In this study, drought in Portugal was assessed using 74 time series of Standardized Precipitation Index (SPI) with a 12-month timescale and 66 years length. A clustering analysis on the SPI Principal Components loadings was performed in order to find regions where SPI drought characteristics are similar. A Fourier analysis was then applied to the SPI time series considering one SPI value per year relative to every month. The analysis focused on the December SPI time series grouped in each of the three identified clusters to investigate the existence of cycles that could be related to the return periods of droughts. The most frequent significant cycles in each of the three clusters were identified and analysed for December and the other months. Results for December show that drought periodicities vary among the three clusters, pointing to a 6-year cycle across the country and a 9.4-year cycle in central and southern Portugal. Both these cycles likely show the influence of the North Atlantic Oscillation (NAO) on the occurrence and severity of droughts in Portugal. Relative to other months it was observed that cycles varied according to the common occurrence of precipitation: for the rainy months – November, December and January – cycles are similar to those for December; for the dry months – May to September – where the lack of precipitation masks the occurrence of drought, the dominant cycles are of short duration and cannot be related to the NAO or other large circulation indices to explain drought variability; for the transition months – February, March, April and October – 6-year and 3-year cycles were identified, the latter being more strongly apparent in central and southern Portugal. NAO influence is again identified relative to the 6-year cycles. The short cycles are apparently associated with positive SPI, thus with wetness, not drought. Overall, results confirm the importance of the NAO as a driving force for dry and wet periods.

Long time series (95 to 135 yr) of the 12-month time scale Standardized Precipitation Index (SPI) relative to 10 locations across Portugal were studied with the aim of investigating if drought frequency and severity are changing through time. Considering four drought severity classes, time series of drought class transitions were computed and later divided into several sub-periods according to the length of SPI time series. Drought class transitions were calculated to form a 2-dimensional contingency table for each sub-period, which refer to the number of transitions among drought severity classes. Two-dimensional log-linear models were fitted to these contingency tables and an ANOVA-like inference was then performed in order to investigate differences relative to drought class transitions among those sub-periods, which were considered as treatments of only one factor. The application of ANOVA-like inference to these data allowed to compare the sub-periods in terms of probabilities of transition between drought classes, which were used to detect a possible trend in droughts frequency and severity. Results for a number of locations show some similarity between alternate sub-periods and differences between consecutive ones regarding the persistency of severe/extreme and sometimes moderate droughts. In global terms, results do not support the assumption of a trend for progressive aggravation of drought occurrence during the last century, but rather suggest the existence of long duration cycles.

This study aims at predicting the Standard Precipitation Index (SPI) drought class transitions in Portugal, considering the influence of the North Atlantic Oscillation (NAO) as one of the main large-scale atmospheric drivers of precipitation and drought fields across the Western European and Mediterranean areas. Log-linear modeling of the drought class transition probabilities on three temporal steps (dimensions) was used in an SPI time series of six- and 12-month time scales (SPI6 and SPI12) obtained from Global Precipitation Climatology Centre (GPCC) precipitation datasets with 1.0 degree of spatial resolution for 10 grid points over Portugal and a length of 112 years (1902–2014). The aim was to model two monthly transitions of SPI drought classes under the influence of the NAO index in its negative and positive phase in order to obtain improvements in the predictions relative to the modeling not including the NAO index. The ratios (odds ratio) between transitional probabilities and their confidence intervals were computed in order to estimate the probability of one drought class transition over another. The prediction results produced by the model with the forcing of NAO were compared with the results produced by the same model without that forcing, using skill scores computed for the entire time series length. Overall results have shown good prediction performance, ranging from 73% to 76% in the percentage of corrects (PC) and 56%–62% in the Heidke skill score (HSS) regarding the SPI6 application and ranging from 82% to 85% in the PC and 72%–76% in the HSS for the SPI12 application. The model with the NAO forcing led to improvements in predictions of about 1%–6% (PC) and 1%–8% (HSS), when applied to SPI6, but regarding SPI12 only seven of the locations presented slight improvements of about 0.4%–1.8% (PC) and 0.7%–3% (HSS).