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Rainfall Prediction in the State of Paraíba, Northeastern Brazil Using Generalized Additive Models
The state of Paraíba 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 Paraíba 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 Paraíba, 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.
Rainfall Prediction in the State of Paraíba, Northeastern Brazil Using Generalized Additive Models
The state of Paraíba 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 Paraíba 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 Paraíba, 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.
Rainfall Prediction in the State of Paraíba, Northeastern Brazil Using Generalized Additive Models
Leydson G. Dantas (author) / Carlos A. C. dos Santos (author) / Ricardo A. de Olinda (author) / José I. B. de Brito (author) / Celso A. G. Santos (author) / Eduardo S. P. R. Martins (author) / Gabriel de Oliveira (author) / Nathaniel A. Brunsell (author)
2020
Article (Journal)
Electronic Resource
Unknown
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