Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2020.104873 |
PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State, Brazil | |
Sobral, Bruno Serafini1,2; de Oliveira-Junior, Jose Francisco2,3; Alecrim, Fabiano2,4; Gois, Givanildo5; Muniz-Junior, Joao Gualberto2; de Bodas Terassi, Paulo Miguel6; Pereira-Junior, Edson Rodrigues1; Lyra, Gustavo Bastos2,7; Zeri, Marcelo8 | |
2020-07-01 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2020 |
卷号 | 238 |
文章类型 | Article |
语种 | 英语 |
国家 | Brazil |
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 (en) 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 Paraiba 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 Sen 's test the intensity of annual rainfall reduction is greater in municipalities of the Middle Paraiba 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. |
英文关键词 | Orbital products Rainfall variability Climate change Trend |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000525323500016 |
WOS关键词 | TESTS ; EXTREMES ; DROUGHT |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/289274 |
专题 | 地球科学 |
作者单位 | 1.State Secretary Environm & Sustainabil SEAS RJ, Land & Cartog Inst Rio de Janeiro ITERJ, BR-20060060 Rio De Janeiro, Brazil; 2.Fed Fluminense Univ UFF, Postgrad Program Biosyst Engn PGEB, BR-24220900 Niteroi, RJ, Brazil; 3.Fed Univ Alagoas UFAL, Atmospher Sci Inst ICAT, BR-57072260 Maceio, Alagoas, Brazil; 4.Empresa Brasileira Pesquisa Agr EMBRAPA SOLOS, BR-22470000 Jardim Botanico, RJ, Brazil; 5.Fed Fluminense Univ UFF, Ind & Met Sch Volta Redonda, BR-27255250 Volta Redonda, RJ, Brazil; 6.Fed Univ Sao Paulo USP, Dept Geog & Postgrad Program Phys Geog, BR-05508000 Sao Paulo, Brazil; 7.Fed Rural Univ Rio de Janeiro UFRRJ, Environm Sci Dept DCA, BR-23890000 Seropedica, RJ, Brazil; 8.Brazilian Natl Ctr Monitoring & Early Warning Nat, BR-12247016 Sao Jose Dos Campos, SP, Brazil |
推荐引用方式 GB/T 7714 | Sobral, Bruno Serafini,de Oliveira-Junior, Jose Francisco,Alecrim, Fabiano,et al. PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State, Brazil[J]. ATMOSPHERIC RESEARCH,2020,238. |
APA | Sobral, Bruno Serafini.,de Oliveira-Junior, Jose Francisco.,Alecrim, Fabiano.,Gois, Givanildo.,Muniz-Junior, Joao Gualberto.,...&Zeri, Marcelo.(2020).PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State, Brazil.ATMOSPHERIC RESEARCH,238. |
MLA | Sobral, Bruno Serafini,et al."PERSIANN-CDR based characterization and trend analysis of annual rainfall in Rio De Janeiro State, Brazil".ATMOSPHERIC RESEARCH 238(2020). |
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