@Article{SilvaJúniorAlSaAnArSi:2018:SpRaTr,
               author = "Silva J{\'u}nior, Celso Henrique Leite and Almeida, Catherine 
                         Torres de and Santos, Jessflan R. N. and Anderson, Liana O. and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Silva, 
                         Fabr{\'{\i}}cio B.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Ceuma University} and 
                         {Centro Nacional de Monitoramento e Alertas de Desastre Naturais 
                         (CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Ceuma University}",
                title = "Spatiotemporal rainfall trends in the Brazilian legal Amazon 
                         between the years 1998 and 2015",
              journal = "Water (Switzerland)",
                 year = "2018",
               volume = "10",
               number = "9",
                pages = "e1220",
                month = "Sept.",
             keywords = "amazon forest, droughts, floods, Mann–Kendall test, TRMM.",
             abstract = "Tropical forests play an important role as a reservoir of carbon 
                         and biodiversity, specifically forests in the Brazilian Amazon. 
                         However, the last decades have been marked by important changes in 
                         the Amazon, particularly those associated with climatic extremes. 
                         Quantifying the variability of rainfall patterns, hence, is 
                         essential for understanding changes and impacts of climate upon 
                         this ecosystem. The aim of this study was to analyse 
                         spatiotemporal trends in rainfall along the Brazilian Legal Amazon 
                         between 1998 and 2015. For this purpose, rainfall data derived 
                         from the Tropical Rainfall Measuring Mission satellite (TRMM) and 
                         nonparametric statistical methods, such as MannKendall and Sens 
                         Slope, were used. Through this approach, some patterns were 
                         identified. No evidence of significant rainfall trends (p 
                         \≤ 0.05) for annual or monthly (except for September, which 
                         showed a significant negative trend) averages was found. However, 
                         significant monthly negative rainfall anomalies were found in 
                         1998, 2005, 2010, and 2015, and positive in 1999, 2000, 2004, 
                         2009, and 2013. The annual pixel-by-pixel analysis showed that 
                         92.3% of the Brazilian Amazon had no rainfall trend during the 
                         period analysed, 4.2% had significant negative trends (p \≤ 
                         0.05), and another 3.5% had significant positive trends (p 
                         \≤ 0.05). Despite no clear temporal rainfall trends for 
                         most of the Amazon had negative trends for September, 
                         corresponding to the peak of dry season in the majority of the 
                         region, and negative rainfall anomalies found in 22% of the years 
                         analysed, which indicate that water-dependent ecological processes 
                         may be negatively affected. Moreover, these processes may be under 
                         increased risk of disruption resulting from other drought-related 
                         events, such as wildfires, which are expect to be intensified by 
                         rainfall reduction during the Amazonian dry season.",
                  doi = "10.3390/w10091220",
                  url = "http://dx.doi.org/10.3390/w10091220",
                 issn = "2073-4441",
             language = "en",
           targetfile = "silva junior_spatiotemporal.pdf",
        urlaccessdate = "27 abr. 2024"
}