Volume 4, Issue 2, April 2016, Page: 56-64
A Regression Model for Estimating Salinity in the South Eastern Mediterranean Sea
Maged Mohamed Abdel Moneim Hussein, Marine Physics Laboratory, Division of Marine Environment, National Institute of Oceanography and Fisheries, Alexandria, Egypt
Received: Mar. 15, 2016;       Accepted: Mar. 24, 2016;       Published: Apr. 5, 2016
DOI: 10.11648/j.ijema.20160402.13      View  4066      Downloads  127
This paper attends the problem of estimating salinity for a southeastern Mediterranean Sea. The main objective of the present study is the estimation of salinity profiles in the upper 500m from measurements of temperature profiles and surface salinity. 465 Temperature and salinity profiles were selected for this study, taken from expeditions carried out by research vessels Yakov Gakkov and Vladimir Parshin, of former Soviet Union during the period 1987-1990. The empirical relationship between salinity and temperature in southeastern Mediterranean Sea is quantified with the help of local regression. Differences in salinity's co-variability with temperature and with longitude, latitude and day of year from eastern to western part of the study area suggested that the region may be achieving more accurate salinity estimates. Eight methods were used for estimating salinity profiles in the present study. The results obtained from method 5 (Surface salinity added to fourth degree polynomial of temperature) were better than other methods for the upper 130m, while method 8 (longitude, latitude and day of year added to third degree polynomial of temperature) were better for the rest depths.
Mediterranean Sea, Salinity, Temperature Profile, Regression
To cite this article
Maged Mohamed Abdel Moneim Hussein, A Regression Model for Estimating Salinity in the South Eastern Mediterranean Sea, International Journal of Environmental Monitoring and Analysis. Vol. 4, No. 2, 2016, pp. 56-64. doi: 10.11648/j.ijema.20160402.13
Copyright © 2016 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.
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