Volume 5, Issue 2, March 2020, Page: 40-46
Spatio-Temporal Assessment of Climate in Response to Solar Radiation Changes over Nigeria Using Satellite Data
Abiem Louis Tersoo, Department of Physics, University of Agriculture, Makurdi, Nigeria
Igbawua Tertsea, Department of Physics, University of Agriculture, Makurdi, Nigeria
Aondoakaa Solomon Igbalumun, Department of Physics, University of Agriculture, Makurdi, Nigeria
Received: Jan. 10, 2020;       Accepted: Feb. 4, 2020;       Published: May 19, 2020
DOI: 10.11648/j.ijees.20200502.12      View  253      Downloads  52
Abstract
Spatiotemporal assessment of climate elements in response to solar radiation changes is vital for understanding the interaction between solar energy budget and climate over Nigeria. In this work, the spatio-temporal assessment of climate changes in response to solar radiation budget was done using regression and correlation analysis on satellite remote sensing and gridded observation data. The satellite data sets include; the Top Net Solar radiation data, obtained from European Medium Range Weather Forecast Reanalysis version 5 data set (ERA5) and Extended Reconstructed Sea Surface (ERSST) data set. The gridded observation climate data sets were obtained from Climate Research Unit (CRU) of University of East Anglia. The 250 x 250 m Digital Elevation data sets were obtained from Shuttle Radar Topographic Mission (SRTM). Results showed the Top net solar radiation (J/m2), precipitation and temperature indicated trends (R-square values) of 8643.9 (0.08), -0.287 (0.06) and 0.019 (0.26) per year respectively. The correlation between Top net radiation and temperature shows, 7, 2 and 91% pixels to be negatively, zero and positively correlated while the correlation between Top net radiation and precipitation shows, 71, 8 and 21% pixels respectively to be negatively, zero and positively correlated. Results shows that there was no direct relationship between Elnino Southern Oscillation (ENSO) but arguably, temperature showed indirect relationship with Top net solar radiation. Also, residual analysis was applied to delineate areas that have no direct relationship between radiation and climate parameters.
Keywords
Climate Change, Nigeria, Solar Radiation, Residual Trends, ENSO
To cite this article
Abiem Louis Tersoo, Igbawua Tertsea, Aondoakaa Solomon Igbalumun, Spatio-Temporal Assessment of Climate in Response to Solar Radiation Changes over Nigeria Using Satellite Data, International Journal of Energy and Environmental Science. Vol. 5, No. 2, 2020, pp. 40-46. doi: 10.11648/j.ijees.20200502.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
International Energy Agency, World Energy Outlook, 2014, OECD/ IEA: CORLET Paris cedex France.
[2]
Smith, K. R.; Woodward, A.; Campbell-Lendrum, D.; Chadee, D. D.; Honda, Y.; Liu, Q.; Olwoch, J. M.; Revich, B.; Sauerborn, R., Climate Change: Impacts, Adaptation, and Vulnerability. 2014, Part A: Global and Sectoral Aspects. Contribution of Working Group II to the 5th Assessment Report of the Intergovernmental Panel on Climate Change: Cambridge, UK; New York, NY, USA, pp. 709–754.
[3]
Morales-Salinas, L.; Cárdenas-Jirón, L. A.; González-Rodríguez, E., A Simple Physical Model to Estimate Global Solar Radiation in the Central Zone of Chile; 2007, Dept of Environmental Sciences and Natural Renewable Resources, Faculty of Agronomy, Univ. of Chile: Santiago, Chile.
[4]
Sambo, A. S., Bala, E. J., Penetration of Solar Photovoltaic into Nigeria's Energy supply mix. in World Renewable Energy Forum (WREF). 2012, Denver, Colorado USA: Curran Association Inc.
[5]
Breyer, C.; Schmid, J., Population Density and Area weighted Solar Irradiation: Global Overview on Solar Resource Conditions for fixed tilted, 1-axis and 2-axes PV Systems. In Proceedings of the 25th European PV Solar Energy Conference and Exhibition, Valencia, Spain; pp. 2019, 4692–4709.
[6]
Agnidé, E. L., Marc, N. and Célestin, M., Solar Irradiance and Temperature Variability and Projected Trends Analysis in Burundi, 2019, Climate, 7, 83; doi: 10.3390/cli7060083 www.mdpi.com/journal/climate accessed 15th November, 2019.
[7]
Fu Q., Solar radiation. In Encyclopedia of Atmospheric Sciences, Holton J, Pyle J, Curry J (eds). Academic Press: Amsterdam; 2003, 1859–1863.
[8]
Bazyomo, S. D. Y. B.; Lawin, E. A.; Coulibaly, O.; Ouedraogo, A.; Lawin, A.; Wisser, D., Forecasted Changes in West Africa Photovoltaic Energy Output by 2045. Climate, 2016, 4, 53.
[9]
Finlayson-Pitts BJ, Pitts JN Jr., Chemistry of the Upper and Lower Atmosphere: Theory, Experiments, and Applications. Academic Press: San Diego, 2000, CA.
[10]
Romanou, A.; Liepert, B.; Schmidt, G. A.; Rossow, W. B.; Ruedy, R. A.; Zhang, Y., 20th century changes in surface solar irradiance in simulations and observations. Geophys. Lett, 34, 2007, 5713–47.
[11]
Bazyomo, S. D.; Lawin, E. A.; Ouedraogo, A. Seasonal Trends in Solar Radiation Available at the Earth’s Surface and Implication of Future Annual Power Outputs Changes on the Photovoltaic Systems with One and Two Tracking Axes. 2017 J. Clim. Forecast, 5, 1000201.
[12]
Birara, H.; Pandey, R. P.; Mishra, S. K., Trend and variability analysis of rainfall and temperature in the Tana basin region, Ethiopia. J. Water Clim. 2018, Chang, 9, 555–569.
[13]
Safari, B., Trend analysis of mean annual temperature conduct in Rwanda during fifty two years. Journal of Environmental Protection, 2012, 3, 538–551.
[14]
Igbawua, T., Zhang, J., Chang, Q. & Yao, F. Vegetation dynamics in relation with climate over Nigeria from 1982 to 2011. Environmental Earth Sciences 75, https://doi.org/10.1007/s12665-015-5106-z (2016).
[15]
Stone, R. C., Hammer, G. L. & Marcussen, T. Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384, 252–255. https://doi.org/10.1038/384252a0 (1996).
[16]
Ibrahim, Y. Z., Balzter, H., Kaduk, J., and Tucker, C. J. (2015) Land degradation Assessment using residual trend analysis of GIMMS NDVI3g. soil moisture and rainfall in Sub-Saharan West Africa from 1982 to 2012, remote sensing, 5471-5494; doi: 10.3390/rs70505471.
[17]
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T. C., Smith, T. M., Thorne, P. W., Woodruff, S. D., Zhang, H. M., 2015. Extended reconstructed sea surface temperature version 4 (ERSST. v4). Part I: Upgrades and intercomparisons. J. Clim. 28, 911–930. http://dx.doi.org/10.1175/JCLI-D-14-00006.1.
[18]
National Oceanic and Atmospheric Administration, Climate Prediction Center [NOAA CPC] (2015) Cold and Warm Episodes by Season. NOAA Center for Weather and Climate Prediction: College Park, Maryland. Retrieved from: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. Accessed 10/20/19.
Browse journals by subject