Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

Yaw Asamoah; Kow Ansah-Mensah

doi:10.1155/2020/3402178

Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

Asamoah, Yaw;Ansah-Mensah, Kow 2020-04-01 00:00:00 Hindawi Advances in Meteorology Volume 2020, Article ID 3402178, 18 pages https://doi.org/10.1155/2020/3402178 Research Article Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana 1 2 Yaw Asamoah and Kow Ansah-Mensah Department of Geography Education, University of Education, Winneba, Ghana Department of Geography and Regional Planning, University of Cape Coast, Cape Coast, Ghana Correspondence should be addressed to Yaw Asamoah; yasamoah@uew.edu.gh and Kow Ansah-Mensah; kow.ansah-mensah@stu.ucc.edu.gh Received 22 July 2019; Revised 4 November 2019; Accepted 5 February 2020; Published 1 April 2020 Academic Editor: Antonio Donateo Copyright © 2020 Yaw Asamoah and Kow Ansah-Mensah. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With varied implications, Ghana’s temperature and rainfall are projected to rise and decline, respectively. A study exposing speciﬁc areas of concern for appropriate responses in this regard is a welcome one. -is study sought to describe the temporal variations in temperature and rainfall in the Bawku Area of Ghana. A forty-year (1976–2015) daily climate data was collected on three meteorological stations from the Ghana Meteorological Agency. Normality test, homogeneity test, Standardised Precip- itation Index (SPI) analysis, Mann–Kendall trend test, and One-way post hoc ANOVA were performed using XLSTAT and DrinC. Over the period under study, the mean annual rainfall pattern was generally erratic, ﬂuctuating between 669.8 mm and 1339.4.6 mm with an annual average of 935.3 mm. -e long-term (40-year period) average temperature of the three stations, on ° ° ° the other hand, was 28.7 C, varying between 26.9 C and 29.9 C annually. Whereas the SPI value of 2006 was ≥2.0, indicating extremely wet year with 2.3% probability of recurring once every 50 years, 1988 was the hottest year with temperature anomaly ° ° ° value of 1.2 C, while coolest years were 1979 (−1.8 C) and 1976 (−1.0 C). -e Mann–Kendall trend test showed a rise in rainfall in Binduri, Garu-Tempane, and Manga, yet none of the rainfall changes were statistically signiﬁcant (P> 0.05). Mean temperature on the other hand experienced a signiﬁcant rise (P< 0.05). With an R-square of 34.7%, the rise in temperature in Manga witnessed the most signiﬁcant change in annual temperature changes. -ere were statistically signiﬁcant (P < 0.05) diﬀerences in the inter- decadal temperature over the 40-year period. Generally, it can be stated that both temperature and rainfall vary in the study area with various degrees of disparities, but temperature assumes an upward trend at a faster rate. We therefore recommend that stakeholders resort to the construction of dams and boreholes to ensure regular availability of water for both domestic and agricultural uses. st range of 1–6 C by the end of the 21 century if nothing is 1. Introduction done to mitigate it [2]. Global warming is described as the Global climate is reported to have witnessed a drastic change basis of climate change which the world is battling to resolve; at least over the last century as observed in the Intergov- these changes are extreme weather conditions, sea level rise, and a shift and erratic precipitation pattern observed in ernmental Panel on Climate Change’s (IPCC) fourth and ﬁfth assessment reports. -e eﬀect of this change has resulted recent times [3]. Spatiotemporal trend analysis of precipi- in the changes and shifts in the patterns of the climate el- tation on global scale has shown a slightly positive trend, ements, particularly precipitation, humidity, and tempera- though variability still exists on some regional and local scales ture across the globe [1]. Accordingly, the Intergovernmental in terms of intensity, amount, and shifts [2, 4]. -ese vari- Panel on Climate Change reported that the global mean abilities and change in climate have signiﬁcant impacts on surface temperature has increased by 0.76 C in the last 150 livelihood and wellbeing of people, particularly those in years and will continue to witness an upward trend in the rainfall-dependent regions. 2 Advances in Meteorology Climate variability and change pose serious threat to and its impacts have been carried out in the country, es- many countries, especially the most vulnerable and poor pecially in the northern regions. -ese studies, including developing ones. United States Environmental Protection those in [16–19], independently concluded that the northern Agency [5] indicates that whereas there is still uncertainty in part will continually experience heating with short periods of rainfall due to the shift and erratic nature, surface tem- rainfall. peratures are continually rising and warming. Meanwhile, Moreover, on the local scale, temperature and humidity studies reveal that the combined eﬀect of varying rainfall and are found to be increasing and decreasing, respectively, in rising temperatures is touted to aﬀect all aspects of human the Bawku East Municipality of the Upper East Region activities [6]. Empirical evidence indicates that a rising trend (UER) [20]. -e Ministry of Food and Agriculture (MoFA) in global temperature is the result of human activities of Ghana estimates that about 70% of the residents in the contributing to the emission of atmospheric gases [7]. Even UER are farmers, mostly practicing rain-fed subsistence though every country around the globe contributes to the farming [21]. While it is undisputable fact that temperature emission and consequently climate change, developed and rainfall are the main direct determinants of agricultural countries are reported to be the major contributors, yet the activities [11, 22], especially in this part of the world, minor contributors, developing countries, are the more Frimpong et al. ignored rainfall trends and anomalies with vulnerable to the eventual impacts [8]. the potential eﬀects on the livelihoods of the poor farmers Africa and West Africa by virtue of their geographical [20]. Hence, this study intends to ﬁll that gap and add to locations are much prone to the shocks of climate change, literature. It is on the backdrop of the above that this study global warming, and the variability in climate. -e IPCC aims to analyse the trends of temperature and rainfall in the report projects an increasing temperature trend in the West Bawku Area of UER of the Savannah agroclimatological African region, within which Ghana is located, with em- zone of Ghana between 1976 and 2015. phasis on the area that lies within the Savannah and Sahel zone [9]. Rising temperatures and erratic precipitation have 2. Materials and Methods a dire consequence on the African population at large due particularly to its dependence on rainfall for its agricultural 2.1.StudyArea. -e Bawku Area of the Upper East Region is practices which in most cases is the major economic driver located in the north easternmost corner of Ghana and shares in the subregion [10]. -is does not come with a surprise borders with two francophone countries, Burkina Faso and because, according to [11], temperature and rainfall are the Togo, to the north and east, respectively. -e area is made up main factors that directly aﬀect agricultural activities. As of ﬁve diﬀerent administrative districts: Bawku Municipality such, erratic rainfall and extreme temperature may likely (Bawku), Bawku West (Zebilla), Binduri (Binduri), Garu- aﬀect agricultural output in the continent, leading to further Tempane (Garu), and Pusiga District (Pusiga). -e study ° ° repercussions on the wellbeing of the people. area in totality is located on latitudes 10 30′ to 11 11 north ° ° -ere have been studies in the recent past on trends in of the equator and longitudes 0 06′ east to 0 40′ west of the climate over Africa countries [3, 10, 12, 13]. -ese studies Greenwich Meridian with an average land area of about basically report on increasing temperatures over the con- 2848 km . -e area lies between 202 and 235 m above sea tinent and the fact that there are variations within countries. level (Table 1). Bawku Municipality is found on latitudes 10 ° ° -e authors separately found a signiﬁcant rising trend in 40′ to 11 11′ north of the equator and longitudes 0 61′ east ° ° temperature, whereas rainfall on the other hand either in- to 0 18′ west, whereas Bawku West is located on latitudes 10 ° ° ° signiﬁcantly assumed an upward trend or was virtually 30′ and 11 10′ north and longitudes 0 20′ to 0 35′ west [23]. constant. Regardless of their ﬁndings, it is more appropriate Binduri District on the other hand is on latitude 11 00′ ° ° for research to also concentrate on local level trend analysis north and longitudes 0 06′ east to 0 18′ west and Garu- ° ° of climate variables since regional and national level studies Tempane is on latitudes 10 38′ to 11 00 north and lon- ° ° have shown variability in climate. Moreover, local level gitudes 0 06 to 0 23′ east [24]. -e study area map is shown adaptation measures to combating climate shocks are in Figure 1. possible and eﬀective only if the local level climate has been Like the whole of UER, Bawku Area falls within the studied for possible variability and change as enshrined in interior continental climate zone of Ghana dominated by the sustainable development goal (SDG) 13: take urgent long dry period and short wet (rainfall) period. -e wet and action to combat climate change and its impacts. dry seasons are determined by the North East Trade Winds In Ghana, projection of increasing trends of climate (also called harmattan) and South West Monsoon that al- elements regardless of the variability indicates that the re- ternate with the seasons. North East Trade Winds originate gions in the northern part of the country (Guinea and Sudan from the Sahara Desert and control the climate of the area. In Savannah) will experience between 2.1 and 2.4 C of tem- eﬀect, the dry season lasts for seven months from October to perature increase, whereas all other regions will be between April [20]. -e wet season is characterized by a unimodal 1.3 and 2.0 C [14]. Holding all other factors constant, the rainfall regime for about ﬁve months between May and Environmental Protection Agency has also indicated that September. -e wet season, however, is inﬂuenced by the historical trends in the climate of Ghana point to the fact that moisture concentrated tropical maritime air mass, which mean minimum and maximum temperatures in the Sa- blows from the Atlantic Ocean [26]. -e area is mainly ° ° vannah areas are expected to increase by 1.10 C and 1.20 C drained by the White and Red Volta River and its tributaries by the year 2040 [15]. Researches into the trends of climate together with other notable rivers and streams: Tamne and Advances in Meteorology 3 Table 1: Geographical locations of the meteorological stations with their elevations. ° ° S/N Gauge station Latitude Longitude Elevation (m) 1 Binduri 10.97 −0.32 202 2 Garu 10.85 −0.18 202 3 Manga 11.02 −0.27 231 Source: [20]. 0°30′0′′W 0°20′0′′W 0°10′0′′W 0°0′0′′ Burkina Faso Bawku Pusiga Yiring Ungu Pusiga Zaboga Kuka Binduri Bawku Zebilla Binduri Bawku West Garu Gubuliga Pialugu Togo Garu-tempane Yanatinga Apotdabogo II Talensi Bunkpurugu Yunyoo 036 12 18 24 East Mamprusi Km 0°30′0′′W 0°20′0′′W 0°10′0′′W 0°0′0′′ Study communities District capital Study area Regional boundary International boundary Figure 1: Study area map in regional and national context. Source: [25]. Pawnaba-Kiyinchongo, respectively. -e rivers ﬂow with the in the area practices subsistence agriculture, the commonest seasons (wet and dry). Rivers overﬂow their banks and ﬂood economic activity there is primary (agriculture, forestry, and nearby communities during the wet season (rainfall season); ﬁshing) and a few secondary (wholesale, retail, and they, however, dry up during the dry season. manufacturing) [21, 27]. With a total population of about 384,151, the area makes up about 36.7% of the UER population, with about 60% being females, 50% youth, 41% children under 15, and about 2.2.Dataset. Time-series data of rainfall and temperature for 9% aged [27]. -e area is largely rural with average the three meteorological stations at Binduri, Garu-Tempane, and Manga communities, all in the Bawku Area of the Upper household size of 6.5. About 60% of the population above 11 years are illiterate, while males constitute the majority East Region were collected and analysed for the period from (53.2%) of the 43% literates. -e largest ethnic group found 1976 to 2015. Table 1 presents the geographical locations and in the area is the Kussasis of the Mole Dagbani group; there elevation of the instruments used to record the data. -e are also the Mamprusis, Moshies, Bimoba, Bisas, Busanga, forty-year-period data was used because of its consistency, Frafra, and Fulani. -e dominant religion is Islam with accuracy, and reliability. -is highly dependable station data majority of the Christian population being Catholics and was collected from the Ghana Meteorological Agency there are also some Traditional African Religious wor- (GMet) in Accra, which is mandated to collect and store shipers. Whereas the predominantly rural population found climate data. Spatial interpolation of data was carried out to 10°40′0′′N 10°50′0′′N 11°0′0′′N 11°10′0′′N Nabdam 10°40′0′′N 10°50′0′′N 11°0′0′′N 11°10′0′′N 4 Advances in Meteorology ﬁll data gaps using arithmetic mean method where data where F is the standard normal distribution function; S(x) before and after the gap were used to generate the missing is the empirical distribution function of the Z values. value. Consequently, years without consistent data were then Jarque–Bera (JB) test is a goodness-of-ﬁt test of whether eliminated from the study, hence the 1976–2015 data. -e sample data have the skewness and kurtosis matching a monthly and annual data used for the analysis in this study normal distribution. JB test is given by the following were compiled from the daily data collected. -e overall formula: average of the 40-year period was calculated to be able to n 3 􏽐 x − x􏼁 i�1 (5) examine the rainfall and temperature anomalies. K � , ns 􏽐 x − x􏼁 i�1 2.3. Data Analysis. -e ﬁrst part of the data analysis was (6) K � − 3. ns done using normality test statistics to understand the nature of the data. -is was necessary to determine the best -e Jarque–Bera test is then determined by putting available test tools suitable for the analysis. As such, both together equations (5) and (6). rainfall and temperature data were subjected to normality test using Shapiro–Wilk, Anderson–Darling, Lilliefors, and 2 2 K 􏼁 K 􏼁 3 4 Jarque–Bera tests. -ese tests were applied because of their (7) JB � n􏼠 + 􏼡, 6 24 suitability and general acceptability for normality testing [28] and the fact that each of the tests will complement the where x is each observation, n is the sample size, s is the others’ weaknesses. With a null hypothesis of normal sample standard deviation, k is skewness, and k is kurtosis. distribution, all the aforementioned tests compare test scores 3 4 Secondly, homogeneity test statistics were applied to of sample to normally distributed scores with the same determine homogenous nature of the data prior to analysing standard deviation and mean. Equations used for the nor- the trend. Homogeneity test was carried out to test for mality tests are provided. homogeneity and suitability of data for trend testing using Shapiro–Wilk (W) is given as Pettitt’s test, Standard Normal Homogeneity Test (SNHT), 􏼐􏽐 a x 􏼑 and the Buishand range test [29]. -ese three tests have i�1 i (i) (1) W � , n similar null hypothesis (H ) that data is homogenously 􏽐 x − x􏼁 i�1 i identical. At a P value of 0.05, H is accepted or rejected. where x is the ordered sample value, a is a constant 1 i generated from the means, variances, and covariance of the 2.3.1. Pettitt’s Test. -is test is a nonparametric test based on ordered statistics, n is number of observations, and x is the rank r of Y and requires no assumption about the distri- i i sample mean. bution of data [29]. Anderson–Darling (AD) test uses cumulative distribution function to determine the normality of a given set of data. T � 2 􏽘 r . (8) y i−y(n+1),y�1,2,...,n -e formula is shown: t−1 AD � −n − 􏽘(2i − 1) ln F x + ln 1 − F x , 􏼂 􏼁 􏼁 􏼁􏼃 i n−i+1 -at is, there is a break in year k when i�1 T � max T . (2) y (9) 0≤x≤1 where n � sample size; F(x) is the cumulative distribution -e value of T is then compared with the [29] critical th function for the speciﬁed distribution; i is the i sample value. when the data is sorted in ascending order. Lilliefors is an improvement in the Kolmogor- ov–Smirnov (K-S) test correcting the tails of the probability 2.4. Standard Normal Homogeneity Test (SNHT). z repre- distributions. One of its main advantages is that even when sents a statistic comparison of the mean of the ﬁrst y years mean and standard deviations are unknown, the test can still with the last years of n-y using P(y). be applied. -e formula is shown as follows: x − x Py � yZ1 + (n − 1)Z2, y � 1, 2, 3 . . . n, (10) Z � , i � 1, 2, . . . , n, (3) where where Z is the individual z-score for every member in the i n 1 y − y sample; X is an individual member/data point. Z1 � 􏽘 , y s -e Lilliefors test statistic which is the empirical dis- i−1 (11) tribution function is then given by 􏼌 􏼌 1 y − y 􏼌 􏼌 i 􏼌 􏼌 Z2 � 􏽘 . T � sup 􏼌F (x) − S(x)􏼌, (4) 1 x n − y s i−y+1 Advances in Meteorology 5 -e year y consisted of break if value of P is maximum. stand-alone software has been used by others (such as -us, the null hypothesis is rejected if [38, 40–42]). Table 2 presents the interpretation of SPI results. P � max P . o y (12) 0≤y≤1 Subsequently, the Mann-Kendall (MK) trend test was applied in this analysis to the annual rainfall and tem- perature values to determine the statistical trend in the 2.4.1. Buishand’s Test. Buishand’s test is applied to variables dataset. MK is a monotonic nonparametric test that is of any distribution. However, properties of the test have widely used for trend testing in climate data [43]. In this especially been studied for normal distribution case [29]. -e test, the null hypothesis (H ) is that there is no trend in the homogeneity test, however, can be based on the cumulative dataset. A positive and negative MK test result signiﬁes an deviations from the mean and hence is given by the fol- increasing and decreasing trend, respectively, and this was lowing formula [30]: supported by Sen’s slope estimator test. Sen’s slope esti- mator, on the other hand, is used to complement the MK ∗ ∗ S � O, S � 􏽘(xi − u), k � 1, 2, . . . T. (13) o K test and also shows the magnitude of the trend. -e closer i�1 the result is to zero (0), the lesser the trend is. -e sign (+, −) of the slope tells if the trend is increasing or decreasing. -us, when the data is homogenous, the value of S will rise and fall around zero. When S is at its maximum or -e MK was adopted because of its robustness and gen- minimum, the year y is said to have a break. Adjusted range erally acceptability and consequent application in many R is then obtained by climate analyses [44]. According to Tigkas et al., MK test statistics are calcu- ∗ ∗ max S − max S 􏼁 1≤t≤T k 1≤t≤T k (14) R � . lated based on the following equations [38]: x−1 x Furthermore, a graphical analysis of the monthly and S � 􏽘 􏽘 Sgn(xj − xi), (16) annual rainfall and temperature was performed to show the i�1 j�i+1 long-term variability in the dataset. -e slope coeﬃcient sign would then indicate whether the data follows a positive or +1, if xj − xk > 0, ⎧ ⎪ negative trend. Temperature anomalies were calculated ⎨ Sgn(x) � 0, if xj − xk � 0, (17) using the arithmetic mean. In this study, temperature anomaly is the number of degrees of temperatures that the −1, if xj − xk < 0. absolute temperature varies from the average (reference point) or the diﬀerence of the absolute temperature from the Xi and Xk are the annual values in years j and k (j> k), average or baseline temperature. respectively. To understand the interannual variability in the rainfall Variance is given as follows: of the study area, the Standardised Precipitation Index (SPI) n(n − 1)(2n + 5) was used to calculate the anomalies in rainfall. (18) Var(S) � , -e SPI is generated using long-term records of pre- cipitation. -is is done by ﬁtting the data into a probability distribution to be transformed into a normal distribution where n is the number of observations and xi (i � 1, . . ., n) such that the mean value of the SPI for the location and its are the independent observations. desired period is zero [31]. -e SPI approach is a widely Z-statistics: used method for precipitation or drought intensity studies. s − 1 ⎧ ⎪ 􏽰�� ⎪ , if s > 0, Results of SPI are originally generated using gamma dis- Var(S) tribution because of its suitability to precipitation time series [32]. However, one can also use the log-normal (19) 0 . . . , if s � 0, distribution approach in generating the SPIs as has been used by others (such as [33–37] because they produce similar results [38]). According to World Meteorological s + 1 􏽰�� ⎩ , if s < 0. Organization, the SPI function is given by the following Var(s) formula: Sen’s slope is estimated by the following formula [38]: X − X i m (15) I(i) � , (Xj − Xk) Qi � , for i � 1, . . . N. (20) j − k where I (i) is the standardised index of year I; X is the value for the year I; X is the average for the year i; and σ is the standard deviation of the time series. But X and X are data values at times j and k; j, however, j k is greater than k (j> k). -e SPI can be calculated for diﬀerent timescales such as 3, 6, 12, 24, and 48 months [32, 39]. -e SPI calculation N is the slope estimator; if there is just one piece of data in each time, then N is given as was done using Drought Indices Calculator (DrinC); this 6 Advances in Meteorology Table 2: SPI values. SPI results Interpretation Probability of occurrence Severity of event ≥2.0 Extremely wet 2.3 1 in 50 years 1.5 to 1.99 Very wet 4.4 1 in 20 years 1.0 to 1.49 Moderately wet 9.2 1 in 10 years 0.0 to 0.99 Mildly wet (NN) 34.1 1 in 03 years 0.0 to −0.99 Mildly dry (NN) 34.1 1 in 03 years −1.0 to −1.49 Moderately dry 9.2 1 in 10 years −1.5 to −1.99 Very dry 4.4 1 in 20 years ≤−2.0 and less Extremely dry 2.3 1 in 50 years NB: “NN” means near normal. Source: [39, 42]. Table 3: Normality test of temperature and rainfall data. Shapiro–Wilk Anderson–Darling Lilliefors Jarque–Bera Variable/test W Sig. A Sig. D Sig. JB obs. Sig. Rainfall Binduri 0.8651 0.0002 1.1828 0.0038 0.1519 0.0209 63.6 <0.0001 Garu-Tempane 0.9144 0.0065 0.9878 0.0433 0.1133 0.2195 09.1 0.0010 Manga 0.9220 0.0089 1.0403 0.0255 0.1092 0.2677 14.42 0.0007 Temperature Binduri 0.8017 <0.0001 2.1485 <0.0001 0.2058 <0.0002 78.0280 <0.0001 Garu-Tempane 0.8089 <0.0001 1.2330 <0.0029 0.1393 <0.0489 167.0084 <0.0001 Manga 0.7318 <0.0001 2.1170 <0.0001 0.1984 <0.0004 301.9649 <0.0001 and data does not follow normal distribution, respectively. n(n − 1) (21) N � , Probability was taken at 95%; H is accepted at P> 0.05 and rejected at P< 0.05. Table 3 and Figure 2 show tests statistics where n is the number of time periods. and graphical results of the normality test. Hence, Sen’s estimator is given as Regarding the temperature, it is observed from Table 3 that all four test tools at all three gauge stations show sig- Q(N + 1) ⎧ ⎪ , if N is odd, niﬁcant deviation from normal distribution with P values ⎪ 2 ≤0.05; hence, H is rejected. -e risk of rejecting H while it o o (22) is true varies from 0.01% to 4.89%. In relation to temperature ⎪ 1 QN Q(N + 2) ⎪ data, the risk of type 1 error is minimal using Shapiro–Wilk 􏼠 + 􏼡 if N is even. 2 2 2 and Jarque–Bera test tools as compared to the others. Meanwhile, with rainfall data, all four tests violate the Lastly, One-way ANOVA post hoc test of multiple normality assumption with P value ≤5% (P< 0.05). comparisons was applied to test for diﬀerences in means Consequently, the null hypothesis for all four tests at of the temperature and rainfall data. -is test was in- Binduri was rejected, whereas one (Lilliefors) each was troduced in this study to purposely complement trend rejected for both Garu and Manga. -at is, only Lilliefors test test results while providing room to understand how best indicated that Garu and Manga rainfall data were normally ANOVA could help understand the diﬀerences in mean distributed. P-P plots and Q-Q plots are shown in Figure 2 scores of rainfall and temperature on decade bases. -is for visual inspection of the normality test. -e risk of type 1 was done by grouping the 40-year data into four groups error, however, ranges between 0.01% and 4.33%. -us, the (Group 1 � 1976–1985, Group 2 �1986–1995, Group results imply that both rainfall and temperature data violate 3 �1996–2005, and Group 4 � 2006–2015). It was to the normality assumption and hence cannot be subjected to identify groups that cause the diﬀerence in temperature a parametric test analysis. over the period. 3.2. Homogeneity Test Analysis. In this study, three types of 3. Results homogeneity test (i.e., Pettitt’s test, the SNHT test, and 3.1. Normality Test Results for Rainfall and Temperature. Buishand’s test) were performed at 5% level of signiﬁcance. Prior to undertaking the homogeneity and Mann–Kendall -is test is used to determine if indeed the datasets are trend test, the data was subjected to normality test using signiﬁcantly homogenous for the trend test analysis. Shapiro–Wilk, Anderson–Darling, Lilliefors, and Jar- In Figure 2, whereas SNHT and Buishand’s test de- que–Bera tests. All four tests have similar null (H ) and o tected the same change point (T) during 2007 at both alternate (H ) hypotheses; data follows normal distribution Binduri and Garu-Tempane, Pettitt’s test was met with a a Advances in Meteorology 7 P-P plot (rainfall) Q-Q plot (rainfall) 0.8 0.6 0.4 0.2 0 0 0 0.5 1 0 200 400 600 800 1000 1200 1400 Empirical cumulative distribution Binduri P-P plot (temperature) Q-Q plot (temperature) 0.8 0.6 0.4 27 0.2 26 0 25 0 0.5 25 26 27 28 29 30 Binduri Empirical cumulative distribution (a) Q-Q plot (rainfall) P-P plot (rainfall) 0.8 0.6 0.4 0.2 0 600 0 0.5 600 700 800 900 1000 1100 1200 1300 Garu-tempane Empirical cumulative distribution Q-Q plot (temperature) P-P plot (temperature) 0.8 0.6 0.4 0.2 0 27 0 0.5 27 28 29 30 31 32 33 Garu-tempane Empirical cumulative distribution (b) Figure 2: Continued. Theoretical cumulative distribution Theoretical cumulative distribution Theoretical cumulative distribution Theoretical cumulative distribution Quantile-normal (903.29, 207.18) Quantile-normal (28.46, 0.70) Quantile-normal (28.91, 0.86) Quantile-normal (966.06, 134.10) 8 Advances in Meteorology Q-Q plot (rainfall) P-P plot (rainfall) 0.8 0.6 0.4 0.2 0 500 0 0.5 500 700 900 1100 1300 1500 1700 Manga Empirical cumulative distribution P-P plot (temperature) Q-Q plot (temperature) 1 31 0.8 0.6 0.4 0.2 0 23 0 0.5 23 24 25 26 27 28 29 30 31 Manga Empirical cumulative distribution (c) Figure 2: Visual inspection of P-P and Q-Q plots for rainfall and temperature. (a) Binduri gauge station. (b) Garu-Tempane gauge station. (c) Manga gauge station. change point at Binduri and Garu-Tempane during 1987 in increase in April but becomes signiﬁcant in May. Rain is mean annual rainfall. However, all three tests had P values highest in August and begins to reduce in volume from ≥0.05, hence meeting the assumption of homogeneity of September to November. -e period between May and rainfall data’s suitability for trend testing and thus implying September is the wet season and that from October to April that at least majority of the datasets were found by the test to is the dry season of the Bawku Area of Ghana. It is observed be of homogenous series and hence useful for the trend that there is a unimodal rainfall regime in the study area irrespective of the location of the gauge station. analysis irrespective of the change point. Figures 3(a)–3(c) show the graphical results of the homogeneity test analysis. Table 4 shows statistical results of the homogeneity 3.4. Trends and Variability in Mean Annual Rainfall. A analysis for both rainfall and temperature and their com- Mann–Kendall (MK) nonparametric test was used to test the parative test statistics (Pettitt’s test, SNHT, and Buishand’s trends available in the rainfall and temperature data whose test). -e table shows that, among the 40 years under study, results are presented in Tables 5 and 6. -e results are based only two (1988 and 1993) were found to be signiﬁcantly on three separate meteorological stations. As a measure of inhomogeneous. In the mean temperature, however, sig- the link between two subsequent annual rainfall and tem- niﬁcant change points were observed in 2010 for Binduri and perature datasets, the MK result is based on the computa- Garu-Tempane and in 1992 for Manga. Both Pettitt’s and tions of Kendall’s tau with a null hypothesis (H ) of no trend Buishand’s tests found a change point (T) in 1992 at Manga, in the dataset. Positive and negative MK test results signify while Buishand’s test was met with a change point in 2010 at an increasing and decreasing trend in dataset, respectively. It both Binduri and Garu-Tempane. is observed in Table 5 that annual rainfall amount at Manga station shows statistically signiﬁcant positive (P< 0.05) 3.3. Seasonal Nature of Monthly Rainfall. In Figure 4, trend; hence, the null hypothesis (H ) is rejected at the monthly rainfall patterns are observed to be similar in all Manga rainfall station. In the same Table 5, Sen’s slope three stations. It is shown in Figure 4 that there is virtually no estimator depicts an upward trend for rainfall in all the rainfall in the period from December to February. -e onset stations: Binduri, Manga, and Garu-Tempane. It, therefore, of rain in the area is March; rainfall amount begins to implies that both Sen’s slope and Mann–Kendall test are in Theoretical cumulative distribution Theoretical cumulative distribution Quantile-normal (28.73, 0.98) Quantile-normal (936.62, 181.00) Advances in Meteorology 9 120 29.5 28.5 60 27.5 26.5 0 25.5 1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020 Years (1976–2015) Years (1976–2015) Mean Mean annual rain mu = 28.463 mu = 75.278 (a) 120 29.5 28.5 60 27.5 26.5 0 25.5 1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020 Period (1976–2015) Period (1976–2015) Mean Mean annual rain mu = 28.463 mu = 75.278 (b) 1970 1990 2010 1970 1980 1990 2000 2010 2020 Period (1976–2015) Period (1976–2015) Mean Mean annual temp mu1 = 67.738 mu1 = 28.012 mu2 = 83.022 mu2 = 29.252 (c) Figure 3: Homogeneity test showing change point T in annual rainfall and temperature. (a) Binduri gauge station. (b) Garu-Tempane gauge station. (c) Manga gauge station. Table 4: Homogeneity test statistics. Pettitt’s test SNHT test Buishand’s test Meteorological station T Sig. T Sig. T Sig. Rainfall Binduri 1987 0.628 2007 0.449 2007 0.447 Garu-Tempane 1987 0.625 2007 0.471 2007 0.460 ∗ ∗ Manga 1988 0.016 1988 0.091 1993 0.017 Temperature Binduri 2009 0.365 2011 0.052 2010 0.020 Garu-Tempane 2009 0.371 2011 0.052 2010 0.022 ∗ ∗ Manga 1992 ≤0.001 1998 0.053 1992 ≤0.001 Sig. at 0.05. Rainfall (mm) Rainfall (mm) Rainfall (mm) Temperature (°C) Temperature (°C) Temperature (°C) 10 Advances in Meteorology Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period (1976–2015) Binduri Garu Manga Figure 4: Rainfall monthly cycles in the Bawku Area. Table 5: Mann–Kendall trend test for annual rainfall. Meteorological station No. of years MK stat. (S) K. tau Min Max M SD P value CI [L–U] Sen’s slope Binduri 40 72.0 0.092 3.5 109.9 75.3 17.5 0.408 −0.256–0.489 0.152 Garu-Tempane 40 112.0 0.14 58.0 103.3 80.5 11.3 0.196 −0.112–0.527 0.192 Manga 40 196.0 0.252 56.3 130.2 78.1 15.3 0.023 0.056–0.882 0.432 Mean 40 132.0 0.169 55.8 111.6 77.9 11.8 0.127 −0.079–0.545 0.219 ∗ ∗ Sig. at 0.05. Reject H at ≤0.05 level or accept H at ≥0.05. 0 1 Table 6: Mann–Kendall trend test for annual temperature. Meteorological station No. of years MK stat. (S) K. tau Min Max M SD P value CI [L–U] Sen’s slope Binduri Mean 40 41.0 0.054 25.6 29.3 28.5 0.7 0.640 0.000–0.006 0.001 Max T 40 195.0 0.264 34.5 36.0 35.3 0.4 0.022 0.010–0.014 0.012 Min T 40 −29.0 −0.038 16.3 22.8 21.7 1.3 0.743 −0.007–0.000 0.000 Garu-Tempane Mean 40 384.0 0.503 27.6 32.9 28.9 0.9 0.0001 0.028–0.050 0.033 Max T 40 484.0 0.634 32.8 36.1 34.9 0.8 0.0001 0.048–0.053 0.050 Min T 40 269.0 0.352 19.6 30.2 22.8 1.4 0.002 0.026–0.033 0.030 Manga Mean 40 403.0 0.529 23.9 30.0 28.7 0.9 0.0001 0.038–0.044 0.041 Max T 40 329.0 0.432 34.6 36.9 35.6 0.6 ≤0.001 0.030–0.037 0.033 Min T 40 352.0 0.458 12.9 23.2 21.9 1.7 0.0001 0.042–0.050 0.046 ∗ ∗ Sig. at 0.05. Reject H at ≤0.05 level or accept H at ≥0.05. 0 1 tandem to the increasing trend of rainfall at Manga and amount was recorded in Binduri in 2008. Total annual Garu-Tempane. rainfall change was computed on the ﬁtted regression Graphically, a linear regression trend line was further line to show the changes in annual rainfall of each rainfall used to show evidence of the rainfall variability and trend station. -e results are −0.029, 2.724, and 5.4613 mm/ in the dataset. As shown in Figure 5, whereas Binduri year in Binduri, Garu-Tempane, and Manga, showed a slightly declining trend (negative trend line) of respectively. total annual rainfall, both Manga and Garu-Tempane showed an upward trend (positive trend line). However, it 3.5. Annual Rainfall Anomalies. -e Standardised Precipi- is observed that, in all the three meteorological stations in tation Index (SPI) was used to generate and describe the the study area, rainfall is erratic and varies with time over the 40-year period. anomalies of total annual rainfall (three stations combined) in the study area over the 40-year study period. -e aim was Again, it is illustrated in Figure 5 that, in all the three meteorological stations, rainfall amount is highly con- to identify years that witnessed more wetness or dryness over the period in the study area. As such, the authors centrated between 600 mm and 1000 mm annually. -e extremely high (1562.7 mm) amount of rainfall recorded combined the data of the three stations to arrive at a common mean that would then be used to generalize for the in all the three stations occurred in 2007 at Manga meteorological station and the extremely low (42.3 mm) area. -us, an average of the three stations was calculated Monthly total rainfall (mm) Advances in Meteorology 11 1800.0 Binduri Garu y = –0.029x + 903.88 y = 2.724x + 910.21 1600.0 2 2 R = 3E-06 R = 0.055 1400.0 1200.0 1000.0 800.0 600.0 400.0 200.0 0.0 Period (1976–2015) Binduri Garu Manga Linear (Binduri) Linear (Garu) Linear (Manga) Figure 5: Long-term variability in annual total rainfall. 3.6. Seasonal Nature of Monthly Temperature. In Ghana, and subsequently used to generate the SPI. Results of SPIs are either positive (wet) or negative (dry), with positive values Temperature is uniformly high throughout the year; implying a greater (>) than median precipitation and negative however, there are ﬂuctuations at the regional and local values indicating less (<) than median precipitation. Of levels. -e three gauge stations do not show signiﬁcant course, this was necessary because of the role rainfall plays in graphical discrepancies in pattern. Overall, temperature ° ° the livelihoods of the residents of this part of the Savannah ranges between 26.4 C and 33.3 C from January to De- agroclimatological zone in Ghana. It is important to note that cember as observed in Figure 7. -ere are two recognizable the SPI can be generated over diﬀerent timescales; however, in peaks of temperature: March-April and October-No- this study, to reﬂect a long-term precipitation pattern, a 12- vember. Temperature is highest during March and April month timescale SPI was adopted to compare the rainfall with highest monthly temperature recorded in April. pattern for 12 consecutive months of the ﬁrst year with 12 August, meanwhile, is the month with the lowest tem- consecutive months of all other years. perature throughout the year, coinciding with the month An SPI result with positive (+) value indicates wetness, with the highest rainfall. Speciﬁcally, temperature is ob- while negative (−) value implies dryness, but the intensity is served to be relatively higher and lower at Garu-Tempane dependent on the value of SPI. It is observed from Figure 6 and Binduri gauge stations, respectively, than all other that it is only in 2006 that the SPI was ≥2.0 to imply an stations as observed from the graph. Wet season’s tem- extremely wet year among the years under study with 2.3% perature is relatively low (25–27 C) between July and probability of recurring once every 50 years. In the years September, while dry season’s temperature is relatively 1993 and 1998, SPI values were ≥1.5 and≤1.99, respectively. higher (27–32 C) from February to April (Figure 7). -is implies very wet years with 4.4% likelihood to recur once every 20 years. Also, in 1988–1991, 1994, 1996, 1997, 1999, 2001–2004, 2008, 2009, 2011, 2012, and 2014, the SPI 3.7. Trend and Variability in Mean Annual Temperature. Table 6 shows the MK trend test results for mean temper- values were ≥0.0 and≤0.99, which suggest mild wetness. -ey are 34.1% likely to recur once in every 3 years. ature of the three stations. It is observed that, at Binduri Conversely, years 1977–1979, 1981–1982, 1985–1987, 1992, gauge station, only the minimum temperature of the area showed a decreasing trend, implying that, on average, 1995, 2000, 2005, and 2010 have their SPI values ≥0.0 and ≤ −0.99, implying mild dryness with 34.1% likelihood of recur- minimum temperature of the area has declined over the years. On the contrary, the maximum temperature of rence once every 3 years. During 1984, 1985, and 2014 SPI values were ≥ −1 and ≤ −1.49, denoting moderate dryness with 9.2% Binduri station showed a positive and signiﬁcant trend. -e null hypothesis (H ) of the mean and minimum temperature recurrence probability once in 10 years. Lastly, 1976, 1980, and 2007 recorded SPI values of ≥ −1.5 and ≤ −1.99 to suggest very is hence accepted as H is rejected. Furthermore, it is observed that, at a P value of 0.05, dry years with 4.4% likelihood of recurrence once in 20 years. However, based on the baseline period, the overall ob- minimum, maximum, and mean temperatures of the Garu- servation shows that the longest dry period occurred from Tempane and Manga stations have positive trend. -us, the 1976 to 1987, marked by negative anomalies, whereas null hypothesis (H ) is rejected at these gauge stations. -at 1988–1991 and 1996–1999 were the longest wet periods is, given the 40-year period, Garu and Manga have witnessed rising temperatures on average. In Table 6, Sen’s slope es- observed. Meanwhile, the period after 1987 witnessed more excess rainfall than shortages. timator shows a positive trend for all temperature datasets in Manga y = 5.4613x + 824.66 R = 0.1213 Annual rainfall (mm) 2014 12 Advances in Meteorology –1 –2 Period (1976–2015) Figure 6: Annual rainfall anomalies. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period (1976–2015) Binduri Garu Manga Figure 7: Monthly cycle of temperature. the meteorological stations. -us, the Mann–Kendall trend 3.8. Temperature Anomalies. Temperature has a profound test and Sen’s slope estimator are in agreement. impact on the hydrological cycle of the Savannah agro- Likewise, Figure 8 shows the graphical representation of the climatological zone and hence the general water security in mean annual temperature over the three stations. A linear the area due to its impact on evapotranspiration rate. As regression trend line was ﬁtted in the graph to show the such, ﬁnding the temperature anomalies over the period is variation of temperature over the study period. It is observed prudent as results could inﬂuence future agricultural plans from Figure 8 that temperature is uniformly high in all the in the area. Temperature anomaly measures the discrepancy three stations with minor extreme cases. On a whole, the of the observed data from the average of the dataset. highest (32.9 C) mean recorded temperature of the three Whereas positive (+) values imply that the 40-year average stations was in 1998 in Garu-Tempane, while the lowest temperature is smaller than the observed, a negative (−) (23.9 C) was recorded in 1979 at Manga meteorological station. value implies a large observed value. With an R-square of 34.7%, the rise in temperature in Figure 9 generally shows that many of the years in perspective witnessed a positive (+) value in temperature Manga was the most signiﬁcant, with just about 14% in Garu-Tempane. Binduri, on the other hand, shows a de- with the anomalies ranging between −1.42 and 1.2 C. Fig- clining mean temperature over the period. Regardless of the ure 9 also shows that, among the years with annual rising above, the mean temperature of the areas witnessed a steady trend in temperature anomalies, 1988 recorded the extreme rise. Also, Figure 8 depicts an average temperature change (1.2 C), suggesting a hot condition. -is was followed by based on meteorological station with a mean annual tem- years 1998, 2001–2007, and 2009–2010 with values ≥0.5 and perature change, ranging from Binduri to Manga, of <1.0, implying warmer conditions than the average. -e rest ° ° −0.0085 C to 0.05 C/year (0.9%–5%) at 95% conﬁdence level. (1990, 1993, 1995–1997, 1999–2000, and 2011) were ≥0.0 and SPI (mm) Mean temperature (°C) 2014 Advances in Meteorology 13 Binduri Garu y = –0.0085x + 45.366 y = 0.0279x – 26.783 2 2 R = 0.0194 R = 0.1391 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Period (1976–2015) Mat binduri Mat garu Mat manga Linear (Mat binduri) Linear (Mat garu) Linear (Mat manga) Figure 8: Long-term variability of mean annual temperature. <0.5. -e above temperature diﬀerence indicates a near- 3.9.1. Binduri Meteorological Station. -e ANOVA results normal temperature condition, while 1980, 1987, and 2008 in Table 7 show no statistically signiﬁcant diﬀerences in the witnessed no change (zero change) in temperature in re- year groups with respect to rainfall. However, there was a lation to the average value, hence a normal temperature. statistically signiﬁcant diﬀerence in the groups at the P< 0.05 level in temperature for the four groups: F (3, 476) � In contrast, Figure 9 further shows that 1979 (−1.8 C) and 1976 (−1.0 C) recorded the lowest temperature 3.623; P � 0.013. Despite reaching statistical signiﬁcance, the anomaly with values ≤−1 C, indicating years with the actual diﬀerence in mean scores between the groups was coolest temperature in comparison with the average of the quite small. -e eﬀect size, calculated using eta-squared, was area. A cooler condition in comparison to the average .02. Post hoc comparisons using the Tukey HSD test indi- temperature of the area was experienced in the years cated that the mean score for Group 3 (M � 28.93, SD � 2.15) 1977–1978, 1983–1984, 2013, and 2014 with recorded was signiﬁcantly diﬀerent from Group 4 (M � 28.02, values ≤−0.5 and > −1.0. Lastly, near-normal conditions SD � 2.25). -ere was, however, no signiﬁcant diﬀerence were observed in the years 1982, 1986, 1989, 1991–1992, between Group 3 (M � 28.93, SD � 2.15) and Group 1 or 1994, 2012, and 2015 with the anomaly ≤−0.5. Group 2. Again, there was no signiﬁcant diﬀerence in means between Group 1 (M � 28.35, SD � 2.23) and Group 2 or Group 4; and there was no signiﬁcant diﬀerence in means 3.9. One-Way Post Hoc ANOVA. A one-way post hoc between Group 2 (M � 28.35, SD � 2.19) and Group 4. ANOVA was conducted to explore the diﬀerences in means of decadal rainfall and temperature measured in 3.9.2. Garu-Tempane Meteorological Station. -e result in millimetres (mm) and degree Celsius ( C), respectively. Table 8 shows that there were no signiﬁcant diﬀerences in -e years were divided into four decades (10 years) from means of the year groups with respect to rainfall, but the year in which recording began as far as this research is temperature showed evidence of statistically signiﬁcant concerned (Group 1: 1976–1985, Group 2: 1986–1995, diﬀerences in the means of the year groups at the P< 0.05 Group 3: 1996–2005, and Group 4; 2006–2015). -is level for the four year groups: F (3, 476) � 3.158; P � 0.025. analysis was done separately for the three meteorological Notwithstanding the level of signiﬁcance, the actual stations and the result is presented in Tables 7–9. In diﬀerence in mean scores between the groups was low. As ANOVA, test of homogeneity of variance uses Levene’s such, the eﬀect size was calculated using eta square and the test, which tests whether the variance in scores is the same result was .02. Meanwhile, in the post hoc comparisons using for each group. If the P value (sig.) is greater than 0.05 the Tukey HSD test, it was found that there was a signiﬁcant (i.e., P> 0.05), it means the homogeneity assumption is diﬀerence in the mean scores of Group 1 (M � 28.17, not violated and hence one can go on to interpret the post SD � 2.17) and Group 3 (M � 29.25, SD � 2.12) and that there hoc ANOVA results. However, if the test results violate were no signiﬁcant diﬀerences between any other groups. the homogeneity assumption (i.e., if P< 0.05), then you consult the robust tests of equality of means and hence use the Welch and Brown–Forsythe ANOVA to interpret your 3.9.3. Manga Meteorological Station. -e result in Table 9 output. But since homogeneity of variance was not vio- shows that there were no signiﬁcant diﬀerences in means of the year groups with respect to rainfall but there was sig- lated in this analysis, Levene’s test was used for the interpretation. niﬁcant diﬀerence in means of temperature at P< 0.05 level Manga y = 0.05x – 71.087 R = 0.3471 Mean temperature (°C) 14 Advances in Meteorology 1.5 0.5 –0.5 –1 –1.5 –2 Period (1976–2015) Figure 9: Mean annual temperature anomalies. for the year groups: F (3, 476) � 12.821; P< 0.001. -e actual agriculturalist for that matter. -e increasing rainfall in the diﬀerence in mean temperature values was moderate evi- area supports the ﬁndings of [46] that found that, in the dence from the eﬀect size of 0.07 calculated using eta- Savannah zone of Ghana, many of the years between 1901 squared. Post hoc multiple comparisons using the Tukey and 2010 experienced above-normal rainfall in the area. HSD test further indicated that the mean score for Group 1 Given that there is an upward trend in rainfall, farmers and the residents in general will be relieved especially in relation (M � 27.66, SD � 2.78) was signiﬁcantly diﬀerent from Group 2 (M � 28.71, SD � 2.16), Group 3 (M � 29.36, to water management towards future use. -is is because whereas crop farmers need water for their plants, those in SD � 2.16), and Group 4 (M � 29.16, SD � 2.11). Group 2 (M � 28.71, SD � 2.16) did not diﬀer signiﬁcantly from either animal husbandry equally rely on water for their animals. Group 3 or Group 4; Group 3 (M � 29.36, SD � 2.16) did not Indeed, the domestic use of water cannot be underestimated. diﬀer signiﬁcantly from Group 4. However, an increasing number of rainfall-deﬁcient years as shown by the anomalies are an indication of drought in those years. -is even worsens the rainfall uncertainty sit- 4. Discussion uation in the area and rather calls for planning because of the In this study, emphasis was placed on the temporal de- signiﬁcant role rainfall plays in the lives of the residents in scription of temperature and rainfall in the Bawku Area of the area. -e fact that there is little or no rainfall from No- Upper East Region of the Savannah zone of Ghana. Similar to other studies, interannual and high values of changes in vember to March suggests the signiﬁcance of the dry season in this part of the country. In the savannah areas of rainfall over the study area are in agreement with [15–17], and therefore it is implied that residents will still be in a state Africa, reports of high temperature and low rainfall have of uncertainty in relation to rainfall in the coming years. -is been established as a consequent of the tropical conti- is because of the diﬃculty in forecasting rainfall in an area nental air mass [47]. -e seasonal nature of the monthly with erratic and varied rainfall pattern. Rainfall variability in rainfall in the area is impacted upon by the dominance of the study area is also consistent with the ﬁndings of [45, 46]. the dry tropical continental air mass and the moist -us, the ﬂuctuations in the rainfall distribution on annual tropical maritime air mass from which we have the North basis are not uncommon in the Savannah zone. However, East Trade Winds (harmattan) and South West Monsoon this ﬁnding is in sharp contrast with [16, 45], in which Winds, respectively. Even though these are the major determinants of the rainfall seasons in Ghana [26, 48], it is rainfall on the whole was found to be decreasing in the northern part in general and Upper East Region of the important to note that the harmattan dominates the Sa- vannah area of Ghana against the inﬂuence of the country. -is could probably be as a result of the fact that their study period was longer and dated back (1954–2010 monsoon, hence leading to the long dry period with little and 1961–2007, respectively) to an earlier time than the amount of rainfall experienced. As such, months of current one and also because it is a wider area in comparison negligible rainfall amount in the study area are generally to the current study area. -us, this study established that all due to the inﬂuence of North East Trade Winds, which are the three meteorological stations on average enjoyed a slight dry and cold in nature. -is is not surprising especially in increase in rainfall distribution. relation to the SPI results witnessing more dryness than Having an increasing rainfall trend is quite a welcoming wetness. -e eﬀect of this air mass on rainfall is similar to result for the residents in the area because of its potential temperature distribution over the area. -us, the wind circulation in West Africa is dominated by the North East agricultural, hydrological, and socioeconomic eﬀects in an area that is highly populated by farmers, rain-fed Trade Winds, which originate from the dry and hot Sahara Temperature anomalies (°C) 2014 Advances in Meteorology 15 Table 7: ANOVA test statistics, Binduri. Binduri SS df MS F Sig. Eta square Rainfall Between groups 13620.562 3 4540.18 0.570 0.635 0.0035 Within groups 3794549.32 476 7971.74 Total 3808169.88 479 Temperature Between groups 52.980 3 17.660 3.623 0.013 0.0223 Within groups 2320.352 476 4.875 Total 2373.332 479 Sig at 0.05 level. Table 8: ANOVA test statistics, Garu-Tempane. Garu-Tempane SS df MS F Sig. Eta square Rainfall: Garu-Tempane Between groups 9451.697 3 3150.57 0.380 0.768 0.002 Within groups 3947291.13 476 8292.63 Total 3956742.83 479 Temperature: Garu-Tempane Between groups 88.512 3 29.504 3.158 0.025 0.019 Within groups 4446.946 476 9.342 Total 4535.461 479 Sig at 0.05 level. Table 9: ANOVA test statistics, Manga. Manga SS df MS F Sig. Eta square Rainfall Between groups 30578.238 3 10192.75 1.111 0.344 0.007 Within groups 4367338.58 476 9175.081 Total 4397916.82 479 Temperature Between groups 206.743 3 68.914 12.82 ≤0.001 0.075 Within groups 2558.597 476 5.375 Total 2765.340 479 Sig at 0.05 level. Desert; this dry and dusty wind has tremendous eﬀect on potential to nullify the rainfall ﬁgures through evapo- transpiration. -e consequence is the scarcity of water in the rainfall and temperature of the study area. -at is not to say, however, that factors including absence of vegetation area as observed in the drying-up of rivers, wells, dams, and cover and absence of river and water bodies have no eﬀects dugouts and soil moisture deﬁciency in the area. -is is a on the local atmospheric circulation in the area. However, major blow to farming in the area because of farmers’ de- the Savannah areas of Northern Ghana have relatively pendence on rain-fed farming. But it is an undeniable fact gentle slopes with rare vegetation and hence we speculate that the rising temperature in the area is partly attributed to less eﬀect of orography in the area. anthropogenic activities through deforestation for charcoal On the contrary, this study also establishes that tem- production, clearing of vegetation for farming, population perature assumes a uniformly increasing trend in the study pressure on the environment, urbanisation, and urban area but varies yearly across the stations, which conforms to heating [52]. global and international studies about variations in tem- Meanwhile, there are decadal diﬀerences in temperature groupings over the area. Whereas at the Binduri station there perature [2, 49]. It is further in consistence with the ﬁndings of [16, 20, 50, 51] that diﬀerently found increasing trends in was an observed diﬀerence in temperature during annual, seasonal, and decadal day and night temperatures in 1996–2005 and 2006–2015, there was a diﬀerence in tem- the northern regions of Ghana. -e increasing temperature perature during 1976–1985 and 1996–2005 in Garu-Tem- over the area carries with it some consequences in the pane. Lastly, diﬀerences exist between 1976–1985 and livelihoods of the residents in terms of drought, dry spells, 1986–1995, 1976–1985 and 1996–2005, and 1976–1985 and water shortage, and the possibility of conﬂict over water 2006–2015 at Manga meteorological station. -is implies resources in the area. -us, the fact that temperature is rising that, during the study period, temperature of the studied signiﬁcantly implies that the rising temperature has the meteorological stations has rarely been constant over the 16 Advances in Meteorology 23, Global International Water Assessment (GIWA) 2003,” decades. -us, the eﬀects of temperature variations felt by 2003, https://www.giwa.net/areas/reports/r23/giwa_regional_ the residents have not been the same across the 40 years. assessment_23.pdf. [2] Intergovernmental Panel on Climate Change (IPCC), Climate 5. Conclusion Change 2007: Impacts, Adaptation and Vulnerability: Contri- bution of Working Group II to Fourth Assessment Report of the -is study has proven to be an addition to the earlier studies Intergovernmental Panel on Climate Change, M. L. Parry, regarding climate variability in the country, especially that of O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and the Savannah agroclimatological zone. It is evident that in the C. E. Hanson, Eds., p. 976, Cambridge University Press, Cam- study area rainfall is averagely low, with erratic pattern. It also bridge, UK, 2008, http://www.cambridge.org/9780521880107. ﬂuctuates and varies in time with some extremely low and high [3] P. V. V. R. Prasada and S. L. Addisu, “Trend analysis and periods of rainfall. Monthly cycles of rainfall and temperature adaptation strategies of climate change in north central show peaks in August and April, respectively. -ere were more Ethiopia,” International Journal of Agricultural Science and years with negative mean annual rainfall anomalies than Research, vol. 3, no. 1, pp. 253–262, 2013, https://www.academia. positives with severe long drought period prior to 1988. -e edu/3407133/TREND_ANALYSIS_AND_ADAPTATION_ STRATEGIES_OF_CLIMATE_CHANGE_IN_NORTH_ temperature, on the other hand, was uniformly high in the CENTRAL_ETHIOPI. study area with minor extreme cases, yet the anomalies in- [4] A. Longobardi and P. Villani, “Trend analysis of annual and dicate rising values of temperatures. -e study is able to es- seasonal rainfall time series in the Mediterranean area,” In- tablish an upward rise in temperature over the study period ternational Journal of Climatology, vol. 30, no. 10, pp. 1538– among the three stations. Despite the insigniﬁcant diﬀerences 1546, 2009. in the interdecadal rainfall amount, temperature witnessed [5] United State Environmental Protection Agency, Ghana interdecadal variations over the years under study. In relation Government Submission to the United Nations Framework to the results, all stakeholders, including nongovernmental Convention on Climate Change Ghana First Biennial Update organisations and government, are recommended to ensure Report, Ministry of Environment, Science, Technology and adequate provision of water supply through the construction Innovation, Accra, Ghana, 2015. of dam’s wells and boreholes. -ese eﬀorts will not just reduce [6] K. K. Azeez, M. M. Abnory, I. H. Sirikyi, and M. Adanusa, water insecurity but will also increase the availability of re- “Climate change and health management in the Central Region, Ghana,” in Climate Change and Sustainable Devel- sources for agricultural activities all-year-round since farmers opment in Africa, Proceedings’ of Second University of Cape will be able to farm even in dry seasons. Coast and University of Ilorin Joint International Conference, Illorin Press, Illorin, Nigeria, 2012. Data Availability [7] Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014: Synthesis Report. Contribution of Working -e data used to support the ﬁndings of this study are Groups I, II, III to the Fifth Assessment Report of the Inter- available from the corresponding author upon request. governmental Panel on Climate Change, Core Writing Team, R. K. Pachauri, and L. A. Meyer, Eds., p. 151, IPCC, Geneva, Conflicts of Interest Switzerland, 2014, https://www.ipcc.ch/site/assets/uploads/2018/ 05/SYR_AR5_FINAL_full_wcover.pdf. -e authors declare no conﬂicts of interest. [8] A. S. Wijaya, “Climate change, global warming and global inequity in developed and developing countries (Analytical Authors’ Contributions perspective, Issue, Problem and Solution),” IOP Conference Series: Earth and Environmental Science, vol. 19, p. 2014, Both authors contributed substantially and equally towards Article ID 012008, 2014. the success of this study, proofread the ﬁnal manuscript, and [9] Intergovernmental Panel on Climate Change (IPCC), Climate Change 2013: Fe Physical Science Basis. Contribution of approved it for publication. Working Group I to Fifth Assessment Report of the Intergov- ernmental Panel on Climate Change, T. F. Stocker, D. Qin, Acknowledgments G.-K. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2020 Yaw Asamoah and Kow Ansah-Mensah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1687-9309
eISSN: 1687-9317
DOI: 10.1155/2020/3402178
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Abstract

Hindawi Advances in Meteorology Volume 2020, Article ID 3402178, 18 pages https://doi.org/10.1155/2020/3402178 Research Article Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana 1 2 Yaw Asamoah and Kow Ansah-Mensah Department of Geography Education, University of Education, Winneba, Ghana Department of Geography and Regional Planning, University of Cape Coast, Cape Coast, Ghana Correspondence should be addressed to Yaw Asamoah; yasamoah@uew.edu.gh and Kow Ansah-Mensah; kow.ansah-mensah@stu.ucc.edu.gh Received 22 July 2019; Revised 4 November 2019; Accepted 5 February 2020; Published 1 April 2020 Academic Editor: Antonio Donateo Copyright © 2020 Yaw Asamoah and Kow Ansah-Mensah. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With varied implications, Ghana’s temperature and rainfall are projected to rise and decline, respectively. A study exposing speciﬁc areas of concern for appropriate responses in this regard is a welcome one. -is study sought to describe the temporal variations in temperature and rainfall in the Bawku Area of Ghana. A forty-year (1976–2015) daily climate data was collected on three meteorological stations from the Ghana Meteorological Agency. Normality test, homogeneity test, Standardised Precip- itation Index (SPI) analysis, Mann–Kendall trend test, and One-way post hoc ANOVA were performed using XLSTAT and DrinC. Over the period under study, the mean annual rainfall pattern was generally erratic, ﬂuctuating between 669.8 mm and 1339.4.6 mm with an annual average of 935.3 mm. -e long-term (40-year period) average temperature of the three stations, on ° ° ° the other hand, was 28.7 C, varying between 26.9 C and 29.9 C annually. Whereas the SPI value of 2006 was ≥2.0, indicating extremely wet year with 2.3% probability of recurring once every 50 years, 1988 was the hottest year with temperature anomaly ° ° ° value of 1.2 C, while coolest years were 1979 (−1.8 C) and 1976 (−1.0 C). -e Mann–Kendall trend test showed a rise in rainfall in Binduri, Garu-Tempane, and Manga, yet none of the rainfall changes were statistically signiﬁcant (P> 0.05). Mean temperature on the other hand experienced a signiﬁcant rise (P< 0.05). With an R-square of 34.7%, the rise in temperature in Manga witnessed the most signiﬁcant change in annual temperature changes. -ere were statistically signiﬁcant (P < 0.05) diﬀerences in the inter- decadal temperature over the 40-year period. Generally, it can be stated that both temperature and rainfall vary in the study area with various degrees of disparities, but temperature assumes an upward trend at a faster rate. We therefore recommend that stakeholders resort to the construction of dams and boreholes to ensure regular availability of water for both domestic and agricultural uses. st range of 1–6 C by the end of the 21 century if nothing is 1. Introduction done to mitigate it [2]. Global warming is described as the Global climate is reported to have witnessed a drastic change basis of climate change which the world is battling to resolve; at least over the last century as observed in the Intergov- these changes are extreme weather conditions, sea level rise, and a shift and erratic precipitation pattern observed in ernmental Panel on Climate Change’s (IPCC) fourth and ﬁfth assessment reports. -e eﬀect of this change has resulted recent times [3]. Spatiotemporal trend analysis of precipi- in the changes and shifts in the patterns of the climate el- tation on global scale has shown a slightly positive trend, ements, particularly precipitation, humidity, and tempera- though variability still exists on some regional and local scales ture across the globe [1]. Accordingly, the Intergovernmental in terms of intensity, amount, and shifts [2, 4]. -ese vari- Panel on Climate Change reported that the global mean abilities and change in climate have signiﬁcant impacts on surface temperature has increased by 0.76 C in the last 150 livelihood and wellbeing of people, particularly those in years and will continue to witness an upward trend in the rainfall-dependent regions. 2 Advances in Meteorology Climate variability and change pose serious threat to and its impacts have been carried out in the country, es- many countries, especially the most vulnerable and poor pecially in the northern regions. -ese studies, including developing ones. United States Environmental Protection those in [16–19], independently concluded that the northern Agency [5] indicates that whereas there is still uncertainty in part will continually experience heating with short periods of rainfall due to the shift and erratic nature, surface tem- rainfall. peratures are continually rising and warming. Meanwhile, Moreover, on the local scale, temperature and humidity studies reveal that the combined eﬀect of varying rainfall and are found to be increasing and decreasing, respectively, in rising temperatures is touted to aﬀect all aspects of human the Bawku East Municipality of the Upper East Region activities [6]. Empirical evidence indicates that a rising trend (UER) [20]. -e Ministry of Food and Agriculture (MoFA) in global temperature is the result of human activities of Ghana estimates that about 70% of the residents in the contributing to the emission of atmospheric gases [7]. Even UER are farmers, mostly practicing rain-fed subsistence though every country around the globe contributes to the farming [21]. While it is undisputable fact that temperature emission and consequently climate change, developed and rainfall are the main direct determinants of agricultural countries are reported to be the major contributors, yet the activities [11, 22], especially in this part of the world, minor contributors, developing countries, are the more Frimpong et al. ignored rainfall trends and anomalies with vulnerable to the eventual impacts [8]. the potential eﬀects on the livelihoods of the poor farmers Africa and West Africa by virtue of their geographical [20]. Hence, this study intends to ﬁll that gap and add to locations are much prone to the shocks of climate change, literature. It is on the backdrop of the above that this study global warming, and the variability in climate. -e IPCC aims to analyse the trends of temperature and rainfall in the report projects an increasing temperature trend in the West Bawku Area of UER of the Savannah agroclimatological African region, within which Ghana is located, with em- zone of Ghana between 1976 and 2015. phasis on the area that lies within the Savannah and Sahel zone [9]. Rising temperatures and erratic precipitation have 2. Materials and Methods a dire consequence on the African population at large due particularly to its dependence on rainfall for its agricultural 2.1.StudyArea. -e Bawku Area of the Upper East Region is practices which in most cases is the major economic driver located in the north easternmost corner of Ghana and shares in the subregion [10]. -is does not come with a surprise borders with two francophone countries, Burkina Faso and because, according to [11], temperature and rainfall are the Togo, to the north and east, respectively. -e area is made up main factors that directly aﬀect agricultural activities. As of ﬁve diﬀerent administrative districts: Bawku Municipality such, erratic rainfall and extreme temperature may likely (Bawku), Bawku West (Zebilla), Binduri (Binduri), Garu- aﬀect agricultural output in the continent, leading to further Tempane (Garu), and Pusiga District (Pusiga). -e study ° ° repercussions on the wellbeing of the people. area in totality is located on latitudes 10 30′ to 11 11 north ° ° -ere have been studies in the recent past on trends in of the equator and longitudes 0 06′ east to 0 40′ west of the climate over Africa countries [3, 10, 12, 13]. -ese studies Greenwich Meridian with an average land area of about basically report on increasing temperatures over the con- 2848 km . -e area lies between 202 and 235 m above sea tinent and the fact that there are variations within countries. level (Table 1). Bawku Municipality is found on latitudes 10 ° ° -e authors separately found a signiﬁcant rising trend in 40′ to 11 11′ north of the equator and longitudes 0 61′ east ° ° temperature, whereas rainfall on the other hand either in- to 0 18′ west, whereas Bawku West is located on latitudes 10 ° ° ° signiﬁcantly assumed an upward trend or was virtually 30′ and 11 10′ north and longitudes 0 20′ to 0 35′ west [23]. constant. Regardless of their ﬁndings, it is more appropriate Binduri District on the other hand is on latitude 11 00′ ° ° for research to also concentrate on local level trend analysis north and longitudes 0 06′ east to 0 18′ west and Garu- ° ° of climate variables since regional and national level studies Tempane is on latitudes 10 38′ to 11 00 north and lon- ° ° have shown variability in climate. Moreover, local level gitudes 0 06 to 0 23′ east [24]. -e study area map is shown adaptation measures to combating climate shocks are in Figure 1. possible and eﬀective only if the local level climate has been Like the whole of UER, Bawku Area falls within the studied for possible variability and change as enshrined in interior continental climate zone of Ghana dominated by the sustainable development goal (SDG) 13: take urgent long dry period and short wet (rainfall) period. -e wet and action to combat climate change and its impacts. dry seasons are determined by the North East Trade Winds In Ghana, projection of increasing trends of climate (also called harmattan) and South West Monsoon that al- elements regardless of the variability indicates that the re- ternate with the seasons. North East Trade Winds originate gions in the northern part of the country (Guinea and Sudan from the Sahara Desert and control the climate of the area. In Savannah) will experience between 2.1 and 2.4 C of tem- eﬀect, the dry season lasts for seven months from October to perature increase, whereas all other regions will be between April [20]. -e wet season is characterized by a unimodal 1.3 and 2.0 C [14]. Holding all other factors constant, the rainfall regime for about ﬁve months between May and Environmental Protection Agency has also indicated that September. -e wet season, however, is inﬂuenced by the historical trends in the climate of Ghana point to the fact that moisture concentrated tropical maritime air mass, which mean minimum and maximum temperatures in the Sa- blows from the Atlantic Ocean [26]. -e area is mainly ° ° vannah areas are expected to increase by 1.10 C and 1.20 C drained by the White and Red Volta River and its tributaries by the year 2040 [15]. Researches into the trends of climate together with other notable rivers and streams: Tamne and Advances in Meteorology 3 Table 1: Geographical locations of the meteorological stations with their elevations. ° ° S/N Gauge station Latitude Longitude Elevation (m) 1 Binduri 10.97 −0.32 202 2 Garu 10.85 −0.18 202 3 Manga 11.02 −0.27 231 Source: [20]. 0°30′0′′W 0°20′0′′W 0°10′0′′W 0°0′0′′ Burkina Faso Bawku Pusiga Yiring Ungu Pusiga Zaboga Kuka Binduri Bawku Zebilla Binduri Bawku West Garu Gubuliga Pialugu Togo Garu-tempane Yanatinga Apotdabogo II Talensi Bunkpurugu Yunyoo 036 12 18 24 East Mamprusi Km 0°30′0′′W 0°20′0′′W 0°10′0′′W 0°0′0′′ Study communities District capital Study area Regional boundary International boundary Figure 1: Study area map in regional and national context. Source: [25]. Pawnaba-Kiyinchongo, respectively. -e rivers ﬂow with the in the area practices subsistence agriculture, the commonest seasons (wet and dry). Rivers overﬂow their banks and ﬂood economic activity there is primary (agriculture, forestry, and nearby communities during the wet season (rainfall season); ﬁshing) and a few secondary (wholesale, retail, and they, however, dry up during the dry season. manufacturing) [21, 27]. With a total population of about 384,151, the area makes up about 36.7% of the UER population, with about 60% being females, 50% youth, 41% children under 15, and about 2.2.Dataset. Time-series data of rainfall and temperature for 9% aged [27]. -e area is largely rural with average the three meteorological stations at Binduri, Garu-Tempane, and Manga communities, all in the Bawku Area of the Upper household size of 6.5. About 60% of the population above 11 years are illiterate, while males constitute the majority East Region were collected and analysed for the period from (53.2%) of the 43% literates. -e largest ethnic group found 1976 to 2015. Table 1 presents the geographical locations and in the area is the Kussasis of the Mole Dagbani group; there elevation of the instruments used to record the data. -e are also the Mamprusis, Moshies, Bimoba, Bisas, Busanga, forty-year-period data was used because of its consistency, Frafra, and Fulani. -e dominant religion is Islam with accuracy, and reliability. -is highly dependable station data majority of the Christian population being Catholics and was collected from the Ghana Meteorological Agency there are also some Traditional African Religious wor- (GMet) in Accra, which is mandated to collect and store shipers. Whereas the predominantly rural population found climate data. Spatial interpolation of data was carried out to 10°40′0′′N 10°50′0′′N 11°0′0′′N 11°10′0′′N Nabdam 10°40′0′′N 10°50′0′′N 11°0′0′′N 11°10′0′′N 4 Advances in Meteorology ﬁll data gaps using arithmetic mean method where data where F is the standard normal distribution function; S(x) before and after the gap were used to generate the missing is the empirical distribution function of the Z values. value. Consequently, years without consistent data were then Jarque–Bera (JB) test is a goodness-of-ﬁt test of whether eliminated from the study, hence the 1976–2015 data. -e sample data have the skewness and kurtosis matching a monthly and annual data used for the analysis in this study normal distribution. JB test is given by the following were compiled from the daily data collected. -e overall formula: average of the 40-year period was calculated to be able to n 3 􏽐 x − x􏼁 i�1 (5) examine the rainfall and temperature anomalies. K � , ns 􏽐 x − x􏼁 i�1 2.3. Data Analysis. -e ﬁrst part of the data analysis was (6) K � − 3. ns done using normality test statistics to understand the nature of the data. -is was necessary to determine the best -e Jarque–Bera test is then determined by putting available test tools suitable for the analysis. As such, both together equations (5) and (6). rainfall and temperature data were subjected to normality test using Shapiro–Wilk, Anderson–Darling, Lilliefors, and 2 2 K 􏼁 K 􏼁 3 4 Jarque–Bera tests. -ese tests were applied because of their (7) JB � n􏼠 + 􏼡, 6 24 suitability and general acceptability for normality testing [28] and the fact that each of the tests will complement the where x is each observation, n is the sample size, s is the others’ weaknesses. With a null hypothesis of normal sample standard deviation, k is skewness, and k is kurtosis. distribution, all the aforementioned tests compare test scores 3 4 Secondly, homogeneity test statistics were applied to of sample to normally distributed scores with the same determine homogenous nature of the data prior to analysing standard deviation and mean. Equations used for the nor- the trend. Homogeneity test was carried out to test for mality tests are provided. homogeneity and suitability of data for trend testing using Shapiro–Wilk (W) is given as Pettitt’s test, Standard Normal Homogeneity Test (SNHT), 􏼐􏽐 a x 􏼑 and the Buishand range test [29]. -ese three tests have i�1 i (i) (1) W � , n similar null hypothesis (H ) that data is homogenously 􏽐 x − x􏼁 i�1 i identical. At a P value of 0.05, H is accepted or rejected. where x is the ordered sample value, a is a constant 1 i generated from the means, variances, and covariance of the 2.3.1. Pettitt’s Test. -is test is a nonparametric test based on ordered statistics, n is number of observations, and x is the rank r of Y and requires no assumption about the distri- i i sample mean. bution of data [29]. Anderson–Darling (AD) test uses cumulative distribution function to determine the normality of a given set of data. T � 2 􏽘 r . (8) y i−y(n+1),y�1,2,...,n -e formula is shown: t−1 AD � −n − 􏽘(2i − 1) ln F x + ln 1 − F x , 􏼂 􏼁 􏼁 􏼁􏼃 i n−i+1 -at is, there is a break in year k when i�1 T � max T . (2) y (9) 0≤x≤1 where n � sample size; F(x) is the cumulative distribution -e value of T is then compared with the [29] critical th function for the speciﬁed distribution; i is the i sample value. when the data is sorted in ascending order. Lilliefors is an improvement in the Kolmogor- ov–Smirnov (K-S) test correcting the tails of the probability 2.4. Standard Normal Homogeneity Test (SNHT). z repre- distributions. One of its main advantages is that even when sents a statistic comparison of the mean of the ﬁrst y years mean and standard deviations are unknown, the test can still with the last years of n-y using P(y). be applied. -e formula is shown as follows: x − x Py � yZ1 + (n − 1)Z2, y � 1, 2, 3 . . . n, (10) Z � , i � 1, 2, . . . , n, (3) where where Z is the individual z-score for every member in the i n 1 y − y sample; X is an individual member/data point. Z1 � 􏽘 , y s -e Lilliefors test statistic which is the empirical dis- i−1 (11) tribution function is then given by 􏼌 􏼌 1 y − y 􏼌 􏼌 i 􏼌 􏼌 Z2 � 􏽘 . T � sup 􏼌F (x) − S(x)􏼌, (4) 1 x n − y s i−y+1 Advances in Meteorology 5 -e year y consisted of break if value of P is maximum. stand-alone software has been used by others (such as -us, the null hypothesis is rejected if [38, 40–42]). Table 2 presents the interpretation of SPI results. P � max P . o y (12) 0≤y≤1 Subsequently, the Mann-Kendall (MK) trend test was applied in this analysis to the annual rainfall and tem- perature values to determine the statistical trend in the 2.4.1. Buishand’s Test. Buishand’s test is applied to variables dataset. MK is a monotonic nonparametric test that is of any distribution. However, properties of the test have widely used for trend testing in climate data [43]. In this especially been studied for normal distribution case [29]. -e test, the null hypothesis (H ) is that there is no trend in the homogeneity test, however, can be based on the cumulative dataset. A positive and negative MK test result signiﬁes an deviations from the mean and hence is given by the fol- increasing and decreasing trend, respectively, and this was lowing formula [30]: supported by Sen’s slope estimator test. Sen’s slope esti- mator, on the other hand, is used to complement the MK ∗ ∗ S � O, S � 􏽘(xi − u), k � 1, 2, . . . T. (13) o K test and also shows the magnitude of the trend. -e closer i�1 the result is to zero (0), the lesser the trend is. -e sign (+, −) of the slope tells if the trend is increasing or decreasing. -us, when the data is homogenous, the value of S will rise and fall around zero. When S is at its maximum or -e MK was adopted because of its robustness and gen- minimum, the year y is said to have a break. Adjusted range erally acceptability and consequent application in many R is then obtained by climate analyses [44]. According to Tigkas et al., MK test statistics are calcu- ∗ ∗ max S − max S 􏼁 1≤t≤T k 1≤t≤T k (14) R � . lated based on the following equations [38]: x−1 x Furthermore, a graphical analysis of the monthly and S � 􏽘 􏽘 Sgn(xj − xi), (16) annual rainfall and temperature was performed to show the i�1 j�i+1 long-term variability in the dataset. -e slope coeﬃcient sign would then indicate whether the data follows a positive or +1, if xj − xk > 0, ⎧ ⎪ negative trend. Temperature anomalies were calculated ⎨ Sgn(x) � 0, if xj − xk � 0, (17) using the arithmetic mean. In this study, temperature anomaly is the number of degrees of temperatures that the −1, if xj − xk < 0. absolute temperature varies from the average (reference point) or the diﬀerence of the absolute temperature from the Xi and Xk are the annual values in years j and k (j> k), average or baseline temperature. respectively. To understand the interannual variability in the rainfall Variance is given as follows: of the study area, the Standardised Precipitation Index (SPI) n(n − 1)(2n + 5) was used to calculate the anomalies in rainfall. (18) Var(S) � , -e SPI is generated using long-term records of pre- cipitation. -is is done by ﬁtting the data into a probability distribution to be transformed into a normal distribution where n is the number of observations and xi (i � 1, . . ., n) such that the mean value of the SPI for the location and its are the independent observations. desired period is zero [31]. -e SPI approach is a widely Z-statistics: used method for precipitation or drought intensity studies. s − 1 ⎧ ⎪ 􏽰�� ⎪ , if s > 0, Results of SPI are originally generated using gamma dis- Var(S) tribution because of its suitability to precipitation time series [32]. However, one can also use the log-normal (19) 0 . . . , if s � 0, distribution approach in generating the SPIs as has been used by others (such as [33–37] because they produce similar results [38]). According to World Meteorological s + 1 􏽰�� ⎩ , if s < 0. Organization, the SPI function is given by the following Var(s) formula: Sen’s slope is estimated by the following formula [38]: X − X i m (15) I(i) � , (Xj − Xk) Qi � , for i � 1, . . . N. (20) j − k where I (i) is the standardised index of year I; X is the value for the year I; X is the average for the year i; and σ is the standard deviation of the time series. But X and X are data values at times j and k; j, however, j k is greater than k (j> k). -e SPI can be calculated for diﬀerent timescales such as 3, 6, 12, 24, and 48 months [32, 39]. -e SPI calculation N is the slope estimator; if there is just one piece of data in each time, then N is given as was done using Drought Indices Calculator (DrinC); this 6 Advances in Meteorology Table 2: SPI values. SPI results Interpretation Probability of occurrence Severity of event ≥2.0 Extremely wet 2.3 1 in 50 years 1.5 to 1.99 Very wet 4.4 1 in 20 years 1.0 to 1.49 Moderately wet 9.2 1 in 10 years 0.0 to 0.99 Mildly wet (NN) 34.1 1 in 03 years 0.0 to −0.99 Mildly dry (NN) 34.1 1 in 03 years −1.0 to −1.49 Moderately dry 9.2 1 in 10 years −1.5 to −1.99 Very dry 4.4 1 in 20 years ≤−2.0 and less Extremely dry 2.3 1 in 50 years NB: “NN” means near normal. Source: [39, 42]. Table 3: Normality test of temperature and rainfall data. Shapiro–Wilk Anderson–Darling Lilliefors Jarque–Bera Variable/test W Sig. A Sig. D Sig. JB obs. Sig. Rainfall Binduri 0.8651 0.0002 1.1828 0.0038 0.1519 0.0209 63.6 <0.0001 Garu-Tempane 0.9144 0.0065 0.9878 0.0433 0.1133 0.2195 09.1 0.0010 Manga 0.9220 0.0089 1.0403 0.0255 0.1092 0.2677 14.42 0.0007 Temperature Binduri 0.8017 <0.0001 2.1485 <0.0001 0.2058 <0.0002 78.0280 <0.0001 Garu-Tempane 0.8089 <0.0001 1.2330 <0.0029 0.1393 <0.0489 167.0084 <0.0001 Manga 0.7318 <0.0001 2.1170 <0.0001 0.1984 <0.0004 301.9649 <0.0001 and data does not follow normal distribution, respectively. n(n − 1) (21) N � , Probability was taken at 95%; H is accepted at P> 0.05 and rejected at P< 0.05. Table 3 and Figure 2 show tests statistics where n is the number of time periods. and graphical results of the normality test. Hence, Sen’s estimator is given as Regarding the temperature, it is observed from Table 3 that all four test tools at all three gauge stations show sig- Q(N + 1) ⎧ ⎪ , if N is odd, niﬁcant deviation from normal distribution with P values ⎪ 2 ≤0.05; hence, H is rejected. -e risk of rejecting H while it o o (22) is true varies from 0.01% to 4.89%. In relation to temperature ⎪ 1 QN Q(N + 2) ⎪ data, the risk of type 1 error is minimal using Shapiro–Wilk 􏼠 + 􏼡 if N is even. 2 2 2 and Jarque–Bera test tools as compared to the others. Meanwhile, with rainfall data, all four tests violate the Lastly, One-way ANOVA post hoc test of multiple normality assumption with P value ≤5% (P< 0.05). comparisons was applied to test for diﬀerences in means Consequently, the null hypothesis for all four tests at of the temperature and rainfall data. -is test was in- Binduri was rejected, whereas one (Lilliefors) each was troduced in this study to purposely complement trend rejected for both Garu and Manga. -at is, only Lilliefors test test results while providing room to understand how best indicated that Garu and Manga rainfall data were normally ANOVA could help understand the diﬀerences in mean distributed. P-P plots and Q-Q plots are shown in Figure 2 scores of rainfall and temperature on decade bases. -is for visual inspection of the normality test. -e risk of type 1 was done by grouping the 40-year data into four groups error, however, ranges between 0.01% and 4.33%. -us, the (Group 1 � 1976–1985, Group 2 �1986–1995, Group results imply that both rainfall and temperature data violate 3 �1996–2005, and Group 4 � 2006–2015). It was to the normality assumption and hence cannot be subjected to identify groups that cause the diﬀerence in temperature a parametric test analysis. over the period. 3.2. Homogeneity Test Analysis. In this study, three types of 3. Results homogeneity test (i.e., Pettitt’s test, the SNHT test, and 3.1. Normality Test Results for Rainfall and Temperature. Buishand’s test) were performed at 5% level of signiﬁcance. Prior to undertaking the homogeneity and Mann–Kendall -is test is used to determine if indeed the datasets are trend test, the data was subjected to normality test using signiﬁcantly homogenous for the trend test analysis. Shapiro–Wilk, Anderson–Darling, Lilliefors, and Jar- In Figure 2, whereas SNHT and Buishand’s test de- que–Bera tests. All four tests have similar null (H ) and o tected the same change point (T) during 2007 at both alternate (H ) hypotheses; data follows normal distribution Binduri and Garu-Tempane, Pettitt’s test was met with a a Advances in Meteorology 7 P-P plot (rainfall) Q-Q plot (rainfall) 0.8 0.6 0.4 0.2 0 0 0 0.5 1 0 200 400 600 800 1000 1200 1400 Empirical cumulative distribution Binduri P-P plot (temperature) Q-Q plot (temperature) 0.8 0.6 0.4 27 0.2 26 0 25 0 0.5 25 26 27 28 29 30 Binduri Empirical cumulative distribution (a) Q-Q plot (rainfall) P-P plot (rainfall) 0.8 0.6 0.4 0.2 0 600 0 0.5 600 700 800 900 1000 1100 1200 1300 Garu-tempane Empirical cumulative distribution Q-Q plot (temperature) P-P plot (temperature) 0.8 0.6 0.4 0.2 0 27 0 0.5 27 28 29 30 31 32 33 Garu-tempane Empirical cumulative distribution (b) Figure 2: Continued. Theoretical cumulative distribution Theoretical cumulative distribution Theoretical cumulative distribution Theoretical cumulative distribution Quantile-normal (903.29, 207.18) Quantile-normal (28.46, 0.70) Quantile-normal (28.91, 0.86) Quantile-normal (966.06, 134.10) 8 Advances in Meteorology Q-Q plot (rainfall) P-P plot (rainfall) 0.8 0.6 0.4 0.2 0 500 0 0.5 500 700 900 1100 1300 1500 1700 Manga Empirical cumulative distribution P-P plot (temperature) Q-Q plot (temperature) 1 31 0.8 0.6 0.4 0.2 0 23 0 0.5 23 24 25 26 27 28 29 30 31 Manga Empirical cumulative distribution (c) Figure 2: Visual inspection of P-P and Q-Q plots for rainfall and temperature. (a) Binduri gauge station. (b) Garu-Tempane gauge station. (c) Manga gauge station. change point at Binduri and Garu-Tempane during 1987 in increase in April but becomes signiﬁcant in May. Rain is mean annual rainfall. However, all three tests had P values highest in August and begins to reduce in volume from ≥0.05, hence meeting the assumption of homogeneity of September to November. -e period between May and rainfall data’s suitability for trend testing and thus implying September is the wet season and that from October to April that at least majority of the datasets were found by the test to is the dry season of the Bawku Area of Ghana. It is observed be of homogenous series and hence useful for the trend that there is a unimodal rainfall regime in the study area irrespective of the location of the gauge station. analysis irrespective of the change point. Figures 3(a)–3(c) show the graphical results of the homogeneity test analysis. Table 4 shows statistical results of the homogeneity 3.4. Trends and Variability in Mean Annual Rainfall. A analysis for both rainfall and temperature and their com- Mann–Kendall (MK) nonparametric test was used to test the parative test statistics (Pettitt’s test, SNHT, and Buishand’s trends available in the rainfall and temperature data whose test). -e table shows that, among the 40 years under study, results are presented in Tables 5 and 6. -e results are based only two (1988 and 1993) were found to be signiﬁcantly on three separate meteorological stations. As a measure of inhomogeneous. In the mean temperature, however, sig- the link between two subsequent annual rainfall and tem- niﬁcant change points were observed in 2010 for Binduri and perature datasets, the MK result is based on the computa- Garu-Tempane and in 1992 for Manga. Both Pettitt’s and tions of Kendall’s tau with a null hypothesis (H ) of no trend Buishand’s tests found a change point (T) in 1992 at Manga, in the dataset. Positive and negative MK test results signify while Buishand’s test was met with a change point in 2010 at an increasing and decreasing trend in dataset, respectively. It both Binduri and Garu-Tempane. is observed in Table 5 that annual rainfall amount at Manga station shows statistically signiﬁcant positive (P< 0.05) 3.3. Seasonal Nature of Monthly Rainfall. In Figure 4, trend; hence, the null hypothesis (H ) is rejected at the monthly rainfall patterns are observed to be similar in all Manga rainfall station. In the same Table 5, Sen’s slope three stations. It is shown in Figure 4 that there is virtually no estimator depicts an upward trend for rainfall in all the rainfall in the period from December to February. -e onset stations: Binduri, Manga, and Garu-Tempane. It, therefore, of rain in the area is March; rainfall amount begins to implies that both Sen’s slope and Mann–Kendall test are in Theoretical cumulative distribution Theoretical cumulative distribution Quantile-normal (28.73, 0.98) Quantile-normal (936.62, 181.00) Advances in Meteorology 9 120 29.5 28.5 60 27.5 26.5 0 25.5 1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020 Years (1976–2015) Years (1976–2015) Mean Mean annual rain mu = 28.463 mu = 75.278 (a) 120 29.5 28.5 60 27.5 26.5 0 25.5 1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020 Period (1976–2015) Period (1976–2015) Mean Mean annual rain mu = 28.463 mu = 75.278 (b) 1970 1990 2010 1970 1980 1990 2000 2010 2020 Period (1976–2015) Period (1976–2015) Mean Mean annual temp mu1 = 67.738 mu1 = 28.012 mu2 = 83.022 mu2 = 29.252 (c) Figure 3: Homogeneity test showing change point T in annual rainfall and temperature. (a) Binduri gauge station. (b) Garu-Tempane gauge station. (c) Manga gauge station. Table 4: Homogeneity test statistics. Pettitt’s test SNHT test Buishand’s test Meteorological station T Sig. T Sig. T Sig. Rainfall Binduri 1987 0.628 2007 0.449 2007 0.447 Garu-Tempane 1987 0.625 2007 0.471 2007 0.460 ∗ ∗ Manga 1988 0.016 1988 0.091 1993 0.017 Temperature Binduri 2009 0.365 2011 0.052 2010 0.020 Garu-Tempane 2009 0.371 2011 0.052 2010 0.022 ∗ ∗ Manga 1992 ≤0.001 1998 0.053 1992 ≤0.001 Sig. at 0.05. Rainfall (mm) Rainfall (mm) Rainfall (mm) Temperature (°C) Temperature (°C) Temperature (°C) 10 Advances in Meteorology Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period (1976–2015) Binduri Garu Manga Figure 4: Rainfall monthly cycles in the Bawku Area. Table 5: Mann–Kendall trend test for annual rainfall. Meteorological station No. of years MK stat. (S) K. tau Min Max M SD P value CI [L–U] Sen’s slope Binduri 40 72.0 0.092 3.5 109.9 75.3 17.5 0.408 −0.256–0.489 0.152 Garu-Tempane 40 112.0 0.14 58.0 103.3 80.5 11.3 0.196 −0.112–0.527 0.192 Manga 40 196.0 0.252 56.3 130.2 78.1 15.3 0.023 0.056–0.882 0.432 Mean 40 132.0 0.169 55.8 111.6 77.9 11.8 0.127 −0.079–0.545 0.219 ∗ ∗ Sig. at 0.05. Reject H at ≤0.05 level or accept H at ≥0.05. 0 1 Table 6: Mann–Kendall trend test for annual temperature. Meteorological station No. of years MK stat. (S) K. tau Min Max M SD P value CI [L–U] Sen’s slope Binduri Mean 40 41.0 0.054 25.6 29.3 28.5 0.7 0.640 0.000–0.006 0.001 Max T 40 195.0 0.264 34.5 36.0 35.3 0.4 0.022 0.010–0.014 0.012 Min T 40 −29.0 −0.038 16.3 22.8 21.7 1.3 0.743 −0.007–0.000 0.000 Garu-Tempane Mean 40 384.0 0.503 27.6 32.9 28.9 0.9 0.0001 0.028–0.050 0.033 Max T 40 484.0 0.634 32.8 36.1 34.9 0.8 0.0001 0.048–0.053 0.050 Min T 40 269.0 0.352 19.6 30.2 22.8 1.4 0.002 0.026–0.033 0.030 Manga Mean 40 403.0 0.529 23.9 30.0 28.7 0.9 0.0001 0.038–0.044 0.041 Max T 40 329.0 0.432 34.6 36.9 35.6 0.6 ≤0.001 0.030–0.037 0.033 Min T 40 352.0 0.458 12.9 23.2 21.9 1.7 0.0001 0.042–0.050 0.046 ∗ ∗ Sig. at 0.05. Reject H at ≤0.05 level or accept H at ≥0.05. 0 1 tandem to the increasing trend of rainfall at Manga and amount was recorded in Binduri in 2008. Total annual Garu-Tempane. rainfall change was computed on the ﬁtted regression Graphically, a linear regression trend line was further line to show the changes in annual rainfall of each rainfall used to show evidence of the rainfall variability and trend station. -e results are −0.029, 2.724, and 5.4613 mm/ in the dataset. As shown in Figure 5, whereas Binduri year in Binduri, Garu-Tempane, and Manga, showed a slightly declining trend (negative trend line) of respectively. total annual rainfall, both Manga and Garu-Tempane showed an upward trend (positive trend line). However, it 3.5. Annual Rainfall Anomalies. -e Standardised Precipi- is observed that, in all the three meteorological stations in tation Index (SPI) was used to generate and describe the the study area, rainfall is erratic and varies with time over the 40-year period. anomalies of total annual rainfall (three stations combined) in the study area over the 40-year study period. -e aim was Again, it is illustrated in Figure 5 that, in all the three meteorological stations, rainfall amount is highly con- to identify years that witnessed more wetness or dryness over the period in the study area. As such, the authors centrated between 600 mm and 1000 mm annually. -e extremely high (1562.7 mm) amount of rainfall recorded combined the data of the three stations to arrive at a common mean that would then be used to generalize for the in all the three stations occurred in 2007 at Manga meteorological station and the extremely low (42.3 mm) area. -us, an average of the three stations was calculated Monthly total rainfall (mm) Advances in Meteorology 11 1800.0 Binduri Garu y = –0.029x + 903.88 y = 2.724x + 910.21 1600.0 2 2 R = 3E-06 R = 0.055 1400.0 1200.0 1000.0 800.0 600.0 400.0 200.0 0.0 Period (1976–2015) Binduri Garu Manga Linear (Binduri) Linear (Garu) Linear (Manga) Figure 5: Long-term variability in annual total rainfall. 3.6. Seasonal Nature of Monthly Temperature. In Ghana, and subsequently used to generate the SPI. Results of SPIs are either positive (wet) or negative (dry), with positive values Temperature is uniformly high throughout the year; implying a greater (>) than median precipitation and negative however, there are ﬂuctuations at the regional and local values indicating less (<) than median precipitation. Of levels. -e three gauge stations do not show signiﬁcant course, this was necessary because of the role rainfall plays in graphical discrepancies in pattern. Overall, temperature ° ° the livelihoods of the residents of this part of the Savannah ranges between 26.4 C and 33.3 C from January to De- agroclimatological zone in Ghana. It is important to note that cember as observed in Figure 7. -ere are two recognizable the SPI can be generated over diﬀerent timescales; however, in peaks of temperature: March-April and October-No- this study, to reﬂect a long-term precipitation pattern, a 12- vember. Temperature is highest during March and April month timescale SPI was adopted to compare the rainfall with highest monthly temperature recorded in April. pattern for 12 consecutive months of the ﬁrst year with 12 August, meanwhile, is the month with the lowest tem- consecutive months of all other years. perature throughout the year, coinciding with the month An SPI result with positive (+) value indicates wetness, with the highest rainfall. Speciﬁcally, temperature is ob- while negative (−) value implies dryness, but the intensity is served to be relatively higher and lower at Garu-Tempane dependent on the value of SPI. It is observed from Figure 6 and Binduri gauge stations, respectively, than all other that it is only in 2006 that the SPI was ≥2.0 to imply an stations as observed from the graph. Wet season’s tem- extremely wet year among the years under study with 2.3% perature is relatively low (25–27 C) between July and probability of recurring once every 50 years. In the years September, while dry season’s temperature is relatively 1993 and 1998, SPI values were ≥1.5 and≤1.99, respectively. higher (27–32 C) from February to April (Figure 7). -is implies very wet years with 4.4% likelihood to recur once every 20 years. Also, in 1988–1991, 1994, 1996, 1997, 1999, 2001–2004, 2008, 2009, 2011, 2012, and 2014, the SPI 3.7. Trend and Variability in Mean Annual Temperature. Table 6 shows the MK trend test results for mean temper- values were ≥0.0 and≤0.99, which suggest mild wetness. -ey are 34.1% likely to recur once in every 3 years. ature of the three stations. It is observed that, at Binduri Conversely, years 1977–1979, 1981–1982, 1985–1987, 1992, gauge station, only the minimum temperature of the area showed a decreasing trend, implying that, on average, 1995, 2000, 2005, and 2010 have their SPI values ≥0.0 and ≤ −0.99, implying mild dryness with 34.1% likelihood of recur- minimum temperature of the area has declined over the years. On the contrary, the maximum temperature of rence once every 3 years. During 1984, 1985, and 2014 SPI values were ≥ −1 and ≤ −1.49, denoting moderate dryness with 9.2% Binduri station showed a positive and signiﬁcant trend. -e null hypothesis (H ) of the mean and minimum temperature recurrence probability once in 10 years. Lastly, 1976, 1980, and 2007 recorded SPI values of ≥ −1.5 and ≤ −1.99 to suggest very is hence accepted as H is rejected. Furthermore, it is observed that, at a P value of 0.05, dry years with 4.4% likelihood of recurrence once in 20 years. However, based on the baseline period, the overall ob- minimum, maximum, and mean temperatures of the Garu- servation shows that the longest dry period occurred from Tempane and Manga stations have positive trend. -us, the 1976 to 1987, marked by negative anomalies, whereas null hypothesis (H ) is rejected at these gauge stations. -at 1988–1991 and 1996–1999 were the longest wet periods is, given the 40-year period, Garu and Manga have witnessed rising temperatures on average. In Table 6, Sen’s slope es- observed. Meanwhile, the period after 1987 witnessed more excess rainfall than shortages. timator shows a positive trend for all temperature datasets in Manga y = 5.4613x + 824.66 R = 0.1213 Annual rainfall (mm) 2014 12 Advances in Meteorology –1 –2 Period (1976–2015) Figure 6: Annual rainfall anomalies. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period (1976–2015) Binduri Garu Manga Figure 7: Monthly cycle of temperature. the meteorological stations. -us, the Mann–Kendall trend 3.8. Temperature Anomalies. Temperature has a profound test and Sen’s slope estimator are in agreement. impact on the hydrological cycle of the Savannah agro- Likewise, Figure 8 shows the graphical representation of the climatological zone and hence the general water security in mean annual temperature over the three stations. A linear the area due to its impact on evapotranspiration rate. As regression trend line was ﬁtted in the graph to show the such, ﬁnding the temperature anomalies over the period is variation of temperature over the study period. It is observed prudent as results could inﬂuence future agricultural plans from Figure 8 that temperature is uniformly high in all the in the area. Temperature anomaly measures the discrepancy three stations with minor extreme cases. On a whole, the of the observed data from the average of the dataset. highest (32.9 C) mean recorded temperature of the three Whereas positive (+) values imply that the 40-year average stations was in 1998 in Garu-Tempane, while the lowest temperature is smaller than the observed, a negative (−) (23.9 C) was recorded in 1979 at Manga meteorological station. value implies a large observed value. With an R-square of 34.7%, the rise in temperature in Figure 9 generally shows that many of the years in perspective witnessed a positive (+) value in temperature Manga was the most signiﬁcant, with just about 14% in Garu-Tempane. Binduri, on the other hand, shows a de- with the anomalies ranging between −1.42 and 1.2 C. Fig- clining mean temperature over the period. Regardless of the ure 9 also shows that, among the years with annual rising above, the mean temperature of the areas witnessed a steady trend in temperature anomalies, 1988 recorded the extreme rise. Also, Figure 8 depicts an average temperature change (1.2 C), suggesting a hot condition. -is was followed by based on meteorological station with a mean annual tem- years 1998, 2001–2007, and 2009–2010 with values ≥0.5 and perature change, ranging from Binduri to Manga, of <1.0, implying warmer conditions than the average. -e rest ° ° −0.0085 C to 0.05 C/year (0.9%–5%) at 95% conﬁdence level. (1990, 1993, 1995–1997, 1999–2000, and 2011) were ≥0.0 and SPI (mm) Mean temperature (°C) 2014 Advances in Meteorology 13 Binduri Garu y = –0.0085x + 45.366 y = 0.0279x – 26.783 2 2 R = 0.0194 R = 0.1391 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Period (1976–2015) Mat binduri Mat garu Mat manga Linear (Mat binduri) Linear (Mat garu) Linear (Mat manga) Figure 8: Long-term variability of mean annual temperature. <0.5. -e above temperature diﬀerence indicates a near- 3.9.1. Binduri Meteorological Station. -e ANOVA results normal temperature condition, while 1980, 1987, and 2008 in Table 7 show no statistically signiﬁcant diﬀerences in the witnessed no change (zero change) in temperature in re- year groups with respect to rainfall. However, there was a lation to the average value, hence a normal temperature. statistically signiﬁcant diﬀerence in the groups at the P< 0.05 level in temperature for the four groups: F (3, 476) � In contrast, Figure 9 further shows that 1979 (−1.8 C) and 1976 (−1.0 C) recorded the lowest temperature 3.623; P � 0.013. Despite reaching statistical signiﬁcance, the anomaly with values ≤−1 C, indicating years with the actual diﬀerence in mean scores between the groups was coolest temperature in comparison with the average of the quite small. -e eﬀect size, calculated using eta-squared, was area. A cooler condition in comparison to the average .02. Post hoc comparisons using the Tukey HSD test indi- temperature of the area was experienced in the years cated that the mean score for Group 3 (M � 28.93, SD � 2.15) 1977–1978, 1983–1984, 2013, and 2014 with recorded was signiﬁcantly diﬀerent from Group 4 (M � 28.02, values ≤−0.5 and > −1.0. Lastly, near-normal conditions SD � 2.25). -ere was, however, no signiﬁcant diﬀerence were observed in the years 1982, 1986, 1989, 1991–1992, between Group 3 (M � 28.93, SD � 2.15) and Group 1 or 1994, 2012, and 2015 with the anomaly ≤−0.5. Group 2. Again, there was no signiﬁcant diﬀerence in means between Group 1 (M � 28.35, SD � 2.23) and Group 2 or Group 4; and there was no signiﬁcant diﬀerence in means 3.9. One-Way Post Hoc ANOVA. A one-way post hoc between Group 2 (M � 28.35, SD � 2.19) and Group 4. ANOVA was conducted to explore the diﬀerences in means of decadal rainfall and temperature measured in 3.9.2. Garu-Tempane Meteorological Station. -e result in millimetres (mm) and degree Celsius ( C), respectively. Table 8 shows that there were no signiﬁcant diﬀerences in -e years were divided into four decades (10 years) from means of the year groups with respect to rainfall, but the year in which recording began as far as this research is temperature showed evidence of statistically signiﬁcant concerned (Group 1: 1976–1985, Group 2: 1986–1995, diﬀerences in the means of the year groups at the P< 0.05 Group 3: 1996–2005, and Group 4; 2006–2015). -is level for the four year groups: F (3, 476) � 3.158; P � 0.025. analysis was done separately for the three meteorological Notwithstanding the level of signiﬁcance, the actual stations and the result is presented in Tables 7–9. In diﬀerence in mean scores between the groups was low. As ANOVA, test of homogeneity of variance uses Levene’s such, the eﬀect size was calculated using eta square and the test, which tests whether the variance in scores is the same result was .02. Meanwhile, in the post hoc comparisons using for each group. If the P value (sig.) is greater than 0.05 the Tukey HSD test, it was found that there was a signiﬁcant (i.e., P> 0.05), it means the homogeneity assumption is diﬀerence in the mean scores of Group 1 (M � 28.17, not violated and hence one can go on to interpret the post SD � 2.17) and Group 3 (M � 29.25, SD � 2.12) and that there hoc ANOVA results. However, if the test results violate were no signiﬁcant diﬀerences between any other groups. the homogeneity assumption (i.e., if P< 0.05), then you consult the robust tests of equality of means and hence use the Welch and Brown–Forsythe ANOVA to interpret your 3.9.3. Manga Meteorological Station. -e result in Table 9 output. But since homogeneity of variance was not vio- shows that there were no signiﬁcant diﬀerences in means of the year groups with respect to rainfall but there was sig- lated in this analysis, Levene’s test was used for the interpretation. niﬁcant diﬀerence in means of temperature at P< 0.05 level Manga y = 0.05x – 71.087 R = 0.3471 Mean temperature (°C) 14 Advances in Meteorology 1.5 0.5 –0.5 –1 –1.5 –2 Period (1976–2015) Figure 9: Mean annual temperature anomalies. for the year groups: F (3, 476) � 12.821; P< 0.001. -e actual agriculturalist for that matter. -e increasing rainfall in the diﬀerence in mean temperature values was moderate evi- area supports the ﬁndings of [46] that found that, in the dence from the eﬀect size of 0.07 calculated using eta- Savannah zone of Ghana, many of the years between 1901 squared. Post hoc multiple comparisons using the Tukey and 2010 experienced above-normal rainfall in the area. HSD test further indicated that the mean score for Group 1 Given that there is an upward trend in rainfall, farmers and the residents in general will be relieved especially in relation (M � 27.66, SD � 2.78) was signiﬁcantly diﬀerent from Group 2 (M � 28.71, SD � 2.16), Group 3 (M � 29.36, to water management towards future use. -is is because whereas crop farmers need water for their plants, those in SD � 2.16), and Group 4 (M � 29.16, SD � 2.11). Group 2 (M � 28.71, SD � 2.16) did not diﬀer signiﬁcantly from either animal husbandry equally rely on water for their animals. Group 3 or Group 4; Group 3 (M � 29.36, SD � 2.16) did not Indeed, the domestic use of water cannot be underestimated. diﬀer signiﬁcantly from Group 4. However, an increasing number of rainfall-deﬁcient years as shown by the anomalies are an indication of drought in those years. -is even worsens the rainfall uncertainty sit- 4. Discussion uation in the area and rather calls for planning because of the In this study, emphasis was placed on the temporal de- signiﬁcant role rainfall plays in the lives of the residents in scription of temperature and rainfall in the Bawku Area of the area. -e fact that there is little or no rainfall from No- Upper East Region of the Savannah zone of Ghana. Similar to other studies, interannual and high values of changes in vember to March suggests the signiﬁcance of the dry season in this part of the country. In the savannah areas of rainfall over the study area are in agreement with [15–17], and therefore it is implied that residents will still be in a state Africa, reports of high temperature and low rainfall have of uncertainty in relation to rainfall in the coming years. -is been established as a consequent of the tropical conti- is because of the diﬃculty in forecasting rainfall in an area nental air mass [47]. -e seasonal nature of the monthly with erratic and varied rainfall pattern. Rainfall variability in rainfall in the area is impacted upon by the dominance of the study area is also consistent with the ﬁndings of [45, 46]. the dry tropical continental air mass and the moist -us, the ﬂuctuations in the rainfall distribution on annual tropical maritime air mass from which we have the North basis are not uncommon in the Savannah zone. However, East Trade Winds (harmattan) and South West Monsoon this ﬁnding is in sharp contrast with [16, 45], in which Winds, respectively. Even though these are the major determinants of the rainfall seasons in Ghana [26, 48], it is rainfall on the whole was found to be decreasing in the northern part in general and Upper East Region of the important to note that the harmattan dominates the Sa- vannah area of Ghana against the inﬂuence of the country. -is could probably be as a result of the fact that their study period was longer and dated back (1954–2010 monsoon, hence leading to the long dry period with little and 1961–2007, respectively) to an earlier time than the amount of rainfall experienced. As such, months of current one and also because it is a wider area in comparison negligible rainfall amount in the study area are generally to the current study area. -us, this study established that all due to the inﬂuence of North East Trade Winds, which are the three meteorological stations on average enjoyed a slight dry and cold in nature. -is is not surprising especially in increase in rainfall distribution. relation to the SPI results witnessing more dryness than Having an increasing rainfall trend is quite a welcoming wetness. -e eﬀect of this air mass on rainfall is similar to result for the residents in the area because of its potential temperature distribution over the area. -us, the wind circulation in West Africa is dominated by the North East agricultural, hydrological, and socioeconomic eﬀects in an area that is highly populated by farmers, rain-fed Trade Winds, which originate from the dry and hot Sahara Temperature anomalies (°C) 2014 Advances in Meteorology 15 Table 7: ANOVA test statistics, Binduri. Binduri SS df MS F Sig. Eta square Rainfall Between groups 13620.562 3 4540.18 0.570 0.635 0.0035 Within groups 3794549.32 476 7971.74 Total 3808169.88 479 Temperature Between groups 52.980 3 17.660 3.623 0.013 0.0223 Within groups 2320.352 476 4.875 Total 2373.332 479 Sig at 0.05 level. Table 8: ANOVA test statistics, Garu-Tempane. Garu-Tempane SS df MS F Sig. Eta square Rainfall: Garu-Tempane Between groups 9451.697 3 3150.57 0.380 0.768 0.002 Within groups 3947291.13 476 8292.63 Total 3956742.83 479 Temperature: Garu-Tempane Between groups 88.512 3 29.504 3.158 0.025 0.019 Within groups 4446.946 476 9.342 Total 4535.461 479 Sig at 0.05 level. Table 9: ANOVA test statistics, Manga. Manga SS df MS F Sig. Eta square Rainfall Between groups 30578.238 3 10192.75 1.111 0.344 0.007 Within groups 4367338.58 476 9175.081 Total 4397916.82 479 Temperature Between groups 206.743 3 68.914 12.82 ≤0.001 0.075 Within groups 2558.597 476 5.375 Total 2765.340 479 Sig at 0.05 level. Desert; this dry and dusty wind has tremendous eﬀect on potential to nullify the rainfall ﬁgures through evapo- transpiration. -e consequence is the scarcity of water in the rainfall and temperature of the study area. -at is not to say, however, that factors including absence of vegetation area as observed in the drying-up of rivers, wells, dams, and cover and absence of river and water bodies have no eﬀects dugouts and soil moisture deﬁciency in the area. -is is a on the local atmospheric circulation in the area. However, major blow to farming in the area because of farmers’ de- the Savannah areas of Northern Ghana have relatively pendence on rain-fed farming. But it is an undeniable fact gentle slopes with rare vegetation and hence we speculate that the rising temperature in the area is partly attributed to less eﬀect of orography in the area. anthropogenic activities through deforestation for charcoal On the contrary, this study also establishes that tem- production, clearing of vegetation for farming, population perature assumes a uniformly increasing trend in the study pressure on the environment, urbanisation, and urban area but varies yearly across the stations, which conforms to heating [52]. global and international studies about variations in tem- Meanwhile, there are decadal diﬀerences in temperature groupings over the area. Whereas at the Binduri station there perature [2, 49]. It is further in consistence with the ﬁndings of [16, 20, 50, 51] that diﬀerently found increasing trends in was an observed diﬀerence in temperature during annual, seasonal, and decadal day and night temperatures in 1996–2005 and 2006–2015, there was a diﬀerence in tem- the northern regions of Ghana. -e increasing temperature perature during 1976–1985 and 1996–2005 in Garu-Tem- over the area carries with it some consequences in the pane. Lastly, diﬀerences exist between 1976–1985 and livelihoods of the residents in terms of drought, dry spells, 1986–1995, 1976–1985 and 1996–2005, and 1976–1985 and water shortage, and the possibility of conﬂict over water 2006–2015 at Manga meteorological station. -is implies resources in the area. -us, the fact that temperature is rising that, during the study period, temperature of the studied signiﬁcantly implies that the rising temperature has the meteorological stations has rarely been constant over the 16 Advances in Meteorology 23, Global International Water Assessment (GIWA) 2003,” decades. -us, the eﬀects of temperature variations felt by 2003, https://www.giwa.net/areas/reports/r23/giwa_regional_ the residents have not been the same across the 40 years. assessment_23.pdf. [2] Intergovernmental Panel on Climate Change (IPCC), Climate 5. Conclusion Change 2007: Impacts, Adaptation and Vulnerability: Contri- bution of Working Group II to Fourth Assessment Report of the -is study has proven to be an addition to the earlier studies Intergovernmental Panel on Climate Change, M. L. Parry, regarding climate variability in the country, especially that of O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and the Savannah agroclimatological zone. It is evident that in the C. E. Hanson, Eds., p. 976, Cambridge University Press, Cam- study area rainfall is averagely low, with erratic pattern. It also bridge, UK, 2008, http://www.cambridge.org/9780521880107. ﬂuctuates and varies in time with some extremely low and high [3] P. V. V. R. Prasada and S. L. Addisu, “Trend analysis and periods of rainfall. Monthly cycles of rainfall and temperature adaptation strategies of climate change in north central show peaks in August and April, respectively. -ere were more Ethiopia,” International Journal of Agricultural Science and years with negative mean annual rainfall anomalies than Research, vol. 3, no. 1, pp. 253–262, 2013, https://www.academia. positives with severe long drought period prior to 1988. -e edu/3407133/TREND_ANALYSIS_AND_ADAPTATION_ STRATEGIES_OF_CLIMATE_CHANGE_IN_NORTH_ temperature, on the other hand, was uniformly high in the CENTRAL_ETHIOPI. study area with minor extreme cases, yet the anomalies in- [4] A. Longobardi and P. Villani, “Trend analysis of annual and dicate rising values of temperatures. -e study is able to es- seasonal rainfall time series in the Mediterranean area,” In- tablish an upward rise in temperature over the study period ternational Journal of Climatology, vol. 30, no. 10, pp. 1538– among the three stations. Despite the insigniﬁcant diﬀerences 1546, 2009. in the interdecadal rainfall amount, temperature witnessed [5] United State Environmental Protection Agency, Ghana interdecadal variations over the years under study. In relation Government Submission to the United Nations Framework to the results, all stakeholders, including nongovernmental Convention on Climate Change Ghana First Biennial Update organisations and government, are recommended to ensure Report, Ministry of Environment, Science, Technology and adequate provision of water supply through the construction Innovation, Accra, Ghana, 2015. of dam’s wells and boreholes. -ese eﬀorts will not just reduce [6] K. K. Azeez, M. M. Abnory, I. H. Sirikyi, and M. Adanusa, water insecurity but will also increase the availability of re- “Climate change and health management in the Central Region, Ghana,” in Climate Change and Sustainable Devel- sources for agricultural activities all-year-round since farmers opment in Africa, Proceedings’ of Second University of Cape will be able to farm even in dry seasons. Coast and University of Ilorin Joint International Conference, Illorin Press, Illorin, Nigeria, 2012. Data Availability [7] Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014: Synthesis Report. Contribution of Working -e data used to support the ﬁndings of this study are Groups I, II, III to the Fifth Assessment Report of the Inter- available from the corresponding author upon request. governmental Panel on Climate Change, Core Writing Team, R. K. Pachauri, and L. A. Meyer, Eds., p. 151, IPCC, Geneva, Conflicts of Interest Switzerland, 2014, https://www.ipcc.ch/site/assets/uploads/2018/ 05/SYR_AR5_FINAL_full_wcover.pdf. -e authors declare no conﬂicts of interest. [8] A. S. Wijaya, “Climate change, global warming and global inequity in developed and developing countries (Analytical Authors’ Contributions perspective, Issue, Problem and Solution),” IOP Conference Series: Earth and Environmental Science, vol. 19, p. 2014, Both authors contributed substantially and equally towards Article ID 012008, 2014. the success of this study, proofread the ﬁnal manuscript, and [9] Intergovernmental Panel on Climate Change (IPCC), Climate Change 2013: Fe Physical Science Basis. Contribution of approved it for publication. Working Group I to Fifth Assessment Report of the Intergov- ernmental Panel on Climate Change, T. F. Stocker, D. Qin, Acknowledgments G.-K. 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Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

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Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

Temporal Description of Annual Temperature and Rainfall in the Bawku Area of Ghana

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