Early Drought Detection in the Greater Horn of Africa Using Satellite-Derived Data [OK??]
SECTION 1: INTRODUCTION
Drought occurs across large portions of Africa, often with devastating consequences for food security, water
supply, crop production, and livestock. Drought often leads to famine, malnutrition, epidemics, and the
displacement of large populations. Together with flooding, drought can significantly impede or erode a country’s
economic growth and development. Drought is affected by a changing climate, and current projections indicate
that it will become more frequent in the future.
Starvation [mw, get graphics. Loss of Livestock: Source KMD
Drought is defined as a persistent and abnormal moisture deficiency that impacts vegetation, animals, and
people. There are four types of drought, based on its impact:
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Meteorological drought expresses the departure of precipitation from normal over a period of time (i.e.,
short-term precipitation deficits).
Hydrological drought usually defines deficiencies in surface and subsurface water supplies. It affects stream
flow, groundwater tables, and reservoir levels. It occurs and recovers over much longer time scales and
reflects the effects and impacts of droughts.
Agricultural drought reflects root-zone soil moisture deficits and impacts on crop yields. It is usually
expressed in terms of needed soil moisture of a particular crop at a particular time.
Socio-economic drought incorporates water supply and demand for health and economic practices.
Meteorological drought is the primary cause of drought. It usually leads to agricultural drought due to lack of soil
water. If precipitation deficiencies continue, hydrological drought develops. The groundwater is usually the last to
be affected and the last to return to normal levels.
Page 2: Forecasting and Monitoring Drought in the GHA
Drought monitoring and forecasting in Africa is limited by the scarcity of reliable ground-based observational data
from, for example, rain gauge networks [is this the only type or just one example??]. There are large spatial gaps
between operational weather stations in most African countries, and individual stations often provide
discontinuous data.
USE THIS?? Temp-upper air observation Stations (Temp Obs.ppt” – single slide in mixed graphics). Get chart
showing synoptic stations from henk!!!!!!!!!!!!!!!!!!!!!!!!! Done
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Therefore, weather forecasters [and hydrologists?] in the Great Horn of Africa (GHA) must rely on data from a
variety of sources when forecasting and monitoring drought. These include satellite images and products, such as
rainfall estimates [and ssts???]; numerical weather prediction forecasts [add examples of types?]; rainfall data
from ???; and sea surface temperature data from ???.
The Greater Horn of Africa (GHA) countries include Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Sudan,
Somalia, Tanzania, and Uganda.
show any of this? Spatial sat data.ppt [4 slides] and/or “Map.avi? in Mixed graphics.
Page 4: About the Case Study [work on this]
There are several ways of monitoring rainfall performance based on the evolution of vegetation. For example,
remote sensing provides very good spatial and temporal coverage of the development and amount of vegetation.
This is strongly related to rainfall and can be used for drought assessment
Data from satellite channels that monitor vegetation are used in vegetation indices (VI). The Normalized
Difference Vegetation Index (NDVI) is a widely used indicator for monitoring the status of the vegetation and
estimating crop production. It’s a simple numerical indicator that indicates the density and health of green
vegetation. The index is widely available in [or from?] meteorological services.
In this module, we will examine ways of assessing the possibility of severe drought in advance using satellite
derived data, particularly the NDVI. We will monitor the NDVIs and their long-term anomalies in two years, 2006
and 2009. We will relate them to the seasonal rainfall performance (the amounts received and forecast) and see
how to anticipate the potential for severe drought in the GHA region.
Due to the lack of observation data in most GHA countries, we will use satellite spatial derived data for the GHA.
For ground truth (“ground truth” is not existing, also data from rain gauges have errors), we will use data from
Kenya, which is one of the countries most affected by severe droughts. Therefore, it will serve as a good
representative for the other GHA countries.
Adverse rain performance in the GHA (either too much or too little) is highly dependent on several factors:
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Sea surface temperatures (SSTs), which are related to La Nina and El Nino episodes
The behavior of the Indian Ocean Dipole (see: http://en.wikipedia.org/wiki/Indian_Ocean_Dipole)
Thus, our process of forecasting drought includes:
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Analysing SSTs to determine the possibility of La Nina/El Nino or an Indian Ocean Dipole
Studying the evolution of NDVI and NDVI anomalies
Examining previous seasonal rainfall performance
Looking at the expected rainfall performance for the next season
From this, we should be able to anticipate the occurrence and severity of an impending drought.
The following data are used in the case study:
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NDVI and long-term NDVI anomalies derived from the SPOT and NOAA satellites [clarify which satellite]
Natural color and day microphysics RGBs from EUMETSAT's MSG satellite
Rainfall Estimates (RFE) from NOAA satellites
ECMWF model surface winds (10 metre)
Sea surface temperatures [from where?]
Nino indices [from where?]
what do with this???
 Satellite data, from NOAA, METOP, MSG??, and SPOT satellites [mention avhrr?]
 Model forecasts (OR say seasonal weather forecasts instead?) from the European Centre for MediumRange Weather Forecast (ECMWF)
 Rainfall estimates (RFE) from the Climate Prediction Centre (CPC) of the National Oceanic and
Atmospheric Administration (NOAA)
For ground truth, we will use the following data:
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Seasonal rainfall forecasts for the long rains (March-April-May or MAM) and short rains (OctoberNovember-December or OND)
 NDVI time-series ???
 Reported rainfall from across the country
 Images of the impact of drought
( I don’t like the expression “ground truthing”. Use a better expression such as: Surface rainfall estimates)
need this??? We will use two RGBs:
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The MSG natural colour RGB for the tenth day of each month; this day was chosen since it corresponds
to the decadal NDVI data
The MSG day microphysics RGB [say more?]
[REARRANGE] The first part describes the region, provides an overview of the case study, and describes the
types and impacts of drought.
The second section uses data to analyze xxx. [orig: Part (ii) gives the data analyses]
The final part presents a process for detecting drought.
The module is intended for use by colleges and universities as well as operational weather services in East Africa.
By the end of the module, students and forecasters should have a better understanding of drought and be better
equipped for its early detection.
look back at 2008, sst/iod, current, future
mention 2 rainfall seasons?
Rainfall Patterns in the GHA
The GHA region has a bimodal (twice yearly) rainfall pattern, with rains in [are these the long rains??] March,
April, May (MAM) and October, November, December (OND).
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In 2009, the AMJ NDVI was very poor compared to the NDVI for the same period in 2006.
Vegetation started off well in January over the western and central parts of Kenya. This was due to the rainfall
performance of the previous season, OND 2008. However the northeast and northwest regions had poor rainfall
performance.
Link to “2008 OND rainfall performance.doc”). Location: “Kenya-06 and 09 Forecasts” –
In this rainfall distribution map for Oct-Nov-Dec 2008, the percentages indicate the level of rainfall received with
respect to the long-term mean. Thus, any amount below 100% implies that less than average rainfall was
received.
Much of Kenya received more than 75% of the long-term average seasonal rainfall, with many areas exceeding
the average by up to 150%. The southeastern part received below-normal rainfall resulting in minimal vegetation
cover. It continued until the long rains season (MAM) when the vegetation picked up. However, it was still below
average.
(Animate “GHA 321 2009” to see this). Location: “GHA MSG 321”
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Fig (xx). RGB321 for 10-1-2009
Which month had the least vegetation?
o May
o April
o November
o September **
Feedback: September had the least vegetation.
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