APPENDIX

This appendix provides full and detailed description of the database and the methodology used in this study.

Hardware and Software

Hardware

Almost the entire GIS analyses were carried out using a Sun Spark Station 10.

The weather generator and BAMnut programs were run on a Pentium III DELL OptiPlex GX1 PC, 1.2 GB, 130,488 KB RAM 17 inch Video Super VGA.

Data transfers between the Sun Spark Station and the Pentium PC were carried out under Microsoft Windows NT.

Software

A Geographic Information System (ARC, Version 7.03, ESRI, Redlands, CA, USA) which has both raster and vector capabilities was used. This system is able to efficiently store geographical referenced database which includes both digital maps and their attribute files.

The weather generator and BAMnut were written in Visual Pascal. BAMnut should be run on at least a 486PC with 8MB of RAM with Windows 95 as the operating system.

World Weather Database

World Weather Data Selection and Extraction

Downloading

Location of climate data for downloads:

http://ipcc-ddc.cru.uea.ac.uk/cru_data/datadownload/observed/climatology_download.html

The following data were selected and downloaded; Maximum Temperature (ctmx6190.zip), Minimum Temperature (ctmn6190.zip), Precipitation (cpre6190.zip), Wet Days (cwet6190.zip), Vapour pressure (cvap6190.zip), Radiation (crad6190.zip), and Windspeed (cwnd6190.zip).

ASCII data format

An extract of the ASCII data file ctmx6190.dat contained in ctmx6190.zp is illustrated below:

Separating data by month

Each zipped file (e.g. ctmn6190.zip) contains 12 monthly ASCII files, so it was necessary to separate these files by month. Due to the very large size of each file (i.e. 259,200 records) the separation of these files was done using software. Once files were separated they were saved into individual files.

The header for each monthly ascii file

Correcting shift

The data downloaded presented a shift at zero latitude, so a shift adjustment had to be made to all the files and this was done automatically using Java software.

Standardising the Database

Adjusting to Potential Evapotranspiration (PET) data from the International Institute for Applied Systems Analysis (IIASA)

The observed climate data from the University of East Anglia Climate Research Unit (CRU) data was found to be the most comprehensive and complete climate data available, however, while displaying this data using Java it was realised that a large number of islands not displayed in the CRU data visualisation page were present (i.e. http://ipcc-ddc.cru.uea.ac.uk/cru_data/visualisation/visual_index.html). To solve this problem, adjustments were made according to PET data from IIASA for the following two reasons; (a) PET data could be used as an input to the model of the present study because it was based on CRU data, and (b) the islands displayed in the PET data were identical to the CRU data visualisation page. The adjustments made are described below:

Converting text files from DOS to UNIX

The following example illustrates how one of the text files was converted from DOS to UNIX using Arc/Info software:

Grid: asciigrid ctmx6190_0.dat ctmx6190_0.txt

Where:
asciigrid = Arc/Info command
ctmx6190_0.dat = ASCII file for maximum temperature for the month of January in DOS format
ctmx6190_0.txt = ASCII file for maximum temperature for the month of January in UNIX format.

Converting data from ASCII to a GRID

The following example illustrates how one of the files was copied and converted into a grid using Arc/Info software:

Grid: asciigrid ctmx6190_0.txt ctmx6190_0

Where:
asciigrid = Arc/Info ASCII to grid conversion command
ctmx6190_0.txt = ASCII file for maximum temperature for the month of January
ctmx6190_0 resulting grid.

Base grid

A grid was used as a template to standardise grid extensions. The cell size and geographical project of PET for the month of January was chosen as the base grid. A description of the base grid is illustrated below:

The base grid

Coordinate System Description
Projection GEOGRAPHIC
Units DD Spheriod CLARKE1866

Based on the number of rows and columns defined above, the base grid had a total of grid cells (i.e. 360 x 720 = 259,200). The number of pixels for the global `land' areas alone was 62,482. Data originally stored in ASCII format or having a different resolution or projection were converted to the base grid. Example:

Grid: setwindow petjan
Grid: setmask petjan
Grid: setcell petjan
Grid: tmx6190_0 = ctmx6190_0

Where:
setwindow, setmask and setcell are Arc/Info commands
petjan = PET grid for the month of January
ctmx6190_0 = maximum temperature for the month of January
tmx6190_0 = output grid.

Summary of Climate Data Used as Inputs to BAMnut

Tables 4, 5, 6 and 7 show statistics of the mean monthly climate datasets for global land areas used in this study as inputs to the model BAMnut developed.

A comparison of climate values between the original CRU data and the adjusted CRU data (i.e. CRU data set to the exact PET format from IIASA) showed no significant differences amongst data, the largest difference was of 0.4 when comparing the mean value of minimum temperature for the month of January.

TABLE 4

Statistics of mean monthly air temperature values for global land areas

TABLE 5

Statistics of mean monthly precipitation and wet days values for global land areas

TABLE 6

Statistics of mean monthly vapour pressure and radiation values for global land areas

TABLE 7

Statistics of mean monthly wind speed values for global land areas

World Weather Generator

Settings Created as Entries for the Daily Data Weather Generator

Table 8 shows the settings that were made for the weather generator. Each folder corresponds to one area of the world, the number of files created from each area and their size in Mb is also shown. Computer memory problems were encountered due to the vast amount of files that the generator had to create, so the information in Table 8 is based on trial-and-error meaning that it is information that should be used to recreate the data or is helpful in creating daily data for another year or even better for enhancing the generator used.

TABLE 8

Settings for weather generator

Output data format for one of the files created for the United Republic of Tanzania, only the first 10 days of the simulated year (1990) are shown:

The weather generator takes approximately 160 hours to generate the required data, however, it can now serve as inputs to current and future versions of BAMnut, thus, there is no need to recreate daily data. The present GIS version of BAMnut takes 80 minutes to create the biomass and pod yield files, and it is expected that the Web version of BAMnut will the take about the same amount of time to generate the two files. However, the user will be able to work in other computer applications and leave the model running, and the Web model will have a report progress function, so that the user will not have to wait for model outputs.

Statistics of Outputs from BAMnut

Converting Grids to an Equal-Area Projection

The result grids were converted to an equal-area projection (Flat Polar Quartic) to calculate the areas covered by each class in each country of Africa.

Arc: project grid bio11feb_5 biomass_pq llpolq.prj
Arc: project grid pod11feb_5 podyield_pq llpolq.prj

To convert grids to Flat Polar Quartic projection the parameters specified in the file llpolq.prj illustrated below were used:

INPUT
PROJECTION GEOGRAPHIC
UNITS DD
PARAMETERS
OUTPUT
PROJECTION FLAT POLAR QUARTIC
UNITS METERS
PARAMETERS
00 00 00
end

Adjusting and Standardising the Projection

After the biomass and pod yield maps had been converted to a polar quartic projection, they presented a distortion in the projection when displayed. To solve this problem a polar quartic projection mask (named templpolq) was used:

Arc: describe templpolq

Coordinate System Description

Procedures for projection adjustment using a mask:

Grid: setmask templpolq
Grid: biomass_pq2 = biomass_pq

Grid: setmask templpolq
Grid: podyield_pq2 = podyield_pq

Converting a World Coverage to a Grid

The coverage WFSCOV, which contains the country boundaries (i.e. areas) was converted to a GRID:

Arc: polygrid wfscov wfscov_grd cntcode

Where:
polygrid = GRID command
wfscov = coverage
wfscov_grd = output grid
cntcode, coverage item containing to country boundary names.

The cell size was set to an equal area projection cell size of 59311.282.

Combining Grids

The GRID wfscov_grd was overlaid on the biomass and pod yield maps in order to produce the statistics by country.

Grid: biomass_stat = combine(wfscov_grd, biomass_pq2)
Grid: podyield_stat = combine(wfscov_grd, podyield_pq2)

Where:
biomass_stat and podyield_stat = output grids
combine = GRID command
wfscov_grd = world grid in flat polar quartic projection
biomass_pq2 and podyield_pq2 = biomass and pod yield grids in flat polar quartic projection.

Creating the text files using Arc/Info INFO

Biomass

ENTER COMMAND >SEL BIOMASS_STAT.VAT
ENTER COMMAND >OUTPUT ../BIOMASS_STAT.TXT
ENTER COMMAND >DISPLAY COUNT,',',WFSCOV_GRD,',',BIOMASS_PQ2 PRINT
ENTER COMMAND > Q STOP

Pod Yield

ENTER COMMAND >SEL PODYIELD_STAT.VAT
ENTER COMMAND >OUTPUT ../PODYIELD_STAT.TXT
ENTER COMMAND >DISPLAY COUNT,',',WFSCOV_GRD,',',PODYIELD_PQ2 PRINT
ENTER COMMAND > Q STOP

Converting text files from UNIX to DOS

The following example illustrates how one of the text files was converted from UNIX to DOS using Arc/Info software:

Grid: gridascii biomass_stat.txt biomass_stat.dat

Where:
gridascii = Arc/Info command
biomass_stat.txt = biomass file in UNIX
biomass_stat.dat = resulting biomass file in DOS format.

Manipulating text files in EXCEL

1. The text files were imported into EXCEL
2. To calculate the areas of each class occurring in the countries, the number of cells (COUNT) was multiplied by the square of the cell size in kilometres (59.30642 x 59.30642).
3. Percentage areas were calculated from each suitability class for each country.

For example: 59.30642 x 59.30642 = 3517.251




Suitability class	Count	Cell size	Count x cell size	Sum	Percentage
2	155	3517.251	545174	622553.5	87.57
2	22	3517.251	77379.53	622553.5	12.42



4. Results were plotted using histograms.

Grid Outputs

The procedures used to create the map compositions for the maps presented in this study are illustrated below using the biomass grid as an example:

Standardising the grid to a common colour range

For purposes of analysis and/or illustration, the biomass grid had to be reclassified to a common colour range.

biomass_5 = reclass (biomass, bio11feb_5.rem)

Where:
biomass_5 = reclassified grid file
reclass = GRID function to reclassify (or change) integer values of the input cells using a remap table on a cell-by-cell basis within the analysis window
biomass = original water requirement grid
biomass_5.rem = remap table.

The remap table biomass_5.rem is shown below:

0 - 0 : 1
1-1,500: 2
1,500-4,500: 3
4,500-8,500: 4
>8,500 : 5

AML to plot the grid

The AML, biomass.aml illustrated below was used in ARCPLOT to generate a map composition for the biomass grid:

killmap biomass_mc
mape biomass
pagesize 11.7 8.3
map biomass_mc
linesymbol 5
box 0.1 0.1 11.6 8.2
mapposition cen cen
shadeset BAMnut.shd
gridnodatasymbol white
grids biomass
linecolor black
arcs/sun1disk1/faogistemp/ctry25m
textset font
textsymbol 1
textsize 0.18
keyarea 0.6 1 10.7 1.23
keybox 0.15 0.1
keyseparation .13 .14
keyshade biomass.leg

Postscript Files

The maps which are presented in this study were converted to eps (i.e. postscript format). The following example illustrates how the biomass grid (map) was converted into and eps file:

gissw2-faogis>> setenv CANVASCOLOR WHITE

Arcplot: &r biomass.aml
Arcplot: display 1040
Arcplot: biomass.gra
Arcplot: plot biomass_mc box 0.1 0.1 11.6 8.2
Arc: postscript biomass.gra biomass.eps

where:
setenv CANVASCOLOR WHITE = sets the background colour to white
&r biomas.aml = automates the map composition of the grid
display 1040 = command used to save the map composition
biomass.gra = graphics file
plot biomass_mc box 0.1 0.1 11.6 8.2 = map composition which was reduced to suit the size required for publication
postscript = ARC command used to create the postscript file.