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1. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
Analytical Study of Rainfall and Temperature Trend in
Catchment States and Stations of the Benin- Owena River
Basin, Nigeria
Obasi, R. A* Ikubuwaje C.O.
Department of Mineral Resources Engineering,
Federal Polytechnic, Ado-Ekiti, P.M.B 5351 Ekiti State, Nigeria.
Tel: 07032448351 Email: romanus914@yahoo.com
Abstract
The impart of climate change is felt worldwide, but the effects are more devastating in countries where
flooding or drought has occurred. In the Nigerian context, the impact of climate change is felt majorly in
terms of rainfall and temperature. It is on the basis of their variations that the analytical study of their trend
is carried out in some catchment States of Nigeria using the Benin- Owena River Basin as case study.
Climatic data of rainfall and temperature for 35years were collected and subjected to Cumulative
Summation (CU-SUM) and the rank-sum tests. The trend analysis shows that as temperature increases
there is a corresponding increase in rainfall. The trend also indicates that no significant departure of these
climatic parameters occurred. The least square regression (r2) and the trend as generated from the Microsoft
Excel computations show that the temperature variation ranges from 0.4% in Delta to 3.5% in Edo, an
indication that the temperature conditions in states understudy are not uniform even though the trend shows
an increase. The rainfall least square regression variation ranges between 0.2% in Zaria and 2.7% in Plateau
states, implying that the rainfall is varying in an upward trend.
Keywords: Analytical study, Climatic variation, Temperature and Rainfall.
1 INTRODUCTION
There is a global concern about climatic changes resulting from greenhouse gas emission from the
industrialized nations. Many scholars have engaged themselves in studying this climatic variability in order
to identify and quantify the extent and trend of the change. Climate variability is the variations of the normal
state and other statistics of the climate on all temporal and spatial scales beyond that of individual weather
events. Variability may result from natural internal processes within the climate system (internal variability)
or from anthropogenic external forces (external variability) (IPCC 2001, 2005). Parida, et a;l.2005 are of
the opinion that the global increase in temperature and changes of other climatic variables such as rainfall
and evaporation are as a result of greenhouse gas emission. Once existing or potential climate change
hazards have been identified, it must be demonstrated that these hazards pose a threat or risk to human
populations and the systems on which they depend. It is therefore necessary to establish coping ranges and
critical thresholds for proper adaptation interventions. This paper is aimed at determining the trend of this
climatic variation with a view to finding out if rainfall and temperature have been influenced by internal or
external factors. This is because precipitation is one of the key climatic variables that affect both the
spatial and temporal patterns on water availability (De Luis et al., 2000).
1.1. Materials and Method
Environmental data of rainfall and temperature were collected from the Meteorological Survey Agency,
Lagos - Nigeria. The rainfall data were subjected to step changes analysis to know whether anthropogenic
or natural factors have brought in inconsistencies and non-homogeneity in the data. Individual rainfall data
from different stations of the Benin-Owena River Basin were also subjected to intervention analysis as well
as trend analysis. Rainfall data used covered an average of thirty five (35) years.
The rainfall data were analyzed using the Cumulative Summation (CU-SUM) and the rank-sum tests. These
tests were used to identify and ascertain if there had been any interventions. The rank-sum test is based on a
non-parametric procedure to overcome the problems of small sample sizes and distributional effects if any
(CRCCH, 2005; Yue et al., 2002). Rainfall and temperature analyses were followed, intervention analysis
9

2. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
(CUSUM and rank-sum techniques), establishment of the null and alternate hypotheses, computation of the
Regression Line over the data point to evaluate the results and the establishment of the trend from the fit of
model.
Intervention analysis was carried out using the Cumulative Summation (CUSUM) technique of Parida et
al., 2003 to determine inconsistencies in rainfall data collected from different rainfall stations. The results
of this analysis will indicate if anthropogenic activities have occurred due to observational errors in data
collection or equipment handling. Using the Parida (2003) equation,
yi=(Xi + Xi-1 + Xi -2 + … + Xn) – I ….₥ ……. . . . . . .. . . . (1)
where n = sample size; ₥ = average of the total series. yi, is plotted against i to generate the oscillatory
graph.
In step change analysis, the rank-sum test is a non-parametric test to find out the difference in median of
two subsets of data representing pre- and post-intervention periods for rainfall data. The standard steps in
computing the rank-sum test statistics as suggested by CRCCH (2005) are indicated below;
(i) Rank all the data, from K (smallest) to M (largest). In the case of ties (equal data values), the average of
ranks is used.
(ii) Compute a statistic 'Sm' as the sum of ranks of the observations in the smaller group (the number of
observations in the smaller group is denoted as k, and the number of observations in the larger group is
denoted as m), and
(iii) Compute the theoretical mean (µ) and standard deviation (σ) for the entire sample, as given by:
µ=k(N+1)/2 and σ = [km(N+1)/2]0.5
The test statistic Zrs is computed as:
Zrs= (Sm-0.5- µ)/σ If Sm > µ
=0 if Sm = µ
= (Sm + 0.5- µ)/σ If Sm < µ
The above step change formulae have been used to analyse the rainfall data for all the stations under study.
Using the Jos rainfall data as an example, the workings are shown as follows;
(i) Ranking the data from K to M
(a) from 2003- 2005 = Small group (K) =7481 and (b) from 1971-2002 = Large group (M) =80,829.
Computing the statistic 'Sm'
Sm = K + M ……………………………………………………………………………….(2)
Sm = 7481 + 80,829 = 88,310
Theoretical mean (µ) and the Standard deviation (σ)
(ii) Computing for µ and σ , we have µ =k(N + 1) ……………………………(3)
2
µ = 7481 (420 +1) = 1574750.5
2
σ = (km(N + 1)0.5
12
…………………………………….(4)
σ =[7481(80,829)(420 + 1]0.5
12
σ= 145651.13
The test statistic Zrs is Computed as; Zrs = (Sm + 0.5 - µ) If Sm < µ ……….(5)
σ
Zrs = 88310 + 0.5 -1574750.5 = -10.21
145651.13
Zrs = -10.21
Since Z0.5 = 1.65 (from normal distribution table)
Accept Ho if Zrs < Z, If otherwise do not accept. Therefore, we have statistical reason to accept Ho.
10

3. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
This step change analyses workings are repeated for Benin, Warri, Lagos, Plateau, Kano, Borno, Niger,
Zaria and Ondo States. The results of the analyses from these towns are presented in Table 1. The null
hypothesis, Ho, presupposes that no change has occurred in the time series or that the two samples come
from the same population (i.e. have the same median). This condition is accepted when the computed Zrs is
less than the Z value derived from a normal distribution. Table at 5% significance level and as worked out
in equation, 5. The combined rainfall and temperature data were later subjected to spatial analyses using
Microsoft excel package in order to generate the curve, the trend patterns and the R-square values (charts
1-9) .
1.2 Results and discussion
In the intervention analysis, the computed CUSUM rainfall values (yi) at any time i for each of the rainfall
stations were obtained and plotted in Figs 1-9. Usually, when there is no intervention, the plot of rainfall
values will oscillate around the horizontal axis (Parida, 2003). Based on this, Figs 1, 3 and 7 of Maiduguri,
Ikeja and Benin respectively show oscillatory departures from the horizontal axis. In Fig 1, the departure
occurred between 1971 and 1973 and between 1979 and 1982. In Fig 3, it occurred between 1976 and 1978
while in Fig 7 (Benin) the departure was between 2003 and 2005. These departures suggest the possibility
of interventions due to the anthropogenic activities of rainfall data collectors. In some of the Figures, the
rainfall values either plot above or below the horizontal axis in most of the computed years. Importantly
some of the Figures show negative and positive slopes on the horizontal axis. Whitting, et al; 2003 suggest
that positive slopes on the horizontal axis indicate wet periods (i.e above average values of rainfall), while
negative slopes are indications of dry periods.
Table 1 shows the result of step change analysis for 9 States in Nigeria, indicating the test statists (Zrs), the
critical value a=0.05 representing Z and the remarks. Using null hypothesis, Ho and applying the results
from equations 2 - 5 of the step change analysis, the test statistics values (Zrs) obtained are less than the
critical values (Z). When this condition prevails (Zrs < Z), it means that Ho is accepted. This implies that
the rainfall data are collected from areas that fall under the same climatic influence. The null hypothesis, Ho
is accepted when the computed Zrs < Z obtained from a normal distribution table at 5% significant level.
This suggests also that no change has occurred in the time series and that the samples come from the same
population (Helse and Hirseh, 2002).
Tables 2 and 3 show the temperature and rainfall values for least square regression and the trend as
generated from the Microsoft Excel computations. The World Almanac and Book of facts (WABF) 1993
suggests that the least square regression calculates the best – fitting line for any observed data by
minimizing the sum of the squares of the vertical deviations from each data point to the line. The r2 values
(the square of the Correction Coefficient) indicate the degree of variation. The temperature variation ranges
from 0.1% in Zaria station to 3.5% in Edo station an indication that the temperature conditions in the
stations/states under study are not uniform even though the trend shows increase. (Table 2).
The percent rainfall variation ranges between 0.2% in Zaria station and 2.7% in Plateau station respectively
(Table 3).
1.3 Conclusion
The trend analysis of temperature and rainfall in nine (9) states of Nigeria for 35years show that as
temperature increases there is a corresponding increase in rainfall. The trend also indicates no significant
departure of the climatic parameters in the nine (9) states under study. The analyses of the meteorological
data of temperature and rainfall show no evidence of serious intervention although some insignificant
departures were observed in station areas of Ikeja, Benin and Maiduguri respectively. This suggests that
the rainfall data are collected from areas that fall under the same climatic influence, an indication that no
significant change has occurred in the time series.
References
CRCCH, ( 2005). Co-operative Research Centre for Catchment Hydrology. TREND User Guide, . pp.(29.)
De Luis, M., Ravenlos. J., Conzalez-Hidalgo, J.C.. Sa"nchez. J.R., Cortina, J,. (2000). Spatial analysis of
rainfall trends in the region of Valencia (east Spain). 2000. International Journal of Climatology 20 (12),
pp.(1451-1469).
IPCC, (2007).Climate Change. Physical science basis. In: Solomon, S., Qin, D.,Manning. M., Chen
Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L (Eds.), Contribution of Working Group I to the Fourth
11

4. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA.
Ndiritu. J.G., (2005), Long-term trends of heavy rainfall in South Africa. Regional hydrological Impacts
of Climatic Change- Hydroclimativ Variability. In: Proceedings of symposium SG
held during the seventh IAHS Scientific Assembly at Foz do iguacu, Brazil, April 2005. IAHS
Publ.296, (p. 178-183)
Parida, B.P., Moalafhi, D.B.. Kcnabatho. P.K..( 2003). Effect of urbanization on runoff coefficient: a case
of Notwane catchment in Botswana. In: Proceedings of the international Conference on Water and
Environment (WE-2003), Bhopal.Watershed Hydrology'. Allied Publishers Pvt. Ltd., (pp. 123-131).
Parida, B.P.. Moalafhi, D.R., Kenabatho. P.K.,( 2006). Forecasting runoff coefficients using ANN for water
resources management: the case of Netware catchment in Eastern Botswana. Physics and Chemistry of the
Earth 31.(pp. 928-934.)
Whiting, J.P., Lambert, M.F., Mercalfe, A.V., (2003). Modeling persistence in annual Australian point
rainfall. Hydrology and Earth System Sciences 7 (2), (pp. 197-211).
Yue, S., Pilon, P., Cavadias, G., 2002. Power of the Mann-Kendall and Spearman's rhotests detecting
monotonic trends in hydrological series. Journal of Hydrology 259, (pp 254-271).
Table 1. Results of step change analysis using rank sum method
STN Test Statistic Critical value at a = 0.05 (Z) Remarks
Zrs = Zrs < Z=Ho
Plateau -10.21 1.65 Accept
Delta -39.62 1.65 Accept
Edo -61.6520 1.65 Accept
Lagos -29.26 1.65 Accept
Kano -19.21 1.65 Accept
Borno -20.94 1.65 Accept
Niger -31.079 1.65 Accept
Ondo -107.6 1.65 Accept
Zaria -28.88 1.65 Accept
Table 2: Results of Temperature Least-Square Regression and Trend
STN Equation on Chart R- Squared value on Chart Correction Coefficient Variation Trend
(%)
Plateau 0.0125X 0.0138 0.1175 1.4 increase
Delta 0.0144X 0.004 0.0633 0.4 increase
Edo 0.0147X 0.035 0.1871 3.5 increase
Lagos 0.0142X 0.017 0.1304 1.7 increase
Ondo 0.0141x 0.034 0.1844 3.4 increase
Kano 0.0148x 0.022 0.1483 2.2 increase
Borno 0.0155x 0.0297 0.1723 3.0 increase
Niger 0.0151x 0.0068 0.0825 0.7 increase
Zaria 0.0142x 0.0152 0.1233 1.5 Increase
12

5. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
Table 3. Results of Rainfall Least-Square Regression and Trend
STN Equation on Chart R- Squared value Correction Variation Trend
Y= on Chart Coefficient (%)
Plateau 0.053x 0.027 0.1643 2.7 Increase
Delta 0.1127x 0.0063 0.0794 0.6 Increase
Edo 0.0911x 0.0068 0.0825 0.7 Increase
Lagos 0.0585x 0.0146 0.1208 1.5 Increase
Ondo 0.066x 0.0077 0.0878 0.7 Increase
Kano 0.0381x 0.0166 0.1288 1.7 Increase
Borno 0.0232x 0.0104 0.1020 1.0 Increase
Niger 0.05x 0.008 0.0894 0.8 Increase
Zaria 0.0427x 0.0015 0.0387 0.2 Increase
500
0
RAINFALL
CUSUM -500
VALUES
-1,000
-1,500
-2,000
1971 1975 1979 1983 1987 1991 1996 2000 2004
Years
Figure.1 CUSUM plot using observed annual rainfall for Maiduguri (1971-2005)
13

6. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
1,000
500
RAINFALL
CUSUM
0
VALUES
-500
-1,000
-1,500
1971 1975 1979 1983 1987 1991 1996 2000 2004
Years
Figure.2 CUSUM plot using observed annual rainfall for Minna (1971-2005)
1,000
0
RAINFALL
-1,000
CUSUM
VALUES
-2,000
-3,000
1971 1975 1981 1985 1989 1993 1998 2002
Years
Figure.3 CUSUM plot using observed annual rainfall for Ikeja (1971-2005)
14

7. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
1,500
1,000
500
RAINFALL
CUSUM
0
VALUES
-500
-1,000
1971 1975 1979 1983 1987 1991 1996 2000 2004
years
Figure.4 CUSUM plot using observed annual rainfall for Jos Rainfall (1971-2005)
0
-2,000
-4,000
-6,000
1971 1972 1974 1976 1977 1979 1981 1982 1984 1986 1987 1989 1991 1992 1994 1996 1997 1999 2001 2002 2004
Sigma level: 3
Figure.5 CUSUM plot using observed annual rainfall for Kano (1971-2005)
15

8. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
1,000
500
RAINFALL
0
CUSUM
VALUES -500
-1,000
-1,500
1971 1975 1979 1983 1987 1991 1996 2000 2004
YEAR
Figure.6 CUSUM plot using observed annual rainfall for Zaria (1971-2005)
3,000
2,000
1,000
RAIN FALL
CUSUM
0
VALUES
-1,000
-2,000
-3,000
1971 1975 1979 1983 1987 1991 1996 2000 2004
year
Figure.7 CUSUM plot using observed annual rainfall for Benin (1971-2005)
2,000
1,000
RAINFALL
0
CUSUM
VALUES
-1,000
-2,000
-3,000
1971 1975 1979 1983 1987 1991 1996 2000 2004
year
Figure.8 CUSUM plot using observed annual rainfall for Warri (1971-2005)
__ __
16

9. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
1,000
RAINFALL
0
CUSUM
VALUES -1,000
-2,000
-3,000
1971 1975 1979 1983 1987 Years
1991 1996 2000 2004
Figure.9 CUSUM plot using observed annual rainfall for Ondo (1971-2005)
__
200.00 y = 0.0125x 30.0
R² = 0.0138
150.00 RNFL
20.0
100.00 TEMP
10.0 Linear (RNFL)
50.00 y = 0.053x
R² = -0.027 Linear (TEMP)
0.00 0.0
1960 1970 1980 1990 2000 2010
Chart 1; Annual mean Rainfall and Temperature for Plateau State years 1971-2005
300 50
45 RNFL
200 y = 0.1127x 40
R² = 0.0063 TEMP
y = 0.0144x 35
100 Linear (RNFL)
R² = -0.004 30
Linear (TEMP)
0 25
1960 1970 1980 1990 2000 2010
Chart 2; Annual mean Rainfall and Temperature for Delta State years 1971-2005
17

10. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
300.00 50.0
y = 0.0911x
250.00
R² = 0.0068
200.00 40.0 RNFL
150.00 TEMP
100.00 30.0 Linear (RNFL)
y = 0.0147x
50.00 Linear (TEMP)
R² = 0.035
0.00 20.0
1960 1970 1980 1990 2000 2010
Chart 3; Annual mean Rainfall and Temperature for Edo State years 1971-2005
180.00 y = 0.0585x 50.0
R² = 0.0146
130.00 40.0 RNFL
TEMP
80.00 30.0
y = 0.0142x Linear (RNFL)
30.00 R² = 0.017 20.0 Linear (TEMP)
-20.001960 1970 1980 1990 2000 201010.0
Chart 4; Lagos State Annual mean Rainfall and Temperature for years 1971-2005
150.00
44.0 RNFL
y = 0.0381x
100.00 R² = 0.0166 TEMP
34.0
y = 0.0148x
50.00 Linear (RNFL)
R² = -0.022
0.00 24.0 Linear (TEMP)
1960 1970 1980 1990 2000 2010
Chart 5; Annual mean Rainfall and Temperature for Kano State years 1971-2005
18

11. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
100.00 50.0
80.00
y = 0.0232x 40.0 RNFL
60.00 R² = 0.0104
TEMP
40.00 Linear (RNFL)
30.0
20.00 y = 0.0155x Linear (TEMP)
R² = 0.0297
0.00 20.0
1960 1970 1980 1990 2000 2010
Chart 6; Annual mean Rainfall and Temperature for Borno State years 1971-2005
140.00 50.0
y = 0.05x 45.0
120.00
R² = -0.008
100.00 40.0
35.0 RNFL
80.00
30.0 TEMP
60.00
y = 0.0151x 25.0 Linear (RNFL)
40.00 20.0
R² = 0.0068 Linear (TEMP)
20.00 15.0
0.00 10.0
1960 1970 1980 1990 2000 2010
Chart 7; Annual mean Rainfall and Temperature for Niger State years 1971-2005
160.00 50.0
y = 0.0427x 45.0
110.00 R² = 0.0015
40.0 RNFL
35.0 TEMP
60.00
30.0 Linear (RNFL)
10.00 25.0 Linear (TEMP)
y = 0.0142x
20.0
1960 1970 1980 1990 R² = 0.0152
2000 2010
-40.00 15.0
Chart 8: Annual mean Rainfall and Temperature for Zaria State years 1971-2005
19

12. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol 2, No.3, 2012
250.0 35.0
30.0
200.0
y = 0.0141x 25.0
RNFL
150.0 R² = 0.0341
20.0
TEMP
100.0 15.0
y = 0.066x Linear (RNFL)
R² = 0.0077 10.0
50.0 Linear (TEMP)
5.0
0.0 0.0
1960.0 1970.0 1980.0 1990.0 2000.0 2010.0
Chart 9; Annual mean Rainfall and Temperature for Ondo State years 1971-2005
20

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