226 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Spatio-Statistical Analysis of Rainfall and Temperature Distribution, Anomaly and Trend in Nigeria Elisha Ademola Adeleke* and Eniola Aminat Orebayo Department of Geography & Environmental Management, University of Ilorin, Ilorin, 1515, Nigeria *Corresponding Author: adelekeelisha@yahoo.com Received 3 June 2019/ Revised 16 May 2020 / Accepted 14 June 2020/ Available Online 23 July 2020 Abstract This study aims to examine the variability of rainfall and temperature based on spatio- statistical analysis. Datazforzthezstudy was gotten from the Nigerian Meteorological Agency and subjected to statistical analysis using mean, standard deviation, time series analysis, t-test and correlation. Thezresultszofzthezstudyzrevealedzthatzthe mean rainfall in the area is 108.6 mm, mean temperature is 28o C and mean sunshine is 4.7 hours. The result further revealed that mean onset date in the state is 13th march, mean cessation date is 10th October while thezmeanzlengthzofzrainyzseason is 223 days. The result also showed that rainfall anomaly index in the area ranged between -2.12 and 2.28 while temperature anomaly ranged between -2.31 and 1.73. The correlation coefficient showed that cessation (0.408) and Length of rainy season (0.406) is significantly related to rainfall, sunshine hours (0.380) and average temperature (0.867) is significantly related to minimum temperature, sunshine hours (-0.389) and average temperature (0.749) is significantly related to maximum temperature while onset (-0.642) and cessation (0.509) is significantly related to length of rainy season. However, there is a significant difference in onsetzdates,zcessationzdateszandzlengthzofzrainyzseasonzinzthezarea. The area is experiencing a significant increase in temperature, rainfall and sunshine hours and recommends that more tress should be planted in the area and Nigerian meteorological agency should also inform farmers about the onset of the rains so as to help the farmers prepare for the planting season. Keywords: Climate; Rainfall; Anomaly; Temperature; Cessation; Onset GEOSFERA INDONESIA p-ISSN 2598-9723, e-ISSN 2614-8528 Vol.5 No. 2 (2020), 226-249, August, 2020 https://jurnal.unej.ac.id/index.php/GEOSI DOI : 10.19184/geosi.v5i2.11250 Accredited by the Ministry of Research , Technology , and Higher Education of the Republic of Indonesia, No. 30/E/KPT/2019. mailto:adelekeelisha@yahoo.com https://jurnal.unej.ac.id/index.php/GEOSI https://drive.google.com/file/d/1rSnVAs6cuHOwHl5BJ87CL2L6K5DQz7s6/view 227 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 1. Introduction Rainfall is one of the major factors affecting food security especially in countries largely or highly dependent on rain-fed agriculture, given that, in addition to evaporation rate and soil characteristics, it controls the state of soil moisture. The role of moisture in agricultural production is even more important in the tropics, especially Nigeria, where rainfall is highly seasonal over most parts and varies from year to year, while the growing season is determined by the availability of rain to meet crop water requirements (Odekunle, 2004). The amount of rainfall that is normally received determines which types of agriculture that can be carried out and which crops can be cultivated in a region. The seasonal distribution of rainfall regulates the agricultural calendar in the tropics (Vellinga et al., 2013). The relationship that exists between rainfall and the tropical occupation of agriculture in Nigeria is that it employs over 80% of the work force. Rainfall is arguably the most important meteorological parameter that has the greatest impact on human activity. Rainfall distribution and pattern has been a major concern to different people in diverse field. i.e. to the agriculturist, rainfall is a crucial factor that determines the planting season and influences the type of crops to be cultivated. To the hydrologist, rainfall is important in generation of Hydro Electric Power. The volume of water in rivers increases during the rainy season and this boost generation of electricity. Morezactionszshouldzbeztakenztozsensitizezthezpubliczzaboutzzthezoccurrences ofwweatherzevents,zwhichziszfrequentznowadayszaszevidencezzofzzchangezzinzzthe climate (Ayanlade et al., 2020). Rainfall in Nigeria is produced by the intertropical discontinuity. Tropical analysts have consequently identified the boundary by several names such as The Intertropical Convergence Zone (ITCZ), Intertropical Fronts (ITF), and recently the Intertropical Discontinuity (ITD). The ITD is a warm, moist maritime air masses yielding heavy rainfall. Rainfall is copious in all mouths with an annual total often being 250 cm (Strahler & Strahler, 1988). It is primarily a region of “maximum” Surface moisture gradient, known as a humidity discontinuity (Oyewo, 2005). 228 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Reductionzinzvolumezofzrainfallzandzsignificantzincreasezinzsurfaceztemperaturezwith thezfarmerszhavingzfirmzperceptionzofzthesezchanges (Tarfa et al., 2020). Warmer temperatures are very likely to produce more vigorous variability in climate such as increase evaporation, capacity of air to hold more moisture and thus heavier rainstorms. Growing industrialization and increasing use of fossil fuels are putting pressure and affecting the regional and global temperatures that are subsequently influencing the overall precipitation levels. Increasingztemperaturezandzchangingzpatternszof precipitationzarezamongzthezmanyzconsequences,zwhichzzarezzattributedzztozzclimate change (Dammo et al., 2016). A change in temperature is an important indicator of global warming that directly determines the impact of climate change. Recent concern about rising global temperature was justified by its negative impact in all sectors of the economy most especially water supply, ecosystems, coastal habitats, and industries. ClimatezchangezcouldzalterzgroundwaterzmainlyZthroughzchangeszinzthez recharge,z leadingztozmodifyzgroundwaterzlevels (Guevara-Ochoa et al., 2020). Analysiszofzthezresponsesztozquestionszonzperceptionszofzextremezclimatezef- fectszindicatedzthatzaboutz70%zofzthezfarmerszwerezawarezofzthezeffectszofzextremez climatezeventszonzcropzproductionzandzyield,zandzoverz50%zindicatedzthatztoozearly rainfall,zlatezrainfall,zprolongedzdrynesszafterzanzzinitialzrainfall,zzexcessivezzrainfall, windstormszwerezthezcommonzweather-relatedzcauseszofzlowzcropzyields (Eludoyin et al.,z2017).zOceaniczand/orzatmosphericzinfluenceszonzthezzrainfallzzinzzBeninzzwere examinedzbyzinvestigatingzthezcorrelationzbetweenzthezprecipitationztimezserieszzand severalzindices (Ahokpossi, 2018). Furthermore, the report on cessation of the rainy season in the country indicated changes from “normal” between 1941 and 1970 to “early cessation” during the 1971 to 2000 period in most stations (NIMET, 2008). NIMET (2012) as certained that the period of the rainy season in the country has decline from 1941 while the signals of late onset and early cessation of the rainy season set in. Thus, the length of the rainy season has remained shrinking, the annual total rainfall is almost the same, thereby giving rise to 229 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 occasional flash floods and drought occurrences during growing period. Temperatures across the country showed an increasing trend from mid-20th century to date. The mean temperature anomaly indicated warming in most locations in the country. Temperatures have increased from 0.2 to 0.5o C in the high ground areas of Jos, Yelwa and Ilorin in the north and Shaki, Iseyin and Ondo in the southwest to 0.9 to 1.9o C over the rest parts of the country. Aiyelokun & Odekoya (2016) stated that the temperature regimes from 1983 to 2013 in Ijebu Ode appears to be varying and changing statistically. Furthermore, average temperature in Ijebu Ode is high in the dry season and low in the wet season and temperature is generally low during August break when rainfall seizes (Aiyelokun & Odekoya, 2016). Thezwatershedzhaszbeenzsufferingzfromzirregularzandzerraticzrainfallzzduringz thezdryzseason.zTemperaturezexhibitedzazstatisticallyzsignificantzzrisingzztrendzzzwith minimumz temperaturezriseszfasterzthanzthatzofzthezmaximumztemperature (Ketema &zSiddaramaiah,z2020).zTrendzforzmeanzandzminimumzaverageztemperatureszthrough timezsignificantlyzwhileztheztrendzforzmaximumztemperaturezexhibitedzaznonsignifi- cantzincreasingztrendz(Asfawzetzal.,z2018).zFactorszthatzmainlyzcorrelatezwithzstream flowzarezprecipitationzandzmaximumztemperaturez(Tesfayezetzal.,z2017).zAzwarming processzandzanzaccelerationzofzthezatmosphericzevaporativezzdemandzzzwhichzzztook zplacezespeciallyzsincezthezmid-1970s (Elferchichi et al., 2017) Ricezyieldzandztemperaturezhadzzgenerallyzzstatisticallyzzsignificantzzpositivez trends,zadaptationzstrategiesztozgeneticallyzmodifyzricezvarietieszandzeffectivezwaterz usezstrategieszinzareaszofzrainfallzdeficitzarezrecommendedztozensurezfoodzzsecurityz (Akinbilezetzal.,z2020).zWarmzextremezdayszarezassociatedzwithzmidtroposphericzsub sidencezmotionzinzverticalzvelocityzanomalyzthatziszconnectedzwithzthezcorezofzzthez thermalzlowzandztheznetzzconvergentzzflowzzinzzNigeriaz(Abatanzetzal.,z2019).zThe trendszinzcoldznightszarezlargerzthanzforzcoldzdayszatzbothzthezannualzandzseasonalz scaleszinzNigeriaz(Abatanzetzal.,z2016). Anzincreasingzztrendzzinzzbothzzrainfallzzand temperaturezinzannualzstatistics,zthough statisticallyzinsignificant (Umar et al., 2019) 230 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Thezhighzvariationszofzrainfallzatzhigherzlatitudeszrevealzthezunreliableznaturezofzrain fallzaszonezprogressesztowardszNorthern Guineazandzvicezversa (Buba et al., 2017). Thezobservedzspatiotemporalztrendszandzvariabilityzinzrainfallzarezimportantzbasiszfor guidingztargetingzofzappropriatezadaptivezmeasureszacrosszmultiplezsectors (Muthoni et al., 2019). There have been limited studies concerned on spatio-statistical analysis of rainfall and temperature. Therefore, this research intends to rainfall and temperature analysis based on onset, cessation and length of rainy season. This study aims to examine the variability of rainfall and temperature based on spatio-statistical analysis. 2. Methods 2.1 Study Area The study area is Ijebu Ode in ogun state, Nigeria (Figure 1 and 2). The area experiences humid tropical climate which is characterized by alternate wet and dry season seasons like the rest of Nigeria. Ijebu-Ode region on annual basis is under the influence of hot-wet tropical maritime air mass during the rainy season (April-October) and hot-dry tropical continental air mass during the dry season (November-March) (Aiyelokun & Odekoya, 2016). Rainfall is generally heavy with peaks occurring in July and September (double maxima) coupled with high temperature, high evapotranspiration and high relative humidity. The average monthly rainfall for the area ranges between 7.1mm in the month of January to 208.27 mm in the month of June. The annual rainfall is between 1575 mm and 2340 mm. The temperature of the area ranges from 23o C during the dry season to 35o C during the rainy season with an average annual temperature is 27.5o C. Furthermore, the area experiences relative humidity of 63 % in the dry season to as high as 95 % during the peak of the rainy season (Onakomaiya, 2000). 231 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Figure 1. Map of Ogun State showing Ijebu Ode Figure 2. Map of Ijebu Ode 232 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 The vegetation of the area is of the tropical rainforest vegetation of Nigeria. In terms of vegetation, the state can be divided into three distinctive zones. Where the state shares a boundary with the Atlantic Ocean, the vegetation is of a swampy type with mangroves and other edaphic trees. about 400 km2 is of basement complex rocks of pre- Cambrian formation. The whole state is drained by flowing coastal rivers such as Ogun, Yewa, and Shasha from Oyo state through the thickly vegetated western part of the state. Numerous small rivers and streams are at the south eastern parts flowing perennially through the swampy terrain. According to the National population commission (2006 census), ijebu-Ode local government area has a population of 154,032, and an estimated population of 222,653 (2007). The State has enormous industrial potentials. There are also other modern industries producing high quality beer, bicycle tyres, ceramic goods, high quality clay bricks, carpet and clothing materials (Onakomaiya, 2000). The data required for the study include Mean and annual temperature, rainfall and sunshine hours between 1983-2017. These data were collected from the Nigeria Meteorological Agency (NIMET) at the headquarters in Oshodi Lagos state while the map of Ijebu ode was extracted from the administrative map of Nigeria using Arc GIS 10.3 software. Onset of the rainy season will be compiled using Walter (1967) formula because of its higher reliability in predicting the onset of the rains among different methods. The formula is expressed in equation 1. Onset = DM (51-A) (1) TM Where DM = number of days of the month containing the onset of rainfall, A = Total rainfall for the previous month, TM = total rainfall for the month in which 51 mm or more is reached. The cessation of rainy season is defined as the last occasion of rainfall that record rainfall of 51 mm and above. Cessation of the rainy season will be compiled using Walter 233 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 (1967) formula because of its higher reliability in predicting the onset of the rains among different methods. The formula is expressed in equation 2. Cessation = DM (51-A) (2) TM Where DM = number of days of the month containing the cessation of rainfall, A = Total rainfall for the previous month, TM = total rainfall for the month in which 51mm or more is reached. Therefore, the length of the rainy season is the total number of days between the Onset and Cessation date. The Anomaly Index expresses the degree of rainfall and temperature anomaly for the relevant periods in relation to the long term mean rainfall and temperature for the study period. To calculate the rainfall and temperature anomaly for the study period, standardize rainfall anomaly index (SAI) will be used. The formula is given in equation 3. SAI = 𝑋− X 𝑆𝐷 (3) Where x = mean annual rainfall or temperature, X = mean of entire series, SD = Standard deviation of the entire series. Correlation will be used to measure the relationship between the rainfall and temperature for the study period. 𝔯 = 𝑛(Σ𝑥𝑦)−(Σ𝑥)(Σ𝑦) √[𝑛Σ𝑥2−(Σ𝑥)2][𝑛Σ𝑦2−(Σ𝑦)2] (4) Where, 𝑦 = temperature, and 𝑥 = rainfall. T test was used to ascertain whether there is a significant difference between onset dates, cessation dates and length of rainy season in the area. Time series is defined as a series of observation assumed by a variable over successive time periods. Time series analysis helps to fit an array of time bound data on a line of best fit, it helps to show the type of trends existing in the data graphically. Time series will be used to examine the trends of rainfall and temperature for the study period and for predict for the future years. The trend line equation of a time series data is as shown in equation 5. 234 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Y= A+B(X) (5) Where, A =intercept, B= rate of change per time, and X= coded values of years. 3. Results and Discussion 3.1 Descriptive Statistics of Onset, Cessation and Length of Rainy Season Table 1 presents the onset dates, cessation dates and length of rainy season in the area for the study period. During the study period, rainfall started lately in 1983, rainy season started on 16th April (106th day of the year) while it started earliest in 2004, rainy season started on February 3rd (34th day of the year). The mean onset date of rain in the area during the study period is 13th March (72nd day of the year) with a standard deviation of 18.8 and a coefficient of variation of 25.7 %, this implies that onset date during the study period is heterogeneous. Furthermore, rainy season started in February in 1982, 1986, 1987, 1991, 1993, 1996, 1999, 2003, 2004 and 2011 respectively, it started in March in 1981, 1984, 1985, 1988, 1989, 1994, 1995, 1997, 2000, 2002, 2005, 2006, 2012, 2013, 2014, 2015 and 2016 respectively while it started in April in 1983, 1990, 1998, 2001, 2007, 2008, 2009, 2010 and 2017. This result shows that rainfall starts within the month of February to April in the study period. Also, during the study period, 27 % of the onset dates was in February, 46 % in March while 27 % was in the month of April. In Table 1, the earliest cessation date of the rainy season in the area was in 1983 which occurred on September 22nd (265st day of the year) while the latest cessation date was in 1990 which occurred in the 10th of December (344st day of the year). The mean cessation date of the rainy season in the area during the study period is October 23rd (296th day of the year) with a standard deviation of 15.8 and a coefficient of variation of 5.3%. This implies that cessation dates during the study period was homogeneous. Also, Cessation dates in 1982, 1983, 1985, 1986, 1987, 1989, 1991, 1993, 1995, 1996, 2000, 2001, 2003, 2004, 2005, 2006 and 2008 are lower than the mean value of 23rd October while cessation dates in 1981, 1984, 1988, 1990, 1997, 1999. 2007, 2009, 2010, 2011, 235 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 2012, 2013, 2014, 2015, 2016 and 2017 are higher than the mean value and cessation dates in 1992, 1994 and 1998 are the same dates with the mean value of 23rd October. Table 1. Onset, cessation and length of rainy season Year Onset Date Julian Day Cessation Date Julian Day Length of Rainy Season 1981 15th March 74th day 1st November 305th 231 1982 25th February 56th day 17th October 290th 234 1983 16th April 106th day 22nd September 265th 159 1984 8th March 68th day 23rd October 297th 229 1985 20th March 79th day 21st October 294th 215 1986 27th February 58th day 11th October 284th 226 1987 14th February 45th day 19th October 292nd 247 1988 6th March 66th day 26th October 300th 234 1989 9th March 68th day 22nd October 295th 227 1990 11th April 101st day 10th December 344th 243 1991 23rd February 54th day 19th October 292nd 238 1992 9th April 100th day 22nd October 296th 196 1993 21st February 52nd day 20th October 293rd 241 1994 15th March 74th day 23rd October 296th 222 1995 10th March 69th day 20th October 293rd 224 1996 17th February 48th day 18th October 292nd 244 1997 23rd March 82nd day 9th December 343rd 261 1998 14th April 104th day 23rd October 296th 192 1999 26th February 57th day 26th October 299th 242 2000 21st March 52nd day 15th October 289th 237 2001 4th April 94th day 26th September 269th 175 2002 29th March 88th day 24th October 297th 209 2003 25th February 56th day 17th October 290th 234 2004 3rd February 34th day 23rd September 267th 233 2005 13th March 72nd day 28th September 271st 199 2006 7th March 66th day 20th October 293rd 227 2007 1st April 91st day 29th October 302nd 211 2008 9th April 100th day 20th October 294th 194 2009 2nd April 92nd day 13th November 317th 225 2010 11th April 101st day 11th November 315th 214 2011 21st February 52nd day 26th October 299th 247 2012 13th March 73rd day 26th October 300th 227 2013 6th March 65th day 26th October 299th 234 2014 11th March 70th day 26th October 299th 229 2015 15th March 74th day 25th October 298th 224 2016 12th March 72nd day 30th October 304th 232 2017 3rd April 93rd day 27th October 300th 207 Mean 13th March 73nd day 23rd October 296th day 223 STD 18.8 15.8 20.8 CV (%) 25.7 5.3 9.3 Note : CV = Climatic Variable, STD = Standard Deviation 236 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 As shown in Table 1, the lowest length of rainy season of 159 days was recorded in 1983 while the highest of 261 days was recorded in 1997. The mean length of rainy season in the area is 223 days with a standard deviation of 20.8 and a coefficient of variation of 9.3%. This implies that the length of rainy season in the area is homogeneous. Furthermore, length of rainy season in 1983, 1985, 1992, 1994, 1998, 2001, 2002, 2005, 2007, 2008, 2010 and 2017 are lower than the mean value of 223 days while length of rainy season in 1981, 1982, 1984, 1986, 1987, 1988, 1989, 1990, 1991, 1993, 1995, 1996, 1997, 1999, 2000, 2003, 2004, 2006, 2009. 2011, 2012, 2013, 2014, 2015 and 2016 are higher than the mean value of 223 days. 3.2 Pattern of Onset Dates Figure 3 presents the pattern of Onset date of rainy season in the area during the study period. The trend of onset date is upward sloping, this implies that rainfall is starting late in the study period. The trend also shows that the latest date of onset was in 1983 while the earliest date was in 2004. This result implies that onset dates in the area is affected by climate change, rain now starts late in the study area. Figure 3. Pattern of Onset Dates y = 0.2065x + 43170 11-Dec 31-Dec 20-Jan 09-Feb 01-Mar 21-Mar 10-Apr 30-Apr 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 O n se t D a te Year Onset Date Linear Trendline 237 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 3.3 Pattern of Cessation Dates As shown in Figure 4, the trend of cessation dates in the area is upward sloping, this implies that cessation dates in the study area is getting late. Furthermore, the earliest cessation date was in 1983 while the latest was in 1990. Also, the trend shows that cessation dates where early between 1981 to 1996 and late between 1997 and 2017. This result shows that climate change has led to late cessation of rainfall in the area. Figure 4. Pattern of Cessation Dates 3.4 Pattern of Length of Rainy Season As shown in Figure 5, the trend of length of rainy season is downward sloping, this implies that there is a decreased in the length of rain season in the area. Also the result of the trend line equation y = -0.0512x + 224.3 is negative, meaning that the area has been experiencing a significant decrease in length of rainy season over the period of 35 years. This further shows a predicted rate of decrease of about -0.0512 per annum. The y = 0.1479x + 43393 08-Aug 28-Aug 17-Sep 07-Oct 27-Oct 16-Nov 06-Dec 26-Dec 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 C e ss a ti o n D a te s Year Cessation Date Linear Trend 238 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 decrease in the length of rainy season is as a result of late onset of rainfall and early cessation. Figure 5. Length of Rainy Season 3.5 Anomalous Rainfall And Temperature Table 2 presents the years with anomalous rainfall in the study area during the study period. 1994 experienced the driest year with an anomaly index of -2.12 while 2011 experienced the wettest year with an anomaly index of 2.28. Furthermore, 1985 recorded the coldest year with a temperature anomaly index of -2.31 while 2006 recorded the hottest year with a temperature anomaly index of 1.73. y = -0.0512x + 224.3 R² = 0.0007 0 50 100 150 200 250 300 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 Le n g th o f th e R a in y S e a so n Year Length of Rainy Season Linear Trendline 239 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Table 2. Anomalous rainfall and temperature Year Anomalous Rainfall Anomalous Temperature 1981 0.92 -0.38 1982 -1.66 -1.15 1983 -1.47 -0.38 1984 -0.67 0 1985 0.03 -2.31 1986 -1.82 -1.35 1987 0.82 1.35 1988 1.08 0 1989 0.81 -0.58 1990 -0.69 1.35 1991 -0.47 0 1992 -0.46 -0.38 1993 -0.39 0.19 1994 -2.12 0 1995 -0.45 0.96 1996 1.09 0.58 1997 0.63 0.01 1998 -0.30 0.03 1999 1.92 0 2000 0.03 0.38 2001 -1.35 0.58 2002 0.23 0.58 2003 -0.32 0.77 2004 -0.16 0.58 2005 -1.39 -0.19 2006 -0.50 1.73 2007 1.12 -1.73 2008 0.38 -0.77 2009 0.61 -1.92 2010 1.00 -1.92 2011 2.28 -0.19 2012 0.26 0.19 2013 0.46 0 2014 0.51 0.19 2015 0.02 1.35 2016 0.32 0.58 2017 -0.29 1.35 In Figure 6, 1982, 1983, 1984, 1986, 1990, 1991, 1992, 1993, 1994, 1995, 1998, 2001, 2003, 2004, 2005, 2006 and 2017 experienced negative anomaly, this implies that these years are dry experiencing rainfall below the normal while 1981, 1985, 1987, 1988, 1989, 1996, 1997, 1999, 2000, 2002, 2007, 2008, 2009, 2010, 2012, 2013, 2014, 2015 240 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 and 2016 experienced positive anomaly, rainfall received in this years are above the normal. Figure 6. Anomalous rainfall Figure 7 presents the graph of temperature anomaly in the area for the study period. Temperature recorded in 1981, 1982, 1983, 1985, 1986, 1989, 1992, 2005, 2007, 2008, 2009, 2010 and 2011 showed a negative anomaly while 1987, 1990, 1993, 1995, 1996, 1997, 1998, 2000, 2001, 2002, 2003, 2004, 2012, 2014, 2015 and 2016 showed a negative anomaly. Also, 1984, 1988, 1994, 1999 and 2013 experienced a normal temperature for the study area. -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Anomalous Rainfall 241 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 Figure 7. Anomalous temperature 3.6 Relationship Between Rainfall and Temperature Table 3 presents the correlation coefficients between rainfall and temperature in the study area. The Minimum temperature (r = 0.084), sunshine hours (r = 0.220), cessation (r = 0.641) and Length of rainy season (0.406) are positive. This implies that minimum temperature, sunshine hours, cessation and length of rainy season are directly related to rainfall amount. However, if minimum temperature, sunshine hours, cessation -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Anomalous Temperature 242 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 and length of rainy season increases, rainfall increases and if minimum temperature, sunshine hours, cessation and length of rainy season decreases, rainfall decreases. Furthermore, the Maximum temperature (r = -0.191), average temperature (r = -0.044) and Onset (r = -0.119) are negative; this implies that maximum temperature, average temperature and onset are inversely related to rainfall amount. If maximum temperature, average temperature and onset increases, rainfall decreases and if maximum temperature, average temperature and onset decreases, rainfall increases. The implication of this maximum temperature and average temperature influences rainfall in the area. As indicated in Table 3, the correlation coefficient of maximum temperature (0.325), sunshine hours (0.380), average temperature (0.867), cessation and length of rainy season (0.080) is positive. This means that there is a directly relationship between minimum temperature and maximum temperature, sunshine hours’ average temperature and length of rainy season. However, implies that if these variables increase, minimum temperature will increase and if these variables decrease, minimum temperature will decrease. Also, Onset (-0.118) and cessation (-0.026) is negative; this implies that there is an inverse relationship between onset date, cessation date and minimum temperature. Table 3. Relationship between the climatic variables Variable Rainfall Min. Temp Max. Temp Sunshine Average Temp. Onset Cessation LRS Rainfall 1 Min T. 0.080 1 Max T. -0.191 0.325* 1 Sunshine 0.220 0.380* -0.389* 1 Average T. -0.044 0.867* 0.749* 0.069 1 Onset -0.119 -0.118 -0.203 -0.141 -0.191 1 Cessation 0.408* -0.026 -0.031 0.079 -0.031 0.318 1 LRS 0.406* 0.080 0.131 0.101 0.131 -0.642* 0.509* 1 Note : LRS = Length of rainy season, Temp./T. = temperature Furthermore, the correlation coefficient Average temperature (r = 0.749) and Length of rainy season (r = 0.131) is positive, this implies that there is a direct relationship between average temperature, length of rainy season and maximum 243 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 temperature. Also, sunshine hours (r = -0.389), onset (r = -0.203) and cessation (r = - 0.031) is negative, this implies that there is an inverse relationship between sunshine hours, onset, cessation and maximum temperature. In the same vein, the correlation coefficient average temperature (r = 0.069), cessation (r = 0.079) and length of rainy season (r = 0.201) is positive, meaning that there is a direct relationship between average temperature, cessation, length of rainy season and sunshine hours. Further, onset (-0.141) is negative; this implies that there is an inverse relationship between onset and sunshine hours. Also, the correlation coefficient length of rainy season (0.189) is positive; this implies that there is a direct relationship between length of rainy season and average temperature while onset (-0.191) and cessation (- 0.031) is negative, this implies that there is an inverse relationship between onset, cessation and average temperature. Furthermore, the correlation coefficient, Length of rainy season (-0.642) is negative, this implies that there is an inverse relationship between length of rainy season and Onset. While onset (-0.642) is inversely related to length of rainy season. In the same vein, cessation (0.509) is directly related to length of rainy season. Furthermore, cessation (0.408*) and length of rainy season (0.406*) are significantly related to rainfall, Minimum temperature (0.325*), sunshine hours (0.380*) and average temperature (0.867*) is significantly related to minimum temperature. In the same vein, sunshine hours (-0.389*) and average temperature (0.749*) are significantly related to maximum temperature. Also, Onset (-0.642*) and cessation (0.509*) are significantly related to length of rainy season. 3.7 Significant Difference Between Climatic Variables Table 4 presents the result of student t test to determine if there is significant difference in Onset dates, cessation dates and length of rainy season. since calculated t (23.539) is greater than p-value (2.028) for onset; we reject the null hypothesis and accept 244 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 the alternative hypothesis which states that there is a significant difference in onset dates in the study area. Table 4. T Test at 0.05 significant level Test Value = 0.95 T Df Sig. Mean Difference 95% Confidence Interval of the Difference Lower Upper Onset 23.539 36 2.028 73.13514 66.8339 79.4364 Cessation 113.527 36 2.028 296.45946 291.1634 301.7555 LRS 65.148 36 2.028 223.32432 216.3721 230.2765 Note : LRS = Length of rainy season In the same vein, since calculated T (113.527) is greater than p-value (2.028) for cessation, thus, we reject the null hypothesis and accept the alternative hypothesis which states that there is a significant difference in cessation dates in the study area. Furthermore, since calculated T (65.148) is greater than p-value (2.028) for length of the rainy season, we reject the null hypothesis and accept the alternative hypothesis which states that there is a significant difference in length of rainy season in the study area. This result implies that onset dates in the area is affected by climate change, rain now starts late in the study area. These results are similar to those reported by Zhang & Fueglistalerz(2019)zthatzchangeszinzthezatmosphericzcirculationzpatternzcanzzenhance rainfallzinzone placezandzsuppresszrainfallzinzanother. Climate change also has led to latezcessationzofzrainfallzinzthezarea.zThesezresultszarezsimilarztozthosezreportedzzby (Baezetzal.,z2019)zthatzbyzthezzoccurrencezzofzzallzzdifferentzztypeszofzzdroughtszisz associatedzwithzextremezmeteorologicalzzdroughts,zzwhichzzarezzmainlyzzcausedzzby precipitationzdeficitszinzthezwetzseasonzorzprolongedzrainfallzshortages.zThesezresults arezsupportedzbyzYacoubz&zTayfurz(2020)zthatztherezziszzpronouncedzzdecreasezzin precipitationzatzthezsouthernzregion,zreachingztozalmostzaztotalzzofz300zzmmzzdeficit innearlyz100-yearzperiod. 245 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 The trend of length of rainy season is downward sloping, this implies that there is azdecreasedzinzthezlengthzofzrainzseasonzinzthezarea.zThesezzresultszzwerezzdifferent fromzthosezofzZhangz&zZhouz(2019),zwhozreportedzthatzzforzzthezzglobalzzmonsoon regionzaszazwhole,zsignificantzzincreaseszinzzextremezprecipitationzzandzzassociations withzglobalzwarmingzare alsozidentified,zhoweverzwithzlimitedzspatialzcoverage. The years with anomalous rainfall in the study area during the study period. Thesezresultszarezsimilarztozthosezreportedzby Olaniyan et al. (2019) zthatzthezInter- tropicalzDiscontinuityz(ITD)zandzzthezzSeazzSurfacezTemperaturezz(SST)zzoverzzthe CentralzPacific,zexhibitzdirectzteleconnectionzwithzthezonset anomaly. These results are supportedzbyzFitzpatrickzetzal.z(2019)zthatzazpotentialzusezofzlocalzonsetzregionszare presentedzthatzshowszthezlinkzbetweenzthezannualzintertropicalzfrontzprogressionzand localzagronomiczonset. If maximum temperature, average temperature and onset increases, rainfall decreases and if maximum temperature, average temperature and onset decreases, rainfall increases. Thesezresultszarezsimilarztozthose reportedzby zSharma et al.z(2018)zthatzthezincreasezzinzzfrequencyzandzzmagnitudezofzextremezzrainfallzzhas beenzattributedztozthezincreasingztrendzzinzzmaximumzzandzzminimumzztemperature. ThesezresultszarezsupportedzbyzMeshramzetzal.z(2018)zthatzthezzmonsoonzzandzzthez winter seasonzexhibited aznegativeztrendzinzrainfallzchangeszoverzthezperiodzofzstudy. 4. Conclusion The area is experiencing a significant increase in minimum temperature, sunshine hours, rainfall and average temperature while it is experiencing a significant decrease in maximum temperature. However, rains start early in the area and ceases late, resulting to a prolonged length of rainy season because the area is experiencing a decline in the length of rainy season. It could also be noted that onset dates of rains and cessation dates are major determinants of length of rainy season as a change in any of the two variables will affect the length of the rainy season. The rainfall anomaly over all the area revealed that there was a composite nature in which. Early warning systems about extreme 246 E.A. Adeleke and E.A. Orebayo et al / GEOSI Vol 5 No 2 (2020) 226-249 temperature and rainfall events should be put in place. This will help reduce the cost of destruction caused by this extreme weather events especially in the case of flood. Early warning about temperature events can help prevent disease such as meningitis that is related with temperature. The response should involve flood forecasting and early warning using rainfall data, rescue and evacuation and post flood impact assessment, recovery and rehabilitation. 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