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IDENTIFYING AND ASSESSING DROUGHT HAZARD

AND RISK IN AFRICA

BY:

M. SINGH

Council for Geoscience, South Africa

Regional Conference on Insurance and Reinsurance for Natural

Catastrophe Risk in Africa,

Casablanca, Morocco, November 12-14, 2006



ii

Abstract

The onset and impacts of drought is of interest to a number of sectors particularly the agricultural

industry, national governments and international relief organizations. Compared with other natural

disasters like hurricanes and floods, drought impacts are the most costly, affect a larger area and are

more frequent.

Drought monitoring and forecasting techniques utilize advanced satellite imagery and high

resolution spatial data to assess agricultural risks on a regional and local scale. Much of this data is

freely available on the internet and the datasets can easily be downloaded and used directly in a GIS

platform. This work looks at the concept of drought in general, drought patterns in Africa, drought

hazard and risk studies performed in Australia and US and drought-related data that is available for

Africa.

Many organizations have their own sets of climate data and forecasts of drought but the key result

of this research shows that a lot of information is available through a number of sources and to

perform a drought risk assessment for most countries in Africa will be highly cost effective.



Table of Contents

Abstract ............................................................................................................................... ii 1 Drought in General.................................................................................................. 5 2 Drought Characteristics .......................................................................................... 8 3 Rainfall patterns in Africa ...................................................................................... 8 4 Assessing Drought Hazard and Risk .................................................................... 15 5 Forecasts and Prediction ....................................................................................... 31 6 Conclusion.............................................................................................................. 35 7 References.............................................................................................................. 36



List of Figures

Figure 1 Rainfall Index for Group 1 Sahel and Sudan from 1960 to 1993 ((Gommes and Petrassi, 1996)) ......................................................................................................... 9

Figure 2 Rainfall Index for Group 5 Southern Africa from 1960 to 1993 ((Gommes and Petrassi, 1996)) ....................................................................................................... 10

Figure 3 Rainfall regions over South Africa (Source: South African National Disaster Management Centre) ............................................................................................... 11

Figure 4 Map of El Nino Regions (Source: Climate Prediction Centre) ............................... 13Figure 5 Graph of Nino 3.4 anomalies for 1980 - 2003 (Source: South African Weather

Service) ................................................................................................................... 14Figure 6 August 1994 to February 1995 Rainfall in mm (Source Rainfall Reliability Wizard,

BRC) (User defines an event and the system shows the amount of rainfall) .............. 19Figure 7 Proportion of years Rainfall has exceeded 200 mm between December and

February (Source Rainfall Reliability Wizard, BRC) (User inputs seasonal target (inmillimetres) and system calculates times per 100 years the target has been exceeded)................................................................................................................................ 19

Figure 8 Integrated vegetation cover (Bureau of Rural Sciences, Integrated Vegetation Cover

2003, Version 1) ...................................................................................................... 20Figure 9 Conceptual capability of BRC's Integrated Toolset ............................................... 21Figure 10 Example of Maps from the US Drought Monitor ................................................. 22Figure 11 Map of the Vegetation Dataset for Eritrea by Africover ...................................... 24Figure 12 NDVI: Normalized Difference Vegetation Index (08/01 - 10, 2006 Dekad 22) ..... 28Figure 13 RFE: Meteosat Rainfall Estimation (08/01 - 10, 2006 Dekad 22) ........................ 29Figure 14 WRSI Water Requirements Satisfaction Index (08/01 - 10, 2006 Dekad 22) ......... 29Figure 15 Plot of sugar cane yield vs NDVI for different sites in South Africa (from Schmidt

et al., 2000) ............................................................................................................. 30Figure 16 FAO NDVI imagery for Eastern Africa derived from SPOT-4 satellite (Difference

between Current Dekad and Average (1998-2004)). The Colors range from dark red to grey to dark green. Dark red represents a large decrease and dark green

represents an increase in greenness of vegetation .................................................... 31Figure 17 Mean Precipitation expected for March 2007 (South African Weather services)GFCSA - Global Forecasting Centre for Southern Africa ........................................ 33

Figure 18 Trimean Precipitation expected for March 2007 (South African Weather services)GFCSA - Global Forecasting Centre for Southern Africa ........................................ 33

Figure 19 Net Forecasting by InfoTech ............................................................................... 34

List of Tables

Table 1 Comparison of significant natural catastrophes in the US with respect to their temporal aspects, fatalities, costs and losses, and spatial extents, Source (NDMC,

2006) ......................................................................................................................... 7Table 2 Chronology of El Niño and Drought/Famine 2 in Ethiopia (P. G. Ambenje CH 11 EWS) ....................................................................................................................... 14

Table 3 WMO search results for climate data on Kenya, Nairobi ........................................ 25



5

1 Drought in General

One of the widely used definitions for Drought is: a protracted period of deficient

precipitation resulting in extensive damage of crops, resulting in loss of yield

(NDMC, 2006).

There are three types of drought (NDMC, 2006):

• Meteorological

• Agricultural

• Hydrological

Meteorological

Meteorological drought is defined on the basis of the degree of dryness (in

comparison to some “normal” or average amount) and the duration of the dry period.

The definition is region specific for e.g.

1. Periods of drought can be identified on the basis of the number of days with

precipitation less than some specified threshold. This definition is only

applicable to year-round precipitation regimes like tropical rainforests, humid

subtropical climates or humid mid-latitude climates e.g. Mannas, Brazil; NewOrleans, Louisiana; London, England.

2. Seasonal Rainfall pattern e.g. central US, Northeast Brazil, West Africa and

Northern Australia

3. Extended periods without rainfall e.g. Omaha, Nebraska (U.S.A.); Fortaleza;

Ceara (Brazil); and Darwin, NW Territory (Australia).

Agricultural

Relates meteorological or hydrological drought to agricultural impacts e.g.

precipitation shortages, differences between actual and potential evapo-transpiration,

soil water deficits and reduced ground water or reservoir levels e.g. Deficient topsoil

moisture at planting may hinder germination, leading to low plant populations per

hectare and a reduction of final yield.



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Hydrological

Associated with the effects of precipitation shortfalls on surface or subsurface water

supply. The frequency and severity of the drought is defined on a watershed or a river

basin scale. Changes in land use e.g. deforestation, land degradation and theconstruction of dams affect the hydrological characteristics of the basin e.g. Changes

in land use upstream can affect infiltration and run-off rates downstream

The agricultural sector is the first to be affected by a drought because of its heavy

dependence on stored soil water. Those who rely on surface water e.g. reservoirs and

lakes are the last to be affected. When precipitation returns to normal the agricultural

sector recovers first and lastly those sectors dependant on stored water. The length of

the recovery period is a function of the intensity of the drought, its duration, and the

quantity of precipitation received when the episode terminates.

Socioeconomic Drought occurs when the demand for an economic good exceeds the

supply as a result of a weather-related shortfall in water supply e.g. in Uruguay 1988-

99, the production of hydroelectric power was affected. The demand for economic

goods increase as a result of increasing population and per capita consumption.

Supply is also increasing because of production efficiency, technology, construction

of reservoirs to increase surface water storage capacity. Hence the vulnerability to

drought increases.

Drought plans help to lesson the effect of drought. Only seven states in the US do not

have a formal drought plan. Dr Donald Wilhite in 1991 derived a 10 step process for

developing drought plans for government.

In comparison with other natural hazards like flood and hurricanes, droughts affect a

much larger spatial area, is not localised and it is difficult to determine the beginning

and the end of a drought (refer to Table 1 ). It is definitely more frequent and the most

costly.



7

Table 1 Comparison of significant natural catastrophes in the US with respect to their temporal aspects, fatalities, costs and losses, and spatial extents, Source (NDMC, 2006)

Drought Floods Hurricanes

Temporal AspectsWarning time up to a year, but

often nonefrom seconds tomonths

36 hours to months

Duration months, years,decades

hours, weeks minutes, weeks

Frequency each year, somepart of the UnitedStates has severe orextreme drought

a stream typicallyoverflows 2 out of 3 years

1.6/year, allintensities; .1/5.75years, class 4 & 5

Fatalities

Annual average 94 (all floods); 136(flash floods) 162

Worst recentevent

Drought is rarely adirect cause of death in the UnitedStates, althoughassociated heatwaves, dust, andstress all contributeto mortality.

48 died in the 1993Mississippi Valleyfloods, 180 in the1985 Puerto Ricoflash floods

49–86 died inHurricane Hugo in1989

Worst recorded unknown6000+ died inGalvestonhurricane in 1990

Costs and Losses Annual average $6–8 billion $2.41 billion $1.2–4.8 billionWorst recent

event

$39–40 billion,

1988–89

Worst recorded 1930s or 1988–89

$15–27.6 billion,1993

$25–33.1 billion,

Hurricane Andrew,1993Aug. 29. 2005hurricane Katrinacaused more than$38 billion indestruction in fourstates.

Spatial Extent

Annual average 18.1% of theUnited States, atpeak intensity

N/A N/A

Worst recentevent

36% of the UnitedStates, July 1988 N/A

Worst recorded 65% of the UnitedStates, July 1934

Mississippi Valleyfloods of 1993

N/A



8

2 Drought Characteristics

Drought is defined by Intensity, Duration and Spatial Coverage

Intensity is defined as the degree of precipitation shortfall and or the severity of

impacts associated with the shortfall. It is generally measured by the departure of

some climatic index from normal and is closely linked to duration in the

determination of the impact.

There are few common indices that are used to measure the drought intensity:

• The decile approach by (Gibbs and Maher, 1967) used in Australia

• Palmer Drought Severity Index (PDMI) which uses Crop Moisture Index

(CMI) (Palmer, 1965,68; Alley, 84) used in the US• Yield Moisture Index (Jose et al. ,1991) used in Philippines

• Standard Precipitation Index (SPI) (by Mckee et al. 1993, 1995)

The duration of a drought lasts for a minimum of 2-3 months with a maximum of a

few years.

The spatial extent of a drought shifts from season to season. In 1934, 65% of the US

was affected by drought. The US NDMC (National Drought Mitigation Centre)

reports that severe and extreme drought affected 25% of the country one out of four

years.

3 Rainfall patterns in Africa

Gommes and Petrassi (1996) reported that the worst drought occurred in 1910, which

affected East and West Africa. They classified Sub-Saharan Africa into eight groups

with variable rainfall patterns based on climate data from 1960 to 1993. This

classification is based on persistence characteristics, trends and pseudocycles with

rainfall patters that are not necessarily independent.



9

A brief description of the rainfall pattern in each group given in Gommes and Petrassi

(1996) will be given below:

Group 1: Sahel and Sudan : Burkina Faso, Cape Verde, Chad, Gambia, Guinea-

Bissau, Mali, Mauritania, Niger, Senegal and Sudan

It was noted that the Sahel and Sudan group was the driest and most variable in

Africa. There is a downward trend of rainfall until 1988 followed by a series of about-

average years. The worst drought years were 1983, 1984, 1972, 1973 and 1977.

Figure 1 Rainfall Index for Group 1 Sahel and Sudan from 1960 to 1993 ((Gommesand Petrassi, 1996))

Group 2: Southern-central Africa and Madagascar: Madagascar, Malawi,

Mozambique, Namibia, Zambia and Zimbabwe

Rainfall patterns are uncorrelated with Group 1. The total amounts are higher and

inter-annual variability is less. The drought of 1991-1992 affected this group.

Group 3: Central Gulf of Guinea countries and Tanzania : Benin, Côte d'Ivoire,

Ghana, Tanzania, And Togo.



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The rainfall patterns are similar to Group 1. Drought occurred in 1977 and 1992.

Countries in this group have bimodal rains. The stability in climate is largely due to

the West African Monsoon. This belt moves north about February and reaches the

Sahelian region in May, which is the start of their season. The rain moves South in

September, when this Group’s season starts and lasts until December.

Group 4: East and West Gulf of Guinea : Cameroon, Central African Republic,

Equatorial Guinea, Gabon, Guinea, Liberia, Nigeria, Sierra Leone

It is the wettest and the least variable of all the groups. The northern half of several

countries has Sahelian features.

Group 5: Southern Africa : Botswana, Lesotho, South Africa, And Swaziland

This group has a relatively low rainfall index and is more variable than the Sahel.

Patterns are similar to Group 2 e.g. Dry years in 1973, 1982, 1983, and 1992. Drought

occurred in 1985 and 1992. Figure 3 shows the variation in rainfall within South

Africa

Figure 2 Rainfall Index for Group 5 Southern Africa from 1960 to 1993 ((Gommes and Petrassi, 1996))



11

Figure 3 Rainfall regions over South Africa (Source: South African National Disaster Management Centre)

Group 6: Horn of Africa and Kenya : Djibouti, Ethiopia, Kenya, Somalia

Have some of the driest places in the world. No correlation in rainfall patterns with

the above groups. There is a slight correlation with Group 8. Characteristic of low

rainfall and high variability. The time series shows a pseudo-periodic behavior with a

cycle of 4 to 5 years (relating to El-Nino events described below).

Group 7: Central-west Africa : Angola, Congo, And Zaire

This is the second wettest group showing a smooth rainfall pattern between 1964 to

1984.

Group 8: Great lakes countries : Burundi, Rwanda, And UgandaRainfall indices are high and not very variable. It is similar to Group 6 with a cycle of

7 years.



13

conditions, which are the result of the 1998-1999-2000 La Niña conditions (Ambenje,

2000).

When past ENSO events are compared with drought and famine periods in Ethiopia,

they show a remarkable association. Some drought years have coincided with ENevents, while others have followed it (Table 2) (Ambenje, 2000).

Computer models today can accurately predict ENSO events at least 9 months ahead.

Online forecasts are available via a number of organizations because its effects are

felt around the world and not only in Africa. An ENSO index has been shown to be

strongly correlated with maize yields in Zimbabwe (Cane et al., 1994) and in Oaxaca,

Mexico. ENSO impacts on crops in Australia have been identified (e.g., Nicholls

1985; 1986). Droughts corresponding to ENSO warm events have been shown to be

associated statistically with disasters in Southeast Asia/Oceania and southern Africa

(Dilley and Heyman, 1995).

Climate change is another factor that directly impacts on rainfall. Scientists predict

that in the future, countries in the Sahel region of Africa will receive more rainfall and

floods while Southern Africa will experience persistent drought in the coming

decades due the arming of the Indian Ocean which is partly due to greenhouse gas

emissions from human activity. These predictions come from looking at 60 computer

models that imitate climate change (Kigoto, 2005).

Figure 4 Map of El Nino Regions (Source: Climate Prediction Centre)



14

Figure 5 Graph of Nino 3.4 anomalies for 1980 - 2003 (Source: South African Weather Service)

Table 2 Chronology of El Niño and Drought/Famine 2 in Ethiopia (Ambenje, 2000).

El Niño Years Drought/Famine Regions 1539-41 1543-1562 Harangue 1618-19 1618 Northern Ethiopia 1828 1828-29 Shea 1864 1864-66 Tigray and Gondar 1874 1876-78 Tigray and Afar 1880 1880 Tigray and Gondar 1887-89 1888-1892* Ethiopia 1899-1900 1899-1900 Ethiopia 1911-1912 1913-1914 Northern Ethiopia 1918-19 1920-22 Ethiopia 1930-32 1932-1934 Ethiopia 1953 1953 Tigray and Wollo 1957-1958 1957-1958 Tigray and Wollo

1965 1964-66 Tigray and Wollo 1972-1973 1973-1974 Tigray and Wollo 1982-1983 1983-1984 Ethiopia 1986-87** 1987-1988** Ethiopia 1991-92 1990-92 Ethiopia 1993 1993-94 Tigray, Wollo, Addis

Sources: Quinn and Neal (1987, 14451); Degefu (1987, 30-31);*Nicholls 1993; Webb and Braun; **Ayalew 1996.



15

4 Assessing Drought Hazard and Risk

Risk due to drought is a product of the region’s exposure to the event (i.e. probability

of occurrence at various severity levels, and the vulnerability of the society to theevent.

In order to assess drought severity and impact for a given area one would need to

track meteorological variables, soil moisture and crop conditions during the growing

season and continually re-evaluating the potential impact of these conditions on the

final yield.

To analyse drought frequency, severity and duration for a given historical periodrequires weather data on an hourly, daily, monthly, or other time scales and impact

data (e.g. crop yield). A drought of the same intensity, duration and spatial

characteristics will have different effects in different regions. How vulnerable a

society is depends on the social factors such as population, technology, policy, social

behaviour, land use patterns, water use, economic development, and diversity of

economic base and cultural composition (Wilhite and Svoboda, 2000).

Australia and the US have quite advanced tools available online for the monitoring

and forecasting of drought. This will be looked at in this section. Thereafter we will

look at what data is available in Africa in order to use similar techniques for drought

monitoring and forecasting.

Drought Policy and Hazard and Risk Capabilities in Australia (Laughlin and

Clarke, 2000 )

In 1992, the National Drought policy encouraged farmers to manage their own risksand to be self-reliant. Subsidies and support to underwrite drought risk was phased

out.

80% of Australian agricultural products are exported. The continent has a highly

unreliable climate. In 1994-1995, there was a severe meteorological drought after



16

which government developed the policy of Drought exceptional Circumstances

(DEC) to provide business and welfare support for affected producers. In 1997,

multiple perils like pests, disease etc. were included in the policy.

Events were defined a rare and severe events with rare being a 1 in 20 year event andsevere being one that lasts for more than 2 months.

The Bureau of Rural Sciences (BRC) was requested to perform a Risk Assessment for

Drought where the Integrated Toolset was developed.

In order to assess the rare event (1 in 20 year event) the risk analysis was performed

in two stages.

Stage 1 required the following:• Establish the context in which agriculture operates in a given region in terms

of

o Climate

o Production

o And natural resource variability

• Risk Characterisation

o Analysis of Commonwealth Bureau of Meteorology climate records

o Application of production data from the Australian Bureau of Statistics

o Grass Growth and production simulation studies

o Monitoring from satellite (and remote-sensed data)

o Sourcing agronomic research

Stage 2 is a Formal Risk Analysis

o The historical frequency of droughts and its agronomic impacts are assessed

by means of

o Assessment of climate records

o Application of crop or pasture simulations

o Production monitoring from field trials

o Interrogation of remote-sensed data



17

The Risk Analysis can be done in two ways:

Point data: Simple presentations of specific events and their long term historical

context are given. It is expressed in terms of millimetres and percentiles. Moving

average windows of 3-36 months are used (Figure 6).

Spatial Analysis: Uses maps of both long term and specific events based on gridded

rainfall data from the Commonwealth Bureau of Meteorology. The maps are based on

100 years of data. Broad scale climatic features can be determined. Maps of rainfall

reliability can be produces i.e. maps of the probability of receiving the seasonal

average in a predetermined way (Figure 7).

Remote Sensing techniques are also used. Two types of data are used: Reflective data:

AVHRR from the NOAA Advanced Very High Resoultion Radiometer and Thematic

Mapper (TM) data from LANDSAT earth sources satellite system (LANDSAT). A

standard multispectral transformation is applied to two week composite AVHRR data:

The Normalised Difference Vegetation Index (NDVI). At 1.1km 2 resolution, imagery

from this approach provides a spatial estimate of plant greenness across the area.

Temporal analysis can be carried out for an area. The NDVI time sequence allow for

between-year comparison of vegetation flushes and estimation of rates of senescence

and vegetation decay. For a small area, risk characterization can be assisted by

classification of LANDSAT TM data (with 30m resolution). Maps of land uses such

as pasture communities, irrigation area and cropping infrastructure can be generated.

For Pasture and Crop Simulation techniques, historical production data is simulated

on computer from the daily climate record. Thereafter frequency and impact analysis

is performed in the dataset. Other simulation models include:

o Construction of the National Drought Alert Strategic Information System by

Climate Applications (by the Queensland Department of Natural resources).

This project delivers national-scale simulation sing the GRASP modelling

framework and the APSIM model

o Temperature production system models like GRAZFEED and GRASSGRO,

developed by the CSIRO Division of Plant and Industry. This serves as

decision support tool for climate risk management practices



18

o Wheat production system simulator, STIN, by Agriculture Western Australia,

provides regular within-season output to assist producers and grain-traders in

making within-crop decisions relating to drought (Stephens, 1998)

Drought Forecasting Models include that of the Climate Variability in AgricultureProgram (CVAP) funded by the Land and Water Research and Development Co-

operation which is continuing development of Australia’s capacity to forecast the

seasons ahead. Other models include the Global Circulation Models (GCM) with and

without coupled oceans which uses Southern Oscillation Index (SOI) based statistical

approaches. The Commonwealth Bureau of Meteorology offers a range of products

and services. Continental –scale maps are provided showing the probability of

exceeding the median rainfall etc.

Note that the Integrated Vegetation Cover Maps (Figure 8) which is crucial for the

risk assessment are generated from a number of datasets listed below:

o 1:100,000 - Agricultural Land Cover Change 1995

o 1:100,000 - Forests of Australia 2003

o 1:2,500,00 - 1996/97 Land Use of Australia, Version 2

o 1:100,000 - Land Use Mapping at the Catchment Scale

o Variable scale - National Vegetation Information System 2000

With all of the above tools, BRC recognized the need to integrate all of this

information so that the results could be easily expressed to the public and decision

makers. Hence the creation of software – which is essentially a GIS plug-in, called the

Integrated Toolset. The Integrated Toolset provides BRS with the capacity to fit

surfaces, with full error diagnostics, from multiple point data, and enhance the

analysis and manipulation of various raster and vector datasets (Figure 9). This

software can be downloaded from BRC’s website.



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Figure 6 August 1994 to February 1995 Rainfall in mm (Source Rainfall Reliability Wizard, BRC)(User defines an event and the system shows the amount of rainfall)

Figure 7 Proportion of years Rainfall has exceeded 200 mm between December and February (Source Rainfall Reliability Wizard, BRC) (User inputs seasonal target (in millimetres) and system calculates

times per 100 years the target has been exceeded)



20

Figure 8 Integrated vegetation cover (Bureau of Rural Sciences, Integrated Vegetation Cover 2003,Version 1)



21

Figure 9 Conceptual capability of BRC's Integrated Toolset

The US Drought Monitor

NDMC’s Drought watch and Drought Monitor provide current information on

drought affecting the US. Weekly Drought Monitor maps are published online.

Forecasts on climate, drought, streamflow, PDSI, and Soil Moisture are provided

(Figure 10 ).



22

Figure 10 Example of Maps from the US Drought Monitor

Datasets on vegetation and other satellite data



23

Claire Englander and Philip Hoehn compiled a Checklist of Online Vegetation and

Plant Distribution Maps ( http://www.lib.berkeley.edu/EART/vegmaps.html ).

As a mere illustration, a search for maps for Eritrea (Figure 11) led the author to the

Africover website.

The purpose of the Africover Project is to establish a digital georeferenced database

on land cover and a geographic referential for the whole of Africa including:

- geodetical homogeneous referential

- toponomy

- roads

- hydrography

The Multipurpose Africover Database for the Environmental Resources (MADE) is

produced at a 1:200,000 scale (1:100,000 for small countries and specific areas)



2 4

F i g u r e 1 1 M a p o f t h e V e g e t a t i o n D a t a s e t f o r E r i t r e a b y A f r i c o v e r



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Climate Data

Historical climate data can be obtained from a number of different organisations. The

first place to look would be the World Meteorological Organisation. The World

Meteorological Organization (WMO) is a Specialized Agency of the United Nations.

It is an intergovernmental organization with a membership of 187 Member States and

Territories. It facilitates the free and unrestricted exchange of data and information,

products and services in real- or near-real time on matters relating to safety and

security of society, economic welfare and the protection of the environment. It

contributes to policy formulation in these areas at national and international levels.

Once you have selected your region of interest you could easily find the respective

organization of the country where the data originated from. For example, a search on

climate data for Nairobi, Kenya gave a listing of the mean temperature and rainfall for

each month in the year (Table 3).

Table 3 WMO search results for climate data on Kenya, Nairobi

!

" ! ! !

# ! $ % $

& ' $ !

# ! (! ! #

' # '

' ' ! ( #

& ) ' #

* ' # ( % #

+, ( # (- # #

, ! # ' ' %



26

Remote Sensed Data

Use of Remote sensed data has become a standard in drought monitoring. The U.S.

Agency for International Development (USAID) Famine Early Warning System

Network (FEWS NET) is a good source of relevant information for Drought Mapping

in Africa.

FEWS NET is an information system designed to identify problems in the food

supply system that potentially lead to famine, flood, or other food-insecure conditions,

in sub-Saharan Africa. It is a multi-disciplinary project that collects, analyzes, and

distributes regional, national and sub-national information to decision makers about

potential or current famine or flood situations, allowing them to authorize timely

measures to prevent food-insecure conditions in these nations. Countries with FEWS

NET representatives are Burkina Faso, Chad, Eritrea, Ethiopia, Kenya, Malawi, Mali,

Mauritania, Mozambique, Niger, Rwanda, Somalia, (southern) Sudan, Tanzania,

Uganda, Zambia, Zimbabwe.

The goal of FEWS NET is to lower the incidence of drought- or flood-induced famine

by providing to decision makers, timely and accurate information regarding potential

food-insecure conditions. With early warning, appropriate decisions regarding

interventions can be made.

The USGS/EROS Data Center (EDC) works with USAID, the National Aeronautics

and Space Administration (NASA), the National Oceanic and Atmospheric

Administration (NOAA), and Chemonics International (Chemonics) to provide the

data, information, and analyses needed for the FEWS NET project. NASA and

NOAA collect and process satellite data that are used to monitor the vegetation

condition (Normalized Difference Vegetation Index, or NDVI) and rainfall (RainFall

Estimate, or RFE) across the entire African continent. The NDVI and RFE data arebut two tools used by FEWS NET to monitor agricultural conditions in Africa.

Normalised Difference Vegetation Index (NDVI) data is calculated from

measurements from NOAA meteorological satellites. NDVI imagery is calculated



27

from the red and near infra-red reflectances observed by the AVHRR (Advanced Very

High Resolution Radiometer) sensor.

The NDVI image (Figure 12) provides an indication of the vigour and density of

vegetation at the surface. Images of NDVI are sometimes referred to as "greennessmaps" since they represent the vegetative vigour of plants. The data are represented as

coloured maps, with colours representing the density of vegetation.

Processed by NASA and the U.S. Geological Survey, the data are represented as

pixels (cells), with each pixel representing an area of 8.0 x 8.0 km. NDVI values

range between -1 and +1, with dense vegetation having higher values (e.g., 0.4 - 0.7),

and lightly vegetated regions having lower values (e.g., 0.1 - 0.2).

The primary use of these images is to compare the current state of vegetation with

previous time periods, for example the same time in an average year to detect

anomalous conditions. In this case the 'normal' situation is taken as the average

measurements over the period 1995 to 1999. Hence yellow and red colours indicate

reduced vegetation this year compared to 'normal', whereas green colours indicate

increased vegetation this year compared to the 'normal'.

Meteosat Rainfall Estimation (RFE) (Figure 13) imagery is an automated (computer-

generated) product which uses Meteosat infrared data, rain gauge reports from the

global telecommunications system, and microwave satellite observations within an

algorithm to provide RFE in mm at an approximate horizontal resolution of 10 km.

The main use of these data is to provide input for hydrological and

agrometeorological models as well as to provide climate information e.g. compare the

current state of rainfall with previous time periods.

This map portrays Water Requirements Satisfaction Index (WRSI) (Figure 14) valuesfor a particular crop from the start of the growing season until this time period. It is

based on the actual estimates of meteorological data to-date. For example, if the

cumulative crop water requirement up to this period was 200 mm and only 180 mm

was supplied in the form of rainfall, the crop experienced a deficit of 20 mm during

the period and thus the WRSI value will be ([180 / 200] * 100 = 90.0%). The current



28

WRSI can increase in value in the later part of the growing season if the demand (crop

water requirement) and supply (rainfall) relationship becomes favorable.

Schmidt et al., (2000) found good correlation with sugar cane yield and the NDVI for

some sites in South Africa (Figure 15).

Similar to the US FEWS NET, the United Nations Food and Agricultural

Organisation (FAO) provided NDVI Data from the SPOT-4 satellite (Figure 16).

Figure 12 NDVI: Normalized Difference Vegetation Index (08/01 - 10, 2006 Dekad 22)



29

Figure 13 RFE: Meteosat Rainfall Estimation (08/01 - 10, 2006 Dekad 22)

Figure 14 WRSI Water Requirements Satisfaction Index (08/01 - 10, 2006 Dekad 22)



30

Figure 15 Plot of sugar cane yield vs NDVI for different sites in South Africa (from Schmidt et al.,2000)



31

Figure 16 FAO NDVI imagery for Eastern Africa derived from SPOT-4 satellite (Difference betweenCurrent Dekad and Average (1998-2004)). The Colors range from dark red to grey to dark green.

Dark red represents a large decrease and dark green represents an increase in greenness of vegetation

5 Forecasts and Prediction

A substantial number of weather related forecasts are available on the Internet.

SASRI Weather watch ( http://www.sasa.org.za/sasri/forecast/index.htm ) brings

together several forecasts relevant to the South African sugar industry.

One of the forecasts include that from the South African Weather Services GFCSA -

Global Forecasting Centre for Southern Africa. The precipitation forecasts are given

for six months lead time. Seasonal averages are given as three month rolling meanswhich correspond to the dynamical anomalies given elsewhere. The scale on each

figure is percentage of normal, blue indicating above normal and red below normal.

Both the mean (Figure 17) and trimean (Figure 18) of the forecasts are displayed.

These are both the same forecast. A forecast consists of ten separate runs with slightly

different starting conditions; each of these runs is termed an ensemble member thus



32

each forecast is run with ten ensemble members. The mean is the average of all

ensemble members of the forecast. The trimean is the average ignoring the extreme

ensemble members (both very wet or very dry ensemble members). The trimean

therefore expresses the central tendency of the ensemble and may give a clearer

indication of the forecast in regions and at times when the normal rainfall is low e.g.during winter it may be that only 1 (out of 10) ensemble members gives an

abnormally high value which will skew the mean towards above normal whereas the

trimean will ignore it. It is therefore advisable that both the mean and trimean are

compared to see if they agree on the forecast.

On the mean forecast map, shading has been introduced to differentiate two levels of

confidence. The clear area (ie. where there is no shading) shows where the mean of

the ensemble forecasts differs from the model climatology (average climate) at the90% significance level (according to a students T-test). It indicates where the model is

more 'confident' that the forecast is different to climatology.

Infotech provides daily forecast of weather for South Africa ( Figure 19 ).



33

Figure 17 Mean Precipitation expected for March 2007 (South African Weather services) GFCSA -Global Forecasting Centre for Southern Africa

Figure 18 Trimean Precipitation expected for March 2007 (South African Weather services) GFCSA -Global Forecasting Centre for Southern Africa



34

Figure 19 Net Forecasting by InfoTech

Clearly a number of organisations provide drought related information, often

duplicating efforts. Among them found in the literature scan, that have not been

researched by the author include:

o United Nations Food and Agriculture Organisation (FAO) Sahel Weather and

Crop Simulation Reports

o FAO’s Global Information and Early Warning System for Africa (GIEWS)

o CPC’s ENSO Advisory Climate and CPC’s African Products

o USAID FEWS current bulletins and reports on conditions in Africa

o The UN International Strategy for Disaster Reduction’s Global, Regional and

National Fire, Weather and Climate Forecasts

o Relief-Web’s national and international news on drought and other disaster-relief efforts

o Africa News Online’s latest information on hazard facing Africa

o The Interagency Task Force on Disaster Reduction of the ISDR( International

strategy for disaster reduction) vulnerability report entitled “ Calculation of

Global Drought Hazard”



35

o WOAB “ World Agricultural Supply and Demand Estimate” reports contain

information and forecasts of world supply-use balances of grains and products.

6

ConclusionThis work looks at the phenomenon of drought in general and its impacts to society.

Drought patterns in Africa are briefly described noting the eight differentt groups in

Africa with characteristic rainfall patterns since 1960.

It is found that ENSO events clearly affect the Horn and Africa group and currently

this is seen with the recent drought in Ethiopia and the more recent drought and floods

in Kenya. Computer models today are able to forecast these events months and

seasons ahead. Another factor that is clearly impacting on the frequency of drought in

Africa is climate change. Scientists predict that, countries in the Sahel region of

Africa will receive more rainfall and floods while Southern Africa will experience

persistent drought in the coming decades due the warming of the Indian Ocean which

is partly due to greenhouse gas emissions from human activity (Kigoto, 2005).

Clearly a number of organisations provide drought monitoring and forecasting tools

for Africa. A pilot study for drought risk in Africa should include a review of all

products supplied by the various organisations, integration of data, collecting of

drought hazard and risk tools available and then interfacing with the insurance

industry to look at or develop a suitable model that can assess the economic risk for

drought in Africa.



36

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