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DOI	10.1016/j.atmosres.2021.105612
	Spatiotemporal variation of dry spells in the State of Rio de Janeiro: Geospatialization and multivariate analysis
	Oliveira B.C.C.D.; Oliveira-Júnior J.F.D.; Pereira C.R.; Sobral B.S.; Gois G.D.; Lyra G.B.; Machado E.A.; Correia Filho W.L.F.; Souza A.D.
发表日期	2021
ISSN	0169-8095
卷号	257
英文摘要	Dry spell studies are of vital importance for agricultural planning and water management. This study characterized dry spells in the state of Rio de Janeiro (SRJ) - southeastern Brazil - based on statistical tests, multivariate analysis and spatial distribution. Daily rainfall data from 1995 to 2017 were obtained from 86 rainfall stations located in the SRJ and neighbouring states. The data were submitted to quality analysis and gap filling of data using the simple linear regression method. The start of a dry spell was considered after three consecutive days with rainfall < 1 mm during the rainy season (November to March). A dry spell was considered the period with at least three consecutive dry days (CDD) and is divided in four classes of dry spells - Class A (3 - 6 days), B (7-10 days), C (11-14 days) and D (15 days or more) – were established for the SRJ. The Shapiro-Wilk (SW), Anderson-Darling (AD), Kolmogorov-Smirnov (KS), Jarque-Bera (JB) and Bartlett (B) tests were also applied to the time series to validate data. The SW (83.72%), AD (74.42%), KS (55.81%) and JB (80.23%) tests indicated non-normality of the data. The classes of dry spells registered different frequencies of occurrence, with Class A (70.03%), B (17.98%), C (6.08%) and D (5.91%). Spatially, there was a high variability of dry spells in the south of the state with the shortest prolonged dry spells, while in the north dry spells are usually longer, with emphasis on February and March. Principal Component Analysis (PCA) was applied to eight variables for Class A (most frequent), and identified latitude, longitude and, particularly elevation, as variables that influence the spatial distribution of dry spells, with highlights for the summer (December and January) season. The high spatial-temporal variability of dry spells in Rio de Janeiro is influenced by multi-scale meteorological systems, with an emphasis on frontal systems and physiographic factors. The applied methodology and presented results can be used to improve public policies regarding water management and mitigate the effects of droughts assuring the quantity and quality of water resources in the development of the SRJ. © 2021 Elsevier B.V.
英文关键词	Dry spell; Multivariate methods; Rainfall; Rio de Janeiro; Spatial variability
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236744
作者单位	Postgraduate Program in Biosystems Engineering (PGEB), Federal University of Fluminense (UFF), Rio de Janeiro, Niterói 24220-900, Brazil; Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), Maceió, Alagoas 57072-260, Brazil; Land and Cartography Institute of Rio de Janeiro (ITERJ), State Secretary of Cities (SECID-RJ), Rua Regente Feijó, 7, Centro, Rio de Janeiro, 20060-060, Brazil; School of Industrial Metallurgical Engineering of Volta Redonda, Technological Center, Federal University of Fluminense (UFF), Rio de Janeiro, Volta Redonda 27255-250, Brazil; Department of Environmental Sciences (DCA), Forest Institute (IF), Federal Rural University of Rio de Janeiro (UFRRJ), Rio de Janeiro, Seropédica 23897-000, Brazil; Federal University of Mato Grosso do Sul (UFMS)Mato Grosso do Sul, Brazil
推荐引用方式 GB/T 7714	Oliveira B.C.C.D.,Oliveira-Júnior J.F.D.,Pereira C.R.,et al. Spatiotemporal variation of dry spells in the State of Rio de Janeiro: Geospatialization and multivariate analysis[J],2021,257.
APA	Oliveira B.C.C.D..,Oliveira-Júnior J.F.D..,Pereira C.R..,Sobral B.S..,Gois G.D..,...&Souza A.D..(2021).Spatiotemporal variation of dry spells in the State of Rio de Janeiro: Geospatialization and multivariate analysis.Atmospheric Research,257.
MLA	Oliveira B.C.C.D.,et al."Spatiotemporal variation of dry spells in the State of Rio de Janeiro: Geospatialization and multivariate analysis".Atmospheric Research 257(2021).