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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
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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.
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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
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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
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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.
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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
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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.
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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))
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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.
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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)
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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.
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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
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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
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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
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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)
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Figure 8 Integrated vegetation cover (Bureau of Rural Sciences, Integrated Vegetation Cover 2003,Version 1)
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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 ).
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Figure 10 Example of Maps from the US Drought Monitor
Datasets on vegetation and other satellite data
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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)
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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
!
" ! ! !
# ! $ % $
& ' $ !
# ! (! ! #
' # '
' ' ! ( #
& ) ' #
* ' # ( % #
+, ( # (- # #
, ! # ' ' %
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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
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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
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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)
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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)
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Figure 15 Plot of sugar cane yield vs NDVI for different sites in South Africa (from Schmidt et al.,2000)
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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
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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 ).
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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
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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”
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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.
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7 References
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