Document Type : Original Article
Authors
- I. U. Siloko ^{} ^{} ^{1}
- E. A. Siloko ^{2}
^{1} Department of Mathematics and Computer Science, Edo State University Uzairue, Nigeria
^{2} Department of Statistics, University of Benin, Benin City, Edo State, Nigeria
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 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.
Keywords
Main Subjects
- Cong, R.-G. and Brady, M., 2012. The interdependence between rainfall and temperature: copula analyses, The Scientific World Journal, 2012. Doi:10.1100/2012/405675
- Islam, M. T. and Zakaria, M., 2019. Interdependency between rainfall and temperature using correlation analysis in the Barishal district of Bangladesh, IOSR Journal of Applied Mathematics, 15(5), pp. 49-55. Doi:10.9790/5728-1505034955
- Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. and Peng, B., 2019. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States, Global Change Biology, 25(7), pp. 2325-2337. Doi:10.1111/gcb.14628
- Siloko, I., Ukhurebor, K., Siloko, E., Enoyoze, E., Bobadoye, A., Ishiekwene, C., Uddin, O. and Nwankwo, W., 2021. Effects of some meteorological variables on cassava production in Edo State, Nigeria via density estimation, Scientific African, 13, pp. e00852. Doi:10.1016/j.sciaf.2021.e00852
- Urban, D. W., Roberts, M. J., Schlenker, W. and Lobell, D. B., 2015. The effects of extremely wet planting conditions on maize and soybean yields, Climatic Change, 130, pp. 247-260. Doi:10.1007/s10584-015-1362-x
- Lesk, C., Coffel, E. and Horton, R., 2020. Net benefits to US soy and maize yields from intensifying hourly rainfall, Nature Climate Change, 10(9), pp. 819-822. Doi:10.1038/s41558-020-0830-0
- Dell, M., Jones, B. F. and Olken, B. A., 2014. What do we learn from the weather? The new climate-economy literature, Journal of Economic Literature, 52(3), pp. 740-798. Doi:10.1257/jel.52.3.740
- Abrignani, M. G., Corrao, S., Biondo, G. B., Lombardo, R. M., Di Girolamo, P., Braschi, A., Di Girolamo, A. and Novo, S., 2012. Effects of ambient temperature, humidity, and other meteorological variables on hospital admissions for angina pectoris, European Journal of Preventive Cardiology, 19(3), pp. 342-348. Doi:10.1177/1741826711402741
- Manatsa, D. and Matarira, C., 2009. Changing dependence of Zimbabwean rainfall variability on ENSO and the Indian Ocean dipole/zonal mode, Theoretical and Applied Climatology, 98, pp. 375-396. Doi:10.1007/s00704-009-0114-0
- Nkuna, T. R. and Odiyo, J. O., 2016. The relationship between temperature and rainfall variability in the Levubu sub-catchment, South Africa, International Journal of Education and Learning Systems, 1. Available at: http://www.iaras.org/iaras/filedownloads/ijes/2016/008-0011.pdf
- Pumo, D., Carlino, G., Arnone, E. and Noto, L. V., 2018. Relationship between extreme rainfall and surface temperature in Sicily (Italy), EPiC Series in Engineering, 3, pp. 1718-1726. Doi:10.29007/rtts
- Aweda, F. and Samson, T., 2022. Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa, Iranian (Iranica) Journal of Energy & Environment, 13(3), pp. 248-257. Doi:10.5829/ijee.2022.13.03.05
- Oloruntade, A. J., Mogaji, K. and Imoukhuede, O., 2018. Rainfall trends and variability over Onitsha, Nigeria, Ruhuna Journal of Science, 9(2). Doi:10.4038/rjs.v9i2.40
- Siloko, I. U., Ukhurebor, K. E., Siloko, E. A., Enoyoze, E. and Ikpotokin, O., 2022. The interactions between temperature and relative humidity: results for Benin City, Nigeria using statistical analysis, Current Applied Science and Technology, 22(1), pp. 1-15. Doi:10.55003/cast.2022.01.22.009
- Ukhurebor, K. E. and Siloko, I. U., 2020. Temperature and rainfall variability studies within South-South region of Nigeria, AU eJournal of Interdisciplinary Research, 5(2), pp. 1-19. Available at:http://www.assumptionjournal.au.edu/index.php/eJIR/article/view/4791
- Wang, D., Hejazi, M., Cai, X. and Valocchi, A. J., 2011. Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois, Water Resources Research, 47(9). Doi:10.1029/2010WR009845
- Silverman, B. W., 2018. Density estimation for statistics and data analysis. New York: Routledge. ISSN:1315140918.
- Siloko, I. U., Nwankwo, W. and Umezuruike, C., 2020. A discourse on smoothing parameterizations using hypothetical dataset, Journal of Applied Sciences, 1(1), pp. 80-88. Available at: https://jasic.kiu.ac.ug/assets/articles/1593455804_a-discourse-on-smoothing-parameterizations-using-hypothetical-dataset.pdf
- Siloko, I. and Siloko, E., Year.Density estimation and data analysis using the kernel method, Proceedings of 56^{th} National Conference of Mathematical Association of Nigeria, pp. 218-226,
- Scott, D. W., 2015. Multivariate density estimation: theory, practice, and visualization. Second edn. New Jersey: John Wiley & Sons. ISSN:0471697559.
- Siloko, I., Ishiekwene, C. and Oyegue, F., 2018. New gradient methods for bandwidth selection in bivariate kernel density estimation, Mathematics and Statistics, 6(1), pp. 1-8. Doi:10.13189/ms.2018.060101
- Nwaopara, A. and Blackies, H., 2014. The incidence of human induced community road dilapidation: A case study of Ekpoma, Edo-Nigeria, International Journal of Community Research, 3(3), pp. 80-85. Avaiable at: https://www.ajol.info/index.php/ijcr/article/view/107686
- Odjugo, P., Enaruvbe, G. and Isibor, H., 2015. Geospatial approach to spatio-temporal pattern of urban growth in Benin City, Nigeria, African Journal of Environmental Science and Technology, 9(3), pp. 166-175. Doi:10.5897/AJEST2014.1715
- Commission, N. P., 2006. Housing and population census result: Edo State National population office, Benin City.
- Ehisuoria, S. E., 2014. Strategies for rural poverty reduction in Ekpoma region of Edo State, Nigeria, Strategies, 4(9), pp. 100-108. Available at: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ae44aeea5c49fb45c256e8e1038c08ac154d4c73
- Jarnicka, J., 2009. Multivariate kernel density estimation with a parametric support, Opuscula Mathematica, 29(1), pp. 41-55. Available at: https://bibliotekanauki.pl/articles/255530.pdf
- Cula, S. G. and Toktamis, O., 2012. Estimation of multivariate probability density function with kernel functions, Journal of the Turkish Statistical Association, 3(1-2), pp. 29-39. Available at: http://jtsa.ieu.edu.tr/Archives/Volume3No12/4.pdf