Modeling Pan Evaporation for Kuwait using Multiple Linear Regression and Time-Series Techniques

Abstract

This study attempts to model evaporation for Kuwait under arid conditions by using a wide range of monthly evaporation data, varying from 0.1 to 40 mm/day, from January 1993 to July 2015. Owing to the reason that the well-known theoretical evaporation models presented in the literature have been justified for a much shorter data range, the paper adopts empirical approaches to fit the data. Two evaporation models are presented based on classical statistical methods, one of multiple linear regression and another of time series analysis. The regression model, which is a function of temperature, relative humidity and wind speed, allows different modifications in the independent variables for more natural evaporation data synthesis. The time series model, which is a function of time only, is convenient for producing forecasts. Both evaporation models have been shown to produce results that are in reasonable agreement with observation values. This study advocates that the specific, rather simple, classical procedures performed to model the evaporation data can be effective alternatives to other theoretical and semi-theoretical methods found in the literature.