Journal of Environment and Earth Science                                                          www.iiste.orgISSN 2224-3...
Journal of Environment and Earth Science                                                        www.iiste.orgISSN 2224-321...
Journal of Environment and Earth Science                                                         www.iiste.orgISSN 2224-32...
Journal of Environment and Earth Science                                                                        www.iiste....
Journal of Environment and Earth Science                                                                                  ...
Journal of Environment and Earth Science                                                                                  ...
Journal of Environment and Earth Science                                                                                  ...
Journal of Environment and Earth Science                                                                                  ...
Journal of Environment and Earth Science                                                                                  ...
Journal of Environment and Earth Science                                              www.iiste.orgISSN 2224-3216 (Paper) ...
Journal of Environment and Earth Science                                                 www.iiste.orgISSN 2224-3216 (Pape...
Journal of Environment and Earth Science                                            www.iiste.orgISSN 2224-3216 (Paper) IS...
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11.[9 20] analytical study of rainfal of nigeria

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11.[9 20] analytical study of rainfal of nigeria

  1. 1. Journal of Environment and Earth Science www.iiste.orgISSN 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.comAbstractThe impart of climate change is felt worldwide, but the effects are more devastating in countries whereflooding or drought has occurred. In the Nigerian context, the impact of climate change is felt majorly interms of rainfall and temperature. It is on the basis of their variations that the analytical study of their trendis 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 CumulativeSummation (CU-SUM) and the rank-sum tests. The trend analysis shows that as temperature increasesthere is a corresponding increase in rainfall. The trend also indicates that no significant departure of theseclimatic parameters occurred. The least square regression (r2) and the trend as generated from the MicrosoftExcel computations show that the temperature variation ranges from 0.4% in Delta to 3.5% in Edo, anindication that the temperature conditions in states understudy are not uniform even though the trend showsan increase. The rainfall least square regression variation ranges between 0.2% in Zaria and 2.7% in Plateaustates, implying that the rainfall is varying in an upward trend.Keywords: Analytical study, Climatic variation, Temperature and Rainfall.1 INTRODUCTIONThere is a global concern about climatic changes resulting from greenhouse gas emission from theindustrialized nations. Many scholars have engaged themselves in studying this climatic variability in orderto identify and quantify the extent and trend of the change. Climate variability is the variations of the normalstate and other statistics of the climate on all temporal and spatial scales beyond that of individual weatherevents. 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 ofthe opinion that the global increase in temperature and changes of other climatic variables such as rainfalland evaporation are as a result of greenhouse gas emission. Once existing or potential climate changehazards have been identified, it must be demonstrated that these hazards pose a threat or risk to humanpopulations and the systems on which they depend. It is therefore necessary to establish coping ranges andcritical thresholds for proper adaptation interventions. This paper is aimed at determining the trend of thisclimatic variation with a view to finding out if rainfall and temperature have been influenced by internal orexternal factors. This is because precipitation is one of the key climatic variables that affect both thespatial and temporal patterns on water availability (De Luis et al., 2000).1.1. Materials and MethodEnvironmental 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 anthropogenicor natural factors have brought in inconsistencies and non-homogeneity in the data. Individual rainfall datafrom different stations of the Benin-Owena River Basin were also subjected to intervention analysis as wellas 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. Thesetests were used to identify and ascertain if there had been any interventions. The rank-sum test is based on anon-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. 2. Journal of Environment and Earth Science www.iiste.orgISSN 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 theRegression Line over the data point to evaluate the results and the establishment of the trend from the fit ofmodel.Intervention analysis was carried out using the Cumulative Summation (CUSUM) technique of Parida etal., 2003 to determine inconsistencies in rainfall data collected from different rainfall stations. The resultsof this analysis will indicate if anthropogenic activities have occurred due to observational errors in datacollection 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 oscillatorygraph.In step change analysis, the rank-sum test is a non-parametric test to find out the difference in median oftwo subsets of data representing pre- and post-intervention periods for rainfall data. The standard steps incomputing 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 ofranks is used.(ii) Compute a statistic Sm as the sum of ranks of the observations in the smaller group (the number ofobservations in the smaller group is denoted as k, and the number of observations in the larger group isdenoted 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]0.5The 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 < µThe 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 SmSm = K + M ……………………………………………………………………………….(2)Sm = 7481 + 80,829 = 88,310Theoretical mean (µ) and the Standard deviation (σ)(ii) Computing for µ and σ , we have µ =k(N + 1) ……………………………(3) 2µ = 7481 (420 +1) = 1574750.5 2σ = (km(N + 1)0.5 12…………………………………….(4)σ =[7481(80,829)(420 + 1]0.5 12σ= 145651.13The 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. 3. Journal of Environment and Earth Science www.iiste.orgISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Vol 2, No.3, 2012This 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 nullhypothesis, Ho, presupposes that no change has occurred in the time series or that the two samples comefrom the same population (i.e. have the same median). This condition is accepted when the computed Zrs isless than the Z value derived from a normal distribution. Table at 5% significance level and as worked outin equation, 5. The combined rainfall and temperature data were later subjected to spatial analyses usingMicrosoft excel package in order to generate the curve, the trend patterns and the R-square values (charts1-9) . 1.2 Results and discussionIn the intervention analysis, the computed CUSUM rainfall values (yi) at any time i for each of the rainfallstations were obtained and plotted in Figs 1-9. Usually, when there is no intervention, the plot of rainfallvalues 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 departureoccurred between 1971 and 1973 and between 1979 and 1982. In Fig 3, it occurred between 1976 and 1978while in Fig 7 (Benin) the departure was between 2003 and 2005. These departures suggest the possibilityof interventions due to the anthropogenic activities of rainfall data collectors. In some of the Figures, therainfall values either plot above or below the horizontal axis in most of the computed years. Importantlysome of the Figures show negative and positive slopes on the horizontal axis. Whitting, et al; 2003 suggestthat positive slopes on the horizontal axis indicate wet periods (i.e above average values of rainfall), whilenegative 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), thecritical value a=0.05 representing Z and the remarks. Using null hypothesis, Ho and applying the resultsfrom equations 2 - 5 of the step change analysis, the test statistics values (Zrs) obtained are less than thecritical values (Z). When this condition prevails (Zrs < Z), it means that Ho is accepted. This implies thatthe rainfall data are collected from areas that fall under the same climatic influence. The null hypothesis, Hois 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 samepopulation (Helse and Hirseh, 2002).Tables 2 and 3 show the temperature and rainfall values for least square regression and the trend asgenerated from the Microsoft Excel computations. The World Almanac and Book of facts (WABF) 1993suggests that the least square regression calculates the best – fitting line for any observed data byminimizing 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 rangesfrom 0.1% in Zaria station to 3.5% in Edo station an indication that the temperature conditions in thestations/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 ConclusionThe trend analysis of temperature and rainfall in nine (9) states of Nigeria for 35years show that astemperature increases there is a corresponding increase in rainfall. The trend also indicates no significantdeparture of the climatic parameters in the nine (9) states under study. The analyses of the meteorologicaldata of temperature and rainfall show no evidence of serious intervention although some insignificantdepartures were observed in station areas of Ikeja, Benin and Maiduguri respectively. This suggests thatthe rainfall data are collected from areas that fall under the same climatic influence, an indication that nosignificant change has occurred in the time series.ReferencesCRCCH, ( 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 ofrainfall 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., ChenMarquis, M., Averyt, K.B., Tignor, M., Miller, H.L (Eds.), Contribution of Working Group I to the Fourth 11
  4. 4. Journal of Environment and Earth Science www.iiste.orgISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Vol 2, No.3, 2012Assessment 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 Impactsof Climatic Change- Hydroclimativ Variability. In: Proceedings of symposium SGheld during the seventh IAHS Scientific Assembly at Foz do iguacu, Brazil, April 2005. IAHSPubl.296, (p. 178-183)Parida, B.P., Moalafhi, D.B.. Kcnabatho. P.K..( 2003). Effect of urbanization on runoff coefficient: a caseof Notwane catchment in Botswana. In: Proceedings of the international Conference on Water andEnvironment (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 waterresources management: the case of Netware catchment in Eastern Botswana. Physics and Chemistry of theEarth 31.(pp. 928-934.)Whiting, J.P., Lambert, M.F., Mercalfe, A.V., (2003). Modeling persistence in annual Australian pointrainfall. Hydrology and Earth System Sciences 7 (2), (pp. 197-211).Yue, S., Pilon, P., Cavadias, G., 2002. Power of the Mann-Kendall and Spearmans rhotests detectingmonotonic trends in hydrological series. Journal of Hydrology 259, (pp 254-271).Table 1. Results of step change analysis using rank sum methodSTN Test Statistic Critical value at a = 0.05 (Z) Remarks Zrs = Zrs < Z=HoPlateau -10.21 1.65 AcceptDelta -39.62 1.65 AcceptEdo -61.6520 1.65 AcceptLagos -29.26 1.65 AcceptKano -19.21 1.65 AcceptBorno -20.94 1.65 AcceptNiger -31.079 1.65 AcceptOndo -107.6 1.65 AcceptZaria -28.88 1.65 AcceptTable 2: Results of Temperature Least-Square Regression and TrendSTN Equation on Chart R- Squared value on Chart Correction Coefficient Variation Trend (%)Plateau 0.0125X 0.0138 0.1175 1.4 increaseDelta 0.0144X 0.004 0.0633 0.4 increaseEdo 0.0147X 0.035 0.1871 3.5 increaseLagos 0.0142X 0.017 0.1304 1.7 increaseOndo 0.0141x 0.034 0.1844 3.4 increaseKano 0.0148x 0.022 0.1483 2.2 increaseBorno 0.0155x 0.0297 0.1723 3.0 increaseNiger 0.0151x 0.0068 0.0825 0.7 increaseZaria 0.0142x 0.0152 0.1233 1.5 Increase 12
  5. 5. Journal of Environment and Earth Science www.iiste.orgISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Vol 2, No.3, 2012Table 3. Results of Rainfall Least-Square Regression and TrendSTN Equation on Chart R- Squared value Correction Variation Trend Y= on Chart Coefficient (%)Plateau 0.053x 0.027 0.1643 2.7 IncreaseDelta 0.1127x 0.0063 0.0794 0.6 IncreaseEdo 0.0911x 0.0068 0.0825 0.7 IncreaseLagos 0.0585x 0.0146 0.1208 1.5 IncreaseOndo 0.066x 0.0077 0.0878 0.7 IncreaseKano 0.0381x 0.0166 0.1288 1.7 IncreaseBorno 0.0232x 0.0104 0.1020 1.0 IncreaseNiger 0.05x 0.008 0.0894 0.8 IncreaseZaria 0.0427x 0.0015 0.0387 0.2 Increase 500 0RAINFALLCUSUM -500VALUES -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. 6. Journal of Environment and Earth Science www.iiste.orgISSN 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. 7. Journal of Environment and Earth Science www.iiste.orgISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Vol 2, No.3, 2012 1,500 1,000 500RAINFALLCUSUM 0VALUES -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. 8. Journal of Environment and Earth Science www.iiste.orgISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)Vol 2, No.3, 2012 1,000 500RAINFALL 0CUSUMVALUES -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 1,000RAIN FALLCUSUM 0VALUES -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. 9. Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol 2, No.3, 2012 1,000RAINFALL 0CUSUMVALUES -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. 10. Journal of Environment and Earth Science www.iiste.orgISSN 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 2010Chart 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 2010Chart 5; Annual mean Rainfall and Temperature for Kano State years 1971-2005 18
  11. 11. Journal of Environment and Earth Science www.iiste.orgISSN 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 2010Chart 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 2010Chart 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 15.0Chart 8: Annual mean Rainfall and Temperature for Zaria State years 1971-2005 19
  12. 12. Journal of Environment and Earth Science www.iiste.orgISSN 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 2010.0Chart 9; Annual mean Rainfall and Temperature for Ondo State years 1971-2005 20
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