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

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

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 occurrenc...

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Bibliographic Details
Published in:International journal of climatology Vol. 40; no. 2; pp. 1197 - 1212
Main Authors: Xavier, Ana Carolina Freitas, Rudke, Anderson Paulo, Fujita, Thais, Blain, Gabriel Constantino, Morais, Marcos Vinicius Bueno, Almeida, Daniela Sanches, Rafee, Sameh Adib Abou, Martins, Leila Droprinchinski, Souza, Rodrigo Augusto Ferreira, Freitas, Edimilson Dias, Martins, Jorge Alberto
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01-02-2020
Wiley Subscription Services, Inc
Subjects:
Brazil
Civil Engineering
Climate
climate change
Daily precipitation
Engineering and Technology
Extreme values
Extreme weather
GEV
Land use
Likelihood ratio
linear distributions
Linear functions
Mathematical models
Maximum likelihood method
monthly
non-stationary
Parameter estimation
Parameters
Precipitation
Probability theory
rainfall
River basins
Rivers
Samhällsbyggnadsteknik
Stations
Teknik
Trends
tropical and subtropical
Vattenteknik
Water Engineering
Weather
Brazil
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Summary: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. The average annual precipitation for recent decades shows a downwards trend and is contrary to what was observed for the last decades of the 20th century over humid subtropical climate areas. Maximum daily precipitations over the Paraná River basin showed trends of decrease or increase, depending on the type of climate and season. In terms of monthly maximum trends, February, July, and October are the months with the highest probability of an extreme weather event occurring in the basin.
Bibliography:Funding information
Agência Nacional de Águas, Grant/Award Number: 23038.003963/2016‐17; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
ISSN:0899-8418
1097-0088
DOI:10.1002/joc.6265