DANTAS, L. G.; DANTAS, LEYDSON GALVÍNCIO.; DANTAS, LEYDSON G.; http://lattes.cnpq.br/6496208673869879; DANTAS, Leydson Galvíncio.
Resumo:
The state of Paraíba makes part of the semiarid region of Brazil, where in recent years, it has
been living with severe droughts, resulting in major socioeconomic losses associated with
climate variability. Understanding how much and how precipitation may be influenced by sea
surface temperature (SST) behavior in the tropical region can assist in mitigating problems like
this. Thus, it is necessary to adjust a model that can capture the influence of the SST on the
precipitation time series. In this study, the generalized additive models for location, scale, and
shape (GAMLSS) was applied to filter the climatic indices with higher predictive efficiency
and consequently to perform climatic precipitation predictions. The results show the frequent
influence of SST in the State, being the collaboration of the tropical Atlantic Ocean more
effective than that of the tropical Pacific Ocean in the distribution of rainfall, highlighting the
TNA, TSA, AMO, SOI and PDO indexes, as the main predictors. The GAMLSS model showed
predictive ability during the summer and austral fall in Paraíba. This performance is verified
during the application of climate forecasts in the years 2016 and 2017, highlighting the
trimesters of JFM, FMA, MAM, and AMJ, as those with the highest predictive potential. The
methodology demonstrates innovative characteristics by the potential in generating climate
prognosis. Allowing different sectors, possibilities for regional and sustainable management of
water resources, which can promote, in a practical way, resilience to climate risk.