气候变化领域集成服务门户(CCPortal): PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State; Brazil

CCPortal

DOI	10.1016/j.atmosres.2020.104873
	PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State; Brazil
	Sobral B.S.; de Oliveira-Júnior J.F.; Alecrim F.; Gois G.; Muniz-Júnior J.G.; de Bodas Terassi P.M.; Pereira-Júnior E.R.; Lyra G.B.; Zeri M.
发表日期	2020
ISSN	0169-8095
卷号	238
英文摘要	Climate Data analysis has become a fundamental tool for scientists who seek to better evaluate changes in climatic variables worldwide. When it comes to rainfall there are many datasets publicly available, and orbital products have been gradually sharpening its results. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR) annual product is used in this study to characterize rainfall variation over the state of Rio de Janeiro (SRJ), considering the period of 1983 to 2017. A rainfall dataset with 35 year long series for each of the 92 municipalities of the SRJ was created using GIS software. Several statistical tests were then applied to the datasets of each municipality in order to verify normality (Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Bera), homogeneity (Pettitt, SNHT, Buishand and von Neumann), trends (Mann-Kendall) and intensity (Şen) of reduction or increase in annual rainfall. The estimated rainfall datasets were classified mainly as normal and homogenous (non-significant breakpoints), but significant breakpoints were registered by the Buishand's test in the dataset of twenty seven (29.34%) municipalities. Twenty municipalities had their estimated datasets compared to local meteorological stations in order to verify PERSIANN-CDR performance over the SRJ. Municipalities in the Middle Paraíba and Center South regions are the wettest of the state, while locations that presented lower average annual rainfalls in the state are concentrated in the North, Coastal Flats and Northwest regions. Alarming trends of reduction in annual rainfall were identified for all municipalities using the MK test, but to the threshold of 95% reliability, results show fifty four (58.69%) municipalities located in the central and western parts of the SRJ. According to Şen ´s test the intensity of annual rainfall reduction is greater in municipalities of the Middle Paraíba and Green Coast regions, but Center South, Metropolitan and Coastal Flats regions also registered disquieting results. PERSIANN-CDR analysis can be considered an efficient methodology in the characterization of rainfall variability and trend detection for the SRJ, being encouraged for future studies addressing rainfall and drought variability over the state. The analysis of the PERSIANN-CDR products should also be applied in other regions of the country, especially considering the remarkable interannual and intraseasonal variability of rainfall in Brazil. © 2020 Elsevier B.V.
英文关键词	Climate change; Orbital products; Rainfall variability; Trend
语种	英语
scopus关键词	Climate change; Clock and data recovery circuits (CDR circuits); Neural networks; Statistical tests; Climate data records; Intraseasonal variability; Meteorological station; Orbital products; Precipitation estimation from remotely sensed information; Rainfall variability; Rainfall variation; Trend; Rain; artificial neural network; climate change; data set; GIS; rainfall; remote sensing; software; trend analysis; Brazil; Rio de Janeiro [Brazil]
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/141955
作者单位	Land and Cartography Institute of Rio de Janeiro (ITERJ), State Secretary for the Environment and Sustainability (SEAS-RJ), Rio de Janeiro, 20060-060, Brazil; Postgraduate Program in Biosystems Engineering (PGEB), Federal Fluminense University (UFF), Niterói, Rio de Janeiro, 24220-900, Brazil; Atmospheric Sciences Institute (ICAT), Federal University of Alagoas (UFAL, Maceió, Alagoas 57072-260, Brazil; Empresa Brasileira de Pesquisa Agropecuária - EMBRAPA-SOLOS, Jardim Botânico, Rio de Janeiro, 22470-000, Brazil; Industrial and Metallurgical School of Volta Redonda, Federal Fluminense University (UFF), Volta Redonda, Rio de Janeiro, 27255-250, Brazil; Department of Geography and Postgraduate Program in Physical Geography, Federal University of São Paulo (USP), São Paulo, 05508-000, Brazil; Environmental Sciences Department (DCA), Federal Rural University of Rio de Janeiro (UFRRJ), 23890-000, Seropédica, Rio de Janeiro, Brazil; Brazilian National Center for Monitoring and Early Warning of Natural Disaster...
推荐引用方式 GB/T 7714	Sobral B.S.,de Oliveira-Júnior J.F.,Alecrim F.,et al. PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State; Brazil[J],2020,238.
APA	Sobral B.S..,de Oliveira-Júnior J.F..,Alecrim F..,Gois G..,Muniz-Júnior J.G..,...&Zeri M..(2020).PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State; Brazil.Atmospheric Research,238.
MLA	Sobral B.S.,et al."PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State; Brazil".Atmospheric Research 238(2020).