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

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Abstract

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.

Original languageEnglish
Pages (from-to)767-783
Number of pages17
JournalNatural Hazards
Volume103
Issue number1
DOIs
StatePublished - 1 Aug 2020

Bibliographical note

Funding Information:
The first author was supported by the Doctoral Scholarship of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 and Universidad del Valle (Cali-Colombia). The second author was supported by the Universidad Mayor de San Andres (UMSA) within the framework provided by the HYdrogéochimie du Bassin AMazonien (HYBAM) program. The third author was supported by the CAPES-Brazil for doctoral studies. The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) of Brazil partially supported the fourth and fifth authors under grants 305611/2019-4 and 302322/2017-5, respectively. The sixth author was supported by The Program for Strengthening Regional Capacities in Research, Technological Development and Innovation in the department of Nariño and the CEIBA Foundation for doctoral studies. We also thanks the Brazilian Amazon State through Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM), for supporting this research. We thanks Álvaro Ávila from Federal University of Viçosa, Omar Gutierrez from Sorbone Université, Hannes Müller from Technische Universität Wien and a anonymous reviewer for improving this study.

Funding Information:
The first author was supported by the Doctoral Scholarship of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 and Universidad del Valle (Cali-Colombia). The second author was supported by the Universidad Mayor de San Andres (UMSA) within the framework provided by the HYdrogéochimie du Bassin AMazonien (HYBAM) program. The third author was supported by the CAPES-Brazil for doctoral studies. The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) of Brazil partially supported the fourth and fifth authors under grants 305611/2019-4 and 302322/2017-5, respectively. The sixth author was supported by The Program for Strengthening Regional Capacities in Research, Technological Development and Innovation in the department of Nariño and the CEIBA Foundation for doctoral studies. We also thanks the Brazilian Amazon State through Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM), for supporting this research. We thanks Álvaro Ávila from Federal University of Viçosa, Omar Gutierrez from Sorbone Université, Hannes Müller from Technische Universität Wien and a anonymous reviewer for improving this study.

Publisher Copyright:
© 2020, Springer Nature B.V.

Keywords

  • CHIRPS
  • La Plata Basin
  • Performance metrics
  • Principal component analysis
  • Satellite precipitation estimate

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