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.

The paper refers to a study on droughts in a small Portuguese Atlantic island, namely Madeira. The study aimed at addressing the problem of dependent drought events and at developing a copula-based bivariate cumulative distribution function for coupling drought duration and magnitude. The droughts were identified based on the Standardized Precipitation Index (SPI) computed at three and six-month timescales at 41 rain gauges distributed over the island and with rainfall data from January 1937 to December 2016. To remove the spurious and short duration-dependent droughts a moving average filter (MA) was used. The run theory was applied to the smoothed SPI series to extract the drought duration, magnitude, and interarrival time for each drought category. The smoothed series were also used to identify homogeneous regions based on principal components analysis (PCA). The study showed that MA is necessary for an improved probabilistic interpretation of drought analysis in Madeira. It also showed that despite the small area of the island, three distinct regions with different drought temporal patterns can be identified. The copulas approach proved that the return period of droughts events can differ significantly depending on the way the relationship between drought duration and magnitude is accounted for.

The standardized precipitation index (SPI), is one of the most used drought indices. However, it is difficult to use to monitor the ongoing drought characteristics because it cannot be expeditiously related to precipitation deficits. It also does not provide information regarding the drought probability nor the temporal evolution of the droughts. By assigning the SPI to drought-triggering precipitation thresholds, a copula-based continuous drought probability monitoring system (CDPMS), was developed aiming to monitor the probability of having a drought as the rainy season advances. In fact, in climates with very pronounced rainy seasonality, the absence of precipitation during the rainy season is the fundamental cause of droughts. After presenting the CDPMS, we describe its application to Mainland Portugal and demonstrate that the system has an increased capability of anticipating drought probability by the end of the rainy season as new precipitation records are collected. The good performance of the system results from the ability of the copula to model complex dependence structures as those existing between precipitations at different time intervals. CDPMS is an innovative and user-friendly tool to monitor precipitation and, consequently, the drought probability, allowing the user to anticipate mitigation and adaptation measures, or even to issue alerts.

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.

This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.