International Journal of Geosciences, 2011, 2, 36-47
doi:10.4236/ijg.2011.21004 Published Online February 2011 (
Copyright © 2011 SciRes. IJG
Rainfall Variability and Its Impact on Normalized
Difference Vegetation Index in Arid and Semi-Arid
Lands of Kenya
C. A. Shisanya1, C. Recha2, A. Anyamba3
1Kenyatta University, Department of Geography, Nairobi, K e ny a
2Chuka University College, Chuka, Kenya
3NASA/Godd ar d Sp ace Fl i ght C e nt er, Biospheric Sciences Branch, USA
E-mail:, csh,
Received September 6, 2010; revised January 7, 2011; accepted J a nuary 14, 2011
Agriculture in arid and semi-arid lands of Kenya is depends on seasonal characteristics of rainfall. This study
seeks to distinguish components of regional climate variability, especially El Niño Southern Oscillation
events and their impact on the growing season normalized difference vegetation index (NDVI). Datasets
used were: 1) rainfall (1961-2003) and 2) NDVI (1981-2003). Results indicate that climate variability is per-
sistent in the arid and semi-arid lands of Kenya and continues to affect vegetation condition and conse-
quently crop production. Correlation calculations between seasonal NDVI and rainfall shows that the Octo-
ber-December (OND) growing season is more reliable than March-May (MAM) season. Results show that
observed biomass trends are not solely explained by rainfall variability but also changes in land cover and
land use. Results show that El Niño and La Niña events in southeast Kenya vary in magnitude, both in time
and space as is their impact on vegetation; and that variation in El Niño intensity is higher than during La
Niña events. It is suggested that farmers should be encouraged to increase use of farm input in their agricul-
tural enterprises during the OND season; particularly when above normal rains are forecast. The close rela-
tionship between rainfall and NDVI yield ground for improvement in the prediction of local level rainfall.
Effective dissemination of this information to stakeholders will go along way to ameliorate the suffering of
many households and enable government to plan ahead of a worse season. This would greatly reduce the
vulnerability of livelihoods to climate related disasters by improving their management.
Keywords: Semi-Arid, NDVI Time Series, Southeast Kenya
1. Introduction
Kenya’s arid and semi-arid lands (ASALs) cover ap-
proximately 83% of the country’s total area [1]. Accord-
ing to recent estimates, about 20% of Kenya’s population
and some 60% of the co untry’s livestock are to be found
in these ASALs [2]. The influx of human population
from the high potential areas of Kenya to these ASALs
has accelerated over the last 20 years [3]. It is estimated
that up to 6 million of Kenya’s population live and ex-
ploit the resources of the ASALs. This means that
ASALs are nationally important in terms of supporting
rural livelihoods. The Kenyan Government acknowl-
edges that one third of the projected increase in agricul-
tural food production is expected to come from these
ASALs [2]. Thus, it would seem that these ASALs will
continue to play a very important role in terms of human
settlement as well as production of subsistence food
crops for the ever increasing human population [4]. The
major environmental factors limiting crop production in
these ASALs of Kenya are high potential evaporation
and rainfall, with the latter being highly variable and
unpredictable in space and time [5]. Apart from the en-
vironmental limitatio ns, the new farming communities in
these ASALs lack the indigenous knowledge in selecting
crops and farming strategies well suited to the stabiliza-
tion and maximization of food production in their dimin-
ished rainfall circumstances.
Rural populations are exposed to the impacts of cli-
mate variability on agricultural production that is con-
Copyright © 2011 SciRes. IJG
sidered as the most rainfall-dependent of all human ac-
tivities [6,7]. This vulnerability is enhanced for the less
economically developed countries in the tropics that, in
many cases, are exposed to high climate variability at
different spatial-temporal scales. Of particular impor-
tance and relevance to Kenya is the El Niño Southern
Oscillation (ENSO) phenomenon that has been linked to
climate variability in many parts of Sub-Saharan Africa
where unique and persistent anomaly patterns have been
detected in the rainfall over parts of southern Africa,
eastern Africa, the Sahel region during periods of strong
and persistent ENSO events [8-12]. The Sub-Saharan
Africa is the only region world -wide where food produc-
tion per capita has decreased over the last twenty years
[13]. Staple crop production occupies an important place
in government policies, and one of the top priorities has
become the stabilization of crop yields [14] in the con-
text of the long-term drought of the last decades [15] and
the uncertainties of the global climate change [16]. With
increased capability to forecast ENSO events well in
advance [17-19], there has emerged a growing convic-
tion and interest in using climatic information in deci-
sion-making process, especially during crop production
[20,21]. The assumption we explore here is that the
Normalized Difference Vegetation Index (NDVI) anoma-
lies are related to ENSO climate teleconnections in af-
fecting agricultural production [20]. These teleconnec-
tions are manifested as short-term perturbations in local
climate that in turn affect crop yields. Agricultural areas
most affected by ENSO-related impacts should be dis-
tinguishable by differences in growing season NDVI
values during ENSO phases. The challenge is to differ-
entiate those components of climate variability related to
ENSO climate teleconnections from the background of
natural variability.
In this paper, the primary objective is to assess the in-
terannual climate variability and the response of vegeta-
tion cover to it by integrating rainfall and satellite-derived
NDVI data sets for semi-arid southeast Kenya (Figure 1).
A secondary objective is to make a contribution towards
meeting the challenge for more local level studies in un-
derstanding the impacts of climate variability and change
on the agricultural sector in the ASALs of Kenya. An
understanding of the historical patterns of dry and wet
cycles in the region could provide some important in-
sights into issues of management of food resources dur-
ing ‘bumper’ years to minimize the effects of recurrent
famine and food sho rt a ges during d ro u ght years.
Figure 1. A map showing the position of the study area in Kenya, administrative districts and distribution of rainfall stations .
Copyright © 2011 SciRes. IJG
2. Studied Area
The study area (Figure 1) is a subset of Kenya’s ASALs
and comprises 20% of the total land surface area [1].
There are two cropping seasons related to the rainy sea-
sons: ‘long’ March-May (MAM; with planting in March
to April), and ‘short’ October-December (OND; with
planting in October to November). Mean annual rainfall
in the study region ranges between 549 mm and 963 mm
a year (Tabl e 1). This amount is insufficient for the pro-
duction of most crops. Occasionally the rains fail or are
below normal for consecutive seasons, leading to
drought. Rainfall variability is a co mmon p heno menon in
the study area and this negatively affects agricultural
production, food security and the general livelihood of
the population. A large majority of the inhabitants are
smallholder subsistence farmers. Agricultural production
is influenced by the significant spatial and temporal
variations that occur in the rainfall. Despite farming
plans being made for both seasons, the October-Decemb-
er season is the most dependent on by farmers and whose
predictability i s quite high [22,23]
We selected the study region for a number of reasons:
A large proportion of the population in the region de-
pends on rainfed maize as staple food crop. The produc-
tion of this cereal is risky in these ASALs due in part to
its sensitivity to year-to-year variability in the amount
and timing of rainfall. The fluctuation in production
leads to loss of income due to reduced yields and above
all threatens the food security of the country. Since this
cereal plays a crucial role in the food security of the
country, it is important that accurate cereal production
estimates are provided to the government and other food
security stakeholders for timely intervention in case of
deficit. This can be by use of vegetation index images
and seasonal rainfall forecast. The predictability of the
short rains at a seasonal time scale is quite high [22] over
the portion of Kenya that encompasses the study area.
Rainfall in this region is strongly linked to the El
Niño-Southern Oscillation (ENSO) [10,12,24,25] raising
the need to assess its impact at varying temporal and
spatial scales.
We selected the study region for a number of reasons:
A large proportion of the population in the region de-
pends on rainfed maize as staple food crop. The produc-
tion of this cereal is risky in these ASALs due in part to
its sensitivity to year-to-year variability in the amount
and timing of rainfall. The fluctuation in production
leads to loss of income due to reduced yields and above
all threatens the food security of the country. Since this
cereal plays a crucial role in the food security of the
country, it is important that accurate cereal production
estimates are provided to the government and other food
security stakeholders for timely intervention in case of
deficit. This can be by use of vegetation index images
and seasonal rainfall forecast. The predictability of the
short rains at a seasonal time scale is quite high [22] over
the portion of Kenya that encompasses the study area.
Rainfall in this region is strongly linked to the El
Niño-Southern Oscillation (ENSO) [10,12,24,25] raising
the need to assess its impact at varying temporal and
spatial scales.
3. Data and Analysis Methods
For this study, the following data sets were used:
Table 1. Geographic location (longitude and altitude) of the study stations mean annual long (MAM) and short (OND) rainfall.
Station Longitude Latitude Altitude Rainfall (mm)
˚E ˚S (m) MAM OND Annual
Kiritiri 37.65 0.68 1143 440.1 425.5 963.5
Ishiara 37.78 0.45 853 350.5 422.9 857.8
Kambo 37.52 0.53 1250 451.6 373.8 938.9
Kalaba 37.38 0.75 1128 489.9 363.2 948.0
Kyuso 38.22 0.53 747 282.5 418.1 779.2
Matiliku 37.53 1.95 1097 324.6 375.8 833.0
Kangundo 37.45 1.20 1280 335.9 339.2 783.5
Katumani 37.23 1.58 1600 276.6 299.9 696.0
Malinda 37.08 1.48 1524 243.0 180.1 549.4
Makindu 37.83 2.28 1000 200.1 338.3 629.3
Kibwezi 37.98 2.40 914 220.2 400.4 710.6
Copyright © 2011 SciRes. IJG
3.1. Rainfall
Daily precipitation data was obtained from the Kenya
Meteorological department (KMD) archives from 1961
to 2003 for the 11 stations used in the study (Table 1).
Rainfall record periods however varied between 25 and
43 years. The selected stations are not well distributed
over the study region (Figure 1) because of closure,
missing data and short record periods. Missing data in
the record periods were estimated using the method de-
scribed by [26]. Homogeneity test on the data sets was
done according to the method of [27]. The study also
sought to investigate ENSO-related variability in rainfall
at annual and seasonal timescales in southeast Kenya for
the period 1960 to 2003. This was achieved by adopting
the National Center for Environmental Prediction (NCEP)
has classified ENSO events since the 1885 world. How-
ever the study limited itself to the period 1960 to 2003
because it was about this time that most of the rainfall
stations were established. According NCEP [28] the
years 1965, 1972, 1982, 1986, 1987, 1991, 1994 and
1997 were classified as El Niño years; whereas 1970,
1973, 1975 1988, 1998 as La Niña years. Quantification
of ENSO-related variability events is to enhance the un-
derstanding of their effect on rainfall and crop yield in
southeast Kenya.
3.2. Normalized Difference Vegetation Index
The NDVI is based on properties of green vegetation to
reflect the incident solar radiation differently in two
spectral wavebands observed by the AVHRR sensor
aboard NOAA polar orb iting satellite series (NOAA-7,9,
11,14,16): visible 550-700 nm (Channel 1) and near-
infrared 730-1000 nm (Channel 2) [29]. The presence of
chlorophyll pigment in g reen vegetation and leaf scatter-
ing mechanisms cause low spectral reflectance in Chan-
nel 1 and high reflectance in Channel 2, respectively.
Reflectance values change in the opposite direction if
vegetation is under stress [30]. Hence, the NDVI meas-
ures vegetation vigour and greenness [31] and is calcu-
lated as follows:
where: NIR and R represent the reflectance of the near
infrared (Channel 2) and the red (Channel 1), respec-
tively. The NDVI is unit-les s, with v alues ran g ing from –1
to +1. Healthy green vegetation normally has the high est
positive values while surfaces without vegetation, such
as bare soil, water, snow, ice or clouds usually have low
NDVI values that are near zero or slightly negative.
Stressed vegetation or vegetation with small leaf area has
positive but reduced NDVI values [3 0,32]. The justifica-
tion for using NDVI data in monitoring ecosystem dy-
namics in arid and semi-arid lands is based on the exten-
sive research agenda in the 1980s and 1990s in arid re-
gions that demonstrated the significant correlation be-
tween NDVI and rainfall variations on seasonal to inter-
annual time scales [33-37]. The established relationship
between NDVI and rainfall formed the basis for using
time series NDVI data for drought monitoring and early
famine warning systems in areas with sparse rainfall
networks [38-40]. Furthermore, NDVI data set has been
used to examine the connection between climate varia-
tions and ecosystem dynamics, particularly those associ-
ated with ENSO phenomenon [41-44] and recently to
investigate long-term trends in vegetation [29,45,46]
Data used in this study were processed by the GIMMS
group at NASA’s Goddard Space Flight Center, as de-
scribed by Anyamba and Tucker [29]. For this research,
NDVI monthly data from 1981-2004 at 8 km spatial res-
olution was used [29]. In addition, monthly NDVI data
from SPOT Vegetation Instrument from May 1998 to
December 2004 at 1 km spatial resolution was utilized.
The satellite data was used to study aspects of spatial
variability that were not captured by the rainfall stations
data. Since NDVI is closely related to rainfall seasonality
[29], analysis was focused on the growing seasons, i.e.
‘long’ and ‘short’ rainy seasons in our case. These sea-
sons for arid and semi-arid Kenya are well differentiated
and details can be found in Jaetzold et al. [47]. The
months of March, April and May, hereafter referred to as
MAM represent the ‘long’ rains growing period, while
October, November and December (OND) represent the
‘short’ rains growing period. The long-term NDVI cli-
matology (1981-2004) was created by averaging data for
all cloud-free pixels for MAM and OND for the same
period. The year to year variability in the NDVI patterns
was examined by calculating yearly MAM and OND
anomalies as follows:
 
where: NDVI are the respective MAM and OND
anomalies, NDVI are individual seasonal MAM and
OND means and NDVI is the long-term MAM and
OND mean. Table 2 shows aggregate NDVI for annual
and seasonal NDVI by station in the study area.
4. Results and Discussion
Figures 2(a-c) show rainfall variability for MAM, OND
and annual for some of the sites in the study. Rainfall in
southeast Kenya deviates from the normal by more than
1.5 standard deviations (For example, 1961, 1967 and
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Table 2. Aggregate NDVI for annual and seasonal NDV I by
station in the study area.
Kiritiri Chief’s Camp, Embu 0.55 0.54 0.51
Ishiara Agricultural Farm 0.61 0.57 0.55
Kambo Kamau’s Farm 0.60 0.62 0.58
Kyuso Agricultural Office, Mwingi0.50 0.51 0.44
Machakos, Ma tiliku Health Centre 0.54 0.53 0.49
Kangundo Kithimani D.O’s Office 0.52 0.47 0.46
Katumani Exp. Res. Station 0.44 0.38 0.38
B&T Malinda Ranch, Lukenya 0.41 0.35 0.35
Makindu Met. Station 0.47 0.45 0.41
Kibwezi, DWA Plantation Ltd. 0.53 0.51 0.47
1987). High rainfall variability makes it difficult for
farmers to plan for agricultural activities [4] and fre-
quently lead to crop failure. It is also significant that
ENSO events have an impact on rainfall in Southeast
Kenya. However, not all ENSO events lead to extreme
climatic events in Southeast Kenya.
Analysis of El Niño years (as defined by NCEP) (Ta-
ble 3(a)) reveals that not all stations in SE Kenya receive
above normal rainfall especially during the OND season.
In fact, of the eight El Niño years (1965, 1972, 1982,
1986, 1987, 199 1, 1994, and 1997), it was in 1982 , 1994
and 1997 when all stations received above normal OND
rainfall: but with varying magnitude. In 1972, out of the
nine stations with analyzed data, five stations recorded
above normal rainfall. Kangundo (–0.26), Katumani
(–0.12), Kiritiri (–0.01) and Kyuso (–0.06) recorded be-
low normal rainfall in 1972 While in 1986, Kambo
(–0.31) and Malinda (–0.29) received below normal
OND rainfall. Of these three years, 1997 was the most
pronounced with ten stations recording a positive stan-
dard deviation of above 1.0 during the OND season.
While more than six stations recorded a positive standard
deviation of above 1.0 for the OND seasons of 1982 and
1994. It is important to note that d espite 1987 being clas-
sified as an El Niño year, all the stations in the study area
recorded below normal rainfall. Analysis of the MAM
rainfall season of El Niño years show that nearly all the
stations recorded below normal rainfall in 1965, 1972,
1987 and 1991. Results further indicate that in most sta-
tions, MAM season preceding OND season in El Niño
years were characterized by suppressed rainfall. Similar
results were recoded by [9] when they characterized
ENSO events in some parts of Kenya.
In La Niña years (1970, 1973, 1975, 1988, 1998) all
stations recorded below normal rainfall amount during
Figure 2. Normalized annual, MAM and OND rainfall at 3
selected stations in the study area. (a) Kibwezi (Makueni);
(b) Katumani (Machakos); (c) Kyuso (Mwingi).
OND season except in 1988 (Table 3(b)). 1973 and 1975
are fairly unique years in that for both MAM and OND
seasons, most of the stations received below normal
rainfall, culminating into a decline in annual rainfall. In
1988, most of the stations received above normal rainfall
during the MAM and OND rainfall seasons despite
NCEP’s classification of the year as La Niña. In 1998,
most stations recorded above normal MAM rainfall ex-
cept Kalaba (–0.69) and Kyuso (–0.69) which recorded
below normal rainfall. The above normal rainfall events
in most of the stations in 1998 MAM season can be at-
tributed to the prolonged effect of the 1997 El Niño
Copyright © 2011 SciRes. IJG
Table 3(a). Analysis of magnitude of El Niño events based on National Center for Environmental Prediction (NCEP) (2005)
1965 1972 1982 1986 1987 1991 1994 1997
–1.38 0.09 1.05 –1.39–1.09–0.23–0.58 –0.16 1.44 0.642.45
KALABA - - - -
–0.48 1.771.270.01 –0.79 –0.24 –0.430.56 0.00 0.90
–0.06 0.46
KAMBO - - - - 0.72 0.81–0.76–0.31–0.40–1.38–0.83–0.32 0.45 1.84 0.172.32
KANGUNDO –1.16 –0.28 –0.77 –0.26 –0.47 0.520.820.94 –0.85–1.26–1.00–0.67 0.37 1.47 0.361.62
KATUMANI –1.14 0.14 –0.97 –0.12 –0.13 1.070.390.07 –1.28–1.28–0.78–0.07 –0.73 2.37 –0.011.52
KIBWEZI –1.24 –0.85 –1.59 0.18 1.45 1.660.210.18 –0.73–1.26–0.01–0.36 –0.34 0.58 –0.17 1.85
KIRITIRI –1.18 –0.62 –1.45 –0.01 –0.02 0.47 –0.05 0.37 –0.97–1.42–0.68-–0.67 –0.62 0.99 –0.04 2.56
KYUSO –0.76 –0.34 –1.19 –0.06 –0.66 0.25 –0.12 0.30–0.63–1.00–0.59–0.68 –0.08 0.59 1.002.18
MAKINDU –1.01 –0.58 –1.79 0.00 0.18 –0.52–1.23–0.64–0.29 –0.31 1.23 0.061.40
–1.31 0.15 –0.33 1.270.81 -0.29–1.01 –0.87 –0.940.33 0.11 1.10 1.194.44
MATILIKU –0.80 –0.79 –1.16 0.54 0.51 –0.97 –0.64 –1.220.58 –0.78 0.26 0.232.86
Note: Bold and unbold indicat e b e l ow and above rainfall e v e n t s , respectively.
Table 3(b). Analysis of magnitude of La Niña events as classified by National Center for Environmental Prediction (NCEP)
(2005) classification.
1970 1973 1975 1988 1998
–0.88 –1.48 0.89 –0.18 1.92 0.24 0.98
KALABA - - - -
–0.48 –0.69 0.82 0.73
–0.68 –1.47
–1.27 –1.09 0.00 –1.16 1.11 2.16 0.16
KANGUNDO 1.21 –1.25 –1.69 –0.99 –0.95 –1.07 1.80 –0.30 0.02 –1.44
KATUMANI 0.77 –1.36 –1.64 –0.73 –0.86 –0.72 0.49 0.23 1.04
–0.92 –0.32 –0.41 –1.54 –1.13 –0.12 1.15 1.44
–0.90 –1.74 –0.95 –0.66 –0.86 0.45 0.76 2.31
KYUSO 1.16
–0.99 –1.01 –0.56 0.50 –0.98 –0.21 0.05 –0.69 –0.75
MAKINDU 0.25 –1.19 –0.78 –1.03 –0.57 –0.97 0.81 –0.02 0.51 –1.34
MALINDA 0.91 –0.95 –0.95 –1.19 –0.42 –0.79 2.00 –0.03 1.45 –1.03
MATILIKU 0.04 –1.13 –1.21 –1.08 –0.17 –0.99 0.62 0.87 0.31
Note: Bold and unbold indicat e b e l ow and above rainfall e v e n t s , respectively.
The result suggests that variation from normal rainfall
and vegetation condition is highest during El Niño than
La Niña events. It also emerged that in El Niño years,
which are usually characterized by above normal rainfall
events during the OND season, there were more stations
with above normal rainfall during OND season than pre-
ceding MAM rainfall (Figure 3). This was with the ex-
ception of 1986 where 75% of the stations received
above normal rainf all during the MAM season compared
to about 48% that received above normal during the
OND season. Results show that ENSO events in south-
east Kenya vary in magnitude, both in time and space.
Notable examples are 1987 and 1988 which were classi-
fied as El Niño and La Niña years respectively but turned
out to be the opposite. Variations on the intensity of El
Niño have also been documented by Ammisah-Arthur et
Copyright © 2011 SciRes. IJG
al. [9]. Previous studies have strongly linked ENSO
events with OND rainfall in Eastern Africa; but there is
scant literature on ENSO-MAM seasonal rainfall in the
region [48]. With reports of improved skill of predicting
ENSO ev ents [19,49] th ese results are th erefore a pointer
to the need to determine the influence of ENSO ev ents at
local level and prepare local communities on the poten-
tial impact of these events.
Figures 4 (a-d) show the effect of inter-annual sea-
sonal rainfall variability on NDVI in selected stations
Machakos and Mwingi districts for MAM and OND
growing season. The results show that the driest years
had the lowest NDVI values while the wettest years had
maximum NDVI values for the same. For instance, dur-
ing the MAM season, 1984, 1993 and 2000 recorded the
lowest amounts of rainfall and NDVI values for April
-June in the region. Many parts of Southeast Kenya and
Eastern Africa recorded failure of the 1984 MAM rains
and low production of staple cereals prompting action
from government and development agencies to secure
food to people [26,50,51]. The 2000 drought was among
the severest on record in Kenya with wide spread
socio-economic impacts [52] that included famine and a
decline in the generation of hydro-electric power. Al-
though inter-annual rainfall variability is low in parts of
Southeast Kenya (Figure 4 (c,d)), the magnitude of
NDVI variation is high. This could imply that rainfall
amount is not the only determinant of NDVI. Tottrup and
Raumussen [53] used NDVI data to analyze long-term
changes in crop production and found that rainfall vari-
ability is not the singular determinant of crop yield in
Peanut Basin in Senegal. Thiam [54] established that in
addition to rainfall amount, factors such as soil type, de-
forestation overgrazing, and agricultural land uses de-
termine primary biological productivity of land in Mau-
ritania. In the case of Mwingi district, rainfall distribution,
alongside bio-physical characteristics could be a major
determinant of biological productivity of vegetation.
Figure 3. Percentage of stations receiving above normal r ainfall in El Niño years for annual, MAM and OND.
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Figures 4. Effect of inter-annual rainfall variability on vegetation during the MAM and OND seasons. (a) Matiliku
(Machakos), MAM; (b) Matiliku (Machakos), OND; (c) Kyuso (Mwingi), MAM; (d) Kyuso (Mwingi), OND; Key: OND-Rn-
October–December Rainfall; MAM-Rn-March-May rainfall; AMJ-ndvi-April-June NDVI; NDJ-ndvi-November-January
Copyright © 2011 SciRes. IJG
Although April and November are the peak rainfall
months in Southeast Kenya, May and December are the
peak NDVI months (Figures 5 (a-b)). Thus, after rainfall
onset, there is a one month lag period for NDVI to reach
its peak. A lagged effect of NDVI was also observed
when MAM and OND rainfall showed high correlation
values with April-June (AMJ) NDVI and Novem-
ber-January (NDJ) NDVI respectively. In other studies,
Anyamba et al. [55] reported a lagged response of rain-
fall and NDVI in Eastern Africa after the 1997/ 1998 El
Niño event. Similarly, Wang and You [56] found that
vegetation response to North Atlantic Oscillation delayed
by 1.5 years. The delayed impact of rainfall on vegeta-
tion has implications on the food web in the ecosystem.
For communities in Southeast Kenya, who are
agro-pastoralists, this has implication on planning for
pasture. Funk and Brown [57] ha ve used the lagged rela-
tionship between rainfall and NDVI to estimate vegeta-
tion response to current climatic conditions, helping to
make early warning systems earlier.
Results of aggregate NDVI values show all districts to
have slightly enhanced green vegetation conditions dur-
ing the OND season than for MAM season (Table 4).
This can be attributed to more and reliable OND rainfall
than MAM rainfall in Southeast Kenya [1,22,58]. The
close coupling between OND and ENSO & related SSTs
has enhanced its predictability [19] providing a window
Figure 5. Effects of rainfall on NDVI at selected stations in
Southeast Kenya (a) Kambo-Embu, (b) Katumani-Machakos.
Table 4. Aggregate NDVI for April-June (AMJNDVI) and
November-January (NDJNDVI) by district.
Embu 0.63 0.69
Makueni 0.47 0.48
Machakos 0.47 0.48
Mwingi 0.50 0.59
Mbeere 0.58 0.62
of opportunity in planning for agriculture, pasture and
managing natural resources in Southeast Kenya. Sea-
sonal variations in vegetation conditions can also be at-
tributed anomalous ENSO events. For instance, An-
yamba et al. [55] demonstrated that the 1997/1998 ENSO
event had the Eastern and Southern parts of Africa ex-
periencing continuous above normal NDVI levels for a
period of over 8 months from October 1997 to May
5. Conclusions
Results presented in this study reinforce earlier findings
that year to year and season to season rainfall variability
is persistent in eastern Africa [9,26,59] and this will con-
tinue to impact on vegetation and rain-fed dependant
livelihoods. This research establishes that the October-
December rains are more reliable as manifested in the
amount of rainfall and the greenness of the vegetation
compared to the March-May rainfall season. Although
these research findings show a common pattern in the
amount of rainfall during ENSO events in southeast
Kenya, all El Niño and La Niña events are not equal in
magnitude. In other words, prediction of an ENSO event
does not always lead to an anomaly in southeast Kenya.
These findings complement those of Amissah-Arthur et
al. [9] who found that all El Niño events are not equal in
terms of their regional impact on Kenyan rainfall. This
variation therefore calls for a need to generate climate
forecasts at a local level (downscaling) with a view to
improving the skill of predicting ENSO events and fore-
cast more accurately. But such predictions of ENSO
events should be accompanied by advisories derived
from knowledge of within-season rainfall characteristics
such as onset, cessation and length of growing season for
effective planning of agricultural decisions. The lagged
response between seasonal rainfall and NDVI can be
used to project crop yield performance over the semi-arid
and food insecure Southeast Kenya. This will go a long
way in assisting food security and reducing the vulner-
ability of local communities. Stakeholders will be able to
put in place relief measures early enough to avert climate
Copyright © 2011 SciRes. IJG
related disasters. The close relationship between rainfall
and NDVI therefore calls for an improvement in local
level rainfall and NDVI prediction and the effective dis-
semination of this information to stakeholders. This
would greatly reduce the vulnerability of livelihoods to
climate related disasters by enhancing their effective
management. However, observed NDVI trends can not
be solely explained by rainfall data. Thus, there is need
to develop a methodology that will distinguish between
climate-induced and human-induced factors.
6. Acknowledgements
This research was funded by START (Global Change
SysTem for Analysis, Research and Training), Washing-
ton, DC, through a grant to the senior author under the
African Global Change Research Community Programme.
Assistance from the following institutions in prov ision of
respective data sets is gratefully acknowledged. The
GIMMS group at NASA’s Goddard Space Flight Centre
(NDVI) and the Kenya Meteorological Department
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