Abstract
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.
Highlights
The high spatial-temporal variability of rainfall in the Northeast of Brazil (NEB) is highly influenced by global teleconnection patterns associated with the El Niño-Southern Oscillation (ENSO), as well asWater 2020, 12, 2478; doi:10.3390/w12092478 www.mdpi.com/journal/waterWater 2020, 12, 2478 the sea surface temperature (SST) in the Atlantic Ocean region [1,2,3,4]
The resampling of these data using the Expectation-Maximization with Bootstrapping (EMB) algorithm, implemented in the Amelia II R package [54], was carried out to fill missing data found in the time series
After obtaining the locations belonging to each homogeneous region, three representative cities were selected from each of the mesoregions, and pre-whitening was applied to calculate the lag between the precipitation time series and the climatic indices obtained from the regions of Niño1 + 2, Niño 3, Niño 3.4, Niño 4, SOI, TNA, and TSA (Table 3), except AMO and PDO
Summary
Water 2020, 12, 2478 the sea surface temperature (SST) in the Atlantic Ocean region [1,2,3,4]. The tropical Atlantic Ocean’s influence on the NEB rainfall regime is very intense, mainly due to its proximity. ENSO events often influence the rainfall regime by either increasing (La Niña) or decreasing (El Niño) its total amount in this region [3]. Several studies concerning the climatic variability in the NEB have been developed, such as [12,13,14,15]. These studies focus on the NEB rainfall dependence on the El Niño and La Niña events
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