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Vol 2, No.3, 2012

9

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

Journal of Environment and Earth Science www.iiste.org

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Vol 2, No.3, 2012

10

(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.

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11

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

Journal of Environment and Earth Science www.iiste.org

ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)

Vol 2, No.3, 2012

12

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

Zrs =

Critical value at a = 0.05 (Z) Remarks

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

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Table 3. Results of Rainfall Least-Square Regression and Trend

STN Equation on Chart

Y=

R- Squared value

on Chart

Correction

Coefficient

Variation

(%)

Trend

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

2004 2000 1996 1991 1987 1983 1979 1975 1971

RAINFALL

CUSUM

VALUES

500

0

-500

-1,000

-1,500

-2,000

YearsFigure.1 CUSUM plot using observed annual rainfall for Maiduguri (1971-2005)

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2002 1998 1993 1989 1985 1981 1975 1971

RAINFALL

CUSUM

VALUES

1,000

0

-1,000

-2,000

-3,000

Years Figure.3 CUSUM plot using observed annual rainfall for Ikeja (1971-2005)

2004

2000

1996

1991

1987

1983

1979

1975

1971

RAINFALL

CUSUM

VALUES

1,000

500

0

-500

-1,000

-1,500

YearsFigure.2 CUSUM plot using observed annual rainfall for Minna (1971-2005)

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2004 2002 2001 1999 1997 1996 1994 1992 1991 1989 1987 1986 1984 1982 1981 1979 1977 1976 1974 1972 1971

0

-2,000

-4,000

-6,000

Sigma level: 3

Figure.5 CUSUM plot using observed annual rainfall for Kano (1971-2005)

2004 2000 1996 1991 1987 1983 1979 1975 1971

RAINFALL

CUSUM

VALUES

1,500

1,000

500

0

-500

-1,000

years

Figure.4 CUSUM plot using observed annual rainfall for Jos Rainfall (1971-2005)

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__ __

year 2004 2000 1996 1991 1987 1983 1979 1975 1971

RAINFALL

CUSUM

VALUES

2,000

1,000

0

-1,000

-2,000

-3,000

Figure.8 CUSUM plot using observed annual rainfall for Warri (1971-2005)

year 2004 2000 1996 1991 1987 1983 1979 1975 1971

RAIN FALL

CUSUM

VALUES

3,000

2,000

1,000

0

-1,000

-2,000

-3,000

Figure.7 CUSUM plot using observed annual rainfall for Benin (1971-2005)

YEAR

2004

2000

1996

1991

1987

1983

1979

1975

1971

RAINFALL

CUSUM

VALUES

1,000

500

0

-500

-1,000

-1,500

Figure.6 CUSUM plot using observed annual rainfall for Zaria (1971-2005)

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__

Chart 1; Annual mean Rainfall and Temperature for Plateau State years 1971-2005

Chart 2; Annual mean Rainfall and Temperature for Delta State years 1971-2005

y = 0.053x

R² = -0.027

y = 0.0125x

R² = 0.0138

0.0

10.0

20.0

30.0

0.00

50.00

100.00

150.00

200.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

y = 0.1127x

R² = 0.0063y = 0.0144x

R² = -0.004

25

30

35

40

45

50

0

100

200

300

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

2004 2000 1996 1991 1987 1983 1979 1975 1971

1,000

0

-1,000

-2,000

-3,000

Years Figure.9 CUSUM plot using observed annual rainfall for Ondo (1971-2005)

RAINFALL

CUSUM

VALUES

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Chart 3; Annual mean Rainfall and Temperature for Edo State years 1971-2005

Chart 4; Lagos State Annual mean Rainfall and Temperature for years 1971-2005

Chart 5; Annual mean Rainfall and Temperature for Kano State years 1971-2005

y = 0.0911x

R² = 0.0068

y = 0.0147x

R² = 0.03520.0

30.0

40.0

50.0

0.00

50.00

100.00

150.00

200.00

250.00

300.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

y = 0.0585x

R² = 0.0146

y = 0.0142x

R² = 0.017

10.0

20.0

30.0

40.0

50.0

-20.00

30.00

80.00

130.00

180.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

y = 0.0381x

R² = 0.0166

y = 0.0148x

R² = -0.022

24.0

34.0

44.0

0.00

50.00

100.00

150.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

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Chart 6; Annual mean Rainfall and Temperature for Borno State years 1971-2005

Chart 7; Annual mean Rainfall and Temperature for Niger State years 1971-2005

Chart 8: Annual mean Rainfall and Temperature for Zaria State years 1971-2005

y = 0.0232x

R² = 0.0104

y = 0.0155x

R² = 0.029720.0

30.0

40.0

50.0

0.00

20.00

40.00

60.00

80.00

100.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

y = 0.05x

R² = -0.008

y = 0.0151x

R² = 0.0068

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

y = 0.0427x

R² = 0.0015

y = 0.0142x

R² = 0.0152

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

-40.00

10.00

60.00

110.00

160.00

1960 1970 1980 1990 2000 2010

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

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Chart 9; Annual mean Rainfall and Temperature for Ondo State years 1971-2005

y = 0.066x

R² = 0.0077

y = 0.0141x

R² = 0.0341

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0.0

50.0

100.0

150.0

200.0

250.0

1960.0 1970.0 1980.0 1990.0 2000.0 2010.0

RNFL

TEMP

Linear (RNFL)

Linear (TEMP)

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