Mathematical Study of Monthly and Annual Rainfall Trends in Nasarawa State, Nigeria

Ekwe, Michael C.; Joshua, Jonah Kunda; Igwe, Johnson Eze; Osinowo, Adekunle Ayodotun

doi:10.9790/5728-10135662

Cited by 13 publications

(1 citation statement)

References 14 publications

(7 reference statements)

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“…The annual rainfall trend indicated variability in the six geopolitical regions with North-East having the lowest range and South-South area with highest. Ologunorisa and Tersoo, 2006;Enete and Ebenebe, 2009;Okonkwo and Mbajiorgu, 2010;Adejuwon, 2011;Azuwike and Enwereuzor, 2011;Ekpoh and Nsa, 2011;Abaje et al, 2012;Babatolu, 2014;Ekwe et al, 2014 studied spatial distribution of rainfall patterns in Nigeria. Researchers are mostly faced with a large number of observations that can be meaningless unless classified into manageable groups.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Agada,

Peter,

Eweh

2024

njtep

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Precipitation is highly unpredictable over space and time, and is an indicator for climate change. The present work classified average yearly precipitation over the 36 states and the Federal Capital Territory (FCT) in Nigeria into various groups. The hierarchical and non- hierarchical (k-means) cluster method was used in the classification of 37- years precipitation data (1984-2020). The results showed that six major clusters were formed. The Precipitation across the 36 states and the FCT in Nigeria can be classified as Light precipitation for Sokoto, Borno, Kastina, Yobe and Kano States; Moderate precipitation for Kebbi, Zamfara, Dutse, Yola, Gombe and Bauchi States; Heavy precipitation for Kaduna, Plateau, Kwara, Taraba, Nassarawa, Benue and Kogi States; Very Heavy precipitation for Osun, Ogun, FCT, Ekiti, Niger and Lagos States; Shower for Edo, Abia, Delta, Anambra, Ebonyi, Enugu, Ondo and Oyo States and lastly, Heavy Shower for Akwaibom, Imo, Rivers, Bayelsa and Cross River States.. The rangs of precipitation based on the six classification are 1.5-1.8mm (Light precipitation), 1.9-2.7mm (Moderate precipitation), 2.8-3.6mm (Heavy precipitation), 3.7-4.3mm (Very Heavy precipitation), 4.4-5.4mm (Shower) and greater than 5.5mm (Heavy Shower). The hierarchical cluster method performed better than the non- hierarchical (k-means) cluster method in structuring the precipitation data and shaping it into groups (clusters) such that observations within the same group are more similar than those in different groups. The results of this study can contribute to a better understanding of the climate condition of the36 States and the FCT in Nigeria.

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Section: Introductionmentioning

confidence: 99%

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Agada,

Peter,

Eweh

2024

njtep

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Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria

Danbatta,

Muhammad,

Varol

et al. 2024

Environ Dev Sustain

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Nigeria’s agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok’s (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.

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Trends analysis in rainfall data series in Duhok city, Kurdistan region, Iraq

Hajani

Klari

2022

Model. Earth Syst. Environ.

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Mathematical Study of Monthly and Annual Rainfall Trends in Nasarawa State, Nigeria

Cited by 13 publications

References 14 publications

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria

Trends analysis in rainfall data series in Duhok city, Kurdistan region, Iraq

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