Stationary and non-stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil
(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.
(Less)
- author
- organization
- publishing date
- 2020-02
- 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 Inc.
- 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
- 2024-03-19 19:29:16
@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}},
keywords = {{Brazil; climate change; GEV; linear distributions; monthly; non-stationary; rainfall; tropical and subtropical}},
language = {{eng}},
number = {{2}},
pages = {{1197--1212}},
publisher = {{John Wiley & Sons Inc.}},
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}},
}