Rainfall In Nigeria Research Articles

Influence of changes in solar activity on Nigerian Rainfall

This paper focuses on the impact of solar activity changes on rainfall in Nigeria. Rainfall data were got from Gridded Time Series (CRU TS v4) of the University of East Anglia, United Kingdom. Various statistical tools like Pearson rank correlation coefficient, regression analysis, covariance, skewness, and kurtosis are employed in the study. The results show that there are generally weak correlations between rainfall and solar activity in most seasons. However, low correlation coefficients (0.0424 and 0.1040, respectively for sunspot numbers and F10.7 mean a very weak positive relationship between early rainfall, main rainfall and annual rainfall and negative for dry season rainfall and Harmattan rainfall. Also, there is a negative time-series relationship between the rainfall amount in dry and Harmattan seasons, with the highest rainfall amount during low solar activity and vice versa. Therefore, it can be concluded that solar activity has little effect on Nigeria's annual and main season rainfall. However, during the dry season and Harmattan, heavy rainfall appears to be associated with low solar activity, while high solar activity appears to be associated with low rainfall. This study will be beneficial to farmers in Nigeria, majority of whom are peasant farmers that practice rain-fed farming.

ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA

Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines the performance of a univariate seasonal ARIMA and seasonal ARIMA which uses monthly temperature and relative humidity as exogenous factors otherwise known as SARIMAX model in forecasting monthly average rainfall in Lokoja, the capital of Kogi state. The study uses monthly data on rainfall, temperature and relative humidity spanning from 2010 to 2022 obtained from Nigeria Meteorological Agency NiMet, Lokoja station. The series were appropriately differenced to attain stationarity. The plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select some tentative models whose parameters would be estimated. The most suitable SARIMA model [SARIMA was chosen based on maximum Coefficient of Determination , and the minimum Akaike information criterion (AIC). However, SARIMAX model outperformed SARIMA model based on the criteria earlier highlighted. SARIMAX model was therefore recommended for modelling and forecasting monthly average rainfall in Kogi state.

Modeling Average Rainfall in Nigeria With Artificial Neural Network (ANN) Models and Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Rainfall prediction is one of the most essential and challenging operational obligations undertaken by meteorological services globally. In this article we conduct a comparative study between the ANN models and the traditional SARIMA models to show the most suitable model for predicting rainfall in Nigeria. Average monthly rainfall data in Nigerian for the period Jan. 1991 to Dec.2020 were considered. The ACF and PACF plots clearly identifies the SARIMA (1,0,2)x(1,1,2)12 as an appropriate model for predicting average monthly rainfall. The performance of the trained Neural Network (NN) analysis clearly favours Levenberg-Marquardt (LM) over the Scaled Conjugate Gradient Descent (SCGD) algorithms and Bayesian Regularization (BR) method with Average Absolute Error 0.000525056. The forecasting performance metric using the RSME and MAE, showed that Neural Network trained by Levenberg-Marquadrt algorithm gives better predicted values of Nigerian rainfall than the SARIMA (1,0,2)x(1,1,2)12.

Statistical Analysis and Forecasting of Rainfall Patterns and Trends in Gombe North-Eastern Nigeria

Rainfall in Nigeria is highly dynamic and variable on a temporal and spatial scale. This has taken a more pronounced dimension due to climate change. In this study, Standard Precipitation Index (SPI) and Mann-Kendall test statistical tools were employed to analyze rainfall trends and patterns in Gombe metropolis between 1990 and 2020 and the ARIMA model was used for making the forecast for ten (10) years. Daily rainfall data of 31 years obtained from Nigerian Meteorological Agency, (NIMET) was used for the study. The daily rainfall data was subjected to several analyses. Standard precipitation index showed that alternation of wet and dry period conditions had been witnessed in the study area. The result obtained showed that there is an upward trend in the annual rainfall amount received in Gombe over the last 31 years at a rate of 3.98 mm/year. The results for the forecast shows that the annual rainfall to be received in Gombe continues in a range above the mean which serves as an indication that the decade will experience more wet years than dry years. The study concludes that the pattern of rainfall in Gombe is a cyclic pattern. The current trend may affect soil moisture, flooding and subsequently lead to ecological change. The study recommends that inhabitants of the study areas should plan their cropping season based on climatic information of their area.

Application of Sarima Models in Modelling and Forecasting Monthly Rainfall in Nigeria

Application of SARIMA model in modelling and forecasting monthly rainfall in Nigeria was considered in this study. The study utilizes the Nigerian monthly rainfall data between 1980-2015 obtained from World Bank Climate Portal. The Box-Jenkin’s methodology was adopted. SARIMA (2,0,1) (2,1,1) [12] was the best model among others that fit the Nigerian rainfall data (1980-2015) with maximum p-value from Box-Pierce Residuals Test. The study forecasts Nigeria’s monthly rainfall from 2018 through 2042. It was discovered that the month of April is the period of onset of rainfall in Nigeria and November is the period of retreat. Based on the findings, Nigeria will experience approximately equal amount of rainfall between 2018 to 2021 and will experience a slight increase in rainfall amount in 2022 to about 1137.078 (mm). There will be a decline of rainfall at 2023 to about 1061 (mm). Rainfall values will raise again to about 1142.756 (mm) in 2024 and continue to fluctuate with decrease in variation between 2024 to 2042, then remain steady to 2046 at approximately 1110.0 (mm). Nigerian Government should provide a more mechanized and drier season farming methods to ease the outage of rainfall in future that may be caused due to natural (or unpredictable) variation.

Relationship between Rainfall Pattern in North Central and Southern Nigeria and Some Tropical Climate Systems

Monthly rainfall data from meteorological stations in Nigeria are analyzed from 1951 to 1992, to establish their relationship with some Tropical climate systems. The climate systems include Tropical South Atlantic (TSA) sea surface temperature index, North Atlantic Ocean (NAO) atmospheric index, Tropical North Atlantic (TNA) sea surface temperature index, Central India Precipitation (CIP) and and Outgoing Longwave Radiation Anomaly (OLRA)). The rainfall pattern was given in terms of the August break (quantified in terms of the monsoon break intensity (MBI) and annual rainfall anomaly index (RAI). Partial correlation analysis was carried out to determine the effect of the tropical climate systems on the linear association between them and the stations in Nigeria. Our results show that the rainfall anomalies although sometimes intense do not have predictable patterns. The tele connection between CIP and total rainfall in Nigeria suggests that the rainfall patterns in Nigeria is likely to be modulated by the Tropical Easterly Jet (TEJ) connecting rainfall pattern in Central India to that in Nigeria. The August break is observed to be highly variable and does not show a clear discernable pattern of variability. Its variability may be connected with multiple forcings from ocean and mesoscale circulations.

A MCDM-based framework for selection of general circulation models and projection of spatio-temporal rainfall changes: A case study of Nigeria

A multi-criteria decision-making approach was used for the selection of GCMs for Nigeria based on their ability to replicate historical rainfall estimated using three entropy-based feature selection methods namely, Entropy Gain (EG), Gain Ratio (GR), and Symmetrical Uncertainty (SU). Performances of four bias correction methods were compared to identify the most suitable method for downscaling and projection of rainfall using the selected GCMs. Random forest (RF) regression was used for the generation of the multi-model ensemble (MME) average of projected rainfall. The ensemble projections for each of the representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 were computed and compared with global precipitation climatology centre (GPCC) historical rainfall of Nigeria to assess the percentage changes in annual rainfall with 95% level of confidence at different ecological zones for three future periods 2010–2039, 2040–2069, and 2070–2099. Quantile regression was used to assess the changes in seasonal rainfall at 95% confidence interval over the present century. The results revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0 and CESM1-CAM5 are the most suitable GCMs for the projection of rainfall in Nigeria. The linear scaling method was found as the most suitable approach for downscaling of rainfall in terms of all the statistical indices used. It was found to downscale rainfall with normalized root mean square error (NRMSE) in the range of 30.7–44.0%, while Nash-Sutcliff efficiency (NSE) was between 0.81 and 0.91, and modified coefficient of agreement (md) was between 0.82 and 0.88. Projection of rainfall showed no significant change in Nigeria over the century under RCP 2.6, 4.5 and 6.5, while RCP 8.5 showed a decrease in the last part of the century (2070–2099). The seasonal changes in rainfall showed an increase in rainfall in the range of 0–20% in most parts of the north. The methodology in this study can reduce the uncertainty inherent in climate change projection and produce better projection of possible spatial and temporal changes in annual and seasonal rainfall.

Characteristics of worst hour rainfall rate for radio wave propagation modelling in Nigeria

Radio waves especially at the millimeter-wave band are known to be attenuated by rain. Radio engineers and designers need to be able to predict the time of the day when radio signal will be attenuated so as to provide measures to mitigate this effect. This is achieved by characterizing the rainfall intensity for a particular region of interest into worst month and worst hour of the day. This paper characterized rainfall in Nigeria into worst year, worst month, and worst hour. It is shown that for the period of study, 2008 and 2009 are the worst years, while September is the most frequent worst month in most of the stations. The evening time (LT) is the worst hours of the day in virtually all the stations.

Effects of Changes in ENSO on Seasonal Mean Temperature and Rainfall in Nigeria

This study investigates the effects of El Niño/Southern Oscillation (ENSO) on temperature and rainfall in Nigeria. The persistent rise in population with more demands for rainfall and water supply in Nigeria requires a better understanding of the impacts of ENSO (La Niña, El Niño) induced changes on the precipitation patterns under future climate conditions. Thus, we compared the sea surface temperature (SST) from the ENSO regions of the Tropical Pacific Ocean (Niño 3 (150°W–90°W, 5°S–5°N) and Niño 4 (160°E–150°W, 5°S–5°N)) with the observed temperature from Nigeria and the temperature is further compared with the associated rainfall. The results show that an increase or decrease in the Niño 3 and Niño 4 SST is accompanied by a corresponding change in the temperature over Nigeria; however, there is better agreement with the Niño 3 SST compared to the Niño 4 SST. The investigation suggests that a slight northward (southward) shift in the mean position of Intertropical Convergence Zone (ITCZ) during a La Niña (El Niño) event is followed by a reduction (increase) in the average temperature within Nigeria while the mean precipitation rises (reduces) over the country. These results could aid weather prediction which might improve the economy as well as save lives and property during climate-related hazards like drought, forest fires and floods.

Seasonal prediction of precipitation over Nigeria

The availability of quantitative means of probing anticipated rainfall is essential for the purpose of planning and policy formulation everywhere in the world. However reliable prediction of rainfall remains a challenge over West Africa and Nigeria in particular. This paper predicts sea-sonal rainfall in Nigeria with the hope of increasing the reliability of predicted seasonal rainfall. Seasonal rainfall data (June-September) from 21 Nigerian stations and monthly Extended Re-constructed Sea Surface Temperature (ERSST) of the tropical Oceans spanning 1960-2012 con-stituted the input data for the formulation of prediction equations using regression analysis. Cross-validation was applied with training period of 1960-1985, verification period of 1986-2012 and vice versa. The correlations between the predicted and the observed seasonal rainfall of the training and verification periods of 1960-1985 were all significant at 5 % level except for one station. While for the training and verification periods of 1986-2012, correlations are not gener-ally significant although some were found to be above the significant level of 5%. For the recon-structed seasonal precipitation, correlations of ten stations were found to be significant at 5 %, but at 10 % level, their number increased to 13. The t-test analysis revealed that there is no sig-nificant difference between the means of predicted and observed seasonal rainfall amount for all stations except for only three stations (Osogbo, Calabar and Enugu). This suggests some degree of skill which is also an indication that Sea Surface Temperature (SST) is a good predictor of June, July, August and September (JJAS) seasonal rainfall in Nigeria. Keywords : Rainfall, Prediction, Tropical Ocean, Cross-validation, Sea Surface Temperature

Hierarchical analysis of rainfall variability across Nigeria

Rainfall in Nigeria is subjected to wide variability both in time and space. This variability has assumed a more pronounced dimension as a result of climate change. In this paper, cluster analyses were used to study rainfall variability in Nigeria. Rainfall data in 20 locations spread across the geopolitical and ecological zones of Nigeria were subjected to hierarchical cluster analysis and analysis of time series and coefficient of variation for over periods spanning 30 years. Maps of spatial variations of mean annual rainfall and mean rainfall coefficient of variation were produced using ARCGIS 10.1. Furthermore, a better understanding of temporal variation of rainfall was sought by an investigation into the time series of rainfall coefficients of variation. It was found that the southern parts of the country were given to more severe rainfall variability/fluctuations than the northern parts. The north central parts exhibited more similarity to the southern parts than the other northern locations. The relationship between average annual rainfall and the coefficient of rainfall variation was found to follow a power law with R2 value approximately 0.7. With respect to variability of annual rainfall, three zones emerged as follows: a linear relationship (R2 = 0.90) exists between coefficient of variation and average annual rainfall for the rainforest zone of the southsouth; a power law (R2 = 0.86) exists between coefficient of variation and average annual rainfall for all rainforest and derived guinea savannah zones of the southeastern and southwestern states; and a logarithmic relationship (R2 = 0.54) exists between coefficient of variation and average annual rainfall for all northern states regardless of ecological zone. Generally, in-year rainfall variability increases from the northwest to the southwest; while between-year (yearly) rainfall variability increases from the north central to the southeast. This study further confirms that rainfall variability over time follows a spatial trend within a certain arbitrary boundary.

Time series analysis of monthly rainfall in Nigeria with emphasis on self-organized criticality

Monthly rainfall data of twenty-one years (1980 – 2000) were analyzed for the six regions of Nigeria using the rescaled range (R/S) statistic, the standard fluctuation analysis (FA) and the detrended fluctuation analysis (DFA). The results indicated that the distribution of monthly rainfall has a Hurst exponent of 1 in the short term which is an evidence of self-organized criticality, and a Hurst exponent of about 0.5 in the long run which is an evidence of a random walk. The Hurst exponents decreased from around 1 for small window sizes and reached 0.5 after 36 months and then remained fairly constant. The R/S statistic was found to perform better than the FA and the linear DFA used in the analysis.

Perception of Nigerian School Boys on the Influence of Contrasting Geographical Locations on Participation in Soccer

The paper investigated the extent to which geographical locations influenced male secondary school students' level of participation in amateur soccer within Zaria and Benin metropolis in the 2006/2007 school year. It also sought to ascertain if relative changes in geographical locations influenced the welfare of soccer players and examined the influence of landscape patterns on participants. A total of 836 respondents formed the sampling unit. Percentages were used to analyse the data for the study. Hindrances to soccer participation in Zaria were cold weather which was attested by (81.5%) respondents, hot weather (78.94%), low humidity (71.05%) and cold winds (69.46%). In Benin metropolis, impediments to soccer participation were reported to be rainfall (83%), thunderstorms (78%), hot weather (70%) and lightning (53.9%). Amateur soccer participants in Zaria were influenced by expenditure on accommodation (84.21%), feeding and first aid treatment (60.42%). However, in Benin metropolis influence on feeding and accommodation recorded (66.82%) while first aid treatment attracted (55%) rating. Although (63.1%) respondents in Zaria, indicated that soccer pitches were undulating, (73.68%) claimed they were dry during the dry season. Some soccer pitches remained dry between May and July, a period that usually experiences some rainfall in Nigeria. Also, (92%) respondents in Benin metropolis remarked that soccer pitches were waterlogged particularly during periods of heavy rainfall, between April and October yearly. The results call for differential planning strategies in organizing soccer training and competition in these two divergent geographical areas in Nigeria.

Evaluation of rainfall trends in Nigeria for 30 years (1978-2007)

The issue of climate change appears to be on the fore burner and it cannot be ascertained to occur without evidence. Rainfall is one of the climatic factors that can indicate climate change. In this work, the non-parametric Mann-Kendall test is used to test for significant trend in rainfall in Nigeria through randomly selected locations in the six geopolitical zones within a 30 years period (1978-2007). The selected locations are; Maiduguri (13°10',11°50') in the North East, Kaduna (7°27',10°31') in the North West, Lokoja (6°45',7°43') in the North Central, Ibadan (3°53',7°22') in the South West, Enugu (7°30',6°27') in the South East and Calabar (8°28',4°57') in the South South. During the 30 years period, the yearly average rainfalls (mm) were 580.5, 1187.5, 1228.7, 1317.2, 1753.8 and 2925.6, respectively. While the rest of the locations had no significant trend yet Maiduguri showed an increasing trend at a rate of 9.88 mm/year, where formally the trend there from the period 1961-1990 was decreasing. This change could either be a problem or blessing to dwellers in that location and there is need for continuous monitoring. Key words: Mann-Kendall test, rainfall, significant trend, six geopolitical zones in Nigeria.

An Empirical Prediction of Seasonal Rainfall in Nigeria

The need to have quantitative means of probing anticipated rainfall is essential and inevitable for the purposes of planning and policy formulation. This paper attempts to present some results of an ongoing experiment on empirical prediction of seasonal rainfall in Nigeria. Rainfall in Nigeria is characterized by both latitudinal and longitudinal variations. In the southern part of the country, the seasons could be classified as March, April, May (MAM), April, May, June (AMJ) and June, July, August, September, (JJAS) while the northern area has one rainfall season - June, July, August, September, (JJAS). The June, July, August and September, (JJAS) season is common to both the southern and northern parts of the country. In the formulation of this experiment, it was assumed that there exists an empirical relationship between some physical parameters (atmospheric, geophysical and oceanic) and rainfall. Rainfall data from twenty Nigeria stations, Sea Surface Temperature Anomalies (SSTA) and Outgoing Longwave Radiation (OLR) constituted the input data for formulation of the prediction equations. The rainfall data and SSTA indices spanned from 1950 to 2006 while OLR indces were from 1979 to 2007. Principal Component Analysis (PCA) of the rainfall data divided the country into six homogenous rainfall zones. The six zones were correlated with global SSTA (Reynolds SST) and OLR indices from the Central part of Africa and South Atlantic Ocean. Correlation between the area averaged JJAS rainfall and the predictors ranges from 0.32 to 0.62, which suggests some degree of skill. Forecast for JJAS 2006 rainfall over the regions saw the OLR becoming a better Predictant. These zones of (SWR, SER, NR2 and NR3) performed creditably well from the equation results.

Investigation of El Niño and La Niña effects and the impact of Atlantic sea surface temperatures (SSTs), on precipitation in Nigeria from 1950 to 1992

Monthly precipitation data from meteorological stations in Nigeria are analysed from 1950 to 1992, in relation to sea surface temperatures (SSTs) in the tropical Pacific and Atlantic Oceans. The analyses have shed some light on understanding the variability of rainfall anomalies observed in Nigeria for this period. The correlation values between rainfall anomaly indices (RAI) and different meteorological indices are not all significant. Thus, the analyses show some indication that rainfall in Nigeria is associated with El Nino-related circulation and rainfall anomalies. The low correlations between RAI and SST in the Pacific confirm low correlations between rainfall and southern oscillation indices (SOI). SST correlations in the tropical Atlantic suggest that warm surface water in this part of the Atlantic moves the Inter Tropical Convergence Zone (ITCZ) southward and away from the SouthEast of Nigeria, indicating less rainfall, while, in SouthWest of Nigeria, the warm surface waters in this part of the Atlantic are likely to be responsible for a more northern position of the ITCZ, which produces more rainfall. The lower correlation in Northern Nigeria may be attributed to its continentality, away from the influence of the sea surface conditions in the Gulf of Guinea and the tropical Atlantic. The drought, or rainfall, cycles in Northern Nigeria are more closely connected to the land surface conditions in the nearby Sahel region.

An analysis of rainfall patterns in Nigeria

The paper is designed to study the rainfall patterns and its implications in Nigeria. Rainfall data from 28 stations for the period of 1970 – 2002 were collected from the Nigeria Meteorological Station, Lagos. While the vegetation map of Nigeria between 1973 and 1995 forms the basis of biodiversity change analysis. The results among others show that rainfall decreases from 1350 mm (1941–1970) to 1276 mm (1970–2002). While there is a general decrease in rainfall in Nigeria, the coastal area is experiencing slight increase. Apart from the general southward shift in rainfall patterns, the duration has also reduced from 80-360 (1941-1970) to 40-280 (1970-2002) rainy days per year. This has created ecological destabilisation and altered the pattern of the vegetation belt especially in the northern fringes of the country. The rainfall pattern has also enhanced wind erosion/desertification, soil erosion and coastal flooding in the north, east and coastal areas of Nigeria respectively. With these impacts, the paper therefore recommends some adaptive and mitigation measures that could help to revert the current situation.Keywords: changing climate, vegetation belts, rainfall pattern and shiftGlobal Journal of Environmental Sciences Vol. 4(2) 2005: 139-145

Geographic Variation in Growing Season Rainfall during three Decades in Nigeria Using Principal Component and Cluster Analyses

Reports of changes in the seasonal and annual rainfall in Nigeria suggests that a more detailed analyses of the geographic extent of these changes and of their impact on agriculture could be of value. Variation in the growing season (April to September) rainfall from stations across Nigeria was analysed over the 30-yr period, 1960–90. Regression analyses were used to examine long-term trends. Principal component and cluster analyses were used to group stations with similar trends in standardised seasonal rainfall. Mean accumulated standardised seasonal rainfall were used to examine short- and medium-term trends for each of the groups identified. Significant (P ≤ 0.05) decreases in rainy season rainfall were found at 8 stations mostly in the Guinea and arid/semi-arid savannas of northern Nigeria, whereas no station showed significant increases. Examination of the monthly (April through September) rainfall showed that only three – Kano, Sokoto and Potiskum in the arid/semi-arid savanna – of the twenty-three stations used in the analysis had declining rainfall trends for each of the months April to September and subsequently declining seasonal rainfall trends. However, 12 to 15 stations had consistently declining rainfall trends in atleast some but not all the growing season months. However, a similar pattern was not the case in terms of increasing rainfall trends, where only one to three stations had consistently increasing rainfall trends in some but not all of the months from April to September. Stations that showed increasing rainfall trends were in the southern parts of Nigeria.

The optimum order of a Markov chain model for daily rainfall in Nigeria

Markov type models are often used to describe the occurrence of daily rainfall. Although models of Order 1 have been successfully employed, there remains uncertainty concerning the optimum order for such models. This paper is concerned with estimation of the optimum order of Markov chains and, in particular, the use of objective criteria of the Akaike and Bayesian Information Criteria (AIC and BIC, respectively). Using daily rainfall series for five stations in Nigeria, it has been found that the AIC and BIC estimates vary with month as well as the value of the rainfall threshold used to define a wet day. There is no apparent system to this variation, although AIC estimates are consistently greater than or equal to BIC estimates, with values of the latter limited to zero or unity. The optimum order is also investigated through generation of synthetic sequences of wet and dry days using the transition matrices of zero-, first- and second-order Markov chains. It was found that the first-order model is superior to the zero-order model in representing the characteristics of the historical sequence as judged using frequency duration curves. There was no discernible difference between the model performance for first- and second-order models. There was no seasonal varation in the model performance, which contrasts with the optimum models identified using AIC and BIC estimates. It is concluded that caution is needed with the use of objective criteria for determining the optimum order of the Markov model and that the use of frequency duration curves can provide a robust alternative method of model identification. Comments are also made on the importance of record length and non-stationarity for model identification

Spatial variation of rainfall in Nigeria during the ‘little dry season’

Rainfall analysis of 33 Nigerian stations for more than 30 years of data has revealed the existence of a latitudinal belt of more pronounced dryness between 6° and 8.5°N within a coastal region of a general rainfall minimum during the so-called ‘little dry season’ of July/August. Temperature, relative humidity and equivalent potential temperature analyses indicate stronger subsidence and inversion in the lower/mid-troposphere over 6°–8.5°N than elsewhere in the coastal region up to 10°N. It is suggested that the lower rainfall within 6° and 8.5°N is due to the stronger subsidence associated with outflows from deep convective systems located to the north of the area.