Multivariate Analysis of Evaporation Drivers in Mbeya, Tanzania Using Principal Component Analysis

Evaporation is a critical process in the hydrological cycle, contributing to approximately 70% of the water loss from Earth's surface. Understanding the drivers of evaporation, including meteorological factors like temperature, humidity, solar radiation, and wind speed, is essential for water resource management and agriculture. Traditional univariate models often oversimplify these interactions, but Principal Component Analysis (PCA) offers a powerful multivariate approach for analysing complex datasets. This study applies PCA to 10 years of meteorological data from Mbeya, Tanzania, including maximum and minimum temperature, wind speed, and solar radiation, to identify key factors influencing evaporation. The analysis highlights that solar radiation (mean = 17.60, SD = 6.01) and sunshine hours (mean = 6.96, SD = 2.87) are the most significant drivers, with strong positive loadings on Principal Component 1 (0.88 and 0.89, respectively). temperature also plays a crucial role, with maximum temperature (mean = 24.16, SD = 2.07) loading heavily on Principal Component 2 (0.75). Together, the first two components explain 60.32% of the total variance. The results demonstrate that PCA effectively reduces dimensionality, providing clearer insights into the dominant meteorological factors affecting evaporation. This dimensionality reduction not only simplifies complex relationships but also improves model predictions, making PCA a valuable tool in environmental studies.