rhe scand averages for eacn 

mapping cell witbin an ecasystem group. At the presenr stage of 

Global Change Jlats Base Afiicu Docwnentu/ion Appendix 8-16 
cevelcament cf this methodology, a carbon density based OR =he 

ranqes and meazs as ~iven canncz be assic.ieC 'cc eack CE,,. Such 

an effcrr re-ires local assesfqen=s of vegetative =we=, laze 

use, soils, copoqrapny, anc climatic facers. 

Uncerzainties for regions known poorly or ir-ferred 

indirectly (by araloqy) will remain for additionEl refinemezzs. 

The di;i:izei map offers a systematic way of locating fczure 

revisi0r.s of boqurdarics anc ecosystem areas. As amounts of 

carbon r?r unit area or their transfer rates are analyzed in morc 

locatior.5, zherr the mean estimates, which are currently aF?liec 

a= l'defaultll values for each place a Given type oczus, can be 

suizably adjusceci fcr variaticns among nations, amcnc clixazic or 

ssll recions of a given ecasystem t-De, or for partic:Laz mi? 

cells. 

(e.g., with Advanced Very Eigh Resolution Radiomecry) is one 

approach to this next stage of analysis. 

changes significantly, whether due to C02 cz to ocher possible 

interacting ca\;ses (Manabe and Uerherald 1967, 1975, 1980; WMO 

1979; Clark 19&2), then the relation of the present pztzerns cf 

vegetation and climate can be used in helping to projecz the 

impacts or, chaiige of vegetatior, and related resouzces. 

Because forests, open woodld~ids, and cmplexes in which 

these ecosyszems a1ter;iate with nonwooaed communities (so-called 

"Inter,rup=cd Woods") , constitute most of the carbon ir. live 

vegetatioa, present uncertainties (about 20 percent) szill center 

on these types. Wheze investigatec most recently, --c?J.zzl woods 

have been identified with even lower biomass and ctzScr, es:irna=es 

than were expected in 1982. 

Some cmiferous forests of mois: regions like the 

nozthweszern coast of Nort-h America are identified vltk=. we2 

higher biomass than the trogicai. rain fores~s. 

A majcr uncertainty exis% in 'he "nonwcocs" ecxlrsten 

concerr,ing the amount of carbcn that should be addec! for 

sciztered trees or woody inclusions not counted in the "WCCCS~~ 

ecasyszerns. neze is considerable allowance from jccmez:= 2nd 

emerience in using the sampled averages differently. 

hs closer refinements are made, the fuzure esckates fzr 

ctrbon densizy in ecosystems will more likely Se revised cswnwzzc 

frm =he meCium estkate cf 560 2 100 petaczms (2s). 

%e database in this package may not aupliczzo, exzczly the 

wcrlci ecosystems complzxes me? chat is part cf :his pack5G-e. 

Revisions to about 20C cells (0.8% of all cells) were maie t3 =he 

?lites used to prim t-30, map. Some of chese revisicxx were nc= 

recorcied and may not have Seen incar?oraced int9 the dat&ase. 

-.1 

Remote sens,.:~ on a continental or world-wide basis 

If the clirnize itself 

-IGlobal Change Data Base Afiica Doctimentation Appendix B-17 
-cHOLDRIDGE LIFE ZONES 

CITATION: Leemans. R., 1990. Global Holdridge Life Zones. Digital Data. 

ILASA, Laxenberg, Austria. 26 MB. 
Blank page retained for pagination 
C. HOLDRIDGE LIFE ZONES 

DATE: 1990 

SOURCE: 

R. Leemans 

hernational Institute for Applied Systems Analysis (IIASA) 

Laxenberg, Austria 

CONTRIBUTOR: 

Peter Schlesinger 

Woods Hole Research Center 

Woods Hole, MA 

DATA CENTER: IIASA 

GEOGRAPHIC SAMPLING: .5 degree grid, cliaracteristic classes 

COVERAGE: Global 

PROJECTION L;lt/lon 

TIME SAMPLING: Decadal composites 

REFERENCES: 

Leemans, R.. 1990. "Global data sets collected and compiled 

by the Biosphere Project." Working paper, IIASA, A-2361. 

(Excerpts included) 

FILE NAMES: AFHOLD Holdridge Life Zones 

Global Change Data Base Africo Docuntenration Appendix C-3 
Blank page retained for pagination 
HOLDRIDGE LXFE ZONES 

DOCUMENTATION FILE 

File Name: AFHOLD.DOC 

image title : Holdridge Life Zones 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : 0.0000000000E+00 

maximum : 3.7000000000E+01 

cell x : 1.66666666673-01 

cell y : 1.66666666673-01 

category 1 : 1 PD POLAR DESERT 

category 2 : 2 SpDT SUBPOLAR DRY TUNDRA 

category 3 : 3 SpMT SUBPOLAR MOIST TUNDRA 

category 4 : 4 SpWT SUBPOLAR WET TUNDRA 

category 5 : 5 SpRT SUBPOLAR RAIN TUNDRA 

category 6 : 6 BD BOREAL DESERT 

category 7 : 7 BDS BOREAL DRY SCRUB 

category 8 : 8 BMF BOREAL MOIST FOREST 

category 9 : 9 BWF BOREAL WET FOREST 

category 10 : 10 BRF BOREAL RAIN FOREST 

category 11 : 11 CTD COOL TEMPERATE DESERT 

category 12 : 12 CTDS COOL TEMPERATE DESERT SCRUB 

category 13 : 13 CTS COOL TEMPERATE STEPPE 

category 14 : 14 CTMF COOL TEMPERATE MOIST FOREST 

category 15 : 15 CTWF COOL TEMPERATE WET FOREST 

category 16 : 16 CTR COOL TEMPERATE RAINFOREST 

category 17 : 17 WTD WARM TEMPERATE DESERT 

category 15 : 18 WTDS WARM TEMPERATE DESERT SCRUB 

category 19 : 19 WTTS WARM TEMPERATE THORN STEPPE 

category 20 : 20 WTDF WARM TEMPERATE DRY FOREST 

category 21 : 21 WTMF WARM TEMPERATE MOIST FOREST 

category 22 : 22 WTWF WARM TEMPERATE WET FOREST 

category 23 : 23 WTR WARM TEMPERATE RAINFOREST 

category 24 : 24 StD SUBTROPICAL DESERT 

category 25 : 25 StDS SUBTROPICAL DESERT SCRUB 

category 26 : 26 StTW SUBTROPICAL THORN WOODLAND 

category 27 : 27 StDF SUBTROPICAL DRY FOREST 

category 28 : 28 StMF SUBTROPICAL MOIST FOREST 

category 29 : 29 StWF SUBTROPICAL WET FOREST 

category 30 : 30 StR SUBTROPICAL RAINFOREST 

category 31 : 31 TD TROPICAL DESERT 

category 32 : 32 TDS TROPICAL DESERT SCRUB 

category 33 : 33 TTW TROPICAL THORN WOODLAND 

category 34 : 34 TVDF TROPICAL VERY DRY FOREST 

category 35 : 35 TDF TROPICAL DRY FOREST 

category 36 : 36 TME' TROPICAL MOIST FOREST 

category 37 : 37 TWF TROPICAL WET FOREST 

legend : 37 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i.e. without rescaling) . 

Global Change Data Base Africa Documelitation Appendix C-5 
HOLDRIDGE LIFE ZONES 

DOCUMENTATION FILE 

File Name: AFHOLD.DOC 

image title : Holdridge Life Zones 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : O.OOOOOOOOOOE+OO 

ma:; imum : 3.7000000000E+01 

cell x : 1.6666666667E-01 

cell Y : 1.66666666673-01 

legen2 ! 37 

category 1 : 1 PD 

category 2 : 2 SpDT 

category 3 : 3 SpMT 

category 4 : 4 SpWT 

category 5 : 5 SpRT 

category 6 : 6 BD 

category 7 ! 7 BDS 

category 8 : 8 BMF 

category 9 : 9 BWF 

category 10 : 10 BRF 

category 11 : 11 CTD 

category 12 : 12 CTDS 

category 13 : 13 CTS 

category 14 : 14 CTMF 

category 15 : 15 CTWE' 

category 16 : 16 CTR 

category 17 : 17 WTD 

category 15 : 18 WTDS 

category 19 : 19 WTTS 

category 20 : 20 WTDF 

category 21 : 21 WTMF 

category 22 : 22 WTWF 

category 23 : 23 WTR 

category 24 : 24 StD 

category 25 : 25 StDS 

category 26 : 26 StTW 

category 27 : 27 StDF 

category 28 : 28 StMF 

category 29 : 29 StWF' 

category 30 : 30 StR 

category 31 : 31 TD 

category 32 : 32 TDS 

category 33 : 33 TTW 

category 34 : 34 TVDF 

category 35 : 35 TDF 

category 36 : 36 TMF 

category 37 : 37 TWF 

POLAR DESERT 

SUBPOLAR DRY TUNDRA 

SUBPOLAR MOIST TUNDRA 

SUBPOLAR WET TUNDRA 

SUBPOLAR RAIN TUNDRA 

BOREAL DESERT 

BOREAL DRY SCRUB 

BOREAL MOIST FOREST 

BOREAL WET FOREST 

BOREAL RAIN FOREST 

COOL TEMPERATE DESERT 

COOL TEMPERATE DESERT SCRUB 

COOL TEMPERATE STEPPE 

COOL TEMPERATE MOIST FOREST 

COOL TEMPERATE WET FOREST 

COOL TEMPERATE RAINFOREST 

WARM TEMPERATE DESERT 

WARM TEMPERATE DESERT SCRUB 

WARM TEMPERATE THORN STEPPE 

WARM TEMPERATE DRY FOREST 

WARM TEMPERATE MOIST FOREST 

WARM TEMPERATE WET FOREST 

WARM TEMPERATE RAINFOREST 

SUBTROPICAL DESERT 

SUBTROPICAL DESERT SCRUB 

SUBTROPICAL THORN WOODLAND 

SUBTROPICAL DRY FOREST 

SUBTROPICAL MOIST FOREST 

SUBTROPICAL WET FOREST 

SUBTROPICAL RAINFOREST 

TROPICAL DESERT 

TROPICAL DESERT SCRUB 

TROPICAL THORN WOODLAND 

TROPICAL VERY DRY FOREST 

TROPICAL DRY FOREST 

TROPICAL MOIST FOREST 

TROPIW WET FOREST 

(category 0 : unclassed) 

[This legend is appropriate for display of the individual classes 

(i.e. without rescaling) . 

Global Change Data Base Africa Docwn~ntution Appendix C-6 
W 0 R KIN G ?A PER 

POSSIBLE CHANGES IN NATURAL VEGETATION 

PATTERNS DUE TO A GLOBAL WARMING 

Rik Lccmans 

February 1990 

wp-90-08 

PUBLICATION NUMBER 108 of the Biosphere Dynamics Projecr 

Global Change Data Base Africa Documentofion Appendix C-7 
POSSIBLE CHANGES IN NATURAL VEGETATION 

PATTERNS DUE TO A GLOBAL WARMING 

Rik Leemans 

February 1990 

WP-90-08 

PUBLICATION NUMBER 108 of the Biosphere Dynamics Project 

Working Papers are interim reports on work of the International Institute for 

Applied Systems Analysis and have received only limited review. Views or 

opinions expressed herein do not necessarily represent those of the Institute or 

of its National Member Organizations. 

INTERNATIONAL INSjT FOR APPLIED SYSTEMS ANALYSIS 

A-2361 Laxenburg, Austria 

Global Change Data Base Africa Documerrrution Appendix C-8 
FOREWORD 

IIASA's projects within the Environment Prognm are devoted to investigating the 

interaction of human development activities and the environment, panicularly in terms of 

the sustainable development of the biosphere. The research js policy-oriented, 

interdisciplinary, international in scope and heavily dependent on collaboration with a 

network of research scientists and institutes in many countries. The importance of IIASA'S 

Environment Program stems from the fact that the many components of the planetary lifesupport systems are being threatened by increasing human activity, and that these problems 

are not susceptible to solution by singular governments or even, international agencies. 

Instead, resolution of the difficulties will demand concerted and cooperative actions by 

many governments and agencies, based on valid undemanding of the earth's environmental 

systems. Establishment of a basis for international cooperation, and production of accurate 

globd environmental perceptions are both hailmarks of IIASA's Environment Program. 

Foremost among the global environmental issua of concern are those involving increasing 

concentrations of greenhouse gases and changing climate. Problem solutions will only 

become apparent after cokction and analysis of pertinent data. testing of relevant 

hypotheses, genesis of mitigation strategies and investigation of the efficacy of the stategies 

which are developed. All of these activities can support deveiopment of, or be supported 

by, the appropriate mathematical models of the biosphere. Therefore, the Biosphere 

Dynamics Project has been focused on the creation of models which can describe the 

processes in the biosphere which result in vegerauon dynamics. The models are being 

designed to define the biotic and ecological results of measures suggested to slow or stop 

increases in greenhouse gases. The models must be capable of documenting whether, and 

if so by how much, vegetational communities would benefit from mitigation actions. as well 

as describing how the terrestrial biosphere will respond in its role as carbon source and sink. 

One such study is aimed at examining the potential future geographic configurations of the 

world's vegetation biomes under specified, steady-state distributions of global climate 

variables. The fint step we have chosen is to document the data seu and classification 

sysrems available for modification to fit our needs, exemplified by the content of this 

working paper. This paper appeared ds pages 105 to 122 in Der Treibhause/fekr. Das 

Problem - Miigliche Folgen - Erjorderiichc Massnahrncn. edited by A. Hack1 and published 

in 1989 by the Akademie fGr Umwelt und Energie. A-2361 Laxenburg, Austria. The 

progress in modifying the models and data sets to fit the peculiar characteristics of global 

environmental change, supported by the present work, will be examined in subsequent 

working papers. 

B.R. Ddas 

Leader 

Environment Program 

Global Change Data Base Africo Documentation Appendix C-9 
ABSTRACT 

The distribution of global vegetation patterns is strongly relate.' to climate patterns. Local 

differences in soil characteristics and water availability, which are in turn also partlv 

determined by climate, force the global vegetation patterns to become even more 

pronounced and the local patterns more diverse. The predicted global climate change, due 

to the increased levels of greenhouse gases, will thus have large impacts on broad-scale 

vegetation patterns. The Holdridge life zone classification is based on biotemperature (a heat 

sum), annual mean precipitation and a potential evapotranspiration ratio; it was used in the 

current paper to create an objective global vegetation (life zonr) map. The map was created 

by interpolation of a large data base of weather stations to a grid of land cells with a 

resolution of 0.5". The climate scenario for future climate under double CO, concentrations 

is based on results of the U.K. Meteorological Office general circulation model. Large 

changes in vegetation patterns are observed between the life zone classifications of present 

and future climate. The polar regions decrease most in their extent. and all boundaries 

between the different latitudinal regions move toward their poles. Most sensitive to climatic 

warming are the borders between the latitudinal regions. Changes are most clearly 

observable at the southern and northern edges of the boreal forest region. Limitations of 

the Holdridge classification to study impacts of climate change are discussed and improved 

approaches are proposed. 

Global Change Data Base Africa Docicmentufion Appendix C- 10 
POSSIBLE CHANGES IN NATURAL VEGETATION PATTERNS 

DUE TO A GLOBAL WARMING 

Rik Lermans 

INTRODUCTION 

Regional vegetation is the result of a close network of interrelations between the 

characteristics of the local environment, natural vegetation succession. and several other 

factors with human management/influence being one of the most important. On a global 

scale, climate is the leading factor in differentiating vegetation. The broad-scale distribution 

of major vegetation types or biomes is roughly in equilibrium with the present climate. 

The notion that the physical environment largely determines the observed vegetation 

patterns, can be found in many textbooks on ecology and geography. Although human 

societies have used local flora and fauna intensively. limitations on the choice of crops and 

maximum limits to productivity are heavily determined by the local environment. Climate 

maps of the world are often based on continental and regional vegetation patterns, which 

were much easier to observe, locate and measure than climatic parameters. And reliable 

networks of meteorological measurement stations were, until recently, not available in large 

parts of the world. 

Vegetation and soil have large effects on microclimate and local climates. Climate operates 

on vegetation in an indirect way, namely through compefition between neighboring 

individuals, probably of different species. This means that a plant will appear only as close 

to its absolute climatic limit when it can overcome the competirion with individuals of other 

species, which are often better adapted to the local environment. Any direct effect of 

climate on vegetation or species distribution should be regarded as an exceptional 

phenomenon, such as those during critical parts of a species life-cycle, like germination and 

establishment. 

In this paper, I use a static equilibrium model, a climate classification. which objectively 

links climate to vegetation and may provide plausible projections of changes in global 

vegetation patterns. I will visualize these changes in the distributions as a result of climate 

change by using a climate scenario based on a Global Circulation Model (GCM). By srudying the extent of these patterns. areas with potential large vegetatjon changes can be 

recognized. 

I will not take into account any other impacts of climate change, like sea-level rise, the 

thawing of permafrost in high latitude soils, and changes in local soil characteristics like 

increased decomposition rates, increased leaching and an accelerated degradation, when 

vegetation cannot take up the extra amount of released nutrients. These impacts can, of 

course, locally be of major importance. An impressbe compilation of such local impacts 

on agriculture and forestry is given by Parry et al. (1988). 

HOLDRIDGE LIFE ZONE CLASSIFICATION 

CIimate consists of a complex mixture of many elements, like temperature, moisture, 

precipitation, wind, etc. Practically any ciimatic element or combinations of those elements 

could influence a single plant's life. Climate can be quantified in terms of various 

parameters. like means, extreme values, durations. Many simple and complex ways have 

been proposed to relate vegetation to climate and vice versa. Tuhkanen (1980) gives a 

historical review of the different methods used, their data requirements, and advantages and 

disadvantages. For this study, I use a combined system of climatic parameters, namely the 

Holdridge life zone classification (Holdridge 1947, 1967). 

This system was used in a climate change impact study by Emanuel et al. (1985a. 1985b). 

Holdridge's underlying assumption is that the differentiation and physiognomy of vegetation 

under relatively undisturbed conditions reflect the effKnt of cljmatjc factors on Vegetation. This means that any climax vegetation in an area can be defined objectively by its climate. 

Topographic, biotic and edaphic factors also depend to a certain extent upon climate, and 

Global Change Data Base Afiica Documerifurion Appendix C- 1 1 
these lead only to a secondary differentiation (Holdridge 1967). Tests of the Holdridge life 

zones have revealed that the system can fit tropical vegetation zones (e.g. Holdridge 1967). 

mediterrancan zones (Steila 1966). as well as the boreal zones, but that it is less applicable 

to cold oceanic or cold arid climates (Hamet-Ahti et al. 1974) where moisture becomes the 

+etcrrnining factor. The ease at which the Holdridge classification can be used in 

5termining the life zones objectively from frequent available climate data (monthly 

~mperature and precipitation) makes this system more suitable for climate change impact 

udies at a global scale than other well known systems. like the Kbppen (1915). 

. hornthwaire (1948) and Box (198 1) classifications which are far more data demanding and 

aependent or are more difficult to relate to actual vegetation patterns. 

The Holdridge life zone classification is based on three climate parameters: biotemperature, 

mean annual precipitation and a potential evapotranspiration (PET) ratio. The 

biotemperature is a temperature sum, related to growing degree da s, and is here defined 

as the sum of daily mean temperatures between 0' and 30 C divided by 365. 

Biotemperature defines the latitudinal or altitudinal belts of life zones. Mean annual 

precipitation is the sum of the mean monthly dues. PET is a simple empirical linear 

function of biotemperature, and ad& no extra information to the model. The PET-ratio is 

defined as PET divided by annual precipitation. The PET ratio defines different humidity 

provinces. The three climatic indices are displayed graphically in a logarithmical system, 

so that each separate life zone becomes equivalent in significance (Figure 1 ). Each life zone 

is named KO suggest a vegetation association. 

I have made use of the Holdridge classification to create a global life zone map by using 

monthly temperature and precipitation values of weather stations scattered around the 

world. The data base was compiled at the International Institute for Applied Systems 

Analysis (IIASA), based on published (Weather Bureau 1959; Walter and Lieth 1960-67; 

Meteorological Office 1972; Miiller 1982) and unpublished (China. USSR) sources. The 

different source data sets were separately screened for errors, outliers and unreliable 

stations. and for doublets. An observation record of between 10 and 40 years (if possible 

1931 -60) was required for the computation of mean monthly temperature and precipitation 

values. The final data set contained ca. 5500 stations worldwide, characterize f by longitude, 

latitude and altitude. 

2 

This array of selected stations was then used to create a global monthly temperature and 

precipitation data base for fand areas on a grid with a resolution of 0.5' (grid cell of about 

55 x 55 km at the equator). The interpolation scheme used for each variable is a smooth 

surface fitting on a triangulation network, based on all stations (Akima 1978). Temperature 

values .ere corrected for changes in altitudes between the stations by applying a moist 

adiabatic lapse-rate of 0.6' C/100 m. Before the interpolation, the altitudes of all stations 

values were calculated as sea-level temperatures and after the interpolation all grid cell 

values were calculated as the modal height of that cell, using a topography datatet (National 

Geophysical Data Center 1988). Mean annual precipitation was computed as the sum of all 

monthly values. The biotemperature was computed by using daily values and generated by 

fitting a quadratic spline function through the monthly values. The specific PET-ntio was 

computed from the biotemperature and toai annual precipitation. ?lese three climate 

i..dices in each land cell (total 62,483 without Antarah) generated a world life zone map 

(Figure 2). 

The life zone patterns (Figure 2) roughly compare with existing vegetation patterns when 

compared with. for example, the digiti& vegetation map of Olson et al. (1982). Precise 

comparison is difficult, however, because of differences in the number and definition of 

vegetation classes. However, the overail globd pattern is clearly visible in both schemes. In 

Northern arc=. where temperature is much more imponant than precipitation, spurious 

classes appear in life zones in comparison with natural vegetation. In addition, in areas 

where soil and toil-moisture chncteristia strongly determine vegetation, differences 

between life zones and actual vegetation are encountered, e.g. the extended 

pampa-grasslands in Argentina (G. Rush, personal communication). Such discrepancies can 

only be overcome if more soil parameters are taken into account, which leads to a more 

complex approach and many othzr problem, like defining the most important factor. Due 

to the use of a moist adiabatic lapse rate, this map reflects the life zones of mountainous 

Global Change Data Hase Afr-icw Docurnentutiion Appendix C- 12 
00 2! 

n DIAGRAM FOR THE CLASSIFICATION OF WORLD LIFE ZOCJES OR PLANT FORMATIONS 

‘. 
Figure 2. A world map with the Holdridge life zone classification based on a 0.5’ gridded 

temperature and precipitation data base. 

Global Change Data Base Africa Documentation Appendix C-14 
areas much better than does the life zone map published by Emanuel et al. (1985a). 

The impact of climate change was determined by using results of simulated climate by a 

general circulation model of the atmosphere (GCM). At present there are several scenarios 

of climate change available. Most available GCMs indicate similar climate change trends, 

although major differences may occur in the magnitude of change and in local patterns. I 

used the output of the model developed by the U.K. Meteorological Office (UK,MO). and 

for a present climate and a doubled CO, (Mitchell 1983). This model was chosen because 

it has a higher horizontal spatial resolution than most other models. This leads to more 

reliable regional patterns (Mitchell, personal communication) needed to give a realistic 

scenario of vegetation change. 

The differences in monthly mean temperature and monthly mean precipitation between the 

two different model runs were used to define the amount of climate change. This quantity 

was then added to the present climate data base to give a doubled CO future climate. The 

combination of the change, simulated by the UKMO GCM-model, and! an authentic climate 

data base has the advantage that existing regional differences in the climate parameters are 

included. and that imperfections in the GCM-model are reduced. The simuiared global 

climate change scenario thus becomes more realistic. 

The ‘climate change’ data base was used to determine a Holdridge life zone classification 

for the future climate under a doubled CO The maps shown here mark only the areas 

undergoing a change; the portions of presentkc zones which would change (Figure 3) and 

portions of future life zones which vegetation could potentially change into (Figure 4). 

Changes are apparent between the two maps: only little more than 5096 of the world’s 

vegetation does not change from one life zone to another between present and future 

climate (Table 1 and Table 2). The climatic warming is obvious in the pattern of life zone 

changes. The latitudinal belts are markedly replaced polewards. This results in the largest 

vegetation change being in the polar regions. This vegetation decreases in area by 33%. 

Given that Greenland remains as a large polar desert (land ice). the largest part of the 

change occurs in the tundra ecosystems. There boreal life tones largely replace the tundra 

which is limited at its northern border by the polar ice-sea, and thus cannot expand 

poleward. The boreal forest, in turn. is replaced at its southern edge by temperate forests. 

but its total area remains almost equal, due to its northward extension. This result contram 

that of Emanuel et al. (1985a. 1985b). who found a larger decrease in the area of the boreal 

forest. A complex pattern of changes OCCUK at the borders between the te:;.perate and 

subtropical regions. The range of most tropic life zones increases. In Africa. the Sahara 

desert expands toward the south and the transitions toward the tropical life zone becomes 

much less gradual than in the present climate. 

DISCUSSION 

Vegetstion Change 

The changes in potential life zones as shown here are large, and affect directly or indirectly 

the whole giobe with all its ecosystems. For example, the tundra systems are important for 

many migrating birds, like geese, for their reprodudon and summer foraging. If these 

grounds disappear or even largely decrease, this will have a catastrophic effect on the 

popUlati0~ Of these birds, and probably many other ecological impacts along their 

migratory path. During the creation of large biosphere reserves. possible effects of climate 

change should be considered; these re~erves should not be created in areas with a high 

probability of change. Unfomrnately, the most diverse systems are located in the transitions 

between ecosystems or biomes. 

The actual impacts of climate change on vegetation will probably not be observed 

immediately after the climate change has occurred. There will be a time lag, ranging from 

decades to decennia. Species and their propagules are not available immediately if the 

circumstancts become appropriate. Especially plants are rather immobile and have slow 

migration rates. Boreal species, for example, have an estimated natural migration capacity 

of 1 to 40 kilometers per century (Davis 1981). Massive, practically impossible, new 
Figure 3. A Holdridge life zone world map with the differences between present climate 

and a double CO, future climate. The marked areas are the original life zones, determined 

on the basis of present climate. 

Global Change Data Base Africa Documentation Appendix c-16 
Figure 4. A Holdridge life zone world map with the differences between present climate 

and double CO, future climate. The marked areas are the future life zones determined on 

the basis of simulated ciimatc. 

Global Change Data Base Africa Dociirnenfution Appendix C-17 
Table 1. Areas (in lo6 km‘) occupied by the different Holdridge life zones for present 

dimate and a probable future dimate under a doubling of CO,. 

Latitudinal Life Zone Current Future Difference 

Region Climate Climate ---.c-IL===-n=w~~-=--~---mnw--~%===R=a=w==R= 

Polar 

Boreal 

Cool Temperare 

Warm Temperate 

Subtropical 

Tropical 

Desert 

Dry Tundra 

Moist Tundra 

Wet Tundra 

Rain Tundra 

Desert 

Dry Bush 

Moist Forest 

Wet Forest 

Rain Forest 

Desert 

Desert Bush 

Steppe 

Moist Forest 

Wet Forest 

Rain Forest 

Desert 

Desert Bush 

Thorn Steppe 

Dry Foresr 

Moist Forest 

Wet Forest 

Rain Forest 

Desert 

Desert Bush 

Thorn Steppe 

Dry Forest 

Moisr Forest 

Wet Fore:. 

Rain Forest 

Desert 

Desert Bush 

Thorn Steppe 

Very Dry Forest 

Dry Forest 

Moist Forest 

Wet Forest 

Rain Forest 

8.3 

0.9 

3.3 

3.5 

2.1 

0.4 

I .8 

10.9 

4.6 

0.9 

1.1 

2.0 

7.5 

8.9 

1 .3 

0.2 

0.9 

1.6 

2.2 

3.7 

3.0 

0.2 

0.0 

7.2 

5.1 

4.8 

8.9 

15.1 

2.1 

0.1 

4.2 

1.1 

1.8 

3.1 

7.6 

4.8 

0.2 

0.0 

4.6 

I .O 

2.4 

2.4 

1.6 

0.4 

1.9 

8.3 

6.0 

I .o 

0.8 

1.8 

7.7 

10.4 

I .9 

0.4 

1 .o 

1.2 

1.7 

2.7 

2.6 

0.3 

0.1 

6.3 

2.8 

4.3 

8.0 

11.7 

1.3 

0.1 

8.6 

0.9 

I .6 

5.6 

13.1 

7.6 

0.7 

0.0 

- 3.6 

+ 0.1 

- 0.9 

- 1.1 

- 0.5 

+ 0.0 

+ 0.1 

- 2.5 

+ 1.3 

+ 0.2 

- 0.1 

+ 0.2 

+ 0.’ 

+ 1.6 

+ 0.6 

+ 0.2 

+ 0.’ 

- 0.5 

- 0.4 

- 1.0 

- 0.3 

+ 0.1 

- 0.0 

- 0.8 

- 2.2 

- 0.5 

- 0.8 

- 3.3 

- 0.8 

+ 0.0 

+ 4.4 

- 0.2 

- 0.2 

+ 2.4 

+ 5.5 

+ 2.9 

+ 0.6 

+ 0.0 

Global Change Data Buse Africu Doc~umetrtuh’on Appendix C- 18 
Table 2. Summary of percentage change in areal extent of the different Holdridge life 

zones for present climate and a probable future climate under a doubling of CO . “STABILITY indicates the area with similar life zones under both climates. “CHANG& 

indicates those which change from the current life zone into another. “EXPANSION” 

indicates those which change from another life zone into the current life zone. (All values 

are relative to the area occupied under present climate.) 

Latitudinal Life Zone Change Stability Expansion Region 

I-IRII=IlIIIII=I 

Polar 

Boreal 

Desert 

Dry Tundra 

Moist Tundra 

Wet Tundra 

Rain Tundra 

Desert 

Dry Bush 

Moist Forest 

Wet Forest 

Rain Forest 

Cool Temperate Desert 

Desert Bush 

Steppe 

Moist Forest 

Wet Forest 

Rain Forest 

Warm Temperate Desert 

Desert Bush 

Thorn Steppe 

Dry Forest 

Moist Forest 

Wet Forest 

Rain Forest 

Subtropical 

Tropical 

Desert 

Desert Bush 

Thorn Steppe 

Dry Forest 

Moist Forest 

Wet Forest 

Rain Forest 

Desert 

Desen Bush 

Thorn Steppe 

Very Dry Forest 

Dry Forest 

Moist Forest 

Wet Forest 

Rain Forest 

56.0 

45.9 

36.9 

25.3 

27.6 

19.6 

41.7 

52.2 

54.6 

43.1 

47.3 

37.1 

58.7 

64.3 

64.0 

61.3 

51.1 

22.2 

16.9 

26.5 

35.5 

56.5 

36.8 

50.6 

23.2 

26.0 

40.4 

54.6 

52.2 

65.9 

94.7 

18.5 

38.4 

63.4 

95.4 

100.0 

as. 1 

- 

44.0 

54.1 

63.1 

74.7 

72.4 

80.4 

58.3 

47.8 

45.4 

56.9 

52.7 

62.9 

41.3 

35.7 

36.0 

38.7 

48.9 

77.8 

83.1 

73.5 

64.5 

43.5 

63.2 

49.4 

76.8 

74.0 

59.6 

45.4 

34.1 

5.3 

81.5 

61.6 

36.6 

14.9 

4.6 

0.0 

47.8 

0.0 

63.8 

35.6 

44.2 

48.7 

87.8 

64.3 

24.2 

73.9 

74.6 

27.4 

51.3 

44.6 

53.4 

89.7 

1 11.3 

66.6 

48.5 

62.7 

47.4 

52.9 

82.3 

18.9 

38.3 

33.1 

63.6 

50.4 

23.3 

10.4 

34.8 

100.2 

62.7 

49.9 

112.3 

86.2 

61.9 

245.4 

- 

Global Change Data Base Afiicu DacumenfuTiorr Appendix C- I9 
platings would be required to sustain its distribution northwards. 

As plant distributions are an outcome of competition between individuals and new, better 

adapted species are not yet available. the species present could remain due to lower 

competitive pressure. If individuals are surviving in an unappropriated environment, higher 

mortality rates will be observed as a result of an increased environmental stress at a species 

environmental limit. The world could then be faced with a vegetation decline on a large 

spatial scale (up to 50% according to the above analysis). This change, occurring during a 

short time-scale (up to one century), has no equivalent in history and as a result, many 

species may become extinct. Such a decrease in biodiversity is irreversible. For example, 

after the sudden crash in species number at the end of the Permian period (some 240 years 

ago), it took more than 5 million years to recover (Erwin 1989). After this crash the 

evolutionary path had changed in a direction leading to the dominance of mammals. 

Improvements of Static Equilibrium Models 

The above-described example of a static equilibrium model has some severe disadvantages. 

like many other models of this type. These models do not include rates of changes, only 

amplitudes of change, and present only the possible distributions of potential vegetation 

types under a specified climate. Transient responses cannot be studied with such models. 

As global climatic warming spreads northwards (locally several hundreds of kilometers), the 

ume-scale will be important. Single plant species often lack migration potential fast enough 

to keep up with expected climate changes. Models, which do not take into account 

competition and possibly migration rates, cannot predict the actual vegetation transients 

under a climatic change. 

Dynamic models for vegetation succession have been developed. The most successful group 

has been developed for forests and is based on the gap-models developed by Botkin et al. 

(1972). Shugart (1984). and Prentice and Leemans (1990). At IIASA, we have combined one 

of these models (FORSKA, Leemans and Prentice 1989) with specific environmental driver 

routines (Bonan 1989) to simulate the global dynamics of boreal forests, because they are 

one of the ecosystems where the largest changes will occur (see above). The boreal forest 

iS also a relatively simple system, with few well known species, located in a few countries 

(Canada, Alaska, Norway, Sweden, Finland, and the USSR). which makes the collection of 

data, impact monitoring networks, and exchange of knowledge much easier. 

The boreal model incorporates the growth of single trees, not in an empirical, but in a 

mechanistic way, and incorporates competition between these trees, resulting in a realistic 

forest structure. The environmental driver includes permafrost dynamics, moisture and 

temperature effects, fire and other ecological disturbances. which all have their specific 

effect on the growth of trees. The model soon will be used to simulate the impacts of 

climatic change on the boreal forest and along its borders. 

The boreal forest covers ca. 10% of the world's land surface. By modeling this part only, 

we can examine no impacts elsewhere. The development of a dynamic global vegetation 

model, including all ecosystems and species, becomes necessary. In cooperation with the 

International Geosphere-Biosphere Program (IGBP), we have started to develop such a 

model. Difficulties which arise here are the different characteristics of terrestrial 

ecosystems, like grasslands, forests and their uansients. The present approach taken at 

IIASA is illustrated in Figure 5 (Prentice et al. 1989). This approach can include a wide 

mnge of environment-vegetation interactions and piant-plant interactions. Single species 

will not be represented; they will be classified into 'functional groups" according to their 

ecological functions. The first, initial results of this model may be ready in mid-1990. 

Environmental D8ta Bases 

All these model exercises need huge amounts of data. At IIASA we are compiling a set of 

environmental data bases of geographical information, such as vegetation, soil, topography 

and climate. All our data bases are linked to a gridded map of the world on a 5' resolution, 

Global Change Data Base Africa Docwrenrution Appendix C-20 
and are model independent. The Holdridge life zone classification was one of the first 

applications to use these data bases. The data bases are updated and improved continuously, 

and new subjects are added, 

Figure 5. A conceptual form of a dynamic vegetation model. The tallest plants shade our 

shoner ones; the number of simulations required for smaller-stature plants is greater than 

for tall plants. Each venjcal layer of vegetation is subdivided into separate layers in which 

different sized individuals of one species can grow (Prentice et al. 1989). 

CONCLUSIONS 

Climate and vegetation are closdly related. Although we have much knowledge about the 

spatial distribution of vegetation patterns, we hardly know' how the different spatial and 

temporal patterns of vegetation are generated in a mechanistic way. The understanding of 

such dynamics are of increasing importance for accessing the possible impacts of any 

climate change. 

I perceive the near future as a composite of many exciting scientific challenges in which 

scientists from different fields, traditions, approaches and countries collaborate seriously 

to unravel the dynamic links within the biosphere. This will be the only way to make 

available the information necessary for policy-makers and the public to decide about a 

sustainable development of mankind within the biosphere. 
REFERENCES 

Akima. H. 1978. A method of bivariate interpolation and smooth surface fitting for 

irregularly distributed datapoints. ACM Trans. Mmh. Sofiware 4:148- 159. 

regimes in boreal forests. Ecoi. Model. 45275-306. 

model of fores. growth. J. Ecol. 60:849-873. 

Phytogeography. Dr. W. Junk Publishers, The Hague. 

Fe-~an. G.B. 1989. A ComDutet model of the solar radiation, soil moisture, and soil thermal 

Kin, D.B., J.F. Janak. and J.R. Wallis. 1972. Some ecological consequences of a computer 

Box. E.O. 198 1. Macroclimate and Plant Forms: An Introduction to Predictive Modeling in 

i 

Davis. M. 1981. Quaternary history and the stability of forest communities. Pages 132-153 

in D.C. West, H.H. Shugart. and D.B. Botkin, eds. Foresr Succession: Conceprs and 

Application. Springer Verlag, New York. 

Emanuel, W.R., H.H. Shugart, and M.P. Stevenson. 1985a. Climatic change and the broadscale distribution of terrestrial ecosystems complexes. Climatic Change 7:29-43. 

Emanuel, W.R., H.H. Shugart, and M.P. Stevenson. 1985b. Response to comment: Climatic 

change and the broad-scale distribution of terrestrial ecosystems complexes. Climaiic 

Change 7:4 5 7 -460. 

Erwin, D.H. 1989. The end-Permian mass extinction: What really happened and did it 

matter? TREE 4225-229. 

Holdridge, L.R. 1947. Determination of world plant formations from simple climatic data. 

Science 105367-368. 

Holdridge, L.R. 1967. Life Zone EcoiogJ#. Tropical Science Center. San Jose. 

Hamet-Ahti, L., T. Ahti, and T. Koponen. 1974. A scheme of vegetation zones for Japan 

Kdppen, W. 1936. Das geographische System der Klimate. In W. Kdppen and R. Geiger, 

Lcemans, R. and 1.C: Prentice. 1989. FORSKA, a general forest succession model. 

and adjacent regions. Atm. Bo[. Fenn. 1139-88. 

eds. Handbuch der Klimaologie. Berlin. 

Meddelanden frdn Vcixbiologiska Institauionen, Uppsaia 1989(2): 1 -60. 

Meteorological Office. 1972. Tables of Temperature. Relmivc Humidify. Precipirmion and 

Sunshine for rhe World. Part Iff. Europe and the Azores. HMSO, London. 228 pp. 

Mitchell, J.F.B. 1953. The seasonal response of a general circulation model to changes in 

CO, and sea .:mperatures. Quart. 1. R. Met. SOC. 109:113- 152. 

Mliller, M.J. 1982. Selected Climatic Data for a Global Set of Standard Smions for 

Vegetation Science. Dr. W. Junk Publishers, The Hague. 

National Geophysical Data Center. 1988. 10-Minute Topography Data Bae. U.S. 

Department of Commerce, Washington, D.C. 

Olson, J., J.A. Watts, and L.J. Allison. 1982. Carbon in Live Vegetmion of Major World 

Ecosysrems. Oak Ridge National Laboratory, Oak Ridge, TN, USA. 

Parry, M.L., T.R. Carter, and N.T. Konijn. 1988. The Impaci oj Climmic Varimions on 

Agriculture. Volume I: Assessments in Cool Temperare and Cold Regions. Kluwer 

Academic Publishen, Dordrecht. 

Global Change Data Base Afiica Documenwtion Appendix C-22 
Prentice. I.C. and R. Leemans. 1990. Pattern and process and the dynamics of forest 

structure. J. Ecoi. (in press). 

Prentice, I.C., R.S. Webb. M.T. Ter-Mikhaelian, A.M. Solomon, T.M. Smith, S.E. 

Pitovranov, N.T. Nikolov, A.A. Minin, R. Leemans. S. Lavorel. M.D. Korzukhin, H.O. 

Helmisaari. J.P. Hrabovszky, S.P. Harrison, W.R. Emanuel. and G.B. Bonan. 1989. 

Developing a global vegetation dynamics model: Results of an IIASA summer 

workshop. RR-89-7. International Institute for Applied Sysrem Analysis, Laxenburg, 

Austria. 

Shugart, H.H. 1984. A Theory of Forest Dynamics. Springer-Verlag, New York. 

Steila. D. 1966. An evaluation of the Thornthwaite and Holdridge classification as applied 

to the mediterranean borderland. Prof. Geogr. 18:358-364. 

Thornthwaitc, C.W. 1948. An approach toward a rational classification of climate. Ceogr. 

Rev. 3835-94. 

Tuhkancn, S. 1980. Climatic parameters and indices in plant geography. Acfa Phyrogeogr. 

Suec. 67: 1 - 1 10. 

Walter, H. and H. Lieth. 1960-67. Klimadiagramm-Welfaflas. Gustav-Fisher-Verlag, 

Stuttgart. 

Weather Bureau. 1959. World Wenrher Records 1941-1950. U.S. Department OC Commerce, 

Washington, D.C. 
-DMATTHEWS VEGETATION, LAND-USE, AND SEASONAL ALBEDO 

CITATION: Matthews. E.. 1984. Global Vegetation. L'ncl-Use, and Seasonal 

Albedo. Diptal Data. NCAR. Boulder. Colorado. .65 MB. 
Blank page retained for pagination 
D. MATTHEWS VEGETATION, LAND-IJSE, AND SEASONAL ALBEDO 

DATE: 1984 

SOURCE: 

Diptized from avrulable sources. 

Elaine Mattliews 

Goddard Institute of Space Science 

Columbia University 

New York. NY 

CONTRIBUTOR: 

Roy Jenne 

NCAR 

P.O. Box 300 

Boulder. CO 80307-3000 

DATA CENTER: NCAR 

GEOGRAPHIC SAMPLING: 1 degree grid, characteristic classes 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Decadal composites 

REFERENCES: 

Matthews. E.. 1983. "Vegetation. Land-Use and Seasonal Albedo Data Sets: 

Dociimentation of Archived Data Tape." NASA Ttx1~ca.l iMemorandurn 

#86 107. 

Matthews, E., 1983. "Global vegetation and land use: New high resoliltion 

data bases for climate studies." J. Clim. Appl. Mereor-., vol. 22, pp. 473-487. 

Znhler. L.. 1986. "A World Soil File for Global Climate Modeling." 

NASA Technical Memorandum #87802. 

FILE NAMES: 

AFMVEG 

AFMCULT 

AFMALBFA 

AFMALBSM 

AFMALBSP 

AFMALBWN 

Matthews Vegetation Type 

Matthews Cultivation Intensity 

Matthews Fall Albedo (x 100) 

Matthews Summer Albedo (x 100) 

Matthews Spring Albedo (x 100) 

Matthews Winter Albedo (x 100) 
Blank page retained for pagination 
MATTHEWS VEGETATION TYPE 

DOCUMENTATION FILE 

File Name: AFMVEG.DOC 

image tit-le : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

Matthews Vegetation Types 

byte 

binary 

438 

4 80 

O.OOOOOOOOOOE+Oo 

3.0000000000E+@1 

1.6666666667E-01 

1.6666666667E-01 

legend : 32 

category 1 : 1 TER 

category 2 : 2 TES 

category 3 : 3 SER 

category 4 : 4 TSP 

category 5 : 5 TSB 

category 6 : 6 EBS 

category 7 : 7 TEN 

category 8 : 8 TSE 

category 9: 9 TSD 

category 10 : 10 CDE 

category 11 : 11 CDF 

category 12 : 12 XFW 

category 13 : 13 ESW 

category 14 : 14 ENW 

category 15 : 15 TDD 

category 16 : 16 CDW 

category 17 : 17 EBT 

category 18 : 18 ENM 

category 19 : 19 DDS 

category 20 : 20 CDS 

category 21 : 21 XSD 

category 22 : 22 ATM 

category 23 : 23 TGW 

category 24 : 24 TGV 

category 25 : 25 TGS 

category 26 : 26 TGN 

category 27 : 27 MGN 

category 28 : 28 MSG 

category 29 : 29 FFO 

category 30 : 30 DES 

category 31 : 31 ICE 

cateaory 32 : 32 CUL 

Tropical evergreen rainforest, mangrove forest 

Tropical/subtropical evergreen seasonal 

broadleaved forest 

Subtropical evergreen rainforest 

Temperate/subpolar evergreen rainforest 

Temperate evergreen seasonal broadleaved 

forest, summer rain 

Evergreen broadleaved sclerophyllous forest, 

winter rain 

Tropical/subtropical evergreen needleleaved 

forest 

Temperate/subpolar evergreen needleleaved 

forest 

Tropical/subtropical drought-deciduous forest 

Cold-deciduous forest, with evergreens 

Cold-deciduous forest, without evergreens 

Xeromo rphi c forest /woodland 

Evergreen broadleaved sclerophyllous woodland 

Evergreen needleleaved woodland 

Tropical/subtropical drought-deciduous 

woodland 

Cold-deciduous woodland 

Evergreen broadleaved shrubland/thicket, 

evergreen dwarf-shrubland 

Evergreen needleleaved or microphyllous 

shrubland/thicket 

Drought-deciduous shrubland/thicket 

Cold-deciduous subalpine/subpolar shrubland, 

cold-deciduous dwarf shrubland 

Xeromorphic shrubland/dwarf shrubland 

Arctic/alpine tundra, mossy bog 

Tall/medium/short grassland with 10-40% woody 

tree cover 

Tall/medium/short grassland with <lo% woody 

tree cover or tuft-plant cover 

Tall/medium/short grassland with shrub cover 

Tall grassland, no woody cover 

Medium grassland, no woody cover 

Meadow, short grassland, no woody cover 

Forb formations 

Desert 

Ice 

Cultivation -- {category 0 : unclassed) 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 
MATTHEWS CULTIVATION INTENSITY 

DOCUMENTATION FILE 

File Name: AFMCULT.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

'category 5 : 

Matthews Cultivation Intensity 

byte 

binary 

438 

480 

0.0000000000E+00 

5.0000000000E+00 

1.6666666667E-01 

1.66666666673-01 

5 

1 0% 

2 20 % 

3 50 % 

4 75 % 

5 100 % 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 
MATTHEWS SEASONAL ALBEDO 

EXAMPLE DOCUMENTATION FILE 

File Names: AFMALBFA.DOC, AFMALBSM.DOC, 

AFMALBSP.DOC, AFMALBWN.DOC 

image title : 

data type : 

file. type : 

rows 

columns 

minimum 

maximum 

cell x 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

category 9 : 

category 10 : 

category 11 : 

category 12 : 

cat=gory 13 : 

category 14 : 

category 15 : 

cell y 

Matthews (Seasonal) Albedo (X 100) 

integer 

binary 

438 

480 

O.OOOOOOOOOOE+OO 

3.2000000000E+03 

1.66666666673-01 

1.66666666673-01 

15 

200 to 399 

400 to 599 

600 to 799 

800 to 999 

1000 to 1199 

1200 to 1399 

1400 to 1599 

1600 to 1799 

1800 to 1999 

2000 to 2199 

2200 to 2399 

2400 to 2599 

2600 to 2799 

2800 to 2999 

3000 to 3199 

{category 0 : 0 to 199) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 
Blank page retained for pagination 
VEGETATION, LAND-USE AND SEASONAL ALBEDO 

DATA SETS: 

DOCUMENTATION OF ARCHIVED DATA TAPE 

MAY 1984 

E. MATTHEWS 

Sigma Data Services 

NASA-Goddard Space F1 i ght Center 

Institute for Space Studies 

2880 Broadway 

New York, New York 

10025 

*NOTE: This ;s a revised version of the documentat'on prov'ded to XCAR in July 

1983. 
GE.VE2AL 3ESC4i?TION 

Global data Sases of vegetation, land use, and land cover have Seen cornp:l?a for 

use ;n climate studies at a 1" latitude x 1" longitude resolution, drawing on 

approx'nately 100 pub1 ished sources complemented by a large col lect'ol of 

satellite imagery. A series of 6 datasets has been prepared and archived at 

NCAR. The first is a vegetation data set (YEGTYPE) representing natural 

(pre-agricul tural ) vegetation based on the UNESCO classificat'on system. The 

second, aerjved from the land-use compilation is a cultivat?on intensity daE3 

set (CULTINT) defining the areal extent of presently cultivated land !n the 1" 

cells. The last four are present integrated surface-albedo data sets (January, 

Aprf 1 , July, October) for snow-free conditions except for permanently 

snow-cavered continental ice, incorporating natural vegetation and cultivation 

character; stics from the vegetation and cul tivation-i ntensi ty data sets. Thesz 

6 data sets include non-zero data for permanent land only, including ccintinental 

'ce; water, including oceans and lakes, is zero. 

Th's paper includes ,f :bt+d-Z: :- , br! ef descr'pt: ons af 

the 'ndividual data SetS.Wu??'=.! sf :be :at: z2:: 25 

For complete discussi.on of the vegetation and land-use data sets see E. Mac'news, 

1983, Giooal vegetation and land use: new high-resolution data bases for 

climate studies, J. Cljrn. Aool. Meteor., 22, 474-487. 

Mattflews , NASA-Goddard Space F1 i ght Center, Institute for Space Stud! es , 2980 

Broadway, New YWk; N.Y. 10025, (212) 678-5500, fls 664-5500. 

Tape prepared by E. 

Acknowledgments. I would like to thank the following people for helpful 

suggest'ons and support during the preparation of th; s report: R.E. 0; CK; nson , 

I.Y. Fung, J.E. Hansen, R. Jenne, L.C. Tsang, and.M. Verstraete. 

Glob:rl Change Data Base Africu Docrrnrarriutiorr Appendix D- 10 
-- -- - 

- FILE I FILE NAME. ARRAY SIZE TYPE 

I VEGIYPE (360,180) 1'2 

2 CULTINT (360,180) 1'2 

3 ALBEDO (36O,lUO)r4 A'4 

v 

DESCW I PT 

land polnts: 1-32 

water points: 0 

see Table I, Fig. 1 

include continental ice 

include lake A ocean 

land polnts: 1-5 

water points: 0 

see Table 2, Fiy. 2 

include continental Ice 

include lake d ocean 

land polnts: 1.-100. 

water polnts: U. 

include continental Ice 

Include lake 6 ocean 

alhedo arrays for 4 months: 

January ,Apri I ,July ,October 

snon-free conditions except 

for permanently snon-covered 

cont inental Ice 

See Iable 3,Fig. 3 - - - - -. - - . - - - - -- -__ - - - - ___ ____ __ I_ - _- - -- ' FI IC ndms dre nqt 011 the tape 
Taole I. VEGTYPE: The thirty two vegetation types descr’oea in Col. 3 are 

identified in VEGTYPE as 1 to 32, as shown in Col. 1. 

Col . 2 lists the 32 associated map symbols used to nao 

VErJTYPE in Fig. 1 

1 2 3 

Vegetsti on Ma P 

TY Pe Symoo 1 

Description 

~ ~~ ~ ~- ~ 

1 ...... 1 ... trop.ica1 evergreen rainforest, mangrove forest 

2 ...... 2 ... tropical /subtropi cal evergreen seasonal 

broad1 eaved forest 

3 ...... 3 ... subtropical evergreen rainforest 

4 ...... 4 ... temperate/subpol ar evergreen rainforest 

5 . . . . 5 , . . temperate evergreen seasonal broadleaved 

6 ...... 6 ... evergreen broadleaved sclerophyllous forest, 

forest, sumner rain 

winter rain 

7 ...... 7 ... tropical/subtropical evergreen needleleaved 

forest 

8 ...... 8 ... temperate/subpolar evergreen needleleaved 

forest 

9 ...... 9 .... tropical/subtropical drought-dec’duous fores: 

10 ...... A ... colddeciduous forest, with evergresns 

11 ...... B ... cold-deciduous fares;, without evergreens 

12 ...... C ... xeromorphic forestjwoodl and 

4 

13 ....... 0 ... evergreen broadleaved sclerophyllous woodland 

14 ...... E ... evergreen needleleaved woodland 

15 ...... F ... tropical/subtropical drought-dec:duous 

wood 1 and 

- 

16 ...... G ... cold-deciduous woodland 

17 ...... H ... evergreen broadleaved shrubland/th’cket, 

evergreen dwarf -shruo 1 and 

18 ...... I ... evergreen needl el eaved or mi crophyl 1 ous 

shrub1 and/thi cket 

19 ...... J ... drought-deciduous shruDland/th!cket 

Clohnl Change Data Base Afiica Docurnentoiiorr Appendix D- 12 
Table 1. (continued) 

1 2 3 

Vegetation rJla P 

TY Pe Syme o 1 

Oescr’ pt i on 

20 ...... K ... cold-deciduous subalpine/subpolar shrubland, 

co 1 d-deci duous dwarf shrub 1 and 

21 ...... L ... xeromorphic shrubland/dwarf shrubland 

22 ...... H ... arctic/alpine tundra, mossy bog 

23 ...... N ... tall/medium/short grassland with 10-40’: woody 

tree cover 

24 ...... 0 ... tal 1 /medium/short grassland with ~10% woody 

tree cover or tuft-plant cover 

25 . . . P . . tall/medium/short grassland with shrub cover 

26 ...... Q ... tall grassland, no woody cover 

27 ...... R ... medium grassland, no woody cover 

28 ...... S ... meadow, short grassland, no woody cover, 

29 ...... T ... for5 formations 

30 ...... U ... desert 

31 ...... V ... ice 

32 ...... U ... cultivation 

Glolial ( ‘Irange Datu Base Africa Docurrrcritution Appendix D- I3 
Table 2. CULTINT: Cultjvation 'ntensities, ranging from 0 to 5 ;n CULTINT 

translats into % cultivated and % natural vegetation as 

5 hown . 

Cult'vat'on 

Intensity 

Descri pti on 

landlwater Z cultivated Z natural 

vegetation 

0 

2 

3 

4 

5 

water 

1 and 

1 and 

1 and 

1 and 

1 and 

0 

20 

50 

75 

100 

- 

100 

80 

50 

25 

0 

Global Change Data Base Aji.ic.u Dcuimentution Appcntlix D- I4 
TABLE 3A . Seasona 1 snow-f ree . integrated a1 bedos for 32 vegetat:on types 1 i st2a 

: n Taole 1 . The winter. spring. sumer and fa1 1 values COrreSpOnd to 

January. Apri 1. July and October in the northern hemisphere and io 

July. October. January and April in the Soutnern hemisphere . The 

appropriate seasonal values were used; in conjunct?on with the VEGTYPE 

and CULTINT data bases. to produce albedo data sets for January. 

April . July and Octooer . 

VEGETATION WINTER SPRING SUMME3 FdLL 

1 .... 11 .... 11 .... 11 .... 11 

2 .... 11 .... 11 .... 11 .... 11 

3 .... 11 .... 11 .... 11 11 

4 .... 12 .... 12 .... 12 12 

5 .... 12 .... 13 .... 14 .... 13 

6 .... 17 .... 14 .... 13 .... 14 

7 .... 13 ..... 14 ... 16 .... 13 

.... 

.... 

8 .... 11 .... 12 .... 15 .... 12 

9 .... 18 .... 16 .... 15 .... 16 

10 .... 12 .... 15 .... 18 .... 13 

11 .... 12 .... 15 .... 18 .... 13 

12 .... 28 .... 32 .... 28 .... 28 

13 .... 15 .... 13 .... 12 .... 13 

14 .... 14 .... 14 .... 16 .... 14 

15 .... 20 .... 18 .... 17 .... 18 

16 .... 14 .... 14 .... 17 .... 14 

17 .... 15 .... 1s .... ia .... 15 

18 .... 15 .... 15 .... 18 .... 15 

19 .... 17 .... 20 .... 20 .... 17 

20 .... 17 .... 20 .... 20 .... 17 

21 .... 28 .... 32 .... 28 .... 28 

22 .... 12 .... 12 .... 17 .... 15 

- 

23 .... 14 .... 15 .... 17 .... 15 

Global Clrlinge Data Uase Afiicu Docunrrrrruiiori Appendix D- IS 
TABLE 3A . (continued) 

VEGETATION . WINTER SPR I NG SUMMER FALL 

24 .... 14 .... 15 .... 16 .... 14 

25 .... 16 .... 18 .... 25 .... 20 

26 .... 17 .... 17 .... 20 .... 17 

27 .... 16 .... 20 .... 20 .... 18 

28 .... 16 .... 20 .... 20 .... 18 

29 .... 16 .... 20 .... 20 .... 18 

30 .... 30 .... 30 .... 30 .... 30 

31 .... 75 .... 75 .... 75 .... 75 

32 .... 16 .... 18 .... 20 .... 18 

Glolwl C'limge Data Base Africa Documerrrution Appendix D- 16 
-EWILSON AND HENDERSON-SELLERS LAND COVER AND SOILS 

FOR GLOBAL CIRCULATION MODELING 

CITATION: Wilson. M. F., and A. Henderson-Sellers, 1985. Glohal Land Cover ,and 

Soils data: digital data. National Center for Atiilosplieric Research, 

Boulder, Colorado. .32 Megabytes. 

Global Change Data Base Africa Documentation Appendix E- 1 
Blank Dacre retained for Dacrination 
E. WILSON AND HENDERSON-SELLERS LAND COVER AND SOILS FOR GCMS 

DATE: 1985 

SOURCE: 

Digitized from available sources. 

M. F. Wilson and A. Henderson-Sellers 

University of Liverpool 

Department of Geography 

P.O. Box 147 

Liverpool, U. K. 

CONTRIBUTOR: 

Roy Jenne 

NCAR 

P.O. Box 3000 

Boulder. CO 80307-3000 

DATA CENTER: NCAR 

GEOGRAPHIC SAMPLING: 1 degree grid, characteristic classes 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Decadal composires 

REFERENCES: 

Wilson. M. F. and A. Henderson-Sellers, 1985. "A global archive of laid 

cover and soils data for use in general circulation climate models." 

Journal of Climarology, vol. 5. pp. 119-143. (Included) 

FILE NAMES: 

AFWSOILS Wilson/Henderson-Sellers Soil Classes 

AFWSOILR Wilson/Henderson-Sellem Soil Class Reliability 

AFWLANDI 

AWL AND^ 

AFWLANDR 

Wilson/Henderson-Sellers Primary Land Cover Classes 

Wilson/Henderson-Sellers Secondary Land Cover Classes 

Wilson/Henderson-Sellers Land Cover Class Reliability 
Blank page retained for pagination 
WILSON/HENDERSON-SELLERS SOIL CLASSES 

DOCUMENTATION FILE 

File Name: AFWSOILS.DOC 

image title : Wilson & Henderson-Sellers Soil Classes 

data type : byte 

file type : binary 

rows : 438 

colms : 480 

minimum : 0.0000000000E+00 

maximum : 3.1000000000E+O1 

cell x : 1.6666666667E-01 

cell y : 1.6666666667E-01 

legend : 31 

category 1 : 1 not used 

category 2 : 2 not used 

category 3 : 3 not used 

category 4 : 4 not used 

category 5 : 5 not used 

category 6 : 6 not used 

category 7 : 7 not used 

category 8 : 8 not used 

category 9 : 9 not used 

category 10 : 10 not used 

category 11 : 11 LCF LIGHT, COARSE, FREE 

category 12 : 12 LIF LIGHT, INTERMEDIATE, FREE 

category 13 : 13 LFF LIGHT, FINE, FREE 

category 14 : 14 LCI LIGHT, COARSE, IMPEDED 

category 15 : 15 LII LIGHT, INTERMEDIATE, IMPEDED 

category 16 : 16 LFI LIGHT, FINE, IMPEDED 

category 17 : 17 MCF MEDIUM, COARSE, FREE 

category 18 : 18 MIF MEDIUM, INTERMEDIATE, FREE 

category 19 : 19 MFF MEDIUM, FINE, FREE 

category 20 : 20 MCI MEDIUM, COARSE, IMPEDED 

category 21 : 21 MI1 MEDIUM, INTERMEDIATE, IMPEDED 

category 22 : 22 MFI MEDIUM, FINE, IMPEDED 

category 23 : 23 DCF DARK, COARSE, FREE 

category 24 : 24 DIF DARK, INTERMEDIATE, FREE 

category 25 : 25 DFF DARK, FINE, FREE 

category 26 : 26 DCI DARK, COARSE, IMPEDED 

category 27 : 27 DII DARK, INTERMEDIATE, IMPEDED 

category 28 : 28 DFI DARK, FINE, IMPEDED 

category 29 : 29 L-P LIGHT, -- , POOR 

category 30 : 30 M-P MEDIUM, -- , POOR 

category 31 : 31 D-P DARK, -- , POOR 

category 34 : 34 ICE ICE 

(category 0 : unclassed) 

Global Change Data Base Afiicu Vocinientution Appelldix E-5 
WILSON/HENDERSON-SELLERS SOIL CLASS RELIABILITY 

DOCUMENTATION FILE 

File Name: AFWSOILR.DOC 

image title : 

data type : 

file type : 

rows 

columns 

mi n imum 

maximum 

cell :i 

cell y 

1 egend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

Wilson/Henderson-Sellers Soil Class Reliability 

byte 

binary 

438 

480 

0.0000000000E+00 

5.0000000000E+00 

1.6666666667E-01 

1.66666666673-01 

5 

1 High 

2 Good 

3 Moderate 

4 Fair 

5 Poor 

(category 0 : unclassed) 

[This leqend is appropriate for display of the individual classes 

(i .e. without rescaling) . 

Global Change Data Uase Africu Documenfution Appendix E-6 
category 39 : 39 PAS 

category 40 : 40 ARC 

category 41 : 41 DFA 

category 42 : 42 NMG 

category 43 : 43 CAS 

category 44 : 44 MA1 

category 45 : 45 COT 

category 46 : 46 COF 

category 47 : 47 VIN 

category 48 : 48 IRG 

category 49 : 49 TEA 

category 50 : 50 ERF 

category 51 : 51 ETC 

category 52 : 52 TBF 

category 53 : 53 Not used 

category 54 : 54 Not used 

category 55 : 55 Not used 

category 56 : 56 Not used 

category 57 : 57 Not used 

category 58 : 58 Not used 

category 59 : 59 Not used 

category 60 : 60 Not used 

category 61 : 61 TUN 

category 62 : 62 DWS 

cat ego ry 

category 

category 

cat ego cy 

cat ego ry 

category 

cat egory 

category 

cat ego ry 

cat ego ry 

category 

63 : 63 Not 

64 : 64 Not 

65 : 65 Not 

66 : 66 Not 

67 : 67 Not 

68 : 68 Not 

69 : 69 Not 

70 : 70 SDB 

71 : 71 SDS 

72 : 72 Not 

73 : 73 SDT 

used 

used 

used 

used 

used 

used 

used 

used 

Pasture + shrub 

Arable cropland 

Cry farm arable 

Nursery and market gardening 

Cane sugar 

Mal ze 

Cotton 

Coffee 

Vineyard 

Irrigated ‘cropland 

Tea 

Equatorial rain forest 

Equatorial tree crop 

Tropical broadleaf forest (slight seasonality) 

Tundra 

Dwarf shrub (tundra transition and high 

altitude wasteland) 

Sand desert and barren land 

Scrub desert and semi desert 

Semi desert + scattered trees 

{cateqory 0 : Open Water} {Category 80: Not Occurring} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) .I 

Globul Change lhta Uase Afiica Docwnteritutiort Appendix E-7 
WILSON/HENDERSON-SELLERS PRIMARY LAND COVER CLASSES 

DOCUMENTATION FILE 

File Name: AFWLAND1.DOC 

i.mage title : 

data type : 

file type : 

rows 

columns 

minimum 

ma:; imum 

cell x 

cell Y 

Wilson/Henderson-Sellers Primary Land Cover Classes 

byte 

binary 

438 

4 80 

o.ooooooOOOOE+OO 

7.3000000000E+01 

1.6666666667E-01 

1.6666666667E-01 

legenh : 73 

category 1 : 1 INW 

category 2 : 2 BOM 

category 3 : 3 ICE 

category 4 : 4 PAR 

category 5 : 5 MAN 

category 6 : 6 Not used 

category 7 : 7 Not used 

category 8 : 8 Not used 

category 9 : 9 Not used 

category 10 : 10 CNE 

category 11 : 11 ONE 

category 12 : 12 DMN 

category 13 : 13 OMN 

cateqory 14 : 14 EBW 

category 15 : 15 EBC 

category 16 : 16 EBS 

category 17 ; 17 .ODN 

cateqory 18 : 18 DDN 

category 19 : 19 DEB 

category 20 : 20 DDB 

cateqory 21 : 21 ODB 

cateqory 22 : 22 DTC 

category 23 : 23 OTW 

cateqory 24 : 24 WOS 

category 25 : 25 DDD 

category 26 : 25 ODD 

category 27 : 27 DES 

category 28 : 28 THS 

category 29 : 29 Not used 

category 30 : 30 TMP 

cateqory 31 : 31 TRG 

cateqory 32 : 32 TGS 

category 33 : 33 TRP 

category 34 : 34 RGS 

category 35 : 35 PAT 

category 36 : 36 SAR 

category 37 : 37 TSG 

category 38 : 38 Not used 

Inland water 

Bog or marsh 

Ice 

Paddy rice 

Mangrove (tree swamp) 

Dense needleleaf evergreen forest 

Open needleleaf evergreen woodland 

Dense mixed needleleaf and broadleaf evergreen 

and deciduous forest 

Open mixed needleleaf and broadleaf, evergreen 

and deciduous woodland 

Evergreen broadleaf woodland 

Evergreen broadleaf cropland 

Evergreen broadleaf shrub 

Open deciduous needleleaf woodland 

Dense deciduous needleleaf forest 

Dense evergreen broadleaf forest 

Dense deciduous broadleaf forest 

Open deciduous broadleaf woodland 

Deciduous tree crops (temperate) 

Open tropical woodland 

Woodland + shrub 

Dense drought deciduous forest 

Open drought deciduous woodland 

Deciduous shrub 

Thorn shrub 

Temperate meadow and permanent pasture 

Temperate rough grazing 

Tropical grassland + shrub 

Tropical pasture 

Rough grazing + shrub 

Pasture + tree 

Semi arid rough grazing 

Tropical Savanna (grassland + tree) 

Global Change Data Base Ajiicu Doc*umentution Apprndix E-8 
WILSON/HENDERSON-SELLERS SECONDARY LAND COVER CLASSES 

DOCUMENTATION FILE 

File Name: AFWLAND2.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell :c 

cell y 

Wilson/Henderson-Sellers Secondary Land Cover Classes 

byte 

binary 

438. 

480 

0.0000000000E+00 

7.3000000000E+01 

1.66666666673-01 

1.6666666667E-01 

legend 

cat ego ry 

category 

category 

cat ego ry 

category 

category 

category 

cat ego ry 

category 

category 

cat ego ry 

category 

: 73 

1: 1INw 

2 : 2 BOM 

3 : 3 ICE 

4: 4PAR 

5: SMAN 

6 :. 6 Not used 

7 : 7 Not used 

8 : 8 Not used 

9 : 9 Not used 

10 : 10 DNE 

11 : 11 ONE 

12 : 12 DMN 

category 13 : 13 OMN 

category 14 : 14 EBW 

category 15 : 15 EBC 

category 16 : 16 EBS 

cateqory 17 : 17 ODN 

category 18 : 18 DDN 

cateqory 19 : 19 DEB 

category 20 : 20 DDB 

category 21 : 21 ODB 

category 22 : 22 DTC 

category 23 :. 23 OTW 

category 24 : 24 WOS 

category 25 : 25 DDD 

cateqory 26 : 26 ODD 

category 27 : 27 DES 

category 28 : 28 THS 

category 29 : 29 Not used 

category 30 : 30 TMP 

cateqory 31 : 31 TRG 

category 32 : 32 TGS 

category 33 : 33 TRP 

category 34 : 34 RGS 

category 35 : 35 PAT 

category 36 : 36 SAR 

category 37 : 37 TSG 

cateqory 38 : 38 Not used 

Inland water 

Bog or marsh 

Ice 

Paddy rice 

Mangrove ( tree swamp 1 

Dense needleleaf evergreen forest 

Open needleleaf evergreen woodland 

Dense mixed needleleaf and broadleaf evergreen 

and deciduous forest 

Open mixed needleleaf and broadleaf, evergreen 

and deciduous woodland 

Evergreen broadleaf woodland 

Evergreen broadleaf cropland 

Evergreen broadleaf shrub 

Open deciduous needleleaf woodland 

Dehse deciduous needleleaf forest 

Dense evergreen broadleaf forest 

Dense deciduous broadleaf forest 

Open deciduous broadleaf woodland 

Deciduous tree crops (temperate) 

Open tropical woodland 

Woodland + shrub 

Dense drought deciduous forest 

Open drought deciduous woodland 

Deciduous shrub 

Thorn shrub 

Temperate meadow and permanent pasture 

Temperate rough grazing 

Tropical grassland + shrub 

Tropical pasture 

Rough grazing + shrub 

Pasture + tree 

Semi arid rough grazing 

Tropical Savanna (grassland + tree) 

Global Change Ihtsa R~se Africa Docrunentutiorr Appendix E-9 
category 39 : 39 PAS 

category 40 : 40 ARC 

category 41 : 41 DFA 

category 42 : 42 NMG 

category 43 : 43 CAS 

category 44 : 44 MA1 

category 45 : 45 COT 

category 46 : 46 COF 

category 47 : 47 VIN 

category 48 : 48 IRG 

category 49 : 49 TEA 

category 50 : 50 ERF 

category 51 : 51 ETC 

category 52 : 52 TBF 

category 53 : 53 Not used 

category 54 : 54 Not used 

category 55 : 55 Not used 

category 56 : 56 Not used 

category 57 : 57 Not used 

category 58 : 58 Not used 

category 59 : 59 Not used 

category 60 : 60 Not used 

category 61 : 61 TUN 

category 62 : 62 DWS 

cat. ego ry 

category 

category 

category 

category 

category 

category 

cat ego ry 

cat ego ry 

cat ego ry 

category 

63 : 63 Not 

64 : 64 Not 

65 : 65 Not 

66 : 66 Not 

67 : 67 Not 

68 : 68 Not 

69 : 69 Not 

70 : 70 SDB 

71 : 71 SDS 

72 : 72 Not 

73 : 73 SDT 

used 

used 

used 

used 

used 

used 

used 

used 

Pasture + shrub 

Arable croFland 

Dry farm arable 

Nursery and market gardening 

Cane sugar 

Maize 

Co t't on 

Coffee 

Vineyard 

Irrigated cropland 

Tea 

Equatorial rain forest 

Equatorial tree crop 

Tropical broadleaf forest (slight seasonality) 

Tundra 

Dwarf shrub (tundra transition and high 

a1 t i t ude wasteland) 

Sand desert and barren land 

Scrub desert and semi desert 

Semi desert + scattered trees 

{category 0 : Open Water} 

{category 10: Not Occurring} 

[This legend is appropriate for display of the individual classes 

(i.e. without rescaling) , ] 

Global Cliunge Dnta Base Aji-ica Documentation Appendix E- 10 
WILSON/HENDERSON-SELLERS LAND COVER CLASS RELIABILITY 

DOCUMENTATION FILE 

image title : 

data type : 

file type : 

rows 

columns 

mi n imum 

ma:.: i mum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

Wilson/Henderson-Sellers Land Cover Class Reliability 

byte 

binary 

438 

4 80 

0.0000000000E+00 

5.0000000000E+00 

1.6666666667E-01 

1.66666666673-01 

5 

1 High 

2 

3 

4 

5 Low 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Global Change 1hta Dase Afiico Docun~rrttatiorr Appendix E- I 1 
Blank page retained for pagination 
FORMAT & BRIEF DESCRIPTION OF SOIL AND VEGETATION DATA 

(University of Liverpool) 

1) The 3 basic data arrays, lox 1' soil data, 1' x 1' primary vegetation 

data, and 1' x 1' secondary vegetation have been merged consecutively 

and stored as one file (called soilveg data) on the tape. 

Each data type was originally stored as an integer array (323its), 

dimensions 360 x 180, (output) in FORTRAN integer format, 4012 i.e., 

1620 lines per data type, 4860 lines in total. 

The 2 re1 iabl il i ty data arrays, 1' x 1' soil re1 iabil i ty, and 1' x 1' 

land cover reliability have been merged consecutively and stored as one 

file (called re1 data) on the tape. 

Each data type is stored as an integer array dimensions 360 x 180 in 

FORTRAN integer format 4011 i.e., 1620 lines per data type, 3240 lines 

in total. 

2) 

File soilves data 

1) 1' x 1' soil data - 360 x 180 array - lines 0 - 1620 

2) 1' x 1' primary veg. data - 360 x 180 array - lines 1621 - 3240 

3) 1' x 1" secondary veg. data - 360 x 180 array, lines 3241 - 4860 

File re1 data 

1) 1' x 1' soil reliability - 360 x 180 array - lines 0 - 1620 

2) 1' x 1' land cover re1 iabil i ty - 360 x 180 array - 1 ines 1621 - 3240 

The tape has been written by an IBM 4341 under VM/CMS using the move-file 

command. 

JCL to write taDe 

The data are output as 80 character logical records, blocked 10 to a physical 

record (800 by tens). 

Fi inmove soilveg data. 

Fi outmove tap1 (lrecl 80 block 800 recfm fb den 1600 9track) 

movef i 1 e 

Fi inmove re1 data A 

Fi outmove tap (lrecl 80 block 800 recfm fb den 1600 9track) 

movef i 1 e 
00 

01 

02 

01 

04 

os 

10 

I1 ia 

13 

14 

15 

I6 

I1 

18 

19 

20 

11 

12 

24 

2s 

26 

28 

IO 

)I 

12 

33 

34 

IS 

I6 

31 

39 

40 

41 

41 

43 

44 

45 

4b 

47 

48 

49 

10 

51 

61 

11 

ai 

sa 

Cod. 

11 

I2 

13 

14 

1s 

16 

17 

18 

19 

20 

11 

22 

23 

24 

25 

32 

27 

28 

29 

IO 

31 

- 

34t 

Colour 

llght 0 

0 

0 

0 

0 

abdlur 0 

0 

.. 

0 

dbrk 0 

0 

. 

0 

. 

I lght 

mbdlur 

dbrk 

I-. 
JOURNAL OF CLIMATOLOGY. VOL. 5. 119-143 (1985) 55 1.5 1355 1.588.3:58 1.524 .-Lt:63 1.4 

A GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 

FOR USE IN GENERAL CIRCULATION CLIMATE MODELS 

M. F. WILSON' AND A. HENDERSON-SELLERS 

Deparment of Geography, Universiry of Liverpool, P.O. Box 147, Liverpool, U.K. 

Received IS March 1984 

Revised 1 June 1984 

ABSTRACT 

A new global archive of soil type and land cover data derived for use in GCM climate models is described. The data 

are archived at a iesolution of 1" latitude x 1" longitude. The method of construction of the component data sets is 

given together wth 'reliability estimates'. The soils data form the only published data set designed specifically for 

use in climate studies. The land cover data are compatible with the soils data, forming a coherent and complete 

data set and seem to be comparable with other land cover information. Recommendations are made about 

combining and coarsening the data to the grid of any general circulation climate model. Methods of parameterizing 

surface radiative and hydrological properties in climate models are proposed. 

KEY WORDS Land surface Climate models Soils Vegetarion Albedo Hydrology 

1. INTRODUCTION 

There is increasing awareness of and concern about the possible causes and consequences of climate 

fluctuations. Major international and interdisciplinary efforts are currently being made to improve our 

understanding of the climate system. The main objectives of the World Climate Research Programme 

(WCRP) are to determine: (i) to what extent climate can be predicted and (ii) the extent of. man's 

influence on climate (Houghton. 1982). The most sophisticated tool with which to study global scale 

climate is the 3-D general circulation model (GCM). 

The physical properties of the soil and the presence and nature of a vegetation cover influence the 

surface energy and moisture balance and the exchange of moisture, heat and momentum between the 

atmosphere and the land surface. Until very recently climate modellers have concentrated upon 

producing successful simulations of the large scale dynamics of the atmosphere and the associated 

transports of energy and moisture. Surface-atmosphere interactions have been represented in climate 

models by using only simple parameterizations. There is, however, an increasing awareness of the 

importance of the interaction of the components of the climate system on various temporal and spatial 

scales. The need for an interdisciplinary approach to climate model development has been recognized 

recently (Dickinson, 1983). 

Development of land surface parameterization and examination of the possible impact of man on 

climate through surface modification are important and currently active fields of research within the 

WCRP. To develop land surface parameterization schemes the global distribution of climatically 

important parameters must be prescribed as input data for the models. An early attempt was made by 

posey and Clapp (1964) to provide information about global vegetation for use in albedo studies 

although these data were produced only in map form and the mislabelling of their Figures 2-5 must be 

~ 

Addressee for Correspondence 
120 M. F. WILSON AND A. HENDERSON-SELLERS 

corrected if comparisons are to be drawn. The only global vegetation data known to the authors to be 

currently available' in digital format are the land cover classifications of Hummel and Reck (1979) ad 

Matthews (1983a). We know of no global soils data in digital form. 

Sensitivity experiments show that many climate models are sensitive to surface albedo changes (e.,. 

Charney, 1975; Charney el al., 1977; Washington and Chervin, 1979; Sud and Fennessy, 1982). More 

recently the sensitivity of climate models to surface moisture and evapotranspiration has been clearly 

demonstrated in the global irrigation experiment undertaken by Shukla and Mintz (1982) using the 

Goddard Laboratory for Atmospheric Science (GLAS) general circulation model and in similar 

experiments undertaken at the U.K. Meteorological Office (UKMO) (Carson and Sangster, 1982). At 

the regional scale, Walker and Rowntree (1977) and Rowntree and Bolton (1983) have shown that 

persistent response to soil moisture anomalies occurs. It is clear that there are many links between 

properties of the energy and moisture budgets. As models are sensitive to soil and land mver 

properties. climate modellers require information about the surface upon which to base parameterization schemes. 

A new global data set of type of land cover and soil designed specifically for use in climate models 

(specifically for the U.K. Meteorological Office 11 layer GCM) has been constructed. The data were 

collated from natural vegetation, forestry, agriculture, land use and soils maps with reference to the 

accompanying descriptive legends. The final data archive is stored in digital format at a 1" latitudexl' 

longitude resolution. The soils data have been averaged from digitized dominant type of soil at a finer 

resolution (112" latitude x 1/2" longitude). The !and cover data also provide information at a finer 

resolution than 1" x 1" as dominant primary and secondary land cover types are archived as separate 

arrays (see Section 3). The identification of secondary land type may be important for cover types such 

as urban development which have a fragmented distribution. The incorporation of both primary and 

secondary land uses results in a finer effective resolution than any data previously incorporated into a 

GCM. The soils data are the only existing global soils data set known to the authors in which soil type is 

classified according to climatically important properties for use in GCMs, although other land cover 

data sets are available and additional data sets of both types are currently being prepared (B. Moore. 

1984. P.c.). The most important feature of the archive described here is that it contains both soil and 

land cover information. Thus. for the first time. parameterization of the hydrological cycle. especially 

evaporation. runoff and soil moisture dependence of albedo, can be incorporated consistently with 

vegetation parameterization schemes. Recommendations concerning the use of the new data in both 

climate modelling and monitoring programmes are made at the end of this paper. 

2. SOILS DATA SET 

The digital soils data described here are presented as a 180 X 360 element array. Each row starts with 

the Greenwich meridian as the western boundary of the first element and progresses eastwards. 

Twenty-one categories of soil based on a combination of colour, texture and drainage classes are 

identified. 

2. I. Criteria for soil caregory selection 

The four major components of soil are inorganic material, organic material, water and air. There are 

many factors which influence the development of soil. These factors vary in time and space such that the 

integral of all soil forming processes produces a multiplicity Of soil profile types. The GCM modeller 

needs information about the large scale distribution of thermal, hydraulic and radiative soil properties 

to determine surface and subsurface temperature and moisture state in a model integration. A simple 

soils data set with relatively few classes is required. 

Surface reflectivity, soil thermal conductivity, specific heat and heat capacity are the most important 

soil parameters influencing the surface thermal regime. Thermal response depends on the conductivity ~ 

CLIMAP (1981) produced a land surface data file at 2" X 2" which we believe is: also available (R. T. Wetherald, 1984, P.c.) 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 121 

bulk average heat capacity of the soil particles, soil porosity and soil moisture content. In general, 

capacity increases and conductivity decreases as texture vanes from coarse to fine in dry soils 

,,hereas both increase with increasing soil moisture content. 

of the numerous factors which contribute to the moisture regime of the soil, surface texture and 

,,,rormation relating to the presence of an impermeable layer impeding vertical drainage are the only 

.,fameters available at a global scale. The soil classification used here is based on a combination of 3 P ,,lour. 3 texture and 3 drainage categories. Surface colour (light, medium or dark) influences the 

,urface albedo. Surface texture (fine, intermediate or coarse) may be used to parameterize rates of 

,,tiltration of moisture into the soil and soil water movement and io assign appropriate thermal and 

moisture capacities for the upper soil layers. Drainage (free, impeded or poor) mav be used as an 

In~icutor ofthe likelihood of waterlogging and the depth to which moisture can penetiate. 

.Id 

_.-. 3 7 Description of data sources 

The main data source used was the FAO/UNESCO Soil Map of the World (S.M.W.). It was 

"ccessary to use the Oxford Regional Economic Atlas of U.S.S.R. and Eastern Europe in conjunction 

,,l[h the Central Asia and East European map sheets to classify the soils of N.E. Asia. as the relevant 

map sheet was not available. The internationally agreed legend of the S.M.W. is not a pedological 

classification system in itself. It was designed to provide a more general framework into which soil 

Clabses of the many existing systems could be fitted. A general legend and 10 regional legends 

,Iccompany the map sheets. Each legend contains details of the environmental conditions. the sources of 

data used and their reliability. Details of morphological, chemical and physical properties of soils from 

,&cted profiles are presented. Colours are given according to the Munsell colour scheme. One 

hundred 2nd six major soil units are identified in the S.M.W. In the S.M.W. the term soil unit is used to 

dcntify soil profile types. 'These units do not correspond to equivalent categories in different 

classification systems' (FAOKJNESCO, 1974. p, 10). It is not possible to assess quantitatively the 

mount of organic matter typical of each of these units. However, from the profile descriptions it is 

possible to distinguish qualitatively soil units in S.M.W. expected to-have low or high organic matter 

content (Table I). High organic matter content is associated with dark soils. It can occur either as a 

result of waterlogging and low decay rates or it can promote aggregation and a structure which improves 

drainage. 

2.3. Derivarion of colour classes 

For climate modelling the colour of the surface layer is required to determine the surface albedo. The 

colour of the surface 'A' or '0' horizon is a function of the mineral material, organic matter content and 

roil moisture content. The S.M.W. units were grouped into 3 colour classes: dark, medium and light. 

The colour class was determined primarily from the description of major soil units given in the general 

legend. Soil colour is given according to the Munsell colour scale. The Munsell colour scale defines 

colour according to three parameters: hue (which is an indication of the tone of a soil colour), chroma 

Iivhich is dependent on the intensity of the colour) and value (which is a measure of the lightness of the 

colour). Soil units characterized by mollic or umbric 'A' horizons or dark organic rich '0' or 'A' 

horizons as ;I diagnostic feature have been classed as dark. Mollic or umbric 'A' horizons are defined in 

the S.M.W. legend as those in which 'both broken and crushed samples have colours (Munsell) with a 

chroma of less than 3.5 when moist, and a value darker than 3.5 when moist'. The remaining soil units 

of S.M.W. either 

(i) have an ochric 'A' horizon or 

(ii) the 'A' horizon characteristics are not a diagnostic feature of the unit. 

An ochric 'A' horizon is defined in the S.M.W. general legend as one that is either 'too light in colour, 

has too high a chroma or too little organic matter. or, is too thin (<lo cm if underlain by rock. 18 cm or 

3 cm where the column is less than and greater than 75 cm respectively) to be classified as mollic or 

umbric or. is both hard and massive when dry'. Therefore it is not possible to assume that all ochric soils 

Global Change Dato Base Africa Docurrreniution Appendix E- 17 
122 M. F. WILSON AND A. HENDERSON-SELLERS 

Table I. Soil units having high 

organic matter content 

gelic histosols 

dystric histosols 

eutric histosols 

mollic solonchaks 

mollic gleysols 

humic gleysols 

mollic andosols 

humic andosols 

humic ferralsols 

mollic planosols 

humic planosols 

rnollic solonetz 

gleyic greyzems 

orthic greyzems 

luvic chernozems 

glossic chernozems 

calcic chernozems 

haplic chernozems 

luvic kastanozems 

calcic kastanozems 

haplic kastanozems 

luvic phaeozems 

glevic phaeoztms 

calcic phaeozems 

haplic phaeozems 

humic cambisols 

humic acrisols 

humic nitosols 

rankers 

rendizinas 

will be lighter in colour than umbric or mollic soils, as a criterion other than colour may have been 

used in distinguishing the unit. To re-classify the remaining soil units of the S.M.W. therefore. the 

descriptions of organic' matter content and colour given for individual sample profiles of the units 

presented in the regional legends were considered. The colour of the surface horizon in individual 

profile descriptions is given in terms of hue. value and chroma in accordance with notation used in the 

Munsell soil colour charts. These colours were subjectively classified as light. medium and dark by 

reference to a colour chart. The indices of hue, value and chroma chosen as the limits of each class are 

shown in Table 11. Where information was available in the profile description about the moisture status 

of the soil. the effect on soil colour was also considered. Problems associated with this technique include 

the limited number of sample profiles described for each of the S.M.W. soil units and lack of 

information about the moisture status of the sample profile at the time of observation. The dark. 

medium and light categories are designed to distinguish soil units that 

(i) range in colour from dark when wet to medium or dark when dry; these soil units were placed in 

(ii) show medium colour characteristics for all moisture states; these soil units were assigned to the 

(iii) have a range of colour that is light when dry and either light or medium when wet; these soil 

Soil units which were found to have variable colour characteristics in different sample profiles were 

assigned to the medium colour class. If only one or no sample profile was available for a soil unit it was 

placed in the medium category except where all other soil units within the soil order (group of soil units, 

e.g. fluvisois) show a specific dark or light colouration. 

the dark category 

medium category 

units were placed in the light category. 

Global Change Ihf:~ R:rsc Africu Docuiiieiitutioir Appendix E- I8 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 123 

Table 11. Hue. value and chroma limits used in 

classifying soil colour 

Assigned 

Hue Value Chroma colour class 

10 R 

2.5 YR 

5 YR 

7.5YR 

10 YR 

9.5 y 

t4 

4 

5 

5 

6 

<-l 

4 

5 

5 

6 

<-I 

4 

5 

5 

6 

<-1 

4 

5 

5 

6 

43 

4 

5 

5 

6 

<I 

4 

5 

6 

6 

<6 

all 

>8 

<8 

all 

<6 

all 

>6 

<6 

all 

<4 

all 

>6 

<6 

all 

all 

a1 1 

>6 

<6 

all 

.c6 

all 

>4 

<-I 

all 

ali 

all 

all 

>6 

<6 

dark 

medium 

medium 

light 

light 

dark 

medium 

medium 

light 

light 

dark 

medium 

medium 

light 

dark 

medium 

medium 

light 

light 

dark 

medium 

medium 

light 

light 

dark 

nedium 

nedium 

le diu m 

ght 

Iight 

-7.4. Deri\,urioti of texrure classes 

Three texture classes are given in the S.M.W. Texture is defined (FAOKJNESCO, 1971) by the 

percentage of sand. silt and clay in the top 3Ocm of the soil profile (Figure 1). The texture class in rhe 

new data set was classified as in the S.M.W. except in 3 cases: 

(i) If a soil unit is noted in the legend lo drain very quickly then in order to prevent excessively slow 

drainage in 3 model simulation. if fine texture was shown, the tcxturc was recorded 3s 

intermediate. 

(iij If 3 slope of <S per cent was coincident with fine texture and low organic matter the soil was 

classified as poorly drained. 

(iii) If fine texture was coincident with high organic matter and _rood drainage it was assumed that the 

organic matter would improve structure and promote good drainage and therefore the texture 

was recorded as intermediate. 

2.5. Derivation of drainage classes 

Three drainage categories are identified in the new data set: free. Poor and impeded. The 

descriptions of drainage characteristics in the S.M.W. legend were reviewed. Soil units which were 

frequentlv noted to have rapid. poor or impeded drainage were identified. These soil units are given in 

Table 111: For characteristically poorly drained soil units. or where fine texture, low organic matter and 

UobaI Change Deta Base qfieicu Documer,tu/jo,f Appciitlix E- 1s) 
124 M. F. WILSON AND A. HENDERSON-SELLERS 

Figure I. Texture triangle used in FAOlUNESCO Soil Map of the World classification 

slope <8 per cent were coincident. or where a marsh overprint symbol occurred, the soil category was 

recorded as poorly drained in this data archive. For the remaining areas. division into free or impeded 

drainage classes was made according to the location relative to the permafrost boundary and information pertaining to the soil phase. Soil phase is indicated by overprint symbols in the S.M.W. and defined 

as subdivisions of soil units based on characteristics which are significant to the use or management of 

the land but are not diagnostic of separation of units themselves. Impeded soils are either (i) located 

within the boundary of a permafrost zone and/or (ii) lithosols and/or (iii) soils having lithic, petrocalcic, 

petrogypsic. petrofernc. phreatic. fragipan or duripan phase. 

2.6. Construction of the soils data archive 

The soil category recorded in the new archive was identified according to the soil unit. slope and 

drainage class which occupied the greatest area in a 1/2" latitudex 112" longitude grid area. If no soil unit 

was dominant in a grid area a subjective appraisal was made of the individual colour. texture and 

drainage characteristics of soil units present. The element was then recorded as the categorv which best 

summarized the mixed properties. The 1" x 1" soils data are shown in Plate I. Plate [(a) is the global 

distribution of the 3 colour classes. In Plates I(b) and I(c) the texture and drainage classes are 

presented. 

From Plate I(a) it is evident that the majority of grid boxes have medium coloured soils. Dark soils 

occur in central North America. eastern Europe and throughout much of central and north-east 

U.S.S.R. north-east India and parts of east Africa and South America. Light soils are identified as 

dominant throughout the desert areas of north and south-west Africa, the Middle East and Mongolia. 

Light coloured soils are also found along the southern and western coasts of Australia and much of 

eastern Canada and most of Scandinavia. 

The soil texture and drainage categories are shown in Plates I(b) and I(c). 'The-three texture 

categories of the freely draining soils are shown in Plate I(b) and of the impeded and poorly draining 

soils are shown in Plate I(c). The most common soil category is the intermediate texture, freely draining 

soil. This occurs throughout most of the U.S.A., Europe and western Asia and is common in Africa and 

eastern Asia. The high northern latitudes have impeded drainage. In many such areas the impermeable 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA I25 

Table III. Soil units dominated by rapid. 

poor and impeded drainage characteristics 

(free. i.e. normal. drainage is associated with 

all the remaining units) ~ ~ 

Rapid chromic luvisols 

cambric arenosols 

luvic arenosols 

ferralic arenosols 

albic arenosols 

eutric regosols 

calcaric regosols 

dystric regosols 

calcaric gleysols 

dystric gleysols 

mollic gleysols 

humic gleysols 

plinthic gleysols 

gelic gleysols 

gleyic solonchaks 

gleyic podzoluvisols 

gleyic misols 

gleyic cambisols 

gleyic luvisols 

gleyic podzols 

gleyic phaeozems 

eleyic greyzems 

iutric histosols 

dystric histosols 

eelic histosols 

khic solonetz 

mollic' solonetz 

gleyic solonetz 

pellic vertisols 

chromic vertisols 

Poor eutric gleysols 

Impeded lithosols 

(All other soil units have free drainage). 

nature of the soil is due to the presence of continuous permafrost. Most desert regions have coarse 

testured soils. Fine textured soils are confined mostly to north-west Alaska. the southern coast of 

fjudson Bay. the northern coast of central U.S.S.R, south-east China and parts of Brazil. 

For each map sheet of the S.IM.W.. a small scale inset map is included showing whether the 

intormation used in constructing the map was derived from (i) systematic soil surveys. (ii) soil 

reconnaissance techniques or (iii) from general information based mainly on the interpretation of land 

forms. geology. vegetation and climate. The maps of source data have been used to show the reliability 

estimate for the data (Plate 11). Only in the Eastern U.S.A. and Eurasia are there large areas where 

information has been derived entirely from systematic survey. These regions are shown as having a high 

level of reliability. General information has been used in most of the sparsely vegetated regions. India 

and the rain-forest regions. The greatest uncertainty in the soil archive is therefore in Canada. N.W. 

Brazil. Africa. the Middle East and southern Asia. New survey information should be incorporated in 

these arcas as soon as possible. 

3. LAND COVER DATA SET 

The land cover data are presented as two 180 x j60 1" latitude X 1" longitude resolution arrays. Each 

type is represented by a two digit code. The 53 possible land cover types used are listed in Table IV. 

The first array contains primary land types. The second array contains secondary land types. The 

Global ('IwnKe Data Ihe Africa Docurnentotion Appitlix E-21 
Llght 

Nan soil 

- 

- 

- lkdlurn Dark 

Plate I(a). Global distribution of dark, medium and light coloured soils 

Interrnedlate, rree 

.- -_ Other __ 

? 

r 

r 

m 

irl 

rA 
-4;L - <KF 3-- ---.. AV.*. 

InLermedlate. impeded Flne. lmpcaea 

_. 

__ - OLher __ 

Plate I(c). Global distribution of soil drainage classes 

Coarse, Impturd 

P00r 

3 &derate (reconnaissance) 

Plate II. Krliahility estiniatrs for the source data used in compiling the soils data file 
Grassland 

wetland and Inland water Barren 

- Other - 

S-1-de-err mnd .para. rs'.t.tion 

Shrualmnd pfll 

- tv.rar..n tr... Cro.1.n. 

:< Olh.r 

Plates TV(a) and IV(b). Distribution of secondary land cover groups 

U 

z 
Declduou. ana nixca tree3 % 

r 

b 

n 

9 

m 

7J 

b 

U 

> 

e 
Plate V. Rcliability rstiinms for thc source data used in cornpiling the land cover tile 

c 

w 

0 

? 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 131 

Table IV. Land cover classes used in new data archive - 

Code Land cover description 

00 Open water 

01 Inland water 

02 Bog or marsh 

03 Ice 

04 Paddy rice 

05 Mangrove (tree swamp) 

10 Dense needleleaf evergreen forest 

11 Open needleleaf evergreen woodland 

12 

13 

14 Evergreen broadleaf woodland 

15 Evergreen broadleaf cropland 

16 Evergreen broadleaf shrub 

17 Open deciduous needleleaf woodland 

18 Dense deciduous needleleaf forest 

19 Dense evergreen broadleaf forest 

20 Dense deciduous broadleaf forest 

21 Open deciduous broadleaf woodland 

*22 Deciduous tree crops (temperate) 

23 Open tropical woodland 

24 Woodland + shrub 

25 Dense drought deciduous forest 

26 Open drought deciduous woodland 

27 Deciduous shrub 

28 Thornshrub 

30 

31 Temperate rough grazing 

32 Tropical grassland + shrub 

33 Tropical pasture 

34 Rough grazing + shrub 

35 Pasture + tree 

36 Semi and rough grazing 

37 

39 Pasture + shrub 

40 Arable cropland 

41 Dry farm arable 

43 Cane sugar 

44 Maize 

45 Cotton 

46 Coffee 

47 Vineyard 

48 Irrigated cropland 

49 Tea 

50 Equatorial rain forest 

51 Equatorial tree crop 

52 

61 Tundra 

62 

70 

71 

73 

80 Urban 

Dense mixed needleleaf and broadleaf. evergreen and deciduous forest 

Open mixed needleleaf and broadleaf, evergreen and deciduous woodland 

Temperate meadow and permanent pasture 

Tropical savanna (grassland + tree) 

'42 Nursery and market gardening 

Tropical broadleaf forest (slight seasonality) 

Dwarf shrub (tundra transition and high altitude wasteland) 

Sand desert and barren land 

Scrub desert and semi desert 

Semi desert + scattered trees 

not identified at 1" x 1" resolution 
132 M. F. WILSON AND A. HENDERSON-SELLERS 

primary land type is one which best represents the land cover type which occupies 250 per cent of the 

grid area. The secondary land type occupies 40 per cent and 225 per cent of the grid area. If no 

secondary land type could be distinguished the primary code was repeated for the secondary land type. 

For grid areas with mixed vegetation or where no single type was clearly dominant, primary and 

secondary types were chosen which most accurately summarized all the component land types. A 

number of mixed categories are incorporated in the classification. 

3. I. Choice of land type categories 

Parameterization of the influence of land cover on surface energy and moisture exchange with the 

atmosphere requires estimates of vegetation height, structure, density, seasonality, ground cover and 

rooting depth at a scale appropriate to the model resolution. 

Four height categories are incorporated in the classification scheme: tall (trees), intermediate (shrub 

and tall grasses and maize), short (grasses and most crops) and very short (tundra and barren land). 

Needleleaf and broadleaf trees have been separated because they differ in structure, interception and 

evapotranspiration characteristics. Density classes range from dense forest, through open woodland, 

shrubland, tree crops. grass or crop with tree shrub, open grassland and cropland to semi-desert, dwarf 

shrub tundra transition, tundra and finally to bare soil. Seasonality is incorporated by distinguishing 

between evergreen and deciduous, temperate and drought shrub and tree formations, temperate and 

tropical ecosystems and between cultivated arable land, permanent pasture and natural grazing. 

Irrigated and dry farm crop systems are distinguished from other arable land due to their different cover 

characteristics and artifically imposed surface hydrology. The combination of both primary and 

secondary land types permits representation of transition zones between dominant land cover types and 

increases the number of types that can be included. 

Fifty-three possible classes divide global vegetation on the basis of parameters important in climate 

modelling studies. Of these 53 classes only 51 were identified at the 1" X 1" resolution (see Table IV). 

Although an agreed international classification for vegetation mapping (UNESCO, 1973) has now been 

published and may be employed in future mapping, existing maps have used diverse classification 

schemes. In this archive the classification is designed specifically to provide information about the gross 

surface features from existing ground based surveys with critical consideration of as many of the 

available data sources as possible. Only a few classes were used for each parameter (e.g. tall, 

intermediate. short and very shodbarren in the height classes), so that it was possible to infer these 

distinctions from most of the data sources available in spite of the disparate classification schemes used. 

The classes have, additionally, been chosen on the basis of expected requirements of land surface 

parameterization schemes in climate models. The data represent a simple classification of major 

vegetation types according to qualitative categorization of gross features of the vegetation cover. In 

classifying vegetation at the large scale appropriate to the climate modelling community it is not 

possible. and probably also not necessary, to identify more than a few broad classes for each of the 

climatically important parameters. Despite the simplified system we have used we are pleased to note 

the similarity between our classification scheme (Table IV) and the subdivisions of major ecosystems 

listed by Matthews (1983a) (her table 4) which were based on the UNESCO (1973) classes. 

3.2. Descripnon of main data sources 

A large number of data sources were used in constructing the land cover data set. The main sources 

(1) The World Atlas of Agriculture (1969-1976), UNESCO) 

(2) The World Forestry Atlas (1961 and continuing) 

(3) Heinernann. Times and Oxford World atlases 

(4) Oxford regional economic atlases 

(5) National and regional atlases (Table V) 

(6) A global vegetation map produced for use in carbon budget studies (Olson and Watts, 1982) 

(7) Northern Hemisphere snow covered a!bedo map (Robinson, 1982). 

were: 

Global Change Data Base Afiicu Documerrtuliorr Aplwntlix E-28 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 133 

Table V. Atlases and map books used in constructing the land cover data file 

- 

\\/urlJ 

,lriav of Economic Geographv. 1975. Humlum. J., 7th edition, Gyldendal, Copenhagen. 

+fryer's Grosser physrtcher Welrarlas 3. 1976. Schmithusen. J. (ed), Atlas zur Biogeographie. Bibliographisches Institut, 

orford Economic Allas ofrhe World. 1972. Jones. D. B. (ed.), 4th edition. Oxford University Press. London. 

o.r{urd World Atlas. 1973. Cohen. S. B. (ed.). Oxford University Press, London. 

The Heinemam World Adas. 1979. Rand McNally (ed.), Heinemann. London. 

The l/niversic Allas. 1977. Fullard. H. and Darby. H. C. (eds), 18th edition. Philip, London. 

T/le Times Atlas of the World. 1968. Comprehensive edition. Times Newspaper. London. 

The Times Concke AIhs of the World. 1972. Times Newspaper. London. 

W&forsradas. 1961 (continuing). Verlag Paul Parey, Hamburg. 

world Aria of Agricuirure. 1969. Committee for the World Atlas Agriculture. Instituto Geografico de Agostini. Novara. 

,Mannheim. 

Ettrope 

Agro-ecologicd Atlas of cereal Growing in Europe. Vol. 1 Agroclimaric Adas of Europe. 1965. Thran. P. and Broekhuizen, S.. 

A~/U of Europe: a profile of Wesrern Europe. 1974. Browne. G. S. (ed.), Banholomew and Warne. London. 

0x)ord Regional Economic Atlas of U.S.S.R. lrnd Eastern Europe. 1963. Oxford University Press, London. 

Ox/ord Regional Economic Allas of Wesrern Europe. 1971. Clayton. K. M. and Kormoss. I. B. F., (eds) Oxford University 

Allas de Belgique. 1951. Comitk National de Geographie Commision d I'Atlas. Brussels. 

A ,\lap Book of rhe Benelur Counmes. 1979. Tussler. A. J. B. and Alden. A. J. L.. Macmillan Education. London. 

Arias Ceskoslovenskei Socialirrickej Republiky. 1962. Svoboda, J. Ustreda sprava gcodCzic a kartografie. Prague. 

.-trh Republiky Ceskoslovenrkk. 1935. Pantoflifek. J. (ed.). Editions Orbis S. A., Prague. 

~ilas of Denmark. 1961. Nielson. N. (cd.). Royal Danish Geographic Society, Copenhagen. 

Danmark Atlas. 1976. Humlum. J. and Nyghrd. K., 2nd edition. Gyldendal. copenhagen. 

Dritucher Planungsarlas. 197G1975. Kuhn. A.. Haubner. K.. Kraus. H. and Witt. W. (cds). Schroedel. Hanover. 

Adus Deurcche Demokrarirche Republik. 1981. Akademie der Wissenchafttcn der D.D.R.. Haack. Gotha. 

An Agriculrural Arlas of England and Wales. 1976. Coppock. J. T. (ed.). Faber and Fabcr. London. 

Arias of Brirain and .Vorihem Ireland. 1963. Blackman. G. E., Caesar. A. A. L.. Gullick; C. F. W. R.. Steers, J. A. and 

Ordnance Sunev Atlas of Great Brirain. 1982. Ordnance Survey, Hamlyn. London. 

Ailas of Ireland. 1979. Irish National Committee for Geography, Royal Irish Academy. Dublin. 

Arias .Vocional de Espana. 1965. Instituto Geografico y Catastral. Madrid. 

Ailus of Finland. 1960. Aario. L.. (ed.). The Geographical Society of Finland. Kustannusosakeyhtio Otava. Helsinki. 

Atlas de France IMenopole). 1953-1959. Comite Nationale de Geographic. Paris. 

A .\.lap Book of France. 1980. Tussler. A. J. B. and Alden. A. J. L.. 4th edition. Macmillan Education. London. 

A .\lop Book of Wesr Germany. 1979. Tussler. A. J. B. and Alden. A. J. L.. Macmillari Education. London, 

Economic and Social Atlas of Greece. 1964. Kayser. B. and Thompson. K.. Center of Economic Research and National 

.VaiionaI Aila of Hungav. 1967. Sandor. R:. (ed.), Canographia. Budapest. 

Adas of Luembourg. 1971. .Ministere de I'Education Nationale. Luxembourg. 

Arias \'an .Vederland. 1963-1977. Government Pnnttng and Publishing Office. The Hague. 

.Verherlanrlr in One Hundred .Mops. 1977. Tamsma. R.. Royal Dutch Geographical Society, Amsterdam. 

Atlas von Niedcrostcrreich. 1951-1958. Hassinger. H. and Becker. A.. Frcytag-Berndt und Anaria. Vienna. 

Ailas der Republik Osrerreich. 1980. Bobek. H. (ed.). Freytag-Bcrndt und Artaria, Vienna. 

Arias Polski. 1952-1961. Centralny Urzqd Gsodezji i Kanografii. Warsaw. 

.Varodoyv Arias PoLski. 1972-1978. Leszozycki. S. (ed.). Polska Akademia Nauk. Instytut Geografii, Warsaw. 

Allas Grografic Republica Socialkid Romania. 1965. Eustatiu. G. and Victor. T.. (eds), Bucharest. 

Adas de la Suuse. 1981. Imhof. E. (ed.). L'Office Federal de Topographic. Wabern. Bemc. 

A .Map Book of Scandinavia. 1979. Tussler. A. J. B. and Buckby, P. R.. Macmillan Education, London. 

Agriculrural Atlas of Scorland. 1976. Coppock. J. T. (ed.), John Donald. Edinburgh. 

Economic Atlas of rhe Sovier Union. 1960. fish. G.. University of Michigan Press. Ann Arbor. 

U.S.S.R. in Maps. 1982. Dewdney. J. C.. Hodder and Stoughton. London. 

Allas over Sverige. 1953-1971. Anrick. C. J.. Hogbom. I., Enebbquist. G. and Lundquist. M.. Kartografiska Institutet. 

Grograjski Arias Jugoslavje. 1961. MardeSiC. P. and Dugatki. Z.. Zagreb, Znanje. 

Elsevier. London. 

Press. London. 

Worswick. G. 0. N.. Clarendon Press. Oxford. 

Statistical SCMCC of Greece. and Social Sciences Centre. Athens. 

Stockholm. 

Asia 

Arias ofrhe Arab World and rhe Middle Ear. 1960. Macmillan. London. 

Atlas of South-Emf Asia. 1964. Macmillan. Amsterdam. 

Oxford Regional Economic Allas of Middle Ear and North Africa. 1960. Oxford University Press. London. 

Sourh Asln in Maps. 1974. Kingsbury, R. C., 2nd edition. Donoyer Geppcrt. Chicago. 

Gmerol Atlas of Afghanrtran. 1970 (continuing). Sahab Geographic and Drafting Institute. Tehran. 

Allas of Land Uriliianon in China. 1950. Chen. C.S.. National Taiwan University, Taipei. 

Global Change httn Bilge AjijrQ ~)o~~?)~~~~~~,j~,~ Appendix E-29 
134 M. F. WILSON AND A. HENDERSON-SELLERS 

Table V (Contd.) 

Atlas of China. 1973. Hsieh. C.-M. and Salter. C. (eds), McGraw-Hill. New York. 

Census Atlas of India. 1970. Mitra. A., Census Commission of India. New Delhi. 

National Atlas of India. 1980. National Atlas and Thematic Mapping Organisation, Department of Science and Technology, 

Atlas of Israel. 19561964. Department of Surveys, Jerusalem. 

Atlas of Japan. 1974. International Society for Educational Information. Tokyo. 

Oxford Economic Atlas of Pakistan. 1955. Gullick. C. F. W. R. (ed.), Oxford University Press. London. 

An Atlas of Pupua and New Guinea. 1970. Ward, R. G. and Lea. D. A. M. (eds), University of Papua New Guinea and 

Geographical Atlas of Taiwan. 1959. Chen. C.-S., National Taiwan Univeristy, Taipei. 

Thailand National Resources Atlas. 1972. Royal Thai Survey Department, Bangkok. 

Soviet Union In Maps. 1972. Fullard, H. (ed.), Phillip. London. 

Government of India. Calcutta. 

Collins-Longman, Giasgow. 

Africa 

Jeune Atlas Afrique. 1974. Chi-Bonnardel, R. V.. Editions Jeune Afrique. Pans. 

Oxford Atlas for East Afnca. 1966. Oxford University Press, London. 

Oxford Regionol Economic Ailas of Africa. 1965. Oxford University Press. London. 

Geography of African Affairs. 1972. Fordham. P.. 3rd edition. Penguin. Harmondswonh. 

Atlas du Burundi. 1979. L’Universite de Bordeaux In, Association Atlas Burundi, Gradignan. 

Atlac du Camcroun. 1960. Institute de Rkcherches Scientifiques du Carneroun, Yaounde. 

Atlas de la Rkpublique Unie du Cameroun. 1979. Laclavere, G., (ed.), Editions Jeune Afnque. Pans. 

Atlas de la Rkpublique Populaire du Congo. 1977. Vennetier. P., (ed.), Editions Jeune Afnque. Pans. 

Atfar de Cbrc d’lvoire. 1979. I’Association de L‘Atlas de ate d’Ivoire. Abidjan. 

Atlac de la C6te d’lvoire. 1980. Vennetier. P.. (ed.), Editions Jeune Afnque. Pans. 

National Atlas of the Ivory Coast. 1971/1972. Office de la Recherche Scientifique et Technique, Abidjan. 

Ghana .Vational Atlac. 1972. Boareng, E. A., (ed.). Council of Scientific and Industrial Research, Accra. 

Atlas de fa Haute-Volta. 1975. Peron, Y. and Zalacain, V.. Editions Jeune Afrique, Paris. 

National Atlas of Kenva. 1970. 3rd edition. Survey of Kenya, Nairobi. 

Liberia in Maps. 1972. Gnielinski. S.. (ed.). University of London Press. London. 

Atlas du Maroc. 1954 (continuing). ComitC National de Gtographie du Maroc. Rabat. 

Malawi in Maps. 1972. Agnew. S. and Stubbs, M. (eds), University of London Press. London. 

Atlac de la Rkpublique Iskmique de Mauntanic. 1977. Toupet, C. and Laclav&re. G. (eds). Editions Jeune Afrique. Pans. 

Atlas d.1 Niger. 1980. Bernus. E. and Hamidou. S. A.. Editions Jeune Afnque. Pans. 

An Agricultural Ath of Nigeria. 1979. Agboola. S. A. (ed.). Oxford University Press. London. 

Nigeria in Maps. 1982. Barbour. K. .M.. Oguntoyinbo. J. S.. Onyemelukwe, J. 0. C. and Nwafor. J. C.. Hodder and 

Federul Atlas of Rhodesia and Nyasuland. 1960-1966. Federal Department of Tngonometrical and Topographic Surveys, 

Atlas of Sierra Leone. j966. Surveys and Land Division. 2nd edition, Stanford. London. 

Sierra Leone in Maps. 1969. Clarke. J. I.. Hodder and Stoughton. London. 

Atlas of Tanzania. 1967. Surveys and Mapping Division. Ministry of Lands. Settlement and Water Development. Dar-esTanzunia in .Maps. 1971. Berry, L. (ed.), University of London Press, London. 

Atlas Prutique du Tchad. 1972. Cabot. J.. Bouquet. C. and Dresch. J. (eds), Institut National Tchadien pour les Sciences 

Atlas of Uganda. 1967. Dawner. E. R. (ed.). Lands and Surveys Department, Government Printer. Entebbe. 

Atlac de la Rkpublique du Zaire. 1978. Laclavere. G. (ed.). Editions Jeune Afnque. Pans. 

Zambia in Maps. 1971. Davies. D. H. (ed.), University of London Press, London. 

Stoughton. London. 

Government Printer. Salisbury. 

Salaam. 

Humaines. Institut Geographique National, Pans. 

Australasia 

Atlas of Ausnalian Resources. 1966-1982. Plumb. T. (ed.), 2nd Series. Department of National Development. Canbena. 

Reader’s Digest Atlas of Ausnalia. 1977. Reader’s Digest (ed.), Reader’s Digest, Sydney. 

New Zealand Atlas. 1976. Wards. I. (ed.), Shearer. Wellington. 

New Zeuland in Maps. 1977. Anderson. A. G.. Hodder and Stoughton. London. 

North America 

Atlas of .Vorth American Affairs. 1969. Adam. D. E. and Rodgers. H. B., Methuen, London. 

Oxlord Regional Economic Atlas of United States and Canada. 1967. Chapman. J. D. and Sherman, J. C. (eds), Oxford 

iVarional .-Was of Canada. 1974. Surveys and Mapping Branch, Department of Energy, Mines and Resources. Macmlllan of 

National Atlas of the United States. 1970. U.S. Department of the Intenor, Geological Survey, Washington, D.C. 

University Press. London. 

Canada. Ottawa. 

Central and South America 

Arfm of Cenrraf America. 1979. Arbingast. S. A. Holz. R. K., Giil. C. C.. Ryan, R. M. and Hezlep, W. L.. Bureau of 

Business Research. University of Texas. Austin. 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 135 

Table v. (Conrd') 

de la Republica Argenrina. 1970. 4th edition. Instituto Geogriifico Militar, Buenos Aires. 

411u for the BahornPc. 1971. Collins and Son. Glasgow. 

.(/lrlr for Barbados. Windwords and Lecwards. 1974. Looby, A., Arthur. J., Hanis, F., Glean. C., Browne. I., Woodley, s., 

de Bolivia. 1958. RenC. R., Instituto Geografico de Agostini. Novara. 

41/a National do Brasi1. 1966. Conselho Nacional de Geografia. Rio de Janeiro. 

'411~ de Columbia. 1969. Diu. A. 2nd edition. Instituto Geografico Agusrln Codazzi. Litograffa Arco, Bogota. 

4rla de Cuba 1978. Instituto Cubano de Geodesia y Cartografia. La Habana. 

411u ,4ocional de Cuba. 1970. Instituto de Geograffa de la Academia de Ciencias de Cuba y Instituto de Gcografia de la 

,411a Geogrbfico del Ecuador. 1975. Jaramillo de Diivila. V., Instituto GeogrtUico Militar, Bogota. 

,A~/,.u National dc Guavmafa. 1972. Instituto GeogrAfico Nacional, Guatemala. 

,qr/a for Guvana. Trinidad and Tobago. 1978. Akai. A. and Matadeen. J., Madan Education, London. 

~//u.I for Jmaica and rhc Warern Caribbean. 1971. Eyre, L. A.. (ed.). Colins-Longman. Glasgow. 

jumaica in Maps. 1974. Clarke. C. G. and Hodgkiss. A. G.. University of London Press, London. 

,A~S of1Mexico. 1975. Arbingast. S. A., Gill. C. C.. Blair. C. D.. Holz. R. K.. Buchanan. J. R.. Marin. C. A., Ryan, R. XI., 

.4r/ac of Peru. 1971. Lbpez Dominovich. A., Guia. Lascano. 

.4r/ac of Venexda. 1979. Minisrerio del Arnbicnte v de 10s Recursos Naturales Renovables. Caracas. 

~~alrhwaite. M. and Kirby, E., Macmillan Education. London. 

Academia de Ciencias de la U.R.S.S. La Habana. 

Bonine. 34. E. and Weiler. J. P., 2nd edition, Bureau of Business Research, University of Texas, Austin. 

-;,z.l. World Atlas of Agriculture (W.A.A.). This contains land-use maps at a scale of 1:5,OOO,OOO or 

larger for all land areas (except northern Canada which is mapped at a scale of 1: 12,500,000). Sources 

in its construction are listed in the atlas. It is therefore possible to assess to some extent the 

Jccuracy of these data. Monographs contain selected bibliographies, descriptions of the physical 

environment (climate. geomorphology and natural vegetation) and details of exploitation of natural 

resources and land use for each country. Sixteen fundamental land use categories are used in this atlas. 

In areas of mixed land use extra classes are used with the land-use categories given in order of 

dominance. 

3.2.2. World Foresrry Atlas (1961 and continuing) (W.F.A.). This source contains maps of major 

forest types at a scale of 1 :5,000,000. Coarser resolution maps are presented of the distribution of 

major species in individual countries. These data were used to augment data presented in the W.A.A. 

and national atlases to determine the distribution of deciduous, evergreen, broadleaf and needleleaf 

species. 

3.2.3. Heinernarm World Atlas (1979), Times Atlas of the World (1968), Oxford Economic World 

(1972') and Oxford Regional (1959-1971) Atlases. Small scale maps of the natural climax vegetation and 

land use presented in these atlases were used to validate data contained in the W.A.A. and to provide 

supplementary information in areas of non-cultivated land. 

3.2.4. Nunonal atlases. Many national atlases were consulted in compiling the new land cover data 

file. Wherever national atlas data were available at a higher resolution or more recent date than the 

source for the W.A.A. the data sources were compared. If disagreement between these sources 

occurred. the most recently compiled of the two data sources was used. 

3.2.5. Ukon and Warts (1982). This is a map of global vegetation types at a scale of 1 : 30,000,000 

designed for use in global carbon budget studies, based on a previous study by Hummel and Reck 

11979). Major ecosystems (forest, grassland, shrubland, desert and cropland) are shown on the map 

with further subdivisions made according to species, density and climatic region. Unfortunately it has 

not been possible to establish the exact sources used to compile this map. Thus aIthough it is a useful 

verification tool. little direct assessment was made from this source. 

3.2.6. Snow covered albedo map (Robinson, 1982). This source contains clear sky surface albedo 

estimated for snow covered land derived in 1" x 1" segments from image processed analyses of Defense 

Meteorological Satellite Program brightness. The brightness data were converted to albedo values by 

linear interpolation between 0.8 for bright tundra and 0.18 for dense coniferous forest. Six albedo 

Globd Change Ihta Rase Afiica Docwtrentation Appendix E-31 
136 M. F. WILSON AND A. HENDERSON-SELLERS 

classes are given in this source. Highest snow covered albedo exists for low and sparse vegetation or a 

bare surface which is completely buried by snow. Areas with tall vegetation have a greater number of 

dark protrusions above the snow surface and therefore have a low snow covered albedo. These dah 

were used to distinguish dense forest, open woodland, grassland, tundra transition and tundra, in hi& 

latitude regions of the northern hemisphere. Although used to add detail to classes already identified 

from other sources these data were not used to delimit the location of major vegetation types if 

disagreement occurred between these data and atlas sources. 

Before it is possible to derive land cover classes suitable for climate modelling from satellite data 

there are many difficulties to overcome (Henderson-Sellers and Wilson, 1983b). No satellite data have 

been directly incorporated in this data set although reference has been made to albedo data pertaining 

to high latitude forests as described above. The data set described represents a land based survey of 

surface cover roughly appropriate to the period 1950-1970. AS such it represents a base (Le. historic) 

data set useful in developing surface monitoring programmes, e.g. within the framework of the 

International Satellite Land Surface Climatology Project (ISLSCP, 1984). 

3.3. Data compilarion 

A 1" x 1" grid was superimposed on data from each of the sources. Land use was determined 

primarily from the World Atlas of Agriculture and national atlases. The other sources were used only to 

add detail, including classification of dominant crop type, tree type and density class and the nature of 

non-agricultural land use. 

To permit illustration of the newly archived data here, the primary and secondary land classes were 

grouped into the 7 categories as listed in Table VI. The grouped data are displayed in Plate I11 

(primary) and Plate IV (secondary). It is important to note that the incorporation of a secondary land 

type also permits identification of, for example, areas of scattered woodland in north-west Europe 

which would be classed as cultivated if a base resolution of 1" X 1" (Le. the primary data only) were 

employed. Differences are bound to occur between this and other data sets; for example, in eastern 

Australia the new data set shows semi desert where Hummel and Reck (1979) indicate the presence of 

Table VI. Grouped land cover classes used in displaying the new data archive 

Group Classes incorporated in groups 

Open water and bog 

Evergreen wood and forest 

Inland water. bog or marsh, paddy rice, mangrove tree swamp. 

Dense needleleaf evergreen forest, open needleleaf evergreen woodland, 

evergreen broadleaf woodland. evergreen broadleaf cropland, dense 

evergreen broadleaf forest, equatorial rain forest, equatorial tree crop, 

tropical broadleaf forest. 

Dense deciduous needleleaf forest, open deciduous needlefeaf woodland, 

dense (mixed) needleleaf and broadleaf, evergreen and deciduous woodland. open mixed needleleaf and broadleaf evergreen and deciduous 

woodland, dense deciduous broadleaf forest, open deciduous broadleaf 

woodland, deciduous tree crops, open tropical woodland, woodland 

and shrub, dense drought deciduous forest, open drought deciduous 

woodland. 

Temperate meadow and permanent pasture, temperate rough grazing, 

savanna, tropical grassland and shrub, tropical pasture, rough grazing and 

shrub, pasture and tree, pasture and shrub 

Arable cropland, dry farm arable, nursery and market gardening, cane 

sugar, maize, irrigated cropland. 

Dwarf shrub, scrub desert and semi-desert, semi-desert and scattered trees, 

semi-arid rough grazing, urban. 

Ice, sand desert and barren land, tundra. 

Evergreen broadleaf shrub, deciduous shrub, thorn shrub, cotton, coffee, 

Mixed and deciduous wood 

and forest 

Grassland 

Cropland 

Semi-arid 

Barren 

Shrub 

vineyard. tea. 

Global Change Data Base Africa Docwttentution Ap~~~lrlix E-32 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 137 

,ru~ing land. This difference is due to the incorporation in the new data archive of a semi-arid rough 

category in an attempt to distinguish different levels of grazing cover type. Other similar 

j,fferences, both real and apparent, would certainly be revealed by a detailed comparison of the 

,Ivailable vegetation data sets (B. Moore, 1984, P.c.). We believe that our land cover information. 

,hc)wn in simplified form in Plates 111 and IV, provides a satisfactory and useful adjunct to our soils data 

jcscribed above. We believe that the most appropriate way to compare global land surface archives is 

1o perform GCM integrations using the same model, initial conditions etc. The results from such a 

would also identify the level of information resolution to which GCMs are sensitive. Such a 

forms the basis of our current research programme. 

problems associated with construction of the new data set occur due to different scales and 

schemes used in the various sources, lack of documentation of the date and scale of source 

data used in construction of atlas data and lack of recent surveys in areas of rapid anthropogenic change 

;lnd in remote areas. 

Throughout the mapping procedure difficulties in assigning a land cover category discussed in the 

w.A.A. were noted. For example. in the monograph for South Korea it is stated that difficulty was 

encountered in classifying the land cover into one of the fundamental categories due to the patchy 

nature of forest clearance for permanent and shifting cultivation. In many semi-arid areas, it is noted 

that much of the land classified as arable is fallow for long periods. Where disagreement occurred 

between the different data sources reviewed. the nature of the discrepancy was recorded. FA0 land use 

gatistics for each country for the years 1961-1965, 1970, 1969-1971 and 1980 were compared. The 

percentage of change in each ten year period was calculated. This information in conjunction with the 

date of the source data and the recorded discrepancies between data sources and difficulties in applying 

W..A.A. classification were used to derive a reliability score between 1 and 5 for each country. These 

reliability values are shown in Plate V. 

A high reliability score is found throughout North America, most of Europe, the Middle East. 

L/'.S.S.R and much of the African continent. This high reliability is indicative of either a very recent 

source of data or a change of C2.5 per cent at the national scale since the date of publication of the map 

source data. The areas of lowest data reliability occur in Central and South America. along the 

south-west coast of northern Africa. Uganda. Rwanda-Burundi. the Phillipines. Thailand. Mongolia 

and Syria. The sensitivity of any climate model simulations to surface forcing in these areas should be 

investigated in order to determine the possible effects of the lack of satisfactory data on model results. 

L 

4. PROPOSED APPLICATION OF THESE DATA IN SPECIFIC CLIMATE 

MODELLIKG AND MONITORING STUDIES 

In deriving and archiving these new data sets it was intended that as much information (spatial and 

classification) as possible should be retained. It is almost inevitable that users will require a coarser 

spatial resolution than the 1" x 1" archived data. The applications are likely to be in parameterization of 

surface albedo (e.g. Dickinson. 1983: Henderson-Sellers and Wilson. 1983a) and surface hydrology 

(e.g. Carson and Sangster. 1982: Rowntree and Bolton. 1983). The methods of use proposed here could 

be modified to suit user requirements. In using the data sets. the data must be corrected to the land/sea 

distribution of the model. 

4. I. Coarsening the resolution of the archireed data 

The resolution of the data (1" x 1") is fine enough to permit summary of features of the data for use at 

the coarser resolution of most GCMs. It is suggested that to derive vegetation files at the resolution 

required by a specific model. percentage amounts of a restricted range of vegetation components should 

be averaged. To undertake this. each vegetation type is broken down into percentage of different 

preselected components. For example. arable land may be broken down into 60 per cent crop, 20 per 

cent grass and 20 per cent bare soil; open mixed woodland into 30 per cent coniferous tree, 30 per cent 

Global Cliarigc I)nh Ihse Africa Docwnerttutiort Appendix E-33 
138 M. F. WlLSON AND A. HENDERSON-SELLERS 

Table VII(a). Annual mean percentage of 24 vegetation components and their associated land types 

Code 1 2 3 1 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

4 

00 

1 

2 

3 

4 

5 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

30 

31 

32 

33 

34 

35 

36 

37 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

51 

61 

62 

70 

71 

73 

80 

100 

100 

100 

25 

25 40 25 

85 

55 30 

45 45 

30 30 30 

60 30 

70 20 

60 25 

60 25 

85 

90 

85 10 

60 5 30 

60 30 

50 25 15 

55 35 

75 5 

55 5 15 

60 30 

50 20 

90 

10 70 5 

5 20 60 

20 65 

20 65 

15 5 75 

15 15 25 

20 75 

15 75 

60 

85 10 

70 

30 50 

75 

20 60 

10 55 

90 

30 50 

10 70 

20 

60 20 

60 20 

10 70 

10 75 

10 10 

10 

30 20 

10 20 10 

40 20 

20 70 

100 

40 

100 

10 

15 

15 

10 

10 

10 

10 

15 

15 

15 

10 

5 

5 

10 

10 

10 

20 

25 

10 

30 

10 

15 

15 

15 

15 

5 

45 

5 

10 

20 

35 

10 

20 

20 

20 

20 

20 

15 

20 

5 

10 

10 

10 

100 

60 

40 

10 

Global Change Data Base Afiicu Dociimertrutiorr Appendix E-34 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 139 

Table VII(b). Twenty-four land types and descriptions 

Type 1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

Description 

Water 

Ice 

Inland lake 

Evergreen needleleaf tree 

Evergreen broadleaf tree 

Deciduous needleleaf tree 

Deciduous broadleaf tree 

Tropical broadleaf tree 

Drought deciduous tree 

Evergreen broadleaf shrub 

Deciduous shrub 

Thorn shrub 

Shorr grass and forbs 

Long grass 

Arable 

Rice 

Sugar 

Maize 

Cotton 

Irrigated crop 

Urban 

Tundra 

Swamp 

Soil 

deciduous tree. 30 per cent grass and forbs and 10 per cent soil etc. Examples of this type of division are 

given in Table VII. Primary and secondary land types may be divided into their respective components 

and then weighted (x 75 per cent of each of the primary type components) + (X25 per cent for each of 

the secondary type components). Thus percentage coveF of the 24 components is determined. The 

averaged values may then either be assigned back into one of the original land type categories or. more 

uscfu&, the paramerenzarion scheme could use the 24 more general classes listed in Table VII. 

The resolution of the soils data may be changed by averaging separately the colour, texture and 

drainaee characteristics for the area. The averaged properties are then re-assigned to one of the original 

classes: The colour and texture classes are averaged by assigning values of 3, 2 and 1 to light, medium 

and dark colour classes and coarse. intermediate and fine texture Classes, respectively, and averaging 

the values Over the required grid area. The colour class at the new resolution is light if the average 

colour. X, is ~2.5. medium. if 1.5 sx f 2.5 and dark if x < 1.5. The drainage properties are most 

accurately summarized by determining the dominant drainage category: free, impeded or poor. An 

example of the major steps in the conversion to a 2" x 2" resolution is shown schematically in Figure 2. 

In the following subsection suggestions about the specific application of these new data are made. 

Potential users are warned. however. that although we are confident about the archived data there is a 

Considerable range of opinion pertaining to albedo (and hydrological) values and their methods of 

Inclusion in climate models. 

J.l. Applicarion to albedo and other parameters 

Once the soils and land type data have been averaged to the required GCM grid size (in this case it is 

assumed that the primary and secondary vegetation types have been combined in the proportions of 75 

3 per cent into one of the 24 land type classes) a grid box albedo can be calculated using the 

Proposed albedos for vegetation (Table VIII) and soil colour (Table IX). It is likely that soil albedos 

be made a function of model-determined soil moisture. Therefore both wet and dry soil albedo 

Global Change Data Base Aj.ic*u Docunientah'on Appendix E-35 
140 

I 

c TYPE 17 

M. F. WILSON 

colcur= 2 = medium 

texture = 3 = cmne 

dminage = free 

AND A. HENDERSON-SELLERS 

* 

Averaging process 

I colour texture drainage I 

type 11 tYpP 17 a 3 3 3 X 

b 2 3 X 

C 2 2 X 

d 1 3 

type 11 tYpP 17 

X 

- - - - 

average 2 2.75 total T 1 0 

I t .. I 

3 X 

Table VIII. Proposed albedo values for use in equation (1) 

5 

.6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

Land type component 

Water 

Ice 

Inland lake 

Evergreen 

Evergreen 

Deciduous 

Deciduous 

Tropical 

Drought 

Evergreen 

Deciduous shrub 

Thorn shrub 

Short grass and forbs 

Tall grass 

Arable 

Rice 

Sugar 

Maize 

Cotton 

Irrigated crop 

Urban 

Tundra 

Swamp 

Soil 

needleleaf tree 

broadleaf tree 

needleleaf tree 

broadleaf tree 

broadleaf tree 

deciduous tree 

broadleaf shrub 

Annual Summer Winter 

0.07 0.07 0.07 

0.75 0.60 0.80 

0.06 0.06 0.06 

0.14 0.14 0.15 

0.14 0.14 0.14 

0.13 0.14 0.12 

0.13 0.14 0. I1 

0.13 0.13 0.13 

0.13 0.13 0.12 

0.17 0.17 0.17 

0.16 0.17 0.15 

0.16 0.16 0.16 

0.19 0.20 0.18 

0.20 0.17 0.22 

0.20 0.25 0.16 

0.12 0.12 0.12 

0.17 0.17 0.17 

0.19 0.22 0.16 

0.19 0.22 0.17 

0.25 0.25 0.25 

0.18 0.18 0.18 

0.15 0.17 0.12 

0.12 0.12 0.12 

See Table IX-colour and moisture 

Global Change Dah Base AJWa Documentation AplXr1dix E-36 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 

Table IX. Typical soil albedos. A, 

Colour class Light Medium Dark 

Moisture state 

Albedos 

Average 

wet dry wet dry wet dry 

0.18 0.35 0.10 0.20 0.07 0.15 

(0.16) (0.15) 0.11) 

141 

vlllue~ are proposed here. It may also be desirable to include seasonality. This can be done either using 

seasonal albedos given in Table VI11 (or others) and/or by modifying the types and percentages of 

basic 24 land type classes specified for the finer archive classes (Table VII). A single grid box 

A, will be given by 

23 

A = Z(P,,A,,) + ASPs (1) 

ill 

\\.here P,, and A,, are the percentages of the 23 vegetation components and their associated albedos: A, 

is the soil albedo for the dominant soil colour type for the grid box (with a prespecified functional 

dependence on soil moisture if desired) and P, is the percentage amounr of soil (Le. P, is identically 

equal to P,.?.J. Sensitivity experiments using the described soil and land cover data using a parameterization of this nature are currently being undertaken with the U.K. Meteorological Office 11 layer 

GCM. 

Additionally it may be considered desirable to incorporate latitudinal dependence for the specified 

vegetation albedos in order to accommodate reduced seasonality at low latitudes and the effect of solar 

renith angle variations in different latitude zones. However, it is considered that the uncertainty in the 

proposed vegetation albedos. A,. listed in Table VIII, is greater than either of these effects. Rather. it 

is recommended that detailed sensitivity studies be carried out (i) to assess the best method of 

incorporating soil moisture dependence and (ii) that this be done before second order terms in the 

albedo calculation are included. For a full review of sources used in compiling Tables VI11 and IX see 

Henderson-Sellers and Wilson (1983b). 

It is also desirabie to use data such as the archive described here to improve the parameterization of 

surface hydrology in GCMs. The amount of water which can be held in a soil is greatly influenced by 

soil texture. The runoff from each model grid square will be critically dependent upon the way in which 

water which reaches the ground surface as either precipitation or snow-melt occurs and will be a 

function of vegetation cover. soil moisture and surface texture. Similarly the amount of moisture 

available for evapotranspiration is a function both of the capacity of the soil and also the extent of the 

vegetation root system (i.e. the effective depth of moisture available to plants). Finally, soil thermal 

properties are influenced by the texture. organic matter content and moisture content of the soil. Using 

the texture classes given in the soils data set and model-predicted moisture content it may be possible to 

improve parameterization of storage and fluxes of heat in the soil surface layers. However it is 

important to note that while the thermal capacity and rate of temperature change could be calculated 

for each texture type as a function of the soil moisture content at a chosen interval in a GCM 

integration the results may depend upon the selected interval and will depend upon the precipitation 

Predicted. Similarly. with respect to the parameterization of evapotranspiration it must be noted that 

the specific effective root depths will differ from real root depths and will be a function of other 

modelled surface properties such as the number of ground layers. Climate sensitivities are as likely to be 

the result of other model features as of the land surface data used. 

5. SUMMARY 

The increasing awareness of the importance of land surface-atmosphere interaction to successful 

climate simulation has prompted a search for improved land surface information. Climate models are 

Global Change Data Base Africa Docmentarion ' Ap~t~~dix E-37 
142 M. F. WlLSON AND A. HENDERSON-SELLERS 

sensitive to surface radiative and hydrological properties (Mintz, 1982). Modellers require relatively 

simple data archives with which to parameterize surface atmosphere moisture, energy and momentum 

exchange. A complete data archive of soil and land cover information has been presented and the 

criteria for category selection have been discussed. The soils and land cover data sets have been 

established at base resolutions finer than 1" X 1". In the case of the land cover classification both 

primary and secondary cover types for each 1" x 1" grid element were recorded, and the soils data were 

archived in the first instance at a resolution of 1/2" X 1/2". This was to ensure that confidence could be 

placed in the 1" latitude by 1" longitude classification presented here. Additionally the higher base 

resolution permits a more accurate procedure for averaging the data to the coarse grid resolutions 

typical of GCMs. The soil classes have been derived by combining three colour, three texture and three 

drainage classes. The land cover classes were chosen on the basis of height, structure, seasonality and 

density criteria. The data described here were designed specifically for inclusion in the U.K. Meteorological Office GCM. It was considered essential that both soil and land cover information were available 

in the same archive and at similar resolutions. Individual features of the archive may be questionable 

since interpolation and subjective assessment was, unfortunately, necessary. The land surface data are 

not directly comparable with the much more detailed data set of Matthews (1983a, b) although an 

assessment of the response of a GCM to an integrated data set containing both her vegetation and some 

other soils data as compared to the response caused by using our data would be most interesting. 

Reliability estimates on an ordinal scale (1-5) are presented for the soils data according to the nature 

of the source data. Similar estimates of reliability are also presented for the land cover data derived 

from information about the rate of change of land use indicated by FA0 statistics, the date of 

publication of the source and difficulties encountered in assigning the chosen categories. A method of 

averaging the data to the coarser resolution of a GCM has been proposed. It is suggested that these data 

be used to derive albedo by weighting the soil and vegetation albedo for each grid box according to an 

annual average or seasonal vegetation cover value. The soil albedo could be made a function of both the 

soil colour class and also the model predicted soil moisture content although parameterization of 

hydrological features is likely to be more complex than inclusion of appropriate radiative terms. 

The new archive described here has the advantage of including soil information as well as land cover 

data information. This combination will permit consistent incorporation of both hydrological and albedo 

parameterizations into climate models. Earlier data archives unfortunately neglected the description of 

soil colour. texture and drainage. Henderson-Sellers and Wilson (1983a) emphasize the importance of 

both these aspects-of the land surface for climate models. It is vital that data sets of a type similar to this 

new archive be incorporated into GCMs in order to assess the sensitivity of the models to land surface 

changes. The data described here are now an integral part of the U.K. Meteorological Office 11 layer 

GCM. Experiments have already been undertaken to compare the effect of establishing surface albedos 

using (i) the Hummel and Reck (1979) data and (ii) the data described here. The results will be 

described in a subsequent publication. Preliminary investigations of the importance of the surface 

hydrological parameterization are also being undertaken. The recommendation (Dickinson, 1983) that a 

single. coherent data archive be used by many climate modelling groups to permit intercomparison of 

GCM sensitivity to land surface parameterization schemes can now be fulfilled. 

ACKNOWLEDGEMENTS 

M.F.W. holds an NERC Studentship. Dr. J. Hummel is thanked for providing quantitative data for 

archive comparisons. We are grateful to Dr. P. Rowntree and P. James who commented constructively 

on the development of the data sets and to Dr. R. Dickinson and Dr. B. Moore who read an earlier 

version of this paper. We wish to acknowledge the considerable assistance and co-operation of the 

dynamical climatology branch of the U.K. Meteorological Office. Finally thanks must be given to the 

large number of scientists who commented constructively upon preliminary versions of this archive 

throughout 1982 and to Dr. N. A. Hughes who performed most of the computer cartography. 

Global Change Data Base Africa Docmeittation Appendix E-38 
GLOBAL ARCHIVE OF LAND COVER AND SOILS DATA 143 

REFERENCES 

C..lr,on. D. and Sangster. A. 1982. 'The influence of land surface albedo and soil moisture on general circulation model 

(.hlrnrv. J. G. 1975. 'Dynamics of deserts and drought in the Sahel', Quarr. J. R. Mer. SOC., 101, 193. 

C'hJrnry. J. G.. Quirk. w. J.. Chow. S. H. and Kornfield, J. 1977, 'A comparative study of the effects of albedo change on 

c'LI>lAp Project Members. 1981. 'Seasonal reconstruction of the earth's surface at the last glacial maximum', Geol. SOC. Amer.. 

Dli)rlnson. R. E. 1983. 'Land surface processes and climate-surface albedos and energy balance', Adv. Geophys., 25, 305. 

Fr\o. 1961. Producrion Year Book. FAO. Rome. 

F,~o. 1976. Producn'on Year Book. FA0 Rome. 

F,~o, 1981. Producrion Year Book, FAO. Rome. 

r,\o!UNESCO. 1974. Soil Map of rhe World, 1 :5,000.000, FAO. Paris. 

I Iclnr.mann. 1979. The Heinemann World Arlas. Rand McNally (ed.), Heinemann, London. 

Ilcnjerson-Sellers, A. and Wilson. M. F. 1983a. 'Albedo observations of the Earth's surface for climate research', Phil. Tram. 

Ilrndrrson-Sellers. A and Wilson. M. F. 1983b. 'Surface albedo data for climatic modelling', Rev. Geophys. Space Phys.. 21. 

Ilou_rhton. J. T. 1982. 'Foreword' in Eagleson, P. S. (ed.), Land Surface P~OCC~SU in Ahnospheric General Circulation ModeLr, 

I(ummel. J. R. and Reck. R. A. 1979. 'A global surface albedo model'. 1. Appl. Mereor.. 18,239. 

ISLSCP. 1984. Report of Workshops of rhe Inrernanonal Sarellire Land Surface Cfimarology Projecr (ISLSCP). UNEP. 

blatthews. E. 1983a. 'Global vegetation and land use: new high-resolution data bases for climate studies', J. Clim. Appl. ,Mereor.. 

22. 474. 

tlatthews. E. 1983b. 'Global vegetation and land use. New high resolution data bases for climate studies', Bull. Am. ,Mer. Soc.. 

63. -93. 

$(lnrz. Y. 1981. 'The sensitivity of numerically simulated climates to land surface conditions'. in Eagleson, P. S. (ed.) Land 

Ticr;ace Processes in Anospheric General Circulan'on Modelr. Cambridge University Press, pp. 109-11 1. 

ohon. J. and Watts. J. A. 1982. 'Major world ecosystem complexes'. in 'Earth's vegetation and atmospheric carbon dioxide'. in 

Cl.irL. W. C. (ed.). Carbon Dioxide Review. Oxford University Press. Oxford. pp. 388-399. 

O.r/ord Economic .4r1as ofrhe World. 1972. Jones. D. B. (ed.), 4th edition. Oxford University Press. London. 

Ox/ord IVorid Arlas. 1973. Cohen. S. B. (ed.). Oxford University Press. London. 

Oxtord Regional Economic Arlac of Middle Easr and Norrh Africa. 1960. Oxford University Press. London. 

U.r/ord Regional Economic Arlas of U.S.S.R. and Easrern Europe. 1963. Oxford University Press. London. 

O.r!ord Regional Economic Arlas of Unired Sraies and Canada. 1967. Oxford University Press. London. 

O.t/ord Regional Economic Arlas of Wesrern Europe. 1971. Clayton, K. M. and Kormoss, I. B. F. (eds), Oxford University Press. 

Powy. J. W. and Clapp. P. F. 1964. 'Global distribution of normal surface albedo'. Geofir. Inr.. 4, 33. 

Robinson. D. A. 1982. 'Maximum estimated surface albedo of snow covered Northern Hemisphere lands', in Remore Sensing and 

Rowtree. P. R. 3nd Bolton. J. A. 1983. 'Simulation of the atmospheric response to soil moisture anomalies over Europe'. Quam 

ShuLla. J. and Wnu. Y. 1982. 'Influence of land-surface evapotranspiration on the Earth's climate'. Science. 215. 1498. 

Sud. Y. C. 3nd Fennessy. M. 1982. 'A study of the influence of surface albedo on July circulation in semi-arid regions using the 

Timcs. 1968. The Times Arlac of the World. Comprehensive Edition. Times Newspaper. London. 

LSESCO. 1973. Inrernan'onal Classificanon and Mapping of Vegerarion, UNESCO, Pans. 

Walker. J. and Rowntree. P. R. 1977. 'The effect of soil moisture on circulation and rainfall in a tropical model'. Quarr. J. R. 

Washington. W. M. md Chemin. R. M. 1979. 'Regional climatic effects of large scale thermal pollution simulation studies with 

IVorid A'as of Agriculrure. 1'169-1976. Committee for the World Atlas of Agriculture. Instituto Geografico de Agostini. Novara. 

World Foresrp ArIas. 1961. Verlag Paul Parey, Hamburg. 

,lmulations'. Numerical Programme Rep 2. U.K. Meteorologcal Office. Bracknell. England, pp. 5.14-5.21. 

drought in semi and regions', J. Amos. Sci.. 34, 1366. 

vap and Chan Series. MC-36, 17pp + microfiche. 

ROY. Soc.. A. 30% 185. 

17.13. 

CJmbridge University Press. 

London. 

rhe .-hnosphere. Remote Sensing Society. Reading. pp. 308-315. 

J. R. Mer. SOC.. 109. 501. * 

GL.-\S GCM'. J. climarol.. 2. 105. 

.Mer. SOC.. 103. 19. 

the NCAR GC,M'. J. .-lppl. .Mereor.. 18. 3. 

Global Change Data Base Ajica Docunienturion Appetlclix E-39 
-FZOBLER SOIL TYPE, SOIL TEXTURE, SURFACE SLOPE, 

AND OTHER PROPERTIES 

CITATION: Staub, B. and C. Rosenweig, 1987. Global Zobler Soil Type. Soil 

Texture, Surface Slope, and Other Properties. Digital Data. NCAR, 

Boulder. Colorado. .45 MB. 

Global Change Data Base Africa Docwnenmiort Appendix F-1 
Blank page retained for pagination 
F. ZORLER SOIL TYPE, SOIL TEXTURE, SURFACE SLOPE, 

AND OTHER PROPERTIES 

DATE: 1987 

SOURCE: 

(1) Leonard Zohler 

Goddard Institute of Space Science 

Columbia University 

New York, N. Y. 

(2) Reprocessed by: 

Brad Staub and Cynthia Rosenweig 

NASA Goddard Space Flight Center 

Institute for Space Studies 

2880 Broadway 

New York, N. Y. 10025 

CONTRIBUTOR: 

Roy Jenne 

NCAR 

P.O. Box 3000 

Boulder, CO 80307-3000 

DATA CENTER: NCAR 

GEOGRAPHIC SAMPLING: 1 Jepee grid, characteristic ciassses 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Decadal composites 

REFERENCES: 

Staub. Brad and Cynthia Rosenzweig. "GIobal Digital Data Sets of Soil Type, Soil 

Texture. Surface Slope, and Other Properties: Documentation of Archived Tape Data." 

NASA Technical Memorandum #100685. 

Henderson-Sellers. A., M.F. Wilson. G. Thomas, R.E. Dickinson, 1986. 

"Current Global Land Surface Data Sets for Use in Chnate-Related Studies." 

NCAR Technical Note 272+STR. 

Matthews, E., 1983. "Global vegetation and land use: New high resolution 

data bases Tor climate studies." J. Clim. Aypl. Meteor., vol. 22, pp. 474-387. 

(continued on text page) 

Global Change Data Base Africa Docrunerrratiorr Appendix F-3 
Matthcws, E., 1984. "Vegetation. Land-Use and Seasonal Albedo Data Sets: 

Documentation of Archived Data Tape." NASA Technical Memorandum 

#86107. 

Wilson. M. F. and A. Henderson-Sellers, 1985. "A global archive of land 

cover and soils data for use in general circulation climate models." 

Journal of CXimasology, vol. 5, pp. 119-143. 

FILE NAMES: 

AFZSOILS 20 bler Soil Types 

AFZTM Zobler Soil Texm 

AEZSLOPE Zobler Surface Slope 

AFZPHS Zobler Phase Codes 

AFZSUBSD Zobler Subsidiary Soil Units 

AFZAREA Zobler Ana Codes 

AFzsP3 Zobler Special Codes 

Global Change Data Base Africu Documentation Appendix F-4 
ZOBLER SOIL CLASSES 

DOCUMENTATION FILE 

File Name: AFZS0ILS.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell Y 

Zobler Soil Classes 

byte 

binary 

438 

480 

1.OOOOOOOOOOE+OO 

6.5000000000E+01 

1.6666666667E-01 

1.66666666673-01 

leqend : 65 

category 1 : 1 No 

category 2 : 2 E 

category 3 : 3 I 

category 4 : 4 La 

category 5 : 5 Qa 

category 6 : 6 Bc 

category 7 : 7 Jc 

category 8 : 8 LC 

category 9 : 9 Qc 

category 10 : 10 Rc 

category 11 : 11 Vc 

category 12 : 12 Bd 

category 13 : 13 Gd 

category 14 : 14 Jd 

category 15 : 15 Ncl 

category 16 : 16 Rd 

category 17 : 17 Be 

category 18 : 18 Ge 

category 19 : 19 Je 

category 20 : 20 Ne 

category 21 : 21 Oe 

category 22 : 22 Re 

category 23 : 23 We 

category 24 : 24 Af 

category 25 : 25 Bf 

category 26 : 26 Lf 

category 27 : 27 Qf 

category 28 : 28 Lg 

category 29 : .29 Zg 

category 30 : 30 Bh 

category 31 : 31 Fh 

category 32 : 32 Gh 

category 33 : 33 Nh 

category 34 : 34 Ph 

category 35 : 35 Xh 

category 36 : 36 Yh 

category 37 : 37 Bk 

category 38 : 38 Kk 

category 39 : 39 Lk 

category 40 : 40 Xk 

category 41 : 41 Yk 

Data Blank 

Rendzinas 

Lithosols 

Albic Luvisols 

Albic Arenosols 

Chromic Cambisols 

Calcaric Fluvisols 

Chromic Luvisols 

Cambic Arenosols 

Calcaric Regospls 

Chromic Vertisols 

Dystric Cambisols 

Dystric Gleysols 

Dystric Fluvisols 

Dystric Nitosols 

Dystric Regosols 

Eutric CambiSOlS 

Eutric Gleysols 

Eutric Fluvisols 

Eutric Nitosols 

Eutric Histosols 

Eutric Regosols 

Eutric Planosols 

Ferric Acrisols 

Ferralic Cambisols 

Ferric Luvisols 

Ferralic Arenosols 

Gleyic Luvisols 

Gleyic Solonchaks 

Humic Cambisols 

Humic Ferralsols 

Humic Gleysols 

Humic Nitosols 

Humic Podzols 

Haplic Xerosols 

Haplic Yermosols 

Calcic Cambisols 

Calcic Kastanozems 

Calcic Luvisols 

Calcic Xerosols 

Calcic Yermosols 

Global Change Delu Base Africa Documentation Appendix F-5 
category 42 : 42 Q1 

category 43 : 43 X1 

category 44 : 44 Y1 

category 45 : 45 Gm 

category 46 : 46 Tm 

category 47 : 47 Ao 

category 48 : 48 Fo 

category 49 : 49 Lo 

category 50 : 50 So 

category 51 : 51 To 

category 52 : 52 20 

category 53 : 53 Ap 

category 54 : 54 Fp 

category 55 : 55'Gp 

category 56 : 56 Lp 

category 57 : 57 Vp 

category 58 : 58 Fr 

category 59 : 59 Ws 

category 60 : 60 Jt 

category 61 : 61 Zt 

category 62 : 62 Bv 

category 63 : 63 Fx 

category 64 : 64 Xy 

category 65 : 65 Yy 

{category 0 : water} 

Luvic Arenosols 

Luvic Xerosols 

Luvic Yermosols 

Mollic Gleysols 

Mollic Andosols 

Orthic Acrisols 

Orthic Ferralsols 

Orthic Luvisols 

Orthic Solonetz 

Ochric Andosols 

Orthic Solonchaks 

Plinthic Acrisols 

Plinthic Ferralsols 

Plinthic Gleysols 

Plinthic Luvisols 

Pellic Vertisols 

Rhodic Ferralsols 

Solodic Planosols 

Thionic Fluvisols 

Takyric Solonchaks 

Vertic Cambisols 

Xanthic Ferralsols 

Gypsic Xerosols 

Gypsic Yermosols 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Global Change Dnto Base Africa Docuntentutiori Appendix F-6 
ZORLER SOIL TEXTURE 

DOCUMENTATION FILE 

File Name: AFZTEXDOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 ; 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 ; 

category 9 : 

Zobler Soil Texture 

byte 

binary 

438 

480 

0.0000000000EtOO 

8.0000000000E+00 

1.6666666667E-01 

1.66666666673-01 

1 COR Coarse 

2 MED Medium 

3 FIN Fine 

4 CM Coarse-medium 

5 CF Coarse-fine 

6MF Medium- f ine 

7 CMF Coarse-medium-fine 

8 ORG Organic 

9 LI Land- I ce 

8 

(category 0 : water} 

[This leqend is approFriate for display of the individual classes 

(i . e. without rescaling) .I 

Global Change Data Base Africa Documentation Appendix F-7 
ZOBLER SURFACE SLOPE 

DOCUMENTATION FILE 

File Name: AFZSLOPE.DOC 

image title : Zobler Surface Slope 

data type : byte 

file type : binary 

rows : 438 

cclumns : 480 

minimum : O.OOOOOOOOOOE+OO 

ma:; imum : 7.0000000000E+00 

cell :c : 1.66666666673-01 

cell y : 1.66666666673-01 

legend :9 

category 1 : 1 0-8% 

category 2 : 2 8-30% 

category 3 : 3 >30% 

category 4 : 4 0-30% 

category 5 : 5 0-8%; >30% 

category 6 : 6 8-30%; >30% 

category 7 : 7 0-8%; 8-30%; >30% 

category 8 : 8 Not Used 

category 9 : 9 Land-Ice 

(category 0 : water} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Global Change Data Base Africo Documentation Appendix F-8 
ZOBLER PHASE CODES 

DOCUMENTATION FILE 

File Name: AFZPHS.DOC 

image title : Zobler Phase Codes 

data type : byte 

file type : binary 

rows : 438 

co 1 umn s : 480 

minimum : 0.0000000000E+00 

ma>: imum : 3.9000000000E+01 

cell x : 1.66666666673-01 

cell y : 1.66666666673-01 

category 1 : 1 No Data Blank 

category 2 : 2 St/Po stony, ponded (1 24) 

category 3 : 3 St/L stony, lithic (1 2) 

category 4 : 4 St/Pg stony, petrogypsic (1 6) 

category 5 : 5 St/R stony, rock debris (1 26) 

category 6 : 6 St/? stony, undocumented (1 27) 

category 7 : 7 St/Du stony, dunes/sands (1 25) 

category 8 : 8 St/L/Pc stony, lithic, petrocalcic (1 2 5) 

category 9 : 9 St/Sa stony, saline (1 9) 

category 10 : 10 St/Pc stony, petrocalcic (1 5) 

category 11 : 11 St stony (1) 

category 12 : 12 So/Po sodic, ponded (13 24) 

category 13 : 13 So sodic (13) 

category 14 : 14 L/Pf lithic, petroferric (2 4) 

category 15 : 15 L/Sa lithic, saline (2 9) 

category 16 : 16 L/Pt lithic, petric (2 3) 

category 17 : 17 L lithic (2) 

category 18 : 18 Po/Du ponded, dunes/sands (24 25) 

category 19 : 19 Po ponded (24) 

category 20 : 20 Du dunes/sands (25) 

cateuory 21 : 21 R/St rock debris, stony (26 1) 

legend : 39 

categor? 22 : 22 R/Pc/St 

cateqory 23 : 23 R/St/Du 

category 24 : 24 R 

category 25 : 25 R/Du 

category 26 : 26 R/St/? 

category 27 : 27 R/Du/St 

category 28 : 28 ? 

category 29 : 29 Pt/Po 

category 30 : 30 Pt 

category 31 : 31 Pf 

category 32 : 32 Pc 

category 33 : 33 Pg 

category 34 : 34 Pg/Po 

category 35 : 35 Dr 

category 36 : 36 Sa/? 

category 37 : 37 Sa 

category 38 : 38 Sa/Po 

category 39 : 39 Sa/So 

{category 0 : water} 

rock debris, petrocalcic, stony (26 5 1) 

rock debris, stony, dunes/sands (26 1 25) 

rock debris (26) 

rock debris, dunes/sands (26 25) 

rock debris, stony, undocumented (26 1 27) 

rock debris, dunes/sands, stony (26 25 1) 

undocumented ( 2 7 ) 

petric, ponded (3 24) 

petric (3) 

petroferric (4) 

petrocalcic (5) 

petrogypsic (6) 

petrogypsic, ponded (6 24) 

duripan (8) 

saline, undocumented (9 27) 

saline (9) 

saline, ponded (9 24) 

saline, sodic (9 13) 

[This legend is appropriate for display of the individual classes 

(i . e. jrithout rescaling) . ] 

Note: The various permutations of the phase codes (in parentheses) in the 

original data have been reassignedto the above categoricalvalues. 

Global Cliutige Data Base Africa Documenrution Appendix F-9 
ZOBLER SUBSIDIARY SOIL UNITS 

DOCUMENTATION FILE 

File Name: AFZSUBSD.DOC 

image title : 

data type : 

file type : 

rows 

columns 

mi n imiim 

maximum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

category 9 : 

category 10 : 

category 11 : 

category 12 : 

category 13 : 

category 14 : 

category 15 : 

category 16 : 

category 17 : 

cateqory 18 : 

category 19 : 

category 20 : 

category 21 : 

category 22 : 

category 23 : 

category 24 : 

category 25 : 

category 26 : 

category 27 : 

category 28 : 

cateaory 29 : 

category 30 : 

cateqory 31 : 

category 32 : 

category 33 : 

category 34 : 

category 35 : 

category 36 : 

category 37 : 

category 38 : 

category 39 : 

category 40 : 

Zobler Associated and Included Subsidiary Soil Units 

byte 

binary 

438 

480 

O.OOOOOOOOOOE+Oo 

1.4300000000E+02 

1.66666666673-01 

1.66666666673-01 

1 No Data (Blank) 

2 AGFA These codes are explained on individual FA0 map sheets 

3 AGFO 

4 BCL 

5 BCTV 

6 BCV 

7 BE 

8 BEE 

9 BF 

10 BKR 

11 BKRC 

12 BKX 

13 BV 

14 BVLC 

15 E 

16 FO 

17 FOFX 

18 FP 

19 JCXK 

20 L 

21 L RE 

22 L Q 

23 LCE 

24 LCRE 

25 LCX 

26 LF 

27 LFNE 

28 LFRD 

29 LFRE 

30 LG 

31 LGRE 

32 R 

33 RCX YK 

34 RCX 

35 RCXK 

36 RCYK 

37 RCZO 

38 RD 

39 Fmso 

40 RE 

143 

Global Change Data Base Africa Documentation Appendix F- 10 
category 41 : 41 REYH 

category 42 : 42 Rxx YH 

category 43 : 43 V 

category 44 : 44 XH 

category 45 : 45 XK 

category 46 : 46 Y 

category 47 : 47 YH 

category 48 : 48 YHYK 

category 49 : 49 YK 

category 50 : 50 YY 

category 51 : 51 20 

category 52 : 52 (1) 

category 53 : 53 (10) 

category 54 : 54 (104) 

category 55 : 55 (11) 

category 56 : 56 (117) 

category 57 : 57 (12) 

category 58 : 58 (120) 

category 59 : 59 (122) 

category 60 : 60 (13) 

category 61 : 61 (14) 

category 62 : 62 (15) 

category 63 : 63 (16) 

category 64 : 64 (17) 

category 65 : 65 (18) 

category 66 : 66 (19) 

category 67 : 67 (2) 

category 68 : 68 (20) 

category 69 : 69 (21) 

category 70 : 70 (22) 

category 71 : 71 (23) 

category 72 : 72 (24) 

category 73 : 73 (25) 

category 74 : 74 (26) 

category 75 : 75 (27) 

category 76 : 76 (28) 

category 77 : 77 (29) 

category 78 : 78 (3) 

cateqory 79 : 79 (30) 

category 80 : 80 (31) 

category 81 : 81 (32) 

category 82 : 82 (33) 

category 83 : 83 (34) 

category 84 : 84 (35) 

category 85 : 85 (36) 

category 86 : 86 (37) 

category 87 : 87 (38) 

category 88 : 88 (39) 

cateqory 89 : 89 (4) 

category 90 : 00 (40) 

category 91 : 91 (41) 

category 92 : 92 (42) 

category 93 : 93 (43) 

category 94 : 94 (44) 

category 95 : 95 (45) 

category 96 : 96 (46) 

category 97 : 97 (47) 

cateqory 98 : 98 (48) 

category 99 : 99 (49) 

Global Cliirrige Ihtn Base Africa Docwnerifulion Appendix F- 1 1 
category100 : 100 (5) 

cateqorylol : 101 (50) 

category102 : 102 (51) 

category103 : 103 (52) 

category104 : 104 (53) 

category105 : 105 (54) 

category106 : 106 (55) 

category107 : 107 (56) 

category108 : 108 (59) 

category109 : 109 (6) 

category110 : 110 (60) 

category111 : 111 (61) 

category112 : 112 (62) 

category113 : 113 (63) 

category114 : 114 (64) 

category115 : 115 (65) 

category116 : 116 (66) 

category117 : 117 (67) 

category118 : 118 (68) 

category119 : 119 (69) 

category120 : 120 (7) 

category121 : 121 (70) 

category122 : 122 (75) 

category123 : 123 (76) 

category124 : 124 (77) 

category125 : 125 (78) 

category126 : 126 (79) 

category127 : 127 (8) 

category128 : 128 (80) 

category129 : 129 (81) 

category130 : 130 (82) 

category131 : 131 (83) 

category132 : 132 (85) 

category133 : 133 (86) 

category134 : 134 (87) 

category135 : 135 (88) 

category136 : 136 (89) 

category137 : 137 (9) 

category138 : 138 (90) 

category139 : 139. (91) . category140 : 140 (92) 

category141 : 141 (93) 

category142 : 142 (94) 

category143 : 143 (97) 

{category 0 : water} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Note: The original subsidiary codes have been reassigned to the above 

categorical values. For further esplanation of these codes see 

individual FA0 map sheets. 

Global Change Data Base Afi’icu Docwnentution Appendix F-12 
ZOBLER AREA CODES 

DOCUMENTATION FILE 

File Name: AFZAREA.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

ma:< imum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

Zobler Area Codes 

byte 

binary 

438 

4 80 

0.0000000000E+OO 

7.0000000000E+00 

1.66666666673-01 

1.66666666673-01 

8 

1 AFR Africa 

2 AUS Australia, South Pacific 

3 EUR Europe 

4 NAM North America 

5 NCA North Central Asia 

6 SAM South America 

7 SAS Southern Asia 

8 SEA South East Asia 

{category 0 : water} 

[This leqend,is appropriate for display of the individual classes 

(i .e. without rescaling) .I 

Note: The area codes that appear in the documentation have been 

reassigned to the above categorical values. 

Global Clienge Data Base Africa Documentation Appendix F- 13 
ZOBLER SPECIAL CODES 

DOCUhlENTATION FILE 

File Name: AFZSP3.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

masimum 

cell x 

cell y 

Zobler Special Codes 

byte 

binary 

438 

4 80 

O.OOOOOOOOOOE+OO 

1.1OOOOOOOOOE+O1 

1.66666666673-01 

1.66666666673-01 

legend : 11 

category 1 : 1 No Data 

category 2 : 2 (33) 

category 3 : 3 (44) 

category 4 : 4 (55 88) 

category 5 : 5 (55) 

category 6 : 6 (66) 

category 7 : 7 (77 88) 

category 8 : 8 (77) 

category 9 : 9 (88) 

category 10 : 10 (88 77) 

category 11, : 11 (99) 

Blank 

Land cell not present on FA0 map; classified 

as soil; SU, TEX, and SLP fields filled in 

Land cell not present on FA0 map; classified 

as land-ice 

(See explanations for codes 55 and 88) 

Cell classified as soil by Zobler, and as 

land-ice by Matthews 

Cell classified as land-ice by Zobler, and as 

vegetation (soil implied) by Matthews 

(See explanations for codes 77 and 88) 

Slope information missing; SLP field filled in 

Texture information missing; TEX field filled in 

(See explanations for codes 88 and 77) 

No soil information present on the map; SU, 

TEX, and SLP fields filled in 

(category 0 : water} 

[This legend is appropriate for display of the individual classes 

(i.e. without rescaling) .I 

Note: The various permutations of the special codes (in parentheses) 

in the original data have been reclassed to the above categorical 

values 

Global Change Data Base Africa Documentation Appendix F- 14 
NASA TECHNICAL MEMORANDUM #I00685 

GLOBAL DIGITAL DATA SETS OF SOIL TYPE, 

SOIL TEXTURE, SURFACE SLOPE, AND OTHER PROPERTIES 

DOCUMENTATION OF ARCHIVED DATA TAPE 

Brad Staub and Cynthia Rosenzweig 

October, 1987 

NASA Goddard Space Flight Center 

Institute for Space Studies 

2880 Broadway 

New York, NY 10025 

Global Change Data Base Afiicu Documentation Appendix F- 15 
ABSTRACT 

This documentation describes the file structure and coding of four soils data 

sets derived from the Zobler (1986) world soil file. These data were 

digitized on a one-degree square grid, a design especially useful for largearea studies such as climate research with general circulation models. They 

are also suitable for studies in forestry, agriculture, soils, hydrology, and 

other research areas that can utilize soils information on this scale. All 

four data sets are based on the Food and Agriculture Organization (FAO, 1974) 

Soil May of the World and on the Mathews (1983; 1984) vegetation data set. 

Data characteristics, coding scheme, and collection methodology are fully 

described by Zobl er (1986). 

The advantages of the Zobler world*soil file are: 1) Missing data have been 

filled in; 2) Subsidiary soils of the designated FA0 soil association for each 

map unit are included; and 3) the data set may be easily converted to 

compatibility in nominal classification of land, land-ice and water cells with 

either the Mathews vegetation file or the FA0 Soil Map of the World. Dot grid 

overlay was used to determine the largest map unit of each one-degree cell. 

The Zobler world soil file differs from the Wilson (described in Wilson and 

Henderson-Sellers 1985) soil data set in that more complete information on 

texture (9 classes), and phase (18) is included for each map unit (106 FA0 

soil types) comprising the largest area of each one-degree square land cell. 

This encoding provides more detailed information at each cell than the Wilson 

soil data set which has a classification scheme (21 types) based on 

combinations of three colors, three textures, and three drainage classes. 

The Zobler world soil file also provides more detailed information about each 

map unit than the Gildea and Moore (described in Henderson-Sellers et a1 ., 1986) FAOSOL global soil archive because it includes the soil types designated 

by FA0 as llassoci ated" and 'Ii ncl uded. I' "Associated" soi 1 s occupy at 1 east 20% 

of the soil ,association and "included" soils occupy less than 20% of the soil 

association of each map unit. The Gildea and Moore global soil archive is at 

0.5' x 0.5' resolution, while the Zobler world soil file is at 1' x 1' 

resol uti on. 

The fourth file on the tape is the SOILWRLD data set. It contains 

information on soil properties of land cells of both Mathews' and FA0 sources, 

reconciles land-classification differences between the two, and has missing 

dat filled in. The first thorough third files on this tape are subsets of the 

SOILWRLD data set that have been ordered in a standard 360 x 180 data matrix 

geometry, have been partially recoded for ease of use, have missing data 

filled in, and agree with Mathews' (1984) nominal classification of land type 

(land, land-ice, water). The first file is a data set of codes for soil unit, 

land-ice, or water, for all the one-degree square cells of the earth. the 

second file is a data set of codes for texture, land-ice, or water, for these 

same soil units. The third file is a data set of codes for slope, land-ice, 

or water for these same soil unites. When used together, these three data 

sets conveniently define a soil type, texture, and slope, for the one-degree 

square cells of the earth. 

Global Change Data Base Africa Documenlation Appendix F-I6 
The Navy fleet Numerical Oceanography Center/(NFNOC) began creating the 

original 10-minute terrain data set in the mid 1960’s. Work extended into the 

early 1970‘s. 

Defense Operational Navigation Charts (ONC), scale 1:1,000,000. For certain 

regions ONC charts were not available; for such areas selected charts from the 

Jet Navigation charts and World Aeronautical Charts were used. 

were hand read out to forms by employees, and then read by optical character 

reader to tape. The values were estimates from contour lines. 

graphs were made for qual ity control, checking terrain features. later, other 

errors were corrected by the National Center for Atmospheric Research in 

Boulder, Colorado. 

The main sources for the data were the U.S. Department of 

The charts 

Isometric 

NFNOC hopes in the future to update the data set using satellite data. This 

update will not be available to the general public for several years, as much 

of the new information will be defense-classified. For further information on 

the history and original sources of the data, you are welcome to contact Leo 

Clark, NFNOC, Code 42, Monterey, California, 93943. 

Global Change Data Base Africa Documentution Appendix F-17 
SECTION 1: Description of SOIL UNIT file. B& SI; Nan-. 2- 

This file consists of a (360. 180) matrix of character-2 SOIL UNIT data baed on the FA0 Soil 

Map of the World. and compiled into digital form by Zobla. hh nutrix clement represents the 

dominant soil unit in a one-degree square crll of the earth's surface. This file wir prepared from 

the SOILWRLD Qta set (the fourth file on this tape). with the addition of the land-ice ells of 

Antarctica. This file conforms exrtly in number. loation. and nominal clasifiation (land. landice. water) to hbtthtars' vegetation data set. 

SECTION 1.A: fi)cfanrrtana Coding. P .-M 

The SOIL UNIT coda in this file an idcntial with the soil unit coda of the SOILWRLD file and the 

FA0 mp (sec Appendix 1 for a list of the 106 soil unit ma and their two-cfuracta coda). WATER 

cells are included in this file and are coded as ' 

'99' (ninety - nine). 

' ldoublc blank). LAND-ICE cells are coded as 

SECTION l.B: Nota 

(1) The latitude and longitude of the northwest corner of any onc-degree square cell tre detmnined 

hy: 

UT-1-90 

LON I - 181 

Cell .Jtituds arc 0 at the equator. increase positive./ northward to +90. and incrczx negatively 

southward to -89. Cell lonaitudcs arc 0 at the prime meridian. in- positively eatward 10 

+179. and inuczsc negatively westward to - 180. This convention holds for a11 four data sets on 

this tape. Scc Appendix 2 for a discussion of map coordinates and matrix indices. 

- 

Global Change Data Base Africa Documentation Appendix F- 18 
(2) Sa: Appendix 3 for a discussion of simple printer-mapping routine for this data set. 

SECTION 2: Description of TEXTURE file. 7 . ER 

This file consists of a (360.180) matrix of &ac~tcr* 1 TEXTURE d~ti ked on the FA0 Soil Map 

of the World. and compiled into digiti1 form by Zobler. Esh matrix elnnent rrpraents the neatsurfs texture (upper 30 cm) of the dominant iOil unit in a one-degree squve cell of the earth's 

surfax. This file was prepared from the SOILWRLD data set 

addition of the Imd-ia ells of Antarcria. This file conforms exrtly in number. location. and 

nominal clluifiation (land. land- k. mta) to Matthews' vegeution &ta set. 

1, with the 

SECTION 2.A: F4&mmem d Coding. LIornn 

' , .. *ape: 

The TMTURE cocks in this data set arc shown in Table 2.1. 

Table 2.1. Texture coda and class in TEX dah set. 

'1' 

'2' 

* 3' 

'4' 

S. 

'6' 

* 7' 

'8' 

'9' 

.. 

CGaBc 

medium 

fine 

co;rrw-medium 

--fine 

medium- I ine 

aorne-medium-fine 

organic 

land- ia 

Wit= 

SECTION 2.B: Nota 

(1) The 'organic' texture clan is applied to those soil units. primarily Histosols. that have no 

Global Change nata Base Africu Documentation Appendix F- 19 
texture symbol on the FAO map. and that arc organic soils (i.e.. possessing an H or 0 master 

horitgn). This texture clat is not an FA0 spaifiation. 

(2) The longitude and latitude convention is identical to the Soil Unit data IC!. u dcvTikd in 

Section l.B.(I). and Appendix 2. 

(3) Thcse data an be mapped. shown in Appendix 3. 

SECTION 3: Description of the SLOPE file. 

This file consists of a (360.180) matrix of chameta-l SLOPE data based on the FA0 Soil Map of 

the World. and compiled into digital fonn by Zobler. tch matrix element rrprsents the rurfaa 

slope of the dominant soil unit in a one-dcgmc squzrc ell of the arth's surface. This file was 

prepared from the SOILWRLD data set (- 1. with the addition of the landiac cells of Anttrctia. This file conforms euctly in number. loation. and nominal classifisatLon 

(land. land-ia. water) lo the Motthews vegetation data set. 

SECTION 3.A: V Coding. I--- fL 

The slope coda in this data set an shown in Table 3.1. 

Table 3.1 Slope codn and clas in the SLP data set. 

'1' 

'2' 

'3' 

- *4' 

'S* 

'6' 

'7' 

'9' 

6. 

0- 8 

8-30 

greater than 30 

0-8; grata than 30 

0-30 

8-30; gmta than 30 

0-8; 8-30; arete th 30 

land- icc 

water 

Global Change Data Base Africa Documentofion Appendix F-20 
SECTION 3.B: Notes 

(1) The longitude and latitude convention is identicll to the Soil Unit data set. IS described in 

Sation I.B.(I). and Appendix 2. 

IZ) Thae data may be easily plotted IS shown in Appendix 3. 

SECTION 4: Description of a WORLD SOIL FILE. U 

This file is the SOILWRLD data set mmpiled by Zoblcr. It consists of 15413 card image records 

of soils information obtained from the FA0 Soil Map of the World. Each record contains information 

on soil type. nar-surf= texture. slope. and phae for the map unit comprising the largest ana of 

a one-degree squue land all. The largest rmp unit wa determined by dot grid overlay. 

data have ben filled in. Conflicting nominal classifiation of some land cells bctwscn FA0 and 

Matthcws' data sets arc identified. and codtd to permit ramciliation rcording to either source. 

Missing 

Water cells and Antarcticr are excluded from this file; otha non-soil land cells. such IS debris 

fields. dunes. and land-ia. trc included. 

SECTION 4.A: File Format and Coding. mQn 

EYh rrcord consists of 9 fields in the order shown in Table 4.1. 

Table 4.1 Data fields of a SOILWRLD data set raord. 

UT 

LON 

su 

SUBSID 

Ex 

SLP 

PHS - 

SP( 3 1 

AREA 

IS 

IS 

A2 

A6 

A3 

A3 

AIS 

AZ 

A3 

field dacrimioq 

LATITUDE of northwest comer of ell. 

LONGITUDE of northwat corner of a1 1. 

Two-letter SOIL UNIT code (set Appendix 1) 

and also a ' (double blank) for Iand-icx: 

(sa aote (1). below). 

Associated and included SUBSIDIARY soil units (see note (21. below). 

TEXTURE code (see note (3). below). 

SLOPE codes (soc note (4). below). 

A string of one- and two-digit PHASE symbols. 

if any (sa note (5). below). 

A thm-element amy of two-digit SPECIAL 

coda (sec note (6). below) used to mask 

mrd attnbuta. 

A three- letter code that nominally ident ifia 

a broad regional Iwtion of the cell 

(sec note (7). below). 

Global Change Data BIlse Africa Documentation Appendix F-21 
ct '. 

Setion 4.B: Note 

(1) The SU (soil unit) coda zre the two-letter symbols for the FA0 soil unit nameS of the dominant 

soil type of the map unit comprising the largest ana within a one-dcgrec land ell. These coda are 

idrntital with the codes in the first file on this tape (see Appendix 1). In addition. land-ict is 

coded as ' (double blank); mall that water ells arc not included. 

(2) The SUBSID (subsidiary) COQ arc the FA0 symbols for 'associated' and 'included' soil types of 

a soil association. The dominant soil type of the association is indiated in the SU field (note 

(I). above). 

cluded' soils oaupy Icu than 20%. These coder arc explained on individual FA0 map sheets. 

'Associated' soils oc~upy at 1~1t 20% of the ana of the soil association. and 'in- 

(3) The TEX (texture) cob arc for the upper 30 cm of the dominant soil unit of a acll (note (1). 

above). The codn are ideatial to the FAO symbols. with the addition of a ' (triple blank) for 

land-ice. and a '9 ' him: double blank) for organic type soils having no texture symbol on the 

FA0 map (sac 2.B. 1). Rac311 that water ells M not included. The iCXtUE ciases and the coda 

arc shown in Table 4.2. 

Global Change Data Base Africa Documentation Appendix F-22 
Table 4.2. Texture coda and class in SOILWRLD data set. 

texture cod€ 

*I - 

'2 ' 

'3 

'12 

'13 

'23 ' 

' 123. 

'9 ' .. 

Iexriirc clas 

me 

medium 

fine 

cotnc-medium 

cuarse- fine 

medium-fine 

axusc-mcdium- fine 

organic 

land - ia 

(4) The SLP (slope) cads are for the dominant soil unit of a cell (see note (1). above). The coda 

are idential to the FA0 symbols with the addition of a ' (triple blank) for land-ia. Reall 

that water cells arc not included. The slope classes and coda arc shown in Table 4.3 

Table 4.3. Slope codes and classes in SOILWRLD data set. 

'A *. 

'E 

'C ' 

'AB ' 

'AC ' 

'BC ' 

'ARC' 

.. 

(5) Soil pa arc properties of a soil that influence its use and murrgcmcnt. but arc not used lo 

classify the soil. Soils may have none. one. or more p-. The PHS (phase) coda in this data set 

arc one- and Iwo-di(it symbols. if any. separated by one or more blanks. not in ordinal sequence. 

The phase cads am shown in Table 4.4. 

Table 4.4. Phpe coda and manins in SOILWRLD data set. 

mSLsQ!&W phase code rnaning 

'1' stony 

92' lithic 

phrcact ic '1 1' 

'12' mdo 

Global Change Data Base Aflicu Documentation Appcndix F-23 
Table 4.4. (Cont.) 

phMC CdC 

'3' 

*4* 

-5. 

6' 

* 7' 

'0' 

'9' 

petrrc 

pctrofmic 

pctrwlcic 

pc t ro gyps ic 

fngipan 

durlpan 

sa1 ine 

sodic 

perrmi ros I 

intcrnii ttent pcnnafros t 

glacier (i.e.. land-id 

pondcd 

dunes. sands 

rock debris 

(6) The SP (special) coda serve two purposes: lo identify land ails that have missing data in 

some of the fields. and to identify ulls that hve a conflicting nominal clasifiation (land. 

land-icc. water) ktwctn Zobla and Matthew,. 

(6.1) Missinn da la. When soil unit. texture. or slope idomtion for the map unit chosen to rcprescnt a land cell was missing on the FA0 map. this information wa added by Zobla and was used to 

fill in the SU. TEX. or SLP fields for such a raord in the file. In addition. J codc was placed in 

the SP field to indiate which fields were filled in (Table 4.5). 

Table 4.5. SP coda for missing d~ta in the SOILWRLD data set. 

'99' NO soil information present on the map; 

SU. TEX. urd SLP fields filled in. 

'88' . TCXIUR information missing; 

TEX field filled in. 

'77' Slope inionrution missing; 

SLP field filled in. 

Thew coda allow a user of this data x! to reproduce the wtual FA0 map unit chosen to 

cfiar;uterite a ell by chaking for these coda in the SP field and masking the appropriate filled 

in field(s1. Note that a *no indiata that a non-soil land cell (such as debris field or dun& 

was pnscnt on the FA0 map. In this instance. in addition to the filled in missing data. the phase 

field contains a code lor the type of non-roil land unit that is indicated on the map. 

(6.2) pominal cl assificsrian conflicx. The nominal classiliation conflicts bctwacn Zoblef and 

Global Change Data Base Africa Documentation Appendix F-24 
,Matthews are of two kinds: land/wata clwifiation. and land- icc/lmd classification. 

A Iand/wrtcr conflict ocEumd whenever Zobla found a all to be absent on the FA0 map and 

present in Matthews. In this instmcr the crll was added to the SOILWRLD data set a either landice or an interpreted soil. in conformity with the Matthews nominal classifiatioa of the 41. In 

addition. a code was placcd in the SP field to indiate this condition (Table 4.6). 

Table 4.6. SP cod6 for Iand/watcr conflicts. 

'44' knd uil not present on FA0 map; classified as land-ia. 

'33' land all not present on FA0 map: classified 1s soil; 

SU. "EX. and SLP fields filled-in. 

A land-iulland conflict OeEUrrrd whenever Matthews and FA0 dingrscd on the land-icc classificrtioa of a all. Records of this kind arc not uniquely classified in this file. and so have 

attribute of both a soil all and a land-icr (i.e.. glacier) cell: the SU. TEX. and SLP fields have 

appropriate coder in them. and the PHS field also contains a glixja code. In addition. a code WM 

plaocd in the SP field to indiate this condition (Table 4.7). 

Table 4.7. SP coda for land-i~/lmd conflicrs. 

'66' Cell clarified as land-icc by Zobla. 

and a vegetat~on (soil implied) by Matthnwr. 

'S5' Cell classified as soil by Zobla. and 

a 1md-k by MatthewS. 

The user of this data set my choose how these raords should be clasified. either its 

Zoblcr-compatible. or a-Matthms-compatible. or as some other union of the two. Acmrding to the 

table below (Table 4.81. this daision amounts to either a row collapse. a column collapse. or other 

masking. taparively. (Note that as an aid to users alredy using the Matthews data sets. the first 

three data sets on this tape.hrve been nude Matthews-compatible). 

' 

Global Change Data Base Africa Docunrentation Appendix F-25 
Table 4.8. Zobla and Matthews compatibility. 

70nm 

Soil Glacier 

When thex ails DC raolvcd axording 10 Matthews. a '66'-type ell baama a soil all 

(dclc!c the glricr code from the PHS field; retain a11 0th- fields). and a 'SS'-typc ell -ma 

a lad-ice el1 (refain the.gIria mde in the PHS field; delete the SU. SUBSID. TEX. and SLP 

fields. and 0th- non-glxia PHS coda). To raoncilc these type cells laording to Zobla. perform the same making operations of the opposing all types: a '66'-type ell becoming a land-ia 

ell. and a '55'-type ull a soil ail. 

(7) The AREA coda indiate territorial rcgrons and are used as broad guida to a all lwtion on 

FA0 map shais. These coda ut shown in Table 4.9 

Table 4.9. AREA code and maning in SOILWRLD data set. 

'AUS' Australia. South Pacific 

'AFR' Afria 

'EUR' Europe 

"AM' North Amaia 

AREA code paning 

'SAM' South Arncrb 

'US' Souti:crn hsrr 

'SU' South Easi Asia 

'h' CA * North C:nin! Ap.15 

(8) The latitude and longitude conventions are the same as those discussed in Settion l.B.(I). and 

Appendix 2. 

Global Change Data Base Afiica Documentation Appendix F-26 
FAO-UNESCO (1974) Soil Map of the World 1:5.000.000. UNESCO. Paris. 

Henderson-Scilcn. A.. M.F. Wilson. G. Thoma. R.E. Dickinson (1986) Currrnt Global Land-Surface Data Sets for Use in Climate-Related Studies. NCAR Technical Note NCARITN-2724TR. 

Matthews. E. (1983) Global vegetation and land use: New high mlution data basa for climate 

studicr. I. Clim. Appl. Metax.. 22. 474-487. 

Matthcwr. E. (1984) Vegetation. &nd-Use and Seaonal Albedo Sets: Documentation of Archived Data Tape. NASA Tdmial Memorandum 186107. 

Wilson. M.F. and A. Henderson-Sellm (1985) A global archive of land cover and soils data for use 

in gmml circulation climate models. J. Clim.. 5, 119-143. 

Zobla. L. (1986) A World Soil File for Global Climate Modeling. NASA Tahnicrl Memorandum 

187802. 

&&gw lednmentf 

We thank Elaine Matthews for ha help in prepring this report. Tina Cary for coding advia. and 

Leonard Zobla for his mcouraga~nt on soils pPrametcrization in hydrology. Chrstopher Shashkin 

typed the report. This work was supported by the NASA CI.imats Program and EPA Contract R812962-010, 

Global Change Data Base Africa Documentation Appendix F-27 
APPENDIX 1 (mnt'd.) 

L LUV~SOLS 

LO Orthic Luvisols 

LC Chromic LuviJols 

LK Calcic LuviJols 

LV Vatic luviwls 

LF Ferric Luvisds 

LA Albic Luvisls 

LP Plinthic Luvisolr 

LG Glcyir: Luvirols 

W PLANOSOLS 

WE Eutric Planosols 

WD Dystric Planosols 

WM Mollic Planosols 

WH Humic Pllncnols 

WS Solodic Planolols 

WX Gclic Planoz~l~ 

,O HISTOSOLS 

OE Eutric Histolois 

OD Dyriric Histosols 

OX Gelic Histosols 

D PODZOLUVISOLS 

DE Euvic Podzoluvisols 

DD Dystric Podzoluvisois 

DG Gleyic Podzoluvisols 

P PODZOLS 

PO Orthic Podzols 

PL Leptiv Podzols 

PF Faric Podzols 

PH Humic Podzols 

PP Plric Podzols 

PG Gleyic Podzols 

F FERRAUOLS 

FO Orthic Femlrols 

FX Xanthic Furalwlr 

FR Rhodic Fanlsools 

FH Humic Fcmlsols 

FA Aaic Femlsols 

FP Plinthic Femlrolt 

A ACRISOLS 

A0 Orthic Acrisols 

A€ Ferric Acrisois 

AH Humic Anisols 

AP Plinthic Acrisols 

AG Gleyic Acrirols 

N NITOSOLS 

NE Eutrrc Nitosols 

ND Dystric Ni tosols 

NH Humic Nimsols 

Global Change Data'Base Africa Documentation Appendix F-28 
-GFA0 SOIL UNITS 

CITATION: Witt, R. (ed.), 1984. Global FA0 Soil Units (IO-min grid). Digital 

Data. UNEP/GFUD, Geneva, Switzerland. 2.3 MB. 

Globd Change Data Rase Afiica Ducmmfutiun Appendix G-1 
Blank page retained for pagination 
G. FAOSOILUNITS 

DATE: 1974 

SOURCE: 

(1) UNESCO/FAO 

(2) ESRI (FA0 contract) 

380 New York Street 

Redlands, CA 92373 

(3) Reprocessed by: 

UNEP/GFUD 

6 rue de la Gabelle 

1227 Carouge 

Geneva, Switzerland 

CONTRIBUTOR: 

Ron Witt 

UNEP/GRID 

Geneva, Switzerland 

DATA CENTER: GRID (Geneva) 

GEOGRAPHIC SAMPLING: 2-min grid, resampled to IO-min, characteristic classes. 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Decadal composites 

REFERENCES: 

_--------------I 

FILE NAMES: AFFAOSOL FA0 Soil Units 

Global Chmge DH~A Base Africa Doctmtentuh’on Appendix (3-3 
Blank page retained for pagination 
FA0 SOIL UNITS 

DOCUMENTATION FILE 

File Name: AFFAOSOL.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

CMESCO/FAO Soil Units 

byte 

binary 

438 

480 

O.OOOOOOOOOOE+OO 

1.33000000003+02 

1.66666666673-01 

1.66666666673-01 

legend : 133 

category 1 : 1 

category 2 : 2 

category 3 : 3 

category 4 : 4 

category 5 : 5 

category 6 : 6 

category 7 : 7 

category 8 : 8 

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category 10 : 10 

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category 39 : 39 

category 40 : 40 

A 

A€ 

Ag 

Ah 

Ao 

AP 

B 

Bc 

Bd 

Be 

Bf 

Bh 

Bk 

Bv 

BX 

C 

Ch 

Ck 

Cl 

D 

Dd 

De 

D9 

E 

F 

Fa 

Fh 

Fo 

FP 

Fr 

Fx 

G 

Gc 

Gd 

Ge 

Gh 

Gm 

GP 

E9 

Cg 

Acti s 01 s 

Ferric Acrisols 

Gleyic Acrisols 

Humic Acrisols 

Orthic Acrisols 

Plinthic'Acrisols 

Cambisols 

Chromic Cambisols 

Dystric Cambisols 

Eutric Cambisols 

Ferralic Cambisols 

Gleyic Cambisols 

Humic Cambisols 

Calcic Cambisols 

Vertic Cambisols 

Gelic Cambisols 

Chernozems 

Glo s sic Cherno zems 

Haplic Chernozems 

Calcic Chernozems 

Liivic Chernozems 

Podzoluvisols 

Dystric Podzoluvisols 

Eutric Podzoluvisols 

Gleyic Podzoluvisols 

Rendzinas 

Ferralsols 

Acric Ferralsols 

Humic Ferralsols 

Orthic Ferralsols 

Plinthic Ferralsols 

Rhodic Ferralsols 

Xanthic Ferralsols 

Gleysols 

Calcaric Gleysols 

Dystric Gleysols 

Eutric Gleysols 

Humic Gleysols 

Mollic Gleysols 

Plinthic Gleysols 

Global Change Data Base Afiica Docmenrution Appendix G-5 
category 41 : 41 

category 42 : 42 

category 43 : 43 

category 44 : 44 

category 45 : 45 

category 46 : 46 

category 47 : 47 

category 48 : 48 

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category 50 : 50 

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category 77 : 17 

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cateuory 79 : 79, 

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category100 : 100 

GX 

H 

Hc 

H9 

Hh 

H1 

I 

J 

Jc 

Jd 

Je 

Jt 

K 

Kh 

Kk 

K1 

L 

La 

Lc 

Lf 

L9 

Lk 

Lo 

LP 

Lv 

M 

M9 

MO 

N 

Nd 

Ne 

Nh 

0 

Od 

Oe 

OX 

P 

Pf 

p9 

Ph . P1 

Po 

PP 

Q 

Qf 

01 

R 

Rc 

Rd 

Re 

R;< 

S 

Sm 

so 

T 

Th 

Tm 

sg 

Gelic Gleysols 

P haeo zems 

Calcaric Phaeozems 

Gleyic Phaeozems 

Haplic Phaeozems 

Luvic Phaeozems 

Litho sols 

F luvi so 1 s 

Calcaric Fluvisols 

Dystric Fluvisols 

Eutric Fluvisols 

Thionic Fluvisols 

Kas t an0 t ems 

Haplic Kastanozems 

Calcic Kastanozems 

Luvic Kastanozems 

Luvi so 1 s 

Albic Luvisols 

Chromic Luvisols 

Ferric Luvisols 

Gleyic Luvisols 

Calcic Luvisols 

Orthic Luvisols 

Plinthic Luvisols 

Vertic Luvisols 

Grey zems 

Gleyic Greyzems 

Orthic Gleyzems 

Nit o sol s 

Dystric Nitosols 

Eutric Nitosols 

Humic Nitosols 

Histosols 

Dystric Histosols 

Eutric Histosols 

Gelic Histosols 

Podzols 

Ferric Podzols 

Gleyic Podzols 

Humic Podzols 

Leptic Podzols 

Orthic Podzols 

Placic Podzols 

Areno sols 

Albic Arenosols 

Cambic Arenosols 

Ferralic Arenosols 

Luvic Arenosols 

Regosols 

Calcaric Regosols 

Dystric Regosols 

Eutric Regosols 

Gelic Regosols 

Solonetz 

Gleyic Solonetz 

Mollic Solonetz 

Orthic Solonetz 

Ando s ol s 

Humic Andosols 

Mollic Andosols 

Global Change Data Base Africa Documeritutiori Appendix G-6 
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{category 0 : 

101 

102 

103 

104 

105 

106 

107 

108 

109 

110 

111 

112 

113 

114 

115 

116 

117 

118 

119 

120 

121 

122 

123 

12 4 

125 

12 6 

127 

12 8 

12 9 

130 

131 

132 

133 

To 

Tv 

U 

V 

vc 

W 

Wd 

We 

Wh 

Wm 

ws 

wx 

X 

Xh 

Xk 

x1 

XY 

Y 

Yh 

Yk 

Y1 

Yt 

YY 

Z 

zg 

Zm 

20 

Zt 

RO 

SA 

WA 

VP 

-- 

unclassed) 

Ochric Andosols 

Vitric Andosols 

Rankers 

Vert i so 1 s 

Chromic Vertisols 

Pellic Vertisols 

Planosols 

Dystric Planosols 

Eutric Planosols 

Humic Planosols 

Mollic Planosols 

Solodic Planosols 

Gelic Planosols 

Xerosols 

Haplic Xerosols 

Calcic Xerosols 

Luvic Xerosols 

Gypsic Xerosols 

Y ermo so 1 s 

Haplic Yermosols 

Calcic Yermosols 

Luvic Yermosols 

Takyric Yermosols 

Gypsic Yermosols 

Solonchaks 

Gleyic Solonchaks 

Mollic Solonchaks 

Orthic Solonchaks 

Takyric Solonchaks 

Rock 

Salt 

Water 

no name 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Glahal Change Data Base Africa Documcnturiori Ayyeiidix G-7 
Blank page retained for pagination 
(me African 80ib database resident at UNEP/=D in Geneva was 

digitized by in ~edlands, California frm the 1973 oNEsco/p~o soil 

nap of the World at 1:SOOOOOO scale. Ime legend of the &il mp of the 

World cuprises an estimated SO00 different map units, uhi& consist of 

soil units or sssociations of mil units occurbag with in the limits of a 

mapable physiograMic entity. At the time of carpilation of lrraps for the 

1973 map there were 1503 map unit8 for Africa (amis VI: 1-2-3). The 

nunt#r of soil units Mi& cmpse the legend of the Soil of the 

Wrld is 106. This number has been increased to 133 at UNEP/~D to 

allw the identification of soib with insufficient fnformatiun to 

specify the daninant soil unit to be that of the dominant 8011 goup 

(Le. A for Acrisols when danimt Aaisol unit is not known). Men a 

map unit is not hcmogeneous, it is CQnpOsed of a danimt soil and of 

associated soils, the latter covering at least to percent of the area; 

important soils bhich mer less than 20 percent of the area are added as 

Fnclus ions . 

Textural classes reflect the relative proportions of clay (fraction less than 2 microns), silt (2-50 microns), and sand (50-2000 

micram). There are three textural classes recognized as shown 

figuratively below, they are coarse, medium, and fine and are given for 

the daninant soil in ea& association and refers to texture in the upper 

30 an of the soil. The texture of a soil horizon is one of its mst 

permanent characteristics and in conjunction with other properties is 

directly related to soil structure, consistence, porosity and cation 

exchange capacity. 

00 

T 

PERCENT SAND 
Slope is an integral part of the land surface as it influences 

Three slope 

Marked by the 

draimge, nm-off, erosion, emsure, and a-sslbility. 

classes are distinguish4 an the Soil Map of the World. 

syatx>ls a,b,c they are: 

a. level to gently undulating: d-t slop ranging 

between 0 and 8 percent; 

b. rolling to hilly: duninant slopes ranging between 

8 and 30 peramt; 

c. steeply dissectd to mountainous: daninant slopes are 

Over 30 percent. 

Phases are subdivisions of soil units based on haracteristics 

me mases on 

which are significant to the use or management of the land but not 

diagnostic for the separation of soil units themselves. 

the Soil Hap of the World are: stony, lithic, petric, petrocdlcic, 

petroqypsic, petroferric, Neatic, fragipan, duripan, saline, sodic and 

cerrado. 

Consideration was given to the insertion of 'climatic variants' 

in the Soil Map of the World. However, the separation of such variants 

wuld have required a general agreement on a climatological 

classification uhi& was beyond the sa3pe of the pblication. 

only the boundaries of pe-mafrost and intermittent pernfrost have been 

shown on the map. 

Resalrce faboratory Software (ELAS) polygon manipulation routines by 

ms reflecting the daninant soil unit, texture of the upper 30 en of 

the daninant soil, and slope classes where available. 

synkoh are fully explained ~1 the reverse of the maps as a ccmposition 

of the association. 

derived using the 133 daninant soil types. 

associated soils, texture, or slope may be developed but currently do not 

exist at UNEF/GRID. 

mviromtal Systans Researcfi Institute (EW) in Redlands, California 

under contract from UNEP fran the Si1 Bp of the World which was in the 

filler ablated stereogra@ic proyxtian. Polygon vertices in digitizer 

table coordinates were processed through ELAS software and geoteferenced 

to latitude and longitude raster database having an equal area projection 

of 0.86 square kilometers per pidcurMement (pixel) or cell with a cell 

size resolution of 927 meters or 30 seconds of 

mese phasis are shown as overprints an the map. 

He~qce, 

The soil associations have been noted on the map and in Eart!! 

Each of the 

me soil dataplane available fran -/- was 

me basic soil units for Africa were digitized by 

Dataplanes depicting 
latitude (+North) and longitude (+East of Greenwib Meridian). 

offset pixel (located at 8can line zero and element zero) has a longitude 

of -72030 seconds (or 72030 seoonds West of GM) and latitude of 136830 

seconds North of -tor. me data tape contains one file of data with 

8762 records. 'Ihe first record is a header record canposed of 1024 bses 

of infomtion about the data file. 

BCM lines) are all 8704 bytes each (8641 picture elemnts mdulated by 

256). There are 133 soil types as noted in the accunpanying type table 

each of whi& has a class nlrmber with an associated colax. 

colour table depicts colour canponents (blue, red, green) ranging in 

value fran zero (darkest) to fifteen (briatest). 

me 

'Ihe rerrraining 8761 records (8761 

me enclosed 
-/FA0 SOIL TIPS 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

29 

30 

31 

32 

33 

28 

SYMSOL 

A 

Af 

Ag 

Ah 

A0 

AP 

B 

Bc 

Bd 

Be 

Bf 

Bg 

Bh 

Bk 

Bv 

BX 

D 

Dd 

De 

09 

E 

P 

Pa 

m 

FO 

PP 

Fr 

PX 

Acr isols 

Ferric Acrisols 

Gleyic Acrisols 

Humic Acrisols 

Orthic Acrisols 

Plintic Acr isols 

Canbisols 

Chrunic carbisols 

Dysttic carbisols 

Eutric cabisols 

Ferralic carbisols 

Gleyic Cmbisols 

Eumic carhisols 

Calcic czinbisols 

Vertic Cabisols 

Gelic cambisols 

chernozems 

Glossic chernozem 

Haplic mernozems 

Calcic chernozems 

Wvic Clernozens 

Podzoluvisols 

Dystric Podzoluvisols 

Eutric Podzoluvisols 

Gleyic Pcdzoluvisols 

Rendzinas 

Fer r als 01s 

Humic Ferralsols 

Orthic Perralsols 

Plinthic Ferralsols 

wlodic Ferrdlsols 

Xanthic Ferralsols 

Acric FerraLsols 
34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

52 

53 

54 

55 

56 

57 

58 

59 

60 

61 

62 

63 

64 

65 

66 

67 

68 

69 

70 

71 

72 

G 

Gc 

Gd 

Ge 

GI 

on 

GP 

GX 

H 

HC 

Hg 

w 

El 

I 

J 

JC 

Jd 

Je 

Jt 

K 

Kh 

m 

Kl 

L 

La 

Lc 

Lf 

Lg 

Lk 

Lo 

LP 

LV 

n 

Mg 

Mo 

N 

Nd 

Ne 

m 

Gleysols 

mcaric Gleysols 

Dystric Gleysols 

Eutric Gleysols 

Humic Gleysols 

Wllic Gleysols 

Plinthic Gleysols 

Gdic Gleysols 

Phaeozm 

Calcaric Phaeozems 

Gleyic Phaeozm 

Haplic Phaeozems 

Luvic Phaeozerns 

Lithosols 

Fluvisols 

cdlcaric Fluvisols 

Dystric Fluvisols 

Eutric pluvisols 

mimic Fluvisols 

Kastanozm 

Haplic Kastanozerns 

Cidcic Kastanoze! 

Luvic Kastanozerrs 

Luvisols 

Albic Luvisols 

Chr ani c Luvisols 

Ferric Uvisols 

Gleyic Luvisols 

Calcic Luvisols 

Orthic Luvisols 

Plinthic Luvisols 

Vertic Luvisols 

Greyzens 

Gleyic Greyzem 

Orthic Greyzem 

Nitosols 

Dystric Nitosols 

Eutric Nitosols 

Humic Nitosols 
73 

74 

75 

76 

77 

78 

79 

80 

81 

82 

a3 

84 

85 

86 

87 

88 

89 

90 

91 

92 

93 

94 

95 

96 

97 

98 

99 

100 

101 

102 

103 

104 

105 

106 

107 

108 

109 

11 0 

0 

od 

02 

ox 

P 

Pf 

pg 

Ph 

P1 

Po 

PP 

0 

Qa 

gc 

Qf 

Q1 

R 

RC 

Rd 

FK 

Re 

S 

s4 

sm 

so 

T 

Tm 

To 

?v 

m 

U 

V 

vc 

vp 

W 

wd 

we 

Iih 

Histosols 

Dystric Histosols 

Eutric Histosols 

Gel ic His tosols 

P3dZOlS 

Ferric Mzols 

Gleyic Podzols 

Humic Pdzols 

Leptic podzols 

Orthic Podzols 

Placic podzols 

Arenosols 

Albic Arenosols 

mic Arenosols 

Ferrdlic Arenosols 

Uvic Arenosols 

RVSoh 

Calcaric Regosols 

Dystric Regosols 

Eutric Regosols 

Gdic Reqosols 

Solonetz 

Gleyic Solone+x 

Mollic Solonetz 

Orthic Solonetz 

Andosols 

Humic Andosols 

Mollic Andosols 

Oc’lric Andosols 

Vitric Andosols 

Rankers 

Ver tisols 

Cxanic Ver tisols 

pellic Vertisols 

planosols 

Dystric Planosols 

mtric Planosols 

Humic Planosols 
111 

11 2 

113 

114 

115 

11 6 

117 

11 8 

119 

120 

121 

122 

123 

124 

125 

126 

127 

128 

129 

130 

131 

132 

133 

wm 

ws 

wx 

X 

xh 

xk 

x1 

xy 

Y 

Yh 

Yk 

Yl 

Yt 

YY 

Z 

zm 

zo 

zg 

Zt 

Hollic Planosols 

Solodic ?l;inosols 

alic Planosols 

xerosols 

Haplk Xerosols 

calcic Xerosols 

Luvic Xerosols 

Gypsic Xerosols 

yermsols 

Haplic Yemsols 

Calcic Yermsols 

mvic Yemsols 

Takyric Yermsols 

bpic Yemsols 

Solonchaks 

Gleyic Solon&aks 

plcliic Solonchaks 

Orthic Solonchaks 

Takyric Solonchaks 

Rock 

salt 

Water 

no name 
-HUNEIVF.40 GRID AFRICA CIS DATA BASE 

CITATION: Witt. R. (ed.), 1984. Africa GIS Data Base. Digital Data. 

UNEP/GRID, Nairobi, Kenya. 150 MB. 

Global Change Data Base Ajkica Docunientution Appendix H- I 
Blank page retained for pagination 
H. UNEP/FAO GRID AFRICA CIS DATA BASE 

DATE: 1984 

SOURCE: 

(1) UI"/FAO 

(2) Environmentd Systems Research Institute 

380 New York Street 

Redlands, CA 92373 

CONTRIBUTOR: 

UNEP/GRlD 

P.O. Box 30552 

Nairobi, Kenya 

DATA CENTER: GRID (Nairobi) 

GEOGRAPHIC SAMPLING: 

1:5,oOo,OOO scale vector points, lines. and polygons with attributes. 

Rasterized to a IO-min grid. 

COVERAGE: Africa 

PROJECTION: Miller Oblate Stereographic for Africa, resampled to lat./lon. 

TIME SAMPLING: Varies by data set. 

REFERENCES: 

UNEP/FAO. 1983. "Provisional Methodology for the Assessment and 

Mapping of Desertifcation." 

ESRI, 1984. "UNEP/J?AO World and Afiica GIS Data Base." 

(Excerpts included.) 

FILE NAMES: 

AFCROPX 

AFRAINX R,ainfall (mm) (Experimental) 

AFsorLx Soils (Experimental) 

AFVEGX Vegetation (Experimental j 

AFWINDX Wind speed (4s) (Experimental) 

AFDESRTX Desertification hazard (Experimental) 

AFTEhlPX 

Crop 1 and (Ex perimen td) 

January temperatures (deg K- 100) (Experimental) 

Global Clinnge Data Base Africa Docrrrrrrrrtuh'orr Appendix H-3 
Blank page retained for pagination 
UNEP/FAO GRID CROPLAND 

DOCUMENTATION FILE 

File Name: AFCROPXDOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 

category 

category 

cat ego ry 

1: 

2: 

3: 

4: 

UNEP/FAO GRID Africa Database--Cropland (Experimental) 

byte 

binary 

438 

480 

O.OOOOOOOOOOE+OO 

9.0000000000E+00 

1.6666600000E-01 

1.66666000003-01 

4 

1 Water Water 

2 No Crop/N Not Cropland or Cropland/Natural Mosaic 

3 Crop/N Mo Cropland/Natural Mosaic 

4 Cropland Cropland 

(category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . I 
UNEPFAO GRID RAINFALL 

DOCUMENTATION FILE 

File Name: AFRAINX.DOC 

image title : UNEP/FAO GRID Africa Database--Rainfall (mm) (Experimen,:al) 

data type : byte 

file type : binary 

rows : 438 

columns : 480 

minimum : O.OOOOOOOOOOE+OO 

maximum : 3.2000000000E+01 

cell x : 1.6666600000E-01 

cell y : 1.66666OOOOOE-01 

legend : 32 

category 1 : 1 0 to 50 

category 2 : 2 50 to 100 

category 3 : 3 100 to 150 

category 4 : 4 100 to 200 

category 5 : 5 150 to 200 

category 6 : 6 200 to 250 

category 7 : 7 200 to 300 

category 8 : 8 200 to 400 

category 9 : 9 250 to 300 

category 10 : 10 250 to 400 

category 11 : 11 300 to 350 

category 12 : 12 300 to 400 

category 13 : 13 350 to 400 

category 14 : 14 400 to 450 

category 15 : 15 400 to 500 

category 16 : 16 400 to 600 

category 17 : 17 450 to 500 

category 18 : 18 450 to 550 

category 19 : 19 500 to 550 

category 20 : 20 500 to 600 

category 21 : 21 550 to 600, 

category 22 : 22 600 to 700 

category 23 : 23 600 to 800 

category 24 : 24 600 to 1000 

category 25 : 25 700 to 800 

category 26 : 26 800 to 900 

category 27 : 27 800 to 1000 

category 28 : 28 900 to 1000 

category 29 : 29 1000 to 1500 

category 30 : 30 1500 to 2000 

category 31 : 31 2000 to 3000 

category 32 : 32 > 3000 

(category 0 : unclassed) 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 
UNEP/FAO GRID SOILS 

DOCUMENTATION FILE 

File Name: AFSOILXDOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

masimum 

cell x 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

category 9 : 

category 10 : 

category 11 : 

category 12 : 

category 13 : 

category 14 : 

category 15 : 

category 16 : 

category 17 : 

category 18 : 

category 19 : 

cateqory 20 : 

category 21 : 

category 22 : 

category 23 : 

category 24 : 

category 25 : 

category 26 : 

category 27 : 

category 28 : 

category 29 : 

category 30 : 

category 31 : 

category 32 : 

UNEP/FAO GRID Africa Database--Major Soil Classes (Exp) 

byte 

binary 

438 

4 80 

0.0000000000E+00 

3.3000000000E+01 

1.66666666673-01 

1.66666666673-01 

32 

1 A Acriso'ls 

2 B Cambisols 

3 C Chernozems 

4 D Podzoluvisols 

5 E Rendzinas 

6 F Ferralsols 

7 G Gleysols 

8 H Phaeozems 

9 I Lithosols 

10 J Fluvisols 

11 K Kastanozems 

12 L Luvisols 

13 M Greyzems 

14 N Nitosols 

15 0 Histosols 

16 P Podzols 

17 Q Arenosols 

18 R Regosols 

19 S Solonetz 

20 T Andosols 

21 U Rankers 

22 V Vertisols 

23 W Planosols 

24 X Xerosols 

25 Y Yermosols 

26 2 Solonchaks 

27 Not used 

28 Not used 

29 Not used 

30 Rock 

31 Salt 

32 Water 

(category 0 : unclassed) 

[This leaend is appropriate for display of the individual classes 

(i.e. without rescaling) .I 
UNEPPAO GRlD VEGETATION 

DOCUMENTATION FILE 

File Name: AFVEGX.DOC 

image title 

data type 

file type 

rows 

co 1 umns 

minimum 

maximum 

cell :i 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

category 11 

category 12 

category 13 

category 14 

category 15 

category 16 

: UNEP/FAO GRID Africa Database--Vegetation (Experimental) 

: byte 

: binary 

: 438 

: 480 

: 0.0000000000E+00 

: 1.6000000000E+01 

: 1.66666666673-01 

: 1.66666666673-01 

: 16 

: 1 IFOR Inundated Forest (11 1 

: 2 ITRE Inundated Tree Savanna (21) 

: 3 IGRS Inundated Grassland (31) 

: 4IMAN Inundated Mangroves (41) 

: 5IMAR Inundated Marsh/Swamps (51) 

: 6 IOAS Inundated Oasis (61) 

: 7 IOTH Inundated Other ( 91) 

: 8 FOR Forest ( 1 9 ) 

: 9 TRE Tree Savanna (29) 

: 10 GRS Grassland (39) 

: 11 MAN Mangroves (4 9) 

: 12 MAR Marsh/Swamp (59) 

: 13 OAS Oasis (69) 

: 14 OTH Other (99) 

: 15 OTHR Other-Rice ( 92 

: 16 WAT Water (88) 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i.e. without rescaling) . J 

Note: The original values that appear in the documentation (in 

parentheses) have been reassigned to the above categorical values. 

Global Change Data Base Africa Uoc~m~entution Appendix €1-8 
UNEPIFAO GRID WIND SPEED 

DOCUMENTATION FILE 

File Name: AFWINDX.DOC 

image title 

data type 

file type 

rows 

columns 

mi n imum 

maximum 

cell x 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

: UNEP/FAO GRID Africa Database--Wind speed (m/s) (Exp) 

: byte 

: binary 

: 438 

: 480 

: 0.0000000000E+00 

: 1.3OOOOOOOOOE+O1 

: 1.66666666673-01 

: 1.66666666673-01 

: 13 

: 1 0.0 to 0.5 

: 2 0.5 to 1.0 

: 3 1.0 to 1.5 

: 4 1.5 to 2.0 

: 5 2.0 to 2.5 

: 6 2.5 to 3.0 

: 7 3.0 to 3.5 

: 8 3.5 to 4.0 

: 9 4.0 to 4.5 

: 10 4.5 to 5.0 -- 

category 11 : 11 5.0 to 5.5 

category 12 : 12 5.5 to 6.0 

category 13 : 13 > 6.0 

(category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . J 

Glohal C’lirtnge nrita Base Afiicu Dociwtcrttntion Appendix H-9 
UNEP/FAO GRID DESERTIFICATION HAZARD 

DOCUMENTATION FILE 

File Name: AFDESRTX.DOC 

imaae title 

data type 

file type 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

cateqory 11 

category 12 

category 13 

category 14 

category 15 

UNEP/FAO GRID Africa Database--Desertification Hazard (Exp) 

byte 

binary 

438 

480 

0.0000000000E+00 

2.55000000003+02 

1.6666666667E-01 

1.66666666673-01 

15 

16-31 slight 

32-47 slight 

48-63 moderate 

64-79 moderate 

80-95 severe 

96-111 severe 

112-127 severe 

128-143 very severe 

144-159 very severe 

160-175 very severe 

176-191 very severe 

192-207 very severe 

208-223 very severe 

224-239 very severe 

240-255 very severe 

{category 0 : 0-15 slight} 

[This leuend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Data Dase Ajfcu Doc'rirrierimion Appendix H-10 
UNEP/FAO GRID JANUARY TEMPERATURES 

DOCUMENTATION FILE 

File Name: AFIXMPX.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

ma:; imum 

cell :i 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

UNEP/GRID Nairobi January Temperatures (degrees K - 100) 

byte 

binary 

438 

4 80 

1.8000000000E+02 

2.1100000000E+02 

1.66666666673-01 

1.6666666667E-01 

15 

182 to 183 

184 to 185 

186 to 187 

188 to 189 

cat ego ry 

cat ego ry 

category 

category 

cat ego ry 

category 

category 

cat eqo ry 

category 

category 

category 

5 : 190 to 191 

6 : 192 to 193 

7 : 194 to 195 

8 : 196 to 197 

9 : 198 to 199 

10 : 200 to 201 

11 : 202 to 203 

12 : 204 to 205 

13 : 2C)6 to 207 

14 : 208 to 209 

1s : 210 to 211 

{category 0 : 180 to l8l} 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Data Base Afiica Docirnicritufiori Appplix H- 1 1 
Blank page retained for pagination 
(Excerpt from Original Docmen+zation 1 

FINAL REPGRT 

UNEP/FAO WSRLD AND AFRICA CIS DATA BASE 

December 1984 

This Report vas Prepared as Part of 

Phase TI of the FAOIUNEP Desertification 

and Mapping Project 

Prepared under UNEP Concracts 83/0012 and 86/0001 

for 

Desertification Branch 

P.O. Box 30552 

Nairobi, Kenya 

United Nations Environment Programme 

and 

Soil Resources * Managemenc and Conservation Service 

Land and Water Development Division 

Food and Agricultural Oraanization of the Uniced Sacions 

Via delle Terne di Caracalla 

00100 Rome, Italy 

Prepared by 

Envi r onment a1 S y s tems Res ear ch Ins t icu t e 

380 Nev Ycrk Strecc 

Redlands, CX.* USA 92373 
PREFACE 

A Geographic Information System (CIS) data base comprised of cartqgraphfc and 

at:: DUC~ i?for?ation vas developed a': varying fntensities for th2 World, 

Devi xping LOY~!. and P.irica as part of Phase 11 of the UNE?/FAO 

Dese:tificaCi..n Lssessment and Hapyfng Project. Most data for t'le data base 

werc provided b: FA0 and were automated at a scale of 1:5,OOO,OCO usfng the 

saxe basemap th.t uas used for the FA0 Soil Map of the World. Tne cita base 

vas used to concxt desertification assessments for the World and Africa and 

co produc? the following draft maps for presentz.:ion at the Governing Council 

of WE17 in Hay 1984: 

Yap of Desertification Hazards Africa. Component Analyses 1:ZS 

Million. Final Hazards Analyses 1.5 Million Scale. 

Soil Elements used in Assessing Desertification and Degradation. World. 

I: LO Million Scale. 

The data base and maps were prepared by ESRI under contr; .t to UNE?. The, 

process and products are generally described in the Map cf Desertification 

Hazards: Explanatory Note prepared for the conference. The methodoiogy for 

the assessment of dzsertification hazard vas prepared and the mzps edited by 

Dr. Todot Boyadgiev of the Soil Resources, Management and Conservation 

Service, Land and Water Developaenc Division, FA3, Rome. The mecSodology is 

described in the FAO/L"E? document, prepared in L983, enti:led, Provisional 

Xechodoloqy tot the Assessment and Maoping of Desertification. 

f1.e data base, built upon an intelligent data structure, v2.i designed to 

support the ongoing assessmenc of desertification and to servc a broad tangocf land analysis, planning, and management interests. It was provided fn 

digital focn to UNE? and EAO. This report primarily describes the processes 

and sources used to create it, and documents its fonnat and conrenc. The 

report, originally submitted in June 1984, was revised in December 1984 to 

accomodace ESBI and FA0 experiences with che installation and use of tSe data 

base at FA0 Headquarters. 
Introduction 

This rcporr vas prepared for the United Nations Environment Programme (L'SEP) 

by fnvlronncntal Systeas Rcccarch Lnstitute (ESRI) under Conrracts 83-0012 and 

84-0031. It describes and documents the process whereby a Geographic 

Information System (CIS) resource data base was developed at varylng 

intensities for the World, Developing World, and Africa and was applied in a 

aulti-scale and multi-level assessment of chc patterns and processes of 

desertification. A full ar.alysfs of desertiffcation hatards vas carried out 

only for Africa. This rep:.rt is principally intended to provide documentation 

for :he automated resource data base in order to facilitate on-going 

assessment of desertification, as vel1 as spacial and tabular analyses relrtad 

to a much vider range of land planning, evaluation and management' interests of 

L'SEP, Food and Agricultural 0rgani:atioa of the United Nations (f.40) and 

governmental and scientific institutions which might acquire it in a digital 

f om. 

The automated data base which vas produced as part of chc contracted effor: is 

composed primarily of data originally developed by FAO. As indicated, it vas 

produced at three levels of fntensicy. For the vorld, the data base contains 

the coordinates and codes for all of the topical information illustrated on 

the eighteen sheets of the FA0 Soil Hop of the World at 1:s million scale. 

This is supplemented by tabular files of descriptive and interpretive 

information which were developed by FA0 and ESXI as part of this project. For 

the developing world, the soils information is supplemented by coordiaate 

information illustrated on the five sheets of the 1:10 dllion scaLe map of 

agro-ecological zones represented in the FM study cncitled, Potential 

Populatioa Supporting Caoacitirs of lands fn ehe Developing World. For 

Africa, attribute codes for the agro-ecological units were developed by FA0 

and vere automated and associated with the cartographic files automated by 

ESXI. Other data, including FA0 climatic and ctnsus data as vel1 as 

physiographic, geologic, detailed soil, vegetation, land use and 

iefrastrucrure data drawn from standard source documents, were also included 

in the automated files for Urica. In addition, several elements lncludiag 

land surface fora, landf oms, and surface modifiers vere interpreted and 

=?ped from icagsry ad topographic map by KSBI staff scientists. 

of data standardization vas eqloycd to insure that all of the data for .Gzi:a 

=ere image-verificd, spatially rectified and cross-compared with related 

components in orcer to insure appropriate cartographic boundary macch. :n 

effec:, the source &ta for Urfca vere cross-coEpared and checked against 

topographic basemaps and hgerp for accuracy and consistency 7rior tu 

aut0Batf0u. 

A 7rocess 

The automation procedures vhich were enploycd vere designed to accurately and 

efficiently capture the coordinates and attributes of all of the mapped dsca. 

High resolution manual digi:iring stations were used to automate all of the 

coordinate da:a except that contained on the compositc/htegrated caps of 

tcrraizi Fnfonation in Urica. Given high data densiry, the terrain caps 

vere autonated on an automatic laser scanning device. All data were processed 
in a vector form[ using ESRI's ARC/INFO software. Interface prograas allowed 

converslon of scaa-entered raster data to a vector forsat and the conversion 

cf vector ffles to raster for laser film display. True coordinate match among 

and becueen related data sets on each map sheet and along sheet boundaries vas 

effected. The coordinates of features which were represented on more than one 

map were encered once, thereafter being joined automatically to ocher 

percinent overlays. 

2nd cuter sheet boundary vas developed ad automated for each map sheet. This 

was automacically superi=posed on all perrinent data in processing. In 

Africa, a true coordlnace match was effected along national boundaries vhich 

followed che stream courses delineated on cha surface hydrology maps. 

che natlcnal boundaries developed as part of a representacicn of 

aaministrative/scaclscical areas at a scale of L:5 million vere used io 

replace the national boundaries represented as part of the agro-ecological 

units maps at l:1C million. For the world soils data and the African data 

used in the analyses, coordinate match vas effected between carcographic 

features on adjoining map sheets. The pertinent map coverages vere 

cransformed from inch references tc true laticude/longitude references using 

computerized algorithms. Adjacent sheets were joined and the coordinaces for 

che adjoining features were matched by fnreractlve machine processing. The 

automated files for the vorld and Africa vhich were edge-umtched were 

subsequently overlaid for use in the final analyses. The overlay files for 

Africa contain soils, agro-ecological, climatic, vegetation and land use data.' 

The final processing step for Africa fnvolved the overlay of the-template 

water features. The overlay for the world outside of Africa involved the 

impositioa of the vater template on the polygon structures which resilted from 

the modeled a;sessment of soil elements. 

A cemplate contahlng che polygonal land/uaccr feacures 

And, 
Cha?tl-r IV: Hodel Development and Iteration 

Introduction 

Conce?cual models were developed by FA0 staff to assess global soils in 

relation to the processes of desertification and to conduct a full 

desertification hazards evaluatfon in Africa. The map sheet for HE Africa vas 

established as a test base for applying both the World and Africa aodels. 

Preliminary versions of the aoc!els were presented to ESRI in October L983. 

Todor Boyadgirv provided direction for model iteration and review over a 

period or' three months, being present at ES2I for four weeks in January - 

February and two weeks in April 1984. Xodels were programmed using EEO 

software and involved boch logical and arithmetic operations. All coordinate 

and attribute manipulations were effected by means of IXFO and A2C software 

respcctfvely . The World models were programmed by ESRI staff to operate on the expansion 

file or' FA0 soils for Africa. Thus, with each mu, all of the soils on the 

concinent were evaluated. The results, however, were initially presented in a 

map form only for the a& sheet. Elsewhere, the results were represented by 

computer listings only. Following iteration and review, the World models were 

also run on the expansion file of FAO soils for the World outside of Africa. 

The resulting model values were added to the expansion files and served as the 

basis for the spatial pacrerning and values displayed on the final maps. 

Africa models were programmed to operate in the expansion files wherever 

possible. Final results were resolved to the attribute components of the 

cvezlay files which were created for each of the three Africa sheets. 

The 

As part of che modrling effort, interpretive values for each of the FA0 soil 

unit.componsnts were developed by FA0 staff. These values, involving ratings 

for grassland productivity, soil constraints and agricultural productivity, 

soil K-values and wind erodibility ratings, were aucomaced and vere used in 

the modeling process through a relational data base operation which tied them 

to the pertinent sofl type. Some of these values were modified during the 

process of model ftrracion. The soil fncerprecive values used fn the final 

models for .Grica are included in the code listings accompanying the final 

data files. The program statements used co produce the final models for 

Africa and the World are,included in Appendix D or' chis report. The concepcs 

and definitions concerning desertification analysis are detailed in the FA0 

taport entitLed, ProvFsignal Xcchodoloqy for Assessment and Mappin3 Of 

Desertiiicacion. The definitions, criteria, and class values employed for 

boch the Africa models and chc World models are ouclined in the Nap of 

Desertification Hazards: Explanatory Zlote, which accompanied the maps 

3repa;od for presencacion in %iy 1984. 

hf tica Yodels 

h sevan-gtagc evaluation of desertification hazards in Africa was conducted. 

Six cmponent analyses vere programed and iterated, each iteration involving 
a reviev of the maps and statistical outputs from the computer analysis. The 

full results of each of the component analyses vere used in the final 

analysis. Thus, it vas necessary to resolve all of the cmponent analyses 

prior to generating a final hazards map for iteration and review. aased upon 

a review of the resulting spatial and statistical distributions from each of 

the analyses, summation criteria for each of the models were specified and 

iterated. The result; of each of the component analyses vere classed in four 

levels: slight, moderate, severe, and very severe. The class limirs vere 

based upon percentages of the highest numeric value for each model. The 

following values and percentages were used: 

Soil Constraint Wind Action Water Action 

Values 3: Values x Values x 

Slight - <4.0 10 - < 3.000 LO - <1.900 10 

Xoder a t e 4.1-12.0 30 3.001-9.000 30 1.901-5.700 30 

Severe 12.1-20.0 50 9.001-15.000 50 5.701-9.500 50 

Very Severe >20.0 >15.000 >9.500 

40 (401100) 29.689 (3O.O=iOO) 18.794 (19.0-100) 

Slight 

Saliniza tion Population Pressure Animal Pressure 

Values rc Values % Values /. 

(3.250 2.5 - <l. 000 2.5 <O. 40 2.5 

T ., 

- 

Hoder at e 3.251-13.000 10 1.001-4.000 10 0.41-1.60 10 

Sever e 13.001-39.000 30 4.001-12.000 30 1.61-4.80 30 

Very Severe >39.000 >12.000 >4.80 

129.594 (130.0=100) 40 (40=100) 25 (16-100) 

The final summary values vere b\::lt using the raw numeric values from each of 

the component analyses. These Yere normalized by use of the factors listed 

below. Each component analysis contributed a maximum value of LOO to the 

sumrary . 

Soil Constraints 2.5 

Wind Action 3.368 

Water Action 5.321 

Salinization 0.772 

Population 2.5 

Animal 6.25 
The class 

The final 

componen t 

lfmits for the sumnary model were established as follows: 

- summfl 

None co Slight - < 42.50 

Modera t e 42.51-85.00 

Severe 85.01-127.50 

Very Severe > 127.50 

mcdel was progratwred to track the relative contribution cf each 

of :he summary value. The two most significant components uere 

identifiw in the summa-& statement for the modei and were displayed ou th~ 

resulting map. For final presentation, the hazard ratings and factors were 

eliminated in areas with greater than 180 days growing period or with cold 

temperatures, and were discriminated in areas with zero and vith 0-180 growing 

periods. 

The results of the modeling program for Africa, including the World models run 

on the African sheets, were stored in the overlay file. 

documented in Appendix N, include buth raw numeric values and final aggregate 

ratings. 

files outlined in Chapter V and documented in Appendix 0. 

These results, 

These served as the basis for developing the final presentation 

Global Change Data Base Ajiico Docurnmution Appendix H- 19 
Blank page retained for pagination 
LJNEPlFAO World and Africa Data Base 

Mapping Procedures and Data Sources 

Integrated Terrain Units 

(Africa 1:5,000,000) 

This manuscript synthesizes and organizes a variety of natural resource 

information and constitutes the basis for the assessment of desertification 

hazards for the African continent. Data sources were either supplied by FAO, 

collected by ESRI, or interpreted from satellite imagery and collateral 

information by ESRI. The data sources for each data ftem are described below. 

The data were prepared at a scale of L.5 million for the African sheets and 

contain polygon coordinate information, first Level attribute codes, and 

second level attribute codes for selected variables. The maps were prepared 

by first creating a set of thematic map sheets for each of the components and 

then integrating these maps to fonn a single map manuscript containing all of 

the map unit boundaries for each module. The method for preparing each of the 

thematic sheets and description of the integration sequence and process is 

provided in the following sections. 

Physiographic Divisions 

Physiographic divisions were derived from the Lobeck Physiographic Diagram of 

Africa. The original which existed at a Scale of apptOXlInately L:35 million, 

was rescaled manually and rectified to the FA0 basemap. The physiographic 

unics were assigned a threedigit descriptive code which was recorded directly 

on the thematic map. Text which accompanied the original diagram was reviewed 

co verify chat the rcscaled and rectified unit boundaries were properly 

located. 

Geomorphological Regions 

Geomorphological regions were derived from those illustrated in Volume VI of 

the FA0 Soil Hap of the World. While largely corollary to the physiographic 

divisions, the geomorphological regions emphasize those regional patterns 

which correspond most directly to changes in pedology. The physiographic 

divisions included ccctonic and geologic considerations. The original 

geomorphological data was treated in the same manner as the physiographic 

divisions, bcisg resealed, rectified and verified. The resulting data was 

reformatted at a scale of L:5 million and were comprised of polygons with 

descriptive numeric codes. The code system vas based on major regions with 

areas of special interest separately identified and dclineated- The texf 

descriptions and comparison of the delineations to the DU topographic sheets 

and the Landsat images served as a base for the verification of this thematic 

map component. 

Appendix H-21 Global Change Ihta Base Africa Docimrenrutiion 
Land Surface Form 

Land surface fora vas interpreted from the topographic film separace of the 

FA0 Soil Nap of the World. The DMA Tooographic Haps of Africa vere used for 

comparison and t3 add further precision to the ratings of slope and profile 

cype. The system for rating land surface form vas based on the method 

developed by Edwin Hammond for small-scale continental landform maps. To 

prepare the thematic maps, a matte drafting film uas affixed to the FA0 

basemap. A grid composed of two-centimeter-square cells uas drawn on this 

overlay. The map interpreters then evaluated the area of the basemap which 

fell vithin each cell. A numeric code vas uritten in each cell. It recorded 

the following: the relative local relief in meters; the percent of the land 

surface within the cell vhich fell into different slope classes; and the 

profile cype indicating the percent of the area characterized by gently 

sloping land and the percent of the area in a lowland versus upland position. 

The gridded information was then overlaid on the Landsat images, and "natural 

landscape units" vere interpreted. These units vere polygonal and conformed 

to major landscape features such as mountain chains, plains, and plateaus. 

The attribute values were recorded on the manuscript. A. final review of the 

polygon data against the DMA topographic sheets verified the attribute values. 

Land f oms 

Landforms/Waterforms were interpreted using numerous sources of collateral 

information. Principal sources included the Landsat mosaics, the USSCS - World 

Soil Hap of Africa, the kite Landform Hap of North Africa, and the UNESCO 

Geological World Atlas. The classification system was developed in 

consultation with the FA0 desertification analyst and vas built around 

pedogenic considerations. The system divides the landscape into mountains, 

piedmont and plain, and then subdivides each according to those gross features 

vhich are most perclnenr to soil development. Mountains and hills were 

divided by rock type and pH. Piedmoncs vere divided into lithic and regolic 

areas as vere plateaus. Plains vere divided by mode of formation. 

Fl3odplalns and deltaic plains vere further divided into age groups. 

landfgrm classification vhich vas employed is outlined later in this Appendix. 

Definitions of the interpreted units are provided below: 

The 

Houncains. 731:. type consists of portions of the earth's crust elevated 

more than 500 mecers above the immediate surrounding surface. These 

features generally have moderate to s:eep slopes, and are often found 

either fr. long chains or ranges, or as single, isolated eminences. These 

forms were Fnterpteced using che Landsat images and the topographic 

basemap primarily. 

Hills. ihis landform cype is comprised of a mass or' material elevated 

less than 500 meters above the surrounding terrain and culminating in a 

vell-developed crest or summir. These features may have slopes ranging 

from slight to steep, and range in height from hillocks, a few hundred 

Global Change Ihtn Rase Afiicu Docuntcrrtuhorr Appeticlix H-22 
meters high, EO larae hills up to five hundred. These forms vere 

interpreted largely using the topographic basemap and Landsat images. 

Lithic piedmont. A piedmont is an area lying at or formed at the base of 

the lower slopes of mountains or hills. The lithic piedmont signifies a 

region in which the rock fragments comprise proportionally more of the 

overlying soil layer than any other constituent, and for vhich this 

mantle is generally fifty centimeters or less in depth over the bedrock. 

Separation of lithic versus regolic piedmonts vas performed in 

conjunction with the USDA, SCS soil data and the satellite imagery. This 

class includes "pediment" areas, which are planar erosion surfaces carved 

into bedrock and generally veneered vith fluvial gravels. These features 

usually occur near piedmont areas, and generally constitute extensive 

bedrock surfaces over vhich the erosion products of the retreating 

mountain fronts are transported to sedimentary basins or plains. 

Regolic Piedmont. The regolic piedmont indicates a region at the lower 

slopes of mountains o r h i l l s with a thickened layer or mantle of 

unconsolidated, incoherent rock detritus and soil materials. These 

sediments are at least fifty centimeters thick over the bedrock. This 

class includes areas comprised of coalescing alluvial fans in similar 

geographic positions as the "pediment" surfaces described under lithic 

piedmonts. 

Lithic Plateau. 

comparatively flat lands vhich are termlnated on at least one margin by a 

steep escarpment. The elevation of a plateau may range videly. The 

.lithic plateau retains a mantle fifty centimeters or less in thickness 

over the bedrock. Extensive dissection of plateaus may produce hills at 

some localities. The distinction between lithic and regolic plateaus is 

detemined through use of the SCS soil data, the Landsat imagery, and DEW 

maps. 

A plateau is comprised of relatively elevated areas of 

Rrgolic Plateau. 

oi materials which are unconsolidated, including rock detritus of 

numerous origins and soil. This mantle is at least fifty centimeters 

thick overlying the bedrock. 

A regolic plateau retains a thickened layer or mantle 

Plains undivided. A plain is comprised of a region of generally uniform, 

comparatively flat slope of considerable extent which is not broken by 

any marked elevations and/or deprcseioas. This class was utilized vhere 

the origin of the plain (example: coastal or lacustrine, flood or 

deltaic) is either unknorn, or not readily apparent. This class vas 

interpreted using the basemaps and Landsat images as primary data. 

Coastal Plain. 

oceanic shoreline, and usually represents an area of emerged sea floor 

sediments. These sediments include beach, bar and spit detritus, coastal 

eolian dunes, and lagoonal, estuarine, bay, tidal marsh and flat 

This type of plain generally has one margin on the 

Global Change Dnfa Base Ajkicu Docrwrcntution A~)PCIKI~X H-23 
deposits. These units uere identified primarily using che geologic naps, 

Landsat and the topographic base map. 

Lacustrine Plain. This type of plain results from the infilling of iake 

basins of former perennial lakes, or by the development of clay or salt 

flats in areas of intermittent lakes. Lacustrine deposits of present or 

foraer perennial lakes are generally coarsest along the margins of the 

relict lakes (beach and shoreline deposits) and tend to become finer as 

the distance from shore increases, becoming finest in texture in the 

central part of the basin. Intermittent lake bottoms are generally finsgrained and/or transitional to evaporitic deposition. Eolian sand 

deposition is common in and around lacustrine plains. This class vas 

also used to designate lacustrine areas of very large size uhich are now 

preserved prisarily as terraces in a larger, undifferentiated ?lain 

(Congo Basi3). Landsat images and the base map served as prisary data 

for these forms. 

Recent Floodplain. The floodplain consists of that portion of a river 

valley adjacent to the river channel which is composed of detritus 

deposited by the stream during flood stages when it overflows its banks. 

As used in this project, this class includes all active, inactive and 

abandoned floodplain areas and most terraces associated with the major 

perennial rivers. This class is also used to designate the larger wash 

or wadi areas of intermittent floodplain activity. Interpretation uas 

based primarily on the Landsat imagery. 

Relict Floodplain. This class was used to designate floodplain areas 

which are now entirely abandoned due to stream piracy, and in a few 

limited cases, for some areas of relatively high terraces uhich uere of 

sufficient size to be mappable at the project scale. The abandoned 

floodplain areas generally retain the full development of the lateal 

structure of active floodplsins (i.e., active, inactive, abandoned 

floodplains, and low and high terraces) despite the general lack of 

flooding activity in the area. Other characteristics of the celicc 

floodplain remain the same as that of the active floodplain. 

Interpretarfon was based on Landsat images within the help of the field 

experience of the FA0 project coordinator. 

Recent Deltaic Plain. This class is a specialized type of plain caused 

by the active deposition of fluvial sediments at the mouth of a river 

into a comparatively still body of vacer. The delta may develop into 

lakes, or into the ocean, and to be mappable at the project gapping 

scale, :he deltas must be unusually large. This designation is reserved 

for currently active deltas. Deltaic deposits may include some minor 

lacustrine deposits, and generally form the transition frsm continental 

to marine influences; salt marshes may occur on the outer flanks of 

deltas extending into ocean waters. Interpretation vas based on Landsat 

iaages and geologic maps. 

Global Clianpe nata Base Africu Dociimerttation Appendix H-24 
Relict Deltaic Plain. This class is used to represent areas of old, 

inactive deltas which ot-.eruise retain the characteristics of recent, 

active deltaic plains. helict deltaic plains are generally isolated, and 

are the result of oceanward advancement of the shoreline such as by 

uplift of the coastal plain, by drainage of the lacustrine area, or by 

stream piracy which transfers the source materials to another locality. 

Interpretation was based on field experience of the FAQ coordinate and 

Landsat imagery. 

Rift Valley. This feature 1s a active, tectonically-produced valley or 

graben originated through subsidence of a longitudinal strip of land 

bounded by two subparallel faults. These valleys are generally steepsided, are frequently filled with water to form lakes in some places. 

These valleys represent areas of separation between crustal plates. 

These areas may have hills occurring wlthin the rift zone itself, and are 

commonly bounded by plateaus on afther margin of the valley. 

Interpretation 1s based primarily on the topograptlc basemap. 

The landform maps were made by drafting interpretations onto drafting film 

registered to the basemap and overlaid on the Landsac images, soil maps, or 

other collateral infonnation. Each interpretation was verified using as many 

supporting documents as possible. Attribute codes were written directly on 

the thematic map. 

Surface Modifiers 

Using the same thematic map sheets on which landforms 'were delineated, the 

interpreters proceeded to further differentiate the landforms according to 

modifying surface conditions including salt cover, sand cover, sand 

orientation, stone cover, degree of dissection and poor drainage areas. Salt 

cover was delineated using the USSCS World Soil Map, FA0 Soil Map of the 

World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and 

Landsat mosaics. Salt areas were mapped if they vera at least 600 sqa km. in 

extent and observed on more than one data source. Sand cover was interpreted 

primarily on the basis of Landsat imagery with assistance from the USSCS maps, 

Raite map, and the DMA topographic maps for areas where dune formations were 

evident. 

interpreted using the Landsat imagery. It should be noted that dune 

orientation interpreted for this effort is on a general scale, local 

observation at a detailed scale could show significant departures from the 

values recorded. Stone cover was laterprated primarily from the FAO and SCC 

soil maps. In North Africa the Ervin blze map va8 very helpful. 

Delineations were verified wing the Landsot images where practical. 

The direction of prevailing alignment of dual structures was 

Degree of dissectioo vas laterprated based on interpretation of surface 

texture as displayed on the Landsat mosaics. The lnterprctations are intended 

to be suggestive rather than definitive because no measure of the depth of 

dissection or of minor surface dissection Fs porsible using Landsat imagery. 

Drainage was taken directly from the DMri topographic sheets by photo reducing 

Global Change Data Base Aflicu Docunrenrution Appendix H-25 
the originals to project scale and then delineating those areas which uere 

snaded using the inundation symbol. 

verification of the poor drainage areas. 

The Landsac images did not allow 

Geology 

Geology vas derived entirely from the UNESCO Geological World Atlas. 

original vas published at 1:lO million so photographic rescaling vas used to 

produce map sheets at 1:5 million. 

data vas printed on exactly the same map projection as the FA0 basemap. To 

the extent possible major rock units uere verfied using the Landsat images. 

The thematic map was prepared by overlaying the rescaled originals vith the 

FA0 basemaps to which a matte drafting film uas registered. 

boundaries vere then traced and subsequently compared to the Landsat images. 

Attribute codes were written on the manuscript for each rock unit. The codes 

include Geologic age, Origin, pH, Types of intrusive, extrusive, or 

metamorphic rock and rock facies for appropriate uni:s. 

The 

No rectification vas required because the 

Rock unit 

Venetation 

Vegetation was originally to be derived from the UNESCO vegetation maps for 

the Mediterranean and the area south of the Sahara as vel1 as the NOAh 

chlorophyl index imagery. However, careful inspection of the data indicated 

that the classifications, based largely on potential vegetation conditions, 

did not provide an adequate estimate of actual vegetative structure or crown 

cover. The vegetation patterning shown on the DMA Topographic Maps of 

Africa shows vegetation structure and average density for forest, tree 

savanna, and bunch grass. 

desertification coordinator to include the DMA vegetation information in the 

data base. 

It vas decided in conjunction vith the FA0 

The DMA sheets originally at L:2 million uere photo reduced as color fila 

transparencies and overlaid by the FA0 basemaps with attached matte film 

overlays. The vegetation patterns uere traced and given a tvo digit code 

number. The first digit described the vegetation type and the second digit 

indicated drainage condition. 

General Land Use 

General Land use vas taken fron the World Atlas of Agriculture. These data 

exist at a scale of 1:s million and show general zones of cultivation. The 

cartographic display on the original maps included both closed polygons 

showing areas vith high density of cultivated land and scattered open dot 

patterns indicating a mosaic of cultivated and non-cultivated lands. The 

latter required reformatting into polygon coverages. This was accomplished by 

drauing lines around clusters of dots and clusters of extremely small poiygons 

vith equal amounts of intervening-natural land. These polygons were classed 

as cultivated/natural aosaic. The resulting polygon coverage vas dram on a 

Globsl C:Iinnge Data Base Africa Docuttierrfu/ion Appendix H-26 
matte drafting fila affixed to the basemap on a light table. The land use 

patterns were not verified against imagery or the vegecation delineations. 

FA0 Soils 

FA0 soils were incorporated into the terrain unit directly from the film 

originals from the FA0 Soil Hap of the World. Rescaling and rectification 

were not required. The first level actribute information vas converted into 

an alphanumeric code list. During a later step secondary attribute codes for 

the Africa soils were prepared. This process is described under the FA0 Soil 

Xanuscript. The structure for first and second level codes is outlined later 

in this Appendix. 

USDA-SCS Soils 

USSCS Soils were obtained at a scale of 1:l million in a rough manuscript 

form. These constitute part of the unpublished USSCS World Soil Map. The 

originals were photographically reduced to 1:5 million. Following the 

rescaling step, it vas necessary to rectify the data to the FA0 basemap by 

carefully registering each map sheet to the hydrologic and topographic 

features and re-drawing the soil patterns. During this reformatting step, it 

was desirable to drop ouc some of the smallest units and to slightly 

generalize the boundary line crenulation. Edgemacch discrepencies were 

identified and corrected by image interpretation and verified through 

discussions with the SCS staff soil scientiscs. SCS code symbols werc 

inscribed on the thematic map. The completed map was compared to the Landsat 

imagery and the topographic basemap to verify the location and extent of map 

units. Secondary attribute code sheets vere developed which included soil 

types, components, dominance, modifiers, landforms, parent material and 

documentation. These basic and expanded codes are outlined later in this 

Appendix. 

Intenration 

After all of the thematic map sheets were complete, they were integrated onto 

a single sheet. The order of map sheet integration was: 

L. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

FA0 Soils 

Geology 

USDA Soils 

Landforms and Surface Modifiers 

Land Surface Forms 

Vegetation 

Land iise 

Physiographic Divisions 

Geomorphological Regions 

AS described earlier, the thematic maps were compared two at a time and slight 

differences were resolved by comparing to the Landsat images and the 

Global Change Data Base Africu Doci~t~ter~/u/iorr Appetitlix 13-27 
copographic basemaps. Each thematic map vas redrawn incorporating the 

revisions and the attribute codes transferred onto the sheets containing the 

new delineations. The boundaries .from all nine data maps were then cornposited 

onto a single sheet. Sequential identification numbers and label placement 

centroids vere drawn on a separate matte film overlay registered to the 

composite polygon map. 

The polygon boundary sheet was automated by laser scanning and the polygon 

centroids vere manually digitized. The two separare files vere later appended 

together during the automation step to form a single spatial file. Attribute 

code forms were filled out by placing the numbered polygons atop the data maps 

each-in-turn and recording the appropriate attribute code in its assigned 

code column. The attributes were then key entered. The maps were edited by 

producing a computer plot of each data category which included boundaries and 

code values. Additional automated editing steps were carried out to identify 

code errors or inconsistencies. These are described in Chapter 111. 

Global Change Data Base Afiica Documentation Appendix H-28 
UNEP/FAO World and Africa Data Base 

Happing Procedures and Data Sources 

Mean Annual Rainfall 

Hean Annual Wind Velocity 

Number of Wet Days per Year 

(Africa 1:5,000,000) 

Three separate climate manuscripts were prepared directly from original hand 

drawn climate maps provided by FAO. The source climate maps uere prepared on 

the FAO/ACS base at a scale of 1:5 million. These were provided by FAQ on a 

relatively stable copy base for each of the Africa sheets. 

The data vere prepared for automation by highlighting and numbering the 

longitude,'latituJe tic marks. A matte drafting film uas affixed to each map 

sheet on which a sequential identification number and centroid was drawa for 

each map polygon. Polygons which intercepted the coastline vere extended into 

the vater to facilitate use of the template. The map sheets were mounted on 

the digitizer tablet and manually digitized. 

lines uere key entered as a part of their arc identification during 

digitizing. Polygon attributes were encoded as first level codes by 

completing and key entering attribute code lists. 

proc'uclng computer generated plots of the lines vith attributes displayed 

using various line symbols and colors and units with polygon values written as 

numeric codes plotted at the polygon Centroids. 

The attribute values for the 

Editing vas done by 

Global Change Data Base Africa Docwrien/uh'on Appendix H-29 
UNEP/FAO AFRICA GIS DATABASE 

1. VEGETATION MAP 

INUNDATED 

CLASS 

11 

21 

31 

41 

51 

(NONE) 61 

91 

92 

88 

NOT INUNDATED OR RICE 

CLASS 

FOREST 19 

TREE SAVANNA 29 

GRASSLAND 39 

MANGROVES 49 (NONE) 

MARSH/SWAMP 59 

OASIS 69 (NONE) 

OTHER 99 

OTHER - RICE 

WATER 

2. RAINFALL (MILLIMETERS) 

1 0-50 

2 50 - 100 

3 100 - 150 

4 100 - 200 

5 150 - 200 

6 200 - 250 

7 200 - 300' 

8 200 - 400 

9 250 - 300 

10 250 - 400 

11 300 - 350 

12 300 - 400 

13 350 - 400 

14 400 - 450 

15 400 - 500 

16 400 - 600 

17 450 - 500 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

450 - 550 

500 - 550 

500 - 600 

550 - 600 

600 - 700 

600 - 800 

600 - 1000 

700 - 800 

800 - 900 

800 - 1000 

900 - 1000 

1000 - 1500 

1500 - 2000 

2000 - 3000 

>3000 

Global Change Data Base Africa Docimiertrution Appendix H-30 
3. WIND (METERS/SECOND) 

CLASS WIND SPEED 

1 0.0 - 0.5 

2 0.5 - 1.0 

3 1.0 - 1.5 

4 1.5 - 2.0 

5 2.0 - 2.5 

6 2.5 - 3.0 

4. SOILS - 90 SOILS GROUPED TO 24 

GREAT GROUP (USA) 

1 A. ACRISOLS 

2 B. CAMBISOLS 

4 D. PODZOLWISOLS 

5 E. RENDZINAS 

6 F. FERRALSOLS 

7 G. GLEYSOLS 

8 H. PHAEDZEMS 

9 I. LITHOSOLS 

10 J. FLWISOLS 

11 K. KASTANOZEMS 

12 L. LWISOLS 

14 N. NITOSOLS 

15 0. HISTOSOLS 

16 P. PODZOLS 

17 Q. ARENOSOLS 

18 R.X REGOSOLS 

19 S. SOLONETZ 

20 T.X ANDOSOLS 

21 U. RANKERS 

22 V.X VERTISOLS 

23 W, PLANOSOLS 

0 

CLASS WIND SPEED 

7 3.0 - 3.5 

8 3.5 - 4.0 

9 4.0 - 4.5 

10 4.5 - 5.0 

11 5.0 - 5.5 

12 5.5 1. 6.0 

13 >6.0 

GREAT GROUP (USA) 

ERODIBILITY PRODUCTIVITY 

6 3 

7 5 

7 6 

9 4 

9 2 

2 4 

7 7- 

8 1 

3 4 

8 8 

6 5 

7 5 

1 3 

8 3 

5 1 

8 2 

2 3 

5 7 

* 

4 

3 

* NO SOILS OF THIS TYPE IN DATA 

9 

2 
-ILEGATES AND WILLMOTT AVERAGE MONTHLY AIR TEMPERATURE 

AND PRECIPITATION 

CITATION: Legates. D. R. and C. J. Willmott. 1989. Global Air Temperature 

and Precipitation. Digital Data. NCAR, Boulder, Colorado. 

13.5 MB. 

Global Change Datu Base Africa Docirmerrtutiun Appelldix I- 1 
Blank page retained for pagination 
I. LEGATES AND WILLMOTT AVERAGE MONTHLY AIR TEMPERATURE AND 

PRECIPITATION 

DATE: 1989 

SOURCE: 

Compiled from multiple soiirces by 

David Legates and Curt Wilhiiott 

Center for Climatic Research 

University of Delaware 

Newark, DE 19716 

(302) 45 1-2344 

CONTRIBUTOR: 

Denn i s Joseph 

NCAR 

P.O. Box 3000 

Boulder, CO 80307-3000 

DATA CENTER: NCAR 

GEOGRAPHIC SAMPLING: .5 degree grid 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Monthly 

I 7ERENCES: 

Legates, David R. and Cort J. Willmott. 1990. "Mean Seasonal and 

and Spatial Variability in Gauge-Corrected Global Precipitation." 

Inrernarionai Journal of Climaroiogy, vol. 10, pp. 1 1 I -I 27. (Included) 

FILE NAMES: 

AFCPRJAN - AFCPRDEC 

AFMPRJAN - AFMPRDEC 

AITMPJAN - AFTMPDEC 

AFERRJAN - AFERRDEC 

(Total of 48 fdes) 

Monthly corrected precipitation 

Monthly measured precipitation 

Monthly mean surface air temperature 

Monthly standard emor for corrected Precipitation 
Blank page retained for pagination 
LEGATES AND WILLMOTT MONTHLY CORRECTED PRECIPITATION 

EXAMPLE DOCUMENTATION FILE 

File Names: AFCPRIANDOC - AFCPRDEC.DOC, (It files) 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 

category 

cat ego ry 

category 

category 

category 

cat ego ry 

category 

cat egoty 

category 

category 

category 

cat ego ty 

cat egory 

category 

1: 

2: 

3: 

4: 

5: 

6: 

7: 

8: 

9: 

10 : 

11 : 

12 : 

13 : 

14 : 

15 : 

Legates & Willmott, (Monthly) Corrected Precipitation (mm/month) 

integer 

binary 

438 

4 80 

0.0000000000E+00 

6.2800000000E+02 

1.66666666673-01 

1.66666666673-01 

15 

55 to 109 

110 to 164 

165 to 219 

220 to 274 

275 to 329 

330 to 384 

385 to 439 

440 to 494 

495 to 549 

550 to 604 

605 to 659 

660 to 714 

715 to 769 

770 to 824 

> 824 

(category .O : 0 to 54) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Note: The minimum and maximum values indicated in the documentation files 

have been standardized to 0 and 628 respectively so that automatic 

scaling during display will show comparable results between monthly 

images. 
LEGATES AND WILLMOTT ANNUAL CORRECTED PRECIPITATION 

DOCUMENTATION FILE 

File Name: AF'CPRA".DOC 

image title 

data type 

file type 

rows 

columns 

minimum 

maximum 

cell :c 

cell y 

1 egend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

category 11 

category 12 

category 13 

category 14 

category 15 

: Legates & Willmott, Annual Corrected Precipitation (mm/year) 

: integer 

: binary 

: 438 

: 480 

: O.OOOOOOOOOOE+OO 

: 5.4690000000E+03 

: 1.66666666673-01 

: 1.66666666673-01 

: 15 

: 341 to 682 

: 683 to 1024 

: 1025 to 1366 

: 1367 to 1708 

: 1709 to 2050 

: 2051 to 2392 

: 2393 to 2734 

: 2735 to 3075 

: 3076 to 3417 

: 3418 to 3759 

: 3760 to 4101 

: 4102 to 4443 

: 4444 to 4705 

: 4706 to 5127 

: 5128 to 5469 

(category 0 : 0 to 3401 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Ihtn Base Africa Docwicrt/a/ion Appcritlix 1-6 
LEGATES AND WILLiMOl" MONTHLY MEASURED PRECIPITATION 

E'XAMPLE DOCUMENTATION FILE 

File Names: AFMPRJANDOC - AFMPRDEC.DOC (12 files) 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

1 egend 

category 1 : 

category 2 : 

Legates C Willmott, (Monthly) Measured Precipitation (mm/month) 

integer 

binary 

438 

480 

0.0000000000E+00 

8.47000000003+02 

1.66666666673-01 

1.66666666673-01 

15 

53 to 105 

~~ 

106 to 158 

category 3 : 159 to 211 

category 4 : 212 to 264 

category 5 : 265 to 317 

category 6 : 318 to 370 

category 7 : 371 to 423 

category 8 : 424 to 476 

category 9 : 477 to 529 

category 10 : 530 to 582 

category 11 : 583 to 635 

category 12 : 636 to 688 

category 13 : 689 to 741 

category 14 : 742 to 794 

category 15 : > 794 

(category 0 : 0 to 52) 

[This leaend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Note: The minimum and maximum values indicated in the documentation files 

have been standardized to 0 and 847 respectively so that automatic 

scaling during display will show comparable results between monthly 

images. 
LEGATES AND WILLMOTT ANNUAL MEASURED PRECIPITATION 

DOCUMENTATION FILE 

File Name: AFMPRA".DOC 

image title : Legates C Willmott, Annual Measured Precipitation (mm/year) 

data type : integer 

file type : binary 

rows : 438 

columns. : 480 

mi n imum : o.ooooOOOOOOE+OO 

maximum : 5.2910000000E+03 

cell x : 1.66666666673-01 

cell y : 1.66666666673-01 

legend : 15 

category 1 : 330 to 660 

category 2 : 661 to 991 

category 3 : 992 to 1322 

category 4 : 1323 to 1652 

category 5 : 1653 to 1983 

category 6 : 1984 to 2314 

category 7 : 2315 to 2645 

category 8 : 2646 to 2975 

category 9 : 2976 to 3306 

category 10 : 3307 to 3637 

category 11 : 3638 to 3968 

category 12 : 3969 to 4298 

category 13 : 4299 to 4629 

category 14 : 4630 to 4961 

category 15 : 4961 to 5291 

(category 0 : 0 to 329} 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global (.hng!c' Ihfa Base Africa Doc'umerrlution Appendix 1-8 
LEGATES AND WILLMOTT MONTHLY STANDARD ERROR 

E'UMPLE DOCUMENTATION FILE 

File Names: AFERRJAN.DOC - AFERRDEC.DOC (12 files) 

image t it le 

data type 

file type 

rows 

columns 

mi n imum 

maximum 

cell x 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

category 11 

category 12 

: Legates & Willmott, (Monthly) Standard Error (mm/month) 

: integer 

: binary 

: 438 

: 480 

: O.OOOOOOOO@OE+OO 

: 2.0700000000E+02 

: 1.66666666673-01 

: I. 66666666673-01 

: 15 

: 13 to 25 

: 26 to 38 

: 39 to 51 

: 52 to 64 

: 65 to 77 

: 78 to 90 

: 91 to 103 

: 104 to 116 

: 117 to 129 

: 130 to 142 

: 143 to 155 

: 156 to 168 

category 13 : 169 to 181 

category 14 : 182 to 194 

category 15 : > 194 

{category 0 : 0 to 12} 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Note: The minimum and maximum values indicated in the documentation files 

have been standardized to 0 and 207 respectively so that automatic 

scaling during display will show comparable results between monthly 

images. 

Clohd Clhrmge Data Base Af,.ic*o Documerrtation Ap~t~dix 1-9 
LEGATES AND WILLMOTT ANNUAL STANDARD ERROR 

DOCUMENTATION FILE 

File Name: AFERW.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell :c 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

cateqory 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

category 9 : 

category 10 : 

category 11 : 

category 12 : 

cateqory 13 : 

category 14 : 

category 15 : 

Legates & Willmott, Annual Standard Error (mm/month) 

integer 

binary 

438 

4 80 

1.0000000000E+00 

1.0200000000E+02 

1.66666666673-01 

1.6666666667E-01 

15 

7 to 12 

13 to 19 

20 to 25 

26 to 31 

32 to 38 

39 to 44 

45 to 51 

52 to 57 

58 to 63 

64 to 70 

71 to 76 

77 to 82 

83 to 89 

90 to 95 

96 to 102 

(cateqory 0 : 1 to 6) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

(;lol>:d t ‘hange Data Base Africa Docnnierrtuh’urr Appericlix I- IO 
LEGATES AND WlLLMOTT MONTHLY AIR TEMPERATURE 

EXAMPLE DOCUMENTATION FILE 

File Names: AFTMPJAN.DOC - AFTMPDEC.DOC (12 fiies) 

image title 

data type 

file type 

rows 

columns 

minimum 

maximum 

cell :c 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

category 11 

cateaory 12 

category 13 

category 14 

category 15 

: Legates & Willmott, (Monthly) Air Temperature (0.1 C) 

: integer 

: binary 

: 438. 

: 480 

: 1.5000000000E+01 

: 3.8600000000E+02 

: 1.66666666673-01 

: 1.66666666673-01 

: 15 

: 38 to 60 

: 61 to 83 

: 84 to 107 

: 108 to 130 

: 131 to 153 

: 154 to 176 

: 177 to 200 

: 201 to 223 

: 224 to 246 

: 247 to 269 

: 270 to 293 

: 294 to 316 

: 317 to 339 

: 340 to 362 : > 362 

(category 0 : < 37} 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Note: The minimum and maximum Tralues indicated in the documentation files 

have been standardized to 15 and 386 respectively so that automatic 

scaling during display will show comparable results between monthly 

images. 

Globul ( 'Ii:rngc* lhI:i Ilase Africu Docunierifufiort Appendix 1- 1 1 
LEGATES AND WILLMOT" ANNUAL AIR TEMPERATURE 

DOCUMENTATION FILE 

File Name: AFI'MPA".DOC 

image title : Legates h Willmott, Annual Air Temperature (0.1 C) 

data type : integer 

file type : binary 

rows : 438 

columns : 480 

minimum : 9.7OOOOOOOOOE+O1 

maximum : 3.1000000000E+02 

cell x : 1.66666666673-01 

cell y : 1.66666666673-01 

legend : 15 

category 1 : 110 to 122 

category 2 : 123 to 136 

category 3 : 137 to 149 

category 4 : 150 to 162 

category 5 : 163 to 176 

category 6 : 177 to 189 

category 7 : 190 to 203 

category 8 : 204 to 216 

category 9 : 217 to 229 

category 10 : 230 to 243 

category 11 : 244 to 256 

category 12 : 257 to 269 

category 13 : 270 to 283 

category 14 : 284 to 296 

category 15 : 297 to 310 

{category 0 : 97 to 1091 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Data Ihe Africu Duciimenruriun Aplxridix I- 12 
GLOBAL AIR TEMPERATURE AMI PRECIPITATION DATA ARCHIVE 

D.R. Legates and C:J. Willmott PO 60% 3000 

Bou\der, CO SO307 

b2sD 

This tape is a 9-track, h680 bpi, unlabelled tape that was created on an IBM3090 at the 

L'niversity of Delaware on - . It contains the following four files: 

fia 1. ism 

File 1 Corrected monthly and annual precipitation interpolated to a 

0.5 X 0.5 ' resolution. Estimated biases in the gage-measurements have 

been removed from these obsemations. This file has a logical record 

length (LRECL) of 80 bytes, a blocksize (BLOCK) of 8000 bytes and contains 260,281 records. The format of each record is: 

V.nL4BLE FORbLqT COLS. 

Latitude (decimal degrees) (Fi.2) 1-7 

Corrected Precipitation (mm month") (Jan. - Dec.) (1215) 13-71 

Longitude (decimal degrees) (Fi.2) 8-14 

.hnual Corrected Precipitation (mm year") (16) 75-80 

File 2 hfeasured monthly and annual precipitation interpolated to a 

0.5' x 0.5' resolution. This file has a logical record length (LFECL) of 

80 bytes, a blocksize (BLOCK) of 8000 bytes and contains 260,281 records. 

The format of each record is: 

V.IRLQLE FORMAT COLS. 

Latitude (decimal degrees) (F7.2) 1-7 

Longitude (decimal degrees) (F1.2) 8-14 

Measured Precipitation (mm month") (Jan. - Dec.) (1313) 1Si4 

hnual Measured Precipitation (mm year-') (16) 75-80 
File 3 Monthly and annual air temperature interpolated to a 0.5 x 11.5 * res+ 

lution. This file has a logical record length fLRECL) of 80 bytes, a blocksize (BLOCK) of 8000 bytes and contains 21'3.281 records. The format of 

each record is: 

Latitude (decimal degrees) (F7.2) 

.Air Temperature (0.1 * C) (Jan. - Dec.) (1 213) 

Annual .Average Air Temperature (0.1 * C) (16) 

Longitude (decimal degrees) (F7 2) 

COLS. 

1-7 

13-74 

75-80 

8-14 

File -1 Standard errors for the precipitation corrections interpolated to a 

0.5 * X 0.3 * resolution. This file has a logical record length (LRECL) of 

80 bytes, a blocksize (BLOCK) of 8000 bytes and contains 260,281 records. 

The format of each record is: 

V.LRL4BLE FORMAT COLS. 

Latitude (decimal degrees) (F7.2) 1-7 

Standard Error (mm month-') (Jan. - Dec.) (1215) 13-T4 

.Annual Standard Error (mm month") (16) is-80 

Longitude (decimal degrees) (F7.2) 8-11 

For additional information concerning this tape, please contact: 

Cort J. b'illmott 

Department of Geography 

Center for Climatic Research 

c'n iversi ty of D eIaw are 

Sewark, DE 19716 

(302) 451-2344 

Global Change nata Base Africa Docitnrcrrfutiorr Appendix 1-14 
MEAN SEASONAL AND SPATIAL V-4RIABILITY IN 

GAUGE-CORRECTED, GLOBAL PRECIPITATION 

DAWD R LEGATES 

Oeporrmmr of Grrmropicp. Cokge of Geosciences. Uniwsity of Oklahm. .VO~M. Oklahoma 73019. USA 

AND 

CORT 1. WILL!!lO~ 

Cmra for CIinmric Ruarrh. Oeparmcmr of Glosopl?*. Univer&.v of odawam. rVlvorlr. Oelawate 19716. USA 

Rueid JJ Aywt 1988 

hid 31 Jmy 1989 

AB!XRACT 

Using traditional land-bwd puge musuremenu and shipboard estimates, a global climatology of man monthly 

precipitation has been developed Data were obtained from ten exhung SOUCCCI screened for coding errors. and 

redundant station records were removed The edited data buc contains 25.635 spatially independent terrestrial station 

records and 2223 oceanic gxid-point records. A procedure for a-g gauge-induced biases is presented and used to 

move systematic errors caused by wind wetting on the interior wails of the gauge. and evaporation from the gaugt 

These 'corrected' monthly precipitation observations wm then interpolated to a 0.J' of latitude by 0.5" of longitude grid 

using a spherically based interpolation procedure. BiYconrcted spatial distributions of the annual mun and intrapnud variance are presented dong with a hamonk decomposition of the intra-annual variance 

KEY woms 61ob.l prrdpiulion Harmonic dysh Rainpup crr00 

IMTODUCITON 

Precipitation is probably the longat. observed and most widely rzcorded hydrologid phenomenon' 

(Strangewayq 1985. p. 41) and is an cssentid component of the hydrologid cycle. A completely reliable 

spatial and temporal representation of $obd precipitation. however, mains unavailable (ct Minu, 198 1: 

Baumganncr. 1982; Jaeger. 1983; Willmott et al, 1985aA even though the demand for such data is increasing 

(National Aeronautics and Space Administration Advisory Council, 1988). Accurate estimates of global 

precipitation arc needed. for example. to help verify general circulation model (GC.M) prognostications of the 

sezsond precipitation cycle. Ground truth for evaluating and calibrating precipitation estimates obtained 

from ~~C~LI-S and satellites also is needed. Xdditiondly, precipitation data can be used in diagnosing GCM 

dynamics. inasmuch as precipitation is a measure ofthe venidly integrated diabatic heating field. Moreover. 

g1ob.d precipitation climatologia ;ire necessary for Iarge-sc3le hydrological and ecological studies and may 

be used Y standards for evaluating climate chmge. 

Deticicncies in existing precipitation dimatologies are refated to inadquate rainpuge networks (highly 

uneven spatial and temporal covcf;lgec), a paucity of oceanic musuremcnts. and the translation of station data 

to overly coarse gnds. Most existing climatologia of ylobal precipitation have a150 used standard mrngau~c 

measurements that have lony been recognized as underestimates ofthe actual prmpitation (cf. Kunyka. 1953: 

Lxson. 1971). Only one existing cfimatology. in fact is based on 'corrected' precipitation data 1 Unesco. 1978). 

althouyh it was developed from very few stations (1042) outside of ;he Soviet Union. (Pnor to 1980. only 

Finland and the Soviet Union corrected precipitation measurements for gauye-induced biases. Recent 

0899-84 1 8: 90.020 I 1 I - I XOS.20 

1990 by the Royal !,IeteorologicaI Society. 
D. R. LEG AT^ AKD C. 1. WILLMOlT 

cooperative and individual dons to remove gauge biases in Europe. however. have been initiated (Sevruk. 

1986a 1.) 

It is reasonable to conclude that our knowledge of large-scale precipitation patterns is incomplete and :hat 

improved climatologks are needed. One such new' climatology is presented here and is based upon (1) a 

spatially dense network of terrestrial stations, (2) relatively accurate estimates of oceanic precipitation and (31 

corrections for the major biases inherent in the gauge collection procedure. Radar and satellite estimates of 

precipitation were not used since the technology is relatively new and long-term means are not yet available. 

As this climatology is independent from remotely sensed prkpitation it may also serve as ground truth for 

evaluating remotely sensed precipitation estimatcs. 

MERGED AND EDITED DATA 

Terrcsrrial precipilarion measuremenis 

Global archives of monthly precipitation have been compiled by Wernstedt (1972). Willmott er al. (1981). and 

the National Center for Atmospheric Research (Spanglcr and Jennc. 1984). and they provided most of the data 

for this ciimatology. The Wernstedt and Willmott er al. data sets contain monthly averages for 17.347 and 

I?,6S9 stations. respectively. Spangler and Jennc. however. give monthly time-series for 3679 stations, from 

which monthly averages had to be computed Wernstedt's Canadian data were not used since snowfall was 

excluded from the averaging. Using only these thra archives, adequate spatial coverage is achieved for much 

of the terrestrial surfact; however, Australia, New Guinea, China. parts of the Far Eass and Antarctica are 

underrepresented. 

To improve the spatial resolution in the above-rnentiontd rcpjons, monthly averages were obtained from 

an additional 208 stations in Australia, New Guinea, and Indonesia (Commonwealth Scientific and Industrial 

Research Organization, 1962-1971; Ausuaiian Department of National Development 1965). S84 stations in 

China and the Far East (Nunonson, 1947; Tcrjung et d., 1985; G. Tas pm. comm, 1986), and ten stations in 

Antarctica (van Roo); 1957). A more dcnsc coverage is not currently possible for Antarctica because these 

data comprise ncarfy alI of the gauge records that exist Ramage (1971) ah0 provided monthly precipitation 

averages for five stauons in the Sahara. 

Temporal comparability was compromised to achitvea denst spatial resolution; that is, virtually all station 

records WM used even though they are bad on differing time-periods. Most of the data. however, were 

observed between 1920 and 1980, and so this climatology is largely representative of that 60-year period; with 

greater weight gives to the more recent (data-rich) years 

Potential coding errors were identified and, where appropriate, corrected using the procedure outlined by 

Legates (1987) and Legates and Willmott (1989). These 35,492 station records then were merged into a single 

data bas& Many stations, however, were represented in more than one archive and therefore, it was necessary 

to combine these 'redundant' records to a single rrcord. Once again, a procedure described by Legates (1987) 

and Legates and Wiilmott (1989) was used 

After editing and merging the data, 24,635 independent station records wen realized (Figure 1). A relatively 

dense station network exists in the industrialized countries of North America, Europe, and East Asia Station 

distributions in arid, mountainous, and polar =@ions, however, an less dense. 

Oceanic precipiration mimares 

Shipboard gauges arc advenely influenad by 'the aerodynamic effm due to the superstructure of the ship, the 

inffucncc of roll and pitch. the capture of spray and the movement of the ship' (World Meteorological 

Organization, 1971, Xvn, pp. ll), as well as by the same biases that affect land-based gauges. Averaged 

shipboard estimates also have a 'fair-weather bias': that k ships tend to avoid storms and othcr severe 

weather (Quayie, 1974). As a conscqucncc, a serious deficiency exists of rciiable precipitation measurements 

over the oceans, and alternative estimates are needed (Jacobs 1968; Reed and Elliott 1973). 

Seved methods have been developed to extrapolate oceanic precipitation from coastal and island 

observations (Reed and Elliott (1973) and Dorman and Bourke (1979) list seven of these studies). In this study, 
"r 

. .- 

. .' 

'*. * 

*. .. 

-. 

, 

liyurc 1. I.wuiflns of iht 2W5 indcpcndcni icrrcririil prccipiirtiuli riotions coniaincd in ihc cdiicd and mcrgcd driu sei. Twclvc mean monihly prccipiiaiion nicusurcnicnir arc 

uvrilrllc Cur crcli rioliun 
oceanic precipitation is estimated according to Dorman and Bourke ( 19781. Their method was derived from a 

technique proposed by J. S. Sawyer and later refined by Tucker I l'hl J. It Is generally more accurate than the 

precjpitation-frequency approaches (cf. Dorman. 1982 Lenata. I Y87). 

Tucker's method was based on an empirical relationship between observed monthly rainfall totals and the 

'current weather' synop[ic code at coastal stations. Ocanic precipitation was estimated by applying this 

relationship to the synoptic weather repons recorded on board ships. Reed and Elliott f 19771, however. 

determined that Tucker's method was suitable for estimating oceanic precipitation only in mid-latitudes. In 

the tropics. the increased rainfall intensity reduced the effectiveness ofTucker's method. Dorman and Bourke 

(I 978) also recognized this problem. but they additionally observed that the error was systematic and highly 

correlated with air temperature. They then developed a set of regression equations (one for each month) to 

remove the error from Tucker's estimates. Using shipboard observations from 1950 through to l97L Dorman 

and Bourke estimated average monthly precipitation for both the Pacific (Dorman and Bourke. 1979) and 

Atlan'tic (Dorman and Bourke. 1981) Ocuns north of 30"s at the vertices of a 2" of latitude by 5' oflongitude 

lattice. These data are used in this study. 

Dorman and Bourke could not obtain reliable estimates for the Indian Ocean and Southcrn Ocean south of 

30"s owing to the paucity of synoptic observations (C. E. Dorman. pen. comm, 1986). For these regions. 

ocemic grid-point estimates were taken from Jaeger's ( 1983) global precipitation climatology. Jaeger used 3 

variation of the typical precipitation-frequency approach. Monthly precipitation frequencies were extracted 

from the United States Marine Climatic Atlas (6. Jaeger. 1983) and Jaeecr interpolated these values to a 4" of 

latitude by So of longitude lattice. Jaeger then converted the interpolated frequencies to annual percentages 

and used estimated values from Geiger's map of mean annual precipitation to translate the percentages into 

rainfall depths (cf. Jaeger. 1983). When integrated. Jaeger's global precipitation climatology gave an annual 

mean global precipitation of 966 mm which, based on other evidence (6. Kessler. 1985). Jaeger felt was too 

low. He then, somewhat arbitrarily. seimcd IO00 mm as the annual man global precipitation for his 

climatology and increased all his oceanic estimates by a factor of 1.062 (Jaeger, 1983). 

Jaeger's estimates and those of Dorman and Bourke, unfonunately, are not commensurate. Systematic 

differences between the two approaches, therefore. had to be resolved prior to their use in this study. Jaeger's 

estimates were convmed into values comparable with the Dorman and Bourke climatolopy by applying 

multiple linur regression to data from the Atlantic and Pacific Oceans north of 30%. Discrepancies were 

assumed to be highly correlated with air temperature and so the regression translation betame. 

(1) 

where u. b, and c arc monthdependent regression coefficients. f J is Jaeger's-monthly precipitation estimate 

(mm), T, is the mean monthly air temperature ("C) at fJ's grid point. and f is the estimated Dorman and 

Bourke value. Grid-point air temperatures were taken from the Comprehensive Ocean-Atmosphere Data Set 

(Fletcher el af, 1983: Woodhff, 1985; SIUK er d, 1985; WoodruBer af, 1987). Equation (1) accounts for 58.7 

per cent of the total variation in Po and slightly more in July, August. and September than in Dmmber. 

January. and February (Legates 1987). 

Mean monthly oceanic precipitation records were compiled or estimated for 223 grid points (Figure 2): 

1235 records were taken from Dorman and Bourke (1979; 1981) and 888 were derived from Jaeger (1983) and 

Equation (1). In addition two fixed-position ship stations were taken from Springier and Jenne (1983). Even 

coverage is apparent over most of the oceans. except for several areas between 15" and 30"s. Infrequent ship 

traverscs largely explain these 'holes' within the Dorman and Bourke fields. In several of these regions. 

however, island-based raingaupe estimata are available (Figure 1). 

P, =a + bPJ + cT, 

GAUGE CORRECTION PROCEDURE 

Since the nineteenth century, wind has been recognized as the largest cause of error in precipitation 

measurements (d. Kurtyka 1953; Lanon. 1971). Koschmiedcr (1934) observed that as the wind speed 

increases. gauge utch dmeascs exponentially. Raingauze shields can reduce but not eliminate the 

measurement bias of standard (elevated) gauges (Neff. 1977). Research has also shown (d. Neff, 1977) that 

'accurate' prenpitation measurzments can be obtained by pit gauges (buned gaupes with their orifices at 

ground level); that is. by gauges sited where the wind speed is at a minimum. Pit gauges, however. have been 

used infrequently and. even today, precipitation usually is measured by an elevated gauge without a shield 

(Sevruk, 1975, 1979. 1987). 

Systematic CITOK in raingauge measurements can also arise from (I) wetting on the internal walls cT !he 

gauge. (2) evaporation from the gauge. (3) splashing into or out of the gauge, and (4) blowing snow (Sevruk, 

1979: Sevruk and Hamon. 1984). Such systematic gauge errors should be identified and removed from existing 

records. Considering five potential sources of errors. Sevruk proposed a general model for precipitation 

correction. 

(2) 

where P, is the corrected precipitation estimatc P, is the precipitation measured by the gauge. li is the wind 

correction factor (n 2 I), AP- is the correction for the wetting loss, APr is the correction for cvaporation. A P, 

is the correction for splashing APb is the comaion for blowing snow, and AP, is the correction for 

unsystematic (random) errors. 

While AP, may be as large as 10 per ant of the actual catch, its expected value is zero when integrated over 

long time periods (Sevruk, 1979). It therefore. is assumed to be zero here. Furthermore, A P, approaches zero 

for nearly all elevated gauges since 'splashing in' is negligible (due to the elevated orifia) and 'splashing out' is 

minimized by the depth of the container. Snow blown into the gauge from the adjacent snow cover ( i A P,) 

and. to a lesser extent snow blown out of the gauge (-AP,) also are considered to be negligible for every 

gauge except the Tretyakov gauge. Struzcr and Bryazgin (1971) and Unaco (1978) observed that 'it is 

significant only for the precipitation gauge used in the USSR [Le- the Tretyakov gauge] and mainly in high 

Istitudes'(Uncsco. 1978, p. 111). Since the specification of n, for the Tretyakov gauge (discussed below) takes 

AP, into accounS there is no neut to explicitly consider it 

Gauge losscs also arc affected by the form of precipitation (Le. Iiquid or solid). Error sources in Equation (2). 

therefore, should be partitioned into liquid (rain) and solid (snow) cornponenu. In view of this, Equation (2) 

can be rewritten as 

P, = K(P, + AP. + APr) 5 AP, -L. APL f AP, 

P,=(1 -R)n,(P,+AP,+AP,)+ Rx,(P,+ AP,+AP,) (3) 

where the subscripts r and s are used for rain and snow, rapcctively, and R denotes the proponion of monthly 

precipitation that falls in solid form. 

With the exception of R. the remaining unknowns (x,. n,, AP,, AP-, AP,,, and AP.,) must be separately 

estimated for each gauge type due to non-trivial differences among gauge designs Legates (1987) categorized 

gauges on the basis of three bias-influencing criteria: the orifice am, whether the gauge was equipped with a 

shield. and the height of the orifice above the ground. He then compiled these parameters for most of the 

world's standard precipitation gauges. His compilation of gauge-type panmeters is used here. 

When a standard puge could not be identified for a country, gauge characteristics had to be inferred 

(Legates, 1987). Kunyka (1953) indicated that the RM.0. Mk I raingauge (elevated at 30 an) is most 

frequently used in British protectorates and countries that formerly were part of the British Commonwealth. 

Unesco (1978) simiiarly concluded that the R.M.O. Mk I was the primary gauge in the Far East. Indonesia, 

New Guinea Libya and Near Eastern countries. Sevruk (1981) identified the Hellmann gauge (elevated at an 

average height of 1 mi as dominant in South America 3nd Africa. In the absence of more reliable information. 

the Sauge types suggested by Kunyka. Unaco, and Sevruk were assigned. 

I Vind 

As mentioned above. wind is the single largest cause of error in measuring precipitation and. therefore. its 

influence (K, and li,) must be estimated. To accomplish this. both wind data and reliable relationships between 

the wind data and the wind corntion factors are required. 

Long-term mean monthly wind-speed data were obtained for 733 terrestrial stations and 6972 oceanic grid 

boxes. The boxes have dimensions of 2" of latitude by 2' of longitude (Legates. 1987). Averaged wind data. 
however, underrepresent the wind speed during pretipitat,on and. thus. average wind speeds dunng 

precipitation events must be estimated. Scvruk (1979. 1982. 1986b) suggcsu that 

wpLw (4) 

where w,, is the estimate of the wind speed during precipitation at anemometer height and w is the mean 

monthly wind speed The empirical coefficient L. depends on the form of precipitation and the number of 

days in a month with precipitation greater than I mm. .M. Equations for estimating L were specified by 

Legates ( 1987) from tabulated values given by Sevruk ( 1979, 1982 1986b) and they are used hen; 

Whiie ,M is available for many stations. it was not encoded for most of the records evaluated here and. 

therefore. it had to be estimated Using data from Spangler and Jenne (1984). Legates (1987) fit 

'bf - 1.09 D,"[ 1~0-(l~00425)p~''.-~"~~] (5) 

where D, is the number of days in the month and is the mean monthly air temperature in "C This equation 

was more accurate than several other equations inasmuch as the mean absoiute error in estimating .bf was lcss 

than 1 days. Equation (5). therefore, is used to estimate LU. 

With an estimate of w,, wind speed during precipitation at the gauge ofice, wh,, can be estimated from 

"hp 31 KL wp (6) 

where K, is the logarithmic coefficient of the wind speed profile and m is an exposure coefficient. Site-speclfk 

estimates of KL are not available for most precipitation 3au3es. Legates (1987). therefore, estimated thae two 

coefficients using Sevruk's ( 1979, 1982) guidelines and o btained 

where h, is the height of the yuge orifice, h, is the height of the anemometer. and : is the roughness length. 

Scvruk (1979. 1982) determined that : (for most puge sites) ranges from 0.005 to 003 m. Estimates of: were 

obtained by scaling roughness lengths of natural vegetation (Wilson and Henderson-Sellen. 1985: 

Henderson-Sellen et af, 1986) to match this range (Legates, 1987). Although gauges are usually piaced in 

clearing, the roughness length at the gauge site shoulci be influenced by the roughness length of the 

surrounding vegetation. 

Sevruk (1979. 1982) indicates that m should range from 06 for well-sheltered sites to 1.0 for unshcltercd 

sites. Site-specific atimates of m were not available for most precipitation gauges. therefore, the natural 

vegetation was ysumcd to be the most significant gauge obstruction. Vegetation heights and cove:;1ge 

parameters were obtained from Willmott and KIink (1986) and an equation developed by Legates (19871 was 

used to estimate the exposure coeriicicnt from these parameters. 

It was necessary to select an equation for K, and K, that was applicable to a wide range of Sauge types. One 

such equation is 

h'= I.o+.4pTw:, (31 

where the coefficient. A. depends on both the form of precipitation (r or SI and gauge type. and p is the 

'coefficient of tnnsfer from the standard atmosphere to its actual condition' (Goilrbev. 1986. p. 1381. Average 

values of A for both rain and snow have been tabulated for se\eral different _pauye :vpes (Legates. 1987). For 

the Tretyakov gauge, A takes into account the etTect oC blowiny snow (Sevruk. 1982). idere. estimates of K, and 

K, are based upon equation 18). 

Other panmetenzitions of K, and K, were used when .4 had not been evaluated. Separate equations. 311 oi 

which are functions of wI,, were used for the Canadian snowgauge. the United States Nat:onal Warher 

Service gauge. and the Swedish SHMI yauge {ci. Legates. 19871. Errors associated with estimating K, by these 

methods were generally small. Ltgata (1987). for instance. examined both standard and pit yaune data at 3 

itations (Sevruk and Hamon. 19841 and reponed 3 mean absolute error of less than 0.027. No jystematic 

biases were observed 
Werring losses 

Wetting iosses (AP.. and AP-) are comprised of moisture that adheres to the internal walls of the gauge 

during pcipitation and during emptying. Sevruk (I 973) estimates that wetting losses cause an underestimate 

in globally averaged precipitation ofabout 2.6 per: cent Wetting losses depend on (1) the design imaterial and 

geometry) of the collmor. (2) the frequency of precipitation measurements. and (3) the form and frequency of 

precipitation. Gauge diKerenccs as weil as the difkrcnt forms of precipitation, therefore. should influence the 

estimation of wetting loss. 

Following Sevruk (1979). wetting loss is estimated from 

AP,=dM (9) 

where the average wetting loss per precipitation event i. depends on both the form of precipitation and gauge 

type. As before, M is the number of precipitation days in the month Values of d arc taken from Sevruk (1979, 

1982 1985) whose compilation inciuded most national precipitation gauges. In general d varies little among 

gauges with the same orifice area and ranges from 0-02 to 0.30 mm per pepitation event. 

Evaporation 

Evaporation from the gauge(AP, and APJ typically accounts for a decrease in the measured precipitation of 

about 0.5 per cent (Scvruk and Hamon. 1984). Consistent with Sevruk (1984). evaporative loss is estimated by 

AP, = i,reM (10) 

where i, is the intensity and r, is the duration'of evaporation (the claspstd time between the end of 

precipitation and its measurement). On averagc 'I, is 0.5 or 0.3 when precipitation is measured once or twice 

daily. respectively. Standard observing practices for many countrk are unknown (S. Unninayar, pen. comm, 

1986). however, and so the average of the two values 0.375, is used 

Procedures to estimate i, an also not well known. It varies with the gauge typc and form of preapitatioa It 

has ais0 been related to man monthly wind spttd air temperature, and more frequently to the average 

monthly vapour pressure deficit. Estimata of i, are obtain& from a procedure that makes use of derived 

vapour pressure deficits (Legates, 1987). His estimates of average monthly vapour prcqurc deficit, using mean 

monthly air temperature and precipitation. arc within 2 mbar of measuremenu. These data thm can be used 

with equations to estimate i, for eight different ~auge types. Precision associated with these equations, fit 

coefficients of determination (9). ranges from 0.288 to 0.887. 

Form of precipitation 

It is relativeiy easy to determine the form of precipitation for daily or hourly totals, but not for long-term 

averages. Lauscher (1954). nevenhclcs, suggested that the proponion of monthly precipitation falling as 

snow (solid) may be estimated from 

R=0.5-M5T, (1 1) 

where T, is the avexge monthly air temperature ("C). Legates (1987). however, found that a logistic cune 

gave a better fit to Lauscher's data. and he gives 

1 

1 + 141(1.3S)'' R= 

Equation (12) estimates R with a fit mean absolute error of less than 0.06. It is used here to partition mean 

monthly precipitation into solid and liquid components. 

INTERPOLATION TO A HIGH-RESOLUTION GRID 

Corrected station data and oceanic gnd-point estimates were interpolated to a 0.5" of latitude by 0.5" of 

longtude lattice. An appropriate interpolation algorithm fiKt had to be selected. It is especially important to 
icrount for the sphericity of the earth when the spatial domain is the entire giooe (Willmott rr a/.. 1985b). 

Legates (1987) evaluated sevcd procedures and concluded that a spherical adaptation of Shepard's ( 1968. 

1384) numerid approximation method (Willmott er ai.. 1985b) was reliable. Seven1 modifications were made 

to this algorithm and they are outlined in Legates and Willmott (1989). A more complete discussion is 

presented by Willmott er al. (1985b) and Legates (1987). Interpolations here are based on this procedure. 

Numerical approximation methods give weighted averages as point estimates on a field. Legates and 

Willmotc (1989) write Shepard's (1968) weight as 

wi = q(l + Di) 

where S, is the distance component for station i and D, isthe directional component Shepard ( 1968, 1984) and 

Willmott et af. (198Sb) used a value of 2.0 for the exponcnk 7. Legates (1987, however, estimated 3n optimal 

exponent of 0.75 for these precipitation data. Herr, then, 0.75 is used. 

GLOBAL PRECIPITATION 

Annul mean 

Mean annual precipitation is heaviest along the Inter-Tropical Convergence Zone (Figure 3). Intense surface 

heating and the confluena of the tropical easterlies result in annual precipitation rates that often exceed 3 mm 

day". Heavy annual rainfall is especially apparent over large areas of the equatorial Pacific as well Y over 

the rain forests of South America. Africa. and Asia. High precipitation ptes also occur over the western sides 

of the continents in the upper mid-latitudes (marine West Coast climates) and over wann ocean currents. 

Warm currents increase both the buoyancy and moisture content of the air. as evidenced by increased 

precipitation rates over the Gulf Strum and Kuroshio currenu, for instance. Monsoonal circulation 

substanually incrrves the annual muns as well. 

Descending air and dur skies associated with the poleward arm of the Hadlcy Cell help minimize 

precipitation in the hot dart regions. Archetypical desert regions include the Sahara, Gobi. Kdahari. 

Atacama. American south-wesf and Australian Outback which. on average, rcnive less than 1 mm day-' 

(Figure 3). Coastal upwelling ductj su-surface temperatures, which cads to extend these deserts over 

adjacent oceans. Pmipitatioa is dso light in polar rq$ons (cold deserts), largely owing to the diminished 

moisture capacity of cold air. 

Mountainous regions (c3. the Rockies and the Alps) exhibit average predpitation rates that vary 

dramatidly over small spatial sulcs (Figure 3). while average precipitation is relatively homogeneous over 

level terrain (e.& over the Australian Outback. the Tibetan Platuy and central North Amenn). The letward 

sides of mountains. of course. 3re much drier than windward sides owing to the nin-shadow effect. These 

pattern are evident especially in the Rockies, the Andes. and the Himalayas 

Averaye precipitation pitterns associated with the Walker CcIl circulation can be seen in the South Pacific 

Ocean between the Equator and 30's (Figure 3). Huvy precipitation is apparent in the wcstr'rn South Pacific 

while light rainfall can be seen od the western coast of South America. Heavier precipitaion occurs over 

eastern Austnlio and the western South Pacific than over Peru and the astern South Pacific 

.J cera+ intra-annul cutidon 

Intra-annual variation in precipitation was evaluated by computing the temporal standard deviation of the 12 

mean monthly values associated with each grid point Standard deviation is. in general. directly comefated 

with annual mean precipitation (Figures 3 and 4). Temporal standard deviations. for instance. are less than 

0.6 mrn day - ' for most amas receiving an average of lets than 2.0 mm day - ' . Areas of heavy precipitation. 

by contrast often have standard deviations that exceed 2.4 mrn Jay-'. Hiyh variability also characterizes the 

monsoon regions of the FY Em. northern Australia and quatorial XfricModcritiny influences that the ocuns exen on precipitation are evident particularly in the Southern 

Hemisphere. Precipitation vanability is especially damped over the extensive southern ocean. Intermittent 

Global Change Data Base Afiicu Docunieritarion Appendix 1-23 


land areas. even small islands. typically increase the seasonal variation and they stand out 1c.g the Falkland 

Islands,. 

Seasotialiry 

Using the same 4' of latitude by 5' of longitude lattice (in projected coordinates) discussed by Legates and 

Willmott (1989). precipitation was decomposed into the annual mean (Figure 3) and the first two annual 

Iimnonics (see Figures 6 and 7). The length of a mapped arrow indicates the amplitude of the harmonic inote 

the logarithmic scale) while the direction locates (approximately) the occurrence in time of the maximum lor 

maxima). A complete discussion of harmonic analysis is given by Rayner (1971). 

,More than 35 percent of the variation in mean monthly precipitation is accounted for by the first two 

harmonics. Explanation of the seasonal variation (9) over the continents is greater than over the oceans 

(Figure 51. Nevertheless, the unexplained variation is not more than 1.0 mmday" for any grid point. 

Maximum precipitation occllfs during the mid- to late high-sun season over much of the subtropics I Figure 

61. Very heavy rain occurs in July and August just north of the Equator and in January and Febtuaiy south of 

the Equator. Monsoonal rains ruch their peak betwan lace July and early September (in the Far East) whilc 

in northern Australia, the height of the wet suson occurs about February. Precipitation between 5'K and 

5"s. particularly in the Atlantic Ocean. has a March or April maximum. which coincides with the maximum 

surface air tempentun for this region (Legates and Willmott. 1989). Equatorial prtcipitation in the Indian 

Ocean is nearly 6 months out of phase with the Atlantic the maximum occurs in September and early 

Ociober. Low-iatitude oceanic precipitation. especially in the subtropics of the Northern Hemisphere. is 

delayed for one or two months relative IO terrestrial precipitation and this is coincident with the occurrence of 

the maximurn surface air temperature. 

First-harmonic amplitudes arc much smaller in mid-latitudes owing to lower precipitation rates. Oceanic 

maxima occur in Deamber and January, in both hemispheres, and their amplitudes tend to be greater than 

terratnnl ampiitudet (at comparable latitudes). Continental interiors pnemlly exhibit a wer-rummer/ 

drywinter cycle while dry-summer climates arc clearly distinguishable (e& [he California coast, the 

.Meditcrranan reZion. coastal western Australia, and the Chilean coast). 

Second harmonia explained only about one-third as much variance as the first (Figure 7). Large 

amplitudes are evident in the quatorial Atlantic and Pacific Oceans where double-maxims occur owing to 

the semi-annual passage or the Inter-Tropical Converyena Zone Over the Atlantic. the maximum phase 

corresponds well with low solar zenith angles but it is delayed in the Pacific Oceanic phases in the tropia. 

once again. are correlated with the surface air temperature cycle (Legates and Willmott, 1989). Large 

amplitudes abo are observed over northern India and they are nearly in phase with the first harmonic This 

attests to the marked seasonality of the Indian monsoon. Amplitudes in mid-latitudes are _eenerally much 

smaller. except wer the southern ocean. 

CONCLUDING REMARKS 

A relatively dense global network of long-term average monthly precipitation records has been compiled from 

existins sources. Gauze measurement C~OK. owing to the effects of the wind. wetting on the internal walls of 

the gauge. and evaporation from the Sauge. have been estimated and removed from the records. These 

irregularly distributed records were then interpolated to a 0.5' of latitude by 0.5' of longitude grid using a 

spherically based interpolation algorithm. 

Precipitation is heaviest in the low latitudes and generaliy decreases toward the poles. Average intra-annual 

variability is positively correlated with aver;lge annual precipitation. Harmonic analysis depicted the marked 

effm that the susond migration of the Inter-Tropical Conversencc Zone has on the timing of precipitation in 

equatorial regions. A double-maxima regime characterizes the seasonal precipitation. cycle between the 

Equator and about IO'N. while heavy unimodal precipitation dominates the subtropics north and south of 

this region. Mid-latitude precipitation is generally less than in the tropics and is usually heavier over the 

xcans rhan over land. 

Figure 6. First tcn\pral barn\oik ul average monthly prrcipitation (mm day- ' ). Amplitude is proportional to the length of the arrow (measured from the centre ol the walckdiill) wliilc 

the crcurrcncc of tlic nirriniuin in time is given by its direction (cdgc ol the rcrlc/dirl) 
I:igurc. 7. Secund tciiipural liariiiuiiic ulovcrage inoiitl~ly precipitation (nm day-’). Amplitude is pruportionsl to the lcngth ulthc arrow (measured lroni the ccnlrc of the scale/dial) 

while thc ucciiiieitc~ of tbc iiuxiina in tiiirc is givcn by its orienlulioiir (edge of tkt sc;llc/dL\) 
Raingauge errors tend to be directly proportional to total precipitation ad amounr 10 nearly 11 per cent of 

the globd catch. An undrrutch of 1s than 5 per cent occurs in the tropics. while more than 40 per ant is 

common at the polts: illustrating the sizeabic: influence of wind on gauge musurementi of snowfall AMMI 

average global precipitation is approximxciy 113 mm (gauge CO~CCION considered), which is consistent 

with values suggested by Kcssler( 1985). It is also within 7 rnm of the gauge-comted global atimare obraincd 

by Unesco (1978). 

ACKNOWLEDGEMENTS 

This work was supponcd by the National L.e:oniiutics 2nd Spaa Administration under grant NAG 5-853. 

Thougi;;ful suggestions from Jam= E Bur: (2Jnivr;::ity of Wisconsin) and cartographic assistance from 

Johanna Feddema (University of Delaware) arc gieatljt appreciated 

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Willmott. C. I.. Rowr C M. Jnd Minu Y. 1985~ 'Climatology of the tcrrcstcd Kyonal water cyde'. J. Climurol- 5. i89-606. 

Willmott. C I.. Rowc C. .M. and Philpot. W. 0. 198Sb. 'Small-sale dimate maps a senritivity analysu of some common assumptions 

Wilson &I. F. and Hendenon-Scllcn. A 1985. 'A global archibe of land cover and SOIL data for wc in gencnl circulation climate models'. 

Woodruir. S D. IY85. 'The comprchenuvc ocean-atmosphenc data M'. fiinl Cunjerence vn Climarr b'uriarionr ulul Snnpsium on 

Woodruff. S. D.. Sluu R. J.. Jennc R. Land Stcurer. P. ,M. 1987. *A comprehensive ocean-atmosphere dam set'. dull. .im .!/ereimi. SocWorld Xleteorologial Organiution IY71. Cuuh fo Mefroroloyrcd lnsrmnr and ObJrrrify Prwficu. lth ed. World .Lleteorologiul 

Lcningnd 663 pp 

ISLSCP ConJirncc. 2-5 December 1985. Rome Europun space Agency SP-ZJII. p. 109. 

world. Pan I: the cutcrn hemisphert Rn 2 the western hcmisphue'. Pub. Climarol.. W (I Jnd 2) 395 pp. and 373 pp. 

auoc~~td with gnd point interpolation and contounng'. Am Cmoyr, I2 SI& 

1. CfimUifJl, 5. 119-143. 

Concrmpurury C/irnure 1950-2100. 7-1 I January 1985. Los An& Amcnan Meteorological Society. p. 14. 

68.139-1230. 

Organimuun. Geneva WMO No. 8. TP. I. 
-JREVISED FNOC ELEVATION, TERRAIN, AND 

SURFACE CHARACTERISTICS 

CITATION: Kineman, J. (ed.), 1985. Revised FNOC Global Elevation, Terrain, 

and Surface Characteristics. Digitai Data. NOAA/NGDC/WDC-A, 

Boulder. Colorado. 28 MB. 

Global Change Data Base Afka Docuntentoll’ort Appendix I- 1 
Blank page retained for pagination 
J. REVISED FNOC ELEVATION, TERRAIN, AND SURFACE 

CHARACTERISTICS 

DATE: 1985 

SOURCES: 

Digitized from. available maps and reprocessed. 

(1) Leo Clark 

USN FNOC 

Code 42 

Monterey, CA 93943 

NCAR 

P.O. Box 3000 

Boulder, CO 80303-3000 

(3) John Kineman 

NOMGDC 

Boulder, CO 

(2) Dennis Joeseph 

CONTRIBUTOR: 

NOAA/WESDIS/NGDC 

Boulder. CO 

DATA CENTER: NGDC/WDC-A 

GEOGRAPHIC SAMPLING: 

1o-min grid 

(I ) Mode, miximum. minimum within cell Elevation) 

(2) Percent cover witllin cell (Urban and Water Cover) 

(3) Characteristic class (Ocean Mask) 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Multiple year composite before 1980 

REFERENCES: 

Cuming, Michael J. and Barham A. tlawkins. 1981. "TERDAT: Tlie FNOC 

System for Terrain Data Extraction and Processing." Technical Report 

MI1 Project M-254 (Second Edition). Prepared for Fleet Numerical 

Oceanography Center (Monterey, CA). Published by Meteorology 

International Incorporated. 

(continued on the next page) 

Global Change Data Base Afr-ica Vocitntenrutiori A1)petdix 3-3 
FILE NAMES: 

AFMAX Maximum eievation (meters) 

AFMIN Minimum elevation (meters) 

AFMODE Modal elevation (mctm) 

AFURBAN Percent urban cover 

AFWATER Percent water 

AFOCEAN Ocean mask 

Global Change hta llase Ajkica Docrivierttution Appendix 5-4 
REVISED FNOC MAXIMUM ELEVATION 

DOCUMENTATION FILE 

File Name: AFMAX.DOC ' 

image title 

data type 

file type 

rows 

columns 

mi n imum 

maximum 

cell x 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category IO 

category 11 

category 12 

category 13 

category 14 

category 15 

: Revised FNOC Maximum Elevation (meters) 

: integer 

: binary 

: 438 

: 480 

: -1.2OOOOOOOOOE+O2 

: 5.8800000000E+03 

: 1.66666OOOOOE-01 

: 1.6666600000E-01 

: 15 

: 255 to 629 

: 630 to 1004 

: 1005 to 1379 

: 1380 to 175A 

: 1755 to 2129 

: 2130 to 2504 

: 2505 to 2879 

: 2880 to 3254 

: 3255 to 3629 

: 3630 to 4004 

: 4005 to 4379 

: 4380 to 4754 

: 4755 to 5129 

: 5130 to 5504 

: 5505 to 5880 

{category 0 : -120 to 254) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Datu Base Ajkicu Docutrrerrtcltion Appendix J-5 
REVISED FNOC MINIMUM ELEVATION 

DOCUMENTATION FILE 

File Name: AFMIN.DOC 

image title : Revised FNOC Minimum Elevation (meters) 

data type : integer 

file type : binary 

rowc : 438 

columns : 480 

minimum : -1.5000000000E+02 

maximum : 3.6600000000E+03 

cell x : 1.66666000003-01 

cell y : 1.66666000003-01 

legend : 15 

category 1 : 88 to 325 

category 2 : 326 to 563 

category 3 : 564 to 801 

category 4 : 802 to 1039 

category 5 : 1040 to 1278 

category 6 : 1279 to 1516 

category 7 : 1517 to 1754 

category 8 : 1755 to 1992 

category 9 : 1993 to 2230 

category 10 : 2231 to 2469 

category 11 : 2470 to 2707 

category 12 : 2708 to 2945 

category 13 : 2946 to 3183 

category 14 : 3184 to 3421 

category 15 : 3422 to 3660 

(category 0 : -150 to 87) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Data Base Africu Docurrrerttuiion Aplwittlix J-6 
REVISED FNOC MODAL ELEVATION 

DOCUMENTATION FILE 

File Name: AFMODE-DOC 

image title : Revised FNOC Modal Elevation (meters) 

data type : integer 

file type : binary 

rows : 438 

columns : 480 

minimum : -1.20O0O0OOOOEt02 

ma:; imum : 3.9600000000E+03 

cell :c : 1.66666660003-01 

cell y : 1.66666660003-01 

leaend : 15 

category 1 : 135 to 389 

category 2 : 390 to 644 

category 3 : 645 to 899 

category 4 : 900 to 1154 

category 5 : 1155 to 1409 

category 6 : 1410 to 1664 

category 7 : 1665 to 1919 

category 8 : 1920 to 2174 

category 9 : 2175 to 2429 

category 10 : 2430 to 2684 

category 11 : 2685 to 2939 

category 12 : 2940 to 3194 

category 13 : 3195 to 3449 

category 14 : 3450 to 3704 

category 15 : 3705 to 3960 

(category 0 : -120 to 134) 

[This leqend is only appropriate for displays which reclass 

(c3ntrast stretch) the continuous values into 16 classes.] 
REVISED FNOC PERCENT URBAN COVER 

DOCUMENTATION FILE 

File Name: AFURBAN.DOC 

image title 

data type 

file type 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 1 

category 2 

category 3 

category 4 

category 5 

category 6 

category 7 

category 8 

category 9 

category 10 

category 11 

: Revised FNOC Percent Urban Cover 

: byte 

: binary 

: 438 

: 480 

: O.OOOOOOOOOOE+OO 

: 6.0000000000E+01 

: 1.66666000003-01 

: 1.66666000003-01 

: 60 

: 1% 

: 2% 

: 3% 

: 4% 

: 5% 

: 6% 

: 7% 

: 8% 

: 9% 

: 10 % 

: 11 % 

category 12 : 12 % 

category 13 : 13 % 

category 14 : 14 % 

category 15 : 15 % 

category 16 : 16 % 

category 17 : 17 % 

category 18 : 18 3 

category 19 : 19 % 

category 20 : 20 % 

capegory 21 : 21 % 

category 22 : 22 % 

careqory 23 : 23 % 

category 24 : 24 % 

cateqory 25 : 25 % 

category 26 : 26 % 

category 27 : 27 % 

cateqory 28 : 28 % 

category 29 : 29 % 

category 30 : 30 % 

category 31 : 31 % 

category 32 : 32 % 

cateuory 33 : 33 % 

category 34 : 34 % 

category 35 : 35 % 

category 36 : 36 % 

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category 39 : 39 S 

cateqory 40 : 40 % 
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cateqory 42 : 42 % 

category 43 : 43 % 

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category 46 : 46 % 

category 47 : 47 3 

category 40 : 48 % 

category 49 : 49 3 

cateqory 50 : 50 % 

category 51 : 51 % 

category 52 : 52 3 

category 53 : 53 % 

category 54 : 54 % 

category 55 : 55 % 

category 56 : 56 3 

category 57 : 57 % 

category 58 : 58 3 

category 59 : 59 % 

category 60 : 60 % 

{category 0 : 0 2) 

(This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 

Global Change Dah Base Africa Doc*u~r~rrrk~tion Appciidix J-9 
REVISED FNOC PERCENT WATER 

DOCUMENTATION FILE 

File Name: AFWATER.DOC 

image title : 

data type : 

file ty9e : 

rows 

columns 

minimum 

maximum 

cell :i 

cell y 

legend 

category 1 : 

category 2 : 

category 3 : 

category 4 : 

category 5 : 

category 6 : 

category 7 : 

category 8 : 

category 9 : 

category 10 : 

category 11 : 

category 12 : 

category 13 : 

category 14 : 

category 15 : 

category 16 : 

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category 21 : 

category 22 : 

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category 25 : 

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category 27 : 

category 28 : 

category 29 : 

category 30 : 

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category 33 : 

category 34 : 

category 35 : 

category 36 : 

category 37 : 

category 38 : 

category 39 : 

category 40 : 

category 41 : 

category 42 : 

category 43 : 

category 44 : 

category 45 : 

Revised FNOC Percent Water Cover 

integer 

binary 

438 

4 80 

o.ooOooOooOOE+OO 

1.0000000000E+02 

1.66666OOOOOE-01 

1.66666OOOOOE-01 

1% 

2% 

3% 

4% 

5% 

6% 

7% 

8% 

9% 

10 % 

11 % 

12 % 

13 % 

14 % 

15 % 

16 % 

17 % 

18 % 

19 % 

20 % 

21 % 

22 % 

23 % 

24 % 

25 % 

26, % 

27 % 

28 % 

29 % 

30 % 

31 % 

32 % 

33 % 

34 % 

35 % 

36 % 

37 % 

38 % 

39 % 

40 % 

41 % 

42 % 

43 % 

44 % 

45 % 

100 

G!obal Cliatige Data Base Africa Docrinrenkitiorr Appendix J-IO 
category 46 : 

category 

category 

category 

category 

category 

category 

cat ego ry 

category 

category 

cat ego ry 

category 

cat ego ry 

cateqory 

cat ego ry 

category 

category 

cat ego ry 

category 

category 

category 

category 

category 

47 : 

48 : 

49 : 

50 : 

51 : 

52 : 

53. : 

54 : 

55 : 

56 : 

57 : 

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60 : 

61 : 

62 : 

63 : 

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65 : 

66 : 

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68 : 

category 69 : 

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category 85 : 

category 86 : 

category 87 : 

cat ego ry 

category 

category 

category 

category 

category 

category 

category 

category 

88 : 

89 : 

90 : 

91 : 

92 : 

93 : 

94 : 

95 : 

96 : 

category 97 : 

category 98 : 

category 99 : 

category100 : 

{category 0 : 

46 % 

47 % 

48 % 

49 % 

50 % 

51 % 

52 % 

53 % 

54 % 

55 % 

56 % 

57 % 

58 % 

59 % 

60 % 

61 % 

62 % 

63 % 

64 % 

65 % 

66 % 

67 % 

68 % 

69 % 

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96 % 

97 % 

98 % 

99 % 

100 % 

0 %I 

[This legend is appropriate for display of the individual classes 

(i. e. without rescaling) . ] 
OCEAN MASK 

DOCUMENTATION FILE 

File Name: AFOCEAN.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

cateqory 1 : 

Ocean mask (produced from Revised FNOC data) 

byte 

binary 

438 

480 

0.0000000000E+00 

1.OOOOOOOOOOE+OO 

1.66666000003-01 

1.66666OOOOOE-01 

1 

Land 

{category 0 : Ocean/Water) 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) .I 

Glohal Change Data Base Afiica Docunrerrkih'on Appendix I- I2 
The Navy Fleet Numerical Oceanography Center/(NFNOC) began creating the 

original 10-minute terrain data set in the mid 1960's. Work extended into the 

early 1970's. 

Defense Operational Navigation Charts (ONC), scale 1:1,000,000. For certdin 

regions ONC charts were not available; for such areas selected charts from the 

Jet Navigation charts and World Aeronautical Charts were used. 

were hand read out to forms by employees, and then read by optical character 

reader to tape. The values were estimates from contour lines. 

graphs were made for quality control, checking terrain features. Later, other 

errors were corrected by the National Center for Atmospheric Research in 

Boulder, Colorado. 

The main sources for the data were the U.S. Department of 

The charts 

Isometric 

NFNOC hopes in the future to update the data set using satellite data. This 

update will not be available to the general public for several years, as much 

of the new information will be defense-classified. 

the history and original sources of the data, you are welcome to contact Leo 

Clark, NFNOC, Code 42, Monterey, California, 93943. 

For further information on 

3/7/85 
SOLID EARTH DIVISION 

325 Broadway 

Boulder, CO 80303 

NOAA/NGDC E/GC-1 

Documentation of the SED/NGDC format of the Navy 

10-minute topographic data 

The Navy IO-minute topography data was reformatted at NOAA into a 40 

character/record ASCII format. 

fields were quality checked for reasonable values (ie., within documented 

limits, etc.). 

listed in the data documentation files ("SUMMARY" and "CHKERRS"). 

listing of the error check definitions: 

All data fields were retained, and all data 

The checks that were performed, and the resulting errors are 

Below is a 

MSG (1) = 'RANGE EXCLUDES MODE' 

MSG 

MSG 

MSG 

MSG (5) = 'SECONDARY TYPE UNDEFINED' 

MSG (6) = 'PRIMARY TYPE UNDEFINED' 

MSG (7) = '%WATER OUT OF LIMIT' 

MSG (8) = '%URBAN OUT OF LIMIT' 

MSG (9) = 'MAX/MIN OUT OF LIMIT' 

MSG (10) = 'SEA TYPE-CODE DISCREPANCY 

(2) = 'SEA HEIGHT AND TYPE MISMATCH' 

(3) = 'RIDGE AZIMUTH OUT OF LIMIT' 

(4) = 'NUMBER OF RIDGES OUT OF LIMIT' 

The NOAA/NGDC/SEGD ASCII format, in Fortran, is: 

(F7.2,1X,F6.2,1X,13,1X,213,1X,412,213) 

The field elements are: 

Format Field contents 

F7.2 Longitude (t/- 180) 

X 

F6.2 Latitude (t/- 90) 

X 

I3 

X 

I3 

I3 

X 

I2 

I2 

I2 

12 

I3 

13 

Modal heights in hundreds of feet 

Maximum height in hundreds of feet 

Minimum hight in hundreds of feet 

Number of significant ridges 

Direction of ridges in 10's of degrees (0-18) 

Primary surface type code 

Secondary surface type code 

Percentage of surface covered by water 

Percentage of surface covered by urban development 

Rer?r to the original binary NCAR/NAVY documentation for details aboub the 

data fields, and the data itself. 

Global Clinnge Data Base Afi-icu Docunreufution Appendix J- 14 
Note: Documentation of data contents and oriainal f orma t : 

Data Format for 

Global 10 Minute Elevation Data 

from the U.S. Navy 

Dennis Joseph 

NCAR, Data Support Section 

April 1992, update Dec. 1984 

Global elevation data at a resolution of 10 minutes was prepared by the Navy 

Fleet Numerical Oceanography Center at Monterey. 

area, the set includes modal elevation, minimum elevation, maximum elevation. 

orientation of ridges, terrain characteristics, and urban development. This 

is archived by the NCAR Data Support Section (DSS) in a packed binary format. 

Parameters available are identifical to those described in documents by 

Meteorological International, Inc. and the Fleet Numerical Oceanography 

Center, but the DSS has made some changes to the set. 

organized somewhat differently with each tape block containing all data for a 

5 x 5 degree area. The data format has been changed and a few coding changes 

were made for consistency with the format. The information content of the 

original set has been preserved entirely. 

For each 10 x 10 minute 

The data is now 

The tape contains 2592 tape blocks, each 962 60-bit words in length. 

block contains all data within a 5 x 5 degree square. Each block is labelled 

with the latitude and longitude of its southwest corner. The tape is ordered 

beginning with the block for 9Os,O and moving eastward for 72 blocks then the 

next 72 blocks beginning at 85s,O etc. 

values for 900 lO‘x10’ squares. 

data points are ordered beginning in the southwest corner of the box and 

scanning eastward then the next row 10 minutes north, and ends with the data 

point for the northeast corner of the box (see figures below). 

A single 

Within a tape block there are data 

These values apply to a 30x30 array where the 

Each tape block has the following format: 

Bits Meaning 

1-9 Latitude of SW corner of box where 

10-18 

19-57618 

57619-57660 Unused. 

57661-57720 

(0=90s,5=85s,90=equator,175=85n,180=90n,etc.) 

Longitude of SW corner of box in degrees east. 

900 64-bit groups containing data for each 

60-bit chucksum of previous 961 60-bit words 

point (see below) 

(can be ignored bymost users). 

Each 64 bit group has the following format: 

Bits Code Description 

1-6 RR Estimate of the number of significant 

7-12 DD General direction of ridges 

13-21 HMO Terrain elevation - Modal height 

ridges 
22-30 HH I Terrain elevation - Maximum height 

31-39 N LO Terrain elevation - Minimum height 

40-45 c1 Primary characteristics of terrain 

46-50 c2 Secondary characteristics of the terrain 

51 -57 www Percentage of water surface 

58-64 URB Percentage of urban development 

Significant Ridges - 

Subjective estimate of number of ridges and their 

orientation in tens of degrees (00-18) 

Terrain Elevation - 

Elevation in feet = (packed value - 100)*100. 

Note that zero values do not necessarily indicate ocean 

surface. Use primary terrain characteristic to identify 

ocean. 

Inland water bodies are coded with the elevation of the water 

surface (except in minimum field where it is always zero). 

Characteristics of the terrain - 

0- 

1- 

2- 

3- 

4- 

5- 

6- 

7- 

8- 

9- 

salt or lake bed 

flat or relatively flat 

desert (or, for high latitudes, glaciers or permanent ice) 

marsh 

lake country or atoll 

major valleys or river beds 

isolated mountains, ridge or peak 

low mountains or hills 

average mountains 

extremely rugged mountains 

62- ocean (identified in primary only). 

Percentage of water - 

For ocean areas at sea level the value is 100, for all other 

areas the range is 00 to 99 (large lakes or inland seas will 

not be coded as 100). 

Percentage of Urban Development - 

Not updated (reflects highly subjective judgments from the maps 

used). 

General Information 

The model terrain height has been contoured at NCAR, and major problems 

identified in these plots have been corrected by the Navy. 

summaries of all parameters indicate that there are still some invalid 

elevation values and unexplained code values, especially in the terrain 

characteristics field. As of this date no further information is available on 

Distribution 
these problems. Occasional occurrences of full range values (all bits on) are 

assumed to indicate missing data. 

The data distribution summaries showed a strong tendency for elevation values 

of cluster around multiples of 500 feet. This is probably due to the contour 

intervals in the original maps or to some other characteristics in the method 

of reading map values. 

The true resolution of the data is reduced to 20 minutes poleward of 70 

degrees latitude, but data values are still present for each 10 minute square. 

More information on the original data format, the sources of the data, the 

methods of reading the data, and the routines designed for the Navy to read 

the original format is available in documentation by the Fleet Numerical 

Oceanography Center and Meteorology International, Inc. (refer to address 

mentioned previously on page 4.) 

General Information UDdate - Dec. 1984 

Various users have noted a large number of bad data points in the minimum 

elevations. A few bad points in the modal and maximum elevations have also 

been identified. An attempt has been made to remove these bad points and 

replace them with estimated values. 

edition of the data were found to have many fewer problems and these values 

were used for all minimum elevations north of 30 South. Checks for 

unreasonable values and gradients were run and comparisons of min, mode, and 

max were made. The results of these tests were manually inspected and where 

estimates seemed better than the original values, they were inserted in the 

set. checks were run on the minimum, modal, and maximum elevation only. No 

checks were run on the other parameters. 

The minimum values from a previous 

There are most likely still some erroneous values in the set, but most of the 

totally unreasonable values have been removed. Note that the minimum 

elevations are coded as zero for all water surfaces regardless of the true 

elevation of the water surface (even when this surface is below sea level). 

In some areas the elevation values are constant over one degree areas 

indicating that the resolution is not truly IO minute in those areas. 

general, the modal elevations seem to be more reliable than the minimum or 

maxi mum. 

In 

This corrected set will be a=the primary archive set, but the uncorrected 

earl i er versions are avai 1 ab1 e on request . 

Rouqhness ComDutations 

Stephano Tibaldi, European Center for Medium range Weather Forecasting, has 

used those elevations to compute estimates of surface roughness. His method 

for computing roughness length (Z) over a suer-defined area containing 

multiple 10' squares is given on the following page. Note that his relative 

maximum are determined by examining the 8 surrounding squares. When looking 

at data which is poleward of 70 degrees, use every other point to compensate 

for the true resolution of 20'. 

Global Cliange Ihta Base Africa Docwrierrtution Appendix J-17 
Formula to compute the roughness length 2, : 

where: 

N 

F 

ni 

& 

4- 

p 

fi 

Pi 

- number of relative g. maximum in the 

user-defined grid square 

- surface area of the user-defined grid square 

- number of significant ridges in the 10' grid square 

- me= height in the 10' grid square 

- maximum height in the 10' grid Square 

- minimum height in the 10' grid square 

- surface area of the 10' grid square 

- pmportion of the user-defined grid square occupied 

by the 10' grid square 

Global Change Data Base Africu Docrmienmtiorr Appeiidix J- 18 
Notes on the FNWC Terrain Data Set 

Navy 10' Global Elevation Values 

27 January 1981 

Dennis Joseph 

NCAR 

Documentation by Meteorology Internationa, Inc. and Fleet Numerical Weather 

central describes in detail the format and content of the elevation data tape. 

The actual layout of tape blocks was not described in these documents, and 

this note explains this layout and make a few other observations about the 

data. On the FNWC system, long logical tape records are segmented into 

shorter tape blocks. Since each block contains an integral number of the 96- 

bit segments, this format has been maintained as a simple way of avoiding 

excessively long tape blocks. 

labels have been removed so that the tape contains only one file. 

The only change made at NCAR is that type 

Each 10 degree square of data consists of one 96-bit identifier segment 

followed by 3600 96-bit data point segments. 

consecutive tape blocks. 

and each contains 320 96-bit segments. 

length and contains 81 96-bit segments. 

of the 12th block. 

the globe. Thus, the tape contains 648 groups for a total of 7776 blocks. 

Each square is recorded in 12 

The first 11 bolocks are 512 60-bit words in length 

The 12th block is 130 60-bit words in 

There are 24 unused bits at the end 

There is one 12-block group for each 10 degree square of 

Users should note carefully the order of the elevation values. 

squares are organized with longitude varying fastest and sequential squares 

moving westward. The hemispheres are separate, NH first, and each is 

organized equator to pole. The labelling conventions have resulted in some 

confusion as to where values apply, especially in the Southern Hemisphere. 

data is labelled with one degree labels, but it is not really organized by one 

degree squares. The one degree box has no effect on the organization in hte 

NH, but does result in unusual ordering or the data in the SH. 

The 10 degree 

In the Northern Hemisphere, ten degree squares and one degree squares are 

labelled with the lat-lon of the SW corner. 

data values are organized with longitude varying fastest, but sequential 

Val ues move eastward. 

International document has diagrams showing this organization on pages 7 and 

9. 

Within 10 degree squars, the 10' 

Lati tude values move pol eward. The Meteol ogy 

In the Southern Hemisphere, ten degree squares are labelled with the lat-lon 

of the NW corner and the one degree squares are labelled with the lat-lon of 

the NE corner. 

within the ten degree square in which they appear on the tape. While one 

degree values scan eastward, the ten minute values within each one degree 

segment actually scan westward. 

within ten degree squares for each hemisphere. 

This results in a column of one degree points which are not 

The following diagrams show the data layout 

We hope to be able to reorganize the data in the near future so that all data 

will scan in the same sense. 

................................ 

Global Cliunge Data Base Africu Docurnerrrutiorr Appendix J- 19 
H0tth.m WhU. 

' os 15 2s 35 45 55 

01 14 24 34 44 54 

03 U 23 33 43 53 

02 12 22 32 42 S2 

01 11 21 31 41 51 

00 10 20 30 60 50 

SI 61 31 21 11 01 

52 42 32 22 12 02 

53 43 33 23 13 03 

54 44 34 24 14 06 

55 45 35 25 15 05 

rmf. coordinate 1 d.8re.a longitude 

BB 

DD 

ridges - 88timat.d numbor of significant ridges 

direction/gamtrl orirntrtioa of th ridges - 0 to 18 dag..range 

terrain dev8tIOn - modal - Wght u read from contour linea 

usht *e n 

s.(n.lraras R 0 

BIII 

HLO 

co&d u positivo oar8 in hundred. of ferc (8.8. 005-500 ft dev.) 

balm sea lam1 is codad 4n (401-100 ft bdov su level) 

Cl primary terrain characteristic - judgod from relative topography 

CODE: ' 

Q and map symbols ..condary " I# 

0 - ult or hka bad 

1 - flat or relatively flat 

2 - -art (7W 19 - glaci.rs or prrnvnr~t ice) 

3 - mnh 

4 - Irk. country or 8tdl 

5 - mjor valleys or rioor b.ds 

6 - isolated wuatal~, rldgm or peak 

7 - lov motmtaizm or hills 

8 - average mountains 

9 - extremely nagged nmunuins 

VWW percentage of vatar surface - eathated from chart details; wm 

laad recorded is 2%; wat8r is 98% or lua. 

m(B percentage of urban developmurt - estimated from map v/populatcd areas 
-KINTEGRATED ELEVATION AND BATHYMETRY 

CITATION: Kineman. J. (ed.), 1989. Intepted Global Elevation and Bathymetry 

Digital Data. NOAA/NGDC/WDC-A, Boulder, Colorado. 4.7 MB. 

Global Change Data Base Africa Docuriieri/ution Appendix K- I 
Blank page retained for pagination 
K. INTEGRATED ELEVATION AND BATHYMETRY 

DATE: 1985 

SOURCE: 

InteFated From best available 5- and IO-min digital sources. 

(1) US Navy Fleet Numeric Oceanographic Center 

(2) US Defense Mapping Agency 

(3) US Naval Oceanographci Observatory 

(4) Bureau of Mineral Resources of Australia, and 

(5) Integration of (1) - (4) above: 

Monterey, CA 

Department of Scientific and Industrial Research of New Zealand 

Margaret Edwards (5-min compilation) 

Washington University 

Earth and Planetary Remote Sensing Laboratory 

St. Louis, MO 

NOWGDC 

Boulder, CO 

(6) 10-min resampling: John Kineman 

CONTRIBUTOR: 

Peter Sloss 

NOMGDC 

Boulder, CO 

DATA CENTER: NGDC 

GEOGRAPHIC SAMPLING: 10-min grid (averaged from 5-min grid) 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Most recent information 

REFERENCES: 

Edwards, Margaret IIelen. 1986. "Digital Itnnge Processing of Local 

and Global Balhymetric Data." Master's Thesis. Washington University, 

Department of Earth and Planetary Sciences, St. Louis, Missouri, 106 p. 

(Excerpt included.) 

(continued on the next page) 

Glob;tl Change Datu Base Afiicu Docurrtenfotion Appcntlix K-3 
Haxby, W. F,, et al, 1983. "Digital Images of Combined Oceanic and 

Continental Data Sets and Their Use in Tectonic Studies." EUS 

Transactions of the American Geophysical Union, voi. 64. no. 52, 

pp. 995-1004. (Excerpt included) 

--------- 

FILENAMES: AFELBA Integrated cievation and bathymetry (feet) 

Global Change Data Rase Apico Documentotion Appendix K-4 
INTEGRATED ELEVATION AND BATHYMETRY 

DOCUMENTATION FILE 

File Name: AFELBADOC 

image title : Integrated Elevation and Bathymetry (feet) 

data type : integer 

file type : binary 

rows : 438 

columns : 480 

mi n imum : -5.9530000000E+03 

maximum : 3.5240000000E+03 

cell x : 1.6656666666E-01 

cell y : 1.66666666673-01 

legend : 15 

category 1 : -5360 -4769 

category 2 : -4768 -4176 

category 3 : -4175 -3584 

category 4 : -3583 -2992 

category 5 : -2991 -2399 

category 6 : -2398 -1807 

category 7 : -1806 -1215 

category 8 : -1214 -622 

category 9 : -621 -30 

category 10 : -29 to 562 

category 11 : 563 to 1154 

category 12 : 1155 to 1746 

category 13 : 1747 to 2339 

category 14 : 2340 to 2931 

category 15 : 2932 to 3524 

(category 0 : -5953 to -5361) 

[This legend is only appropriate for displays which reclass 

(contrast stretch) the continuous values into 16 classes.] 

Global Change Duta Base Ajkica Donrmeriturion Appendix K-5 
Blank page retained for pagination 
WASHINGTON UNIVERSITY 

Department of Earth and Planetary Sciences 

DIGITAL IMAGE PROCESSING AhD INTERPRETATION OF 

LOCAL AND GLOBAL BATWMETRiC DATA 

by 

Margaret Helen Edwards 

A thesis presented to the 

Graduate School of Arts and Sciences 

of Washington University in 

pdd fulfillment of the 

requirements for the 

degree of Master of Ans 

December, 1986 

Saint Louis, Missoflri 

Global Change Data Base Afiica Dociinrenkltiorr Appeiitiix K-7 
Blank page retained for pagination 
5. '3BDB5 STCDY 

5.1. INTRODUCllON 

The second srudy involves digital manipukuon and interpretadon of cfie $obd 

bathyrncuic database, DBDBS. The fmt step in this study was to generate a global 

topo,mphic dxaset using the global bathymetry and three condnental darases, all with 10" by 

10" resolution or better. The Slobal topographic dataset was used to generate 3 selies of three 

map snetts of Ea~h topo~phy in shaded relief and colorcoded shaded relief form chat have 

betn published by N.O.A.A.'s Xadonal Geokhysicd Data Center [ l985]. Subsequesdy, a 

subset of the bathyrnezrk dataset was used to examine the flexure that occ~113 dons hcmt 

zones in the East Pacific. Fi,w 5.1 is a flow diagram depic~ns the image proctssing 

techiques that werc used to generate the global topographic dataset 

5.2. GEXRATION OF THE GLOBAL ELFJATION DATASET 

The first portion of *hs srudy involves the genemuon of a global, dis$;,tal eievadon 

dataset Four sqaratc datasets with resolurions of 10" by 10' or berm wert mtd to zene=Titc 

the g!obaI map. The datasets were incrged in such a way that the best spanal nsoiurion da.u 

were rcrcained for any given Iocsaon. In addidon, the data were merged using a priority 

scfierce that sc!ecxd data on the basis of data dabiliry. For example, all of the octanic dat:. 

we= :&en hm the bathymezic database even though dl thrct of the conkend dames 

contained occvlic data for areas adjacEqt to the coastkes, bec3use the bQ,thyme*c ciause: 

was assumed to have mo,rc.tcLiable ccemic dara The data were displayed in difkxt mu 

projeczons with ae?Welevadon ;e?rcsented as colors, 

Tne resuicne - hazes de?ict many textsaid and marine landforms such as mountains, ?iains. 

ridges, rcnches, seamounts, and condnend margins. In this seckx the various datases 

and tec.hniques used to genmte :he giobal dataset art ~~SC.JSS&. 

values, and shaded :ckf 33~s. 

Global Change Data Base Africa Docuntc.rrtutiorr Appendix K-9 
DBDBS IMAGE PROCESSING 

< 

Place datasets 

in gridded format 

Expand pourtr 

rcsoluaon data 

I Merge datasets according - 

to priority scheme: 

1) DBDBS 

2) Nonhern 5' topo 

4) 10' tODO darastr 

3) southan 5' topo 

Scale globd dataset 

to numaid age of 

eEYJ coior imaees 

Scale global dawer 

to numerical range of 

A Data ready 

( to be displayed as 

vded imagv 

Figure 5.1 - This diagram regreriem rhe image processing rechiques thr were used ro 

generare :he global dararer. In this figure, inpu and oqu are represenred as ovak and 

processes are represenred as boxes. The flow of processing is represenzed by rhe arrows. 
The global e!evadon dataset was compiled from four seprate darners (Table 1). T 

&met us& as the bast map is Digital Bahpcmc Database 5 (DBDBS; formtriy known 

SYYBAPS) available from H.O.A.A.'s National Geophysical Data Cexcr. This databas( 

composed of 5' barhymcnic avenges and was compiled using bathyrnemc contour cham 

(set Secrion 2 for a detailed description of the compilation tdhiques). The bathymeec 

datase~ was used as the base map because it has the most covtr-age a~ the best resolution. 

second and third darasets in he priority scheme have comparable resolution to the 

bathymetric darasct but cover smaller land artas. The second dataset contains 5' avcrqe 

elevations for pars of North America and Empe and is also availabk at the Yationai 

Geophysical Data Center. The third dataset covers Ausualia, Tasmania, and Yew Zedai~i 

is composed of 5' average eievarions, and was obtained from William Haxby and Je5e:r 

Weisst1 at Lamont-Doheny Geological Obsavatory. The Ausnalian dataset was compiik 

by the Burcw of Mineral Resources, Canberra, and the New Zealand Deparunent of 

ScienrifTc and Industrial Research waxby er af., 19831. The founfi and final dataset was 

mared by the U.S. Navy Fleet Numerical Octvrography Center using 100 foot contour 

charts. This darastt has the poorcsz rcsolurion but covers the m of the remaining land 

arm. Data in the fourrh dataset have 10' resolution and are composed of modal elevauor,s 

rarher than average elevations (e.g. rather than averafig the values within an ma, his 

dataset contains the devarion most kquentiy encoutehd within the m3). Areal covmgc 

of r.he four darasets is ilIusnated in the simple cylindrical map of the Eyrh shown in E;.x 

5.2. The formars on digital tape of each of the four datasets varied considerably and hex= 

sey-ate programs werc developed to convert the data to a added image format (Appenku 
TXBLE 5. I Darascs used to generate global digital elevation daraset 

~amt/Coverage Resolunon Type of Measurement Avdability/Cornpilarion 

DBDBS - Major 5' by 5' average elevations Available from NOAA's 

Wies of water NGDC; Compiled by U.S. 

Naval Oceanographic Office 

Ccnd and 5' by 5' average elevations Available from NOAA's 

Xonh America, NGDC; Prepared by the 

and Eiuope Defense Mapping Agency 

Ausnalh, 5' by 5' average elevations Compiled by the Bureau of 

Tasmania, and Mineral Resources, 

?Jew Zealand 

---- ---- ------------------------- - 

_----- --- -------------------------- - 

_-_-------_I---- --------_I----------------- 

-----_I----- -- ---I_-------------- 

Canberra, and the New 

Zealand Depanment of 

Scientific and Indusuial 

Research; Gridded by Wm. 

Haxby and colleagues, 

Lamont-Dohesry Geological 

Obstrvatory 

Afiica, Asia, lo' by 10' modal elevations Compiled by the U.S. meet 

south America, Numaical Oceanography 

Nonhern Canada, Ccntm, Available hrn 

etc. NOAA's NGDC 

- - ---I----- 

Figure 52 - Mq of rhe four daursers used to generare the global ropographic dutases. The 

dara shown as biue represent rhe global barhymerric darner; rhe dma in yellow cover 

Awalia, New Zealand, and Tamuutia; green areas represent rhe &a ured for most of Norrh 

America and Wessern Ewope; the &mer shown as red was used w fill in all remaining 

area. The elmracier and availabiliry of each of these &men is described in Table 5.1. 

Glol):tl C'hange Data Base Africa Docwarentution Appendix K- 12 
Tne iir ste? afrer conve.xing the data to image format was :o expzid he 10' dx3 by 2 

fac:or of two using the biiinev interpolation expansion algorithm described in Secrion 3.3. 

The poorer resolution data werc expanded rathc rhan decrexiqg the resolution of the other 

darasets to preserve the ma..um information content Data values in the combined darast.: 

ranged from a minimum value of approximately -10800 metars 10 a maxinun value of 

+8750 meters. For processing prposes it was convenient to scale the data to fit into smaller 

numerical ranges. The data were scaled in two ways; to a range from 0 to 255 and to a range 

benveen 0 and 500. The reason for the mo different scales was that it was more conveniect 

for the processing system hardware to display the data with the smaller numerid mge 

while the data that ranged hm 0 to 500 wert used for computational purposes. T3e data 

werc rcerged to make wo global topoppnic maps of the Earth, one map containing byte 

information (range 0 to 255) for each grid& location, and another containing data ranging 

hm 0 to 500 for each location. These two maps were then combined to produce the global 

elevarion dataset shown in Fi,prc 5.3. 

The merged dam wert displayed in a variety of map projections with elevation data 

re?rtsented as gay values, colors, or shaded relief maps (Egurcs 5.4 and 5.5). The shaded 

relief maps werc generated using the algorithm described in Sccion 3.3 and Appendix C. In 

this instance the simuiated sun was assumed to be 20 degrees above the western horizon. 

The shaded relief maus in F;,,gres 5.4b and 5.5a were senerated using the data that were 

scaled 5om 0 to 500. Attempts to creace shaded relief maps hm the data Wirh the srdc 

numeic range resulted in subdued expression of many topogaphic fcintrts. For example, 

the Mid-hdanuc Ridge is much more discdNe in Figure 5.5a than it is in Figure 53. Tie 

reason for the subdued appeuanc: of the data in Fi,.;urt 5.5b is that alaosc wenty kilometers 

of relief have been compressed to 3 mgc two orders of magnitude smaller than the mge of 

the orignal d3ta. Using the data that had been scaled from 0 to 500 increased the value of the 

auuarcnt *- slope of the dara, thereby dqic5ng -3ter de*d in the shaded =Lief map in Fip 

5.5a Trial and mr experimentxion determined that a range of relief much ~~CUE than 0 to 

500 led to disrorrion in the images due to KOO high slopes during computation. 

Global Change nata Base Africa DocrtnwntuTion Appendix K-13 
Figure 53 - Mercmr map projecbn of rhe global KOpOgraphy of the Emh. The resolunon 

of the dara has been decreased by a facror of 4 so rhar rk enrire duuzser could be 

phorographed in om inage. Lanzz.de Limiu are 72 " S KO 72 " N; longirude Limirs are 90" W to 

I20 O W. Conrinemal orrtiines and 10 O grid mark are shown in black Colors in the bar ar the 

bortorn of rhe image have been assigned ro represenr approximarely eqwl areu under rhe 

h+ypsomerric cwe for rhe Eanh. 

Global Clinnge Dnta Base Africa Docurnentation Appendix K- 14 
Figure 5.4 - In Figrue 5.4a gray values have been convened to colors uing one of the color 

look-up tables given in Appendix D. The colors are the same as those sbwn in the color bar 

in Figure 53. Tk dara range from &id IO " S to 70 "N and from Iongirude 90 O W to IO O 

E. Figwe 3.46 is a color-coded shaded relief map of the same area. In rhs image rhe colors 

from Figure 5.4a he been overlaid onro the shaded rei&-map shown in Figwe 5ia. The 

advansage of combining color comours wirh a skded reliefmap (both of which acrually 

&Tic; the same infonnanon) is rhar rhe combined/conn is a pseudo-rhree-dimensional image. 

Global Cliange Data Base Afric-u Docunicvttutiort Appetidix K- 15 
Figure 55 - Figure 55a is a shaded relitf map of rhe rwrrhern Arlannc Ocean generared 

using dara which had been compressed ro a nmric range of 0 to 500. Figure 55b is a 

shader! re!itfmap ojrhe same areu generased using byte dara wirh a range of 0 lo 255. Note 

ihar ihe ropography in ihis image is subdued compared io rhe shaded relicif map in Figwe 

?Sa. 

Global Clitlrige Data Base Aflicci Docirrireritutiori Appendix K- I6 
As in the Seakam study, knages of the global topography were generated wich color 

contours overlaid on top of a shaded relief map. Several experiments with color were 

performai before the final color scale was chosen (Appendix E). The final color scde 

shown in Figures 5.3 and 5.4 was panerned after "The World," Series 1145, Edition 

l-DiMATC, which was prepared and published under the direcdon of the Depamnent of 

Defense by the Defense Mapping Agency and the Topographic and Hydrogzphic Centers. 

The color scde was dettxmhed using a data disaibuuon CUN~ (approximately the equivalent 

of a hypsornemc curve) for the elevation dataset Colors were assigned to represent 

approximatc!y equal arcs under the cme. The colon were chosen so that oceanic data were 

rqrescnted as various shades of blue, the deepest blues occurring at trench locations and :he 

lightest blues occming at dephs close to se3 level. The continental data were reixesentec by 

multiple hues ranging from gcen at se3 level through yellow, red, and finally to white at the 

highest elevations. A color bar of the final color scde is shown in Fi,gurc 5.3. In total, 

thirry-eight colon werc used to generate the frnai color scale. A color could represent an 

elevation range ;is smaU as 75 mtftrs or as large as 2 kilometers based on where the color fell 

along the "hypsomcrsic" color CUNC. The colorcoded data were displayed as a simple color 

map or as color mcduktd by a shaded rciitf map. The gray tone and color-coded maps 

were then displayed in a variety of map projections including Mercator projec5ons, polar 

stereographic projections, and projections that simulate spacecraft views of the Emh (FI, 

5.6). The resultant images dqicr a variety of intercsdng morphological features inc!ucLn% 

seamounts, ridges, mnches, and continental margins. 

* UlJy 

Global Change Data Base Africa Docunicrrtc~tion Appendix K- 17 
Figure 5.6 - Figure 5.6a is a polar srerwgraphic map of the Nonh Pole. The fonnulas for 

convening from a sh.oie cylindrical lo a polar slereographic projecnon are given in Appendix 

A. Consinend ourlines are shown in black. The dara in thir image range to a larinrde of55 4 

Figure 5.6b is a simulared spacemfi view ofthe Emh cenrered over 135 O E and 30 ON. 

The disrance fiom rhe cenrer of Ihe planer to rhe hgorhencal focal plane is 580,000 

kilomerers. The theory used to generate these images is discussed in Appendix B. 

Cannnenral ourlines are shown in while. 

Global Change Data Base Africa Doc~irr~rcrtfutiarl Appendix K- 18 
-LMICRO WORLD DATA BANK II (MWDB-II): COASTLINES, COUNTRY 

BOUNDARIES, ISLANDS, LAKES, AND RIVERS 

CITATION: Pospeschill, F.. 1988. Micro World Data Bank II (MWDB-11): 

Coastlines, Country Boundaries, Islands, Lakes, and Rivers. Digital 

Data. NOAA/NGDC/WDC-A, Boulder, Colorado. 2.5 MB. 

Global Change Data Base Ajhico Docutrienturion Appendix L-1 
Blank page retained for pagination 
L. MICRO WORLD DATA BANK II (MWDB-TI): COASTLINES, 

COUNTRY BOUNDARIES, ISLANDS, LAKES, AND RIVERS 

DATE: 1988 

SOURCE: 

Digitized from available sources. 

(1) CIA WDB-I1 

(2) USAF (Generalized to 1-min.) 

(3) Fred Pospeschil 

Micro Doc 

3108 Jackson Street 

Bellevue, Nebraska 68005 

CONTRIBUTOR: 

Fred Pospeschil 

Micro Doc 

3 108 Jackson Street 

Bellevue, Nebraska 68005 

DATA CENTER: NGDC/WDC-A 

GEOGRAPHIC SAMPLING: 1:3,000,000 scale vector lines, unlabeled 

COVERAGE: Global 

PROJECTION: Lat/lon 

TIME SAMPLING: Most recent information 

REFERENCES: 

FTLE NAMES: 

COASTS 

COUNTRY 

ISLANDS 

LAKES 

RIVERS 

African Coastlines 

African Country Boundaries 

African Islands 

African Lakes 

Mican Rivers 
MICRO WORLD DATA BANK IT: COASTLINES 

DOCUMENTATION FILE 

File Name: COASTS.DVC 

title 

data type 

file type 

object type 

coord. span 

coord. unit 

min X 

max X 

min Y 

max Y 

MWDB-I1 Coasts, Detail Level 1 

integer 

ascii 

line 

1.6666666660E-02 

-1.2000000000E+03 

3.6000000000E+03 

-2.1000000000E+03 

de9 

2.28000000003+03 

Global Change Data Base Afiicu Doctmrerrfun'on Appendix L-4 
MICRO WORLD DATA BANK II: COUNTRIES 

DOCUMENTATION FI4I 

File Name: COUNTRY.DVC 

title 

data type : 

file type : 

object type : 

coord. span : 

coord. unit : 

min X 

max X 

min Y 

max Y 

MWDB-I1 Countries, Detail Level 1 

integer 

ascii 

line 

deg 

1.6666666660E-02 

-1.2000000000E+03 

-2.1000000000E+03 

3.6000000000E+03 

2.2800000000E+03 
MICRO WORLD DATA BANK II: ISLANDS 

DOCUMENTATION FILE 

File Name: ISLANDS.DVC 

title 

data type 

file type 

object type 

coord. span 

coord. unit 

min X 

max X 

min Y 

max Y 

MWDB-I1 Islands, Detail Level 1 

integer 

ascii 

line 

deg 

1.6666666666E-02 

-1.2000000000E+03 

3.6000000000E+03 

-2.1000000000E+03 

2.28000000003+03 
MICRO WORLD DATA BANK II: LAKES 

DOCUMENTATION FILE 

File Name: LAKES.DVC 

title 

data type 

file type 

object type 

coord. span 

coord. unit 

min X 

max X 

min Y 

max Y 

MWDB-I1 Lakes, Detail Level 1 

integer 

ascii 

line 

1.66666666003-02 

-1.2000000000E+03 

-2.1000000000E+03 

de9 

3.6000000000E+03 

2.2800000000E+03 

Globul Change Data Base Aj?icu Dociaitcrttahort Appcriclix L-7 
MICRO WORLD DATA BAiW II: RIVERS 

DOCUMENTATION FILE 

File Name: RIVERS.DVC 

title 

data type 

file type 

object type 

coord. span 

coord. unit 

min X 

max X 

min Y 

max Y 

MWDB-I1 Rivers, Detail Level 1 

integer 

ascii 

line 

de9 

1.6666666666E-02 

-1.2000000000E+03 

3.6000000000~+03 

-2.1000000000E+03 

2.28000000003+03 
Micro World Data Bank I1 (MWDB-11) 

The full WDB-I1 is a digitial map data base produced by the 

Central Intelligence Agency (CIA) and distributed by the National 

Tec.hnica1 Information Service (NTIS) , U.S. Department of Commerce, 

5285 Port Royal Road, Springfield, VA, 22161. Miczo WDB-I1 is a 

highly compressed version which is suitable for use on micro 

computers and was put in this configuration by Micro Doc. Before 

describing Micro WDB-I1 a few words should be said about the source 

file - WDB-11. The following paragraphs are paraphrased from the 

NTIS overview of WDB-11. 

WDB-I1 is a digitial representation of the world 

coastlines and boundries suitable for use in automated mapping 

systems. It contains approximately six million discrete 

geographic points and was digitized using all available sources 

of information. Map scales used range from 1:750,000 to 

1:4,000,000 with a nominal scale of 1:3,000,000. These points 

are grouped by and identified as describing (1) coast lines, 

(2) country boundries, (3) state boundries (USA only) , (4) 

islands, (5) lakes, and (6) rivers. Each of these groupings is 

further broken down into features and subordinate 

classifications/ranks. These ranks are hierarchically 

structured, and are also used for plotting symbol definition. 

Clearly, WDB-I1 is an excellent data source when making large (4 

x 6 foot) plots on a mainframe or minicomputer. It is, however, 

somewhat large (150 - 200 megabytes) for use on microcomputers. Even 

on the larger commercial online graphics systems, many points have to 

be filtered out before generating displays. For this reason many 

people have spent considerable time over the past to filter and 

compress this data into a form which could be used in desktop 

computers. To these people we wish to express our appreciation - 

particularly Antonio Riveria who provided us with a copy of the 

latest download of the file. 

The present version began as a three megabyte ASCII text file 

which contained some 179,000 points selected from all six of the line 

types desczibed above. This file was then converted into a sixteen 

bit integer format which reduced the size to just over one megabyte. 

Since this was still a little large for most five inch disk formats 

the file was divided into six files - one for each of the six line 

types. The coast line file was further divided into two files as it 

was over 400 KB. With this processing completed the file was 
configured such t3at it could be readily moved to most desk top nicrgcomputers using the MSDOS disk format. 

?rovides a Srief statistical overview of the six line types which are 

csntained in the data base. 

The following table 

Detail COAST 

Level .PNT 

1 29150 

2 35077 

3 5470 

4 4000 

Total 75175 

5 1478 

Lines 208 

Size (KB) 449 

COUNTRY 

. PNT 

a573 

10172 

1496 

1249 

869 

22359 

301 

134 

STATE 

. PNT 

798 

95 9 

153 

101 

248 

2259 

111 

14 

ISLAND 

. PNT 

13781 

16077, 

2524 

1945 

35171 

a44 

344 

211 

LAKE 

. PNT 

5886 

7080 

1023 

820 

309 

15118 

103 

90 

RIVER Total . PNT 

11146 69339 

13217 82599 

1254 9502 

760 5365 

28194 179331 

1817 12526 

196 1~63 

169 1067 

The above table shows the number of pcints which are in each 

file and for each level of detail. 

segments in each file and the file sizes in kilobytes. 

Detail level 5 contains tbe fewest points and produces the least 

detailed graphics image. The points at each level of detail are 

additive to the points a t a l l lower levels. 

detail level 4 the points from both levels 4 and 5 must be used / 

retrieved, Therefore, if one were drawing coast lines at detail 

level 4, a total of 5478 (4000 + 1478) paints would be precessed. 

These 5487 points will describe 208 line segments of various lengths. 

of detail. 

selected, there will always be 208 line segments described by the 

c3ast line data file. In some cases, mostly in the islands file, 

line segments degenerate to single paints at the lower levels of 

derail. Therefore, programs which process this data must check to 

see whether each line segment header record is followed by a point 

rzcord or another line segment record. 

larer. 

Actual use of the data in these files has shown that most 

displays whicfi cover a reasonably large area do not need all of the 

detail provided at level 1. 

the U.S. 

and greatly reduces the number of points which must be processed. 

support Generation of large area displays, four compositefiles were 

created. These are WDB3.U, WDB4.ALLr WDBS.AU, and WDB5.CCI. The 

fksc three. contain all line types for the indicated detail level 

(WDB3.U is level 3, etc.) . WDB5.CCI contains only country, coast 

lhe, and island line segments and is intended for 'quick and diztj!' 

testing work. 

It also shows the number of line 

For example, when using 

Note that the same number of line segments exist at all levels 

For example, regardless of which level of detail is 

More will be said about this 

A large area would be a major portion of 

To 

For large area plots level 3 or 4 is normally suf2icient 

Micro WDB-11 Composite Files D~IIp3=~IIpP~I~~~~=~~==n= 
Detail WDB3 WDB4 WDBS WDBS 

Level .ALL .ALL .ALL .CCI 

3 12480 0 0 0 

4 9365 9502 0 0 

5 4315 5365 4315 3664 

Total 26160 14867 4315 3664 

Lines 1263 1263 1263 853 

Site (XB) 157 82 25 17 

All Micro WDB-I1 files are sequential files composed of fixed 

length six byte records which can be accessed randomly. Each record 

is composed of three 16-bit signed integer fields which can be read 

by all microcomputer languages. There are no record seperators used 

in these files. The record layout is : 

I Code I Latitude I Longitude I 

In Pascal, the record definition is : 

POINT = RECORD 

CODE : INTEGER; 

LATITUDE : INTEGER; 

LONGITUDE : INTEGER; 

END; 

Within the MWDBII files the records are stored as shown below. 

In some cases there may be hundreds of points in a single line 

segment. In other cases there may be consecutive header records 

which locate individual points to be plotted. 

The same format is used for both the line header records and 

point records. The usage of each field is as follows : 

CODE - Line Header Records. 

The CODE field is used to : 

1. Identify the type of line segment which is described by 

the point records which follow it, 

2. Identify the line or point within the line type, and 

3. Provide the coordinates if the record describes a point. 
In header records the CODE field is always a positive four digit 

number in the foLm of : ABBB where 

A =' BBB = CODE = 

1 = Coast lines 001 -> 208 1001 -> 1208 

2 - Country boundries 001 -> 301 2001 -> 2301 

4 = State boundries 001 -> 111 4001 -> 4111 

5 = Islands 001 -> 344 500i -> 5344 

6 = Lakes 001 -> 103 6001 -> 6103 

7 = Rivers 001 -> 196 7001 -> 7196 

CODE - Point records. 

In point records the CODE field contains the detail level. The 

detail level is a value in the range of 1 through 5. To properly 

retrieve points at any detail level use the following comparison : 

IF CODE >= DESIRED - DETAIL - LEVEL THEN ... -_ 

LATITUDE and LONGITUDE - In both header and point records. 

The latitude and longitude fields contain the coordinate values 

expressed in signed minutes. 

with the formula COORD VALUE := (Degrees 60) + Minutes. Thus, the 

ranges of coordinate values in MWDBII are : 

The values in these fields are computed 

90 OON = 5400 

90 00s - -5400 

180 OOE - 10800 

180 OOW a -10800 

MWDBII uses the standard four quadrant Cartesian coordinate 

system in which north, south, east, and west are referenced to the 

equator and the Greenwich meridian. 

coordinates in the header record are the beginning of the line. When 

a header record is followed by another header record then the 

coordinates in the header record should be plotted as a single point. 

When working at low levels of detail it is common to find multiple 

consecutive header records which locate isolated points. There may 

also be gaps between some of the lines. 

public domain and may?qncorporated into other products without fee so 

long as appropriate credits are included. An appropriate credit line 

would be - "This product contains/uses data and/or code placed in the 

public domain by Fred Pospeschil and Antonio Riveria. 

coordinate data was created by the Central Intelligence Agency." 

Graphics support was from Flexi-Graph, New Orleans General Data 

Services, 7230 Chadbourne Drive, New Orleans, LA 70126 and Micro Doc, 

3108 Jackson St., Bellevue, NE 68005. 

When a header record is followed by point records the 

This documentation and the associated files are placed in the 

Original 

All programs were compiled with MS Pascal Version 3.20. 

ALL USERS OF THESE MATERIALS ARE TOTALLY RESPONSIBLE FOR THE13 

USE AS THESE MATERIALS ARE PROVIDED WITHOUT EITFER EXPRESS OR IMPLIZD 

WARRANTIES OF ANY KIND. 

Global Change Data Base Ajkicu Doc*untrrr/otiort Apixendix L- I 2 
-MWORLD DATA BANK II (WBDII): COUNTRY REGIONS AND RIVERS 

CITATION: EROS Data Center, 1990. African Nations and Rivers. Digital Data. 

EDC. Sioux Falls. S.D. .42 MB. 

Global Change Datu Dase Africa Duc*irnrcrrtutiori Appendix M- 1 
Blank page retained for pagination 
M. WORLD DATA BANK II: COUNTRY REGIONS AND RIVERS 

DATE: 1990 

SOURCE: 

(I) CIA-World Data Bank I1 

(2) Reprocessed by: 

EROS Data Center 

Sioux Falls, SD 

CONTRIBUTOR: 

EROS Data Center 

Sioux Falls. SD 

DATA CENTER: EDC 

GEOGRAPHIC SAMPLING: 1: 1,000,000 vector lines and polygons with labels 

COVERAGE: Africa 

PROJECTION: Lat/lon 

TIME SAMPLING: Most recent idormation 

REFERENCES: 

FILE NAMES: 

AFNATION African countries 

AFRNERS African rivers 

(;lolv:d Change Data Uase Afrh Docrcmeitfurion Appendix M-3 
WDB-II AFRICAN RIVERS 

DOCUMENTATION FILES 

File Name: AFRNERS.DOC 

image title : 

data type : 

file type : 

rows 

columns 

minimum 

maximum 

cell x 

cell y 

legend 

category 1 : 

WDB-XI African Rivers 

byte 

binary 

438 

4 80 

O.OOOOOOOOOOE+OO 

1.0000000000E+00 

1.6666666667E-01 

1.66666666673-01 

1 

1 Rivers 

{category 0 : unclassed} 

[This legend is appropriate for display of the individual classes 

(i .e. without rescaling) . ] 
WDB-TI AFRICAN NATIONS 

DOCUMENTATION FILE 

File Name: AFNATION.DOC 

image title : WDB-I1 African Countries 

data type : byte 

file type : binary 

rows : 438 

co 1 umn s : 480 

minimum : 0.0000000000E+00 

maximum : 6.8000000000Et01 

cell :c : 1.66667000003-01 

cell y : 1.66667000003-01 

category 1 : TUNISIA Tunisia (TS) 

category 2 : ALGERIA Algeria (AG) 

category 3 : MOROCCO Morocco (MO) 

category 4 : JERBA IS Jerba Island 

category 5 : LIBYA Libya (LY) 

category 6 : MADEIRA IS Madeira Island 

category 7 : EGYPT Egypt (EG) 

category 8 : CANARY IS Canary Island 

category 9 : CANARY IS Canary Island 

category 10 : CANARY IS Canary Island 

category 11 : CANARY IS Canary Island 

category 12 : CANARY IS Canary Island 

category 13 : CANARY IS Canary Island 

category 14 : W SAHARA Western Sahara (WI) 

category 15 : MAURITANIA Mauritania (MR) 

category 16 : MALI Mali (ML) . 

catqgory 17 : NIGER Niger (NG) 

category 18 : CHAD Chad (CD) 

cateaorjt 19 : SUDAN Sudan (SUI 

category 20 : ETHIOPIA Ethiopia (ET) 

category 21 : SENEGAL Senegal (SG) 

category 22 : BURKINA FASO Burkina Faso (Dahlak Arch) 

category 23 : UPPER VOLTA Upper Volta (W) 

cateaory 24 : NIGERIA Nigeria (NI) ~ 

category 25 : THE GAMBIA The Gambia (GA) 

cateqory 26 : CAMEROON Cameroon (CM) 

category 27 : DJIBOUTI Djibouti (DJ) 

category 28 : GUI-BISSAU Guinea-Bissau (PU) 

category 29 : GUINEA Guinea (GV) 

cateqory 30 : BENIN Eenin (BN) 

category 31 : SOMALIA Somalia (SO) 

category 32 : AR BIJAGOS Arquipelago dos Bijagos 

category 33 : GHANA Ghana (GH) 

cateqory 34 : TOGO Togo (TO) 

category 35 : CEN AFR REP Central African Republic (CT) 

cateqory 36 : IVORY COAST Ivory Coast (IV) 

category 37 : SIERRA LEONE Sierra Leone (SL) 

category 38 : LIBERIA Liberia (LI) 

category 39 : SHERBRO IS Sherbro Island 

csteqory 40 : ZAIRE Zaire (CG) 

category 41 : KENYA Kenya (KE) 

legend : 6a 
category 42 : TJGANDA 

category 43 : EQ GUINEA 

category 44 : CONGO 

category 45 : RIO MUNI 

category 46 : GABON 

category 47 : S TOME PRINC 

category 48 : TANZANIA 

category 49 : RWANDA 

category 50 : BURUNDI 

category 51 : CABINDA 

category 52 : PEMBA IS 

category 53 : ZANZIBAR 

category 54 : ANGOLA 

category 55 : MAFIA IS 

category 56 : ZAMBIA 

category 57 : MALAWI 

category 58 : COMOROS IS 

category 59 : MADAGASCAR 

category 60 : COMOROS IS 

category 61 : NAMIBIA 

category 62 : BOTSWANA 

category 63 : SOUTH AFRICA 

category 64 : WALVIS BAY 

category 65 : SWAZILAND 

category 66 : LESOTHO 

category 67 : MOZAMBIQUE 

category 68 : ZIMBABWE 

Uganda (UG) 

Equatorial Guinea (EK) 

Congo (CF) 

Rio Muni 

Gabon (GB) 

Sao Tome & Principe (TP) 

Tanzania (TZ) 

Rwanda (RW) 

Burundi (BY) 

Cab i nda 

Pemba Island 

Zanzibar 

Angola (A01 

Mafia Island 

Zambia (ZA) 

Malawi (MI) 

Comoros Island 

Madagascar (MA) 

Comoros Island 

Namibia (WA) 

Bot Swana ( BC) 

South Africa (SF) 

Walvis Bay 

Swaziland (WZ) 

Lesotho (LT) 

Mozambique (MZ) 

Zimbabwe (21) 

{category 0 : OCEAN) 

[This le?end is appropriate for display of the individual classes 

(i .e. without rescaling) . J 

(;lob ;rl Cliaii ge Data Base Afr-icw Docitnr en tu tiori Appendix M-6 
The Description OF WDBII 

WDBII contains five geographic areas such as North America, South America 

including Central America, Europe, AFrica and Asia including Australia. Each 

area is further subdivided into separate files for coastlines, islands and 

lakes (CIL), rivers (RIV), and international boundaries (BDY). In the case of 

North America, additional file PBY) further delineates the individual states 

of the USA and the provinces of Canada. WDBII uses map scales ranged from 1:l 

million in the case of the Middle East, to 1:4 million for the USSR. 

Nominally, a scale of 1:3 million is used to achieve the amount of detail 

desi red. 

The following is the subordinate classification/ranks used by CIL, RIV, BDY 

and PBY files. 

CIL file: - Coastlines, islands and lakes 

Code No Ranks 

01 

02 

03 

04 

06 

07 

08 

09 

10 

13 

14 

15 

Coastlines, islands and lakes on all maps 

Additional major islands and lakes 

Intermediate island and 1 i kes 

Mi nor i sl ands and 1 akes 

Intermittent major 1 akes 

Intermittent minor lakes 

Reefs 

Salt pans - major 

Salt pans - minor 

Ice shelves - major 

Ice shelves - minor 

G1 aci ers 

RIV file: - Rivers 

Code No Ranks 

01 

02 

03 

04 

05 

06 

07 

08 

10 

11 

12 

Permanent major rivers 

Additional major rivers 

Additional rivers 

Minor rivers 

Double-1 ined rivers 

Intermittent rivers - major 

Intermittent rivers - additional 

Intermittent rivers - minor 

Major canal s 

Canal s of lesser importance 

Canals - irrigation type 

BDY file: - International boundaries or limits of sovereignty 

Code No Ranks 

01 Demarcated or delimited 

02 Indefinite or in dispute 

Global Change Data base Africu Docwireiriuiiorr Appendix M-7 
03 Other line of separation or sovereignty on land 

PBY file: - Internal boundaries 

Code No Ranks 

01 First order administration 

Each WDBII file contains two different types of records. 

two types of records are shown below: 

The formats of these 

Line Identifier Record (Line Record) 

Field Description Position Lensth Format 

1 Line identifier 1-7 7 I7 

2 Rank code no. 0-9 2 I2 

3 No. of points in the line (NP 10 - 15 5 I6 

4 Zeros 15 - 20 5 I5 

Lati tude/Longi tude Record (Point Record) 

Field DescriDtion Position Lenqth Format 

1 Latitude (DDMMSSH) 1-7 7 313,Al 

2 Longitude (DDDMMSSH) 5 - 15 8 13,212 ,A1 

3 Sequence Count 15 - 20 5 I5 

As can be surmised from the above formats, there exists a single record for 

each line identifier, with NP subsequent latitude/longitude records, one for 

each point contained in the line. 

bytes. 

The record size of both formats is 20 

THE DESCRIPTION OF THE REFORMATTED WDBII DATA BASE 

RFWBII incorporates the input files into one large file. The record size of 

the file is 16 bytes. RFWBII converts the input data from ASCII format into 

binary format. 

The following shows the file numbers and the code numbers assigned by RFWBII 

to the 16 files presently included in the WDBII:: 

File No. Code No. Area File TvDe 

11 

12 

13 

14 

21 

22 

23 

24 

31 

North America - CIL 

North America - RIV 

North America - BDY 

North America - PBY 

South America - CIL 

South America - RIV 

South America - BDY 

Vacant 

Europe - CIL 

Global Change Data Base Ajbicu Docwneniution Appendix M-8 
10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

32 

33 

34 

41 

42 

43 

44 

51 

52 

53 

54 

Europe - RIV 

Europe - BDY 

Vacant 

Africa - CIL 

Africa - RIV 

Africa - BDY 

Vacant 

Asia - CIL 

Asia - RIV 

Asia - BDY 

Vacant 

In addition to the line and point records, RFWBII contains 13 header records. 

The formats of these records are shown below: 

Rec. No. Word No. TVDe Pescri Dti on 

1 

I1 

I1 

11 

2 

I1 

I1 

I1 

1 INT*4 World boundary data base identifier 

( - 555) 

Maximum no. of input files (20) 

No. of actual input 1 nes 

2 

3 

4 

1 

2 

3 

4 Re a 

II 

II 

II 

I1 

Total no. of records 

Total no. of segments 

Not used 

Not used 

Approximation thresh0 d 
-NTHE WORLD DATA CENTER SYSTEM 

Global Change Data Base Africa Dorumenlatiori Appendix N- I 
Blank page retained for pagination 
APPENDIX N. THE WORLD DATA CENTER SYSTEM 

Description of World Data Centers 

The World Data Centers (WDCs) were created in 1957 to provide archiving for the observational 

data resulting from the International Geophysical Year (IGY). In the years following the IGY, 

the International Council of Scientific Unions (ICSU) recommended that the WDCs continue to 

collect, archive, and redistribute data. This new system for exchanging geophysical data was 

found to be very effective, and the operations of the WDCs were extended by ICSU on a 

continuing basis to other international programs. The WDCs were under the supervision of the 

Comite International de Geophysique for the period 1960 through 1967 and are now supervised 

by the ICSU Panel on World Data Centres. 

World Data Centers have been established in a variety of countries: WDC-A is located in the 

USA; WDC-B in the USSR; WDC-C in western Europe, Australia, and Japan; and WDC-D in 

the People’s Republic of China. The Centers collect and distribute data for a number of 

disciplines: 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

meteorology 

oceanography 

astronomy 

rockets and satellites 

solar-terrestrial physics: solar and interplanetary phenomena, ionospheric phenomena, 

flare-associated events, geomagnetic phenomena, aurora, cosmic rays, airglow 

nuclear radiation 

glaciology (snow and ice) and geocryology 

marine geology and geophysics: gravity, magnetics, bathymetry, seismic profiles, 

marine sediment, rock analyses 

solid earth geology and geophysics: seismology, tsunamis, gravimetry, Earth tides, 

recent movements of Earth’s crust, Earth’s rotation, magnetic measurements, 

paleomagnetism and archeomagnetism, volcanology, geothermics 

renewable resources and environment 

In each discipline, the scientific community determines the nature and form of data exchange, 

based on research needs. Thus, the type and amount of data in the WDCs differ from discipline 

to discipline. However, each WDC is responsible for: 

0 collecting data in the field or discipline for which it is responsible 

0 protecting the incoming data 

Global Change Data Base Africa Documentation Appendix N-3 
0 copying and reproducing data, maintaining adequate standards of clarity and 

durability 

0 supplying copies of data to other WDCs 

0 preparing catalogs of data 

0 making data available to the scientific community. 

All the Centers are staffed, funded, and maintained exclusively by the countries in which they 

are located. The WDCs catalog the data and make them available to scientists in all countries 

upon written request or personal visit. Minimal charges may be requested to cover costs of 

processing the requested data. 

World Data Center A 

World Data Center A was established in the United States under the auspices of the National 

Academy of Sciences. WDC-A is operated with national resources, but follows ICSU guidelines. 

The National Academy of Sciences has overall responsibility through the Geophysics Research 

Forum and its Committee on Geophysical Data. WDC-A consists of a Coordination Office and 

nine subcenters at scientific institutions in various parts of the United States. Most WDC-A 

subcenters are at corresponding national data centers, whose large national collections are 

available through the WDC-A subcenters. 

Organizations wishing to contribute data or establish exchange agreements should contact the 

appropriate World Data Center A. 

WORLD DATA CENTER A 

COORDINATION OFFICE 

National Academy of Sciences 

2101 Constitution Avenue, NW 

Washington, DC 20418, USA 

Telephone: 202-334-3368 

WORLD DATA CENTER A: GLACIOLOGY (SNOW AND ICE) 

Cooperative Institute for Research in Environmental Sciences 

University of Colorado 

Boulder, Colorado 80309, U.S.A. 

Telephone: 303-492-5 17 1 

Global Change Data Base Africa Documentation Appendix N-4 
WORLD DATA CENTER A FOR MARINE GEOLOGY AND GEOPHYSICS 

National Geophysical Data Center 

NOAA, E/GC3 

325 Broadway 

Boulder, Colorado 80303-3328, U.S.A. 

Telephone: 303-497-6487 

WORLD DATA CENTER A: METEOROLOGY 

National Climatic Data Center 

NOAA, E/CC 

Federal Building 

Asheville, North Carolina 28801, USA. 

Telephone: 704-259-0682 

WORLD DATA CENTER A: OCEANOGRAPHY 

National Oceanographic Data Center 

NOAA, E/OC 

1825 Connecticut Avenue, NW 

Universal Building, Room 406 

Washington, DC 20235, U.S.A. 

Telephone: 202-673-5594 

WORLD DATA CENTER A: ROCKETS AND SATELLITES 

NASNGoddard Space Flight Center 

Code 630.2 

Greenbelt, Maryland 20771, U.S.A. 

Telephone: 301-286-7354 

WORLD DATA CENTER A: ROTATION OF THE EARTH 

U.S. Naval Observatory 

Washington, DC 20392-5100, U.S.A. 

Telephone: 202-653-1529 or 1527 

WORLD DATA CENTER A: SEISMOLOGY 

U.S. Geological Survey 

Branch of Global Seismology and Geomagnetism 

Box 250436, Mail Stop 967 

Denver Federal Center 

Denver, Colorado 80225, U.S.A. 

Telephone: 303-236- 1500 

Global Change Data Base Africa Documentation Appendix N-5 
WORLD DATA CENTER A FOR SOLAR-TERRESTRIAL PHYSICS 

National Geophysical Data Center 

NOAA, EjGC2 

325 Broadway 

Boulder, Colorado 80303-3328, U.S.A. 

Telephone: 303-497-6324 

WORLD DATA CENTER A FOR SOLID EARTH GEOPHYSICS 

National Geophysical Data Center 

NOAA, E/GCl 

325 Broadway 

Boulder, Colorado 80303-3328, U.S.A. 

Telephone: 303-497-6521 

WDC-A for Solid Earth Geophysics Reports 

World Data Center A for Solid Earth Geophysics has a variety of reports related to its activities. 

This publication, SE-47, is one in a series. To obtain ordering information about the other 

publications (listed below), call 303-497-6277, fax 303-497-65 13, telex 5928 11 NOAA MASC 

BDR, or write to: 

WORLD DATA CENTER-A FOR SOLID EARTH GEOPHYSICS 

NATIONAL GEOPHYSICAL DATA CENTER 

NOAA, CODE E/GCl 

325 BROADWAY 

BOULDER, COLORADO 80303-3328, U.S.A. 

SE- 1 

SE-2 

SE-3 

SE-4 

SE-5 

SE-6 

SE-7 

SE-8 

SE-9 

SE-10 

Catalog of Tsunamis in Alaska 

Geodynamics International-9 

Summary of Earthquake Focal Mechanisms for the Western Pacific-Indonesian 

Region, 1929-1973 

Catalog of Tsunamis in Hawaii 

Geodynamics International- 10 

Catalog of Seismograms and Strong-Motion Records 

Directory of Seismograph Stations 

Survey of Practice in Determining Magnitudes of Near Earthquakes, Part 2: Europe, 

Asia, Africa, Australia, the Pacific 

Survey of Practice in Determining Magnitudes of Near Earthquakes, Part 1: North, 

Central, and South America 

Geodynamics International- 1 1 

Global Change Data Base Africa Documentation Appendix N-6 
SE- 1 1 

SE- 12 Geodynamics International-12 

SE- 13 

SE- 14 

SE- 15 Geodynamics International- 13 

The Information Explosion and Its Consequences for Data Acquisition, Documentation, 

and Processing: An Additional Aspect of the Limits to Growth 

Bibliography of Statistical Aspects of Seismicity 

Directory of U.S. Data Repositories Supporting the International Geodynamics Project 

SE- 16 Geodynamics International-14 

SE- 17 

SE- 18 

SE- 19 Geodynamics International- 15 

SE-20 Manual of Seismological Practice 

Annual Mean Values of Geomagnetic Components for Selected Observatories, 1940- 

1973 

Homogenous Magnitude System of the Eurasian Continent: P-Waves 

SE-21 Geomagnetic Observatories, 1978 

SE-22 

SE-23 Geodynamics International- 16 

SE-24 

SE-25 

Historical Seismogram Filming Project: First Progress Report 

Historical Seismogram Filming Project: Second Progress Report 

Directory of World Seismograph Stations, Volume 1. The Americas-Part 1. United 

States, Canada, Bermuda 

SE-26 

SE-27 

SE-28 

SE-29 

SE-30 

Geodynamics International- 17: Final Report 

Catalog of Significant Earthquakes, 2000 B.C.- 1979 

Historical Seismogram Filming Project: Third Progress Report 

Strong-Motion Data from Japanese Earthquakes 

Progress Report on Selected Geophysical Activities of the United States, 1977-1981 

SE-3 1 

SE-32 

SE-33 

SE-34 

SE-35 Documentation of Earthquake Algorithms 

New Catalog of Strong Earthquakes in the U.S.S.R. from Ancient Times Through 1977 

Directory of World Digital Seismic Stations 

Historical Seismogram Filming Project: Fourth Progress Report 

Homogeneous Magnitude System of the Eurasian Continent: S and L Waves 

SE-36 

SE-37 

SE-38 

SE-39 Tsunamis in Peru-Chile 

SE-40 

Catalog of Submarine Volcanoes and Hydrological Phenomena Associated with 

Volcanic Events: 1500 B.C. to December 31, 1899 

Inventory of Filmed Historical Seismograms and Station Bulletins at World Data 

Center A 

Catalog of Strong-Motion Accelerograph Records 

Earthquake Catalog for the Middle East Countries 1900-1983 

Global Change Data Base Africa Documentation Appendix N-7 
SE-41 

SE-42 

SE-43 

SE-44 

SE-45 

Directory of World Seismograph Stations, Volume 11. East Asia-China, Japan, 

Korea, and Mongolia 

Catalog of Submarine Volcanoes and Hydrological Phenomena Associated with 

Volcanic Events: January 1,1900 to December 31,1959 

A Directory of Geomagnetic Observatories with Digital Recording Magnetometers, 

1987 

Directory of Data Sources for Lithospheric Investigations, Volume 1 

A Report on Geomagnetic Observatory Operations, 1990 

SE-46 

SE-47 

SE-48 

Enhancement of Earth Science Research and Educational Capabilities in the Developing 

Nations Through the Use of Compact Disc Technology; Report on the Pilot Project 

Global ChangeDataBase: Pilot (Diskette) Project for Africa; DataBase Documentation, 

Version 1.1 

Global Change Data Base: Training Exercise Manual; Exploring Earth's Environment, 

Africa as an Example 

*U.S. GOVERNMENT PRINTING OFFICE:1992-673-025/69016 

Global Change Data Base Africa Documentation Appendix N-8