A spatiotemporal assessment of the high-resolution CHIRPS rainfall dataset in southwestern Colombia using combined principal component analysis

Abstract

Long and temporal time-consistency rainfall time series are essential for studying climate; nevertheless, raingauge stations are unevenly distributed across southwestern Colombia. This research paper assesses the consistency of the satellite rainfall estimate from Climate Hazards Group Infrared Precipitation (CHIRPS) via pixel-to-point comparison with 46 observed monthly rainfall time series using four pairwise metrics and Combined Principal Component Analysis (CPCA). Two Combined Principal Components (CPC) were also used to determine the relationship with the Sea Surface Temperature (SST) through simultaneous linear correlation maps. The results showed that CHIRPS has a better performance in the Andean region than in the Pacific region. The correlation between CPC1 (CPC2) and SST showed a typical El Niño Southern Oscillation pattern with an inverse (direct) relationship between the rainfall in the Andean (Pacific) Region. Finally, our results validate that CHIRPS can be included in further studies of the spatiotemporal dynamics of rainfall in Southwestern Colombia.

Keywords

• Combined Principal Component Analyses
• Pairwise comparison
• Satellite rainfall estimates and Southwestern Colombia