Spatiotemporal Analysis of Extreme Rainfall Frequency in the Northeast Region of Brazil

被引:5
|
作者
Morales, Fidel Ernesto Castro [1 ]
Rodrigues, Daniele Torres [2 ,3 ]
Marques, Thiago Valentim [4 ]
Amorim, Ana Cleide Bezerra [5 ]
de Oliveira, Priscilla Teles [6 ]
Silva, Claudio Moises Santos e [3 ]
Goncalves, Weber Andrade [3 ]
Lucio, Paulo Sergio [3 ]
机构
[1] Univ Fed Rio Grande do Norte, Dept Stat, Ave Senador Salgado Filho 3000, BR-59078970 Natal, RN, Brazil
[2] Univ Fed Piaui, Dept Stat, Ave Campus Univ Minist Petronio Portella, BR-64049550 Ininga, Teresina, Brazil
[3] Univ Fed Rio Grande do Norte, Dept Climate & Atmospher Sci, Climate Sci Postgrad Program, Ave Senador Salgado Filho 3000, BR-59078970 Natal, RN, Brazil
[4] IFRN, Fed Inst Educ Sci & Technol Rio Grande Norte, Rua Brusque 2926, BR-59112490 Natal, RN, Brazil
[5] ISI ER SENAI Innovat Inst Renewable Energies, Ave Capitao Mor Gouveia 2770, BR-59071355 Natal, RN, Brazil
[6] Univ Estadual Paulista, Fac Ciencias, Ave Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, Brazil
关键词
climatological modeling; climate change; extreme rainfall frequency; environmental data; nonhomogeneous Poisson processes; anisotropic processes; TROPICAL ATLANTIC; PRECIPITATION; VARIABILITY; EVENTS; TEMPERATURE; INTENSITY; TRENDS; CLIMATOLOGY; SENSITIVITY; WEATHER;
D O I
10.3390/atmos14030531
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Climate extreme events are becoming increasingly frequent worldwide, causing floods, drought, forest fires, landslides and heat or cold waves. Several studies have been developed on the assessment of trends in the occurrence of extreme events. However, most of these studies used traditional models, such as Poisson or negative binomial models. Thus, the main objective of this study is to use a space-time data counting approach in the modeling of the number of days with extreme precipitation as an alternative to the commonly used statistical methods. The study area is the Northeast Brazil region, and the analysis was carried out for the period between 1 January 1980 and 31 December 2010, by assessing the frequency of extreme precipitation represented by the R10 mm, R20 mm and R* indices.
引用
收藏
页数:22
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