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Hydrological Sciences Journal
ISSN: 0262-6667 (Print) 2150-3435 (Online) Journal homepage: http://www.tandfonline.com/loi/thsj20
Evaluation of recent hydro-climatic changes in
four tributaries of the Niger River Basin (West
Africa)
Djigbo Félicien Badou, Evison Kapangaziwiri, Bernd Diekkrüger, Jean
Hounkpè & Abel Afouda
To cite this article: Djigbo Félicien Badou, Evison Kapangaziwiri, Bernd Diekkrüger, Jean
Hounkpè & Abel Afouda (2017) Evaluation of recent hydro-climatic changes in four tributaries
of the Niger River Basin (West Africa), Hydrological Sciences Journal, 62:5, 715-728, DOI:
10.1080/02626667.2016.1250898
To link to this article: http://dx.doi.org/10.1080/02626667.2016.1250898
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Evaluation of recent hydro-climatic changes in four tributaries of the Niger River
Basin (West Africa)
Djigbo Félicien Badou
a
, Evison Kapangaziwiri
b
, Bernd Diekkrüger
c
, Jean Hounkpè
a
and Abel Afouda
a
a
Graduate Research Programme on Climate Change and Water Resources (GRP CCWR), West African Science Service Centre on Climate
Change and Adapted Land Use (WASCAL), University of Abomey-Calavi, Cotonou, Benin;
b
Hydrosciences Research Group, CSIR Natural
Resources and the Environment, Pretoria, South Africa;
c
Department of Geography, University of Bonn, Bonn, Germany
ABSTRACT
West Africa experienced severe drought during the 1970s and 1980s, posing a threat to water
resources. A wetter climate more recently suggests recovery from the drought. The Mann-Kendall
trend and Theil-Sen’s slope estimator were applied to detect probable trends in weather ele-
ments in four sub-basins of the Niger River Basin between 1970 and 2010. The cross-entropy
method was used to detect breakpoints in rainfall and runoff, Spearman’s rank test for correlation
between the two, and cross-correlation analysis for possible lags. Results showed an overall
increase in rainfall and runoff and a decrease in sunshine duration. Spearman’s coefficients
suggest significant (5%) moderate to strong rainfall–runoff correlation for three sub-basins. A
significant lower runoff was observed around 1979, with a rainfall break around 1992, indicating
possible cessation of the drought. Temperatures increased significantly, at 0.02–0.05°C year
-1
,
with a negative wind speed trend for most stations. Half of the stations exhibited an increase in
potential evapotranspiration.
ARTICLE HISTORY
Received 18 June 2015
Accepted 2 September 2016
EDITOR
M.C. Acreman
ASSOCIATE EDITOR
Not assigned
KEYWORDS
Climate variability; climate
trends; breakpoint analysis;
cross-entropy method; Benin
1 Introduction
Scientists are unanimous on the fact that, during the
early 1970s, a break occurred in the hydro-climate of
West Africa and this was followed by the great drought
and famine of the 1980s (e.g. Nicholson 2001,L’Hôte
et al. 2002, Peugeot et al. 2003, Séguis et al. 2004, Mahé
et al. 2005, Goula et al. 2007, Omotosho 2008, Lebel
et al. 2009). The first works to highlight the great
drought were conducted in the framework of the
HAPEX-Sahel project (e.g. Le Barbé and Lebel 1997,
Lebel et al. 1997) . The AMMA-CATCH project was set
up to document the great drought and its impacts on
West African populations (Lebel et al. 2009). It was
reported that the mean annual discharge of several
Sahelian rivers decreased drastically as a consequence
of the rainfall decrease (Andersen et al. 2005, Mahé
et al. 2005). This decrease in annual discharge was
coupled with an increase in the groundwater table in
certain aquifers of the Sahel, leading to the “Sahelian
Paradox”(Descroix et al. 2009). The reduction in land
cover caused an increase in the runoff coefficient,
which in turn favoured the filling of ponds and there-
fore groundwater recharge.
While certain authors insist that the great drought
still prevails, others think that it has ended. Omotosho
(2008) reported a shift to wetter conditions at the
station of Kano (Sahelian zone of Nigeria), with the
period 1996–2000 being the wettest 5-year period since
1931. L’Hôte et al. (2003,2002), however, claimed that
the drought of the 1980s continued and Kasei et al.
(2010) reported that moderate to extreme droughts
dominated the climate of the Volta River Basin during
the period 1961–2005. In an early study in the same
basin (i.e. Volta River Basin), Oguntunde et al. (2006)
reported that rainfall decreased at a rate of 6 mm year
-1
during the 1970–2002 sub-period against an increase of
49 mm year
-1
during the sub-period 1901–1969.
Paturel et al.(1998) pointed out the succession of wet
and dry decades during both halves of the twentieth
century along with a heterogeneous spatial manifesta-
tion of the drought within 16 non-Sahelian West and
Central African countries. For Ali and Lebel (2009), the
years 1990–2006 had different characteristics in com-
parison with the four previous decades and the great
drought of the 1970s and 1980s. Ali and Lebel (2009)
found that wet years were dominant in the eastern
Sahel, whereas the opposite was true in the western
Sahel. In another report, Lebel and Ali (2009) specified
that recovery from the drought was real in the eastern
Sahel, almost real in the central Sahel, but not yet
CONTACT Djigbo Félicien Badou fdbadou@gmail.com
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES, 2017
VOL. 62, NO. 5, 715–728
http://dx.doi.org/10.1080/02626667.2016.1250898
© 2016 IAHS
actual in the western Sahel. An investigation based on a
total of 54 stations located between latitudes 4°N and
17°N and longitudes 3°W and 16°W revealed that the
drought had ended for only six stations (Ardoin et al.
2003). Ozer et al. (2003) noticed that for some stations
located in Chad, Mali, Niger and Senegal there was an
average lag of 12 years between the “beginning of the
drought”and its “statistical identification”. Ozer et al.
(2003) thus opposed the conclusion of L’Hôte et al.
(2003,2002) and suggested that data for approximately
10 years (2001–2010) were necessary to confirm or
reject the shift of the climate to wetter conditions.
Studies using recent data on the “re-greening”of the
Sahel (Dardel et al.2014a,2014b) and on recent trends
in heavy rainfall in the Central Sahel (Panthou et al.
2014) seem to confirm the hypothesis of Ozer et al.
(2003).
In this debate, certain zones get little attention, as is the
casefortheupperRepublicofBenin,wherethestudyarea
for this paper is located. The research area has been the
focus of very few studies (Vissin 2007). Amogu et al. (2010)
studied 29 West African catchments including the current
research area. However, the rainfall and runoff time series
considered were limited to the period 1950–2000, so did
not include series for the last decade (2001–2010). A recent
studybyOyerindeetal.(2014) showed good agreement
between the hydro-climatic change for the period
1950–2010 and indigenous perceptions. Nevertheless, the
study did not thoroughly analyse recent patterns in rainfall
and runoff, and focused mainly on the municipality of
Malanville and Lake Kainji, both located in the northern
part of the study area.
TherecommendationofOzeretal.(2003)––that data
for 10 years (2001–2010) were necessary to verify the end
of the great drought––is relevant for two reasons. The first
rationale, as reported by a number of studies, is that West
Africa is a region of high spatial and temporal rainfall
variability (e.g. Katz and Glantz 1986,Adejuwonetal.
1990,Nicholson2001,Lawin2007). The second reason is
that land-use change hampers the detectability of the end
of the great drought. This is important as land-use change
is known to act together with climate change to intensify
the hydrological cycle (Giertz et al. 2005,Huntington2006,
Descroix et al. 2009). In fact, as a result of population
growth, human pressure on land cover in West Africa
has led to significant land-use change (Vissin 2007,
Amogu et al. 2010). The demand for agricultural “cotton-
land”in northern Benin, for example, has increased from
49 813 ha in 1990 to 205 675 ha in 2013, implying an
annual demand of 6494 ha (CeRPA Borgou-Alibori 2014).
Inspired by the recommendations of Ozer et al.
(2003), the key objective of this study is to investigate
the post-1970 hydro-climatic change in four non-
Sahelian tributaries of the Niger River located in
Benin. In order to have a comprehensive understand-
ing of the environmental change, the analyses are not
based solely on rainfall and runoff time series, but also
include temperature, sunshine duration, potential eva-
potranspiration and near-surface wind speed.
2 Materials and methods
2.1 Study area
The study area is made up of four tributaries of the Niger
River Basin located in the Sudanian zone of West Africa.
These are Mekrou (10 552 km
2
), Alibori (13 684 km
2
), Sota
(13 449 km
2
) and Kompa-Gorou (2041 km
2
). Ninety-five
percent of the area of these basins is located in the Republic
of Benin and situated between 1°50ʹ–3°75ʹWlongitudeand
10°0ʹ–12°30ʹNlatitude(seeFig. 1). With a unimodal rain-
fall regime, the mean annual rainfall of the area for the
period 1970–2010 is about 936 mm. The mean minimum
and maximum temperatures are 21.5 and 34.6°C, respec-
tively. The research area is home to more than 1.5 million
people (INSAE 2015).Itisthelargestzoneforcottonand
vegetable production, as well as cattle breeding, in Benin.
In addition, it contains the W- Park, which is one of the
most important West African wildlife parks.
2.2 Data sources
As shown in Table 1, data were collected from various
sources. Apart from potential evapotranspiration,
which was computed using the Penman-Monteith for-
mula (Monteith 1965), the other data are historical
observed measurements.
2.3 Data quality control
Data quality control was conducted for all variables
with an emphasis on the rainfall and runoff time series.
For each station, years with 5–10% of missing data
within the rainy season (July–September) and those
with more than 10% of records missing, regardless of
the season, were excluded (Table 2).
For runoff, hydrographs were built from the
observed data and periods of non-negligible flow deter-
mined. Mean runoff of a given month is arbitrarily
deemed negligible if it is less than 25% of the mean
monthly discharge of the entire period. Records with
more than one month of missing data within the per-
iod of non-negligible flow were excluded. Thus, the
range of “good”runoff data was not homogeneous
across stations (see Table 2) and the gaps were mainly
noticeable during the period 1993–2002.This is due to
716 D. F. BADOU ET AL.
delays between the breakdown of gauging instruments
and their repair or replacement (DG-Eau 2008).
2.4 Data analysis
2.4.1 Post-1970 rainfall trend
In order to capture the recent trend in rainfall, a 5-year
moving average of mean annual rainfall was plotted. A
5-year moving average was preferred to a 1-year
average because of the high inter-annual rainfall varia-
bility in the region.
In addition, two common non-parametric trend
detection tests were applied. These are the Mann-
Kendall test (Mann 1945, Kendall 1975) and the
Theil-Sen slope estimator (Theil 1950, Sen 1968). The
two tests have been described in detail by several
authors (e.g. Kahya and Kalaycı2004, Dinpashoh
et al. 2011, Tabari et al. 2011) and are often used in
studies investigating trends in hydro-climatic time
Figure 1. (a) Location of the study area in West Africa within the Niger River Basin; (b) DEM and (c) overview over the four
tributaries.
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES 717
series (e.g. Dinpashoh et al. 2011, Oguntunde et al.
2011,2012, Tabari et al. 2011, Hounkpè et al. 2015).
We used the R-package Kendall version 2.2 (McLeod
2014) to implement the Mann-Kendall test for our
dataset, while a web-based application developed by
Vannest et al. (2011) was used in the case of the
Theil-Sen slope estimator.
2.4.2 Breakpoint analysis of rainfall and runoff
The breakpoint analysis pursues two goals: to iden-
tify the number of breaks within a dataset, and then
to determine the location of the detected breaks.
Breakpointdetectioninhydro-climatologyhelps
identify regime shifts (i.e. a shift from a dry period
to a humid one and vice versa). While there are
different methods to detect breakpoints in hydro-
climatic data, in this study the cross-entropy
method (CE) (Priyadarshana and Sofronov 2015)
embeddedintheR-softwarewasselected.TheCE
is based on a stochastic optimization technique and
was originally developed to estimate the number
and the position of breakpoints in continuous bio-
logical data. Priyadarshana and Sofronov (2015)
define it as an “exact search method”capable of
identifying no breakpoint or the maximum number
of breakpoints specified by the user. Details on the
assumptions, the algorithm, and the strengths and
weaknesses of the method are fully discussed in
Priyadarshana and Sofronov (2015), who reported
that, except for a processing time, CE either
Table 1. Summary of the data collected for the study. DMN:
Direction Météorologique Nationale; DGEau: Direction Générale
de l’Eau of Benin.
Data
Number of
stations and
period
covered Data type Sources
Climate 27 stations Rainfall DMN Benin,
DMN Burkina
Faso and
DMN Niger
1970–2010
a
6 stations Temperature, sunshine
duration, potential
evapotranspiration
and wind speed
1970–2010
b
Hydrology 5 stations Discharge DGEau
1970–2010
a
One rainfall station has a data range of 1976–2010 and seven have a range
of 1981–2010.
b
One synoptic station has a data range of 1981–2010.
Table 2. Quality check of rainfall and runoff data. + and * indicate that the period considered is
1976–2010 or 1981–2010, respectively; otherwise, the period is assumed to be 1970–2010.
Station Longitude (°) Latitude (°) No. of years of missing data Missing data (%)
Rainfall
Alfakoara 3.07 11.45 9 22
Banikoara 2.43 11.30 3 7
Bembereke 2.67 10.20 2 5
Birni 1.50 10.00 12 29
Boukoumbe 1.10 10.17 20 49
Diapaga* 1.78 12.07 2 7
Djougou 1.67 9.70 12 29
Fada
N’gourma*
0.35 12.07 0 0
Gaya 3.45 11.88 0 0
Ina 2.73 9.97 13 32
Kalale 3.38 10.30 8 20
Kandi 2.93 11.13 0 0
Karimama+ 3.18 12.07 16 39
Kerou 2.10 10.83 24 59
Kouande 1.68 10.33 7 17
Mahadaga* 1.75 11.70 1 3
Malanville 3.40 11.87 3 7
Namounou* 1.70 11.87 5 17
Natitingou 1.38 10.32 0 0
Niamey 2.17 13.48 0 0
Nikki 3.20 9.93 13 32
Ouna* 3.15 12.17 5 17
Parakou 2.60 9.35 0 0
Segbana 3.70 10.93 24 59
Tamou* 2.17 12.75 11 37
Tanguieta 1.27 10.62 5 12
Tapoa* 2.40 12.47 11 37
Runoff
Couberi 3.33 11.74 10 24
Gbasse 3.25 10.98 20 49
Yankin 2.66 11.25 13 32
Kompongou 2.20 11.40 20 49
Malanville 3.40 11.88 13 32
718 D. F. BADOU ET AL.
performs similarly to or outperforms other compe-
titive methods in determining the location of
breakpoints.
In a previous study (not shown here) CE was
compared to the Pettitt test (Pettitt 1979), the
Bayesian approach of Lee and Heghinian (1977)and
Hubert’s segmentation (Hubert et al. 1989)for17
rainfall stations located in and around the Benin
part of the Niger River Basin. The Pettitt and Lee
and Heghinian tests provided only one breakpoint
whatever the length and variability in the data.
However, Hubert’s segmentation yielded multiple
breaks, though within a time series even just one
extreme value could result as a break (Ardoin et al.
2003).TheCEprovidesmultiplebreakpointsand
offers the possibility to choose the number of breaks
desired. Also, CE replicated 78%, 78% and 100% of
the breaks detected by the Lee and Heghinian, Pettitt
and Hubert approaches, respectively. In contrast, the
likelihood of the Lee and Heghinian, Pettitt and
Hubert tests to replicate a break detected by the CE
was 52%, 16% and 28%, respectively. Thus, the CE
was the most robust of the tests considered and was
preferred for investigating the breakpoints in rainfall
and runoff time series.
Determination of the breakpoints in rainfall data was
first done for individual raingauges using the CE techni-
que. We then deduced the breakpoint of the entire study
area by applying the t-test at the 5% significance level. As
a result of the gap in the runoff record for the years
1993–2002, breakpoint detection in the runoff time series
was done in two stages. Firstly, the first and second break-
points of each hydrometric station were determined.
Then, the two corresponding breakpoints for the research
area were computed using the t-test at 5% significance
level. Note that, because of the gaps in the runoff time
series, a second break was necessary in order to increase
the chance of capturing the “true”year of break. Actually,
limiting the analysis to only one break could be mislead-
ing given the poor quality of the runoff time series.
2.4.3 Correlation of the rainfall–runoff relationship
The mean annual catchment precipitation was com-
puted using the Thiessen polygon method (Thiessen
1911). The rainfall–runoff correlation was investigated
by computing Spearman’s rank coefficients (Spearman
1904) for Kompongou, Yankin, Gbasse and Couberi.
The Spearman test measures the strength of the mono-
tonic relationship between paired variables and was
chosen because of its non-parametric character.
Malanville was not included because the Niger River
at this outlet covers 1 000 000 km
2
and access to rain-
fall data for this large area was problematic.
Furthermore, we investigated the lag between the
two variables in order to detect whether an increase
(decrease) in runoff is preceded by an increase
(decrease) in rainfall. To this end, we performed a
cross-correlation analysis. For a sample of Npairs of
two variables (x
t
,y
t
), the cross-correlation function
(ccf) is computed in two steps. Firstly, the sample
cross-covariance function (ccvf) was calculated, as
described in Chatfield (2004):
CxyðkÞ¼ 1
NX
N
t¼1k
ðxt
xÞðytþk
yÞ
k¼0;1; :::; ðN1Þ
(1a)
CxyðkÞ¼ 1
NX
N
t¼1k
ðxt
xÞðytþk
yÞ
k¼1;2; :::; ðN1Þ
(1b)
where Nis the length of the data,
xand
yare the means
of x
t
and y
t
, and kis the lag.
Secondly, the sample ccvf was scaled by the variances
of the two variables to obtain the sample ccf as given by:
rðkÞ¼ CxyðkÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Cxxð0ÞCyy ð0Þ
p(2)
where Cxxð0Þand Cyy ð0Þare the variances of x
t
and y
t
.
As recommended by Chatfield (2004), the data were
pre-whitened before the cross-correlation analysis
because the result of a cross-correlation analysis
might be biased if the individual time series are auto-
correlated. To avoid such uncertainty, data should be
pre-whitened before estimating their sample ccf. We
used the R-package time series analysis (Chan and
Ripley 2015) version 1.01 both for pre-whitening the
data and for computing sample ccf.
2.4.4 Other climate variables
The other variables investigated were temperature
(minimum, maximum and their difference), sunshine
duration, potential evapotranspiration and near-surface
wind speed. We also evaluated the long-term trends of
these variables by applying the Mann-Kendall test and
the Theil-Sen slope estimator method, as described in
Section 2.4.1. The gradient of each variable was taken
as equal to the slope given by the Theil-Sen test. Prior
to the application of the two statistical tests, for each
year, the absolute values of minimum and maximum
temperatures were extracted and the average values
computed. Likewise, annual totals were calculated for
sunshine duration and potential evapotranspiration,
whereas annual mean daily values were computed for
near-surface wind speed.
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES 719
3 Results and discussion
3.1 Post-1970 rainfall trend
As shown in Table 3, an increase in rainfall was
observed for 26 of the 27 raingauges, with
Boukoumbe the only station where a statistically non-
significant decreasing trend was noted. We believe this
might be due to the high percentage of missing data
(i.e. 49%, see Table 2) in the record. Figure 2 displays
the post-1970 rainfall trend of selected stations and a
mixed pattern is observed. For some gauges in the
south (e.g. Bembereke and Kouande), an increasing
trend is evident, whereas for certain gauges located in
the centre (Kandi and Banikoara), it is less evident. The
mixed pattern observed in the study area is in line with
the high spatial rainfall variability of the region (e.g.
Adejuwon et al. 1990, Omotosho 2008), Also from
Figure 2 and as reported in earlier studies (Omotosho
2008, Lebel and Ali 2009, Yabi and Afouda 2012), the
1990s decade corresponds to the wettest period since
1970 for almost all the stations of the Benin part of the
Niger River Basin.
3.2 Breakpoint analysis of rainfall and runoff
The results of breakpoint analyses at individual raingauges
are given in Table 3. For example, at Parakou (south),
Kandi (centre) and Niamey (north), the break years are
1988, 1998 and 1989, respectively, which gives two sub-
periods for each station: 1970–1987 and 1988–2010 in the
case of Parakou, 1970–1997 and 1998–2010 at Kandi, and
1970–1988 and 1989–2010 at Niamey.
Application of the t-test resulted, for the entire area,
in a break around 1992 ± 2.5 years. In the southern
part of the region (Bembereke, Birni, Boukoumbe, Ina,
Kalale, Kouande, Nikki, Parakou and Tanguieta, for
which the mean annual rainfall of 1970–2010 was
greater than 1050 mm), the break occurred around
1989. The shift in the central zone of the region
(Kerou, Segbana, Kandi, Banikoara and Alfakoara, for
which the mean annual rainfall of the period
1970–2010 is between 900 and 1050 mm) took place
around 1993. For the raingauges of Diapaga,
Namounou, Mahadaga, Ouna, Fada N’gourma,
Tamou, Tapoa and Niamey, located in the north of
the region and for which the mean annual rainfall of
Table 3. Mann-Kendall trend, Theil-Sen slope and year of break in rainfall data. Raingauges are
ordered from the south to the north of the study area. + means increasing trend, –signifies
decreasing trend.
Station Longitude (°) Latitude (°) Mann-Kendall trend Theil-Sen slope (mm year
-1
) Year of break
South
Parakou 2.6 9.35 + 3.49 1988
Djougou 1.67 9.7 + 6.32
c
2003
Nikki 3.2 9.93 + 4.16 1988
Ina 2.73 9.97 + 2.89 1988
Birni 1.5 10 + 7.47 1997
Boukoumbe 1.1 10.17 −−0.96 1991
Bembereke 2.67 10.2 + 3.37
c
1988
Kalale 3.38 10.3 + 1.59 1989
Natitingou 1.38 10.32 + 2.86 2003
Kouande 1.68 10.33 + 1.29 1991
Tanguieta 1.27 10.62 + 4.67
c
1991
Centre
Kerou 2.1 10.83 + 1.39 1979
Segbana 3.7 10.93 + 11.85 1996
Kandi 2.93 11.13 + 0.05 1998
Banikoara 2.43 11.3 + 0.52 1994
Alfakoara 3.07 11.45 + 1.35 1997
North
Mahadaga 1.75 11.7 + 3.86 1986
Malanville 3.4 11.87 + 2.47 2003
Namounou 1.7 11.87 + 6.86
c
1988
Gaya 3.45 11.88 + 1.57 2003
Karimama 3.18 12.07 + 2.28 No break
Diapaga 1.78 12.07 + 11.04
a
1994
Fada N’gourma 0.35 12.07 + 4.53 1991
Ouna 3.15 12.17 + 6.75
c
1989
Tapoa 2.4 12.47 + 9.52
b
1994
Tamou 2.17 12.75 + 4.94 1987
Niamey 2.17 13.48 + 1.95 1989
a
Trend is significant at α= 0.01
b
Trend is significant at α= 0.05
c
Trend is significant at α= 0.1
720 D. F. BADOU ET AL.
1970–2010 was less than 900 mm, 1989 was detected as
the breakpoint. The raingauges of Natitingou and
Djougou (south of the area) and Malanville and Gaya
(north of the area) showed breaks in 2002. For these
four raingauges the shift occurred 10 years after 1992
(i.e. average year of the break in the study area). Similar
results were found by Ozer et al. (2003) in the Sahel.
Ozer et al. (2003) noticed a lag of 12 years between the
“beginning of the drought”and its “statistical identifi-
cation”for some stations of the Sahel.
Table 4 displays the two breakpoints in the runoff
data. For the entire area, the application of the t-test
resulted in 1979 ± 6.9 and 1991 ± 5.4 years for Break 1
and Break 2, respectively. The year of Break 1 reflects
the period of significant low flow observed in most of
the West African rivers (Vissin 2007, Descroix et al.
2009, Mahé 2009, Amoussou et al. 2012) during the
drought of the 1980s. As for the second break (1991), it
almost perfectly matches the year of the break in rain-
fall data (1992). This finding suggests that a shift to
wetter conditions undoubtedly occurred in the research
area in the early 1990s.
On the basis of the year of break found for the
rainfall time series (i.e. 1992), we compared the hydro-
graph of the sub-period 1970–1992 to that of
1993–2010 (Fig. 3) to observe the change that occurred
in the runoff time series. We chose the year of break
detected in rainfall data as the “reference”because of
the better quality of the rainfall time series, making the
result from the rainfall series more reliable. From
Figure 3, it can be seen that the recovery is noticeable
for all outlets except at Malanville.
Unlike the other stations, at Malanville two peaks
are recorded. The first peak matches those of the other
stations and is due to the contribution of the closest
tributaries (i.e. Mekrou and Alibori, Fig. 1) to the flow
at Malanville. As explained by Le Barbé et al. (1993),
the second peak is the result of the water coming from
the farthest tributaries located in the western Sahel. At
Malanville, the shift to wetter conditions can be
observed for the June–October flows but not for the
November–March flows. It is interesting to note that
the June–October seasonal flow at Malanville corre-
sponds to the period of high discharge at the other
runoff stations (Gbasse, Couberi, Yankin and
Kompongou). However, for the November–March sea-
sonal flow, the limb of the hydrograph of 1970–1992 is
only slightly higher than that of 1993–2010. Hence,
Figure 3 gives us a clear but contrasting picture: the
recovery is observed for the flows of June–October (i.e.
for our research area) but the drought still prevails for
the flows of November–March (i.e. for the western
Sahel). These results signify that, until 2010, the
drought was still noticeable in the western Sahel. The
Figure 2. Post-1970 trends in rainfall data. The year of shift corresponds to the year of break as shown in Section 3.2. Bembereke,
Kalalé and Kouande are located in the south of the area, Kandi and Banikoara in the centre and Gaya in the north.
Table 4. Year of break in runoff time series. Years marked in
bold show the most important breaks, in the sense that, if one
wishes to consider a break, these years should be considered.
Station Longitude (°) Latitude (°) Year of break 1 Year of break 2
Kompongou 2.20 11.40 1976 1986
Yankin 2.66 11.25 1975 1994
Malanville 3.40 11.88 1982 1994
Gbasse 3.25 10.98 1988 1996
Couberi 3.33 11.74 1976 1988
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES 721
recovery observed in our research area is supported by
the work of Amoussou et al. (2012), who studied the
hydro-climatology of the Mono-Couffo region (south-
eastern Benin) for the period 1961–2000 and found
that the climate had shifted slightly to wetter condi-
tions since 1990. Another study (Hounkpè et al. 2016)
over a nearby catchment, the Ouémé River basin,
reported that the majority of the stations displayed a
statistically significant positive change over the period
1921–2012 for heavy rainfall, and we know that an
increase in the number of extreme rain events implies
an increase in total annual rainfall (Panthou et al. 2014,
Hounkpè et al. 2016). Likewise, Lebel and Ali (2009)
and Omotosho (2008) reported the recovery in the
eastern Sahel, based on a station at Kano in northern
Nigeria. Notwithstanding this, in a very recent study,
Descroix et al. (2015) found that the shift to normal
rainfall conditions is now real in Senegambia and in
the Middle Niger River Basin (both located in the
western Sahel), although it should be noted that the
dataset used in that study includes the period
2011–2013, unlike our case, where the time series end
in 2010. In addition, Descroix et al. (2015) found that
the shift to normal rainfall conditions occurred around
1999, instead of the early 1990s as observed in our
research area and in the eastern Sahel. Hence, the end
of the drought occurred in Senegambia and in the
Middle Niger River Basin nearly 7 years after its occur-
rence in our research area. This finding of Descroix
et al. (2015) supports the conclusion of Ozer et al.
(2003) that a 10-year period is necessary to verify the
end of the great drought for certain regions of the West
African subcontinent.
3.3 Correlation of the rainfall–runoff relationship
The correlation between two variables is either very
weak, weak, moderate, strong or very strong for a
Spearman rank coefficient, r
s
, in the ranges 0.00–0.19,
0.20–0.39, 0.40–0.59, 0.60–0.79 or 0.80–1.00, respec-
tively. As can be seen in Table 5, the Spearman rank
coefficient of the rainfall–runoff correlation was mod-
erate to strong for all catchments, with the exception of
Gbasse. These results suggest that large values of runoff
are associated with large values of rainfall at the outlets
of Kompongou, Yankin and Couberi. The poor quality
of runoff time series at Gbasse (see Table 2) probably
does not allow for proper capture of the rainfall–runoff
correlation.
The results of the cross-correlation analyses are
presented in Figure 4. Only the Couberi station
showed a statistically significant (5%) positive sample
ccf for a lag of –4. Gbasse, Yankin and Kompongou
Figure 3. Comparison of monthly runoff of the two sub-periods (1970–1992 and 1993–2010) based on the break in rainfall data of
1992. The range of “good”runoff data is not homogeneous from one station to the next.
Table 5. Spearman rank coefficients (r
s
)ofrain-
fall–runoff correlation and the corresponding
p-values.
Station r
s
p-value
Kompongou 0.57 1.85E-02
Yankin 0.66 1.66E-04
Couberi 0.66 7.96E-05
Gbasse 0.33 0.141
722 D. F. BADOU ET AL.
stations exhibited insignificant positive sample ccf at
lags of –3, –4 and +5, respectively. The positive
sample ccf obtained for all the stations signifies that
rainfall and runoff are positively correlated, and this
confirms the results of Spearman’s rank test. The lags
of –3 at Gbasse station and –4 at Couberi and Yankin
stations imply that an increase in runoff time series
occurs after a 3–4-year period of rainfall increase.
From a statistical viewpoint, the lag of +5 at
Kompongou station means that runoff leads rainfall,
which is physically untrue. As mentioned for the
Spearman rank test, the quality of the data might
justify the result obtained at Kompongou station.
The results for Couberi, Gbasse and Yankin, how-
ever, denote that 3–4 years are needed to fill natural
reservoirs before an increase in runoff can be
observed.
The quasi-concomitant increase of rainfall and
runoff observed here has also been noticed in a
nearby region, the Mono-Couffo River (Amoussou
et al. 2012). Now the question to consider is
whether the observed increase in runoff is due to
climatic or land-use change. However, this question
isbeyondthescopeofthepresentstudy.Thecon-
clusions from previous studies on the question (e.g.
Descroix et al. 2012,Mahéetal.2013, Panthou
et al. 2013,2014) can be summarized as follows: in
the context of reduction of soil retention capacity
(due to land degradation), an increase in runoff is
due to an increase in total annual rainfall, which is
itself due to an increase in the number of heavy rain
events. We also refer the reader to Aich et al.
(2015), who reported that rainfall and land use
contribute equally to floods in the Sirba catchment,
and Casse and Gosset (2015), who showed that
rainfall is the dominant driver of the recent floods
recorded in Niamey.
3.4 Other climate variables
3.4.1 Temperature
Statistically significant increases of absolute maxi-
mum temperature (T
max
), mean minimum tempera-
ture (T
min
*) and mean maximum temperature
(T
max
*) were recorded for all the investigated sta-
tions. Likewise, a significant increase of absolute
minimum temperature (T
min
) was observed for all
the stations with the exception of Natitingou, where
a non-significant decreasing trend was noted (see
Table 6). The increase in T
min
and T
max
,for
instance, reached 0.04–0.08 and 0.02–0.04°C year
-1
,
respectively. Columns 4 and 7 of Table 6 also depict
a decreasing trend in the difference between max-
imum and minimum temperatures (ΔT) for five of
the six stations for both absolute and mean tem-
peratures. This indicates that the minimum tem-
peratures are increasing faster than the maximum
temperatures (see also column 5 of Table 6). Hence,
Figure 4. Graphs of the cross-correlation function between rainfall and runoff for Kompongou, Yankin, Gbasse and Couberi stations.
Coef. denotes the cross-correlation function coefficient. The horizontal (blue) lines represent the lower and upper 95% confidence
level for the significance of the ccf.
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES 723
not only have the maximum temperatures increased,
but higher minimum temperatures were recorded
during the last four decades. Our results are in
agreement with previous studies, which investigated
trends in temperature time series. New et al. (2006)
evaluated the trends in daily climate extremes of 14
southern and western African countries, including
Nigeria, for the period 1961–2000. They found that
minimum and maximum temperatures exhibited
statistically significant warming patterns.
Furthermore, in a study focusing on the climate of
the tropical rainforest regions encompassing some
zones of the Republic of Benin, Malhi and Wright
(2004) noted that since the mid-1970s mean tem-
perature has increased at the rate of 0.26 ± 0.05°C
per decade. In addition, Sultan et al. (2015), cited
by Descroix et al. (2015), documented the warming
climate observed in several West African countries
(e.g. Burkina Faso, Niger, Senegal, etc.) since the
1950s. Tao et al. (2014) drew similar conclusion to
ours for the climate of the Poyang Lake Basin in
China. Our results are also supported by the con-
clusions of Working Group I of the
Intergovernmental Panel on Climate Change (IPCC
2013) that, since 1850, the last three decades have
been successively warmer.
3.4.2 Sunshine duration, wind speed and potential
evapotranspiration
As depicted in Table 7(columns 2 and 5), a significant
decreasing trend of annual sunshine hours (SD) was
observed. It was only at Parakou station (south of the
study area) that a non-significant decreasing trend was
recorded. Furthermore, we notice that the rate of
decrease of SD was not latitude dependent. Actually,
the highest gradient was recorded at Kandi station
located in the centre of the research area. Also, SD
decreased faster at Niamey station than at Natitingou,
although the latter is 3.2° (nearly 350 km) north of the
former (Table 7,Fig.1). In comparison with earlier
studies, Liao et al. (2007) and Stanhill and Cohen
(2005) noted a similar decrease of sunshine duration
in Tibet (China) for the period 1971–2005 and in the
USA for 1950–1987, respectively. More interestingly,
the decrease in sunshine duration in the research area
is concomitant with the increase in rainfall mentioned
in Sections 3.1 and 3.2. Such a negative correlation
(decrease in sunshine duration in a context of
increased rainfall) was also observed by Stanhill and
Cohen (2005) in the USA during the 1910s, 1950s and
1970s.
The results of trend detection in near-surface wind
speed are displayed in Table 7 (columns 3 and 6).
Wind speed exhibited a negative trend for the majority
of the stations (two-thirds of the stations depicted a
negative trend, while one-third indicated a positive
trend). The negative trend of wind speed during the
last decades seems to be a global phenomenon
(McVicar et al. 2012) and is even termed “stilling”
(Roderick et al. 2007). Numerous works have docu-
mented a decreasing wind speed trend across the five
continents (e.g. Xu et al. 2006, Roderick et al. 2007,
McVicar et al. 2008, Brazdil et al. 2009, Vautard et al.
2010, Wan et al. 2010, Wever 2012, Knorr 2013). In a
recent review of 148 studies, including 21 related to
Africa, McVicar et al. (2012) noted that, for West
Table 6. Mann-Kendall trend and Theil-Sen slope for absolute minimum (T
min
) and absolute maximum (T
max
) temperatures, their
difference (ΔT=T
max
–T
min
), mean minimum (T
min
*) and mean maximum (T
max
*) temperatures, and their difference (ΔṪ*=T
max
*–T
min
*)
during the period 1970–2010. + and –signify increasing trend and decreasing trend, respectively.
Mann-Kendall trend Theil-Sen slope (°C year
-1
)
Station Minimum Maximum ΔTMinimum Maximum ΔT
Absolute temperature
Parakou + + −0.06
a
0.03
a
−0.03
Natitingou −+ + 0.00 0.02
b
0.01
Kandi + + −0.05
a
0.02
a
−0.03
b
Gaya + + −0.05
a
0.04
a
−0.02
Fada N’gourma + + −0.08
a
0.03
b
−0.0
b
Niamey + + −0.04
a
0.02
c
−0.02
Mean temperature ΔṪ*ΔṪ*
Parakou + + −0.05
a
0.03
a
−0.02
b
Natitingou + + −0.03
a
0.02
a
−0.01
c
Kandi + + −0.04
a
0.02
b
−0.03
a
Gaya + + + 0.03
a
0.03
a
0.01
Fada N’gourma + + −0.05
a
0.02
b
−0.03
a
Niamey + + −0.03
a
0.02
a
−0.01
b
a
Trend is significant at α= 0.01
b
Trend is significant at α= 0.05
c
Trend is significant at α= 0.1
724 D. F. BADOU ET AL.
Africa as a whole, the decreasing wind speed trend is
predominant. Likewise, Michels et al. (1999) cited by
McVicar et al. 2012), O’Higgins (2007) and Oguntunde
et al. (2011) reported negative trends for the stations of
Sadoré (Niger), Manga (Ghana) and Ibadan (Nigeria),
respectively. The work of Ogolo (2011), however, con-
tradicts our results and the studies cited here; finding
an overall positive trend of wind speed for a sample of
21 sites located in Nigeria.
The results concerning potential evapotranspiration
(PET) are shown in columns 4 and 7 of Table 7.
Although, on average, PET decreased at a rate of
0.9 mm year
-1
, we did not observe a common trend.
While half of the stations displayed an increasing trend,
the other half showed a decreasing trend. The decrease
of PET on average recorded in the research is in agree-
ment with the results of Oguntunde et al. (2012) for the
station of Ibadan (Nigeria) and McVicar et al. (2012)at
the global scale. Nevertheless, Oguntunde et al. (2006)
noticed that potential evaporation was still increasing
from 1901 to 2002 over the Volta Basin.
In addition, it appears that stations displaying an
increasing trend were unevenly distributed across the
study area and that this applies also for stations with a
decreasing trend. For example, an increasing trend was
seen at Natitingou (south) and Gaya (north) stations,
while Parakou (upper south) and Niamey (upper
north) showed a decreasing trend. Wang et al. (2012)
also reported the absence of spatial coherence in the
trends in reference evapotranspiration for 80 stations
located in the Loess Plateau (China).
Previous studies investigating the causes of change
in PET with respect to climatic factors reported that
wind speed is the main driving factor for certain sta-
tions, whereas different variables (radiation, relative
humidity, etc.) are predominant for other stations in
the same research area. At Ibadan (southwestern
Nigeria), Oguntunde et al. (2012) reported that the
variability in pan evaporation is partly due to radiation,
wind speed and vapour pressure deficit. Within the
Yellow River Basin (China), Wang et al. (2012) showed
that wind speed is the principal cause of change in
reference ET in the west and north, whereas sunshine
duration and temperature are the dominant factors in
the middle–lower Yellow River Plain and the source
area of the Yellow River Basin, respectively. In the
Qinghai–Tibetan Plateau, Zhang et al. (2009) found
that the predominant cause of change in reference
evapotranspiration in the north is wind speed, but it
is radiation in the southeast. At the global scale, wind
speed has been found to be the dominant climatic
factor influencing PET (McVicar et al. 2012).
4 Conclusions
In this study, we have documented the recent hydro-
climatic change of four non-Sahelian tributaries of the
Niger River Basin. We found that runoff and rainfall
showed a post-1970 increase, but sunshine duration
exhibited a significant negative trend. Despite the
poor quality of runoff time series, the rainfall–runoff
correlation was statistically significant and moderate to
strong (0.57–0.66) for three of the four investigated
catchments, and the breaks in rainfall and runoff data
were nearly consistent. Significant lower runoff was
observed around 1979 followed by a regime shift
around 1991. The shift to wetter conditions, based on
rainfall time series, occurred around 1992. Thus, we
conclude that, in the study area, the great drought of
the 1970s and 1980s ended in 1992. A yearly increase in
temperature of 0.02–0.05°C was recorded over the last
four decades, and the minimum temperature increased
by nearly twice as much as the maximum temperature.
As indicated by the negative trend shown by most of
the stations, near-surface wind speed is “stilling”. Half
of the stations exhibited an increasing trend for poten-
tial evapotranspiration, while the remainder showed a
decreasing trend. Apart from rainfall, we did not detect
particular spatial patterns for the other climate vari-
ables investigated.
Table 7. Mann-Kendall trend and Theil-Sen slope for annual sunshine duration (SD), annual potential evapotranspiration (PET), and daily
wind speed (WS) for 1970–2010. + and –denote increasing trend and decreasing trend, respectively. Stations are ordered from the south
to the north of the research area.
Mann-Kendall trend Theil-Sen slope
Station SD WS PET SD (h year
-1
)WS(ms
-1
year
-1
) PET (mm year
-1
)
Parakou −− − −0.73 −0.02
a
−1.73
b
Natitingou −++ −2.95
b
0.01 0.96
Kandi −− − −9.49
a
−0.01
c
−1.74
Gaya −− +−3.92
b
−0.05
a
3.26
Fada N’gourma −− +−7.05
b
−0.01 0.65
Niamey −+−−3.64
c
1E-03 −7.00
a
Trend is significant at α= 0.01
b
Trend is significant at α= 0.05
c
Trend is significant at α= 0.1
HYDROLOGICAL SCIENCES JOURNAL –JOURNAL DES SCIENCES HYDROLOGIQUES 725
Knowledge of the shift in the climate to wetter
conditions is useful for stakeholders involved in agri-
culture and other water-related sectors. Indeed, more
water storage will be required to meet the current
growing water demand resulting from population
growth and global warming. Beyond the framework
of statistical hydrology, the update of the climatic
information presented here could also be useful in
hydrological modelling by assisting the choice of cali-
bration and validation periods.
Acknowledgements
The authors are grateful to the German Ministry of Education
and Research (BMBF), the West African Science Service
Centre on Climate Change and Adapted Land Use
(WASCAL), the Graduate Research Programme “Climate
Change and Water Resources”of the University of Abomey-
Calavi and the Council for Scientific and Industrial Research
(CSIR). The institutes that provided climate and runoff data
are also acknowledged: the Agence pour la Sécurité de la
Navigation Aérienne en Afrique et à Madagascar (ASECNA)
of Benin, Burkina Faso and Niger, the Centre Régional pour la
Promotion Agricole (CeRPA) and the Direction Générale de
l’Eau (DGEau). Finally, the authors are very thankful to the two
reviewers and the Associate Editor (Dr Julian Thompson),
whose comments helped to improve the original manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
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