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.

Evaluation of approaches to estimate fish catches on Lake Victoria: a case study using CAS and frame data from Uganda

Since 2005, harmonized catch assessment surveys (CASs) have been implemented on Lake Victoria in the three riparian countries Uganda, Kenya, and Tanzania to monitor the commercial fish stocks and provide their management advice. The regionally harmonized standard operating procedures for CASs have not been wholly followed due to logistical difficulties. Yet the new approaches adopted have not been documented. This study investigated the alternative approaches used to estimate fish catches on the lake with the aim of determining the most reliable one for providing management advice and also the effect of current sampling routine on the precision of catch estimates provided. The study found the currently used lake-wide approach less reliable and more biased in providing catch estimates compared to the district based approach. Noticeable differences were detected in catch estimates between different months of the year. The study recommends future analyses of CAS data collected on the lake to follow the district based approach. Future CASs should also consider seasonal variations in the sampling design by providing for replication of sampling. The SOPs need updating to document the procedures that deviate from the original sampling design.