Natural Resources, 2011, 2, 155-166
doi:10.4236/nr.2011.23021 Published Online September 2011 (http://www.SciRP.org/journal/nr)
155
Local Climate Forcing and Eco-Climatic
Complexes in the Wooded Savannah of Western
Nigeria
Mayowa Fasona1, Mark Tadross2, Babatunde Abiodun3, Ademola Omojola4
1,4Department of Geography, University of Lagos, Lagos, Nigeria; 2,3Climate Systems Analysis Group, Environmental and Geo-
graphical Sciences Department, University of Cape Town, Cape Town, South Africa.
Received April 15th, 2011; revised May 15th, 2011; accepted June 1st, 2011.
ABSTRACT
Many climate impact applications are sensitive to local differentials in the climate system. This study investigates how
eco-geographic factors influence the local climate and propagate eco-climatic complexes that vary spatio-temporally.
Local geography data including elevation, slope, aspect, rainfall, temperature, vegetation, population dens ity, and soil
potential for agriculture were integrated and analyzed using geographic information system and principal component
analysis. The result was profiled for local climate drivers and associated spatial structures in present and future cli-
mate (2046-2065) scenarios. The results sugg est a local climate system driven by the couplin g between terrain, rainfa ll
and temperature in all seasons. In the present climate, this coupling creates eco-climatic complexes that extend from
the southeast to northwest corridor in all seasons except June-July-August (JJA) when it is shifted to the northeast axis.
This pattern is projected to continue in the future clima te scenario, but its spa tial influence and intens ity would weaken
around the northwest axis and rainfall will become less significant in the system in JJA. The clustering of rural settle-
ments these complexes suggests the climate-positives produced by the system significantly support rural livelihoods.
Thus, these eco-climatic complexes represent climate sensitive natural resource systems that should be targeted as a
fulcrum for climate change mitigation and adaptation in the wooded savannah .
Keywords: Climate Change, Geographic Factors, Eco-Climatic Complex, GIS, PCA, Adaptation, Savannah, Nigeria
1. Introduction
Climate change is expected to have significant impact on
the terrestrial ecosystems and biodiversity. Developing
countries of Africa have low adaptive capacity because
of low level of technology and preparedness. Most of
these countries also depend on rain-fed agriculture and
the natural resource stock which make them severely
vulnerable to climate change. The arid and semi-arid
regions of West Africa (i.e. the Savannah and Sahel)
harbour large population. This accelerates land transfor-
mation, degradation and increase resource conflicts.
These conflicts may become fiercer in future as these
regions have been predicted to get drier. Depending on
the emission scenario current temperatures are predicted
to rise in the order of 1.4 to 5.8℃ by 2100 [1]. Long
term rainfall signals have already become more erratic in
space and time distribution [2-4], whereas only 3.7 % of
the total agricultural land in the entire sub-Saharan Af-
rica is irrigated [5]. Rainfed agriculture is the major
source of livelihood for large rural population in the Ni-
gerian savannah [3,6,7]. Thus, the water footprint may
become more critical in defining the future pattern and
trajectory of settlements and agrarian land uses and the
concomitant water challenge may overwhelm current
traditional agriculture and water management practices.
A degree of local forcing that varies by region and
season complements synoptic-scale forcing to influence
local climate [8]. Local perturbations including terrain,
land cover, and land-water boundary often exert strong
influence on the local climate and create eco-climatic
structures or complexes that support the natural resource
capita on which livelihoods of rural population thrive.
The influence of local perturbation tends to occur at rela-
tively fine resolution that cannot be captured by global
and most regional climate models. The degree and extent
to which these processes can feedback to impact the local
climate still represents an element of uncertainty in cli-

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 156
mate projections [8]. Global and regional climate models
provide insights into dynamic interaction between land
surface and atmospheric processes which drive global
and regional climates. But their relatively coarse resolu-
tions often mask large differentials in local forcing in-
cluding terrain, land cover, agricultural practices, energy
policies and socio-economic orientations and details
about local scale circulation and perturbation induced by
landscape complexity are often eliminated [9-11]. Many
impact applications including place-based and con-
text-specific adaptation strategies, ecosystems manage-
ment, and local mitigation actions are very sensitive to
fine scale climate variations that are parameterized in
coarse scale models. They require the equivalent of point
climate observation. The West Africa region is part of
those regions around the world with highly variable cli-
mate on seasonal and decadal time scales [11]. The me-
soscale convective process (MCS) relies on the complex-
ity in terrain and land cover to propagate and accounts
for over 75% of rainfall received in the West Africa sa-
vannah [12]. Understanding the nature and role of such
local scale forcings is important for planning strategies
for climate adaptation and local level mitigation in the
Nigerian savannah.
Empirical downscaling is one way to generate point
scale data that captures fine scale variations in local cli-
mate. It is a widely used technique for exploring regional
and local-scale response to global climate change as
simulated by comparatively low-resolution global cli-
mate models (GCM). It represents the cross-scale rela-
tionships between the larger scale circulation from the
GCM and local climate responses based on the premise
that the local-scale climate is in some measure a response
to the larger, synoptic-scale forcing. Station observa-
tional data are used to derive a relationship between the
synoptic-scale and local climates which can then be used
with comparable resolution fields of a GCM to generate
information on the local climate consistent with the
GCM forcing [8,13]. Empirical downscaling is important
for generating regional and local scale scenarios of future
climate to make climate change information available for
impacts and vulnerability assessments, policy formula-
tion, and climate change adaptation at regional and local
scales. It has the advantage to downscale to point scales
which matches the observational data characteristics. It
also provides regional detail that is consistent with the
actual spatial gradients over the region. However, the
downscaling is forced by GCMs which means inherent
errors in the GCMs data is also transferred into the
downscaled data. In addition, because the possibility of
going beyond original scale of data is limited, the down-
scaled data cannot be regarded as a complete substitute
for fine scale climate measurements obtained across
space. The evaluation of the extent to which local forc-
ings will interface with the climate system and the con-
comitant spatial influence produced is a key question
addressed in this study. Downscaled climate data can be
integrated and analyzed with the drivers of the local cli-
mate system and this enables their spatial pattern of in-
fluence and impact on the local climate system to be de-
ciphered and quantified. We present evidence that local
geographic forcings including terrain and land cover
have the potential to influence the local climate system
across space and seasons and determine spatial patterns
of climate-positives and eco-climatic complexes that
produce the natural resource capita which supports live-
lihood systems across the wooded savannah of western
Nigeria.
2. Materials and Methods
2.1. Regional Setting
The study area is roughly defined by Latitudes 8˚ to 9˚15′
North and Longitudes 3˚50′ to 5˚50′ East. It covers about
40,000 km2 in western Nigeria, extending from the bor-
der with Benin Republic in the west to the Niger flood-
plains in central Nigeria covering parts of Oyo, Kwara,
Kogi, Niger, Ekiti and Osun States (Figure 1). The study
area is covered by the wooded savannah ecology which
approximates the transitional zone between the southern
rainforest and the northern grassland savannah. Average
elevation is about 300m but outcrops rising above 500m
in the eastern axis. Vegetation consists of mixture of
trees and grasses, as well as moist peri-forest mixed with
savannah of anthropic degradation and patchy landscape
[14,15]. Generally, the area is characterized by a
sub-humid Koppen’s Aw climate [16]. Annual rainfall
received falls between 900mm and 1300mm and mean
maximum temperature range is between 28℃ and 36℃
with peak temperature occurring around February and
March. The southern part shares the bimodal rainfall
pattern of the southern rainforest belt with peaks in mid
June to July and September. The highest monthly rainfall
occurs in September as opposed to July for the rainforest
belt. Rainfall is the most critical limiting factor of human
activity in the Nigerian Savannah. Prolonged change in
quantity and regime of rainfall is an index of climatic
variability and change. Monsoonal wind and MCS are
the dominant rain producing forces over the region. The
MCS predominates and produces over 75% of rainfall
received [12]. Population density is high and a pov-
erty-environment linkage is very strong. Survival for
large rural population depends on small-holder rainfed
agriculture [3,6,7] and the natural capital contributes sig-
nificantly to human well-being [11]. The study area is
important for root, tuber and cereal cultivation. Intense

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 157
Figure 1. Study area and observation stations.
land use pressure has increased the frequency of savan-
nah fire, forest conversion to agricultural land, and incur-
sions into marginal riparian forests. Uncontrolled har-
vesting of trees for fuel-wood and charcoal are important
livelihood activities [17,18]. Due to its large pasture un-
dergrowth, the study area has in recent years become
important for extensive grazing for migrating pastoralists.
This has increased the frequency of land resource con-
flict [19-21].
2.2. Data and Data Sources
2.2.1. NDVI
Dekad 2 long term mean (1982-2008) seasonal normal-
ized difference vegetation index (NDVI) data for January,
April, July, and October were accessed from the archive
of the Famine Early Warning Systems Network
(FEWS-NET) African Data Dissemination Service1. The
NDVI is derived from data collected by National Oceanic
and Atmospheric Administration (NOAA) Advanced
Very High Resolution Radiometer (AVHRR) satellites,
and processed by the Global Inventory Monitoring and
Modeling Studies Group (GIMMS) at the National
Aeronautical and Space Administration (NASA). The
NDVI is calculated from the near-infrared (NIR) and
visible (VIS) wavelengths of the AVHRR sensor using
the following algorithm:

 
NDVI=NIRVISNIR+VIS (1)
The NDVI (also referred to as NDVI-g) dataset de-
rived principally from NOAA-17 inter-calibrated with
NOAA-16 and previous NDVI products were also in-
ter-calibrated with SPOT-Vegetation NDVI [22-25]. The
spatial resolution of the NDVI data is 8 km in both X and
Y directions and its was reprojected for overlay with
other datasets to UTM-31 and WGS spheroid from its
original Albers Equal Area Conic and Clarke 1866 sphe-
roid. The NDVI computed data range of −1.0 to +1.0
original scaled to the range of 0 to 200 (where computed
−1.0 equals 0 and computed 0 equals 100 and computed
1.0 equals 200) was re-scaled to the normal NDVI range
of 0 to 1 using the straight line function:
1
100
Y
X

 (2)
where X is the rescaled NDVI value and Y is the value in
the range 0 to 200.
The close coupling between rainfall and the growth of
vegetation make it plausible to utilize NDVI data as
1http://earlywarning.usgs.gov/adds/imgdatas2.php?imgtype=nd&extent
=w

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 158
proxy for land surface response to precipitation variabil-
ity and changes in the biophysical properties of vegeta-
tion [25-27].
2.2.2. Terrain
Terrain data was extracted from the 3 arc-second eleva-
tion data from the Shuttle Radar Topography Mission
(SRTM) obtained in February 2000 and accessed from
the archive of the global land cover facility2. The world
reference systems (WRS-2) scenes SRTM_ffB03,
p190r054 and p190r054 were downloaded and processed
to obtain derivatives including contour and spot heights,
slope, and aspect.
2.2.3. Climate
Data for historical daily rainfall and maximum tempera-
ture (1960 to 2005) for 12 climatic stations around the
study area was sourced from the Nigerian Meteorological
Agency (NIMET) in Lagos. Because the study area is
adjacent to the Nigeria/Benin Republic border, rainfall
and maximum temperature data was also sourced for 3
adjacent stations in Benin Republic from the portal of the
Climate Systems Analysis Group (CSAG) of the Univer-
sity of Cape Town to improve the surface interpolation
process. Statistical downscaling of the climate data was
carried out in CSAG. The statistical downscaling tech-
nique employed matching of GCM data with self organ-
ized map (SOM) characterization of atmospheric states
and forced by SRES A2 emissions scenario (8,Hewitson
and Crane 2006). The driving GCMs were adopted from
the Coupled Model Intercomparison Project Phase Three
(CMIP3) archive3, which makes statistical downscaling
possible for the non-seamless periods 2046-2065 (near
future) and 2081-2100 (far future) only. The statistical
downscaling process reproduced the observational data
and also generated both near-future and far-future pro-
jections for 10 GCMs plus NCEP reanalysis. Studies that
compare climate model output often use ensemble sets of
all the models. A comparability study of 18 GCM out-
puts (including all the 10 generated by the downscaling
process) at the process level by [28] suggests that the
MRI CGCM 2.3.2 model provides the most reliable
simulation of the twenty-first century climate over West
Africa. The MRI CGCM 2.3.2 model was thus adopted
to characterize the future climate. The comparison be-
tween the model output and the observation data and
NCEP reanalysis suggests a significant agreement in spa-
tial and temporal pattern of rainfall and maximum tem-
perature across the study area.
2.2.4. Other Datasets
Data on forested lands, protected areas, disturbance index,
potential of soils for agriculture, and population density
were also developed and integrated. Forested and pro-
tected areas were extracted from Landsat TM and ETM+
data. Disturbance indices were computed by criteria
evaluation of 12 land cover categories derived from the
Landsat imageries. The land cover categories were ana-
lyzed according to current state and the extent to which
they have been, or are likely to be impacted by human
activity. Soil data extracted from the 1:650,000 digital
Soils map of western Nigeria sourced from Soils Survey
Division of the Ministry of Agriculture and Natural Re-
sources was analyzed for the ability of each pedologic
unit to support crop production. Population density data
was computed at local government level using data from
the 2006 census collated from the National Population
Commission.
2.3. Procedure
The GIS as a tool for data integration and analysis sup-
ports the building of multiple spatial data layers. Features
extracted and derivates generated from all data layers are
integrated to generate composite results that reveal spa-
tial pattern and processes that are not discernable in indi-
vidual data layers. The datasets were analyzed to produce
gridded derivatives which were integrated into a common
GIS database for collocation analysis (Figure 2). The
output from the gridded derivatives was exported in AS-
CII text to statistical software and subjected to principal
component analysis (PCA). The seasonal correlations
and principal factors were generated and the result was
transferred back into GIS for spatial interpolation to de-
rive PCA maps. PCA is an exploratory multivariate sta-
tistics that transforms series of variables into a set of
components that are orthogonal in both time and space
and ordered them in terms of the amount of variance they
explain from the series. It is a powerful technique for
analyzing variability over space and time [29] and very
effective in organizing the underlying sources of vari-
ability in data. PCA has been used in related studies on
variability in data with climatic and ecological signifi-
cance and air pollution [30,31], biological diversity
[32,33], groundwater and geochemical data variability
[34,35], and sources of heavy metals in soil [36].
The seasonal NDVI data produced 798 grid data points
which was adopted as the frame for extracting point val-
ues (in ASCII) from all the other gridded datasets (rain-
fall, maximum temperature, aspect, slope, contour, agri-
cultural potential of soils, population density, protected
areas, forested areas, and disturbance indexes). The AS-
CII values extracted were digitally written into the attrib-
ute files of the seasonal NDVI data. This process pro-
duced a collocated attribute data tables with data fields
for the seasonal NDVI and all other datasets. This ex-
2http://www.landcover.org/data/srtm/
3http://www.pcmdi.llnl.gov/projects/cmip/Table.php

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 159
Figure 2. The framework for integration of eco-geographic variables for PCA.
tended attribute data files were imported into STATIS-
TICA® 9 software4 and subjected to PCA. With the ei-
genvalues cut-off set at 1, the correlation matrix option
produced standardized PCA. The slope of the scree plot
was used to decide the number of factors to retain in each
case thereby eliminating inconsequential principal com-
ponents. Factor coordinates for all cases were transferred
into GIS and PCA surfaces were generated using the in-
verse distance weights (IDW) spatial interpolation algo-
rithm.
Efficient computation of PCA is done with matrix al-
gebra and starts with a matrix of inter-correlations which
produces standardized PCA. Data reduction is achieved
by finding linear combinations (principal components) of
the original variables which account for as much of the
original total variance as possible given by:
112 2
PCV VV
ii ini
aa a  n
(3)
where PCi is principal component i and a1i is the loading
(correlation coefficient) of the original variable V1 [31].
The successive linear combinations are extracted in
such a way that they are uncorrelated with each other and
account for successive smaller amounts of total variance
[31].
3. Results
3.1. Pattern of Feedbacks Between Climate and
Eco-Geographic Factors
The feedbacks and associated spatial patterns created by
the interaction between climatic elements and eco-geo-
graphical variables are critical for planning adaptation to
climate change especially at local levels. The regional
climate of the wooded savannah is strongly influenced
the MCS, a coupled system of local processes including
the influence of terrain, land-cover and moisture gradi-
ent.
The seasonal correlations across space between rain-
fall and maximum temperature on one hand, and NDVI
and elevation on the other are shown on Tables 1 and 2
respectively for present and future climates.
NDVI is positively correlated with elevation (r = 0.56,
p < 0.01) and rainfall (r = 0.53, p < 0.01) in March-
April-May (MAM). MAM represents the onset of the
rains when the wooded savannah recovers after the dry
period of December-January-February (DJF). The more
rainfall experienced during this period the greener the
region becomes. Elevated areas normally have higher
chance of receiving early rainfall due to the strong effect
of MCS which requires the lifting force provided by ter-
rain to produce rain. This may also explain why some
areas around relatively rugged terrain are wetter than the
surrounding areas.
This well known relationship between terrain and cli-
mate is also demonstrated by the positive correlation
between elevation and rainfall and the negative correla-
tion of elevation with temperature (in all seasons) except
in June-July-August (JJA) when the pervading system is
reversed. Thus rainfall shows strong negative correlation
with elevation (r = −0.62, p < 0.01) and strong positive
correlation with Tmax (r = 0.76 p < 0.01) in JJA. This
strongly suggests the dominance of the West African
monsoon (WAM) system in JJA. Normally, the MCS
requires a local forcing such as higher altitude or vegeta-
tion to lift the gathered moisture to saturation, condensa-
tion and precipitation stages. However, in JJA because of
the influence of the WAM system which predominates
during this period, the influence of MCS and the local
forcings are weakened because the atmosphere is satu-
rated already. Hence, less rainfall is produced by the lo-
cal systems even at higher altitudes.
4www.statsoft.com

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 160
Table 1. Correlation of climate and eco-geographic vari-
ables for present climate.
Variables Season NDVI Elevation Rain Tmax
DJF −0.161 −0.184 0.298
MAM 0.563 0.530 −0.382
JJA −0.073 0.347 0.250
NDVI
SON 0.261 0.023 −0.089
DJF 0.678 −0.774
MAM 0.499 −0.670
JJA −0.624 −0.641
Elevation
SON 0.151 −0.666
DJF −0.777
MAM −0.733
JJA 0.759
Rain_
SON −0.096
All correlations significant at the 0.01 level (2-tailed).
Table 2. Correlations of climate and eco-geographic vari-
ables for future climate (2046-2065).
Variables Season Elevation Rain Tmax
DJF 0.739 −0.761
MAM 0.620 −0.739
JJA 0.034 −0.683
Elevation
SON 0.389 −0.756
DJF −0.887
MAM −0.658
JJA 0.351
Rain_
SON −0.431
All correlations significant at the 0.01 level (2-tailed).
The WAM system is essentially driven by land-ocean
pressure differential driven by heating on the land. The
sun is overhead on the tropic of cancer in June (boreal
summer) and overhead around the West Africa savannah
in July/August on its way back to the equator. The sa-
vannah thus receives direct insolation which creates a
low pressure zone on land. This low pressure drives the
monsoon from the ocean to the land to produce rainfall,
hence the positive correlation between rainfall and tem-
perature. This is especially significant because it repre-
sents a reversal of the existing system dominated by
MCS and allows areas around the inland basins and the
northeast axis to experience maximum rainfall. The sys-
tem is reversed again in September-October-November
(SON) and this appears to be responsible for the strongly
delineated bimodal rainfall peak received in the region.
The strong association of rainfall and temperature with
terrain in DJF, MAM and SON is expected to continue in
future scenario. However, the no correlation (r = 0.03, p
< 0.01) and weak positive (r = 0.35, p < 0.01) association
between rainfall and temperature respectively with ter-
rain in JJA suggest that the coupling between rainfall,
temperature and terrain will become weaker in future
scenario. This may have serious implications for rainfall
in the inland basins around the northeast axis which rely
on the reversal of the system in JJA.
3.2. Analysis of the Controlling Systems
Eighteen (18) variables (15 for future climate) were gen-
erated, integrated and analyzed. The target is to identify
the combination of factors (i.e. factors coupled into sys-
tems) that have impacts on the local climate. Tables 3
and 4 show the rotated (varimax with Kaiser Normaliza-
tion) results of component matrix generated through cor-
relation matrix for the present and future climates respec-
tively.
Six principal components explain 65.6% of the total
variance between the extracted data. The first principal
component couples the climate-orographic complex and
explains 20% of the total variance. It accounts for the
coupled system between elevation, temperature and rain-
fall. Elevation is inversely related to temperature and
directly related to rainfall. It also supports the assumption
that mesoscale processes which relies on orographic
forces controls the local climate. The second, third and
fourth principal components show inter-correlations be-
tween the same set of variables i.e. rainfall, NDVI and
forested areas respectively. Principal components five and
six, though explain only 7% and 6% of the variance re-
spectively, combine factors which include aspect, forested
area, slope and soil potential for agriculture which are
important for the ecological systems and use of the land.
For the future climate, 6 principal components ac-
counted for 69% of the total variance. The coupled cli-
mate-orographic complex still remains the controlling
system and accounts for about 24% of the total variance.
The second principal component establishes in-
ter-relationship between the forested areas in two differ-
ent periods, and the third principal component establishes
the direct positive feedback between rainfall and pro-
tected areas.
3.3. Spatial Pattern of the Controlling Systems
The dominance of ‘climate-terrain’ complex on the local
climate system is unassailable in both present and pro-
jected future climates. In both cases, elevation exerts
positive influence on rainfall and negative influence on
temperature. This pattern predominates from the south-
east to northwest corridor and it is more pronounced in
areas south of the city of Ilorin and around ‘Oke-ogun’
areas. The seasonal analyses suggest that this pattern
predominates for present and future climates in DJF
(Figure 3), MAM (Figure 4), SON (Figure 5) and for
the annual average (Figure 7). The system is reversed in
the monsoon season of JJA (Figure 6) when the inland
basins across the Niger and northeast corridor experi-

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 161
Table 3. Extracted principal components for present climate.
Component
Variables 1 2 3 4 5 6
Aspect 0.129 −0.116 0.220 −0.138 0.621 −0.287
Slope −0.075 0.156 0.236 0.274 0.014 −0.534
Elevation −0.818 −0.018 0.292 0.063 0.059 0.060
Population density −0.168 0.234 −0.062 0.289 −0.062 0.312
Soil potential for agric 0.099 0.081 0.320 0.130 0.119 0.663
Distance to water −0.076 −0.023 0.111 0.534 −0.017 0.154
Protected areas 0.175 0.215 −0.292 0.503 −0.135 −0.001
NDVI for 1986 −0.210 −0.001 0.770 0.156 −0.059 −0.026
NDVI for 2006 −0.096 0.156 0.756 −0.195 0.018 0.041
Average Tmax for 1986 0.958 0.033 −0.045 0.050 0.028 0.057
Average Tmax for 2006 0.961 −0.037 −0.049 0.087 −0.007 0.054
Average rainfall for 1986 0.125 0.931 0.008 −0.005 −0.024 −0.019
Average rainfall for 2006 −0.650 0.690 0.069 −0.045 0.056 −0.013
Disturbance index for 1986 −0.162 −0.063 −0.030 0.097 0.760 0.292
Disturbance index for 2006 0.055 −0.200 0.126 0.642 0.215 −0.117
Forested areas in 1986 0.001 −0.090 0.465 −0.176 −0.660 −0.019
Forested areas in 2006 −0.121 −0.058 0.393 −0.681 −0.070 0.136
Long-term mean rainfall −0.048 0.915 0.097 0.007 −0.080 0.035
Table 4. Extracted principal components for future climate (2046-2065).
Component
Variables 1 2 3 4 5 6
Aspect −0.035 −0.094 0.431 0.383 0.052 0.387
Slope 0.133 −0.150 −0.199 −0.208 −0.031 0.756
Elevation 0.823 −0.157 0.192 −0.197 −0.120 0.007
Population Density 0.151 −0.285 −0.286 −0.049 −0.197 −0.446
Soil potential for agriculture −0.005 −0.085 0.010 0.172 −0.848 0.022
Distance to water −0.029 −0.387 −0.016 −0.376 −0.317 −0.008
Protected area −0.241 −0.406 −0.504 −0.085 0.108 −0.039
Disturbance index for 1986 0.100 −0.535 0.438 0.421 −0.152 −0.077
Disturbance index for 2006 −0.182 −0.523 0.217 −0.372 −0.110 0.173
Forest area in 1986 0.112 0.640 −0.086 −0.482 −0.267 0.058
Forest area in 2006 0.295 0.654 0.288 0.157 −0.243 0.005
Long term average rainfall 0.746 −0.025 −0.518 0.294 −0.035 0.108
Monthly average rainfall 0.746 −0.026 −0.518 0.294 −0.034 0.107
Long term average Tmax −0.867 0.114 −0.292 0.207 −0.169 0.093
Mean monthly Tmax −0.867 0.104 −0.283 0.209 −0.172 0.096
Figure 3. DJF: Elevation varies directly with Rainfall and Inversely with Tmax in present (Left) and future (Right) climate.

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 162
Figure 4. MAM: Rainfall and Tmax vary inversely with Elevation for present climate (including NDVI) (Left) and in future
climate (Right).
Figure 5. JJA: Rainfall and Tmax varies inversely with Elevation in present climate (Left), and future climate (Right)—when
rainfall is no longer significant in the system.
Figure 6. SON: Elevation Varies inversely with Tmax only in present climate (Left), and also directly with rain in future cli-
mate (Right).

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 163
Figure7. The annual average: Rain and Tmax sensitivity to terrain in Present climate (−Elevation, −rain, +Tmax) (Left), and
future climate (+Elevation, +rain, −Tmax) (Right).
ence higher rainfall and cooler temperature. Onset of
rains in the southeast to northwest corridor is around
April and most of the early rains are from mesoscale
processes, thus giving the area a double peak rainfall in
June and September. Incidentally, the agricultural land-
use around the southeast to northwest corridor is domi-
nated by rainfed small-holder root, tuber and cereal cul-
tivation which are well suited to the optimum rainfall and
lower temperature that prevail in this axis. On the other
hand, onset of rains in the inland basins across the Niger
is around May which coincides with the approach of the
WAM. Peak rainfall is received in August, the same time
when ‘the little-dry season’ pervades the southeast to
northwest corridor. These feedbacks also contrast the
general notion of a regular rainfall gradient that de-
creases with latitude in the Nigerian savannah.
This spatial pattern is projected to continue in future
climate but with diminishing influence. While the system
is projected to become pronounced in the highland areas
located at the edge of the rainforest zone in the southeast
axis, its influence around the northwest corridor espe-
cially in ‘Okeogun’ areas will diminish. This may have
severe implication for large rural population that depends
on the system for livelihood.
The expected upturn of the system in JJA will also
become severely weakened in future scenario (Figure 5)
because rainfall will no longer be significant in the sys-
tem. A cool temperature without significant rainfall is
expected to pervade the inland basins across the Niger.
This will also pose serious implications for rural liveli-
hoods in areas around the inland basins in the northeast
axis that rely on the up-turn of the system in JJA to opti-
mize their peasant agriculture.
The pattern of influence of terrain on local climate and
the resulting eco-climatic complexes was compared with
the present pattern of distribution of rural communities
and the drainage pattern. The distribution of rural settle-
ments across space is clustered (average nearest neigh-
bour index: = 0.58 and Z score = −39.21 Std (p < 0.01))
around the areas where the terrain positively influences
the climate for most seasons. This suggests a strong
feedback between the rural livelihood systems and the
local climate system. It also suggests that the ‘cli-
mate-positives’ of the southeast to northwest corridor has
long been recognized by local communities as eco-
climatic asset on which their livelihood depends. It also
underscores the importance of incorporating indigenous
knowledge into climate change mitigation and adaptation
planning. The integration of the local climate system
maps with the drainage pattern also suggests that this
eco-climatic asset is the nerve center for the drainage
systems of western Nigeria. It is the headwaters for ma-
jor rivers catchments including Okpara, Oyan, Ogun,
Oba, Oshun, Asa and Ero Rivers that drain the western
Nigeria. This suggests that the local climate system pro-
duces distinct space-specific ecological and natural re-
source systems which directly and indirectly support
livelihoods and other provisioning services for very large
population in the savannah.
4. Discussions
Climate change mitigation options may range from local,
regional to global, but adaptation is highly localized,
place-specific and sometimes contextual. Rural liveli-
hoods are also localized and rely mainly on local natural
resource systems created by local climate-positives.
Large population of peasant farmers and pastoralists in
dry and semi-dry environments depend on the interaction
between climate and natural resources for survival. Cli-
mate sensitive but well managed natural resource sys-

Local Climate Forcing and Eco-Climatic Complexes in the Wooded Savannah of Western Nigeria 164
tems support sustainable livelihoods and also enhance
long-term climate regulation services. If these resource
systems are poorly managed, they tend to exacerbate
climate change impacts. The pattern created by the inter-
action between terrain, land cover and the local climate
varies spatio-temporally. In the present climate, the cli-
mate-positive produces eco-climatic complex which
stretches from southeast to northwest corridor and par-
ticularly pronounced around the ‘Oke-ogun’ in the
west-central area of the region. The ‘Oke-ogun’ area is
locally acknowledged as the food basket of western Ni-
geria because its highly productive land supports produc-
tion of arable crops including yam, cassava, maize and
sorghum. This area has also witnessed significant ru-
ral-to-rural migration in recent past. Conflict over land
resources especially between sedentary cultivators and
pastoralists is becoming rampant. Because the cli-
mate-positive advantage is projected to weaken in future,
severe consequences on livelihoods and increase in re-
source conflict are also likely. A reversed rural-to-rural
migration may also upset existing socio-economic bal-
ance and increase land resource conflicts in newly suit-
able lands.
The people in the inland basins across the Niger River
around the northeast axis also depend on peasant rainfed
agriculture for livelihood. Farming is programmed to
take full advantage of the rainy months except in the
floodplains. The situation may become more critical in
future because the expected reversal which will likely
lead to the weakening of the MCS in the southeast to
northwest corridor is projected to become more unstable,
causing less rainfall in JJA in the northeast axis than
presently experience. This suggests these areas will be-
come drier in future. This will no doubt challenge exist-
ing traditional agricultural systems. Innovative adaptive
capacities and strategies are therefore required to avert
food insecurity.
The pattern exhibited by location of communities
suggests the livelihood systems are directly connected to
the eco-climatic corridors. In essence, the terrain sys-
tem modifies the local climate to produce favorable con-
dition that supports rainfed agriculture for most seasons
of the year. On the other hand, communities located in
the inland basin across the Niger River rely mainly on
the upturning of the system in JJA before planting their
crops. This could be responsible for why flood plain ag-
riculture and small scale irrigation farming is common in
the inland basins than the southeast to northwest corridor.
The eco-climatic complex as a natural resource system
area also creates a super watershed that drains the entire
western Nigeria. This suggests that ecological breakdown
resulting from climate change, which is suggested by the
future climate scenario, may spell disaster for human
livelihoods as well as water availability. The green and
blue water footprint in the region may in future become
the footprint of fierce resource competition.
Aggressive vegetation and albedo enhancement strate-
gies to restore moisture and energy balances, maintain
ecological function and enhance climate regulation ser-
vices may be necessary to guarantee the local climate
system. The eco-climatic resources together with the
river catchments constitute important climate sensitive
natural resource system that needs to be protected. This
makes it attractive to the reduced emission from defores-
tation and land degradation (REDD) and clean develop-
ment mechanisms (CDM) initiatives.
5. Conclusions
The study has attempted to provide insights into the rela-
tions between the local climate system and the control-
ling factors using the PCA. The principal factors show a
strong coupling between the climatic elements and the
terrain which suggests the dominance of the mesoscale
convective systems. The variation in the spatial pattern of
influence is consistent with the locational pattern of
communities and the livelihood systems which suggests
dependence on the eco-climatic resource complex. While
the observed pattern is projected to continue in future,
the spatial influence will severely diminish and some
areas especially around ‘Oke-ogun’ that presently sup-
port large population and viable rural livelihoods may be
negatively affected. The areas of strong positive cli-
mate-terrain feedback corridor also correspond to a super
catchment for the major drainages of western Nigeria.
We conclude that these eco-climatic complexes consti-
tute an important natural resource system for climate
change mitigation and adaptation in the wooded savan-
nah of western Nigeria.
6. Acknowledgments
This research was carried out under the African Climate
Change Fellowship Programme (ACCFP) postdoctoral
fellowship awarded to MF. The ACCFP is supported by
a grant from the Climate Change Adaptation in Africa
(CCAA) jointly funded by IDRC and DFID. The Interna-
tional START Secretariat is the implementing agency in
collaboration with the Institute of Resource Assessment
(IRA) of the University of Dar es Salaam and the African
Academic of Sciences (AAS).
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