What Are The Factors That Affect Evaporation

CHAPTER 2

LITERATURE REVIEW

2.1 Evaporation
Evaporation over large water bodies has a crucial role in the global hydrological cycle. Evaporation occurs whenever there is a vapour pressure deficit between a water surface and the atmosphere. In addition, the available energy needs to be sufficient for vaporization. Vaporization is to enable a phase change from liquid to gaseous state (Abdelrady, 2013).
There are four main factors that affect evaporation which are the area of exposed water surface, the temperature increases loss, the movement if air above the water surface and the relative humidity (Magin & Randall, 1960). According to the kinetic theory of evaporation, molecules of water are in a constant state of random motion, colliding

The following model has been selected from the literature as it is mathematically straight forward and as a result should be relatively easy. The equation for evaporation in outdoor swimming pools (Smith, Lof and Jones, 1994) is given as: E= ((30.6+32.1 x U)(Pw-Pa))/∆H (2.4) Where E is evaporation rate in kilograms per meter squared per hour, U is the wind speed propagating over the water surfaces in metres per second, Pw is the saturation vapour pressure for specific water temperature in units of mm Hg, Pa is the saturation vapour pressure at the air dew point temperature in units of mm Hg and ΔH is the latent heat of water for specified water temperature in the tank in units of kJ per

Frequently, it used to know how and to what extent a certain response variable is related to a set of explanatory variables. The development of the Regression Model can predict the value of a dependent variables, Y based upon the value of particular of a single explanatory variable, X. Correlation analysis is used to measure the strength of association between variable X and Y.

2.7.1 Simple Regression Analysis
In a simple regression analysis, a linear model developed from which the values of dependent variable where it can predict based on particular values of a single independent variables. Simple regression model consider only one explanatory variables, X and assume that the statistical relationship between the response variable Y and this explanatory variables is linear. The straight line model can be represent as

Y_i= β_o+ β_1 x_i+ ε_i , i = 1, 2, ..., n. (2.6)
Where:
Yi : the response that corresponds to the setting xi of the explanatory variable, i = 1, 2, ..., n. xi : the ith observation on the explanatory variable. β0 and β1 : the coefficients in the linear relationship; β0 is the intercept and β1 is the