SST Indexes in the Tropical South Atlantic for Forecasting Rainy Seasons in Northeast Brazil

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará's operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

Assessment of total evaporation rates and its surface distribution by tridimensional modelling and remote sensing

In arid and semiarid environments, evaporation is responsible for significant water losses from reservoirs. This condition is of special concern in the Brazilian Northeast region, as this is one of the most populous semiarid areas in the planet. The present study aims to assess the spatio-temporal variability of evaporation rates on the water surface of Pentecoste reservoir, located in the Brazilian semiarid region, by using both the hydrodynamic model Delft3D and a remote sensing technique (RS). While RS has already been used to evaluate the spatial distribution of evaporation rates in lakes, Delft3D was innovatively tested and applied for this purpose for the first time in this study. The calibration results showed an accurate reproduction of the water level variability (r² of 0.997), along with a satisfactory calibration of the reservoir's thermal structure for the full water column (MAE of 0.539 °C, RMSE of 0.572 °C, and NMAE of 0.008). Curves relating monthly evaporation rates with air temperature and wind speed showed strong correlation between those variables (r² of 0.817 for air temperature and 0.849 for wind speed). Also, the averaged evaporation rates modeled by Delft3D differed by less than 5% compared to RS. Regarding the spatial distribution results, for the wet period the evaporation patterns were similar to those of RS, while in the dry period RS provided a more stable evaporation pattern when compared to Delft3D. The innovative approach proposed in the present study can be used to better understand the evaporation dynamics in surface waters and optimize the location of damping evaporation structures, namely air diffusers, shading systems, and floating solar panels, which are important for improving water availability, not only in drylands.

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.

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

The 2012–2018 drought was such an extreme event in the drought-prone area of Northeast Brazil that it triggered a discussion about proactive drought management. This paper aims at understanding the causes and consequences of this event and analyzes its frequency. A consecutive sequence of sea surface temperature anomalies in the Pacific and Atlantic Oceans, at both the decadal and interannual scales, led to this severe and persistent drought. Drought duration and severity were analyzed using run theory at the hydrographic region scale as decision-makers understand impact analysis better at this scale. Copula functions were used to properly model drought joint characteristics as they presented different marginal distributions and an asymmetric behavior. The 2012–2018 drought in Ceará State had the highest mean bivariate return period ever recorded, estimated at 240 years. Considering drought duration and severity simultaneously at the level of the hydrographic regions improves risk assessment. This result advances our understanding of exceptional events. In this sense, the present work proposes the use of this analysis as a tool for proactive drought planning.