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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temperature. Agricultural and Forest Meteorology, in press.

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contiguous United States. Soil Sci. Soc. Am. J. 58:439-455.

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capacity in the contiguous United States. Soil Sci. Soc. Am.

J. 59:1126-1133.

Kern, J.S. (1996) Errata to \"Geographic patterns of soil water-

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Bromberg, T.H. Painter, N.A. Rosenbloom, W.J. Parton, and F.

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(GENESIS) with a land-surface-transfer scheme (LSX). Part 2:

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Project: Comparing biogeography and biogeochemistry models in

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to climate change and CO2 doubling. Global Biogeochem. Cycles

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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.