Energy
I. U. Siloko; E. Enoyoze
Abstract
Wind is a significant weather variable and its study has gained convincing attention recently due to its increasing importance as a source of renewable energy as well as its role in various natural phenomena like erosion, precipitation, and spread of wildfires. This paper investigates wind speed distribution ...
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Wind is a significant weather variable and its study has gained convincing attention recently due to its increasing importance as a source of renewable energy as well as its role in various natural phenomena like erosion, precipitation, and spread of wildfires. This paper investigates wind speed distribution in Delta State, Nigeria using a nonparametric statistical technique for ten consecutive years spanning from 2011 to 2020 across three stations. The nonparametric statistical approach is the kernel density estimation with focus on Gaussian kernel estimator. The results of the investigated period revealed that wind speed in Asaba that is located in Delta North is higher in comparison with the wind speed in Patani which is situated in Southern region of the State while the wind speed is low at Sapele in Delta Central. Therefore, installation of wind power generation system is more profiting in the Northern part because the amount of wind energy generated is determine by the wind speed. Again, the performance of agricultural or industrial activities that depend on wind speed for their proper execution is optimum in 2018 while the least performances were recorded in 2015 and 2016 respectively for the period explored.
Environment
I. U. Siloko; E. A. Siloko
Abstract
This paper focuses on the interdependence between rainfall and temperature and their joint effect. Rainfall and temperature are vital climatic variables for agricultural productivity and other human activities. Despite the importance of rainfall and temperature, there are difficulties associated with ...
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This paper focuses on the interdependence between rainfall and temperature and their joint effect. Rainfall and temperature are vital climatic variables for agricultural productivity and other human activities. Despite the importance of rainfall and temperature, there are difficulties associated with accurate analysis of their joint distribution due to the possibility of interrelationship between the variables. Several studies have been conducted by researchers on the interaction between climatic variables in order to ascertain their effects on the environment because temperatures are observed to be undergoing changes regularly. The analysis of rainfall and temperature for exploratory and visualization purposes is investigated because underlying structures and patterns do form the basis of decisions by government and regulatory agencies. This study employs the statistical approach in investigating the interdependence between rainfall and temperature in Ekpoma, Edo State, Nigeria for a period of five consecutive years from 2016 to 2020 using the Gaussian kernel estimator. The results of the investigations using some statistical indicators establish that there is irregular pattern of rainfall which is occasioned by changes in temperature. The variability of rainfall is mostly prominent in two years which are 2017 and 2019 with 29.43mm and 27.74mm as maximum amount of rainfall respectively. The results also demonstrate that the performance of years with high standard deviations are better than that of low standard deviations. Again, the performance of years with high negative correlation coefficients and high negative covariance of rainfall and temperature is better than years with weak correlations and low covariance.