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Ehigiator, 1:12http://dx.doi.org/10.4172/scientificreports.574

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Volume 1 • Issue 12 • 2013

Keywords: Rainfall; Trend; Drought; Flood; Non-parametric; Tests

Introduction The trend analysis of hydrological series is of practical importance

for the detection of the gradual trends over time in the face of global climate change. World-wide interest in global warming and climate change has led to numerous trend detection studies to determine if the values of a random variable generally increase (or decrease) over some period of time in statistical terms [1-6]. Several authors have analyzed hydro-meteorological time series in West Africa from Niger to Senegal [7-11]. They pointed out the non-stationarity of the series and a climatic jump down between 1965 and 1972; the majority of the shifts appearing between 1969 and 1970. A step decrease of the precipitation in 1970 has been identified in parts of Bulgaria and Romania [12]. Precipitation in the Great Plains of the United States of America also shows a significant change with an increase since the late 60´s, the last two decades being the wettest of the 20th century [13]. The Brazilian Amazon basin precipitation records show a shift near 1975, downward in the northern area and upward in the southern part [14]. The earth global air temperature was relatively stable from the 30´s to the 70´s; it has since then been showing a strong increase. All these studies suggest the possibility of a worldwide new climatic phase since the start of the 70´s. The origin of the climatic change has been adduced to a general perturbation of the atmospheric and oceanic circulation at a planetary scale with different regional impacts [1,5]. The essential results of these selected studies have been highlighted here in order to place the results found with the Owena records in a general context. Although climatic changes do not occur in one day, it is known that threshold effects and small variations in latitude of atmospheric systems such as jet-streams and low pressure convergence zones can result in relatively abrupt change in local mean precipitation [15].

Continuous monitoring of water, sediment and morphology at watershed level is imperative for maximizing our understanding of water and sediment responses to varying environmental in-put parameters. Moreover, many hydrological issues could be approached if, and only if, there are prototype databases on a wide spectrum of hydrological processes. There are many different ways in which changes

*Corresponding author: Ehigiator O.A, Department of Civil Engineering, Federal University of Technology, Minna, Nigeria, E-mail: ayuduehigiator@yahoo.com

Received January 01, 2012; Published January 15, 2013

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

Copyright: © 2013 Ehigiator OA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

AbstractPrecipitation trend analysis on different spatial and temporal scales has been of great concern during the past

century because of the attention given to global climate change. The aim of this study is to analyse rainfall time series over a fifty year period at an experimental watershed in South-Western Nigeria. Percentage monthly contributions of each of the wet season months (June-October) to annual rainfall for each of the years were computed while trend analysis was performed using non-parametric statistical tests to detect potential trends, seasonal patterns of rainfall and measurement of their significance. The average annual rainfall was 1600 mm with extreme values of 1156 mm in 1958 (the driest year) and 2543 mm in 1985 (the wettest year) for the 50 year period. There were no significant monotonic trends in the area arising from the long-term rainfall records. Thus, in general, it can be rightly assumed that rainfall pattern has been fairly stable over the period examined. However, for the assessment of the region’s water resources, it is recommended to use hydrometeorological records only over the 1971-2000 periods corresponding to the new World Meteorological Organization (WMO) standard. The use of this period for the quantification of the water resources will give a better picture of the current available water resources. The onset of dry season was becoming much earlier with the passage of time as highlighted from analysis of the database over the reference period for the months of October through to January with the attendant implication of a warmer weather pattern and increase in evapotranspiration losses.

Rainfall Trends in a Tropical Watershed in South-Western NigeriaEhigiator O. A*Department of Civil Engineering, Federal University of Technology, Minna, Nigeria

in hydro-meteorological series can take place. A change can occur abruptly (step change) or gradually (trend) or may take more complex form. Climate change is often recognized as a progressive trend. Studies of precipitation change are typically complicated by factors such as missing values, seasonal and other short-term fluctuations (climate variability) and by lack of homogeneity of the data (e.g. due to changes in instrument and observation techniques).The existence of a trend in a hydrological time series is detected by statistical tests. There is no best forecasting procedure as the choice is essentially dependent on the objective of the forecast, degree of accuracy and the properties of the time series [16].

Parametric or non-parametric statistical tests can be used to decide whether there is a statistically significant trend. Parametric tests assume that the random variable is normally distributed and homosedastic (homogeneous variance). Non-parametric tests make no assumption for probability distribution while the t-test for trend detection is based on linear regression, and therefore checks only for a linear trend [17]. There is no such restriction for the Spearman's rho test. The Spearman test is expected to be less affected by the outliers because its statistic is based on the sign of differences, not directly on the values of the random variable. Another non-parametric test for trend detection is the Mann-Kendall (M-K) test which [18] showed provides results almost identical to those obtained for the Spearman test. If indeed one were interested in detecting a trend in a particular month then one could use the Mann-Kendall trend test for that particular month or group of months [19,20]. All statistical tests involve two kinds of errors. These are the type I error (rejecting the null hypothesis when it is true),

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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and the type II error (not rejecting the null hypothesis when it is false). It is important to consider the power of a test, where power is defined as one minus the probability of type II error. A powerful test will reject a false hypothesis with a high probability.

MethodologyThere are many approaches that can be used to detect trends

and other forms of non-stationarity in hydro-meteorological data. In deciding which approach to take, it is necessary to be aware of which test procedures are valid (i.e. the data meets the required test assumptions) and which procedures are most useful (likely to correctly find a change when it is present). The present study is realized with the annual and monthly precipitation records covering Owena River Basin; an experimental forested watershed which discharges into Owena river between latitudes 7° 17’ and 8° 15’ North of the Equator and Longitudes 5° 01’ E and 5° 45’ E in South Western Nigeria (Figure 1). The data were obtained directly from Ondo State Water Corporation and Benin Owena River Basin Development Authority (BORBDA). The drainage area of the river is about 790 sq. kilometres and lies on a basement complex, the broken edge of a widely extending high rocky plateau that dominates the transitional region between the equatorial and tropical climatic zones with two main seasons, the dry season and the wet season. Average annual rainfall is 1600mm with extreme values of 2540mm and 1150mm (Figure 2). Mean monthly temperature ranges between 27°C and 29°C. The highest temperature occurs usually between February and April while the coolest months are July and August with temperature of 27°C. The relative humidity of the air masses averages about 80% during the wet seasons and about 50% throughout the year.

Statistical analysis of the data series was carried out involving the determination of parameters such as mean, standard deviation, coefficient of variation etc. In order to examine whether the contribution of each month’s rainfall to the annual rainfall shows any significant trend, a time series of contribution of rainfall for each month towards the annual total rainfall for each year in percentage was prepared and the results presented in figure 3. Trend analysis was conducted for the rainy season months of June to October and the onset of the dry season in November. The mean, standard deviation and coefficient of variation of the annual precipitation were determined for the region over the 30 years WMO periods: 1941-70, 1951-80, 1961-90 and 1971-2000. A step change in mean precipitation was effected to check whether it is possible to divide the time-series in two stationary periods. According to [15] if there has been a marked step change in a data series then it is likely that a test for trend will give significant results, even though there is no trend. To check if this was the case for the annual precipitation time series of Owena, the time was split into two periods (1943-1969 and 1970-1992) according to Hubert segmentation procedure [21] and the results presented in table 1. The Spearman rank test was used to determine possible existence of any significant trend in the data series. The values were evaluated as an ordered time series. Each data value is compared to all subsequent data values. The test compares the relative magnitudes of sample data rather than the data values themselves [22,23]. One benefit of this test is that the data need not conform to any particular distribution. Moreover, data reported as non-detects can be included by assigning them a common value that is smaller than the smallest measured value in the data set.

The calculation of the Spearman rank statistics rs requires that the

THE STUDY AREALEGEND :

DAM

WEAR

TOWN

CATCHMENT BOUNDARY

RIVER

STATE BOINDARY

HYTE OCEAN ANNUAL

Figure 1: Owena watershed.

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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original observations xi’s be transformed to ranks ki’s by arranging them in increasing order of magnitudes. Then a parameter di is computed as

di = ki – i, where i ranges from 1 to n,

rs is defined by the equation

( )

2

12

61

1)== −−

∑n

ii

s

dr

n n (1)

for large n, the value of rs can be tested for significance by calculating the quantity ts given by the equation

s s 2

2t r1−

=− s

nr

(2)

If the computed ts lie within the desired confidence limits of the two-tailed test, we conclude that there is no trend in the data series.

A second approach adopted for the data evaluation was the Mann-

Kendal non-parametric test, based on the statistic S with each pair of observed values yi, yj (i > j) of the random variable inspected to determine whether yi > yj or yi < yj. Assuming the number of the former type of pairs is P, and the number of the latter type of pairs is M then S is defined as

S =P–M (3)

For n > 10, the sampling distribution of S and Z follow the standard normal distribution where

s

s

( –1) / if S 00 if S 0

( –1) / if S 0

σ

σ

>= = <

ZS

S (4)

( 1)(2 5) / 18σ = − +s n n n (5)

There is a correction for ties when yi = yj [24]. The null hypothesis that there is no trend is rejected when the computed Z value is greater than Za/2 in absolute value.

Results and Discussions The statistical analysis of the rainfall data on a monthly basis for the

fifty years record is presented in table 2. The rainfall amount showed a fluctuating pattern-the wettest month was September with a fifty year average depth of slightly over 255mm. In fact, rainfall increased steadily from an average of 10.44 mm in January (the driest month) to 238 mm in July followed by a decrease in August (August break) to 157 mm, then a spike to 255 mm in September which decreased to 190 mm in October and finally decreasing to 53 mm and 18 mm in November and December respectively. Results suggest that contribution of August rainfall exhibited significant increasing trend in the second half of the period examined while contributions from June and September remained within fairly constant bands throughout the years (Figure 4). Coefficient of variability of June (0.296) and September (0.369) rainfall are less compared to July (0.48) and August (0.731) rainfall. It is observed that contribution of October rainfall decreased appreciably with the passage of time. The analysis shows insignificant changes for the total annual rainfall series as the coefficient of variation was barely 0.207. The Hubert segmentation procedure suggests the segmentation of the time series between the hydrological years 1969-70 and 1970-71. The mean of the recent period is lower than the mean of the first period. The high differences between the coefficients of variation in the decadal distribution of precipitation during the months of August, November and December shown in table 3, are due to isolate precipitation storms during these dry months. The existence of rare precipitation event during these dry months leads to high standard deviation compare to the mean precipitation. This decrease as reflected in table 1 range between 16.5% in October and 46.2% in January of the mean precipitation of the first period. The decrease of the annual precipitation is essentially due to a decrease of the precipitation during the months of November, December and January. The mean, standard deviation and coefficient of variation of the annual precipitation determined over the 30 years WMO periods are presented in table 4. For the region, the three parameters are relatively stable with values around 1600 mm, 330 mm and 0.21 respectively.

Table 5 and figure 5 show the decadal mean as well as frequency of drought and flood years. The deficient or excess wet years are defined for those years where the rainfall percentage departures from the mean rainfall are less or more than the standard deviation (20.73% of mean). In the decade 1943-50 there was neither drought (deficient) nor

0

500

1000

1500

2000

2500

3000

1943

1948

1953

1958

1963

1968

1973

1978

1983

1988 Year

To

tal A

nn

ual

rai

nfa

ll (m

m)

Figure 2: Annual rainfall amount for the period 1943-1992.

0

2

4

6

8

10

12

14

16

18

Jan

MarMay

Ju

lSep Nov

month

% o

f an

nu

al r

ain

fall

Figure 3: Percentage monthly contribution to annual rainfall.

Periods June July Aug. Sept. Oct. Nov. Dec. Jan. Feb.1943-1969 (1st

period) 207.9 237.2 157 251.3 205 66.9 22.8 13.2 29.4

1970-1992 (2nd period) 213.2 239 158.3 259.9 171.4 37.2 13.7 7.1 42.5

Table 1: Seasonal distribution of mean monthly precipitation (mm).

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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flood (excess) year. During the dry period of 1951-60, there were two deficient years and one excess year. In the course of the next decade (1960-1970), there were two flood years of excess wet period with

no deficient year throughout the decade as highlighted in figure 6. Analyses of fluctuations in the long term monthly mean rainfall using the Spearman rank statistics are presented in table 6 while the Mann-

RAIN DATA AT ONDO METOEROLOGICAL STATION (mm)Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total1943 96 7 132 139 186 169 115 103 161 219 176 21 15241944 13 33 101 114 133 97 213 108 370 197 83 0 14621945 0 2 13 157 156 145 329 125 157 172 22 23 13011946 22 45 72 183 162 260 30 30 272 200 48 54 13781947 0 92 66 201 151 228 182 295 336 130 51 34 17661948 0 28 73 219 195 143 93 79 220 178 50 0 12781949 0 15 88 123 146 212 481 197 174 244 91 0 17711950 11 7 44 211 177 206 291 37 153 183 43 14 13771951 22 14 151 59 179 150 345 132 392 211 98 0 17531952 0 40 121 149 156 188 229 109 252 293 52 23 16121953 3 58 75 112 227 298 265 61 256 137 38 17 15471954 8 34 107 138 238 344 145 211 247 235 56 3 17661955 18 20 115 90 159 262 206 204 308 211 59 3 16551956 0 37 118 214 100 106 108 17 231 206 42 57 12361957 0 24 123 201 235 319 411 236 449 306 84 86 24741958 1 6 103 150 170 167 29 65 151 216 56 42 11561959 29 38 153 100 199 138 240 83 313 155 117 3 15681960 8 22 81 151 158 220 157 142 231 125 36 71 14021961 21 0 72 102 95 163 394 39 221 150 49 32 13381962 0 15 164 83 187 186 288 103 146 317 169 49 17071963 15 82 87 78 134 383 238 488 433 486 4 14 24421964 0 8 87 217 125 239 240 59 139 186 77 8 13851965 70 24 93 106 156 219 385 264 140 155 20 0 16321966 7 30 116 186 192 163 232 276 192 92 98 1 15851967 0 16 131 150 127 195 181 53 224 162 22 23 12841968 11 55 105 242 126 188 313 529 391 175 66 37 22381969 1 41 135 204 86 223 264 194 226 207 98 0 16791970 22 0 131 107 179 220 51 80 294 234 21 0 13391971 3 61 93 127 147 133 281 80 323 109 60 25 14421972 31 64 106 81 149 203 133 106 180 120 58 39 12701973 0 32 56 121 190 178 272 275 177 180 3 16 15001974 0 44 112 191 183 229 265 153 330 133 16 0 16561975 0 61 97 221 301 105 464 110 130 210 82 36 18171976 13 79 106 141 172 191 71 90 89 243 81 0 12761977 19 33 56 136 164 275 210 106 105 188 3 40 13351978 0 63 126 288 204 233 425 72 296 168 8 2 18851979 9 13 96 115 140 169 297 291 364 174 106 5 17791980 0 51 127 189 125 304 186 361 349 304 52 22 20701981 0 5 93 140 166 253 210 150 183 133 50 0 13831982 10 101 61 209 165 177 156 45 186 182 0 0 12921983 0 18 35 127 157 283 129 45 312 80 47 22 12551984 0 0 172 121 200 131 215 233 264 125 17 1 14791985 0 42 213 483 411 260 335 285 372 109 33 0 25431986 40 125 191 42 211 272 171 102 257 132 23 0 15661987 0 32 145 91 87 141 244 355 231 263 1 7 15971988 4 92 94 194 240 223 142 101 302 245 28 30 16951989 0 0 115 201 245 230 300 211 159 124 24 0 16091990 12 1 0 141 121 197 210 116 277 150 76 56 13571991 0 55 171 295 204 281 552 224 310 205 0 13 23101992 0 5 75 97 196 224 185 52 488 131 67 0 1520Mean

(1943-92) 10 35 103 158 174 210 238 157 255 189 53 18 1605

S.D. 18.1 29.7 41.9 72.9 54.8 62.4 115.2 115.8 94.3 70.4 39.4 21.4 332.8C.V. 1.744 0.838 0.403 0.459 0.315 0.296 0.484 0.731 0.369 0.371 0.741 1.151 0.207

Table 2: Precipitation data at Owena watershed from 1943-1992.

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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25

20

15

10

5

0

1 5 9 13 17 21 25 29 33 37 41 45 49

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(Ju

ne) t

o a

nnua

l rai

nfal

l

25

20

15

10

5

01 5 9 13 17 21 25 29 33 37 41 45 49

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(Au

g) to

ann

ual r

ainf

all

25

20

15

10

5

01 5 9 13 17 21 25 29 33 37 41 45 49

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(O

ctob

er) t

o a

nnua

l rai

nfal

l

35

30

25

20

15

10

5

01 5 9 13 17 21 25 29 33 37 41 45 49

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(Ju

ly) t

o an

nual

rain

fall

35

30

25

20

15

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5

01 5 9 13 17 21 25 29 33 37 41 45 49

1 5 9 13 17 21 25 29 33 37 41 45 49

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(Se

ptem

ber)

toan

nual

rain

fall

14

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6

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2

0

No of Years (1943-1992)

Perc

enta

ge c

ontri

butio

n(N

ovem

ber)

toan

nual

rain

fall

Figure 4: Time series of percentage contributions of the wet season (June-Oct.) months & Nov. to annual rainfall.

DecadeJun Jul Aug Sept

Mean S.D. C.V. Mean S.D. C.V. Mean S.D. C.V. Mean S.D. C.V.1941-50 182.5 53.4 0.293 216.8 146.2 0.673 121.8 87.6 0.719 230.4 86.1 0.3741951-60 219.2 82.6 0.377 213.5 112.1 0.525 126 72.8 0.578 283 86.1 0.3041961-70 217.9 63.4 0.291 258.6 99.3 0.384 208.5 180 0.863 240 102.8 0.4281971-80 202 60.9 0.301 260.4 120.5 0.462 164.4 104.4 0.635 234 108.4 0.4621981-90 216.7 53.6 0.247 211.2 67.1 0.318 164.3 103.7 0.631 254.3 66.2 0.260

DecadeOct Nov Dec Annual

Mean S.D. C.V. Mean S.D. C.V. Mean S.D. C.V. Mean S.D. C.V.1941-50 190.3 33.9 0.178 70.5 48.0 0.681 18.25 19.2 1.05 1482.1 193.7 0.1301951-60 209.5 60 0.286 63.8 27.1 0.425 30.5 31.6 1.04 1616.9 363.3 0.2251961-70 216.4 111.8 0.516 62.4 50.4 0.808 16.4 17.9 1.09 1662.9 391.3 0.2351971-80 182.9 58.9 0.322 46.9 37.3 0.795 18.5 16.3 0.881 1603 279.6 0.1741981-90 154.3 58.8 0.381 29.9 23.1 0.772 11.6 18.9 1.63 1577.6 369.6 0.234

Table 3: Decadal variations in seasonal distribution of monthly and annual precipitation (mm).

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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DecadeDecadal mean rainfall (mm) Decadal mean (%)

Mthly Annual departure from normal Freq. of deficient year Freq. of excess year1943-1950 123.5104 1482.125 -7.7 0 01951-1960 146.1083 1616.9 0.69 2 11961-1970 138.575 1662.9 3.56 0 21971-1980 133.5833 1603 -0.18 2 11981-1990 131.4667 1577.6 -1.74 1 11991-1992 159.5833 1915 19.25 0 1

Table 5: Decadal mean (% departure from normal), frequency of drought and flood years.

Kendall rank statistics was performed only for evaluation of the wet-seasonal variation as shown in table 7.

For the Spearman test, if ts lies between ± rs then the series does not have a trend. The direction of trends can be determined by using either technique [20]. For the Mann-Kendall statistics, if Z lies inside the range of Za/2, then the trend is not significant in the rainfall time series. In other words, when the values of Z are higher – in absolute value – than 1.96, it implies a trend or a change in the time series.

The initial value of the Mann-Kendall statistic [25], S, is assumed to be 0 (i.e., no trend). If a data value from a later time period is higher than that from an earlier time period, S is incremented by 1. On the other hand, if the data value from a later time period is lower than a data value sampled earlier, S is decremented by 1. The net result of all such increments and decrements yields the final value of S. A very high positive value of S is an indicator of an increasing trend, and a very low negative value indicates a decreasing trend. However, it is necessary to compute the probability associated with S and the sample size, n, to statistically quantify the significance of the trend. For the long-range study, both tests indicate that there are significant trends for the months of October and November compared to other months at the 95% confidence level. In addition, M-K statistics revealed significant trend for the month of June. The displayed negative Spearman statistics for the months of October through to January are manifestations of

downward trends in the rainfall pattern and this could be an indication of a temporal climatic change.

It is pertinent to mention that any given test brings with it precise mathematical definition of what is meant by "no trend", including a set of background assumptions usually related to type of distribution and serial correlation. The outcome of the test is a "decision" either the null hypothesis H0 is rejected or not rejected. Failing to reject H0 does not mean that it was "proven" that there is no trend. Rather, it is a statement that the evidence available is not sufficient to conclude that there is a trend [26].

ConclusionsChanges in catchment response as a result of climate change and/

or variability continue to be of concern to hydrologists and water managers and thus identification and study of changes in rainfall as a major input into the hydrologic system remains pivotal in water resources management and planning for reliable input into modelling catchment response systems. Statistical analysis of the database over the reference period highlight that (i) There were no significant monotonic trends in the area arising from the long-term rainfall records; (ii) The precipitation time series present a step change or shift around 1970 and (iii) The 50-year period can be divided in two separate periods (1943-1969 and 1970-1992). From 1943 to 1969 the precipitation records do

1991-1992

1981-1990

1971-1980

1961-1970

1951-1960

1943-1950

-20 0 20 40

Dec

adal

inte

rval

Decadal Mean departure from normalrainfall

Decad. mean%departure fromnormal

Figure 5: Decadal mean (%) departure from normal.

Freq

uenc

y

2.5

2

1.5

1

0.5

0

Freq. of deficientyear

Freq. of excessyear

1943- 1951- 1961- 1971- 1981- 1991- 1950 1960 1970 1980 1990 1992

Decadal interval

Figure 6: Frequency of drought & flood.

WMO Periods1941-70 51-80 61-90 71-2000

Mean( annual precipitation) 1594.8 1614 1614.5 1619STDEV 333.2 326.8 339.8 340.80

C.V. 0.209 0.202 0.210 0.211

Table 4: Variation in mean WMO periodic precipitation (mm).

Citation: Ehigiator OA (2013) Rainfall Trends in a Tropical Watershed in South-Western Nigeria. 1:574 doi:10.4172/scientificreports.574

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Jan Feb Mar Apr May June July Aug Sept Oct Nov Decrs -0.162 0.099 0.159 0.053 0.172 0.201 0.064 0.151 0.113 -0..277 -0.353 -0.178

DF 50 50 50 50 50 50 50 50 50 50 50 50P 0.261 0.495 0.268 0.715 0.231 0.162 0.657 0.294 0.433 0.052 0.012 0.125ts -1.14 0.69 1.12 0.37 1.21 1.42 0.44 1.06 0.79 -1.998 -2.61 -1.25

Trend NA NA NA NA NA NA NA NA NA E E NA

Table 6: Spearman Rank Correlation Results where NA and E symbolise the no-association and existence of trend respectively.

June July August Sept October

σs 119.6 119.6 119.6 119.6 119.6Z 0.017 0 -0.033 -0.033 0.033

Trend E NA NA NA E

Table 7: Summary of M-K statistics of rainfall for the wet seasons.

not show any trend while from 1970 to 1992, the data show significant shift or step change in the mean value of the time series for the dry season starting from October through to February but this trend is not significant compared to the variations from year to year. This finding is in consonance with observed global trends.

Thus, in general, it can be rightly assumed that rainfall pattern has been fairly stable over the period examined. However, for the reassessment of the region’s water resources, it is recommended to use hydrometeorological records only over the 1971-2000 period corresponding to the new WMO standard. The use of this period for the quantification of the water resources will give a better picture of the current available water resources. The onset of dry season was becoming much earlier with the passage of time as highlighted from the results of the data for the months of October through to January with the attendant implication of a warmer weather pattern and increase in evapotranspiration losses.

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