A study of the climate of Egypt with special reference to agriculture

Abstract

This thesis contains a study of the climate of Egypt with special reference to agriculture. It is organised in three main sections. The main body of the thesis, beginning with Chapter 3 (Temperatures in Egypt), deals with surface, earth and water temperatures in detail. Analysis of daily maximum temperatures at selected stations during the 15 year period (1960 - 1974) showed that spring is the period during which the frequency and severity of hot spells is greatest. Analysis of precipitation over Egypt showed that the rainy season in Egypt occurs between October and May and the maximum rainfall over the coastal Mediterranean region occurs in January, but in Middle and Upper Egypt this maximum occurs in October and May. Evaporation and evapotranspiration was estimated using the Penman equation and the Thornthwaite formula for selected stations in Egypt. These indicated that the evaporation and evapotranspiration values for each month are very high at Aswan, Kharga and Dakhla stations, in comparison to Alexandria and Tanta. The main weather features which are sufficient to specify what is called Khamsin weather have been analysed. These are excessively high surface temperatures and extremely low humidities, associated with the invasion of south and south-east winds which produce rising dust and duststorms. It is also seen that there are two types of Khamsin depressions. The first originates over the Atlantic and the second forms to the south of the Atlas mountains. Factor analysis techniques have been used to classify the climate of Egypt. The results showed that the climate of Egypt can basically be divided into two climatic zones. The first comprises the Mediterranean Coastal area, including the Delta. The second zone covers the rest of the country south of Cairo. Multiple regression equations have been used to explain the relationships between the crop yields and selected climatic variables. The results derived show that the multiple regression equations analysing climatic data have good predictive qualities in terms of annual yields.