@Article{DantasSOBSMOB:2020:RaPrSt,
               author = "Dantas, Leydson Galv{\'{\i}}ncio and Santos, Carlos A. C. dos 
                         and Olinda, Ricardo A. de and Brito, Jos{\'e} Ivaldo B. de and 
                         Santos, Celso A. G. and Martins, Eduardo S. P. R. and Oliveira, 
                         Gabriel de and Brunsell, Nathaniel A.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Campina Grande (UFCG)} and {Universidade 
                         Estadual da Para{\'{\i}}ba (UEPB)} and {Universidade Federal de 
                         Campina Grande (UFCG)} and {Universidade Federal da 
                         Para{\'{\i}}ba (UFPB)} and {Funda{\c{c}}{\~a}o Cearense de 
                         Meterologia e Recursos H{\'{\i}}dricos (FUNCEME)} and 
                         {University of Toronto} and {University of Kansas}",
                title = "Rainfall prediction in the state of Para{\'{\i}}ba, Northeastern 
                         Brazil using generalized additive models",
              journal = "Water (Switzerland)",
                 year = "2020",
               volume = "12",
               number = "9",
                pages = "e2478",
                month = "Sept.",
             keywords = "non-stationary, water resources, SST indices, Northeast of Brazil, 
                         zero adjusted Gamma distribution (ZAGA).",
             abstract = "The state of Para{\'{\i}}ba is part of the semi-arid region of 
                         Brazil, where severe droughts have occurred in recent years, 
                         resulting in significant socio-economic losses associated with 
                         climate variability. Thus, understanding to what extent 
                         precipitation can be influenced by sea surface temperature (SST) 
                         patterns in the tropical region can help, along with a monitoring 
                         system, to set up an early warning system, the first pillar in 
                         drought management. In this study, Generalized Additive Models for 
                         Location, Scale and Shape (GAMLSS) were used to filter climatic 
                         indices with higher predictive efficiency and, as a result, to 
                         perform rainfall predictions. The results show the persistent 
                         influence of tropical SST patterns in Para{\'{\i}}ba rainfall, 
                         the tropical Atlantic Ocean impacting the rainfall distribution 
                         more effectively than the tropical Pacific Ocean. The GAMLSS model 
                         showed predictive capability during summer and southern autumn in 
                         Para{\'{\i}}ba, highlighting the JFM (January, February and 
                         March), FMA (February, March and April), MAM (March, April and 
                         May), and AMJ (April, May and June) trimesters as those with the 
                         highest predictive potential. The methodology demonstrates the 
                         ability to be integrated with regional forecasting models 
                         (ensemble). Such information has the potential to inform decisions 
                         in multiple sectors, such as agriculture and water resources, 
                         aiming at the sustainable management of water resources and 
                         resilience to climate risk.",
                  doi = "10.3390/w12092478",
                  url = "http://dx.doi.org/10.3390/w12092478",
                 issn = "2073-4441",
             language = "en",
           targetfile = "dantas_rainfall.pdf",
        urlaccessdate = "27 abr. 2024"
}