**4. Results and discussions**

#### **4.1. Spatial and temporal distribution of rainfall**

The **Figure 3** above, it shows the rainfall concentration and distribution that exist in the South Eastern zone of Nigeria. From the coefficient of variation obtained, it is evident that the rainfall data of the area shows greater consistency with an average of 22% in these zones. Also in **Figure 4** below, the influence of Udi Plateau is also seen in the dispersion of the rain from the mean. On the location axis, it is seen that from Nkwelle–Akwette, there is a positive value in the rainfall difference which shows the escarpment in contact with the wind bearing rain while the other side of the escarpment is noticed by the negative result obtained from Nsukka–Awka. This has significantly resulted to a variance in rainfall pattern in areas around or within the escarpment.

Furthermore, it is clearly seen that Awka has the highest negative dispersion and this is attributed to its closeness to the plateau top with the effect of the distance of each of the stations under observation not neglected.

Generally, South Eastern Nigeria rainfall follow the same pattern as other parts of Southern Nigeria with bi-modal rainfall between May–October, that is, wet season and Nov-April dry season. The rainfall indicates a double peak in July and September.

**Figure 4.** A graph showing the dispersion of rainfall from mean value.

#### **4.2. Distribution characteristics of rainfall**

The rainfalls of the South East Nigeria have high concentration from the coast reducing inland towards the Udi escarpment. From this research, as seen in **Table 2** above, the mean rainfall of the zone was found to be 1744 mm, and Awka having a mean of 1153 m. The heaviest rainfall of the area is around Owerri/Umudike axis at 2349 mm.

From the relation of elevation vs. annual rainfall **Figure 5** above, it is seen that rainfall decreases with increase in elevation. However elevation is not as significant as the effect of latitude and distance from the sea **Figures 6** and **7** below.

From **Table 1** above, it shows that the coefficient of variation of wind is lowest around the coast progressing up to the far-North where it is highest. This pattern has reversed analogous to the rainfall system of the country, which has high value in the coast and low rainfall in the North. Camberlin and Wairoto [31] have shown that there exist a relationship between the Westerlies anomalous wind pattern of Western Kenya and the rainfall of the area. More work

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern…

http://dx.doi.org/10.5772/intechopen.72867

107

Another important observation to make is that most cities with high altitude especially in the far north have good/higher wind speed and hence wind power density. This is illustrated in

**Figure 8** below where elevation values are plotted against average wind speed.

need to be done on Nigeria wind speed and rainfall pattern.

**Figure 7.** A graph of distance from sea (m) against annual rainfall (mm).

**Figure 6.** A graph showing latitude against annual rainfall.

**Figure 5.** A graph showing elevation (m) against annual rainfall (mm).

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern… http://dx.doi.org/10.5772/intechopen.72867 107

**Figure 6.** A graph showing latitude against annual rainfall.

**4.2. Distribution characteristics of rainfall**

106 Engineering and Mathematical Topics in Rainfall

of the area is around Owerri/Umudike axis at 2349 mm.

**Figure 4.** A graph showing the dispersion of rainfall from mean value.

latitude and distance from the sea **Figures 6** and **7** below.

**Figure 5.** A graph showing elevation (m) against annual rainfall (mm).

The rainfalls of the South East Nigeria have high concentration from the coast reducing inland towards the Udi escarpment. From this research, as seen in **Table 2** above, the mean rainfall of the zone was found to be 1744 mm, and Awka having a mean of 1153 m. The heaviest rainfall

From the relation of elevation vs. annual rainfall **Figure 5** above, it is seen that rainfall decreases with increase in elevation. However elevation is not as significant as the effect of

**Figure 7.** A graph of distance from sea (m) against annual rainfall (mm).

From **Table 1** above, it shows that the coefficient of variation of wind is lowest around the coast progressing up to the far-North where it is highest. This pattern has reversed analogous to the rainfall system of the country, which has high value in the coast and low rainfall in the North. Camberlin and Wairoto [31] have shown that there exist a relationship between the Westerlies anomalous wind pattern of Western Kenya and the rainfall of the area. More work need to be done on Nigeria wind speed and rainfall pattern.

Another important observation to make is that most cities with high altitude especially in the far north have good/higher wind speed and hence wind power density. This is illustrated in **Figure 8** below where elevation values are plotted against average wind speed.

**Figure 8.** Annual average wind speed (m/s) against station elevation (m).

However, Bauchi which is at the base of Jos plateau with an altitude of 628 m is of class 1, the same can be said of Yola, which is on the foot of Alantika mountains. Both Weibull scale and shape factors c and k, are related by a second degree polynomial as shown in **Figure 9** below:

Inland and Middle Belt areas have strong correlation coefficient above 0.8 while Coastal and Far-Northern zone show weak correlation below 0.25.This study has also show that Nigeria wind system exhibit bimodal peak pattern especially in the Coastal and the Inland cities corresponding with the rainfall seasons of the country while the middle belt and far-north has unimodal peak mode. From **Figures 11a** and **b** below, the Coast and Inland cities have their minimum wind speed in November at an average of 3.3 and 3.2 m/s respectively. The maximum wind speed for the zones are however highest in April and August with values of 4.6 m/s for both months in the Coast and 4.9 and 4.3 m/s for Inland cities. The middle belt and far north have minimum wind speed of 4.1 and 4.4 m/s respectively in October and maximum of 6.0 and 6.8 m/s respectively in April. For all the regions, as shown from the graphs above, the maximum wind speed occurs in April–May which is the onset of Rainy season and minimum in October–November the start

**Figure 11.** (a) Seasonal variation of average monthly wind speed (m/s) for Coastal cities against months of the year. (b)

Seasonal variation of average monthly wind speed (m/s) for Inland cities against months of the year.

**Figure 10.** (a) Trend showing annual average wind speed (m/s) for Coastal cities against year. (b) Trend showing annual

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern…

http://dx.doi.org/10.5772/intechopen.72867

109

of Dry season in Nigeria.

average wind speed (m/s) for Inland cities against year.

In Nigeria, average yearly wind speed varies from region to region. Analysis of this average indicates that Coastal area has 4.1 m/s, Inland cities has 4.2 m/s, Middle Belt area 5.0 m/s, and Far-Northern cities 5.9 m/s. This is illustrated in **Figure 10a** and **b** below for coastal and inland cities.

**Figure 9.** Scale factor c (m/s) against shape factor.

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern… http://dx.doi.org/10.5772/intechopen.72867 109

**Figure 10.** (a) Trend showing annual average wind speed (m/s) for Coastal cities against year. (b) Trend showing annual average wind speed (m/s) for Inland cities against year.

Inland and Middle Belt areas have strong correlation coefficient above 0.8 while Coastal and Far-Northern zone show weak correlation below 0.25.This study has also show that Nigeria wind system exhibit bimodal peak pattern especially in the Coastal and the Inland cities corresponding with the rainfall seasons of the country while the middle belt and far-north has unimodal peak mode. From **Figures 11a** and **b** below, the Coast and Inland cities have their minimum wind speed in November at an average of 3.3 and 3.2 m/s respectively. The maximum wind speed for the zones are however highest in April and August with values of 4.6 m/s for both months in the Coast and 4.9 and 4.3 m/s for Inland cities. The middle belt and far north have minimum wind speed of 4.1 and 4.4 m/s respectively in October and maximum of 6.0 and 6.8 m/s respectively in April.

For all the regions, as shown from the graphs above, the maximum wind speed occurs in April–May which is the onset of Rainy season and minimum in October–November the start of Dry season in Nigeria.

**Figure 11.** (a) Seasonal variation of average monthly wind speed (m/s) for Coastal cities against months of the year. (b) Seasonal variation of average monthly wind speed (m/s) for Inland cities against months of the year.

**Figure 9.** Scale factor c (m/s) against shape factor.

cities.

However, Bauchi which is at the base of Jos plateau with an altitude of 628 m is of class 1, the same can be said of Yola, which is on the foot of Alantika mountains. Both Weibull scale and shape factors c and k, are related by a second degree polynomial as shown in **Figure 9** below: In Nigeria, average yearly wind speed varies from region to region. Analysis of this average indicates that Coastal area has 4.1 m/s, Inland cities has 4.2 m/s, Middle Belt area 5.0 m/s, and Far-Northern cities 5.9 m/s. This is illustrated in **Figure 10a** and **b** below for coastal and inland

**Figure 8.** Annual average wind speed (m/s) against station elevation (m).

108 Engineering and Mathematical Topics in Rainfall

#### **4.3. Frequency analysis results**

Frequency analysis of rainfall data for South Eastern Nigeria is computed comparatively using normal, log-normal, extreme value type I, pearson type III and log-pearson type III distributions with rainfall data average 8 years and above. For the 12 stations in the zone, logpearson type III has the best probability distribution at 50% of stations in the zone followed by pearson type III and with Log-normal and extreme value type I having no significance in the zones.as shown in **Figure 12** below.

From **Table 4**, component 1, shows the strongest correlation exist between rainfall and distance from the sea, rainfall with latitude and rainfall with elevation. In all the cases there is a decrease in rainfall for increase in those parameters, that is, places with high elevation correspond with high latitude and shortest distance from sea and high rainfall value. As for the second component there is an increase in longitude and distance from nearest neighbour with increase in this component. This suggests that places with long distance between stations are also along increase

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern…

http://dx.doi.org/10.5772/intechopen.72867

111

The result of this study indicates that out of the five frequency distributions, log-pearson type III best describes the frequency distribution of 50% of stations. It is necessary to observe that the rainfall data used is on average 8 years and above with some missing data. A better result could be achieved with increase in number of data available to a higher accuracy. From the principal component analysis, the variation can be explained with two components and in these, the elevation, latitude, distance from sea and rainfall are the main factors, while others such as temperature, humidity, air pressure, presence of acid rain can be checked for. Since elevation has influence, as one of the main factors, it is evident that the presence of Udi escarp-

ment is affecting the value of rainfall of the South Eastern Nigeria.

From the computed wind parameters of the 24 stations in Nigeria, it is obvious that:

Most of the Coastal regions are of class 1, that is, poor wind power density. Ikeja in the south-western part of the country is however an exception with good wind power density, hence good location for siting wind power generators. In the Inland region most cities show poor wind power density of class 1. Enugu and Ikom are however in class 2. The center of

in longitude.

**5. Conclusions**

**Table 4.** The principal components determined.

#### **4.4. Results from principal component analysis**

Variables used in this analysis consist of annual rainfall, elevation, latitude, longitude, distance from the sea and distance from the nearest neighbour data. Principal component analysis is carried out to transform the original data with the correlation and eigenvalues determined. The two components are capable of interpreting 71.85% of the entire information as shown in **Table 3** below.


**Table 3.** Extraction method: Principal component analysis.

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern… http://dx.doi.org/10.5772/intechopen.72867 111


From **Table 4**, component 1, shows the strongest correlation exist between rainfall and distance from the sea, rainfall with latitude and rainfall with elevation. In all the cases there is a decrease in rainfall for increase in those parameters, that is, places with high elevation correspond with high latitude and shortest distance from sea and high rainfall value. As for the second component there is an increase in longitude and distance from nearest neighbour with increase in this component. This suggests that places with long distance between stations are also along increase in longitude.

## **5. Conclusions**

**4.3. Frequency analysis results**

110 Engineering and Mathematical Topics in Rainfall

the zones.as shown in **Figure 12** below.

**4.4. Results from principal component analysis**

**Table 3.** Extraction method: Principal component analysis.

**Figure 12.** Frequency analysis results.

in **Table 3** below.

Frequency analysis of rainfall data for South Eastern Nigeria is computed comparatively using normal, log-normal, extreme value type I, pearson type III and log-pearson type III distributions with rainfall data average 8 years and above. For the 12 stations in the zone, logpearson type III has the best probability distribution at 50% of stations in the zone followed by pearson type III and with Log-normal and extreme value type I having no significance in

Variables used in this analysis consist of annual rainfall, elevation, latitude, longitude, distance from the sea and distance from the nearest neighbour data. Principal component analysis is carried out to transform the original data with the correlation and eigenvalues determined. The two components are capable of interpreting 71.85% of the entire information as shown

The result of this study indicates that out of the five frequency distributions, log-pearson type III best describes the frequency distribution of 50% of stations. It is necessary to observe that the rainfall data used is on average 8 years and above with some missing data. A better result could be achieved with increase in number of data available to a higher accuracy. From the principal component analysis, the variation can be explained with two components and in these, the elevation, latitude, distance from sea and rainfall are the main factors, while others such as temperature, humidity, air pressure, presence of acid rain can be checked for. Since elevation has influence, as one of the main factors, it is evident that the presence of Udi escarpment is affecting the value of rainfall of the South Eastern Nigeria.

From the computed wind parameters of the 24 stations in Nigeria, it is obvious that:

Most of the Coastal regions are of class 1, that is, poor wind power density. Ikeja in the south-western part of the country is however an exception with good wind power density, hence good location for siting wind power generators. In the Inland region most cities show poor wind power density of class 1. Enugu and Ikom are however in class 2. The center of Udi escarpment Enugu has high rain bearing wind speed but not very good for siting wind generators.

[8] Ngene BU, Agunwamba JC, Nwachukwu BC, Okoro BC. Comparing network design approaches in areal rainfall estimate of Nigeria river basins. ARPN Journal of Engineering

Geography of Udi Cuesta Contribution to Hydro-Meteorological Pattern of the South Eastern…

http://dx.doi.org/10.5772/intechopen.72867

113

[10] Back EL, Bretherton SC. The relationship between wind speed and precipitation in the

[11] Mays WL. Water Resources Engineering. 2nd ed. United Kingdom: John Wiley and

[12] Ayoade JO. Introduction to Climatology for the Tropics. 2nd ed. Nigeria: Spectrum

[13] Manwell JF, McGowan JG, Roger AL. Wind Energy Explained: Theory, Design and

[14] Abdel Halim AS, Metwally E, El-desoukky MM. Environmental pollution study around a large industrial area near Cairo-Egypt. J Radioanal. Nucl. Chem. 2003;**257**:123-124 [15] Anthony T, Eniayejuni, Mary AA. Biometrics Approach to Population census and national identification in Nigeria: A Prerequisite for Planning and Development. Asian

[16] Omole DO, Ndambuki JM. Sustainable living in Africa: Case of water, sanitation, air

[17] Ajayi OO. The potential for wind energy in Nigeria. Wind Engineering, Multi-Science

[18] Aidan J, Ododo JC. Wind speed distribution and power densities of some cities in

[19] Odo FC, Akubue, GU. Comparative assessment of three models for estimating weibull parameters for wind energy applications in a Nigerian location. International Journal of

[20] Oyedepo SO, Adaramola MS, Paul SS. Analysis of Wind Speed data and Wind Energy Potential in Three Selected Locations in South-east Nigeria. Int. Journal of Energy and

[21] Dikko I, Yahaya DB. Evaluation of wind power density in Gombe, Yola and Maiduguri, North eastern Nigeria. Journal of Research in Peace, Gender and Development.

[22] Houghton J. Global Warming: The Complete Briefing. 3rd ed. United Kingdom:

[23] Akintola JO. Rainfall Distribution in Nigeria. 1892-1983. Nigeria: Impact publishers; 1986 [24] Chow VT. A general formula for hydrologic frequency analysis. Transactions American

Northern Nigeria. Journal of Energy and Applied Sciences. 2010;**5**:420-426

Application. West Sussex, United Kingdom: John Wiley and Sons; 2002

[9] Iloeje NP. A New Geography of Nigeria. 5th ed. Nigeria: Longman; 2009

pacific ITCZ. Journal of Climate. 2003;**18**:4317-4328

Transactions on Basic & Applied Sciences. 2011;**1**:60-67

pollution & energy. Sustainability. 2014;**6**:5187-5202

and applied science. 2015;**10**:1-16

Sons; 2011

Books; 2004

Pub. 2010;**34**:303-312

2012;**2**:115-122

energy science (IJES). 2012;**2**:22-25

Cambridge University Press; 2004

Geophysical Union. 1951;**32**:231-237

Environmental Engineering. 2012;**3**:1-7

It is suggested that another study should be conducted to include the South–South zone in order to have a better picture of the influence of the main variables on the rainfall pattern of the Southern part of Nigeria.
