The zooplankton population structure and their trend of variations in the southern of Caspian Sea (Iranian waters)

Caspian Sea has gone under a lot of changes due to human influences and the unwanted presence of a ctenophora Menomiopsis leidyi which has greatly changed the structure of planktons in the last recent years. Therefore, this study was carried out in order to determine these changes in the zooplankton community. the Sampling was done in 8 transacts in Astara, Anzali, Sefidrood, Tonekaboun, Noushahr, Babolsar, Amirabad and Bandar Torkaman coastal waters at 5 different depths including 5, 10, 20, 50 and 100 m. Sampling was carried out in four seasons of spring, summer, autumn and winter during 2008, 2009 and 2010 on board of R/V Gilan. Altogether, 12 species of zooplankton were identified in 2008, 22 species in 2009 and 14 species in 2010. The zooplankton included four groups: copepoda (4 species), cladocera (8species), rotatoria (10 species) and protozoa (2 species).The increase of diversity in 2009 was due to cladocera and rotatoria groups. The abundance of zooplankton in the spring was 5074 + 7807 ind/m3 more than other season in 2008. The abundance of copepoda in the summer reached the highest value of 3332 ind/m3 and since autumn the abundance gradually decreases and in the winter reached to the lowest value. The most abundance of cladocera was 797 ind/m3 in winter and decreased in summer and autumn. The abundance of rotatoria was 2189 ind/m3 in winter. rotifera and copepoda consisted the main population of Zooplanktons in the winter. The results of 2009 and 2010 showed that the abundance of zooplankton in winter was 2.6 fold of autumn, 1.6 fold of summer and 1.1 fold (1/9 fold in 2010)of spring. After increasing increased of temperature, phytoplankton, and zooplankton in summer, M.leidyi increased too. In the autumn M. leidyi reached to the highest rate and decreased zooplankton. The maximum population of zooplankton was in the layer 0-20 m and in the layer more than 20 meters, the abundance of zooplankton decreased very much. In 216 2008, 2009 and 2010, the abundance of zooplankton was 87, 77 and 77 percent in the layer 0-20 m respectively. In this study, the thermocline was observed in the layer 10 – 20 meters in the spring, that formed a thin layer but in the summer it was in the layer 20 to 50 meters. Temperature decreased between 11 to 15 oC in this layer. The variation of temperature between surfaces to bottom was 10 to 13 oC in spring, 19 to 21 in summer, about 9 oC in autumn and maximum 3 oC in winter. The most biomass of zooplankton was in the west. The biomass of zooplankton in central west and east of Southern of Caspian Sea was 54 %, 22 % and 24 % respectively in 2008, in 2009 was 48%, 33% and 20% respectively and in 2010 was 54 %, 29 % and 16 % respectively .The biomass decreased from west to east. The model of zooplankton designed by principal component analysis (PCA)and linear regression for Southern of Caspian Sea.

Investigation into the meteorological aspects of the variations in the level of Lake Victoria

The level of Lake Victoria has, since 1961, reached a height which caused serious flood damage. Already the financial implications are considerable for Kenya, Tanzania and Uganda. If further rises can be anticipated, expenditure on flood control measures to the tune of several million pounds sterling must be envisaged. If such rises should lead to uncontrolled discharge at the Owen Falls Dam site because of overshooting, downstream districts of Uganda and the Sudan may be seriously flooded. All this merits a thorough study, and any indication of the future behaviour of lake levels, even when associated with a low probability, must be taken into account. In these circumstances the Water Development Department of Kenya approached the East African Meteorological Department in November, 1964, on behalf of all parties concerned with the request to study the meteorological background of the Iake level variation, with a view to forecasting future behaviour.

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.