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Africa is already experiencing the impact of climate change. Some of the manifestations of climate change in Africa are, changing weather patterns resulting in, flooding and drought. Temperature change has impacted health, livelihoods, productivity of food, availability of water, and state of security. This study examines the long-term climate variations in Central African Countries (Gabon, Cameroon, Republic of Congo, Central Africa Republic, Chad and Democratic Republic of Congo) for the period 1901 to 2015, and then investigates the possible influence of increases in greenhouse gas concentrations. To investigate climate patterns and trends in the Central African Countries, precipitation and temperature were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal. Data was further aggregated using annual average blocks of 10 years. Linear and polynomial regression was performed. Also, linear time series slopes were analyzed to investigate the spatial and temporal trends of climate variability in Central African countries. Results of the analyses indicated that the mean annual temperature and precipitation records in some of the Central African Countries had both warming and cooling trends over the study period from 1901 to 2015. For example, differences between the maximum and the minimum rainfall data for Democratic Republic of Congo, Cameroon and Gabon were 13 mm, 13 mm and 11.1 mm, which corresponded to 11.04%, 10.03% and 10.44% respectively. The study also found the temperature of Chad to have significantly risen from 1901 to 2015 by almost 20%, while its rainfall’s variation was limited. Although the variation in rainfall in Chad was not dramatic, the temperature per 10 year rose by almost 20%. Chad’s temperature rose according to a cubic model from about 24.5 °C to just below 27 °C during the period 1901-1940. This was followed by a brief drop between 1940 and 1960. From 1960 to 2015 it rose according to the model to almost 28 °C. By 2040 the temperature is expected to reach about 29.5 °C if this trend continues. Gabon was found to be the wettest country in Central Africa. Between 1901 and 1960, its average rainfall rose from about 144 mm to a maximum of approximately 160 mm. It had a general average rainfall/10 year’s increase from 1901 to 2015. The paper concludes by outlining policy recommendations in the form of improving national and regional environmental policies and regulations in the region, community involvement in decision making processes both at local and the national levels so as to contribute their input in the daily management of the forest resources, poverty alleviation in the region as well as building regional information system (RIS) incorporating Geographic Information System (GIS), remote sensing and other environmental and socio-economic data to help reduce anthropogenic emissions of greenhouse gases.

Climate is among important drivers of ecosystems’ health, their compositions and other earth systems. Rainfall, atmospheric pressure, temperature and humidity are among the variables that control weather and climate respectively. Climate describes statistical variations of components of weather over time spanning from months to many centuries [

Global changes especially warming could impact regional climates, especially rainfall patterns in the long-term. Local activities such as modification of land cover and disruption of local and regional hydrology could also impact climates [

Central Africa is a region of the African continent comprising of Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of the Congo, the Republic of the Congo, Equatorial Guinea, Gabon, Rwanda, and São Tomé and Príncipe. This study focused on data from Democratic Republic of Congo (DRC), Republic of Congo, Cameroun, Chad and Gabon (

upon an unprecedented phase of economic expansion, it faces several environmental challenges fueled partly by the pressures caused by human activities through economic activities, such as housing development, and road construction for transportation, economic development and demographic changes. In the last few decades, the region has been experiencing increasing resources degradation, fueling climate change [

For this study, temporal analysis of rainfall data from, the Democratic Republic of Congo, Cameroun, Gabon, Republic of Congo, Central African Republic and Chad were used. Rainfall and temperature data sets of 1950-2015 for the stated countries were downloaded from World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers [

Month | Rainfall | Year | Month | Rainfall | Year | Month | Rainfall | Year | Month | Rainfall | Year |
---|---|---|---|---|---|---|---|---|---|---|---|

January | 110.3 | 1911 | June | 42.2 | 1912 | December | 140 | 1914 | May | 122.9 | 1916 |

February | 138.7 | 1911 | July | 55.2 | 1912 | January | 103.8 | 1915 | June | 63.1 | 1916 |

March | 157 | 1911 | August | 80.4 | 1912 | February | 110.8 | 1915 | July | 41 | 1916 |

April | 141.9 | 1911 | September | 141.1 | 1912 | March | 153.5 | 1915 | August | 84.4 | 1916 |

May | 111 | 1911 | October | 145.8 | 1912 | September | 135 | 1916 | February | 104.1 | 1918 |

June | 59.5 | 1911 | November | 191.9 | 1912 | October | 177.9 | 1916 | March | 175.1 | 1918 |

July | 62.1 | 1911 | December | 142.1 | 1912 | November | 189.2 | 1916 | April | 156.6 | 1918 |

August | 77.6 | 1911 | January | 112.2 | 1913 | December | 171.6 | 1916 | May | 92.9 | 1918 |

September | 137.7 | 1911 | February | 126.4 | 1913 | January | 153.3 | 1917 | June | 68.8 | 1918 |

October | 177 | 1911 | March | 148.4 | 1913 | February | 143.8 | 1917 | July | 60.4 | 1918 |

November | 187.5 | 1911 | April | 147.5 | 1913 | March | 154.9 | 1917 | August | 85.7 | 1918 |
---|---|---|---|---|---|---|---|---|---|---|---|

December | 160.6 | 1911 | May | 115.4 | 1913 | April | 155.8 | 1917 | September | 123.2 | 1918 |

January | 128.2 | 1912 | June | 64.4 | 1913 | May | 123.5 | 1917 | October | 146.2 | 1918 |

February | 148.6 | 1912 | July | 45.4 | 1913 | June | 58.7 | 1917 | November | 170.3 | 1918 |

March | 144.7 | 1912 | August | 68.2 | 1913 | July | 40 | 1917 | December | 191.2 | 1918 |

April | 152.2 | 1912 | September | 86 | 1913 | August | 93.9 | 1917 | January | 184 | 1919 |

May | 118.8 | 1912 | October | 174.1 | 1913 | September | 141.3 | 1917 | February | 145.5 | 1919 |

November | 131 | 1913 | April | 135 | 1915 | October | 180.1 | 1917 | March | 148.4 | 1919 |

December | 109.1 | 1913 | May | 91 | 1915 | November | 137.9 | 1917 | April | 129.5 | 1919 |

January | 124.8 | 1914 | June | 70.3 | 1915 | December | 134.2 | 1917 | May | 98.3 | 1919 |

February | 128.2 | 1914 | July | 37.4 | 1915 | January | 93.2 | 1918 | June | 93.7 | 1919 |

March | 179 | 1914 | August | 75.6 | 1915 | July | 82.3 | 1919 | April | 140.7 | 1920 |

April | 157.3 | 1914 | September | 160.4 | 1915 | August | 117.2 | 1919 | May | 135.9 | 1920 |

May | 107.6 | 1914 | October | 134.7 | 1915 | September | 101.9 | 1919 | June | 65.1 | 1920 |

June | 71.6 | 1914 | November | 159.8 | 1915 | October | 148.1 | 1919 | July | 69.6 | 1920 |

July | 67.7 | 1914 | December | 177.2 | 1915 | November | 192.5 | 1919 | August | 58.6 | 1920 |

August | 86.4 | 1914 | January | 116.7 | 1916 | December | 168.9 | 1919 | September | 116.2 | 1920 |

September | 134.4 | 1914 | February | 130.1 | 1916 | January | 116.3 | 1920 | October | 172.6 | 1920 |

October | 193.2 | 1914 | March | 148.9 | 1916 | February | 112.1 | 1920 | November | 162.9 | 1920 |

November | 174.3 | 1914 | April | 168.6 | 1916 | March | 166.9 | 1920 | December | 157 | 1920 |

Average rainfall, mm/month | 124.4 |

Average rainfall mm/10 years (Democratic Republic of Congo) | |
---|---|

Years after 1900 | Average rainfall (mm) |

1901 | 122.8631 |

1910 | 126.487 |

1920 | 124.44 |

1930 | 124.3271 |

1940 | 126.6208 |

1950 | 126.0523 |

1960 | 128.9018 |

1970 | 132.4034 |

1980 | 125.5405 |

1990 | 127.811 |

2000 | 119.2098 |

2010 | 124.8244 |

2015 | 127.3143 |

Year | Average rain/10 years |
---|---|

1901 | 137.8247 |

1910 | 137.8247 |

1920 | 137.0419 |

1930 | 135.0912 |

1940 | 136.6843 |

1950 | 134.2571 |

1960 | 133.1015 |

1970 | 140.1571 |

1980 | 139.6232 |

1990 | 133.0645 |

2000 | 127.3796 |

2010 | 134.3287 |

2015 | 130.0457 |

Year | Average rain/10 years |
---|---|

1901 | 144.4524 |

1910 | 146.2408 |

1920 | 144.9641 |

1930 | 150.4393 |

1940 | 148.9023 |

1950 | 152.9573 |

1960 | 159.5338 |

1970 | 156.1968 |

1980 | 150.1271 |

1990 | 150.6118 |

2000 | 152.3903 |

2010 | 151.0454 |

2015 | 155.4274 |

To develop models that identify trends in rainfall and temperature data sets, aggregation of data using batches of 10 years was adopted for all the countries. In the modelling of the variations between variables in atmospheric data, regression was used.

The models for average rainfall and temperature based on data sets of 10 consecutive years were modeled based on the following respective formulations.

Rainfall , R ∝ f (t)

For cubic model,

R = A n + 1 t n + A n t n − 1 + A n − 1 + t n − 2 + A n − 2 (1)

For quadratic model,

R = A n + 1 t n + A n t n − 1 + A n − 1 + t n − 2 + A n − 2 (2)

For linear model,

R = A t + B (3)

where, R is the average rainfall based on 10 consecutive years in mm/year, n is the polynomial index and t is time in years. A_{1}, A_{2}, A_{3} and B are constants.

Temperature T ∝ f (t)

For cubic model,

T = A n + 1 t n + A n t n − 1 + A n − 1 + t n − 2 + A n − 2 (1)

For quadratic model,

T = A n + 1 t n + A n t n − 1 + A n − 1 + t n − 2 + A n − 2 (2)

where, T is the average temperature based on 10 consecutive years in ˚C/year, n is the polynomial index and t is time in years. A_{1}, A_{2} and A_{3} are constants.

Statistical software (SPSS, Microsoft Excel data analysis toolkit, etc.) was used to fit data and build models. The strength of model developed was based on the magnitude of the coefficients of correlations between variables (R square), which ranged from 0 to 1.0 (0% - 100%).

A model was accepted if the corresponding coefficient of correlation was close to 100%. Linear and curvilinear modellings were applied depending on the correlations between data pairs, which attempts to develop single models for data sets spanning from 1901 to 2015 yielded models of low coefficients of correlations for most data sets. To develop models that were more representative of the data sets, illustrating the rises and falls in environmental data, data aggregates from time spans shorter than the entire data collection range of time (1901-2015) were used in modeling. Modeling for rainfall trends was carried out for, the Democratic Republic of Congo (DRC), Gabon and Cameroun since the difference between their maxima and minima rainfall data was at least 10%. Only Chad had a percentage change of temperature higher than 15%. Hence temperature change was only analyzed for Chad. The modeling processes and corresponding results for each of the countries are discussed as follows.

The data used in modeling rainfall for the Democratic Republic of Congo (DRC) is presented in

The difference between the maximum and the minimum rainfall data was 13 mm. The percentage rise from the minimum to maximum temperature was ((132.4 − 119.2)/119.2) × 100 = 11.04%. Regression analysis was used to model the Democratic Republic of Congo’s average monthly rainfall data per consecutive 10 years versus years. The data from 1901 to 1940 was fitted into a third order polynomial equation. The general average rainfall trend from 1901 to 1940 is represented by a gradual rise from about 123 mm in 1901 to a maximum quantity of 126 mm in 1910 (

A polynomial function of order 3 was used to model rainfall data from 1940 to 1980 by curve fitting. The equation and the curve are given and illustrated in

The average rainfall/10 years dropped from about 127 mm in 1940 to a local minimum of 125 mm in 1945. It then rose to a local maximum of about132 mm in 1970, and then gradually dropped to a value between 125 mm and 126 mm in

1980 (^{2} = 0.9605).

The average rainfall data/10 consecutive years for the time span 1980-2015 was fitted into a third order polynomial equation. The equation for the trend and the curve is illustrated in

The difference between the maximum and the minimum rainfall data for Cameroun’s data is 13 mm. Hence, the percentage rise from the minimum to maximum

temperature was ((140.157 − 127.38)/127.38) × 100 = 10.03%.The data used in modeling rainfall Cameroun is presented in

The mean rainfall data per10 consecutive years was fitted to a cubic equation. The following model was developed. y = −2E−05x^{3} + 0.1287x^{2} − 250.73x + 162992 (

Where, y is the average rainfall based on every 10 consecutive years in mm/year and x is time in years.

This model suggested a decreasing trend for rainfall from 138 mm to 130 mm. R squared for the cubic model was 0.4038; hence, it represents or explains about 40% of the data.

Better models for this data were developed by fitting the data for 1901-1960 to a quadratic polynomial using Microsoft Excel curve fitting tool. The model had R squared equal to 0.8465. Hence, the model explains about 85% of the data (

The data for 1960-2015 was fitted to a third order polynomial model with R squared equal to 0.732 (

Stronger models for the data for 1980-2015 were developed by modeling the data for the time periods of 1980-2000 and 2000-2015. The corresponding models are illustrated in

The model for the period 2000-2015 (

The model illustrates a rise from 127 mm to about 135mm in 2008, followed a drop to 130 mm by 2015. According to the model, Cameroun’s average rainfall was expected to drop as years progressed beyond 2015.

The difference between the maximum and the minimum rainfall data is 15 units. The data used in modeling rainfall Gabon is presented in

The data was fitted to a second order polynomial model using regression. Hence, the percentage increase from the minimum to the maximum temperature was ((155.5 − 144.4)/144.4) × 100 = 10.44%. The rainfall data for Gabon from 1901-2015 follows a second order polynomial function with R^{2} = 0.5662. The model explains about 56.6% of the variation. The model is represented as, y = −0.0016x^{2} + 6.4385x + 6222.3. According to this model, the average rainfall per 10 years rose from about144 mm in 1901 to approximately 154 mm in 1970 and then dropped gradually to 152 mm (

Better models for Gabon’s average rainfall were developed by modeling the data piecewise. A quadratic model was developed for average the rainfall between, 1901 and 1960. A strong model with R^{2} = 0.9155 was developed (^{2} − 16.856x + 16,198.

A quadratic model was developed for rainfall data for the period 1960-2015. The model is presented as follows. y = 0.0087x^{2} − 34.763x + 34791, with R^{2} = 0.8493. The model explains about 85% of the average rainfall/10 year’s variation during the period 1960-2015 (

Where, y is the average rainfall based on every 10 consecutive years in mm/year and x is time in years.

The difference between the maximum and the minimum average temperatures per ten-year data aggregates was 3.5˚C (5.3 F), which corresponded to a percentage rise equal to (4.5/24.5) = 18.36%.

The average monthly temperature for Chad per 10 years is presented in

Year | Average monthly temperature ˚C/10 years |
---|---|

1901 | 24.37034 |

1910 | 26.61626 |

1920 | 26.57028 |

1930 | 26.66089 |

1940 | 26.80198 |

1950 | 26.45233 |

1960 | 26.36165 |

1970 | 26.39722 |

1980 | 26.71711 |

1990 | 26.84742 |

2000 | 26.84741 |

2010 | 27.85025 |

2015 | 27.63414 |

A correlation analysis for the average temperatures per 10 years data and was run, using SPSS 20 software. The correlation between the average temperatures per 10 consecutive years was found to be statistically significant (p = 0.006), with coefficient of correlation being equal to 71% (

Microsoft Excel software was used to fit a cubic model the variation between the data sets using regression. The model is given by the equation, y = 1E−05x^{3} − 0.0725x^{2} + 142.07x + 23.783 − 92,751, R^{2} = 0.8016 (

According to the model, the temperature rose from an initial approximate

Correlations | |||
---|---|---|---|

Mean temperature | Years | ||

Mean temperature | Pearson correlation | 1 | 0.712** |

Sig. (2-tailed) | 0.006 | ||

N | 13 | 13 | |

Years | Pearson correlation | 0.712** | 1 |

Sig. (2-tailed) | 0.006 | ||

N | 13 | 13 |

**. Correlation is significant at the 0.01 level (2-tailed).

value of just below 24.5˚C in 1901 to almost 27˚C in 1940. It then marginally dropped to about 26.5˚C in 1980. During the period 1980-2015 it rose to approximately 28˚C. Stronger models were developed for variation of the data through shorter time spans (1901-1950) and (1950-2015). The corresponding cubic and quadratic models had R squared (coefficients of determination) equal to, 0.907 and 0.901 respectively. These models are illustrated in

Between 1995 and 2015 the average temperature of chad rose sharply. According to the models illustrated in

If the temperature continues to rise according to the model, Microsoft Excel statistical tool kit can be used to model its value by 1940 by entering 15 years in the forecasting tab. The temperature for the period 2015-2040 is illustrated in the following graph (

The average rainfall of the Democratic Republic of Congo had a first general trend of rising, followed by a decrease to a minimum of about 123 mm. It then rose gradually to about 127 mm. The average rainfall for Cameroun dropped from about 138 mm in 1905 to 133 mm in 1960. It then rose to about 140 mm in 1980, followed by a drop to about 139mm. In general, Cameroun’s rainfall data tended to decrease from 1905 to 2015 (

Anthropogenic activities such as clearing of land for agriculture, industry, slash-and-burn agriculture and other human activities in the region have an impact on the concentration of greenhouse gases which ultimately leads to global warming. Perhaps, introduction of a policy which focuses on improving national and regional environmental policies and regulations in the region would go a long way to minimize some of the anthropogenic activities especially disforestation which are very rampant in the region [

The authors declare no conflicts of interest regarding the publication of this paper.

Twumasi, Y.A., Merem, E.C., Namwamba, J.B., Ayala-Silva, T., Abdollahi, K., Okwemba, R., Lukongo, O.E.B., Akinrinwoye, C.O., Tate, J. and La Cour-Conant, K. (2020) Analysis of Precipitation and Temperature Variability over Central Africa (1901-2015). Atmospheric and Climate Sciences, 10, 220-239. https://doi.org/10.4236/acs.2020.102012