The Northeast region of Brazil (NRB) is the most populous semiarid area in the world and is extremely susceptible to droughts. The severity and duration of these droughts depend on several factors, and they do not necessarily follow the same behavior. The aim of this work is to evaluate the frequency of droughts in the NRB and calculate the return period of each drought event using the copula technique, which integrates the duration and severity of the drought in the NRB in a joint bivariate distribution. Monthly precipitation data from 96 meteorological stations spatially distributed in the NRB, ranging from 1961 to 2017, are used. The copula technique is applied to the Standardized Precipitation Index (SPI) on the three-month time scale, testing three families of Archimedean copula functions (Gumbel–Hougaard, Clayton and Frank) to reveal which model is best suited for the data. Averagely, the most frequent droughts observed in the NRB are concentrated in the northern sector of the region, with an observed duration varying from three and a half to five and a half months. However, the eastern NRB experiences the most severe droughts, lasting for 14 to 24 months. The probability distributions that perform better in modeling the series of severity and duration of droughts are exponential, normal and lognormal. The observed severity and duration values show that, for average values, the return period across the region is approximately 24 months. Still in this regard, the southernmost tip of the NRB stands out for having a return period of over 35 months. Regarding maximum observed values of severity and duration, the NRB eastern strip has the longest return period (>60 months), mainly in the southeastern portion where a return period above 90 months was observed. The northern NRB shows the shortest return period (~45 months), indicating that it is the NRB sector with the highest frequency of intense droughts. These results provide useful information for drought risk management in the NRB.

Drought causes serious social and environmental problems that have great impact on the lives of thousands of people all around the world. The purpose of this research was to investigate the trends in humid conditions in the northeast of Brazil (NEB) in the highest climatic precipitation quarters, November–December–January (NDJ), February–March–April (FMA), and May–June–July (MJJ), through the standardized precipitation and evapotranspiration index (SPEI), considering an alternative statistical approach. Precipitation and potential evapotranspiration (PET) time series for the calculation of the SPEI were extracted for the 1794 NEB municipalities between 1980 and 2015 from a grid dataset with a resolution of 0.25° × 0.25° using the bilinear interpolation method. The trends and statistical significance of the SPEI were estimated by quantile regression (QR) and the bootstrap test. In NDJ, opposite trends were seen in the eastern NEB (~0.5 SPEI/decade) and in the south (~−0.6 SPEI/decade). In FMA, most of NEB presented negative trends in the 0.50 and 0.95 quantiles (~−0.3 SPEI/decade), while in MJJ, most of NEB presented positive trends in all quantiles studied (~0.4 SPEI/decade). The results are consistent with observational analyses of extreme rainfall.

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.

Resumo A Evapotranspiração (ET) é a segunda variável mais importante do ciclo hidrológico e influencia inúmeros processos da atmosfera. Desta forma, é interessante estudar as mudanças desta variável sob o contexto das mudanças climáticas antropogênicas e da constante ocorrência de secas no Nordeste Brasileiro (NEB). Foram analisadas as tendências da ET no NEB entre 1980 e 2016 a partir de dados em grade com alta resolução de 0.25° x 0.25º, constituindo 2071 pontos no NEB. Uma análise de agrupamentos mostrou que é plausível dividir a região em quatro grupos homogêneos. O grupo 1 é referente a área do semiárido na porção central do NEB. O grupo 2 é a faixa que circunda o semiárido. O grupo 3 localiza-se ao norte do Maranhão e o 4 na costa e noroeste do NEB. Analisando-se a tendência média da ET, observa-se que os grupos 2, 3 e 4 apresentaram tendências significativas de aumento da ET de 2,7 mm/ano, 3,51 mm/ano e 2,57 mm/ano, respectivamente. Em análise ponto por ponto, a porção central do NEB e semiárido apresentaram tendências positivas de ET anual (~0.8 mm/ano), enquanto o litoral noroeste e uma parte da região central da Bahia apresentaram tendências negativas (~1 mm/ano). Tais resultados devem estar associados as recentes tendências de temperatura e chuvas observadas no NEB, com impactos importantes nos núcleos de desertificação observados em muitas áreas da região, servindo de alerta a gestores ambientais e de recursos hídricos.

A thermal comfort index for the Northeast of Brazil was analyzed for two scenarios of climatic changes, A1B and A2, for 2021-2080, and compared with the reference period 1961-1990. A technique of regionalization was applied to rainfall, maximum and minimum temperature data from meteorological stations, obtained by statistical downscaling of projections from four global climate models. The results pointed to a significant reduction of rainfall and an increase of temperature for three different climatically homogeneous subregions. Regarding the thermal comfort index, the results point to an increase in days with heat discomfort between 2021 and 2080. In the northern portion, the higher percentage of days with heat discomfort will be significant since the first half of the period under appreciation, i.e., from 2021 to 2050. Conversely, in the eastern of northeastern Brazil, the increase of days with heat discomfort should happen in the period from 2051 to 2080, whereas the central-western part of the region, which, in the reference period, had recorded less than 1% of days with heat discomfort, might see an elevation of that percentage to 7% between 2021 and 2050, potentially reaching 48% of its days made uncomfortable by heat between 2051 and 2080.