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Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil

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Abstract

Spatial and temporal patterns of rainfall were identified over the state of Rio de Janeiro, southeast Brazil. The proximity to the coast and the complex topography create great diversity of rainfall over space and time. The dataset consisted of time series (1967–2013) of monthly rainfall over 100 meteorological stations. Clustering analysis made it possible to divide the stations into six groups (G1, G2, G3, G4, G5 and G6) with similar rainfall spatio-temporal patterns. A linear regression model was applied to a time series and a reference. The reference series was calculated from the average rainfall within a group, using nearby stations with higher correlation (Pearson). Based on t-test (p < 0.05) all stations had a linear spatiotemporal trend. According to the clustering analysis, the first group (G1) contains stations located over the coastal lowlands and also over the ocean facing area of Serra do Mar (Sea ridge), a 1500 km long mountain range over the coastal Southeastern Brazil. The second group (G2) contains stations over all the state, from Serra da Mantiqueira (Mantiqueira Mountains) and Costa Verde (Green coast), to the south, up to stations in the Northern parts of the state. Group 3 (G3) contains stations in the highlands over the state (Serrana region), while group 4 (G4) has stations over the northern areas and the continent-facing side of Serra do Mar. The last two groups were formed with stations around Paraíba River (G5) and the metropolitan area of the city of Rio de Janeiro (G6). The driest months in all regions were June, July and August, while November, December and January were the rainiest months. Sharp transitions occurred when considering monthly accumulated rainfall: from January to February, and from February to March, likely associated with episodes of “veranicos”, i.e., periods of 4–15 days of duration with no rainfall.

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Acknowledgments

The authors are grateful to Fundação Superintendência Estadual de Rios e Lagos (SERLA), Instituto Nacional de Meteorologia (INMET), Companhia de Pesquisa de Recursos Minerais (CPRM) and Light Serviços e Eletricidade (LIGHT) for providing the pluviometric data available at the Hidroweb system. The authors are grateful to Conselho Nacional de Desenvolvimento Científico (CNPq) for the financial support (Processes 483643/2011-4 and 454928/2012-2). We thank Gisleine Cunha Zeri for the helpful comments on the final manuscript.

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Authors and Affiliations

  1. Pós-Graduação em Engenharia de Biossistemas, Escola de Engenharia, Universidade Federal Fluminense (UFF), 24210-240, Niterói, RJ, Brazil

    Thábata T. Brito

  2. Departamento de Ciências Ambientais, Instituto de Florestas, Universidade Federal Rural do Rio de Janeiro (UFRRJ), 23890-000, Seropédica, RJ, Brazil

    José F. Oliveira-Júnior, Gustavo B. Lyra & Givanildo Gois

  3. National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), Estrada Doutor Altino Bondesan, 500, Eugênio de Melo, 12247-016, São José dos Campos, SP, Brazil

    Marcelo Zeri

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  1. Thábata T. Brito
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  2. José F. Oliveira-Júnior
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Correspondence to Marcelo Zeri.

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Responsible Editor: M. T. Prtenjak.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Figure S1. Number of years of rainfall series for each station (TIFF 3036 kb)

Figure S2. Percentage (%) of missing data for each station (TIFF 2905 kb)

Figure S3. Map of regression coefficient β0 (intercept) (TIFF 1123 kb)

Figure S4. Map of standard deviation of regression coefficient β0 (TIFF 1115 kb)

Figure S5. Map of regression coefficient β1 (slope) (TIFF 1094 kb)

Figure S6. Map of standard deviation of regression coefficient β1 (TIFF 1087 kb)

Figure S7. Map of p-values calculated for regression coefficient β0 (TIFF 1080 kb)

Figure S8. Map showing t-test results for regression coefficient β0 (TIFF 1076 kb)

Figure S9. Map of p-values calculated for regression coefficient β1 (TIFF 1066 kb)

Figure S10. Map showing t-test results for regression coefficient β1 (TIFF 1102 kb)

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Brito, T.T., Oliveira-Júnior, J.F., Lyra, G.B. et al. Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil. Meteorol Atmos Phys 129, 469–478 (2017). https://doi.org/10.1007/s00703-016-0481-x

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