A principal component analysis approach to assess CHIRPS precipitation dataset for the study of climate variability of the La Plata Basin, Southern South America

Wilmar Loaiza Cerón, Jorge Molina-Carpio, Irma Ayes Rivera, Rita Valeria Andreoli, Mary Toshie Kayano, Teresita Canchala

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

6 Citas (Scopus)

Resumen

This article assesses the consistency of the satellite precipitation estimate CHIRPS v.2 to describe the spatiotemporal rainfall variability in the La Plata Basin (LPB), the second largest hydrographic basin in South America, by (a) pixel-to-point comparison of CHIRPS data with 167 observed monthly precipitation time series using three pairwise metrics (coefficient of correlation, bias and root mean square error) and (b) principal component analysis (PCA) to evaluate the large-scale coherence between CHIRPS and rain gauge data. The pairwise metrics indicate that CHIRPS better represents the rainfall in the coastal, northeastern and southeastern parts of the basin than in the Andean region to the west. The PCA shows that CHIRPS describes most of the observed rainfall variability in the LPB, but contains more variability, especially during December–February and March–May seasons. The two major modes observed are highly correlated spatially (empirical orthogonal functions—EOFs) and temporally (principal components—PCs) with the corresponding CHIRPS modes. The PCA allows the determination of the main rainfall variability modes and their possible relations with climate variability modes. Besides, the analyses of the precipitation anomaly modes show that the El Niño Southern Oscillation explains the first EOF modes of datasets. The PCA provides an alternative and effective means of assessing the consistency of CHIRPS data in representing spatial and temporal rainfall variability in the LPB.

Idioma originalInglés
Páginas (desde-hasta)767-783
Número de páginas17
PublicaciónNatural Hazards
Volumen103
N.º1
DOI
EstadoPublicada - 1 ago. 2020

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© 2020, Springer Nature B.V.

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