Volume 7, Issue 3, September 2019, Page: 46-55
Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria
Davidson Odafe Akpootu, Department of Physics, Usmanu Danfodiyo University, Sokoto, Nigeria
Wahidat Mustapha, Nigerian Meteorological Agency (NIMET), Abuja, Nigeria
Ashiru Muhammad Rabiu, Sokoto Energy Research Centre, Usmanu Danfodiyo University, Sokoto, Nigeria
MukhtarIsah Iliyasu, Physics Unit, Umaru Ali Shinkafi Polytechnic, Sokoto, Nigeria
Mohammed Bello Abubakar, Physics Unit, Umaru Ali Shinkafi Polytechnic, Sokoto, Nigeria
Mukhtar Isah Iliyasu, Department of Physics, Arthur Jarvis University, Calabar, Nigeria
Simeon Imaben Salifu, Department of Physics, Kogi State College of Education Technical, Kabba, Nigeria
Received: Aug. 26, 2019;       Accepted: Sep. 18, 2019;       Published: Oct. 9, 2019
DOI: 10.11648/j.hyd.20190703.12      View  62      Downloads  6
Abstract
In this paper, the monthly variation of Surface Water Vapour Density (SWVD) with meteorological parameters of monthly average daily mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours during the period of sixteen years (2000 – 2015) for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91m above sea level) were investigated. The daily variation of surface water vapour density for the two distinct seasons considering two typical months in each during the period of year 2015 was examined. The results showed fluctuation in the amount of surface water vapour density in each day of the month for the period under investigation. The monthly average daily values indicated that the surface water vapour densities are greater during the raining season than in the dry season. It was observed that the maximum average value of surface water vapour density of 21.002gm-3 occurred in the month of June during the raining season and minimum value of 14.653gm-3 in the month of January during the dry season. The highest value of surface water vapour density was observed on 9th May, 2015 and the lowest on 14th January, 2015. The comparison assessment of the developed SWVD based models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). The developed multivariate correlation regression model that relates temperature and relative humidity with R2=99.9% MBE=0.1259 RMSE=0.1462 MPE=-0.6739 NSE=99.8402% and IA=99.9611% was found more suitable for surface water vapour density estimation with good fitting and therefore can be used for estimating surface water vapour density in the location under investigation and region with similar climatic information. The results of the descriptive statistical analysis revealed that the surface water vapour density, mean temperature, relative humidity, cloud cover and sunshine hours data spread out more to the left of their mean value (negatively skewed), while the surface pressure data spread out more to the right of their mean value (positively skewed). The surface water vapour density data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.
Keywords
Surface Water Vapour Density, Raining Season, Dry Season, Mean Temperature and Relative Humidity
To cite this article
Davidson Odafe Akpootu, Wahidat Mustapha, Ashiru Muhammad Rabiu, MukhtarIsah Iliyasu, Mohammed Bello Abubakar, Mukhtar Isah Iliyasu, Simeon Imaben Salifu, Estimation of Surface Water Vapour Density and Its Variation with Other Meteorological Parameters Over Owerri, South Eastern, Nigeria, Hydrology. Vol. 7, No. 3, 2019, pp. 46-55. doi: 10.11648/j.hyd.20190703.12
Copyright
Copyright © 2019 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]
AGU (1995). American Geophysical Union’s Special Report on Water Vapour in the Climate System. Washington, DC 20009.
[2]
Adeyemi, B and Ogolo, E. O (2014). Diurnal and seasonal variations of surface water vapour density over some meteorological stations in Nigeria. Ife Journal of Science vol. 16: no. 2.
[3]
Gerding, M., Christopher, R and Neuber, R (2002). Tropospheric water vapour observations by ground based Lidar. Sixth Ny-Alesund International scientific seminar, NPI report series, NorskPolarinstituH.
[4]
Ajayi, G. O (1989). Physics of the tropospheric radio propagation. Proceedings of the ICTP College on Theoretical and Experimental Radio Propagation Physics, 6–24 February 1989, Trieste, Italy.
[5]
Harries, J. E (1997). Atmospheric radiation and atmospheric humidity. Quarterly Journal of Meteorol. Society.123: 2173-2186.
[6]
Schulz, J., Albert, P., Behr, H. D et al (2009). Operational climate monitoring from space: the EUMETSAT satellite application facility on climate monitoring (CM-SAF). Atmos. Chem. Phys. Discussion. net/8/8517/2008/.
[7]
Wentz, F. J and Schabel, M (2000). Precise Climate Monitoring Using Complementary Satellite Data Sets. Nature.403: 414-416.
[8]
Hegg, D. A., Hobbs, P. V., Gasso, S et al (1996). Aerosol measurements in the Arctic relative to direct and indirect radiative forcing J. Geophy. Res. 101: 23349-23363.
[9]
Ramanathan, V., Crutzen, P. J., Kiehl, J. T et al (2001). Aerosols, climate and the hydrological cycle. Sci., 294: 2119-2124.
[10]
IPCC (2001). Inter governmental Panel on Climate change. Third Assessment Report: Climate change 2001. WGI: The scientific basis, summary for policy makers, Geneva, Switzerland.
[11]
Okorie, F. C., Okeke, I., Nnaji, A et al (2012). Evidence of Climate Variability in Imo State of Southeastern Nigeria. Journal of Earth Science and Engineering. 2 (2012): 544-553.
[12]
Okorie, F. C (2010). Great Ogberuru in Its Contemporary Geography, Cape Publishers, Owerri, Nigeria.
[13]
El-Sebaii, A and Trabea, A (2005). Estimation of Global Solar Radiation on Horizontal Surfaces Over Egypt, Egypt. J. Solids. 28(1): 163-175.
[14]
Chen, R., Ersi, K., Yang, J et al (2004). Validation of five global radiation Models with measured daily data in China. Energy Conversion and Management.45, 1759-1769.
[15]
Merges, H. O., Ertekin, C and Sonmete, M. H (2006). Evaluation of global solar radiation Models for Konya, Turkey. Energy Conversion and Management. 47: 3149-3173.
[16]
Akpootu, D. O., Iliyasu, M. I., Mustapha, W et al (2017). The Influence of Meteorological Parameters on Atmospheric Visibility over Ikeja, Nigeria. Archives of Current Research International. 9(3): 1-12. doi: 10.9734/ACRI/2017/36010.
[17]
Hejase, H. A. N and Assi, A. H (2011). Time-Series Regression Model for Prediction of Monthly and Daily Average Global Solar Radiation in Al Ain City-UAE. Proceedings of the Global Conference on Global Warming held on 11 – 14 July, 2011, Lisbon, Portugal. Pp 1-11.
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