A USER'S GUIDE TO THE
VEMAP PHASE I DATABASE
                                 
Nan Rosenbloom
Timothy G.F. Kittel
                                 
To accompany CDROM and World Wide Web versions of:
                                 
THE VEMAP PHASE I DATABASE:
AN INTEGRATED INPUT DATASET FOR ECOSYSTEM AND VEGETATION MODELING
FOR THE CONTERMINOUS UNITED STATES
                                 
                                 
T.G.F. Kittel, N.A. Rosenbloom, T.H. Painter, D.S. Schimel, H.H.  Fisher,
A. Grimsdell, VEMAP Participants, C. Daly, and E.R. Hunt, Jr.

Ecosystem Dynamics and the Atmosphere Section
Climate and Global Dynamics Division
National Center for Atmospheric Research
and
Climate System Modeling Program
University Corporation for Atmospheric Research

			TABLE OF CONTENTS

1  INTRODUCTION                                                  
     1.1  VEMAP and the VEMAP Database                          
     1.2  Citations and User Access Acknowledgments              
     1.3  VEMAP Mailing List                                     

2  DATA FILE ACCESS                                              
     2.1  CDROM                                                  
     2.2  UCAR World Wide Web Site                               
     2.3  UCAR Anonymous FTP Server                             
     2.4  GNU Compressed UNIX Tarfiles                         

3  THE VEMAP GRID                                             

4  FILE STRUCTURE                                            
     4.1  SVF File Format                                   
     4.2  Binary File Format                               
     4.3  ASCII Column Format for Site Files              

5  SCALING FACTORS, BACKGROUND VALUES, AND LANDCOVER MASK
     5.1  Scaling Factors                               
     5.2  Background Values                            
     5.3  Landcover Mask                              

6  ROAD MAP TO FILE AND VARIABLE DESCRIPTIONS        

7  GEOREFERENCING VARIABLES AND CELL AREAS          
     7.1  Summary of Geographic Variables          
     7.2  Filename Protocol                       
     7.3  Variable Descriptions                  

8  DAILY, MONTHLY, AND ANNUAL CLIMATE DATASETS  
     8.1  Summary of Climate Variables         
     8.2  Climate Filename Protocol           
     8.3  Creation of Climate Variables      
     8.4  Maximum, Minimum, and Mean Temperature 
     8.5  Precipitation                         
     8.6  Solar Radiation                      
     8.7  Humidity                            
     8.8  Surface Wind Speed                 

9  SOILS                                    
     9.1  Summary of Soil Variables        
     9.2  Soil Filename Protocol          
     9.3  Creation of the VEMAP Soils Dataset    
     9.4  Hierarchical Division of Soils into Mineral and
        Organic Components and Texture Classes   
     9.5  Soil Files                            

10  VEGETATION                                 
     10.1  Summary of Vegetation Variables    
     10.2  Creation of the Vegetation Dataset   
     10.3  Vegetation Files                    

11  CLIMATE CHANGE SCENARIOS                  
     11.1  Summary of Climate Scenario Files    
     11.2  Scenario Filename Protocol           
     11.3  Development of Climate Change Scenarios 
     11.4  Climate Change Scenario Variables      
     11.5  Creation of New Climates:  Application of Change 
	   Fields to Base Climate and Tests for Physical 
	   Constraints                           

12  SITE FILES                                  
     12.1  Site File Content and Structure     
     12.2  Site File Naming Protocol          

13  ACKNOWLEDGMENTS                          

14  CONTACTS                                

15  REFERENCES                             

A1  APPENDIX 1:  CDROM AND FTP SITE DIRECTORY STRUCTURE 

A2  APPENDIX 2:  DETERMINATION OF ABSOLUTE SOIL AREA
    FOR EACH CELL                                      
     A2.1  Absolute Area for Soil Modes and Components
     A2.2  Application to Model Outputs              

A3  APPENDIX 3:  AGGREGATION OF KUCHLER VEGETATION CODES
    TO VEMAP VEGETATION TYPES                       

A4  APPENDIX 4:  STATE IDENTIFICATION NUMBERS      

A5  APPENDIX 5:  VEMAP MAILING LIST               
     A5.1  Description of the VEMAP Mailing List 
     A5.2  How to Subscribe to the VEMAP Mailing List
     A5.3  Listserver Commands                      


                    LIST OF TABLES AND FIGURES

Table 1.  Datasets available on the VEMAP CDROM, Web site, 
	  and FTP site					
Table 2.  VEMAP grid corners defining the minimum bounding 
	  rectangle				
Table 3.  Format of data records in site files
Table 4a. Number of background cells (excluding wetlands)
Table 4b. Number of background cells (including wetlands)
Table 5.  Geographic variables 					
Table 6.  Climate variables 				
Table 7.  Soil variables  			
Table 8.  Vegetation variable name code and description
Table 9.  VEMAP vegetation types 		
Table 10.  Availability of climate variables for each climate 
	   scenario 			
Table A3.1 Aggregation of Kuchler vegetation types to VEMAP 
	   vegetation types            
Table A3.2 Kuchler Vegetation Type Names and Identifying Codes


Figure 1. Layout of the VEMAP gridded array                  
Figure 2. Schematic illustration of relationships among 
	  radiation variables in the climate dataset        


------
1  INTRODUCTION

1.1  VEMAP and the VEMAP Database

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is
an ongoing multi-institutional, international effort addressing the
response of biogeography and biogeochemistry to environmental
variability in climate and other drivers in both space and time
domains.  The objectives of VEMAP are the intercomparison of
biogeochemistry models and vegetation-type distribution models
(biogeography models) and determination of their sensitivity to
changing climate, elevated atmospheric carbon dioxide
concentrations, and other sources of altered forcing.  The VEMAP
exercise allows us to identify important commonalties and
differences among model controls and responses.  Where the models
differ, the comparision highlights areas of uncertainty or error
and identifies problems for future research.  Inter-model
differences also help to quantify the uncertainty in modeled
responses to changing climate and other drivers.

The completed Phase I of the project was structured as a
sensitivity analysis, with factorial combinations of climate
(current and projected under doubled CO2), atmospheric CO2, and
mapped and model-generated vegetation distributions.  The highly
structured nature of the intercomparison allowed rigorous analysis
of results, while constraining the range of questions explored.
Maps of climate, climate change scenarios, soil properties, and
potential natural vegetation were prepared as common boundary
conditions and driving variables for the models (Kittel et al.
1995).  As a consequence, differences in model results arose only
from differences among model algorithms and their implementation
rather than from differences in inputs.  Results from VEMAP I are
reported in VEMAP Members (1995) and selected files are available
through UCAR's anonymous FTP server (see Section 2.3).

The VEMAP input database for the Phase I model intercomparison is
documented in this Technical Note.  It includes compiled and model-
generated datasets of long-term mean climate, soils, vegetation,
and climate change scenarios for the conterminous United States.
The data are on a 0.5 degree latitude/longitude grid.  There are both
daily and monthly representations of the mean climate.  The climate
data and climate change scenarios are presented in both gridded and
time-sequential format.  We developed the time-sequential, "site"
file format to facilitate extractions of information for individual
grid cells (Sections 4.3 and 12).
1.2  Citations and User Access Acknowledgments

The citations for the VEMAP database are:

Kittel, T.G.F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, H.H.
  Fisher, A. Grimsdell, VEMAP Participants1, C. Daly, and E.R.
  Hunt, Jr.  (1996)  The VEMAP Phase I Database:  An Integrated
  Input Dataset for Ecosystem and Vegetation Modeling for the
  Conterminous United States. CDROM and World Wide Web
  (URL=http://www.cgd.ucar.edu/vemap/).

Kittel, T.G.F., N.A. Rosenbloom, T.H. Painter, D.S. Schimel, and
  VEMAP Modeling Participants2  (1995)  The VEMAP integrated
  database for modeling United States ecosystem/vegetation
  sensitivity to climate change.  Journal of Biogeography 22(4-
  5):857-862.

An additional reference for VEMAP is:

VEMAP Members3  (1995)  Vegetation/Ecosystem Modeling and Analysis
  Project (VEMAP):  Comparing biogeography and biogeochemistry
  models in a continental-scale study of terrestrial ecosystem
  responses to climate change and CO2 doubling.  Global
  Biogeochemical Cycles 9(4):407-437.

Users are requested to acknowledge that access to the dataset was
provided by the Climate System Modeling Program, University
Corporation for Atmospheric Research, and the Ecosystem Dynamics
and the Atmosphere Section, Climate and Global Dynamics Division,
National Center for Atmospheric Research.

Development of the VEMAP database was supported by NASA Mission to
Planet Earth, Electric Power Research Institute (EPRI), USDA Forest
Service Southern Region Global Change Research Program, and NSF-ATM
Climate Dynamics Program through the University Corporation for
Atmospheric Research's Climate System Modeling Program.

1.3  VEMAP Mailing List

We have instituted an email list to keep VEMAP users informed of
updates, future releases, and other information related to the
VEMAP database.  Our intent is to use this service as an electronic
message board to quickly and easily disseminate pertinent database
information.  Archived list messages are available using the get
command described in Appendix 5.3.  For information on how to
subscribe to the VEMAP mailing list, see Appendix 5.


2  DATA FILE ACCESS

The VEMAP database is available on CDROM, through the Internet on
the VEMAP Web site, or via anonymous FTP from the UCAR anonymous
FTP server.

2.1  CDROM

The VEMAP database CDROM contains the complete set of input files
used for the VEMAP model intercomparison.  We present the monthly
data files in both gridded format (SVF) and time-sequential
columnar format (site files), and the daily variables in binary
format.  Table 1 contains a list of the files available on the
CDROM and Appendix A1 provides an overview of the CDROM directory
structure.  A README file resides under each subdirectory
describing the files within that directory.  We have included
example postscript images under the subdirectory /images.  Updated
documentation and files for the CDROM can be found on the UCAR
World Wide Web site:

         http://www.cgd.ucar.edu/vemap/CDROM_updates.html

or obtained through the anonymous FTP site (see Section 2.3), under
the /vUPDATES subdirectory.

Table 1.  Datasets available on the VEMAP CDROM, Web site (www),
and FTP site.

Category               File Structure     File Format    Access
Georeferencing         Gridded data       SVF*           CDROM,www,FTP
Monthly Climate        Gridded data       SVF            CDROM,www,FTP
Daily Climate          Sequential data    IEEE Binary    CDROM,www,FTP
Soil                   Gridded data       SVF            CDROM,www,FTP
Vegetation             Gridded data       SVF            CDROM,www,FTP
Climate Scenarios      Gridded data       SVF            CDROM,www,FTP
Site Files             Sequential data    ASCII Columnar CDROM,www,FTP
Bulk Transfer          Multiple files     UNIX Tarfiles  www,FTP
Phase I Model Results  Gridded data       SVF            www,FTP

*SVF = ASCII gridded "Single Variable Format"  (see Section 4.1)

2.2  UCAR World Wide Web Site

The VEMAP files are accessible from the Internet.  Using an
Internet browser (e.g., Mosiac, Netscape, etc.), enter the URL:

                  http://www.cgd.ucar.edu/vemap/

Note for Macintosh Users:  If you are using an early version of
Netscape on a Macintosh, you may have difficulty downloading files.
In this case, it is advisable to obtain the VEMAP files via
anonymous FTP from ftp.ucar.edu (see Section 2.3).

The VEMAP home page contains a short description of the VEMAP
project and a directory to additional pages.  To gain access to the
VEMAP dataset from the VEMAP home page, click on "Access to the
VEMAP Dataset" under the heading, "The VEMAP Dataset".  A listing
of available data files by dataset category is given in Table 1.

2.3  UCAR Anonymous FTP Server - ftp.ucar.edu

To gain access to the VEMAP files via anonymous FTP from the UCAR
FTP site, type:

     > ftp ftp.ucar.edu
     Name:  anonymous
     Password:  <your_login>
     ftp> cd cgd/vemap
     ftp> cd <subdirectory>
     ftp> get <filename>

Available datasets are listed in Table 1 and the FTP directory
structure is presented in Appendix 1.

2.4  GNU Compressed UNIX Tarfiles (Web Site and FTP Site Only)

2.4.1  Description of GNU Compressed Tarfiles

We have archived multiple files into compressed UNIX tarfiles for
more efficient data transfer.  For example, all 12 gridded monthly
maximum temperature files are stored in TX_MON.tar.gz (a .gz file
suffix indicates that the tarfile has been compressed using the GNU
software utility gzip, see below).

Tarfiles can be found on the FTP site under the subdirectory
/tarFiles.  To use these files, first FTP the desired tarfile to
your home machine (remember to set the transfer mode to binary
before FTP'ing the tarfiles).  Then, decompress the .gz file and
extract the archived files using the GNU gzip and tar commands
appropriate to your machine.
For example, on a UNIX system, type:

     > gunzip <filename.gz>
     > tar xvf <filename>

This process will create a complete set of files in your current
local directory.  The tarfiles frequently contain a considerable
number of files and require sufficient space in your current
directory.  For a complete listing of the space required for the
contents of each tarfile, download the file:

     /tarFiles/README.tarFiles

from ftp.ucar.edu.

Characteristic decompressed and/or extracted individual file sizes
are:

     Daily binary:     ~4700 kBytes/file
     Gridded SVF:        ~33 kBytes/file
     Columnar Site:     ~370 kBytes/file

2.4.2  How to Obtain Free GNU Software

The gzip utility is provided by the Free Software Foundation GNU
project.  It is available for multiple system platforms, and may be
freely downloaded from the Internet.  The Macintosh gzip version is
available at:


http://persephone.cps.unizar.es/general/gente/spd/gzip/gzip.html

UNIX and MS-DOS versions, along with other GNU software, can be
found at:

     ftp://prep.ai.mit.edu/pub/gnu/

2.4.3  Hints for Using gzip on Selected Systems

UNIX:  To compress files, use the command gzip; gunzip will
decompress a .gz file.   The command man gzip will provide more
information on how to use gzip.

MS-DOS:  To compress files, use the command gzip; gzip -d will
decompress a .gz file.  The command gzip -h will provide more
information on how to use gzip.

Macintosh:  The Macintosh web site provides full instructions on
how to use MacGzip.

3  THE VEMAP GRID

The grid used for the VEMAP coverage is a 0.5 degree latitude 0.5 degree
longitude grid covering the conterminous U.S.  Grid edges are
aligned with 1.0 degree and 0.5 degree latitude-longitude lines; grid centers
are located at 0.25 degree and 0.75 degree latitude-longitude intersections.
Latitude and longitude for each cell are included in the VEMAP
dataset (Section 7.3).

The grid's minimum bounding rectangle (MBR) is defined by grid
domain corners given in Table 2.  The full 0.5 degree VEMAP grid contains
5520 cells, with 115 columns and 48 rows.

          Table 2.  VEMAP grid corners defining the minimum
          bounding rectangle (MBR).

  Grid Position     Longitude*      Latitude
   Lower Left Corner  -124.5         25.0
   Upper Right Corner  -67.0          49.0

        *Negative longitudes are degrees West.


4  FILE STRUCTURE

We use three file formats throughout the VEMAP dataset:

   (1)  ASCII SVF format for gridded monthly current climate,
       climate change scenarios, soils, vegetation, and
       georeferencing files.
   (2)  Binary time-sequential format for daily climate data.
       Each record contains the "characteristic year" of daily data
       for a grid cell (Section 8.3).  Records are indexed by grid
       cell.
   (3)  ASCII column format for sequential monthly climate data
       and climate change scenarios.  Each line presents 12 monthly
       values for a single grid cell.  Records are indexed by grid
       cell (site files, Section 12).

4.1  SVF File Format

All gridded VEMAP data files are in an ASCII format based on, but
not identical to, the SVF format specified by the GENAMAP
Geographic Information System (GIS).  Typical SVF files have 2
header lines followed by a 6-digit integer array.  In contrast,
VEMAP files have 5 header lines.

The first 2 lines are a VEMAP data access policy statement,
followed by a blank line.  These first 3 lines must be removed in
order to convert the file to standard SVF format.

The 4th header line is a title line identifying the gridded
variable and its units.  For continuous data (i.e., non-categorical
datasets), we also include the scale factor used to convert values
to stored integers (Section 5.1).  Division by this factor will
restore the original value.  Version number and revision date are
also in the title line.

The 5th header line gives the gridded array's column and row
indices (as four 6-digit integers):  1, 115 and 1, 48.

The header lines are followed by the gridded VEMAP integer array,
which is dimensioned 115 columns by 48 rows (Section 3).  The 6-
digit integers in the VEMAP array include at least one blank space
so that values in the file are space delimited.  The array starts
in the northwest corner of the grid, with the column index running
west to east and row index running north to south (Fig. 1).

        Column                          
        1                                 115
 Row 1  1    2    3    4    5    6...  ...115
        116  117  118...               ...230
        231                                 
        ...
    48                                ...5520

             
             Figure 1.  Layout of the VEMAP gridded array, with
             grid cell ID numbers.


The full grid contains 5520 grid cells, 3261 of which are within
the boundaries of the conterminous U.S. and predominantly covered
by land (see Section 5.2).  Background cells (ocean and inland
water cells) are assigned the value of -9999.

4.2  Binary File Format

All daily variables are stored in IEEE binary format.  We have
provided a FORTRAN program to read the binary files:
                                 
                        /programs/biread.f

Each binary file contains 365 days of data for the 3261 grid cells
with landcover in the U.S.  Background grid cells are not included.

The files begin with two header lines containing information about
the data.  The first lists the variable name, units, scaling
factor, and version number.  The second describes the content of
each data record with the following string:

                    gridpt|lon|lat|day (1->365)

The two header lines are followed by 3261 data records.  Each
record includes the grid point identifying number (ID), longitude,
latitude, and a year's worth of scaled daily values.  Only one year
of data is given per grid cell, representing a "characteristic"
year (see Section 8.3).  Grid cell ID numbers begin at the top left
corner of the grid and proceed left to right, top to bottom (Fig.
1).

Daily files on the NCAR FTP site or the WWW are available in GNU
compressed format (Section 2.4) to speed FTP transmission.

4.3  ASCII Column Format for Site Files

Site files contain the monthly climate data and scenarios in column
format, with each record containing 12 monthly values for a single
variable.  This time-sequential format was developed to facilitate
data extraction for individual grid cells.  Site files contain 8
header lines, beginning with a 2 line VEMAP data policy statement,
followed by a blank line.  The 4th header line is a title line
identifying the gridded variable and its units.  For continuous
data (i.e., non-categorical datasets), we also include the scale
factor used to convert values to stored integers (see Section 5.1).
Division by this factor will restore the original value.  Version
number and revision date are also in the title line.  The next 4
lines provide column headings for the data records.

In addition to the 12 monthly data values, each record contains
auxillary geographic information for each cell:  grid cell ID,
latitude, longitude, elevation, VEMAP vegetation type (Section 10
and Table 9), Kuchler vegetation type (Appendix Table A3.2), and
state identification number.  The format of each data record is
given in Table 3.  VEMAP vegetation types listed in the site files
are from version 2 of vveg (vveg.v2, Table 9).  State identifying
codes are listed in Appendix 4.  Order of monthly values is January
to December.

Table 3.  Format of data records in site files.

Variable        Variable Variable   Column  Column    Scaling
                   Type   Width   Start    End      Factor
                         (columns)
Grid Cell ID      numeric    4       1        4         1
Latitude          numeric    4       7       10        100
Longitude         numeric    5       12      16       -100
Elevation         numeric    4       19      22         1
vveg2 Vegetation  numeric    2       27      28         1
  Code  
Kuchler           numeric    3       32      34         1
  Vegetation Code
State ID          numeric    2       39      40         1
12 Monthly        numeric    6       41     112        (*)
Values 

*Scaling factor for monthly values is stored in the 4th header
line.

Site files include only non-background grid cells, so that there
are 3261 data records per file.

5  SCALING FACTORS, BACKGROUND VALUES, AND LANDCOVER MASK

5.1  Scaling Factors

In SVF, site, and daily binary files, data values are represented
by scaled integers. We produced these integers by multiplying the
original data values by a scaling factor (e.g., 100.0, 0.001) which
is included in the fourth header line of the SVF and site files and
first header of the daily binary files.  Division by this scaling
factor will restore the original value.  For example, if the listed
value equals 297 and the scaling factor equals 10.0, the actual
value equals:  (297/10.0 = 29.7).

5.2  Background Values

Data files contain roughly 2200 cells that are outside the physical
or political boundaries of the conterminous U.S. (i.e., outside the
VEMAP domain).  In the SVF gridded files, these cells are set to
the stored background value of -9999.  In addition, cells dominated
by large inland water bodies (e.g., Lake Michigan, Great Salt Lake)
are also set to -9999.

In some files, generally those containing VEMAP Phase I model
results, cells classified as wetlands (in vveg.vx, Table 9) are
also set to background.  Counts of background cells and data cells,
either including or excluding wetlands, are given in Tables 4a and
4b, respectively.

          Table 4a.  Number of background cells and cells within
          the VEMAP domain with land cover (including wetlands).

Cell Type                                Count
Non-background grid cells                 3261
  (including wetlands)
Background cells (-9999)                  2259
                                   Total  5520

          Table 4b.  Number of background cells (with wetland cells
          set to -9999) and cells
          within the VEMAP domain with land cover.

Cell Type                                Count
Non-background grid cells                 3168
  (excluding wetlands)                      
Background cells (-9999)                  2352
  (with wetland cells set to -9999)          
                                    Total 5520

Most VEMAP data files contain data for wetland cells, such that the
non-background cell count for data files is 3261 cells (Table 4a).
Exceptions are latitude and longitude files (Section 7) in which
all cells are filled with data (non-background cell count = 5520),
and elevation and vegetation files (non-background cell count =
3385).  Typical non-background grid cell counts for VEMAP Phase I
results files are 3168 cells because most of the models were not
run for the wetland cells (Table 4b).

5.3  Landcover Mask

Any soil or gridded climate file can be used as a VEMAP domain land
mask.  In these files, background values (-9999) indicate cells
outside the domain or over inland water bodies, and all other
values identify non-water cells within the domain.


6  ROAD MAP TO FILE AND VARIABLE DESCRIPTIONS

In the following sections (Sections 7 - 11), we describe VEMAP
database variables and associated files.  Each of these sections
follows this general outline:

      * Summary of available variables
      * File naming protocol
      * Derivation of variables (for most sections)
      * Description of individual variables

For subsections that describe individual variables (e.g., Section
7.3.1), subsection headings include the variable name code used in
filenames (in parentheses) and units (in square brackets).

Descriptions include data sources and derivations where
appropriate.  At the end of each subsection, names of gridded SVF
files, daily binary files (when present), and scaling factors are
listed.  We list a background cell value of "N/A" if all cells are
filled with data.


7  GEOREFERENCING VARIABLES AND CELL AREAS

7.1  Summary of Geographic Variables

The VEMAP dataset includes three georeferencing and three cell area
variables (Table 5).  These are described in more detail in Section
7.3.  On the CDROM and FTP site, these data files are located under
the subdirectory /geog.  Note that the area variables are related:

                    varea = (areap/100) * area


      Table 5.   Geographic variables.  Variable name codes are
      those used in filenames (Section 7.2).
     
Variable   Description
Name Code
elev       Average grid cell elevation
lat        Latitude of grid cell center
lon        Longitude of grid cell center
area       Absolute area of a grid cell
areap      Percent of a grid cell covered by land and
           within U.S. borders
varea      Absolute area of a grid cell covered by land
           and within U.S. borders

7.2  Filename Protocol

The filename protocol for area and georeferencing files is:

                                VAR
                                 
where:

              VAR =  Variable name
             elev    elevation [m]
              lat    latitude [degrees and decimal degrees]
              lon    longitude [degrees and decimal degrees]
             area    absolute area  [km2]
            areap    percent VEMAP domain area [%]
            varea    absolute VEMAP domain area [km2]
                     

7.3  Variable Descriptions

7.3.1  Elevation (VAR = elev) [m]

   Elevation was aggregated from 10-minute Navy Fleet Numeric
   Oceanographic Center (NFNOC 1985) data (C. Vorosmarty, personal
   communication).  Aggregated elevation for each 0.5 degree cell was
   computed as a simple mean of nine 10-minute grid cell modal
   values.  Elevations for inland water bodies are included; non-
   background cell count = 3385 (see Section 5.2).

          Gridded SVF file:        elev
          Scaling factor:          1.0
  
7.3.2  Latitude (VAR = lat) [degrees and decimal degrees]

   Latitude of grid cell center.  Positive for North latitudes.
   All cells are filled with latitude values; there are no
   background cells.
   
          Gridded SVF file:        lat
          Scaling factor:          100.0

7.3.3  Longitude (VAR = lon) [degrees and decimal degrees]

   Longitude of cell center.  Scaling factor gives negative
   degrees for West longitudes.  All cells are filled with
   longitude values; there are no background cells.

          Gridded SVF file:        lon
          Scaling factor:          -100.0

7.3.4  Area (VAR = area) [km2]

   Absolute area of a grid cell.  Determined by coordinate
   geometry.

          Gridded SVF files:      area
          Scaling factor:          1.0

7.3.5  Percent Land Area (VAR = areap) [%]

   Percent of the area of a 0.5 degree latitude/longitude grid cell that
   is covered by land and within the VEMAP domain (the
   conterminous U.S.).  Derived from the Kern U.S. EPA 10-km
   gridded soil coverage (Section 9), this is the number of non-
   zero 10-km pixels relative to the total number of pixels in a
   0.5 degree cell.

          Gridded SVF files:     areap
          Scaling factor:          1.0

7.3.6  Absolute Land Area (VAR = varea) [km2]

   Absolute area of a grid cell that is covered by land and within
   the VEMAP domain (the conterminous U.S.).  Absolute land area
   is determined as:
   varea = (area) * (areap/100)

          Gridded SVF files:     varea
          Scaling factor:          1.0


8  DAILY, MONTHLY, AND ANNUAL CLIMATE DATASETS

8.1  Summary of Climate Variables

The database includes 21 climate variables (Table 6), which are
described in Sections 8.4 - 8.8, and are presented in daily,
monthly, and annual files.  Section 8.3 discusses development of
the daily and monthly versions.  On the CDROM and FTP site, these


data are in the subdirectories /daily and /monthly.  Selected
climate variables are also available in site file format (Sections
4.3 and 12).

Table 6.  Climate variables.  Variable name codes are those used in
filenames (Section 8.2).

Variable Name    Description
Code
tx, tn, tm       Maximum, minimum, mean temperature
r_atmax, r_atmin Record absolute maximum, minimum temperature
r_mtmax, r_mtmin Month of occurrence of record absolute
                 maximum, minimum temperature
c_atmax, c_atmin Characteristic year absolute maximum, minimum
                 temperature
c_mtmax, c_mtmin Month of occurrence of characteristic year
                   absolute maximum, minimum temperature
p                Accumulated precipitation
sr               Total incident solar radiation at surface
fsr              'sr' as fraction potential total solar
                   radiation at top of atmosphere (total
                   atmospheric transmissivity:  clear sky +
                   cloud effects)
fsr_sfc          'sr' as fraction potential total solar
                   radiation at surface (cloud transmissivity)
psr              Potential total solar radiation at top of
                 atmosphere
psr_sfc          Potential total solar radiation at surface
irr              Mean daily irradiance
vp               Vapor pressure
rh               Relative humidity (mean for daylight hours)
w                Wind speed

8.2  Climate Filename Protocol

8.2.1  Gridded Monthly and Annual SVF Files
  
The filename protocol for gridded monthly and annual climate SVF
files, with the exception of gridded absolute temperature files
(Section 8.2.2), is:

                              VAR.MMM
where:
  
     VAR or         =  Variable name
     VAR_sfc
                tx     maximum temperature [degrees C]
                tn     minimum temperature [degrees C]
                tm     mean temperature [degrees C]
                 p     precipitation [mm]
                sr     total incident solar radiation [kJ/m2]
               fsr     'sr' as fraction potential total
                         solar radiation at top of atmosphere [0-1]
           fsr_sfc     'sr' as fraction potential total
                         solar radiation at surface [0-1]
               psr     potential total solar radiation at
                       top of atmosphere [kJ/m2]
           psr_sfc     potential total solar radiation at
                       surface [kJ/m2]
               irr     mean irradiance [W/m2]
                vp     vapor pressure [mb]
                rh     mean daylight relative humidity [%]
                 w     wind speed [m/s]
                       
              .MMM  =  Period
                       month (jan, feb, etc.) or annual
                       (ann)
                       

8.2.2  Gridded Absolute Temperature SVF Files
  
Protocol for naming gridded absolute temperature and month of
occurrence of absolute temperature files is:
  
                               P_VAR
  
where:
  
              P_  = Period
               r    20-year WGEN record
               c    characteristic year
                    
             VAR  = Variable
           atmax    absolute maximum temperature
           atmin    absolute minimum temperature
           mtmax    month of occurrence of absolute maximum
                    temperature (e.g., 7 = July)
           mtmin    month of occurrence of absolute minimum
                    temperature (e.g., 1 = January)
                    

8.2.3  Binary Daily Files

Filenames for binary daily files follow the form:

                            VAR. BI, or
                            VAR_sfc. BI

where:

     VAR = Variable name
      BI = Binary daily file

and where variable name codes are the same as for monthly and
annual SVF files (Section 8.2.1).

8.3   Creation of Climate Variables

The VEMAP dataset includes daily, monthly, and annual climate data
for the conterminous U.S. including maximum, minimum, and mean
temperature, precipitation, solar radiation, and humidity.
Seasonal mean surface wind speed is also provided.  The monthly,
seasonal, and annual data are long-term climatological means and
are on the CDROM and FTP site under the subdirectory /monthly.
Annual averages are simple means of the 12 monthly fluxes.  The
daily set presents a "characteristic year" in which monthly
averages or accumulations of the daily values match the long-term
monthly climatology but where the daily series has variances and
covariances characteristic of a station's weather record.  The
daily data are on the CDROM and FTP site in the subdirectory
/daily.

We used two processes to create the daily climate data (Kittel et
al. 1995):

   (1) statistical simulation of daily temperature and
       precipitation records, and
   (2) empirical estimation of corresponding daily radiation and
       humidity records.

8.3.1  Temperature and Precipitation Records

In the first process, we generated one year of daily precipitation
and maximum and minimum temperature for each VEMAP grid cell.
These records were produced using a stochastic daily weather
generator, WGEN (Richardson 1981, Richardson and Wright 1984),
which we modified to better utilize temporal statistics created by
its accompanying parameterization program, WGENPAR.
Parameterization of WGEN was based on daily records from 870
stations.  WGEN was run for each grid cell with parameters assigned
from the closest station.  Climate records created by WGEN have
realistic daily variances and temporal autocorrelations (e.g.,
persistence of wet and dry days) and maintain physical
relationships between daily precipitation and temperature.  For
example, in the WGEN records, days with precipitation tend to have
lower maximum temperatures than days with no precipitation.

To obtain the one year daily series, we first produced a 20-year
weather record using WGEN.  From this 20-year record, we derived
the VEMAP characteristic year by choosing 12 individual months
whose monthly means most closely matched the corresponding long-
term historical monthly means (e.g., January from year 5, February
from year 2, etc.).  Daily values of the selected months were
adjusted so that their monthly sum (for precipitation) or mean (for
temperature) exactly matched the historical long-term monthly
means.

As the final step in this process, we determined the absolute
maximum and minimum temperatures and their month of occurence for
the characteristic year (c_atmax, c_mtmax, c_atmin, c_mtmin).  We
also saved "record" absolute maximum and minimum temperatures and
their month of occurence (r_atmax, r_mtmax, r_atmin, r_mtmin) from
the full 20-year WGEN simulation which includes interannual
variation about the long-term mean.

8.3.2  Solar Radiation and Humidity Records

We used CLIMSIM (Running et al. 1987) to generate daily records of
solar radiation and surface air humidity from daily maximum and
minimum temperatures and precipitation.  We produced 6 solar
radiation variables:  total incident solar radiation at the surface
(sr), sr as a fraction of potential total solar radiation at the
top of the atmosphere (fsr) and at the surface (fsr_sfc), potential
total solar radiation at the top of the atmosphere (psr) and at the
surface (psr_sfc), and mean daily irradiance at the surface (irr).
Humidity variables generated were vapor pressure (vp) and mean
daylight relative humidity (rh).  Because of biases in the method
used in CLIMSIM to generate humidities from daily minimum
temperature (Kimball et al. 1996), daily vapor pressure values were
adjusted so that monthly means match the long-term means of Marks
(1990).  More details on this adjustment are given in Section
8.7.1.  Monthly means of solar radiation and humidity variables
were created from the daily CLIMSIM output.  Because the solar
radiation and humidity data are based on temperatures and
precipitation that are constrained to match their long-term means
and because the humidity data are additionally constrained by the
Marks (1990) means, monthly means of the solar radiation and
humidity dailies are taken to represent the climatological means of
these variables.

For radiation variables, monthly and annual files contain either
averages or totals of daily values.  To distinguish between these,
refer to units and file descriptions (e.g., "Average monthly file"
vs. "Total monthly file") in Section 8.6.

8.4  Maximum, Minimum, and Mean Temperature [degrees C]
   
8.4.1  Maximum, Minimum, and Mean Temperature (VAR = tx, tn tm)
   [degrees C]

   Long-term monthly mean daily maximum and minimum temperatures
   were interpolated to the VEMAP grid from 4613 station 1961-1980
   normals (NCDC 1992, dataset TD-9641).  Station values were
   adiabatically lowered to sea level (Marks and Dozier 1992),
   interpolated to the 0.5 degree VEMAP grid, and then re-adjusted to
   the new grid elevation.  Mean temperatures were computed as a
   simple average of the gridded maximum and minimum monthly
   temperatures.  We then generated daily maximum and minimum
   temperatures for each grid point, as described in Section
   8.3.1.  Daily temperatures were constrained in the generation
   process so that their monthly means matched the interpolated
   long-term monthly normals.  Daily mean temperatures are not
   provided.
   
   Daily binary files:     tx.BI        tn.BI
   Average monthly SVF files:           tx.MMM      tn.MMM
   tm.MMM
   Average annual SVF files:            tx.ann      tn.ann
   tm.ann
   Scaling factor:         10.0

8.4.2  Record Absolute Maximum and Minimum Temperature (VAR =
   r_atmax, r_atmin) [degrees C]

   Absolute daily maximum and minimum temperature in the 20-yr
   WGEN record.

   SVF file:               r_atmax      r_atmin
   Scaling factor:         10.0

8.4.3  Month of Occurrence of Record Absolute Maximum and Minimum
   Temperature (VAR = r_mtmax, r_mtmin) [month id: 1-12]

   The month of occurrence of absolute maximum and minimum
   temperature in the 20-yr WGEN record.  Month identifier runs
   from 1 to 12, corresponding to months January through December.

   SVF file:               r_mtmax      r_mtmin
   Scaling factor:         1.0

8.4.4  Characteristic Year Absolute Maximum and Minimum Temperature
   (VAR = c_atmax, c_atmin) [degrees C]

   Absolute maximum and minimum temperature found in the VEMAP
   characteristic year.

   SVF file:               c_atmax      c_atmin
   Scaling factor:         10.0

8.4.5  Month of Occurrence of Characteristic Year Absolute Maximum
   and Minimum Temperature (VAR = c_mtmax, c_mtmin) [month id: 1-
   12]
   
   The month of occurrence of absolute maximum and minimum
   temperature found in the VEMAP characteristic year.  Month
   identifier runs from 1 to 12, corresponding to months January
   through December.

   SVF format:             c_mtmax      c_mtmin
   Scaling factor:         1.0

8.5  Precipitation (VAR=p) [mm/day, month, or year]

Long-term mean monthly precipitation was spatially aggregated from
a 10-km gridded U.S. dataset developed using PRISM by Daly et al.
(1994).  PRISM models precipitation distribution by (1) dividing
the terrain into topographic facets of similar aspect, (2)
developing precipitation-elevation regressions for each facet type
for a given region based on station data, and (3) using these
regressions to spatially extrapolate station precipitation to 10-km
cells that are on similar facets.

We generated daily precipitation for each grid point using WGEN, as
described in Section 8.3.  Daily values were constrained such that
monthly rainfall accumulations for each grid point matched the long-
term monthly means.

Note:  Units and scaling factors differ for daily, monthly, and
annual files.

       Daily binary files: 		p.BI         [mm/day]
       Scaling factor:     		10.0
       
       Total monthly SVF files:         p.MMM       [mm/month]
       Total annual SVF file:           p.ann       [mm/year]
       Scaling factor:     		1.0

8.6  Solar Radiation

8.6.1  Relationship Among Solar Radiation Variables

Six solar radiation variables are included in the climate dataset
(Table 6).  These variables are either measures of solar radiation
inputs at the top of the atmosphere (psr) and the surface (psr_sfc,
sr, and irr) or of cloud and total transmissivity (fsr_sfc and fsr,
respectively).  Relationships among these variables on a daily
basis are illustrated in Fig. 2 and are as follows.

  (1)  Potential total incident solar radiation at the surface
  (psr_sfc) is the potential at the top of the atmosphere (psr)
  reduced by clear sky effects on transmissivity, such that:

               psr_sfc = psr * (clear sky transmissivity)

  (2)  Total incident solar radiation at the surface (sr) is
  derived from potential solar radiation at the top of the
  atmosphere (psr) diminished by total atmospheric (clear sky and
  cloud) effects on transmissivity (fsr, ranging from 0 to 1), so
  that:

               sr = psr * fsr

  (3)  Total incident solar radiation at the surface (sr) is also
  related to potential at the surface (psr_sfc) (which accounts for
  only clear sky effects on transmissivity), by further reducing
  psr_sfc by cloud effects:

               sr = psr_sfc * fsr_sfc

  where fsr_sfc is cloud transmissivity (0 - 1).

  (4)  Atmospheric transmissivity variables are related to each
  other, such that total atmospheric transmissivity (fsr) is the
  product of cloud (fsr_sfc) and clear sky transmissivities:

               fsr = fsr_sfc * (clear sky transmissivity)

  (5)  Daily mean surface irradiance for daylight hours (irr) is
  derived from sr and day length, such that, with unit conversion:

               irr = sr * (1 day/day length) * (1000J/1kJ)

  where day length is in seconds.
  
  Note that because radiation variables were determined on a daily
  basis, these relationships do not precisely hold for monthly
  averages or accumulations (see notes in Sections 8.6.5 and
  8.6.6).

Top of                                              
Atmosphere          psr                            psr
                                                    
                                                    
                                                clear sky
                                             transmissivity
                                                    
                   total                            
              transmissivity
                   (fsr)
           = clear sky + cloud                      
              transmissivity
                                                    
                                                    
                                cloud               
                           transmissivity
                              (fsr_sfc)             
                                                    
Surface             sr                           psr_sfc

Figure 2.  Schematic illustration of relationships among radiation
variables in the climate dataset.

8.6.2  Total Incident Solar Radiation (VAR = sr) [kJ m-2 day-1 or
   kJ m-2 yr-1]

   Total incident solar radiation at the surface.  Generated by
   CLIMSIM, sr is based on daily potential solar radiation at the
   top of the atmosphere (psr) and an estimate of daily
   atmospheric transmissivity (reported in this dataset as
   "fraction potential total solar radiation", fsr), such that:
  
               sr(daily)  =  psr_daily * fsr(daily)

   We report sr as daily (sr.BI) and monthly (sr.MMM) average
   values, and as an annual summation of daily values (sr.ann).

          Daily binary files:    	sr.BI    [kJ m-2 day-1]
          Average monthly SVF files:    sr.MMM   [kJ m-2 day-1]
          Total annual SVF file: 	sr.ann   [kJ m-2  yr-1]
          Scaling factor:         	1.0

8.6.3  Surface Total Solar Radiation as Fraction of Top of
   Atmosphere Potential Total Solar Radiation (VAR = fsr)
   [fraction, 0-1]

   Ratio of total incident solar radiation at the surface (sr) to
   potential total solar radiation at the top of the atmosphere
   (psr), or total atmospheric transmissivity.  CLIMSIM generates
   fsr as an estimate of atmospheric transmissivity (reported as
   "trans" in CLIMSIM).  In CLIMSIM, atmospheric transmissivity is
   estimated first from clear sky transmissivity, which is a
   function of elevation.  Clear sky transmissivity is then
   diminished by a surrogate for cloudiness, based on the
   occurrence of precipitation and the diurnal temperature range
   using the method of Bristow and Campbell (1984).  Daily
   temperatures and precipitation used in these calculations are
   from the WGEN-generated record (tx.BI, tn.BI, p.BI).
   
   We report fsr as daily (fsr.BI) and monthly (fsr.MMM) average
   values, and as an average of the 12 monthly mean values
   (fsr.ann).

          Daily binary files:      	fsrday.BI
          Average monthly SVF files:    fsr.MMM
          Average annual SVF file: 	fsr.ann
          Scaling factor:          	1000.0

8.6.4  Surface Total Solar Radiation as Fraction of Surface
   Potential Total Solar Radiation (VAR_sfc = fsr_sfc) [fraction,
   0-1]

   Ratio of total incident solar radiation at the surface (sr) to
   potential solar radiation at the surface (psr_sfc), or cloud
   transmissivity.  Because psr_sfc already accounts for clear sky
   transmissivity, fsr_sfc represents a further reduction in
   transmissivity due to cloud cover.  (See discussion of
   transmissivity calculations in the subsection on fsr, Section 8.6.3.)
   
   Therefore, fsr_sfc can be used as a surrogate for percent
   possible hours of sunshine or for (1 - % cloudiness).  However,
   these 3 variables are not strictly the same.  Percent hours of
   sunshine is determined at meteorological stations by a sunshine
   switch, and percent cloudiness by hourly observations of
   fractional cloud cover.
   
   We report fsr_sfc as daily (fsr_sfc.BI) and monthly
   (fsr_sfc.MMM) average values, and as an annual average of the
   monthly means (fsr_sfc.ann).

          Daily binary files:           fsr_sfc.BI
          Average monthly SVF files:    fsr_sfc.MMM
          Average annual SVF file:      fsr_sfc.ann
          Scaling factor:               1000.0

8.6.5  Potential Total Solar Radiation at the Top of the Atmosphere
    (VAR = psr) [kJ m-2 day -1, mo-1,  or yr-1]

   Monthly and annual accumulated potential total incident solar
   radiation generated by CLIMSIM.  Potential total solar
   radiation is based on latitude and solar geometry using the
   method outlined by Gates (1981).
   
   We report psr as daily (psr.BI), monthly (psr.MMM), and annual
   (psr.ann) accumulations of daily values.
   
   Note:  Because psr, fsr, and sr were determined on a daily
   basis, it is not possible to reproduce the monthly sr value
   based on monthly accumulated psr and mean monthly fsr values
   (i.e., [sr.MMM] * [days/month]  !=  [psr.MMM] * [fsr.MMM]).

          Daily binary files:      psr.BI    [kJ m-2 day-1]
          Total monthly SVF files: psr.MMM   [kJ m-2 mo-1]
          Total annual SVF file:   psr.ann   [kJ m-2 yr-1]
          Scaling factor:          0.01

8.6.6  Potential Total Solar Radiation at the Surface (VAR_sfc =
    psr_sfc) [kJ m-2 day -1, mo-1,  or yr-1]

   Monthly and annual accumulated potential total incident solar
   radiation at the surface generated by CLIMSIM.  Potential total
   solar radiation at the top of the atmosphere (based on latitude
   and solar geometry, Gates 1981) is modified by clear sky
   transmissivity to estimate potential solar radiation at the
   surface.
   
   We report psr_sfc as daily (psr_sfc. BI), monthly
   (psr_sfc.MMM), and annual (psr_sfc.ann) accumulations.
   
   Note:  Because psr_sfc, fsr_sfc, and sr were determined on a
   daily basis, it is not possible to reproduce the monthly sr
   value based on the monthly accumulated psr_sfc and the mean
   monthly fsr_sfc values (i.e., [sr.MMM] * [days/month] != 
   [psr_sfc.MMM] * [fsr_sfc.MMM]).

          Daily binary files:        psr_sfc.BI   [kJ m-2 day-1]
          Total monthly SVF files:   psr_sfc.MMM  [kJ m-2 mo-1]
          Total annual SVF file:     psr_sfc.ann  [kJ m-2 yr-1]
          Scaling factor:            0.01

8.6.7  Daily Mean Irradiance (VAR = irr) [W m-2]

   Daily mean irradiance for daylight hours, derived from CLIMSIM
   calculations of total incident solar radiation (sr.BI) and day
   length.

          Daily binary files:           irr.BI   [W/m2]
          Average monthly SVF files:    irr.MMM  [W/m2]
          Average annual SVF file:      irr.ann  [W/m2]
          Scaling factor:               100.0

8.7  Humidity

8.7.1  Vapor Pressure (VAR = vp) [mb]

   Vapor pressures were generated by CLIMSIM using WGEN-produced
   daily minimum temperature.  CLIMSIM estimates surface air
   humidity by assuming that dew point temperature is equal to
   daily minimum temperature.
   
   To account for arid regions where the minimum temperature may
   not be an adequate estimate of dew point temperature, we
   modified vapor pressure and relative humidity values to more
   closely match long-term monthly means calculated by Marks
   (1990) (Kittel et al. 1995).  If the Marks vapor pressure was
   less than CLIMSIM monthly mean vapor pressure, daily vapor
   pressures were adjusted by the corresponding monthly ratio:

        ratio(month) = [vp_MARKS(month)/vp_CLIMSIM(month)]

   If the Marks vapor pressure was equal to or higher than CLIMSIM
   (ratio < 1.0), no adjustment was made to daily vp and rh.  New
   monthly mean vapor pressures were calculated from the adjusted
   values.

          Daily binary files:    	   vp.BI
          Average monthly SVF files:       vp.MMM
          Average annual SVF file:         vp.ann
          Scaling factor:        	   100.0

8.7.2  Mean Daylight Relative Humidity (VAR = rh) [%]

   Generated by CLIMSIM with WGEN-generated temperature input (see
   Section 8.7.1).  The mean is for daylight hours, as CLIMSIM
   calculates relative humidity relative to the saturated vapor
   pressure for a computed daylight-period temperature mean.  If
   daily vapor pressures were adjusted (see Section 8.7.1),
   relative humidities were modified accordingly.

          Daily binary files:    	rh.BI
          Average monthly SVF files:    rh.MMM
          Average annual SVF file: 	rh.ann
          Scaling factor:        	10.0

8.8  Surface Wind Speed (VAR=w) [m/s]

Grid-averaged seasonal wind speed at 10-meter height.  These data
are based on a 10-km EPA dataset (Marks 1990), which is in turn
based on DOE seasonal (3-month) mean wind speeds with some
topographic adjustment (Elliott et al. 1986).  Wind speeds reported
here in monthly files are the same within each season (e.g., winter
= January, February, March).

          Average monthly SVF files:    w.MMM
          Average annual SVF file: 	w.ann
          Scaling factor:        	10.0


9  SOILS

9.1  Summary of Soil Variables

The soils dataset includes 18 variables (Table 7).  These are
described in more detail in Sections 9.3 - 9.5.  For most
variables, soil data are provided for 2 layers:

          (1)  0 ->  50 cm
          (2) 50 -> 150 cm

Relationships among area variables (ma, oa, map, tap) are presented
in Appendix 2.1.  On the CDROM and FTP site, soil data can be found
in the /soil subdirectory.

    Table 7.  Soil variables.  Variable name codes and layer codes
    (L) are those used in filenames
    (Section 9.2).

  Variable Name  Layers (L*)  Description
           Code
          modes       -       Number of modal soil profiles per cell
            map       -       Percent areal coverage of mineral soil 
			      component within a modal or average
                              soil  (Corresponding variable for organic soil
                              component is not included; 1 - map)
            tap       -       Percent areal coverage of a given modal 
			      soil (or average soil) within the VEMAP land
                              area of a grid cell
         ma, oa       -       Absolute areal coverage of mineral 
			      (or organic) soil component within a modal 
			      or average soil
            mbd     1, 2      Bulk density
         mz, oz       -       Soil depth
 msa,msi,mcl,moc    1, 2**    Texture:  % sand, silt, clay, organic content
       mrf, orf     1, 2      Rock fragments
       mwh, owh     1, 2      Water holding capacity
   tsoc, tsoc20 (0-100 cm,    Soil organic carbon
                 0- 20 cm)

   *layer ID code, L:  1 = 0 - 50 cm, 2 = 50 - 150 cm
   **moc is not available for layer 2

9.2  Soil Filename Protocol

File names for modal soils follow the form:

            CVARL_mM

and for average soils:

            CVARL_ave
where:

                   C  = Soil component type
                   m    mineral soil
                   o    organic soil
                   t    total (both components combined)
                        
                 VAR  = Variable name
                    
Mineral soils only (C = m)                                     
                  ap    areal coverage of mineral soil [% of modal soil area]
                  sa    sand content of mineral soil [% by weight]
                  si    silt content of mineral soil [% by weight]
                  cl    clay content of mineral soil [% by weight]
                  bd    bulk density of mineral soil [g/cm3]
                  oc    organic content of mineral soil [% by weight]
                        
Mineral and organic soils (C = m, o)                                     
                  wh    water holding capacity [cm H2O]
                   z    depth [cm]
                   a    area [km2]
                  rf    rock fragments [% by volume]
                                            
Total (C = t; omitted for modes)                              
               modes    number of modal soil profiles per cell
                 tap    mode area [% of cell land area]
                tsoc    soil organic carbon (0-100 cm) [mg C/ha]
              tsoc20    soil organic carbon (0-20 cm) [mg C/ha]
                        
                   L  = Layer
                   1    0 - 50 cm
                   2    50 - 150 cm
                        
          _mM or .mM  = Modal soil profile id# (M = 1 to 4, with m1 
			representing the most dominant profile
        _ave or .ave  = Average soil profile
                        

9.3  Creation of the VEMAP Soils Dataset

9.3.1  Source Data

Soil properties were based on a 10-km gridded EPA soil database
developed by Kern (1994, 1995).  Two soil coverages are provided in
the Kern dataset:  one from the USDA Soil Conservation Service
(SCS) national soil database (NATSGO) and the other from the United
Nations Food and Agriculture Organization soil database (FAO 1974-
78).  Only the SCS NATSGO soils are included in the VEMAP set.

9.3.2  Model Representation of Cell Soil Data

Physical consistency in soils data was incorporated by representing
a grid cell's soil by a set of dominant (modal) soil profiles,
rather than by a simple average of soil properties.  Because soil
processes, such as soil organic matter turnover and water balance,
are non-linearly related to soil texture and other soil parameters,
simulations based on dominant soil profiles and their frequency
distribution can account for soil dynamics that would be lost if
averaged soil properties were used.

To spatially aggregate Kern data to the 0.5 degree grid, we used cluster
analysis to group the subgrid 10-km elements into up to 4 modal
soil catagories (Kittel et al. 1995).  In this statistical
approach, cell soil properties are represented by the set of modal
soils, rather than by an "average soil."  We also provide cell-
averaged soil data.

See Appendix 2 for determination of the absolute area represented
by the entire cell (or by each modal soil within a cell) and the
application of these quantities to model results.  These areal
values are included in the database as variables ma and oa (Section
9.5.4) and varea (Section 7.3.6).

9.3.3  Missing Code

When a soil mode is not present, the cell value is set to a stored
value of -98 (i.e., -98 is not scaled by the scaling factor).  If a
mode is absent, no additional soil profiles are present for the
cell from that mode level on.  For example, if mode 3 is not
present, then all the soil information for that cell is contained
in the previous modes (modes 1 and 2), and mode 4 will also be
absent.

9.4  Hierarchical Division of Soils into Mineral and Organic
     Components and Texture Classes

Structure of the VEMAP soil dataset follows the hierarchical
division of a cell's soil in the Kern sets.  In the Kern SCS NATSGO
database, each soil type is represented by 2 component soils:  a
mineral soil (C=m) and an organic soil (C=o), each with its own
profile of soil properties.  Both mineral and organic soils are
further differentiated into rock fragments and finer elements.
Rock fractions are presented as a percentage of the entire soil
volume for each of these component soils.  VEMAP Phase I
simulations used the mineral soil component of mode 1 soils.

The finer elements of the mineral soil are defined texturally in
terms of mineral (msa, msi, mcl) and organic (moc) content.
Percent organic matter (by weight) is relative to the combined
mineral and organic fractions of the mineral soil.  Percent by
weight of sand, silt, and clay are relative to the mineral portion
only.

Values are averages for each modal (or cell average) soil.  Percent
sand, silt, and clay add up to 100% (+/- 1% due to rounding error).

9.5  Soil Files

9.5.1  Modes per Cell  (VAR = modes) [units = number of modes]

   Number of modal soils per cell.  Number of modes range from 1
   to 4.
   
      Gridded SVF files:  	modes
      Scaling factor:           1.0

9.5.2  Mineral Soil Percent Areal Coverage within a Given Modal (or
       Average) Soil (CVAR = map) [% of modal soil area, or % of area 
       of all modal soils]

   Relative area covered by mineral soils as percent of the total
   area covered by a given modal soil (map_mM) or for all soils in
   a cell (map_ave).  Note that the area covered by organic soils
   equals (1 - map) for the corresponding modal soil or cell
   average.
   
      Gridded SVF files:
          Mineral soils  	map_mM         map_ave
      Scaling factor:           1.0

9.5.3  Modal Soil Percent Areal Coverage (CVAR = tap) [% of cell land area]

   Relative area covered by modal soil M as percent of area
   covered by land (and within U.S. borders) for each 0.5 degree grid
   cell.  Includes both mineral and organic components.

      Gridded SVF files:  	tap_mM
      Scaling factor:          	1.0

9.5.4  Absolute Areal Coverage of a Modal (or Average) Mineral or Organic Soil
       (CVAR = ma, oa) [km2]

   Areal coverage of the mineral (ma) or organic (oa) component
   soil in a cell for either a modal (_mM) or average (_ave)
   profile.  (See Appendix 2.1 for calculation of this variable.)

      Gridded SVF files:
          Mineral soils        ma_mM          ma_ave
          Organic soils        oa_mM          oa_ave
      Scaling factor:          1.0

9.5.5  Bulk Density (CVAR = mbd) [g/cm3]

   Bulk density of the mineral soil component for layer L and soil
   mode M (or cell average).

      Gridded SVF files:
          Mineral soils  	mbdL_mM        mbdL_ave
      Scaling factor:          	100.0

9.5.6  Soil Depth (CVAR = mz, oz) [cm]

   Soil depth for mineral and organic soils for soil mode M (or
   cell average).

      Gridded SVF files:
          Mineral soils  	mz_mM          mz_ave
          Organic soils       	oz_mM          oz_ave
      Scaling factor:          	1.0

9.5.7  Texture (CVAR = msa, msi, mcl, moc) [% by weight]

   Percent sand, silt, and clay of mineral portion of mineral soil
   and percent organic content of entire mineral soil (see Section
   9.4) for layer L and soil mode M (or cell average).

Note:  This coverage is for mineral soils only.

   sand/silt/clay -
      Gridded SVF files:
          sand      		msaL_mM        msaL_ave
          silt      		msiL_mM        msiL_ave
          clay      		mclL_mM        mclL_ave
      Scaling factor:   	1.0

   organic matter - (for layer 1 only)
      Gridded SVF files:  	moc1_mM         moc1_ave
      Scaling factor:          	100.0

9.5.8  Rock Fragments (CVAR = mrf, orf) [% by volume]

   Rock fragments for mineral and organic soils for layer L and
   soil mode M (or cell average).

      Gridded SVF files:
          Mineral soils        mrfL_mM        mrfL_ave
          Organic soils        orfL_mM        orfL_ave
      Scaling factor:          1.0

9.5.9  Water Holding Capacity (CVAR = mwh, owh) [cm H2O]

   For mineral soils, Kern (1995) provides water holding capacity
   (WHC) based on Rawls et al. (1982) and Saxton et al. (1986).
   We used the Rawls et al. WHC for layer 1, because this method
   utilizes organic matter content, and the Saxton et al. WHC for
   layer 2, where organic content information is not present
   (Saxton et al. calculations are based only on %
   sand/silt/clay).  For WHC of organic soils, Kern used Paivanen
   (1973) and Boelter (1969).  There was an error in the original
   Kern WHC values for layer 1.  This is corrected as per Kern
   (1996).

      Gridded SVF files:
          Mineral soils  	mwhL_mM        mwhL_ave
          Organic soils  	owhL_mM        owhL_ave
      
      Scaling factor:
          Mineral soils  	10.0
          Organic soils  	 1.0

9.5.10  Soil Organic Carbon (CVAR = tsoc20, tsoc) [mg C/ha]

   Soil organic carbon (SOC) for 0-20 cm and 0-100 cm layers for
   modal and average soils.  Calculated from mean SOC values in
   the Kern EPA soil database, based on SCS NATSGO data.  SOC is
   for both mineral and organic soil combined.  SCS data are for
   current SOC levels, including for agricultural soils where
   present.  Kern's SOC values are adjusted for rock fragment
   content and actual soil depth.  SOC 0-20 cm was derived using
   mean SOC values for 4 soil layers in the Kern EPA database by
   (1) integrating between 15 and 20 cm along a spline function
   that was fit to values for 0-8, 8-15, 15-30, and 30-75 cm
   layers and (2) adding the integrated value to the sum of 0-8
   and 8-15 cm SOC.

      Gridded SVF files:  	tsoc20_mM      tsoc20_ave
                         	tsoc_mM        tsoc_ave
      Scaling factor:           1.0


10  VEGETATION

10.1  Summary of Vegetation Variables

The vegetation dataset includes one variable:  vegetation type
(Table 8).  This coverage is of potential natural vegetation under
current conditions (see Section 10.2).  We include the original
coverage used in VEMAP Phase I simulations (vveg.v1), as well as a
slightly modified version (vveg.v2).  Vegetation files can be found
in the subdirectory /geog on the CDROM and FTP site.

          Table 8.  Vegetation variable name code and description.

Variable Name  Description
Code
     vveg      Current distribution of VEMAP vegetation class



Table 9.  VEMAP vegetation types:  vveg identifying code and
corresponding VEMAP vegetation type.  Where type description
differs between vveg versions, the version is identified in
parentheses.

vveg Code            Vegetation Type
TUNDRA               
1                    Tundra

FOREST               
2                    Boreal Coniferous Forest
                      (includes Boreal/Temperate Transitional
                     and Temperate Subalpine Forests)
3                    Maritime Temperate Coniferous Forest
4                    Continental Temperate Coniferous Forest
5                    Cool Temperate Mixed Forest
6                    Warm Temperate/Subtropical Mixed Forest
7                    Temperate Deciduous Forest
8                    Tropical Deciduous Forest (not present)**
9                    Tropical Evergreen Forest (not present)

XEROMORPHIC WOODLANDS and FORESTS
10                   Temperate Mixed Xeromorphic Woodland
11                   Temperate Conifer Xeromorphic Woodland
12                   Tropical Thorn Woodland (not present)

SAVANNAS             
13                   Temperate/Subtropical Deciduous Savanna (.v1)
                     Temperate Deciduous Savanna (.v2)
14                   Warm Temperate / Subtropical Mixed Savanna
15                   Temperate Conifer Savanna
16                   Tropical Deciduous Savanna (not present)

GRASSLANDS           
17                   C3 Grasslands  (includes Short, Mid-, and Tall C3 Grasslands)
18                   C4 Grasslands  (includes Short, Mid-, and Tall C4 Grasslands)
                     
SHRUBLANDS           
19                   Mediterranean Shrubland
20                   Temperate Arid Shrubland
21                   Subtropical Arid Shrubland

EXCLUDED SURFACE     
TYPES
90                   Ice (not present)
91                   Inland Water Bodies (includes ocean inlets)
92                   Wetlands (includes floodplains and strands)
**  not present = vegetation type is not present in the current
distribution of types for the U.S. on the 0.5 degree grid (vveg.v1,
vveg.v2).  These types are included because they are outputs of
VEMAP biogeographical models where vegetation distribution could
change under altered climate and CO2 forcing,  and they were used
as inputs to selected biogeochemical model runs.

10.2  Creation of the Vegetation Dataset

Vegetation types are defined physiognomically in terms of dominant
lifeform and leaf characteristics (including leaf seasonal
duration, shape, and size) and, in the case of grasslands,
physiologically with respect to dominance of species with the C3
versus C4 photosynthetic pathway (Table 9).  The physiognomic
classification criteria are based on our understanding of
vegetation characteristics that influence biogeochemical dynamics
(Running et al. 1994).  The U.S. distribution of these types is
based on a 0.5 degree latitude/longitude gridded map of Kuchler's (1964,
1975) potential natural vegetation provided by the TEM group (D.
Kicklighter and A.D. McGuire, personal communication).  Kuchler's
map is based on current vegetation and historical information and,
for purposes of VEMAP Phase I model experiments, is presumed to
represent potential vegetation under current climate and
atmospheric CO2 concentrations (355 ppm).  The aggregation of
Kuchler to VEMAP vegetation types for versions 1 and 2 is given in
Appendix 3.

10.3  Vegetation Files

10.3.1  vveg.v1

   Current distribution of potential natural vegetation,
   aggregated from Kuchler's (1964, 1975) potential natural
   vegetation map (Appendix 3).  VEMAP Phase I used vveg.v1 for
   simulations that input current potential natural vegetation.

      Gridded SVF file:        vveg.v1
      Scaling factor:          1.0

10.3.2  vveg.v2

   Similar to vveg.v1 but with slight variations in vegetation
   distribution based on a modification of the VEMAP aggregation
   of Kuchler types (Appendix 3).  The updated distribution (.v2)
   is used in the site files (see Section 12).

      Gridded SVF file:        vveg.v2
      Scaling factor:          1.0


11  CLIMATE CHANGE SCENARIOS

11.1  Summary of Climate Scenario Files

There are 8 climate change scenarios in the VEMAP database (Table
10, Section 11.3.2).  These are based on doubled-CO2 climate model
experiments and are described in Section 11.3.  Not all variables
are available for each scenario (Table 10).  We report changes as
either differences or change ratios, depending on the variable
(Section 11.3, Table 10).  The scenarios can be found in the
directory /scenario on the CDROM and FTP site.

Table 10.  Availability of climate variables for each climate
scenario and description of the change field (diff = difference,
ratio = change ratio).  Climate scenarios are based on climate
model experiments discussed in Section 11.3.

                                Climate Scenario
Variab  Change CCC   GFDL   GFDL   GFDL  GISS  RegCM OSU   UKMO
  le    Field         R15   R15    R30
 Name    Type         Q-
                     flux
  tx     diff                                    X           
  tn     diff                                    X           
t, tm    diff   X      X     X      X     X      X    X      X
  rh     diff          X     X      X     X           X      X
  p     ratio   X      X     X      X     X      X    X      X
  sr    ratio   X      X     X      X     X      X    X      X
  vp    ratio          X     X      X     X      X    X      X
  w     ratio   X      X     X      X     X      X    X      X
                                 
11.2  Scenario Filename Protocol

The naming protocol for scenario files is:

                            VAR_GGG.MMM
where:

      VAR  = Variable name                                  
    t, tm    surface air mean temperature difference
             (2xCO2-1xCO2)
   tx, tn    surface air maximum or minimum temperature
             difference (2xCO2-1xCO2)
       rh    relative humidity difference (2xCO2-1xCO2)
        p    precipitation change ratio (2xCO2/1xCO2)
       sr    total incident solar radiation change ratio
             (2xCO2/1xCO2)
       vp    surface vapor pressure change ratio
             (2xCO2/1xCO2)
        w    surface wind speed change ratio (2xCO2/1xCO2)
                                                            
     _GGG  = Climate model experiment                       
      ccc    CCC                                            
      gf1    GFDL R15                                       
      gfq    GFDL R15 Q-flux                                
      gf3    GFDL R30                                       
      gis    GISS                                           
      mm4    RegCM (MM4)                                    
      osu    OSU                                            
      ukm    UKMO                                           
                                                            
     .MMM  = Period
             month (e.g., jan, feb) or annual (ann)
             

11.3  Development of Climate Change Scenarios

11.3.1  Overview

Climate scenarios from eight climate change experiments are
included in the database.  Seven of these experiments are from
atmospheric general circulation model (GCM) 1xCO2 and 2xCO2
equilibrium runs (Section 11.3.2).  These GCMs were implemented
with a simple "mixed-layer" ocean representation that includes
ocean heat storage and vertical exchange of heat and moisture with
the atmosphere, but omits or specifies (rather than calculates)
horizontal ocean heat transport.  The eighth scenario is from a
limited-area nested regional climate model (RegCM) experiment for
the U.S. (see Section 11.3.2) which was supported by the Model
Evaluation Consortium for Climate Assessment (MECCA).  The CCC and
GFDL R30 runs are among the high resolution GCM experiments
reported in IPCC (1990).

Changes in monthly mean temperature and relative humidity were
represented as differences (2xCO2 climate value - 1xCO2 climate
value) and those for monthly precipitation, solar radiation, vapor
pressure, and horizontal wind speed as change ratios (2xCO2 climate
value/1xCO2 climate value).  GCM grid point change values were
derived from archives at the National Center for Atmospheric
Research (NCAR; Jenne 1992) and spatially interpolated to the 0.5 degree
VEMAP grid.  Wind speed changes are for the lowest model level.
For GISS runs, we calculated winds from vector components and then
determined the change ratio.  Values from the 60-km RegCM grid were
reprojected to the 0.5 degree grid.  For calculation of relative humidity
changes, see Section 11.3.3.  Vapor pressure (and relative
humidity) were not available for the CCC run; relative humidity
changes were not determined for the RegCM experiment.

A key issue in the generation of altered climates based on climate
model output is the strong possibility of physical inconsistencies
in the new climates.  Change ratios from the NCAR archive have an
imposed upper limit of 5.0, providing some constraint on these
changes.  An exception is that the GISS wind speed change ratios do
not have this limit imposed (most GISS wind speed change ratios
were less than 5).  In the creation of the climates, we suggest
additional checks for physical consistency in Section 11.5.

For a discussion of the utility and limitations of using climate
model experiment outputs for exploring ecological sensitivity to
climate change, see Sulzman et al. (1995).

11.3.2  Model Experiments

The 8 climate model experiments are:

     CCC  - Canadian Climate Centre (Boer, McFarlane, and Lazare 1992)

     GISS - Goddard Institute for Space Studies (Hansen et al. 1984)

     GFDL - Geophysical Fluid Dynamics Laboratory.  Three
     experiments:
     
        (1)  GFDL R15:   R15 (4.5 degree by 7.5 degree grid) runs without Q-
               flux corrections (Manabe and Wetherald, 1987).
        (2)  GFDL R15 Q-flux:  R15 resolution (4.5 degree by 7.5 degree grid)
               runs with Q-flux corrections (Manabe and Wetherald
               1990, Wetherald and Manabe 1990).
        (3)  GFDL R30:   R30 (2.22 degree by 3.75 degree grid) run with Q-flux
               corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990).
     
     OSU  - Oregon State University (Schlesinger and Zhao 1989)
     
     UKMO - United Kingdom Meteorological Office (Wilson and Mitchell 1987)
     
     RegCM (MM4) - National Center for Atmospheric Research (NCAR)
         nested regional climate model (climate version of the
         Pennsylvania State University/NCAR mesoscale model MM4;
         Giorgi, Brodeur and Bates 1994).  Conterminous U.S.
         simulations were on a 60-km interval grid and were driven
         by 1x and 2xCO2 equilibrium GCM runs (Thompson and Pollard
         1995a, 1995b).  1x and 2xCO2 RegCM runs were each 3 years
         in length.  Climate changes were based on averages for
         these runs.

11.3.3  Determination of Surface Humidity Change

Surface humidity is reported in the NCAR archives as mixing ratio
(r) for OSU and GFDL runs and as specific humidity (q) for UKMO and
GISS runs; no humidity variable was archived for CCC runs.  We
converted q and r to vapor pressure and calculated a change ratio.

Determination of new monthly mean daytime relative humidities (RH)
from monthly change ratios of vapor pressure (VP) on a monthly
basis and independent of a base or control climate is problematic.
This is because of non-linear relationships among VP, RH, and
temperature and between daily mean daylight temperature and monthly
temperature means.  While recognizing these limitations, we
estimated monthly mean RH for each scenario from corresponding
monthly VP and temperature means, mimicking the daily method in
CLIMSIM.  New climate monthly values were constrained to be between
0 and 100%.  We assumed that changes in monthly mean RH are a good
estimate of changes in monthly mean daylight RH.

11.4  Climate Change Scenario Variables

All variables in the scenario dataset are change fields.  The
reader is referred to the section on methods and cautions for
creating new climate inputs based on these fields (Section 11.5).

11.4.1  Difference Fields

   Temperature - [degrees C]
      Difference in monthly or annual mean monthly temperature.
      
          Gridded SVF files:       t_GGG.MMM
          Scaling factor:          10.0

   Relative humidity - [%]
      Difference in monthly or annual mean daylight relative
      humidity (see Section 11.3.3).
      
          Gridded SVF files:       rh_GGG.MMM
          Scaling factor:          10.0

11.4.2  Change Ratios [ratio, 0-1]

   Precipitation -
      Change ratios for monthly or annual accumulated
      precipitation.
          
          Gridded SVF files:       p_GGG.MMM
          Scaling factor:          1000.0

   Solar radiation -
      Change ratios for monthly or annual mean total incident solar
      radiation.
      
          Gridded SVF files:       sr_GGG.MMM
          Scaling factor:          1000.0

   Vapor pressure -
      Change ratios for monthly or annual mean vapor pressure.
          
          Gridded SVF files:       vp_GGG.MMM
          Scaling factor:          1000.0

   Wind speed -
      Change ratios for monthly or annual mean near-surface wind
      speed.
         
         Gridded SVF files:       w_GGG.MMM
         Scaling factor:          1000.0

11.5  Creation of New Climates:  Application of Change Fields to
    Base Climate and Tests for Physical Constraints

11.5.1  Creation of Altered Climate Fields

To create new climates for a given scenario, modify monthly or
daily VEMAP base climate (Section 8) by monthly scenario change
fields according to the following processes.  Then check for
physical inconsistencies (Section 11.5.2)

   (1)  For maximum, minimum, and mean temperature and for
   relative humidity:

   Add the corresponding month's temperature or relative humidity
   differences to the base climate's monthly or daily values.

   (2)  For precipitation, solar radiation, vapor pressure, and
   wind speed:

   Multiply base climate monthly or daily values by the
   corresponding monthly change ratios.
   
Note that these procedures may not result in daily RH values that
are strictly consistent with the new daily temperature and vapor
pressure record because RH, vapor pressure, and temperature changes
are applied evenly across a month.

11.5.2  Checks for Physical Consistency

We recommend that users of the climate scenarios apply the
following rules to limit physical inconsistencies arising from the
generation of altered climates:

   (1)  Apply an upper limit of 5.0 on RegCM (MM4) change ratio
   values and on GISS wind speed change ratios.  This avoids
   extreme values and maintains consistency with the upper limit
   already built into the change fields for the other models.
   
   (2)  For solar radiation:  Limit new values of total incident
   solar radiation (sr) so as not to exceed potential solar input
   at the surface (psr_sfc).
   
   (3)  For vapor pressure:  Check that new vapor pressure values
   do not exceed saturated vapor pressure (vpsat).  To calculate
   saturated VP based on daylight average temperature (tdaylt), we
   present here code adapted from CLIMSIM that is consistent with
   that used in the calculation of daily relative humidity
   (Section 8.3.2):
        
   tdaylt = [{tmax - [(tmax+tmin)/2]} * 0.35]  + [(tmax+tmin)/2]
   
               vpsat = 6.1078 * exp[(17.269 * tdaylt)/(237.3 + tdaylt)]
   
   Where tmin and tmax are minimum and maximum temperatures,
   respectively.  This constraint is appropriately applied on a
   daily basis.  When applied monthly, it may overly constrain
   monthly mean vapor pressures.
   
   (4)  For relative humidity:  Set any relative humidity values
   greater than one hundred percent to 100% and values less than
   zero percent to 0%.
   
   (5)  For wind speed:  Use caution in deciding whether or not to
   apply surface wind speed changes.  Changes in wind speed from
   the GCM runs are locally extreme (e.g., by a factor of 3 or
   more).  Wind change fields were not used in the VEMAP I
   simulations.
     
These tests do not cover all possible physical inconsistencies, but
provide a minimum set of checks.  Note that for any month in which
rules (2) - (5) are applied, monthly means of new daily values may
not exactly match new monthly values that are obtained by applying
monthly changes to VEMAP base climate monthly means.  This is
because the above constraints have differential effects when
applied at daily versus monthly timesteps.


12  SITE FILES

12.1  Site File Content and Structure

Site files contain monthly climate and scenario data in column
format.  We developed this time-sequential format to facilitate the
extraction of data for individual stations.  README files included
under the /siteFiles directory give instructions on how to find a
particular grid cell.  Site files omit background grid cells, with
a new line for each grid cell (3261 data records).  Each file lists
12 monthly values (January-December) as a single record.  A record
also contains geographic information about the associated grid
point such as latitude, longitude, elevation, state identification
number, and Kuchler and VEMAP vveg.v2 vegetation types (See Section 4.3).

12.2  Site File Naming Protocol

The naming protocol for the files is VAR or VAR_GGG, where VAR
describes the variable (as in Section 8.2.1) and, in the case of
climate scenario files, GGG gives the climate model experiment from
which the scenarios were extracted (as in Section 11.2).  If the
filename does not include a GGG suffix, the data were extracted
from the monthly climate files.


13  ACKNOWLEDGMENTS

Development of the VEMAP database was supported by VEMAP sponsors
(NASA Mission to Planet Earth, Electric Power Research Institute,
and USDA Forest Service Southern Region Global Change Research
Program) and by the National Science Foundation Climate Dynamics
Program through UCAR's Climate System Modeling Program (CSMP).  We
thank Lou Pitelka, Susan Fox, Tony Janetos, and Hermann Gucinski
for their support of VEMAP.  Thanks to Donna Beller, Hank Fisher,
Alison Grimsdell, and Tom Painter for programming and data
management support, Susan Chavez for administrative support,
Gaylynn Potemkin for manuscript preparation, Roy Barnes, Chris
Daly, Filippo Giorgi, E. Raymond Hunt, Jr., Roy Jenne, Dennis
Joseph, Jeff Kern, Danny Marks, Christine Shields, Dennis Shea, and
Will Spangler for access to datasets and model output, and Jeff
Kuehn and NCAR's Climate and Global Dynamics Division for computer
systems support.  We thank Rick Katz, Dennis Shea, David Schimel,
VEMAP participants, and other users for document review and dataset
evaluation.  Linda Mearns, Rick Katz, and Dennis Shea also provided
comments on daily climate dataset design.  We wish to thank Genasys
II, StatSci, and NCAR's Scientific Computing Division for technical
support.  NCAR is supported by the National Science Foundation.


14  CONTACTS

Direct enquiries and comments regarding the VEMAP dataset to:

Nan Rosenbloom
     telephone:     303-497-1617
     email:  nanr@ucar.edu
Tim Kittel
     telephone:     303-497-1606
     email:  kittel@ucar.edu

Mailing address and fax number are:

     Ecosystem Dynamics and the Atmosphere Section
     Climate and Global Dynamics Division
     NCAR
     P.O. Box 3000
     Boulder, CO 80307-3000
     USA

     Fax:  303-497-1695

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     database for modeling United States ecosystem/vegetation
     sensitivity to climate change.  J. Biogeog. 22:857-862.

Kuchler, A.W.  (1964)  Manual to Accompany the Map, Potential
     Natural Vegetation of the Conterminous United States.  Spec.
     Pub. No. 36.  American Geographical Society, New York.  143 pp.

Kuchler, A.W.  (1975)  Potential Natural Vegetation of the
     Conterminous United States.  (2nd ed.)  (Map 1:3,168,000)
     American Geographical Society, New York.

Manabe, S. and R.T. Wetherald  (1987)  Large-scale changes in soil
     wetness induced by increase in carbon dioxide.  J. Atmos. Sci.
     44:1211-1235.

Manabe, S. and Wetherald, R.T. (1990) [Reported in:  Mitchell,
     J.F.B., S. Manabe, V. Meleshko, T. Tokioka.  Equilibrium
     Climate Change and its Implications for the Future. Pp. 131-
     172, in:  Climate Change:  The IPCC Scientific Assessment.
     Houghton, J.T., G.J. Jenkins, and J.J. Ephraums (eds).
     Cambridge University Press, Cambridge, UK.]

Marks, D.  (1990)  The sensitivity of potential evapotranspiration
     to climate change over the continental United States.  Pp. IV-
     1 - IV-31, in:  Biospheric Feedbacks to Climate Change:  The
     Sensitivity of Regional Trace Gas Emissions,
     Evapotranspiration, and Energy Balance to Vegetation
     Redistribution.  H. Gucinski, D. Marks, and D.P. Turner (eds).
     EPA/600/3-90/078.  U.S. Environmental Protection Agency,
     Corvallis, OR.
 
Marks, D. and J. Dozier  (1992)  Climate and energy exchange at the
     snow surface in the alpine region of the Sierra Nevada:  2.
     Snow cover energy balance. Water Resources Research 28:3043-3054.
 
NFNOC (Navy Fleet Numeric Oceanographic Center)  (1985)  10-minute
     Global Elevation Terrain, and Surface Characteristics.  (Re-
     processed by NCAR and NGDC).  NOAA National Geophysical Data
     Center.  Digital dataset.

NCDC (National Climatic Data Center)  (1992)  1961-1990 Monthly
     Station Normals Tape.  U.S. Department of Commerce.  Digital
     dataset:  TD 9641.

Paivanen, J.  (1973)  Hydraulic conductivity and water retention in
     peat soils.  Acta Forestalia Fennica 129:1-70.

Rawls, W.L., D.L. Brakensiek, and K.E. Saxton  (1982)  Estimation
     of soil water properties.  Trans. American Society of
     Agricultural Engineers 25:1316-1320.

Richardson, C.W.  (1981)  Stochastic simulation of daily
     precipitation, temperature and solar radiation.  Water
     Resources Research 17:182-190.

Richardson, C.W. and D.A. Wright  (1984)  WGEN:  A Model for
     Generating Daily Weather Variables.  U.S. Department of
     Agriculture, Agricultural Research Service, ARS-8.  83 pp.

Running, S.W., R.R. Nemani, and R.D. Hungerford  (1987)
     Extrapolation of synoptic meteorological data in mountainous
     terrain and its use for simulating forest evapotranspiration
     and photosynthesis.  Can. J. For. Res. 17:472-483.

Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick
     (1986)  Estimating generalized soil-water characteristics from
     texture.  Soil Sci. Am. J. 50:1031-1036.

Schlesinger, M.E. and Z.C. Zhao  (1989)  Seasonal climate changes
     induced by doubled CO2 as simulated by the OSU atmospheric GCM-
     mixed layer ocean model.  J. Climate 2:459-495.

Sulzman, E.W., K.A. Poiani, and T.G.F. Kittel  (1995)   Modeling
     human-induced climatic change:  A summary for environmental
     managers.  Environmental Management 19:197-224.

Thompson, S.L. and D. Pollard  (1995a)  A global climate model
     (GENESIS) with a land-surface-transfer scheme (LSX).  Part 1:
     Present-day climate.  J. Climate 8:732-761.

Thompson, S.L. and D. Pollard  (1995b)  A global climate model
     (GENESIS) with a land-surface-transfer scheme (LSX).  Part 2:
     CO2 sensitivity.  J. Climate 8:1104-1121.

VEMAP Members  (1995)  Vegetation/Ecosystem Modeling and Analysis
     Project:  Comparing biogeography and biogeochemistry models in
     a continental-scale study of terrestrial ecosystem responses
     to climate change and CO2 doubling.  Global Biogeochem. Cycles
     9:407-437.

Wetherald, R.T. and S. Manabe  (1990)  [Reported in:  Cubasch, U.,
     and R.D. Cess.  Processes and Modeling.  Pp. 69-91, in:
     Climate Change:  The IPCC Scientific Assessment. Houghton,
     J.T., G.J. Jenkins, and J.J. Ephraums (eds).  Cambridge
     University Press, Cambridge, UK.]

Wilson, C.A. and J.F.B. Mitchell  (1987)  A doubled CO2 climate
     sensitivity experiment with a global climate model including a
     simple ocean.  J. Geophys. Res. 92 (D11):13,315-13,343.
A1  APPENDIX 1:  CDROM AND FTP SITE DIRECTORY STRUCTURE

Directories on the CDROM (Section 2.1) and FTP site (Section 2.3)
have the following structure:

   /daily
   /docs
   /geog
   /images
   /monthly
   /programs
   /soil
       /mineral
       /organic
       /total
   /scenario
       /ccc
       /gfdl_qfx
       /gfdl_r15
       /gfdl_r30
       /giss
       /mm4
       /osu
       /ukmo
   /siteFile
   /tarFiles (FTP and Web site only)
   /vresults (FTP and Web site only)
       /bgc
       /biome2
       /century
       /doly
       /mapss
       /tem
   /vUPDATES (FTP and Web site only)

Readme files residing in each subdirectory describe files in that
directory.

A2  APPENDIX 2:  DETERMINATION OF ABSOLUTE SOIL AREA FOR EACH CELL

A2.1  Absolute Area for Soil Modes and Components

A useful quantity for spatially explicit modeling is the absolute
area represented by each cell (varea, Section 7.3.6) or portion of
a cell being simulated (such as the area for mode 1's mineral
soil).  The soils database includes the area covered by mineral and
organic components of the modal and average soils (ma and oa,
Section 9.5.4).  These were determined as follows:

For mineral soils:

  Absolute areal cover of mode M mineral soils for a cell =
  
     (relative extent of mineral soils within mode M soils) 
     (relative extent of mode M soils within VEMAP land area) 
     (absolute area of VEMAP land area in a cell)
 
  or, in terms of the datasets by filename:
 
     ma_mM  = (map_mM/100) * (tap_mM/100) * varea
 
  and for average soils, by filename:
 
     ma_ave = (map_ave/100) * (tap_ave/100) * varea
 
For organic soils:

  Absolute areal cover of mode M organic soils for a cell =

     (relative extent of organic soils within mode M soils) 
     (relative extent of mode M soils within VEMAP land area) 
     (absolute area of VEMAP land area in a cell)

  or, in terms of the datasets by filename:

     oa_mM = [1 - (map_mM/100)] * (tap_mM/100) * varea

  and for average soils, by filename:

     oa_ave = [1 - (map_ave/100)] * (tap_ave/100) * varea

A2.2  Application to Model Outputs

Model experiments can be run either with the dominant soil, average
soil, or a suite of modal types.  In the first and second cases,
where a single modal or average mineral soil is assumed to
represent the land area for a cell, then model output can be
multiplied by the cell's land area within the VEMAP domain:
  
     Model variable cell total =
        (model variable) * varea * km2/(model unit area)

where varea is the area of VEMAP land area in a cell (Section 7).

In the third case, model outputs must be weighted by the relative
areal coverage of each soil category in each cell to give results
for the entire cell.

For simulations run with all soil modes (1-4) and/or both soil
components (organic and mineral), weighted model outputs (e.g., for
net primary production) are generated by the following method
(Kittel at al. 1996).  Outputs for each soil component and mode are
multiplied by the cell area represented by the corresponding modal
soil, summed across modes to provide a weighted total for each
component.  If both components are present, component totals are
summed, again weighted by corresponding areas.
  
For a more explicit description of how to determine the weighted total for 
each component and both combined, see the the VEMAP User's Guide on the 
World Wide Web (http://www.cgd.ucar.edu/vemap/users_guide.html), or 
order the "User's Guide to the VEMAP Phase I Database" (NCAR Technical 
Note TN-431+IA) available from:

	EDAS
	Box 3000
	NCAR
	Boulder, CO  80307-3000


A3  APPENDIX 3:  AGGREGATION OF KUCHLER VEGETATION CODES TO VEMAP
   VEGETATION TYPES

Table A3.1  Aggregation of Kuchler vegetation types to VEMAP
vegetation types (vveg versions 1 and 2, Section 10).  Names of
Kuchler types are given in Table A3.2.

VVEG VEMAP Vegetation              Kuchler Vegetation Types
     Type 			vveg.v1              vveg.v2
________________________________________________________________
1   Tundra                     52                   52
2   Boreal coniferous forest   15, 21, 93, 96       15, 21, 93, 96
      forest
3   Temperate maritime         1, 2, 3, 4, 5, 6     1, 2, 3, 4, 5, 6
      coniferous forest
4   Temperate                  8, 10, 11, 12, 13,   8, 10, 11, 12, 13,
      continental coniferous   14, 16, 17, 18, 19,  14, 16, 17, 18,
      forest          	       20, 95               19, 20, 95
5   Cool temperate             28, 106, 107, 108,   106, 107, 108,
      mixed forest             109, 110             109, 110
6   Warm temperate/            29, 89, 90, 111,     26, 28, 29, 89,
      subtropical mixed forest 112                  90, 111, 112
7   Temperate deciduous forest 26, 98, 99, 100,     98, 99, 100, 101,
       			       101, 102, 103, 104   102, 103, 104
8   Tropical deciduous forest  not present          not present
9   Tropical evergreen forest  not present          not present
10  Temperate mixed            30, 31, 32, 36, 37   30, 31, 32, 36, 37
      xeromorphic woodland
11  Temperate conifer          23                   23
      xeromorphic woodland
12  Tropical thorn woodland    not present          not present
13  (v1) Temperate             61, 71, 81, 82, 84,  71, 81, 82, 84, 88
      deciduous savanna        87, 88
    (v2) Temperate/subtropical
      deciduous savanna
14  Warm temperate/subtropical 60, 62, 83, 85, 86   60, 61, 62, 83,
      mixed savanna                  	     	    85, 86, 87
15  Temperate conifer savanna  24                   24
16  Tropical deciduous savanna not present          not present
17  C3 grasslands              47, 48, 50, 51, 63,  47, 48, 50, 51,
                               64, 66, 67, 68       63, 64, 66, 67, 68
18  C4 grasslands              53, 54, 65, 69, 70,  53, 54, 65, 69,
                               74, 75, 76, 77       70, 74, 75, 76, 77
19  Mediterranean shrubland    33, 34, 35           33, 34, 35
20  Temperate arid shrubland   38, 39, 40, 46, 55,  38, 39, 40, 46,
     			       56, 57               55, 56, 57
21  Subtropical arid shrubland 41, 42, 43, 44, 45,  41, 42, 43, 44,
     			       58, 59               45, 58, 59
90  Ice                        not present          not present
91  Inland water bodies        no symbol            no symbol
92  Wetlands                   49, 78, 79, 80, 92,  49, 78, 79, 80,
                               94, 113, 114         92, 94, 113, 114


           Table A3.2  Kuchler Vegetation Type Names and
           Identifying Codes
           (Kuchler 1964, 1975).

Code      Kuchler Vegetation Type
_____________________________________
WESTERN FORESTS
          
Needleleaf Forests
1         Spruce-cedar hemlock forest
2         Cedar-hemlock-Douglas fir forest
3         Silver fir-Douglas fir forest
4         Fir-hemlock forest
5         Mixed conifer forest
6         Redwood forest
7         Red fir forest
8         Lodgepole pine-subalpine forest
9         Pine-cypress forest
10        Ponderosa shrub forest
11        Western ponderosa forest
12        Douglas fir forest
13        Cedar-hemlock-pine forest
14        Grand fir-Douglas fir forest
15        Western spruce-fir forest
16        Eastern ponderosa forest
17        Black Hills pine forest
18        Pine-Douglas fir forest
19        Arizona pine forest
20        Spruce-fir-Douglas fir forest
21        Southwestern spruce-fir forest
22        Great Basin pine forest
23        Juniper-pinyon woodland
24        Juniper steppe woodland
          
Broadleaf forests
25        Alder-ash forest
26        Oregon oakwoods
27        Mesquite bosques
          
Broadleaf and needleleaf forests
28        Mosaic of numbers 2 and 26
29        California mixed evergreen forest
30        California oakwoods
31        Oak-juniper woodland
32        Transition between 31 and 37
          
WESTERN SHRUB AND GRASSLAND
          
Shrub     
33        Chaparral
34        Montane chaparral
35        Coastal sagebrush
36        Mosaic of numbers 30 and 35
37        Mountain mahogany-oak scrub
38        Great Basin sagebrush
39        Blackbrush
40        Saltbush-greasewood
41        Creosote bush
42        Creosote bush-bur sage
43        Palo verde-cactus shrub
44        Creosote bush-tarbush
45        Ceniza shrub
46        Desert: vegetation largely
          absent
          
Grasslands
47        Fescue-oatgrass
48        California steppe
49        Tule marshes
50        Fescue-wheatgrass
51        Wheatgrass-bluegrass
52        Alpine meadows and barren
53        Grama-galleta steppe
54        Grama-tobosa prairie
          
Shrub and grasslands combinations
55        Sagebrush steppe
56        Wheatgrass-needlegrass shrubsteppe
57        Galleta-three awn shrubsteppe
58        Grama-tobosa shrubsteppe
59        Trans-Pecos shrub savanna
60        Mesquite savanna
61        Mesquite-acacia savanna
62        Mesquite-live oak savanna
          
CENTRAL AND EASTERN GRASSLANDS
          
Grasslan  
ds
63        Foothills prairie
64        Grama-needlegrass-wheatgrass
65        Grama-buffalo grass
66        Wheatgrass-needlegrass
67        Wheatgrass-bluestem-needlegrass
68        Wheatgrass-grama-buffalo grass
69        Bluestem-grama prairie
70        Sandsage-bluestem prairie
71        Shinnery
72        Sea oats prairie
73        Northern cordgrass prairie
74        Bluestem prairie
75        Nebraska Sandhills prairie
76        Blackland prairie
77        Bluestem-sacahuista prairie
78        Southern cordgrass prairie
79        Palmetto prairie
          
Grassland and forest combinations
80        Marl-Everglades
81        Oak savanna
82        Mosaic of numbers 74 and 100
83        Cedar glades
84        Cross timbers
85        Mesquite-buffalo grass
86        Juniper-oak savanna
87        Mesquite-oak savanna
88        Fayette prairie
89        Blackbelt
90        Live oak-sea oats
91        Cypress savanna
92        Everglades
          
EASTERN FORESTS
          
Needleleaf forests
93        Great Lakes spruce-fir forest
94        Conifer bog
95        Great Lakes pine forest
96        Northeastern spruce-fir forest
          
Broadleaf forests
98        Northern floodplain forest
99        Maple-basswood forest
100       Oak-hickory forest
101       Elm-ash forest
102       Beech-maple forest
103       Mixed mesophytic forest
104       Appalachian oak forest
105       Mangrove
          
Broadleaf and needleleaf forests
106       Northern hardwoods
107       Northern hardwoods-fir forest
108       Northern hardwoods-spruce forest
109       Transition between numbers 104 and 106
110       Northeastern oak-pine forest
111       Oak-hickory-pine forest
112       Southern mixed forest
113       Southern floodplain forest
114       Pocosin
115       Sand pine scrub
116       Subtropical pine forest


A4  APPENDIX 4:  STATE IDENTIFICATION NUMBERS

State identification codes used in the site files (Section 12) are:

1 	Alabama
2 	Arizona
3 	Arkansas
4 	California
5 	Colorado
6 	Connecticut
7 	Delaware
8 	Florida
9 	Georgia
10   	Idaho
11   	Illinois
12   	Indiana
13   	Iowa
14   	Kansas
15   	Kentucky
16   	Louisiana
17   	Maine
18   	Maryland
19   	Massachusetts
20   	Michigan
21   	Minnesota
22   	Mississippi
23   	Missouri
24   	Montana
25   	Nebraska
26   	Nevada
27   	New Hampshire
28   	New Jersey
29   	New Mexico
30 	New York
31   	North Carolina
32   	North Dakota
33   	Ohio
34   	Oklahoma
35   	Oregon
36   	Pennsylvania
37   	Rhode Island
38   	South Carolina
39   	South Dakota
40   	Tennessee
41   	Texas
42   	Utah
43   	Vermont
44   	Virginia
45   	Washington
46   	West Virginia
47   	Wisconsin
48   	Wyoming
49   	unassigned
50   	Alaska


A5  APPENDIX 5:  VEMAP MAILING LIST

A5.1  Description of the VEMAP Mailing List

The VEMAP mailing list is called "vemap_users" and is a moderated
list as opposed to a discussion list.  A moderated list allows only
the list owner to send messages to list subscribers.  A discussion
list allows any individual subscriber to send messages to the
entire list.

A5.2  How to Subscribe to the VEMAP Mailing List

In order to subscribe to the VEMAP users list, send a message to:

     majordomo@ucar.edu

Leave the subject blank, then type in the body of the letter:

     subscribe vemap_users
     end

The end command is optional and is only needed if your outgoing
email messages include an appended footer, or signature (e.g., your
name and address appended to the bottom of each message).

In response to this message, you will receive an email reply from
majordomo@ucar.edu telling you of your successful subscription.  If
you wish to unsubscribe at any time, use the instructions below.

A5.3  Listserver Commands

In the description below, items contained in [ ]'s are optional.
When providing the item, do not include the [ ]'s or <>'s shown
with the command.

subscribe <list> [<address>]
     Subscribe yourself (or <address> if specified) to the named <list>.

unsubscribe <list> [<address>]
     Unsubscribe yourself (or <address> if specified) from the named <list>.

get <list> <filename>
     Get a file related to <list>.

index <list>
     Return an index of files you can "get" for <list>.

which [<address>]
     Find out which lists you (or <address>, if specified) are on.

who <list>
     Find out who is on the named <list>.

info <list>
     Retrieve the general introductory information for the named <list>.

lists
     Show the lists served by this Majordomo server.

help
     Retrieve this message.

end
     Stop processing commands (useful if your mailer adds a signature).

Commands should be sent in the body of an email message to
Majordomo@ucar.edu.

Note that commands in the "Subject:" line are not processed.  If
you have any questions or problems, please contact the list owner
via email at Majordomo-Owner@ucar.edu.

_______________________________
1 J.M. Melillo (Chair), J. Borchers, J. Chaney, A. Haxeltine, D.W.
Kicklighter, A.D. McGuire, R. McKeown, R.P. Neilson, R.R. Nemani,
D.S. Ojima, Y. Pan, W.J. Parton, L.L. Pierce, I.C. Prentice, W.M.
Pulliam, B. Rizzo, S.W. Running, S. Sitch, T.M. Smith, and F.I.
Woodward.
2 J.M. Melillo (Chair), J. Chaney, A. Haxeltine, E.R. Hunt, Jr.,
D.W. Kicklighter, A.D. McGuire, R. McKeown, R.P. Neilson, R.R.
Nemani, D.S. Ojima, Y. Pan, W.J. Parton, L.L. Pierce, I.C.
Prentice, W.M. Pulliam, B. Rizzo, S.W. Running, T.M. Smith, and
F.I. Woodward.
3 J.M. Melillo (Chair), J. Borchers, J. Chaney, H. Fisher, S. Fox,
A. Haxeltine, A. Janetos, D.W. Kicklighter, T.G.F. Kittel, A.D.
McGuire, R. McKeown, R. Neilson, R. Nemani, D.S. Ojima, T. Painter,
Y. Pan, W.J. Parton, L. Pierce, L. Pitelka, C. Prentice, B. Rizzo,
N. Rosenbloom, S. Running, D.S. Schimel, S. Sitch, T. Smith, and
F.I. Woodward.