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Stationary and non-stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

Xavier, Ana Carolina Freitas ; Rudke, Anderson Paulo ; Fujita, Thais ; Blain, Gabriel Constantino ; de Morais, Marcos Vinicius Bueno ; de Almeida, Daniela Sanches ; Rafee, Sameh Adib Abou LU ; Martins, Leila Droprinchinski LU ; de Souza, Rodrigo Augusto Ferreira and de Freitas, Edimilson Dias , et al. (2020) In International Journal of Climatology 40(2). p.1197-1212
Abstract

The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non-stationary for location parameter), and Model 3 (non-stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non-stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the... (More)

The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non-stationary for location parameter), and Model 3 (non-stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non-stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non-stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

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type
Contribution to journal
publication status
published
subject
keywords
Brazil, climate change, GEV, linear distributions, monthly, non-stationary, rainfall, tropical and subtropical
in
International Journal of Climatology
volume
40
issue
2
pages
16 pages
publisher
John Wiley & Sons
external identifiers
  • scopus:85071386684
ISSN
0899-8418
DOI
10.1002/joc.6265
language
English
LU publication?
yes
id
330f6d9a-4b7c-48a1-b269-19cfaa343386
date added to LUP
2019-09-12 11:51:39
date last changed
2020-02-11 09:53:37
@article{330f6d9a-4b7c-48a1-b269-19cfaa343386,
  abstract     = {<p>The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non-stationary for location parameter), and Model 3 (non-stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non-stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non-stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.</p>},
  author       = {Xavier, Ana Carolina Freitas and Rudke, Anderson Paulo and Fujita, Thais and Blain, Gabriel Constantino and de Morais, Marcos Vinicius Bueno and de Almeida, Daniela Sanches and Rafee, Sameh Adib Abou and Martins, Leila Droprinchinski and de Souza, Rodrigo Augusto Ferreira and de Freitas, Edimilson Dias and Martins, Jorge Alberto},
  issn         = {0899-8418},
  language     = {eng},
  number       = {2},
  pages        = {1197--1212},
  publisher    = {John Wiley & Sons},
  series       = {International Journal of Climatology},
  title        = {Stationary and non-stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil},
  url          = {http://dx.doi.org/10.1002/joc.6265},
  doi          = {10.1002/joc.6265},
  volume       = {40},
  year         = {2020},
}