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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.

In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.