Modeling of rainfall variability using functional principal component method: a case study of Taiz region, Yemen

The sophisticated functional techniques can efficiently analyze and model various earth systems, such as the climate change system. The major objective of this study is to adapt the functional principal component analysis (FPCA) method for rainfall data to capture the variations over time intervals and establish a functional model of the rainfall patterns. Furthermore, this work contributes to discovering and modeling the main rainfall features. It could be useful for a better understanding of the Taiz region's rainfall regime during the past 2 decades. The analysis was conducted according to the average monthly rainfall of the Taiz region from 1998 to 2019 and processed using R software. The proposed approach of functional principal components has been successfully carried out and demonstrated significant findings for rainfall patterns and temporal variations. According to the results of the current study, the first four functional principal components illustrated 33%, 23.3%, 17.3%, and 13.3% of the total variance of the rainfall variability, respectively. Moreover, the FPCs model showed that the amount of rainfall over the Taiz region during the last 2 decades has influenced by the monsoonal intertropical and Red Sea convergence zones.

Automated summary

The study utilized the FPCA method to analyze rainfall data in Taiz region over the past 20 years, successfully establishing a functional model of rainfall patterns and highlighting the influence of monsoonal and convergence zones on rainfall variability.