Evidence of climate variability from rainfall and temperature fluctuations in semi-arid region of the tropics

Atmospheric Research 224 (2019) 52–64

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Evidence of climate variability from rainfall and temperature fluctuations in semi-arid region of the tropics

T

Da'u Abba Umara, , Mohammad Firuz Ramlib, Ahmad Zaharin Arisc, Nor Rohaizah Jamilb, Adebayo Abel Aderemid ⁎

a

Department of Environmental Sciences, Faculty of Science, Federal University Dutse, Dutse, Nigeria Faculty of Environmental Studies, Universiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia c Environmental Forensic Research Centre, Faculty of Environmental Studies, Universiti Putra Malaysia, UPM 43400 Serdang, Selangor, Malaysia d Department of Geography, Madibbo Adama University of Technology, Yola Adamawa, Nigeria b

ARTICLE INFO

ABSTRACT

Keywords: Rainfall Climate variability Mann-Kendall Semi-Arid Nigeria

The pattern of rainfall and temperature behaviour in the Hadejia River Basin (HRB) has been assessed. The behaviour of rainfall and temperature have been used as proxies in detecting the presence of climate variability. Historical rainfall and temperature monthly data spanning thirty-six years (1980–2015) obtained from the Nigerian Meteorological Agency (NIMET) was used in this study. ANOVA and Mann-Kendall trend test was used for the data analysis. The ANOVA results showed significant variation in rainfall, maximum and minimum temperature between the stations. The Mann-Kendall trend test result shows an increasing trend in both rainfall and temperature in annual statistics, though statistically insignificant. However, the monthly trends result showed mixed results of both significant and insignificant as well as increasing and decreasing trends. The mean, standard deviation and the coefficient of variation were spatially interpolated using inverse distance weightage technique for easy comprehension. Even though the annual increasing trends result was statistically insignificant except for two out of the ten stations, it is still crucial for planning water-related activities and programs considering the sensitivity and fragility of the region to minor climatic variations.

1. Introduction The impact of climate change and variability have been studied and reported worldwide, (Hoque et al., 2016; Li et al., 2016; Wen et al., 2017), and more studies are currently ongoing. The changes and variability in climate occur at all spatial scales (Elsanabary and Gan, 2015). However, in recent time interest have shifted to regional and basin scales studies, which provides more details and crucial information for the management and planning of local economic and societal activities (Elsanabary and Gan, 2015), and for the prudent environmental protection and management Rainfall and temperature dynamics have been used as proxies to investigate climate change and variability “e.g.” rainfall was used by; (Mohammed et al., 2015; Huang et al., 2018; Karki et al., 2018); temperature by (Iqbal et al., 2016; Salman et al., 2018) and both were used by (Kusangaya et al., 2014; Nayak et al., 2018). Besides rainfall and temperature fluctuation, increasing occurrence of extreme events such as droughts, floods and heavy storms in Africa has been cited as important characteristics of climate variability

⁎

(Ogungbenro and Morakinyo, 2014; Suleiman and Ifabiyi, 2015). These extreme weather events are associated with the increased frequency of to anthropogenically motivated rise in greenhouse gas (GHG) concentration and land use changes (Reason, 2007) and their consequences are the intensifications of global warming and hydrological cycle (Huntington, 2006). The manifestation of that was in the increased spatiotemporal variability of the basic climate parameters (rainfall and temperature) which has been reported to increase in West particularly with regard to rainfall anomalies (De Longueville et al., 2016). However, many studies have demonstrated rainfall recovery in recent years (De Longueville et al., 2016; Awotwi et al., 2015; Sanogo et al., 2015). The recovery is echoed both in more rainy days linked with a long wet spell. duration and more extreme rainfall events. Moreover, in the entire Sahelian region, the largest rainfall recovery is displayed in the August–October period. The trend in the rainfall behaviour also shows larger inter-annual rainfall variability especially along the Coast of Guinea (e.g. in Nigeria and Ghana) (Sanogo et al., 2015). In the semi-arid region of Nigeria, increased fluctuation in rainfall

Corresponding author. E-mail addresses: [email protected] (D.A. Umar), [email protected] (M.F. Ramli).

https://doi.org/10.1016/j.atmosres.2019.03.023 Received 12 February 2019; Accepted 17 March 2019 Available online 18 March 2019 0169-8095/ © 2019 Published by Elsevier B.V.

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Fig. 1. Meteorological data stations within HRB. Modified after: IUCN, 2003.

Fig. 2. Elevation of the weather stations.

and temperature possesses the potential of worsening the already existing environmental degradation and water scarcity (Ogungbenro and Morakinyo, 2014; Balogun et al., 2016), which have been projected to affects food security, human health, environmental flow, economic development (Balogun et al., 2016) and communal conflicts (Roma, 2008; Audu, 2013; Umar and Ankidawa, 2016). The pattern of rainfall behaviour and the increased inter-annual rainfall variability in the area has long been associated with climate change and variability (Adakayi, 2012; Mohammed et al., 2015). In this region, freshwater availability is the major future challenge to sustainable development (Global International Water Assessment (GIWA), 2004; Sobowale et al., 2010; Umar and Ankidawa, 2016), and is largely attributed to climate change and variability (Olagunju, 2015; Sawa et al., 2015). Thus, the current study is aimed at assessing the spatiotemporal dynamics and presence of a trend in rainfall and temperature time

series as the basis of establishing the presence of climate change and variability in Hadejia River Basin (HRB). 2. Materials and method 2.1. Study area Hadejia River Basin is sub-catchment of the Lake Chad Basin. It has a total spatial extent of about 24,680 km2 (Adakayi, 2012). The area is climatically control by two air masses, the South West (SW) and the North East (NE) trade winds. The SW trade winds bring moisture all over the North from the Atlantic Ocean with high temperature (summer) between the months of May to September. The NE trade wind, on the other hand, comes along with dry cold winds from the Sahara Desert between Octobers to April (winter). The interphase of these two wind systems is the Inter-tropical convergence zone or the 53

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Fig. 3. Descriptive summaries (a) rainfall (b) Tmax (c) Tmin.

inter-tropical discontinuity (ITCZ or ITD) and its migration determines the wetness or dryness of any location within the basin perimeter (Ebele and Emodi, 2016).

records, which were used in the analysis of the spatiotemporal variation and the presence of a trend in the data set. Although there exist other meteorological stations outside the basin, however, only stations within the basin are considered for this study (Fig. 1) for reliability and the completeness of data. The elevation of the weather stations was shown in Fig. 2. The drainage system is draining toward the northeast direction in compliance with the elevation characteristics. Tiga station located at the southern tip of the basin has the highest elevation, and Hadejia

2.2. Data for the study Rainfall and temperature records for thirty-six years from four synoptic meteorological stations were obtained from the Nigerian Meteorological Agency (NIMET). The data contained daily and monthly 54

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Fig. 4. Spatial interpolation of mean, SD and CV; (a, b and c) rainfall (d, e and f) Tmax and (g, h and i) Tmin.

situated at the north-eastern fringe has the lowest elevation among the weather stations considered for this study (Fig. 2). Prior to the application of desired statistics, the monthly data used were subjected to quality assurance and quality control (QA/QC) analysis as a requisite to the application of suitable statistics (Duhan and Pandey, 2013). These monthly data were checked for irregularities such as missing data and outliers. The result of QA/QC scrutiny revealed that the data were statistically hygienic except for a few missing data which constitute < 10% of the whole dataset. They were replaced with the means of the last two recorded observations that wrapped the missing observations (Chatterjee and Hadi, 2015).

2.3.1. Descriptive statistics Descriptive statistics are used to label the basic features of the data in a study. They offer simple summaries about the sample and the measures. They are brief coefficients that summarize a given data set, which can either be the entire population or a sample of it. Descriptive statistics reveals the measures of central tendency and variation/spread in data (Bluman, 2008). They form the foundation of quantitative analysis of data. 2.3.2. Analysis of Variance (ANOVA) One-way analysis of variance (ANOVA) is one of the most famous statistical tool used in determining the existence of variation between two or more groups of observations. It is used to test whether the means of two or more independent groups are equal. This statistical tool tests the null hypothesis that samples in two or more groups were drawn from the same population (Chatterjee and Hadi, 2015).

2.3. Data analysis The spatial and temporal variations of rainfall and temperature in the data were analyzed using descriptive statistics, one-way ANOVA and Mann-Kendall trend test. The spatial variation of the chosen parameters was analyzed using ANOVA, while the trends were evaluated using the Mann-Kendall trend test. The mean, SD and CV were interpolated via IDW technique in the GIS environment. The IDW results ease the understanding of the spatiotemporal dynamics of rainfall and temperature. Meanwhile, the cluster analysis was conducted to strengthen the ANOVA and Tukey HSD Test results.

2.3.3. Mann-Kendall Trend Test and Sen Slope Estimator Nonparametric tests have been the most commonly used tests for establishing the temporal variations in hydro-meteorological variables (Zhang et al., 2014; Li et al., 2016). Mann–Kendal (MK) trend test is the most promoted approach among the nonparametric statistical families (Yürekli, 2015). Its wide applications were due to its numerous advantages such as; accommodating missing values and outliers, as well as data with skewed distributions (Ab Razak et al., 2016). The null 55

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Fig. 5. Rainfall trend by station (a) Doguwa (b) Rano (c) Madobi (d) Tiga (e) Kadawa (f) Challawa (g) Kano (h) Gaya (l) Ringim and (j) Hadejia. n 1

Table 1 ANOVA test of meteorological stations on mean annual rainfall. Variable Rainfall Doguwa Rano Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

n 36 36 36 36 36 36 36 36 36 36

Mean ± SD 1136.25 ± 226.06 960.42 ± 168.63 948.20 ± 270.27 962.64 ± 291.43 938.20 ± 271.700 984.43 ± 147.70 978.98 ± 153.11 742.59 ± 176.91 740.59 ± 176.91 380.91 ± 89.81

n

S=

sign (xj

xk )

k=1 j=k+1

df

F

P

9

36.986

0.000

(1)

where S is the Man-Kendall test values, and are the sequential data values, j, k, and n are the length of the data. Sign (xj - xk) is a pointer function which assumes any of the values 1, 0, and − 1, subject to the sign of. (xj - xk); i.e.

350

hypothesis (Ho) of the MK test is that time series values have no trend while the alternative hypothesis (H1) states that, there is a trend in the data set. In this current study, a significant trend is indicated in the test when the P value is < 0.05. This test is based on the statistic.

sgn (xj

xk ) = 1 if xj

sgn (xj

xk ) 0 if xj

sgn (xj

xk ) =

xk > 0

xk = 0

1 if xj

xk < 0

(2) (3) (4)

The Sen Slope estimator is used to estimate the true slope of ManKendall's trend analysis (Dorigo et al., 2012). This test calculates the magnitude of any significant trend detected in the Mann-Kendall test (Gocic and Trajkovic, 2013). The Sen Slope estimator can be calculated using this equation. 56

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3. Results and discussion

Table 2 Tukey HSD test of mean annual rainfall, maximum and minimum temperature between stations. Doguwa

Rainfall

Tmax

3.1. Descriptive statistics

Tmin

MD

Sig

MD

Sig.

MD

Sig.

Doguwa Rano Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

175.83333* 188.05556* 173.61111* 198.05556* 151.82222 157.26667* 393.66667* 395.66667* 755.34444*

0.013 0.005 0.015 0.002 0.061 0.044 0.000 0.000 0.000

−0.3 −0.4 −0.49889* −0.70000* −1.12000* −1.82917* −1.92000* −1.86444* −2.41444*

0.569 0.168 0.026 0.000 0.000 0.000 0.000 0.000 0.000

−1.19139* −0.69139* −0.50806* −1.00806* −0.96361* −1.46222* −1.05139* −1.55139* −2.19333*

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rano Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

12.22222 −2.22222 22.22222 −24.01111 −18.56667 217.83333* 219.83333* 579.51111*

1.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000

−0.1 −0.19889 −0.4 −0.82000* −1.52917* −1.62000* −1.56444* −2.11444*

1.000 0.94 0.168 0.000 0.000 0.000 0.000 0.000

0.50000* 0.68333* 0.18333 0.22778 −0.27083 0.14 −0.36000* −1.00194*

0.000 0.000 0.669 0.353 0.138 0.91 0.008 0.000

Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

−14.44444 10 −36.23333 −30.78889 205.61111* 207.61111* 567.28889*

1.000 1.000 0.999 1.000 0.001 0.001 0.000

−0.09889 −0.3 −0.72000* −1.42917* −1.52000* −1.46444* −2.01444*

1.000 0.569 0.000 0.000 0.000 0.000 0.000

0.18333 −0.31667* −0.27222 −0.77083* −0.36000* −0.86000* −1.50194*

0.669 0.037 0.134 0.000 0.008 0.000 0.000

Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

24.44444 −21.78889 −16.34444 220.05556* 222.05556* 581.73333*

1.000 1.000 1.000 0.000 0.000 0.000

−0.20111 −0.62111* −1.33028* −1.42111* −1.36556* −1.91556*

0.936 0.001 0.000 0.000 0.000 0.000

−0.50000* −0.45556* −0.95417* −0.54333* −1.04333* −1.68528*

0.000 0.000 0.000 0.000 0.000 0.000

Kadawa Challawa Kano Gaya Ringim Hadejia

−46.23333 −40.78889 195.61111* 197.61111* 557.28889*

0.995 0.998 0.003 0.002 0.000

−0.42 −1.12917* −1.22000* −1.16444* −1.71444*

0.121 0.000 0.000 0.000 0.000

0.04444 −0.45417* −0.04333 −0.54333* −1.18528*

1.000 0.000 1.000 0.000 0.000

Challawa Kano Gaya Ringim Hadejia

5.44444 241.84444* 243.84444* 603.52222*

1.000 0.000 0.000 0.000

−0.70917* −0.80000* −0.74444* −1.29444*

0.000 0.000 0.000 0.000

−0.49861* −0.08778 −0.58778* −1.22972*

0.000 0.996 0.000 0.000

Kano Gaya Ringim Hadejia

236.40000* 238.40000* 598.07778*

0.000 0.000 0.000

−0.09083 −0.03528 −0.58528*

1.000 1.000 0.003

0.41083* −0.08917 −0.73111*

0.001 0.996 0.000

Gaya Ringim Hadejia

2 361.67778*

1.000 0.000

0.05556 −0.49444*

1.000 0.029

−0.50000* −1.14194*

0.000 0.000

Ringim Hadejia

359.67778*

0.000

−0.55000*

0.008

−0.64194*

0.000

The descriptive summaries revealed that both rainfall mean and maximum values were higher at Doguwa station and were both lowest at Hadejia station (Fig. 3a). However, the highest and lowest minimum rainfall was recorded at Challawa and Hadejia stations respectively (Fig. 3a). Meanwhile, highest rainfall range and SD were at Doguwa station and the lowest at Hadejia station (Fig. 3a and 4b), but the rainfall CV was higher at Tiga station and lower at Kano and Challawa stations (Fig. 4c). The highest mean, maximum and minimum Tmax values were found to be higher at Hadejia station and the lower at Doguwa station (Fig. 3b). The Tmax ranges, SD's and CV's were higher at Doguwa, Rano and Tiga stations and lower at Kano and Ringim stations respectively (Figs. 4b, 5e and f). Similarly, the Tmin mean, maximum and minimum values were also found to be higher at Hadejia station and lower at Doguwa station (Fig. 3c). However, the standard deviation (SD) and coefficient of variation (CV) and Tmin ranges were found to be higher at Doguwa, Tiga and Kadawa and lower at Challawa, Kano and Madobi stations respectively (Figs. 4c, 5h and i). 3.2. One way Analysis of Variance (ANOVA) A one-way analysis of variance (ANOVA) was conducted to compare the difference between four meteorological stations on annual mean rainfall. The result shows significant difference in annual mean rainfall based on meteorological stations computed [F (9, 350) = 36.986, P < .05] (Table 1). Multiple comparisons test using Tukey HSD further revealed that only two stations (Doguwa and Hadejia) were found to have a significant difference between all the stations. However, Rano, Madobi, Tiga, Kadawa, Challawa and Kano stations were found to have a significant difference with Gaya, Ringim, and Hadejia. Thus, there is no significant difference between Rano, Madobi, Tiga, Kadawa, Challawa and Kano stations, and between Gaya and Ringim (Table 2). The overall rainfall spatial behavior has unveiled the spatial changes in rainfall mean in the area. The highest rainfall mean was in Doguwa (1136.25 mm) and the least was in Hadejia stations (380.91 mm), probably due to their locational disparity (Fig. 1), the Table 3 ANOVA test of meteorological stations on mean annual maximum and minimum temperature.

The bold and asterisk shows that the mean difference is significant at the 0.05 level, MD = Mean Difference; Sig. = Significance.

?? =

xj j

xk k

(5)

where ?? is the value of Sen Slope estimator, and are data values at time j and k.

57

Variable

n

Mean ± SD

Temperature maximum Doguwa Rano Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

36 36 36 36 36 36 36 36 36 36

31.37 31.68 31.77 31.87 32.07 32.49 33.20 33.29 33.24 33.79

Temperature Minimum Doguwa Rano Madobi Tiga Kadawa Challawa Kano Gaya Ringim Hadejia

36 36 36 36 36 36 36 36 36 36

18. 66 ± 0.47 19. 86 ± 0.41 19. 36 ± 0.41 19. 17 ± 0.47 19. 67 ± 0.47 19. 63 ± 0.33 20.13 ± 0.34 19. 72 ± 0.35 20.22 ± 0.35 20.86 ± 0.46

± ± ± ± ± ± ± ± ± ±

0.77 0.77 0.77 0.77 0.77 0.42 0.34 0.42 0.38 0.54

df

F

P

9

65.704

0.000

78.282

0.000

350

9

350

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Fig. 6. Tmax trend by station (a) Doguwa (b) Rano (c) Madobi (d) Tiga (e) Kadawa (f) Challawa (g) Kano (h) Gaya (l) Ringim and (j) Hadejia.

former is in the southern extreme of the basin, while the latter in the northern fringe of the basin. The results indicated that both rainfall mean, SD and CV are higher in the southern part of the basin and decreases northward (Fig. 4a, b, and c). Table 3 is the ANOVA result for maximum and minimum temperature. The results shows a significant difference in annual mean maximum [F (9, 350) = 65.704, P < .05] and minimum [F (9, 350) = 78.282, P < .05] temperature based on the meteorological stations (Table 3). Similarly, the ANOVA test was conducted for the minimum temperature for the meteorological stations (Table 3). The result has shown a significant difference in annual mean minimum temperature based on the meteorological stations [F (9.350) = 78.282, P < .05] (Table 3). Furthermore, multiple comparisons test using Tukey HSD for the maximum and minimum temperature conducted confirmed the ANOVA results, and the results were displayed in Table 2. The result of the Tukey test for maximum and minimum temperature has confirmed the ANOVA results that there are spatial variations between the studied stations. However, the Tukey test has clearly shown the extent of the variations, statistically significant and or statistically insignificant

(Table 2). The disparities were likely due to the relative topographic and climatic inhomogeneity. Higher mean maximum and minimum temperature was at Hadejia stations (Tmax; 33.79, Tmin; 20.86), while the least was at Doguwa station (Tmax; 31.37, Tmin; 18. 66) (Fig. 6). These findings corroborate previous studies that temperature is higher in the northern part of the basin and decreases southward (Adakayi, 2012). Meanwhile, rainfall is higher at the southern locations and decreases northward. Thus, the north-eastern locations have high temperature and low rainfall and the reverse was the case in the southern part of the basin (Ifabiyi and Ojoye, 2013). 3.3. Trend analysis Temporal behaviour of rainfall and temperature in the area was detected using the Man-Kendall trend test (Table 4a and b). The monthly rainfall trend test results in all the stations showed an increasing trend statistically significant in April at Doguwa, Madobi and Hadejia stations, in June and September at Rano, Madobi, Kadawa, Challawa, Gaya and Ringim stations, in October at Doguwa, Madobi, Tiga and Hadejia stations and lastly in July only at Rano station. 58

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Table 4 Mann–Kendall test statistic for monthly precipitation by stations (1980–2015). Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

a Month Apr May Jun Jul Aug Sept. Oct. Annual

Doguwa 3.05 1.44 1.73 −0.56 0.64 1.73 2.63 2.71

1.668 1.232 1.846 −0.875 0.804 1.657 1.660 9.980

↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑

Rano 0.91 0.01 3.12 2.26 −0.56 3.20 0.89 2.41

0.500 0.023 2.540 2.252 −1.022 3.298 0.382 7.753

↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑

Madobi 2.40 0.72 2.67 1.51 −0.40 2.17 2.70 1.78

0.481 0.354 2.639 2.796 −1.109 2.483 0.832 8.975

↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑

Tiga 0.19 0.90 0.10 1.74 −0.04 1.78 2.51 1.51

0.000 0.644 0.220 3.917 −0.130 2.180 0.660 0.746

↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑

Kadawa 0.98 1.21 2.45 1.76 −0.07 2.33 1.49 1.51

0.142 0.581 2.119 2.509 −0.192 2.228 0.317 8.444

↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑

b Month Apr May Jun Jul Aug Sept. Oct. Annual

Challawa −0.93 −1.57 2.00 0.98 −0.69 2.25 −1.05 0.99

−0.161 −0.557 1.421 0.985 −0.887 1.840 −0.177 2.400

↓ ↓ ↑ ↑ ↓ ↑ ↓ ↑

Kano −0.01 −0.50 1.59 −0.18 0.56 1.02 −0.49 0.67

−0.020 −0.225 0.903 −0.131 0.800 1.312 −0.208 1.637

↓ ↓ ↑ ↓ ↑ ↑ ↓ ↑

Gaya −0.65 −1.10 2.30 1.72 −0.46 2.57 −1.94 1.54

−0.044 −0.382 2.245 2.208 −1.316 2.332 −0.120 4.614

↓ ↓ ↑ ↑ ↓ ↑ ↓ ↑

Ringim −1.39 −1.09 2.33 1.59 −0.44 2.56 −0.54 1.48

−0.077 −0.413 2.279 2.243 −1.298 2.342 0.000 4.436

↓ ↓ ↑ ↑ ↓ ↑ ↓ ↑

Hadejia 0.00 1.36 1.25 0.15 0.30 1.53 1.01 1.65

0.000 0.221 0.757 1.058 1.782 0.870 0.213 0.175

↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑

The mean difference is significant at 0.05 level; bold values are significant. Table 5 Annual and monthly maximum temperature trends (M-K) in the HRB (1980–2015). Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

a Month Jan Feb Mar Apr May Jun Jul Aug Sept. Oct. Nov Dec Annual

Doguwa 1.53 0.68 0.15 0.12 0.04 1.58 1.96 2.36 1.57 −0.44 0.07 1.66 1.62

0.041 0.016 0.006 0.004 0.000 0.040 0.057 0.061 0.026 −0.006 0.000 0.032 0.021

↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ ↑

Rano 1.31 1.30 0.10 −0.33 1.94 2.25 2.32 1.73 1.87 0.22 −0.68 −0.59 1.62

0.029 0.032 0.000 −0.009 0.047 0.047 0.069 0.045 0.030 0.004 −0.013 −0.012 0.021

↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↑

Madobi 0.78 2.22 0.74 2.02 1.51 −1.30 0.30 −1.21 1.01 −0.71 0.95 1.55 1.62

0.018 0.082 0.032 0.044 0.042 −0.026 0.004 −0.025 0.018 −0.013 0.026 0.033 0.021

↑ ↑ ↑ ↑ ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↑ ↑

Tiga 1.13 1.43 −0.01 −0.18 2.07 2.17 2.35 1.65 2.06 0.23 −0.55 −0.67 1.63

0.026 0.034 0.000 −0.007 0.050 0.045 0.069 0.045 0.028 0.004 −0.011 −0.013 0.021

↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↑

Kadawa 0.91 2.60 0.41 0.87 1.34 −1.34 −0.59 −1.05 0.55 −0.07 2.41 3.42 1.62

0.020 0.093 0.008 0.011 0.041 −0.029 −0.017 −0.024 0.005 0.000 0.042 0.085 0.021

↑ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑

b Month Jan Feb Mar Apr May Jun Jul Aug Sept. Oct. Nov Dec Annual

Challawa −0.89 1.57 0.14 0.19 1.43 1.06 −0.44 −1.04 −0.71 −1.64 −0.89 −1.39 −0.25

−0.020 0.034 0.002 0.002 0.031 0.025 −0.007 −0.020 −0.009 −0.025 −0.012 −0.021 0.000

↓ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↑

Kano 0.35 2.13 0.30 0.71 1.04 −2.25 −0.20 −2.58 −0.27 −1.33 0.48 0.68 1.44

0.004 0.068 0.012 0.013 0.023 −0.040 0.000 −0.040 0.000 −0.027 0.011 0.016 0.008

↑ ↑ ↑ ↑ ↑ ↓ ↑ ↓ ↑ ↓ ↑ ↑ ↑

Gaya −0.87 1.39 0.14 0.48 1.46 0.95 −0.37 −1.16 −0.75 −1.66 −1.64 −1.64 −0.25

−0.020 0.029 0.004 0.007 0.031 0.024 −0.003 −0.020 −0.012 −0.025 −0.020 −0.025 0.000

↓ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↑

Ringim 0.23 2.25 −0.33 −0.83 0.79 −2.42 −1.56 −1.79 −0.57 −0.48 1.57 2.92 0.60

0.002 0.076 −0.008 −0.008 0.014 −0.045 −0.027 −0.036 −0.007 −0.012 0.018 0.056 0.004

↑ ↑ ↓ ↓ ↑ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑

Hadejia −0.90 1.73 1.01 0.61 1.75 0.72 0.18 −0.96 0.53 −0.83 −0.56 −1.71 0.78

−0.018 0.033 0.022 0.012 0.036 0.022 0.000 −0.021 0.006 −0.009 −0.008 −0.020 0.005

↓ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↓ ↓ ↓ ↑

The mean difference is significant at 0.05 level; bold values are significant.

However, there are no statistically significant decreasing trends reported from the monthly trend results (Table 4a and b). Suggesting rainfall recovery as reported by some previous studies (Sanogo et al., 2015). The statistically significant increasing trends were largely displayed in June and September (six stations in each), and least shown in July (only one station). This is an indication of increasing rainfall/climate variability in the area, particularly that none of these months was hitherto recognized as the rainfall peak months (July and August) in the area. Meanwhile, the overall rainfall annual trend was increasing at all

stations, however not statistically significant (Table 4 a and b, Fig. 4a-j). This result was in agreement with the results of some previous studies, that there is a noticeable improvement in precipitation over the region (Mohammed et al., 2015; Suleiman and Ifabiyi, 2015). The result of the trend statistics of annual maximum temperature was generally increasing for all the stations, however not statistically significant (Table 5a and b). Meanwhile, the monthly trend results showed statistically increasing and decreasing trends. The statistically significant increasing trends are in February at Madobi, Kadawa, and Ringim stations; in April at Madobi; in May at 59

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Table 6 Annual and monthly minimum temperature trends (M-K) in the HRB (1980–2015). Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

Z

Q

Trend

a Month Jan Feb Mar Apr May Jun Jul Aug Sept. Oct. Nov Dec Annual

Doguwa 1.28 0.76 −0.65 −1.52 −1.24 −2.01 0.34 0.83 1.39 1.62 −1.55 1.90 0.19

0.024 0.006 −0.023 −0.041 −0.022 −0.028 0.000 0.009 0.020 0.027 −0.027 0.021 0.002

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑ ↑

Rano 1.66 1.94 −0.12 −3.14 −1.54 −1.84 1.57 1.13 1.01 1.53 −0.56 2.06 0.74

0.032 0.028 −0.004 −0.055 −0.014 −0.021 0.020 0.014 0.008 0.033 −0.008 0.032 0.003

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑ ↑

Madobi 1.75 1.91 −0.16 −3.15 −1.30 −2.00 1.65 1.24 0.96 1.71 −0.57 2.06 0.74

0.033 0.027 −0.005 −0.052 −0.013 −0.020 0.018 0.015 0.008 0.033 −0.007 0.030 0.003

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑ ↑

Tiga 1.38 1.91 −0.20 −1.46 −1.21 −0.41 0.67 1.54 0.68 0.29 0.85 0.40 1.20

0.026 0.052 −0.005 −0.028 −0.024 −0.004 0.009 0.015 0.006 0.004 0.011 0.005 0.12

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑

Kadawa 1.38 0.64 −0.65 −2.32 −2.06 −2.28 0.56 0.34 0.79 1.42 −1.76 1.86 0.19

0.025 0.009 −0.023 −0.059 −0.033 −0.023 0.006 0.005 0.010 0.027 −0.025 0.020 0.002

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑ ↑

b Month Jan Feb Mar Apr May Jun Jul Aug Sept. Oct. Nov Dec Annual

Challawa 1.29 1.78 0.25 −1.77 −1.32 −1.60 0.33 1.00 0.31 0.44 0.93 −0.16 1.52

0.028 0.047 0.006 −0.023 −0.021 −0.020 0.000 0.013 0.000 0.005 0.012 0.000 0.009

↑ ↑ ↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑

Kano 0.05 0.89 −0.35 0.79 −1.39 −0.34 3.00 2.76 1.47 −0.12 −1.15 −0.82 1.66

0.000 0.025 −0.009 0.011 −0.019 −0.004 0.039 0.032 0.020 0.000 −0.021 −0.011 0.010

↑ ↑ ↓ ↑ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↓ ↑

Gaya 1.13 1.61 −0.15 −2.37 −1.94 −1.85 −0.11 0.49 −0.30 −0.05 0.19 0.00 0.31

0.021 0.040 −0.003 −0.025 −0.030 −0.025 0.000 0.006 0.000 0.000 0.000 0.000 0.002

↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑

Ringim 0.05 0.89 −0.15 0.79 −1.39 −0.34 3.00 2.76 1.47 −0.12 −1.15 −1.86 1.39

0.000 0.025 0.000 0.011 −0.019 −0.004 0.039 0.032 0.020 0.000 −0.021 −0.025 0.009

↑ ↑ ↑ ↑ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↓ ↑

Hadejia −0.35 2.60 3.28 −1.36 2.39 −0.56 3.75 1.78 1.09 1.21 −2.12 0.01 1.53

0.006 0.068 0.090 −0.026 0.029 0.000 0.026 0.013 0.011 0.025 −0.032 0.000 0.013

↑ ↑ ↑ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑

The mean difference is significant at 0.05 level; bold values are significant.

Tiga; in June at Rano and Tiga; in July at Doguwa, Rano and Tiga; in August at Doguwa; in September at Tiga; in November at Kadawa and in December at Kadawa and Ringim stations respectively (Table 5a and b). However, the statistically significant decreasing trend was in June at Kano and Ringim and in August at Kano station (Table 5b). Despite the fact that Hadejia station has the highest temperature mean, yet none of the monthly trend in the station report statistically significant increasing trends a scenario linked to increased climate variability in the area (Sawa et al., 2015; Umar and Ankidawa, 2016). Similarly, the annual Tmin trend results have revealed a statistically insignificant increasing trend (Table 6a and b; Fig. 7 a-j) for all the stations. However, the monthly minimum temperature trends results showed an increasing and decreasing trends with the statistically significant increasing trends in December at Rano and Madobi stations, July and August at Kano and Ringim stations; February, March, May, and July at Hadejia station (Table 6a and b). Furthermore, the only statistically significant decreasing trend is in June at Doguwa, Madobi and Kadawa stations; in April at Rano, Madobi and Kadawa stations and in May and November at Kadawa and Hadejia stations respectively (Table 6a and b).

2001 while the extreme low was in 1982, 1983, 1984 and 1987 respectively (Fig. 8a). These years have rainfall > ± 10 mm and were found to correspond to the period of floods and droughts incidence in the areas (Table 7). In terms of drought disasters, Hadejia station is the most affected, probably due to its location in the core semi-arid premises of the country. However, the severity of flood disaster was more pronounced in Tiga, Kano and Ringim stations respectively (Table 7). Historical records have revealed that floods and droughts have resulted in untold consequences to over 15 million inhabitants depending on the Hadejia river basin for their livelihoods (Sobowale et al., 2010). 4. Discussion The spatiotemporal dynamics and presence of a trend in rainfall and temperature time series were assessed aiming at establishing the presence of climate change and variability in Hadejia River Basin (HRB), northern Nigeria. The overall findings were that rainfall and temperature varied significantly over space and time. However, the spatial variation in temperature is more visible than that of rainfall. On the other hand, temporal changes are more discernible in rainfall series than in the temperature series. Thus, the annual increasing trend in rainfall and temperature, though statistically insignificant is an indication of a variable climate in the area. More so, due to the sensitivity of the region to even the least climatic changes, the results is crucial for the planning of climate-related activities and programs and will form the basis for future climate variability studies in the area. The spatial variations of rainfall and temperature were analyzed via ANOVA, and the results showed that there was statistically significant difference between the meteorological stations on mean annual rainfall [F (3, 140) = 67.012, P < .05], maximum [F (3, 110) = 77.683, P < .05] and minimum temperature [F (3, 110) = 88.171, P < .05]. The multiple comparison tests using the Tukey HSD test has

3.4. Occurrence of extreme weather events The occurrence of extreme weather events such as floods and droughts/aridity are considered important evidence of climate variability in the area. Rainfall is the most variable parameter among all the climatic elements in the region (Ekpoh and Nsa, 2011). The pattern of rainfall behaviour and the increased inter-annual rainfall variability in the area has long been associated with climate change and variability (Adakayi, 2012; Mohammed et al., 2015). Although both rainfall and temperature vary with time and space, rainfall variability is more visible than that of temperature. Thus, rainfall extreme (high and low) values were given precedence in the discussion of extreme weather events. The extreme high rainfall events were in the 1994, 1998 and 60

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Fig. 7. Tmin trend by station (a) Doguwa (b) Rano (c) Madobi (d) Tiga (e) Kadawa (f) Challawa (g) Kano (h) Gaya (l) Ringim and (j) Hadejia.

corroborated the ANOVA results, sustaining the significant variation between the studied stations. Both results (ANOVA and Turkey test) have sustained the earlier findings that, the southern part of the basin has higher rainfall and lower temperatures, while the northern frontier of the basin has low rainfall and high temperature (Adakayi, 2012 and Mohammed et al., 2015) and that, rainfall is less variable spatially compared to temperature (Thelma, 2015). A similar scenario was reported for the Hanjiang River Basin in China, where higher rainfall pattern was observed in the southern part of the basin and a lower rainfall over the northern part of the basin (Deng et al., 2018) Moreover, spatial interpolation of the mean, SD and CV has further justified the spatial characteristics of rainfall and temperature. Although the clear dichotomy of rainfall and temperature between the south and northern part of the basin, there is an elevated value of temperature around Kano station, perhaps due to the location of this station within the urban centre. The trend results of the rainfall and temperature via the MannKendall trend test showed a general increasing annual trend for all stations statistically significant only at Doguwa and Rano stations. The change magnitude ranged from 0.175–4.436 for rainfall and

0.009–0.013; 0.004–0.021 for minimum and maximum temperatures respectively. The annual and decadal rainfall analysis showed that 1983 and 1994 were the driest and wettest years respectively. Similarly, the decades 1980s and 1990s were the driest and wettest decades in the area. This result matched with the finding of Chamani et al. (2018), that the decade 1990s was the wettest in the recent past. However, the temperature series has undergone seasonal, annual and decadal analysis. The annual mean temperature analysis showed that 1987 and 1992 were the hottest and the coldest years in the series year (Fig. 9a). Of the four seasonal temperature categories (DJF, MAM, JJA, and SON), MAM season has the highest mean temperature values (Fig. 9b). Meanwhile, the decadal analysis had revealed the 1990's and 2000's decades as the coldest and the hottest decades in the region (Fig. 9c). Although the annual trend in the rainfall and temperature series was statistically insignificant except for Doguwa and Rano stations, it is yet crucial for the understanding of the climatic behaviour of the region, and for the planning of climate-related events and activities such rainfed and irrigation agriculture, water supply as well as other socio-economic activities such as fishing and recreation. This because, unlike other parts of the country, this region is very sensitive to even little 61

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Fig. 8. Rainfall annual (a) and decadal (b) variability and extremes events in the Hadejia river basin. Table 7 Rainfall extremes and corresponding year of floods/droughts in the studied stations. Stations

Parameters

Tiga Kano

Rainfall

Ringim

Rainfall

Hadejia

Rainfall

Extremes

Historical records

Droughts magnitude

Floods magnitude

Source

High years

Low years

Droughts years

Floods years

1999 2009 2001 1998 2001 2003 1994 2005 2010

1983 1984 1987 1983 1984 1987 1983 1984 1991

1983/84 1986/87

1999/ 2010

Moderate Moderate

Moderate Severe

(Adefolalu, 1985) (Olagunju, 2015)

1983/84 1986/87

2001 2003

Severe Moderate

Severe Severe

1983/84 1986/87 1991/92

1994 2005 2010

Severe Severe Severe

Severe Severe Moderate

(Adefolalu, 1985) (Olagunju, 2015) (Olaniran, 1991) (Ekpoh and Nsa, 2011) (Olagunju, 2015)

Source of the extreme values are from the data.

climatic variations (Adakayi, 2012). Thus, rainfall and temperature trend results from this study revealed nothing other than increased climate variability not change as previously reported (Ekpoh and Nsa, 2011; Ifabiyi and Ojoye, 2013; Suleiman and Ifabiyi, 2015). This is also in line with the findings of Byakatonda et al. (2018) that semi-arid areas exhibit increased climate variability in the recent past, manifested by the occurrences of extreme weather events associated to the El-Niño Southern Oscillation (ENSO). In a similar vein, Salih et al. (2018) stated that the Sudano-Sahelian region of Africa is highly vulnerable to climate variability, usually manifested in term of rainfall irregularities. They further stated that “weak to moderate” rainfall type dominate the precipitation scenarios in the region accounting for about 60–75% of the rains events.

Moreover, besides increased climate variability and its potential impacts in the area, it is feared that the effects of other factors such as the changing land use, uncontrolled human and livestock population expansion may pose additional challenges in the area (Sawa et al., 2015). The congregational effects of these factors altogether possessed the potentials of intensifying the sensitivity of the region to minor climatic changes as was the case with the upper part of the Chad Basin (AbdulKadir et al., 2015), and in Iraq (Salman et al., 2018), Argentina (Antonio et al., 2018), and Greece (Douka and Karacostas, 2018). Consequently, the vulnerability of the inhabitants of the area and their societal resources, particularly water resources and agriculture will be heightened.

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Meanwhile, the significant spatial variations will guide in the establishment of crop-area and climate variability relationship to boost the choice of crop to grow in an area while taking due consideration of the immediate local climate. Thus, careful consideration should be apportioned to both spatial and temporal fluctuations to make the best use of them. Conflict of interest None. Acknowledgement Profound gratitude to the management of Federal University Dutse (FUD), Dutse Nigeria and the Hadejia-Jama'are River Basin Development Authority (HJRBDA) for their unquantifiable support. References Ab Razak, N.H., Aris, A.Z., Ramli, M.F., Looi, L.J., Juahir, H., 2018. Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling. J. Flood Risk Manag. 11, S794–S804. AbdulKadir, A., Usman, M.T., Shaba, A.H., 2015. An integrated approach to delineation of the eco-climatic zones in Northern Nigeria. J. Ecol. Nat. Environ. 7 (9), 247–255. Adakayi, P.E., 2012. An Assessment of Rainfall and Temperature Variations in Selected Stations in Parts of Northern Nigeria. University of Jos Institutional Repository Theses and Dissertations Faculty of Environmental Sciences. Adefolalu, D.O., 1985. Further aspects of the Sahelian drought as evident from rainfall regime in Nigeria. Archiv. Meteorol. Geophys. Bioclimatol. 36, 277–295. Antonio, C., Ovando, G.G., Díaz, G.J., 2018. Secular variation of rainfall regime in the central region of Argentina. Atmos. Res. 213, 196–210. Audu, S.D., 2013. Conflicts among farmers and pastoralists in Northern Nigeria induced by freshwater scarcity. Dev. Country Stud. 3 (12), 25–32. Awotwi, A., Kumi, M., Jansson, P., Yeboah, F., Nti, I., 2015. Predicting hydrological response to climate change in the White Volta catchment, West Africa. J. Earth Sci. Clim. Change 6 (1), 1–7. Balogun, I.I., Sojobi, A.O., Oyedepo, B.O., 2016. Assessment of rainfall variability, rainwater harvesting potential and storage requirements in Odeda Local Government Area of Ogun State in South-western Nigeria. Cogent Environ. Sci. 2 (1), 1138597. Bluman, A.G., 2008. Elementary Statistics: A Step by Step Approach, a Brief Version. McGraw Hill, New York. Byakatonda, J., Parida, B., Moalafhi, D., Kenabatho, P.K., 2018. Analysis of long term drought severity characteristics and trends across semiarid Botswana using two drought indices. Atmos. Res. 213, 492–508. Chamani, R., Monkam, D., Djomou, Z.Y., 2018. Return times and return levels of July–September extreme rainfall over the major climatic sub-regions in Sahel. Atmos. Res. 212, 77–90. Chatterjee, S., Hadi, A.S., 2015. Regression Analysis by Example. John Wiley & Sons. De Longueville, F., Hountondji, Y.C., Kindo, I., Gemenne, F., Ozer, P., 2016. Long-term analysis of rainfall and temperature data in Burkina Faso (1950–2013). Int. J. Climatol. 36 (13), 4393–4405. Deng, P., Zhang, M., Guo, H., Xu, C., Bing, J., Jia, J., 2018. Error analysis and correction of the daily GSMaP products over Hanjiang River Basin of China. Atmos. Res. 214, 121–134. Dorigo, W., Jeu, R., Chung, D., Parinussa, R., Liu, Y., Wagner, W., 2012. Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture. Geophys. Res. Lett. 39 (18). Douka, M., Karacostas, T., 2018. Statistical analyses of extreme rainfall events in Thessaloniki, Greece. Atmos. Res. 208, 60–77. Duhan, D., Pandey, A., 2013. Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmos. Res. 122, 136–149. Ebele, N.E., Emodi, N.V., 2016. Climate change and its impact in Nigerian economy. J. Sci. Res. Rep. 527. Ekpoh, I.J., Nsa, E., 2011. Extreme climatic variability in North-western Nigeria: an analysis of rainfall trends and patterns. J. Geogr. Geol. 3 (1), 51. Elsanabary, M.H., Gan, T.Y., 2015. Evaluation of climate anomalies impacts on the Upper Blue Nile Basin in Ethiopia using a distributed and a lumped hydrologic model. J. Hydrol. 530, 225–240. GIWA, 2004. Global International Waters Assessment Regional Assessment 43: Lake Chad Basin. Published by the University of Kalmar on behalf of United Nations Environment Programme. Gocic, M., Trajkovic, S., 2013. Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Glob. Planet. Chang. 100, 172–182. Hoque, M.A., Scheelbeek, P.F.D., Vineis, P., Khan, A.E., 2016. Drinking water vulnerability to climate change and alternatives for adaptation in coastal South and South East Asia. Clim. Chang. 136 (2), 247–263. Huang, W.-R., Chang, Y.-H., Liu, P.-Y., 2018. Assessment of IMERG precipitation over Taiwan at multiple timescales. Atmos. Res. 214, 239–249. Huntington, T.G., 2006. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319 (1), 83–95. Ifabiyi, I., Ojoye, S., 2013. Rainfall trends in the Sudano-Sahelian ecological zone of

Fig. 9. Temperature mean (a) annual (b) seasonal and (c) decadal.

5. Conclusion Rainfall and temperature series has been used to assess the presence and increased climate variability in the Hadejia river basin. Generally, the annual trends of both rainfall and temperatures in all the studied stations showed an increasing trend with a statistically significant trend in rainfall series at two (Doguwa and Rano) out of ten stations considered. These two stations were located at the high relief areas in the southern part of the basin. However, the monthly trends produced mixed results of statistically significant and insignificant increasing and decreasing trends. Considering the sensitivity of the region to minor climatic changes, both results are crucial for the planning and management of water-related activities and programs such as water supply and agriculture. The noticeable moisture recovery particularly the significant increasing trends in rainfall in some of the rainfall stations create hope for the renewal of the shrinking/drying of the surface water bodies (dams and reservoirs) built for irrigations and water supply in the area. 63

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