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Article

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

1
Climatology Group, Department of Geography, University of Barcelona, 08001 Barcelona, Spain
2
Department of Geography, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Atmosphere 2021, 12(8), 941; https://doi.org/10.3390/atmos12080941
Submission received: 7 July 2021 / Accepted: 19 July 2021 / Published: 22 July 2021

Abstract

:
Drought is one of the most complex climate-related phenomena and is expected to progressively affect our lives by causing very serious environmental and socioeconomic damage by the end of the 21st century. In this study, we have extracted a dataset of exceptional meteorological drought events between 1975 and 2019 at the country and subregional scales. Each drought event was described by its start and end date, intensity, severity, duration, areal extent, peak month and peak area. To define such drought events and their characteristics, separate analyses based on three drought indices were performed at 12-month timescale: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). A multivariate combined drought index (DXI) was developed by merging the previous three indices for more understanding of droughts’ features at the country and subregional levels. Principal component analysis (PCA) was used to identify five different drought subregions based on DXI-12 values for 312 Mediterranean stations and a new special score was defined to classify the multi-subregional exceptional drought events across the Mediterranean Basin (MED). The results indicated that extensive drought events occurred more frequently since the late 1990s, showing several drought hotspots in the last decades in the southeastern Mediterranean and northwest Africa. In addition, the results showed that the most severe events were more detected when more than single drought index was used. The highest percentage area under drought was also observed through combining the variations of three drought indices. Furthermore, the drought area in both dry and humid areas in the MED has also experienced a remarkable increase since the late 1990s. Based on a comparison of the drought events during the two periods—1975–1996 and 1997–2019—we find that the current dry conditions in the MED are more severe, intense, and frequent than the earlier period; moreover, the strongest dry conditions occurred in last two decades. The SPEI-12 and RDI-12 have a higher capacity in providing a more comprehensive description of the dry conditions because of the inclusion of temperature or atmospheric evaporative demand in their scheme. A complex range of atmospheric circulation patterns, particularly the Western Mediterranean Oscillation (WeMO) and East Atlantic/West Russia (EATL/WRUS), appear to play an important role in severe, intense and region-wide droughts, including the two most severe droughts, 1999–2001 and 2007–2012, with lesser influence of the NAO, ULMO and SCAND.

1. Introduction

1.1. Climate Change and the Growing Risk of Drought Hazard

Climate change, in combination with accelerated population growth, has been described as the biggest human threat of the 21st century, putting natural systems and, thereby, sustainable human and environmental resource development at increased risk [1]. The intensive climate extremes are projected to result in a change in water availability, exacerbate carbon losses in terrestrial ecosystems, and further raise water vapor content in the atmosphere, thus amplifying the warming impacts and increasing mega-droughts worldwide [2,3,4]. Drought is one such recurring, widespread, complicated, costliest and disruptive climate extreme that frequently occurs as a result of differential responses to climate warming in different climatic regimes [5,6,7]. Recently, drought has begun to be seen not as a purely natural hazard, but partially as a result of human action, which has altered many of the characteristics of drought [1,8]. One-fifth of the global destruction caused by natural disasters can be attributed to droughts [9], where the dramatic population and economic growth has resulted in increasing demand for water that substantially intensified the frequency of global drought events [10]. Pandemics can potentially exacerbate drought impacts [11], as it was found that drought is among the most obvious reasons for displacement and disturbance during the coronavirus pandemic [12,13]. Droughts in the 21st century are identified as multifaceted, challenging natural disasters [14,15] and are characterised by longer duration, higher severity, larger spatial extent and hotter temperature that can potentially result in many deleterious impacts on ecological security, with non-linear alterations in ecosystem functions and resilience [3,4]. Systematic reviews [16,17] have indicated an accelerated transition to a more arid climate over several areas as the result of an increased tendency of frequent and intense droughts. Meanwhile, human-induced global warming and rapidly expanding human populations have already influenced water availability and storage, increasing pressure on water supplies, affecting the long-term ecosystem and increasing their sensitivity to droughts [4,18]. As a result of 1.5 to 2 °C global warming, two-thirds of the world population will experience increasing droughts; the drought magnitude is likely to double in 30% of the global land by the end of this century [19,20]. An important part of drought problem is that its current definitions refer to the drought only from the perspective of human dimensions, focusing primarily on meteorological, agricultural, socioeconomic and cultural impacts without addressing its ecological dimensions [21]. The occurrence of widespread drought in developing and developed societies has underscored the sensitivity of all communities to this natural hazard. It is not easy to know whether the drought frequency is increasing, or rather the community exposure to it. This tendency appears to be accelerating as a result of the increasing demand on both local and regional water resources [22]. The socioeconomically devastating impacts of frequent drought events have recently resulted in several global assessments of future drought conditions to better support populations and improve management plans in order to reduce direct and indirect cascading drought impacts [23,24,25]. Combining the natural and human dimensions of drought is one of the most fundamental steps in addressing the increased risk of drought in the 21st century [18,26].

1.2. Drought, Climate Change and the Mediterranean Context

The MED is identified as one of the most responsive hotspots of the climate system in the face of increased dryness at global warming levels beyond 1.5 °C [27]. The IPCC 1.5 degree Special Report (SR15) has pointed out that the increased anthropogenic warming in the MED has contributed to increased drying in the northern hemisphere mid-latitude areas including the MED [28,29]. Furthermore, with global warming of 2 °C, desertification is also predicted to occur in the MED by the end of the 21st century, resulting in an expansion of areas with significant decreases in water availability, with an accompanying increase in aridity, driving irreversible terrestrial biodiversity loss and affecting the Mediterranean ecosystem carbon storage in the coming decades [30]. The MED experienced more frequent and severe meteorological and hydrological droughts in recent decades, which is in line with the expected trend towards high frequencies of drought periods in a future warmer climate [31,32]. Above 2 °C, the MED could become more vulnerable to drought, calling for the development of a variety of adaptation mechanisms and the pursuit of drastic adaptive responses to cope with these extreme climate events, including mitigation strategies in addition to radical changes in the social structure and human communities [33,34]. The complexity of the Mediterranean climate, with its high rainfall variability and its unequal seasonal distribution in addition to the observed dynamical and physical atmospheric process complexities [35,36], produced the conditions that have led to the high sensibility and vulnerability of this region to droughts [37]. Droughts are not spatially coherent in the MED [38] demonstrating different spatial patterns even at the regional scale [39,40].
The main goal of our study is the identify and characterise meteorological drought events over the MED by constructing a robust list of the most relevant drought events that occurred in the MED between 1975 and 2019, and analysing these events. The severity, intensity, spatial extent, peak month, area involved at peak month and the frequency of drought occurrence are the essential characteristics that we have investigated. Compared to the existing drought datasets, we have provided some important developments and novelties that include a new and detailed database of drought episodes over the MED. The importance of this dataset is that it deals in details with the drought events in the Eastern Mediterranean and Middle East (EMME), where drought studies are still relatively few. Based on quality-checked and homogenised data, three drought indices were applied in order to obtain detailed information and better understanding of drought events’ features over the MED in addition to formulating a strong dataset of these events, taking into account the impacts of both precipitation (PP) and temperature (TEMP). The obtained drought events records have specific entries at both the country and subregional scales, which can then be used to extract the main drought characteristics for each subregion.
The remainder of this paper is structured into five main sections. In Section 2, we present the input data, study area and the selected drought indices. The main characteristics of different drought events and the new applied system for classifying the drought events are also demonstrated in this part. Section 3 displays the analysis results and discussion and describes, separately: the database structure; the collected records at the country and subregional scales; the biggest constructed drought events; and the drought dynamics in terms of large-scale teleconnection patterns. Finally, Section 4 derives some conclusions from the main results and includes some critical research questions for evaluating future studies of drought in the region.

2. Data and Methods

2.1. Study Area and Data

The Mediterranean basin, with latitude boundaries between 30° and 49° N and longitude boundaries between 10° W and 45° E, is the study area in this work. This study area reaches portions of three continents: Africa, Asia, and Europe. Climate is very variable within this area and characterised by warm to hot, dry summers and mild to cool, wet winters [40]. The daily meteorological datasets (1975–2019) used in this study are compiled by both the European Climate Assessment & Dataset (ECA & D; [41]) and the Global Historical Climatology Network (GHCN; [42]) Data for Syria, Lebanon, Algeria, and Jordan are directly obtained from national meteorological and hydrological services and some regional research projects. Main statistical characteristics of total annual precipitation and mean annual temperature for all stations are shown in Appendix A. After an initial quality check, the mean monthly maximum and minimum temperature (Tmax, °C), (Tmin, °C) were computed as an arithmetic average of daily maximum and minimum temperatures, respectively. The mean temperature values for any given month were considered if there were no more three missing values in this month. Precipitation amounts (P) were transformed from daily values into monthly sums if no more than two daily values were missing. Monthly temperature and precipitation series have also undergone further quality checks and data errors and inhomogeneities are eliminated using the MASH V3.03 (Multiple Analysis of Series for Homogenization [43,44]) homogenization procedures and 312 stations were finally selected for our study. About 20% of total stations failed the homogeneity tests, more than 90% of which are located in Greece, Italy, Algeria and Turkey. Depending on these results, we discarded 78 stations and completed this study with 312 meteorological stations distributed evenly located at the west–east axis of the Mediterranean basin (Figure 1). The values NOA, EATL/WRUS and SCAND indices used in the study were obtained through the NOAA CPC website: https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml, accessed on 21 July 2021 while the following websites have been used to obtain the values of the WeMO and ULMO, respectively: https://crudata.uea.ac.uk/cru/data/moi/, http://www.ub.edu/gc/data_base/, accessed on 21 July 2021.

2.2. Selection of Drought Indicators: The SPI, SPEI and RDI

Drought indices are important and effective elements for providing a comprehensive picture of drought to plan, monitor, and quantify drought and its risk management [45,46]. Multiscale drought meteorological drought indices are commonly used as proxies for characterising soil moisture at different layers [47,48]. However, due to drought complexity, using a single index is not sufficient for precisely identifying the drought dimensions [49], and the combined use of several different drought indices would give an actual and clearer picture of drought conditions [50]. Drought is defined for each station using the Standardized Precipitation Index (SPI; McKee et al. [51,52,53]), which is based on precipitation data only and measures the normalised anomalies in accumulated precipitation through a given number of months. The SPI was recommended by the World Meteorological Organization (WMO) in 2012 to characterise the meteorological droughts and quantify the precipitation deficit at multiple time scales [54]. The later Standardized Precipitation Evapotranspiration Index (SPEI; [55,56]) is also applied. The SPEI is designed to represent climatic water balance (the difference between precipitation and potential evapotranspiration, PET), which captures the main effects on water requirement as a result of evapotranspiration. Using the ratio between cumulative precipitation and PET, the Reconnaissance Drought Index (RDI; [57,58]) is also used. The SPEI and RDI provide a more comprehensive representation of drought characteristics due to their inclusion of precipitation along with potential evapotranspiration that can be effectively used to compare the drought events under different climate conditions [59,60]. Droughts in the MED have very different dimensions and affect several sectors, but currently, the most important impacts relate to the hydrological one, which is largely affected by frequent droughts [61]. In this paper, the drought indices values are generated using a 12-month aggregation period because of its great importance in estimating the severity of hydrological droughts, determining the possibility of drought persistence conditions for long periods of time [62] and considering the management of water supplies [40,63]. This paper focuses on long term drought indices (12-month timescale) because of their great importance in estimating the severity of hydrological droughts and determining the possibility of drought persistence conditions for long periods of time [62]. The input data are fitted to a two-parameter gamma probability density function for SPI-12 and RDI-12, whereas the SPEI-12 follows the log-logistic probability distributions. The PET is derived using the Hargreaves–Samani equation [64,65] which indirectly estimates extra-terrestrial radiation using Tmax and Tmin. Although the estimated evapotranspiration using temperature-based models may magnify the impacts of the current global warming [66], it has been indicated that drought indices provide reliable estimates of the true global drought trend when using the temperature-based models [67]. The temperature-based approaches, such as Thornthwaite, have produced reliable results [68]. The Hargreaves–Samani outputs show high correlation with Penman–Monteith method [56] and confirm high reliability in arid, semi-arid, Mediterranean, and very humid areas [69]. SPI-12 and SPEI-12 values are obtained using the SPEI package developed by Beguería and Vicente-Serrano (2017), available in R (Version.3.6.3, Development Core Team 2020) [70].

2.3. Drought Concepts and Characterisation, a Multivariate Perspective for a New Combined Index

A single index usually considers particular processes while ignoring others, leading to it not capturing an accurate picture of hydrological changes, and resulting in an incorrect interpretation [71]. To overcome the single-index problem, the three drought indices were integrated into one. For each station, a new combined drought index, DXI-12, was constructed by averaging the monthly values of the SPI-12, the SPEI-12, and the RDI-12 as a multivariate drought index to incorporate multiple drought-related values. The combination of multiple drought-related variables was successfully applied for drought monitoring [72], detecting [73,74], and prediction [75,76]. For example, results based on [77] have suggested a combined use of meteorological drought indices (SPI and SPEI) in the case of lack of streamflow measurements to provide the best performances in monitoring hydrological droughts. The development of a combined drought indicator gives a synoptic and synthetic overview and provides an integrated approach that fully describes the meteorological drought conditions across the MED [72]. The selected time scale relates to the time required for the effects of drought to appear on water resources and other different life sectors [78]. Recently, the use of multivariate indices has concretely increased [79], especially in cases that take into consideration the multiple characteristics of drought events. The combined index describes droughts in more detail, showing a set of drought characteristics such as intensity, severity, duration, start and end drought period, and areal extent for each region. Applicability of the combination of several drought indices is based on the fact that drought, as a natural phenomenon, results from a combination of different factors such as lack of precipitation, prolonged periods with below average rainfall, and increased evaporative demand as a result of temperature excess [80]. For each constructed drought event, we have identified the essential aspects of drought characteristics. In this paper, seven parameters (severity, intensity, duration, areal extent, peak area, peak month, area involved with peak month) were used to describe the constructed drought events. Details about the definition of these parameters can be found in Table 1. The degree of dryness (DDI) was also assessed as an indicator to evaluate the drought intensity for different drought categories in each area and for each subregion. More details about DDI calculation can be found in Salehnia et al. [81].

2.4. Principal Component Analysis (PCA)

PCA is a multivariate technique that is extensively used in meteorological, climatological, hydrological studies, and drought regionalisation as a dimensionality reduction statistical method for extracting structural information in a dataset, and allowing a clearer interpretation of large datasets of dependent variables without losing much of the original information [82]. In this paper, the PCA was used to capture the major spatial patterns of drought across the MED by reducing dimensionality in a group of the DXI-12. Specifically, the S-mode PCA with the varimax orthogonal rotation was applied to the DXI-12 at 312 stations for identification of the regions within the MED that have homogeneous drought features and conditions. The S-mode PCA enables the analysis to describe the areas with close temporal fluctuations [83]. Using the REdaS R package developed in 2015, the Kaiser–Meyer–Olkin (KMO) test [84] was utilized to examine the quality of the principal components of the time series of all drought indices before the application of the PCA method, and thus, to check the suitability of the data for factor analysis. Both the scree plot of eigenvalues and the rule of thumb [85] were used to make the decision on how many loadings to retain for rotation. The PCA was performed using the “prcomp” function in R.

2.5. Definition of the Constructed Meteorological Drought Event Database

According to Henriques and Santos [86], a drought event occurs when the value of the variable falls below a fixed threshold. Many thresholds were selected for classifying drought events. McKee et al. [51] established that a drought event starts when the SPI value reaches −1 and ends when it becomes positive. Spinoni et al. [76] indicated that a drought spell requires at least two consecutive months with a negative drought indicator measured. Spinoni et al. [28,87] assumed that a drought event starts when the indicator values fall below a certain value corresponding to a given negative standard deviation ( X i = 1 σ ) for at least two consecutive months and ends when this value turns positive. In this study, drought durations for each country/subregion were constructed by creating sequences of the number of two or more consecutive months over which the value of indicator persists below a given threshold ( X i = 1 σ ). The time series of drought durations for each station was constructed by creating a succession of the number of consecutive months, at least two, over which the value of the drought indicator persists below this threshold. The drought event ends when the indicator value turns positive. Each drought event within the constructed database has its own property which reflects the drought dimensions and characteristics. We have investigated the drought events at the national and subregional level to determine the biggest droughts that occurred in each region over the MED during last four decades. Monthly series for DXI-12 during 1975–2019 were obtained for each station, and then the corresponding series for each country were constructed. Finally, the subregional series was derived for each subregion. We have also assembled a list of the overall most relevant Mediterranean drought events that took place between 1975 and 2019 by also considering the events that involved more than one region (multi-subregional events).

2.6. Developing a New System for Classifying the Drought Events According to Three Different Attributes

Spinoni et al. [28] compared the drought events at different scales according to a special scoring system that was derived from an aggregation of six different parameters. To further identify the most exceptional drought events over the past 40 years, a similar approach will be applied using only the three most important parameters (severity, intensity and the areal extent) to describe and classify the drought events in different areas over the MED. Since the drought is a three dimensional interdisciplinary and multi-scalar phenomenon [73,88], our new approach to classification of the drought events is modified to incorporate these three drought parameters. The new classification is modified to evaluate the drought events depending on the previous grouped parameters. The main objective of this method is to identify coherent time and coverage structures through a three-dimensional array (severity, intensity, and areal extent) of drought period identified by the DXI-12 in order to transfer this qualitative classification into a quantitative one. In other words, a given parameter received grades from 1 to 6, relying on the event itself compared to others in the series, at a regional scale. While this approach has been tested for two different accumulation periods (3 and 12-month timescale), in this paper, we only present the results for 12-month accumulations. The percentile is used as a way to classify the status of each parameter and different percentiles are examined to assess the drought event condition through the combination of the three aforementioned indicators. Table 2 shows the classes of the three selected parameters and their respective grades, and this adopted drought events classification system has established the categories of “extreme drought events” for total grades from 18 to 15, “severe drought events” for total grades from 14 to 11, and “moderate drought events” for grades from 10 to 1 as a new attempt to classify the drought periods according to these attributes. The new classification is applied for each constructed drought event through the DXI-12.

2.7. Atmospheric Circulation–Drought Conditions Analyses

The MED is prone to the effects of the large-scale teleconnection patterns which considerably vary in their strength within the region [89]. Several studies have considered the association between fluctuations in the atmospheric teleconnection patterns (ATPs) and droughts [90,91,92] and their different impacts on the hydrological regime in the MED [91,93]. Here, we focus on the North Atlantic Oscillation (NAO) [94], the Upper-Level Mediterranean Oscillation index (ULMO) [95], the Western Mediterranean Oscillation (WeMO) [96], the East Atlantic/West Russia (EATL/WRUS) [97] and the Scandinavia pattern (SCAND) [98] because a preliminary analysis has showed that they are the best candidates among existing teleconnection indices which describe the drought conditions in the region of interest. Relationships between atmospheric circulation and the percent area of drought based on SPI-12, SPEI-12 and RDI-12 were explored using Pearson’s correlation analysis.

3. Results and Discussion

3.1. Loading Patterns of SPI-12, RDI-12, SPEI-12 and DXI-12 Derived from PCA

The SPI-12, SPEI-12, RDI-12 and DXI-12 were used to identify drought patterns for the 1975–2019 period. For all drought indices, five principal components (PCs) were retained identifying five regions that are well-structured and spatially coherent and show the highest values of loadings and explain the highest variance. The obtained regions are characterised by different drought variability due to different precipitation and temperature regimes in these areas.
High values obtained in the KMO test (>0.71) suggest that all proposed drought indices are adequate for the PCA, which also indicates that we can apply the PCA method to all selected time series. The KMO measures of sample adequacy applied to the drought indices are, respectively, 0.71, 0.95, 0.69, and 0.92 for the SPI-12, SPEI-12, RDI-12 and DXI-12, suggesting that all are adequate for PCA analysis (KMO test > 0.50). Appling the varimax rotation to the PCs has allowed the identification of subregions within the MED that have homogeneous drought conditions. Generally, there is a spatial coherence in the distribution of stations associated with each PC. Some stations have shown the highest correlation with more than one component identified in the PCA analysis. Generally, the main spatial patterns of all drought indices’ variability characterise the MED uniformly. The patterns were harmonious and had a coherent spatial structure that was consistent with climate influences over the MED. Although the general drought patterns existing over the MED are captured by all indices, the proportions of variance by the leading PCs are relatively higher in the case of the SPEI-12, RDI-12, and DXI-12, indicating the significant role of PET and the large response of drought to temperature variations. This means that the sensitivity of a purely precipitation-based drought index to drought variability differs from the precipitation and temperature-based indices.
The percentages of the total variance explained by the first retained loadings for the previous drought indices, which explain 10.34%, 15.53%, 17.78%, and 17.58% of total variance, respectively, clearly exhibit consistent spatial structures as well as temporal variation. The second rotated loadings account for 10.08%, 10.5%, 10.49%, and 10.99% of the total variance, respectively, with a mean cumulative variance of almost 59.9% for the five PCs for all indices. Thus, the rotated loadings for SPI-12, SPEI-12, RDI-12 and DXI-12 successfully reflect a coherent structure with five distinct subregions that are characterised by different drought variability levels, which are possibly related to the different rainfall regimes in these areas. The spatial patterns of the first five principal components are almost the same for SPI-12, SPEI-12 and RDI-12 but, in the case of the SPEI-12 and RDI-12, the loadings are higher and correspond to a wider extent than SPI. Therefore, our study is based on the analysis of the drought-event records, which are constructed according to DXI-12 for five subregions spread over 20 countries along the Mediterranean area (Figure 2 and Table 3).

3.2. Meteorological Drought Events Identified by DXI-12 and Their Characteristics at Country and Subregional Scales

Based on the analysis of the DXI-12, a new dataset of meteorological drought events was constructed during the period between 1975 and 2019. The overall number of drought events is 375, 169 of which occurred after 1996 at the country level. Any drought event that involved more than one country was allocated to represent its subregion. At the subregional scale, 19 drought events are detected in IBE–NWA, whereas the dataset has reported 20, 13, 13 and 15 drought events in EMME, AEG, BLK–FR and NA, respectively. The spatiotemporal characteristics of each drought event, including its duration, severity, intensity, areal extent and area involved with peak were examined at the country and subregional scales. Considering that the area affected by drought is a very important drought characteristic to evaluate the drought risk, we first reported the widest drought events in each region that hit more than one country with a peak regional area that exceeded 50% (Table 4). The corresponding Degree of Dryness (DDI) values for the peak year for each drought event at the subregional scale are also presented.
The areal extent of annual droughts (Table 4) shows many wide droughts over the MED that have struck more than 40% of the area. The exceptional multi-year drought event (1999–2001) might be marked as critical in all subregions, as the MED was exposed to drought episodes which affected the wider area by more than 50% in the IBW–NWA, EMME and AEG, while 37% of the area of NA was affected by this exception event. In terms of individual drought events and their other main characteristics, the differences were also significant between subregions. The meteorological drought events in the MED usually exhibit spiky behavior, inherently related to the high temporal variability of precipitation in this region. Generally, the most pronounced drought events in the last two decades experienced larger areas than other periods, compared with the late 1970s and 1980s. Sub regional drought area reached its highest level in several years after 2015 with severe and large coverage drought episodes during 2015–2018. Considering both the average of involved area and DDI values, the 1999–2001 period is the period of the broadest drought spell in the MED as a whole and the driest period in the EMME, with the drought also extended until 2002 in the NA, at which point it had the largest severity and highest intensity (Table 4). These findings reinforce what is already established by Hoell et al. [99] and Barlow et al. [100] who considered the catastrophic 1999–2001 droughts to be one of the worst drought periods in the EMME and the period with the widest drought impacts in the Southeastern Mediterranean. Furthermore, it appears that drought distribution within a multi-year is one of the most important factors in determining the severity of drought episodes. In Europe as a whole, a very spatially extensive event was shown between 2017 and 2018 that covered at least 35% of the total area and peaked in December 2017 with 74.3% in the Iberian subregion. The highest severities were also recorded during the 2017–2018 drought incidents in the IBE–NWA, as shown in Table 4. These results are in agreement with other studies [101] that ranked the 2017 drought period as one of the most severe drought events in Europe since 1979. In the IBE–NWA, an extensive coverage drought period (52.7%) was also notable in the mid-2000s, typified by high severe conditions (DDI = 11.9), and peaked at 74.2% in August 2005. The same degree of dryness was identical for the AEG in 1990, with a peak area of 77.6%. Severe drought events with high percentage areal extent were experienced in the AGE through the 1990s (59.2% in 1990), 2000s (65.3 in 2001) and mid 2010s (50% in 2014), with an almost complete lack of wide drought events in 1980s.
A wide drought event was recorded in the BLK–FR in the early 1990s, covering more than 50% of this area and followed by multiple periods of large drought extent in the mid-2000s with more 45% of spatial coverage that were not repeated until the late-2010s. Moreover, large sectors of the Adriatic and west central Mediterranean (BLK–FR) suffered from a persistent spring and summer drought in 2003 affecting 31% of this area and reaching its largest coverage in December 2003 (50%). This intensive drought episode was accompanied with an exceptional heatwave that broke the temperature record, particularly cross southern and central France [102,103], indicating a very good example of a compound drought and heat event. In the NA, drought events have been recorded in different periods and there was a succession of wide drought events since the 1980s but the most severe and widest drought event was recorded in the late 1990s, a period characterised by extremely severe long-lasting droughts. The latter half of the twentieth century also emerged as the driest of the last nine hundred years in the NA [104]. Furthermore, the multi-year drought (1999–2002) in the NA was reported as the most severe of the last five centuries [105].
Additionally, it is apparent that the late 1980s drought episode in the IBW–NWA, AGE, and NA droughts are not such extreme events compared with other drought episodes. The results suggest they display considerably lower severity and intensity with respect to the multi-year 1999–2001, 2005–2007 and 2015–2018 droughts. Table 5 and Figure 3 list the top five drought events that struck each subregion ranked by duration, severity, intensity, areal extent and degree of dryness. Several drought hotspots were indicated during last two decades along the MED. The most severe and intense droughts of the 1990s and late 2010s are clearly visible for the region as a whole.
The 1990s and 2000s also stand out in terms of spatial area in drought. The most spatially extensive events were two of the five long-lasting droughts (2008–2009) in the southeastern Mediterranean across the EMME, and in 2002 in NA. The multi-year drought in 2015–2018 displayed a highly unusual spatial pattern that affected huge parts of Europe including the AEG and BLK–FR, as shown in Table 5. Dry conditions were reported in over 90% of central-western Europe, which hit record-breaking values in more than 25% of the area [100]. Furthermore, the spatial coverage of the top drought events in all subregions has displayed large affected areas with the highest coverage in the EMME (≈76%) in the late 2000s.
For the same period, this area also displayed its most long-lasting drought event, lasting 47 months, which was the longest drought episode in the last 900 years in the EMME [38]. This prolonged drought episode (2007–2012) was one of the worst long-term droughts in modern history, and was associated with the steepest decline in crop productivity in thousands of years [100]. With this continuous harsh drought phenomenon, hundreds of thousands of people in have been displaced in the Euphrates and Tigris catchment basins, leading to an increased risk of regional conflicts, and thus contributing to the outbreak of war and helping to spark the Syrian Civil War [106,107]. The most intense drought events were recorded in the late 1990s and mid-2000s in the IBE–NWA, EMME, and NA, whereas the late 2010s experienced the most intense events in the AEG. Results have revealed top drought clusters in specific decades such as the mid-2000s and late 2010s, and more widespread events were most notable during the 2000s, with the highest degrees of dryness also shown after 1990 in the region as a whole. The highest degree of dryness was recorded in the 1990s in the EMME, NA and AEG, while the other subregions recorded their highest scores in the mid-2000s. These results are in line with other previous ones which confirmed that there is clear evidence of a strong drying trend in the EMME starting in the early 1960s, with the lowest precipitation amounts associated with the driest years in the late 1990s [99,108]. Very similar findings have been obtained by computing the DDI to describe the area affected by the droughts in the AEG. This result was also reported by Korner et al., 2005 [109], who indicated that the most crop yield declined in the Aegean area, namely in Greece, during the 1990s due to the consecutive severe droughts that hit this area. Varol and Ertugrul, 2015 [110] have also reported an increase of 14.75 times in the burned forest area in the Aegean region after 1990 due to increased droughts in this area. Finally, the results suggest the MED as a region which is characterised by a marked and high spatial variability of drought events, even for the most extreme and exceptional episodes recorded in the region, which is consistent with the results of previous observation [111]. The higher values of the DDIs during the last two decades also represent the trends toward drier conditions in all subregions (Figure 4).
Figure 4 presents the temporal variations in the DDIs over the MED during the 1975–2019 period. The results demonstrate that the MED has exhibited a high drought tendency during the past four decades with more frequent and severe droughts occurring after the 1990s and in the 2000s, and fewer droughts in the 1980s. Based on Figure 4, the DDIs change in the MED exhibits a large subregional increase over the past four decades, with a remarkable upward (drying) shift since the late 1990s. Generally, the MED displays the highest tendency toward extreme dry conditions, represented by higher DDIs values (>10) in the IBE–NWA, EMME and AEG, where the highest DDI was recorded in 1999 in the EMME (14.3). Large dry conditions were also present for the other four subregions in the 2000s, while relatively small dry conditions were present from the early-1980s into the mid-1990s.

3.3. Three-Dimensional Identification and Characterisation of Meteorological Drought Events at Subregional and Regional Scales

According to the new classification system that we previously described, multiple drought events have been recorded between 1975 and 2019 at the national and regional scales. Following the new classification, the meteorological drought events were firstly investigated at the country scale (Table 6), as the impacts of drought, corresponding damages and other economic losses are generally recorded at the national scale for each drought event [112]. Then, all events at the country scale which involved more than one country have been grouped to create a new list of drought events at the subregional scale (Table 7). Only drought events with high grade (>13) during 1975–2019 have been considered for both the national and regional scales. In order to prove the effectiveness of our approach in extracting drought periods, we have listed some scientific literature and scientific text-based reports that discussed single or several drought case studies across the MED. Generally, the literature is not as extensive about all drought events in the MED, particularly in southern and southeastern areas. However, all the drought events reported in Table 6 and Table 7 has been previously documented. The spatial patterns of the obtained drought events in this study are in good agreement with most of the major drought events presented in the international database of text-based reports and add a series of other drought events, particularly in the Eastern Mediterranean and Maghreb countries. As shown in Table 6, the largest numbers of droughts occurred from the 1990s up to the present and are considered to be exceptional events. All countries have experienced extreme drought events becoming more severe and more intense in the last two decades. The areal extent of annual droughts (Table 6) shows that many wide droughts, in all Mediterranean countries, have struck more than 50% of the area. The period after the late 1990s might be marked as critical, as the MED was exposed to drought episodes which affected more than 90% of countries in the wider area. For example, the event in 1989–1991 was the most severe one in Greece, Italy, and Croatia over the last four decades and the area under these very dry conditions has dramatically expanded to reach its maximum (97%) in Croatia in this exceptional drought spell. The late 1990s were characterised by very exceptional events that hit the Southern Levant countries (Syria, Lebanon, Israel, Jordan, Palestine, and Turkey), two of them ending in the early 2000s (Syria and Turkey). The 1999 widespread drought also affected neighboring Jordan and Israel, which were hit by their worst drought over the past 100 years in 1999 [113]. The highest drought severity ever recorded at the country level was detected in Algeria in the late 1990s, namely between 1999 and 2002 (Table 6). The recent drought (1999–2002) in the Mediterranean parts of Algeria and Tunisia was classified as the longest period of consecutive drought years since at least the middle of the 15th century and the worst drought event that struck these two countries since at least the middle of the 15th century [105,114]. The period between 2000 and 2001 included very severe and prolonged droughts in France, Italy, and Greece, which started in 1999 in Greece. This period is seen as the most serious event in Italy since the 1920s [115]. The extensive and persistent droughts in the mid-2000s significantly exceeded the other events in their spatial extent to include all the Mediterranean countries. Table 6 shows many wide droughts that struck more than 40% of the area, such as the event which spanned between 2003 and 2004 in Croatia and affected 74% of the entire country. The results display that around 34% of the area that was hit by drought in 2006 and substantial parts of Morocco and Spain (50 % and 70%, respectively) were also drought-prone between 2004 and 2005. Very dry conditions occupied several western and eastern countries in the mid and late-2010s as one of the most recent exceptional drought periods was consistent with a major drought in Spain, Italy, France, Algeria, and Cyprus and severe drought from 2016 to 2018 in Turkey. Based on these results, the two decades of 2000–2010 can be classified as a period with the most severe and long-lasting droughts during the last 50 years that appear in all Mediterranean countries [87,116]. More specifically, the droughts of the late 1990s, mid-2000s, and the multi-year drought series around 2015, appear to be the most serious and large spatial extent drought episodes, which covered almost all Mediterranean countries and reaching high severity levels. It should be noted, however, that recent droughts at particular locations (e.g., Algeria and Tunisia) are more severe than in other Mediterranean countries, as Algeria and Tunisia recorded the highest drought severity in 2002 and 1981, respectively. Although the other countries have a broader spatial extent, the NA experienced droughts in the late 20th century that were exceptional in the context of the prior 500 years [117].
It should be noted, however, that recent droughts at particular locations (e.g., Algeria and Tunisia) are more severe than other sites, although the others have broader spatial extent as the NA experienced droughts in the late 20th century that were exceptional in the context of the prior 500 years [117]. Compared to previous works on global-scale drought change [28,66], we provide, through this paper, a more comprehensive and detailed drought-event dataset for the MED by considering multiple meteorological drought indices to identify drought in this region. Table 7 reports a corresponding compiled list of the extraordinary, extreme and severe drought events, with scores exceeding 13 at the subregional scale between 1975 and 2019 identified by the three-dimensional array system, and presents twenty-four multiregion severe events detected by the DXI-12. Moreover, Table 7 is based on input data up to December 2019, but a few drought events were still ongoing at the end of 2019. These updates will be included in the coming dataset in future research.
A notable uneven temporal distribution of the drought events is detected as most events occurred after the 2000s, which have been characterised by increasing and more severe and extreme rainfall events mixed with prolonged dry conditions [40,143]. In the 2000s, 17% of the global population (1.1 billion people), who mostly live in the Middle East, North Africa, and South and East Asia, experienced critical water deficiency and high water stress [33] with 61–89% of the population under water shortage. Severe, intense, wide, and long-lasting drought events are frequent in all subregions across the MED over the last decades and were more pronounced in the Middle East and some parts of the Eastern Mediterranean. The largest drought severity was recorded in the EMME (>53) with very high intensity (>2.1) between 2014 and 2019. The multi-drought event in the mid-2000s also experienced high severity (>40) with very high intensity (2.2). The common pattern of drought occurrence displays marked spatial variability of the Mediterranean drought events, as droughts in this region seldom affect the area as a whole; the matter could be explained through the east–west climate dipole as the dominant component of variability between western and eastern sites of the MED [144]. Moreover, the EMME reflects transition between summer subtropical in the north and semi-arid climates in the south, making it more vulnerable to climatic sensitivity [141]. Combining the results reported in Table 6 and Table 7 and Figure 4 makes it possible to clearly identify the “hotspots” hit by more severe, more intense and wider meteorological drought events at the national and regional scales during 1975–2019. The recent drought in the Levant region between 2008 and 2012 was probably the worst drought of the past nine centuries. It was 50% drier than the worst drought period of the previous 500 years and 20% drier than the driest spell over the past 900 years [38]. A very severe drought period struck the northern parts of Africa between 2004 and 2005, leading to a reduction in the economic growth rate by 2.2% [142]. In 2005, severe droughts were recorded in the west of the Iberian Peninsula, southeast France, which is consistent with the results supported by Quintana-Seguí et al. [141].
In 2017, one of the worst drought periods in 150 years hit large parts of Europe and threatened the Balkans, eastern and southern Spain, and most of the Italian peninsula. Table 7 shows that exceptional droughts occurred in the 2010s and encompassed five subregions with grades higher than 13, while the AEG experienced the most exceptional drought events in the early 1990s and the mid-2000s with grade (16).

3.4. Evaluation of Different Drought Events Parameters during Two Subperiods

3.4.1. Change in Severity, Intensity and Frequency of Drought Events between 1975–1996 and 1997–2019

According to DXI-12, the number of drought events for each subregion was assessed for two consecutive periods 1975–1996 and 1997–2019. Their average values for duration, severity and intensity, and the degree of dryness were computed (Table 8). Table 8 illustrates the number of drought events at the regional scale and their average duration, severity, intensity and their spatial extent identified by the DXI-12.
The average degree of dryness was also reported in Table 8. In order to evaluate the spatial and temporal patterns of drought events, we have compared the average severity and intensity (Figure 5), frequency and spatial extent through the two subperiods. The drought periods identified by the DXI-12 were longer, wider, most severe, most intense and most frequent over 1997–2019 (Table 8 and Figure 5). The DDI values have also exerted visible upward tendency in all subregions. The Table 8 shows the frequency of the drought events in all subregions has increased from 1975–1996 to 1997–2019. The same applies to other parameters, as the drought intensity, severity and spatial coverage experienced a remarkable change in all subregions. Additionally, the DXI-12 identified dryer conditions and an increase in the area affected by droughts over all subregions. A more remarkable change is shown across the EMME, which indicates the highest drought exposure in the second period. The arid and semi-arid areas over the EMME are generally characterised by high dry conditions induced by increases in temperature and evaporation [38,107]. The highest increase in the different drought parameters was demonstrated in this subregion, while the change towards largest number of drought events mainly occurred in the IBE–NWA. Furthermore, the dry climate of EMME has not only tended to experience more intense drought events over the last two decades, as shown in this research, but also more prolonged and long-lasting events. This was confirmed by Tabari and Willems (2018) [145], who found evidence that the number of dry days is increasing and the drought episodes will be longer (up to 90%) over 80% of the Middle East. The most relevant decrease in severity was clearly evident in some parts of BLK–FR area and NA, mainly in Croatia, southeast France and Spain, and Tunisia. The results have shown a clear correspondence between the severity and intensity of drought events, particularly in the Levant area and Turkey.
Based on the DXI-12, a significant change towards larger frequencies of moderate, severe and extreme drought events was evident, in all subregions, in 75%, 81% and 82% of the total stations, respectively (Figure 6).
The increases in the number of droughts over the MED have been detected between the subperiods 1975–1996 and 1997–2019 in all subregions at different categories. During the second period (1997–2019), the increasing trend in drought frequency based on DXI-12 was experienced over the majority of the MED. The results revealed a clear signal concerning the increase in the average number of drought events over the MED by 1.36 events per decade (not shown). The EMME demonstrates the highest increase in drought occurrence by more than one event per decade (+1.9 event/per decade), where Syria and Israel show the highest increase in drought frequency (more than two events per decade). On a spatial basis and based on the DXI-12, the frequency of moderate droughts shows similar patterns for all subregions, but the moderate drought events display a larger increase in the last two decades than both severe and extreme ones. The EMME retained the largest share of the increase in all extreme, severe and moderate events, with increases of almost one moderate event, 0.7 severe events, and 0.3 extreme events per decade (Figure 6). The AEG, for example, experienced a pronounced increase in severe drought events by 0.2 and 0.3 events per decade in severe and drought events, respectively. A very clear increase in the moderate events was also evident in the BLK–FR during 1997–2019, with an approximate increase of 0.6 events per decade. The increase in extreme periods was clear, at 1.2 events per decade, in some localised areas over Italy, Greece, Tunisia and Levant countries, while some other areas in south France and Spain have experienced an eminent increase in severe droughts by 1.1 events per decade. Such results confirm what has been reported by Spinoni et al. and Dai [28,76] following different approaches (Figure 6).
Thus, drought occurrence has shown an increasing trend in addition to more prolonged and frequent periods, in last two decades, at the regional level (Table 8). The change in drought event frequency was also assessed according to the SPI, SPEI-12 and RDI-12 between the subperiods 1975–1996 and 1997–2019 (Figure 7). An overall increase in the frequency of dry spells appears over the MED for all the combinations of SPI-12, SPEI-12 and RDI-12 during 1997–2019, with clear spatial patterns, suggesting that droughts were more frequent in the last two decades. Based on the SPI-12, SPEI-12 and RDI-12, the results indicated that 70%, 96% and 84% of the total stations have reported an increase in the frequency of drought periods during 1996–2019, respectively (Figure 7).
Although there is a coherent change in the characteristics of the original drought events, which was identified by all indicators, there is a general increase in the frequency of the drought events that were studied using SPEI-12 and RDI-12 compared with those obtained by the SPI-12. Therefore, their capacity to detect drought conditions is potentially higher than the SPI-12 due to their incorporating of the influence of temperature on multi-temporal drought. The representation of the full water balance by SPEI and RDI makes them superior to SPI in terms of the accurate assessment of meteorological droughts with increasing climate [146,147,148]. Most parts of the Iberian Peninsula have also displayed consistent characteristics and experienced a drought frequency increase identified by the different drought indicators, and these were more evident when measured by the SPEI-12 and RDI-12. The results further suggest that drought events could exhibit entirely different characteristics when we include the influence of the temperature in the drought indices.

3.4.2. Change in Precipitation (PP) and Potential Evapotranspiration (PET) between 1975–1996 and 1997–2019

Changes in precipitation amounts and PET values between 1975–1996 and 1997–2019 were analysed to explore whether the dry and wet tendency reflected by the SPI-12, SPEI-12 and RDI-12 was driven by the precipitation or temperature anomalies—that were included in PET—, or both (Figure 8a,b). It is not easy to understand the actual roles of both temperature and precipitation variations in drought changes over the MED during the last two decades. This is due to nonlinear interactions among temperature, precipitation, and droughts. Although an extended period of reduced precipitation plays an important role in producing drought conditions over the MED, the effect of increased temperature is notable as a significant factor in enhancing drought by enhancing the evapotranspiration rate. According to SPI-12, 62% of total stations experienced a drying tendency. In the last two decades, some areas across west Turkey, south Spain, and the Adriatic experienced a wetting tendency. The more extreme negative values for both the SPEI-12 and RDI-12 compared with the SPI-12 (not shown), particularly in the semi-arid areas in the southeastern Mediterranean, indicate the impacts of temperature that may initiate or increase drought via the enhancement of PET. Although the RDI-12 identified dryer conditions in the last two decades compared with the SPI-12, the SPEI-12 displays the highest contribution of PET to drought conditions compared with the SPI-12 and RDI-12, particularly in the Balkans, north-east Tunisia, and west Turkey. It was concluded that the DXI-12 was able to provide a physically sound, temporally flexible index, which can be directly combined with all possible variants and linked to the climate conditions, that allowed us to list hundreds drought periods and characterise several exceptional drought events between 1975 and 2019. In this sense, DXI-12 can evaluate the actual drought conditions and effectively lessen overestimation of any drought conditions by SPEI-12 in terms of drought duration and intensity. All drought events detected by the DXI-12 were previously confirmed and documented at both the country and subregional scales, while some drought accidents, especially the moderate and short ones, could not be detected by SPI-12 and RDI-12 as well.
According to the SPEI-12, the dry conditions across the MED have been enhanced in recent years as a result of increases in temperature and decreases in precipitation in approximately 63% of total stations. The findings also show that approximately 28% of the total stations experienced drier conditions which resulted only from positive temperature anomalies over the last two decades, where the SPI-12 showed an increasing trend, while the rest of the indicators showed a significant decreasing trend (Figure 8). There is a detectable and significant decreasing trend of precipitation and increasing temperature over the Mediterranean and Middle Eastern countries [33,100,107]. Thus, this result indicates that the positive trend of precipitation is not strong enough to outweigh the temperature increase in this area, and that the drought exhibits a larger response to temperature variations, which is more than its response to the precipitation anomalies in these areas. This proportion decreases to approximately 15% for RDI-12. Generally, the MED as a whole displays a clear example of the worst drying combination (decrease in precipitation amount associated with increase in the PET).

3.5. Drought Dynamics, Large Scale Atmospheric Controls of Widespread Drought Events over the MED

The correlation of the annual percent of drought area extent based on the SPI-12, SPEI-12, RDI-12 and DXI-12 indices with large-scale atmospheric fields over the whole study period (1975–2019) is investigated. The correlation analysis gives a more detailed evaluation of the processes linking large-scale atmospheric variation to Mediterranean meteorological droughts. In addition to the general relationship between the percentage of MED experiencing meteorological droughts and ATPs (Table 9), the widespread droughts and their atmospheric drivers were also investigated. The spatial pattern of correlation is virtually identical for the SPEI-12, RDI-12 and RDI-12, with positive correlations with NAO and ULMO and negative ones with WeMO, EATL/WRUS and SCAND (Table 9).
Despite the similarity of the spatial patterns of correlation coefficients presented in Table 9, the correlation of the area in drought with the NAO is the weakest. Generally, the areas in drought obtained by the SPEI-12 and RDI-12 are better related to the ATPs in comparison to SPI-12 and detected the highest correlations with DXI-12 (Table 9). Statistically, the WeMO and EATL/WRUS are remarkably correlated with the area in drought identified by SPEI-12, RDI-12 and DXI-12. The widespread dry conditions over central and Western Europe (BLK–FR) were associated with negative phases of WeMO and SCAND and a positive phase of ULMO, as shown in Table 9. The results presented here show a major role played by the WeMO in drought within the EMME and BLK–FR. The negative phase of the WeMO generally shows very wide spatial coverage of drought over the EMME and BLK–FR. The large area under drought noted over the EMME during 2011–2012 (47%) was often linked with severe negative values of the WeMO. The severe negative phase of the WeMO (<−1.5) over 2017 was also connected with the largest percentage of the affected area in the EMME. Similarly, the ongoing negative annual phase of the WeMO between 1985 and 1991 was associated with very large area under drought in the BLK–FR. This affected area spanned over large territories in France and Croatia which were hit by severe drought episodes and involved 61.2% of the entire subregion. The consecutive low annual negative values of the WeMO between 2002 and 2018 were found to be associated with very high percentages of the area under drought in the BLK–FR. With the exception of the BLK–FR, the negative phase of EATL/WRUS was often associated with large drought spatial coverage within the MED. The persistent negative anomalies in the EATL/WRUS values in the late 2010s (July 2017–September 2018) were associated with high spatial coverage (40%) in the IBE–NWA. Furthermore, the exceptional multi-year 2007–2012 drought in the EMME was developed in a predominantly negative phase of both the WeMO and EATL/WRUS patterns. The combination of the negative phase of WeMO and EATL/WRUS over 2008 resulted in a strong influence on the drought areal extent in the EMME (76%), as shown in Table 5.
The major drought episodes that occurred between 1999 and 2002 in the NA were driven by qualitatively similar conditions with the complete domination of the negative EATL/WRUS, which had the largest mean drought area in 2002. The negative phase of the EATL/WRUS is usually linked to below-average precipitation and above-average temperatures in these areas [148,149], which clearly explains the strong negative relationship between the EATL/WRUS and the area in drought except for the BLK–FR.
The impact of the ULMO significantly appeared through both the 1994 and 2012 exceptional droughts. The very large areas of the IBE–NWA affected by droughts during 1992, 1999 and 2012 (52%, 44% and 62%, respectively) were concordant with the positive phase of ULMO. The widest affected area in in the BLK–FR during 2002 (Table 5) was remarkably associated with a large predominance of negative values of the WeMO, SCAND and positive values of the ULMO. These findings perhaps suggest that the WeMO and EATL/WRUS patterns show most potential role in explaining the spatial evolution and characteristics of large-scale drought over the MED with lesser influence of the NAO ULMO, and SCAND. In NA, the EATL/WRUS may determine the climatic water balance drought, while the NAO and ULMO play a significant role in regulating the internal rainfall variability, and thereby the spread of drought events. These results correspond with previous findings [150,151], which indicated high drought conditions during the positive phase on the NAO in southern Europe and North Africa.
At the regional scale, similar correlation patterns have been detected with drought severity, intensity and area in drought. Correlation patterns of all selected ATPs indicate relationships of Mediterranean drought severity, intensity with atmospheric circulation at the regional level (Figure 9). High drought intensities and severities are significantly associated with the negative phases of both WeMO and EATL/WRUS across the MED. The results confirm the higher capability of the SPEI-12 and RDI-12, and DXI-12 compared with SPI-12 in improving climatic water deficit detection in the MED, which confirms the capacity of temperature in detecting drought conditions. The drought intensities and severities obtained by the DXI-12 showed the highest negative correlation coefficient values with EATL/WRUS and WeMO, respectively.

4. Conclusions

This study has provided a comprehensive dataset of exceptional meteorological drought events over the Mediterranean during the 1975–2019 period that contains hundreds of constructed drought events at the national and subregional scales. Three drought indices were computed at 12-month accumulation scales and a new combined indicator was obtained (DXI-12) as the average of these three indices to identify the meteorological drought due to both rainfall shortage and climatic water balance. The drought event databases are identified by monthly series of the DXI-12 at the national and subregional scales. Based on the drought-event dataset, we have also investigated in detail the main characteristics of the obtained drought events in different subregions across the MED, including duration, severity, intensity, spatial coverage, peak month, area involved at peak month and the frequency of drought occurrence. This new approach has proved to be effective in detecting the most relevant Mediterranean drought events at the national and subregional scales as it is based on data obtained with high-quality control, a multi-indicator method, and a strong statistical setting. The evaluated drought quantification, which includes these three indices, gives a more reasonable estimation of water demand as a result of including evapotranspiration. Generally, our newly developed DXI-12 provides better information about drought identification and more realistic characteristics of dry spells compared to a single drought index and, thereby, provides a better tool for monitoring water resources, especially in semi-arid and arid ecosystems. According to the results presented in this study, the MED is shown to be a region that is water-stressed and prone to severe and exceptional droughts. The drought episodes show that the EMME and most areas of the NA and southern Europe, including south Italy and Spain, underwent a drying phase during the study period, with the highest drought severity and intensity from the late 1990s onwards. These findings are in line with recent scientific literature [28,101]. The combination of individual indicators depicts the spatial extent of a drought situation across the MED and gives an overview of the drought drivers. The DXI-12 indicates that droughts over the MED were mainly driven by temperature increases with the inherent increase in the PET, particularly in the humid area, whereas rainfall did not change appreciably. A new grade classification approach for identifying the most relevant extreme events is identified by the three-dimensional array (severity, intensity and spatial coverage). The new classification approach and database extracted from combined indices the time series have listed twenty-four multi-region big drought events, which were previously documented. The new classification has allowed us to discover the most hotspots that have displayed the highest severity and intensity and included the largest area over the MED. The significant trends towards larger drought areas and higher drought severities and intensities in all subregions and mainly occurred in the EMME. Although the actual precipitation anomalies exhibit relatively large effects on drought occurrence, we can also conclude that the rapid warming of the MED in recent decades presents a relatively greater role in determining droughts than that of precipitation variations in several areas, especially in the Balkans and west Turkey.
In this paper, we have detected several drought hotspots that have tended to experience more severe, intense wide and more long-lasting drought events between the subperiods 1975–1996 and 1997–2019 in all subregions. Dynamically, the region lies at a crossroads of large-scale influence from the Atlantic Ocean and the Mediterranean Sea, which results in regionally severe and prolonged droughts. Dynamically, the region is a junction of regional influence from the Mediterranean Sea, Europe, and Asia, as well as large-scale influence from the Atlantic Ocean and the Mediterranean Sea. A complex range of atmospheric circulation patterns influences have been shown to have at least some influence on the droughts of the MED, both nationally and regionwide, particularly the WeMO and the EATL/WRUS.
The WeMO pattern and the EATL/WRUS appear to play the strongest role in forcing wide droughts at the subregional scale, including the two widest and most devastating droughts of the last 40 years (1999–2001 and 2007–2012). A relative role is played by the NAO and ULMO in the NA in these episodes; in addition, a relationship between the SCAND and the area in drought in western and the Adriatic is found. The drought event datasets produced by this study still need to be improved in some respects. The extension of the data to drought events to include more historical data is significant for studies of climatic dynamics and ATPs in order to understand the long-term variation of droughts and develop monitoring and forecasting tools to help to adapt to and mitigate drought. Moreover, including realistic socio-economic data will strongly improve the prospects for drought risk assessment in the future as many combined meteorological, hydrological, and social drivers contribute to drought occurrence. Finally, these constructed exceptional drought events at the national and subregional scales can help researchers and relevant stakeholders in dealing with the multitude of such drought events and assess their risk by investigating the correlation between the severity and duration of each event and documented impacts in different sectors. As an initial possible application, we are planning to exploit this dataset in exploring the synergy and combination between these exceptional droughts and heatwaves across the MED.

Author Contributions

Conceptualization, S.M. and J.A.L.-B.; methodology, S.M.; software, S.M. and D.R.; formal analysis, S.M. and D.R.; data curation, S.M.; writing—original draft preparation, S.M.; writing—review and editing, J.M.-V., J.A.L.-B., D.R. and S.M.; supervision, J.M.-V. and J.A.L.-B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Spanish CLICES project (CGL2017-83866-C3-2-R, AEI/FEDER, UE).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The historical climate data are available at the European Climate Assessment & Dataset (ECA&D; https://www.ecad.eu/; accessed on 21 July 2021) and the Daily Global Historical Climatology Network (GHCN; https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn, accessed on 21 July 2021). The values NOA, EATL/WRUS and SCAND indices were obtained through the NOAA CPC website: https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml, while the following websites have been used to obtain the values of the WeMO and ULMO, respectively: https://crudata.uea.ac.uk/cru/data/moi/ and http://www.ub.edu/gc/data_base/, accessed on 21 July 2021.

Acknowledgments

This study was achieved within the framework of Climatology Group of the University of Barcelona (2017 SGR 1362, Catalan Government) and the Spanish CLICES project (CGL2017-83866-C3-2-R, AEI/FEDER, UE).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Main statistical characteristics of total annual precipitation over the MED during 1975–2019.
Table A1. Main statistical characteristics of total annual precipitation over the MED during 1975–2019.
IDLongLatMeanMedMaxMinSkKuCV%LCLUCL
ST017.4836.50656.4622.41126.661.7−0.22.926.3606.0706.9
ST021.2036.12384.2400.1618.061.7−0.20.030.4350.0418.3
ST033.2536.68604.2606.6884.9269.00.0−0.826.6557.2651.1
ST042.1436.18729.1726.91069.661.7−0.60.629.5666.3791.9
ST05−0.6035.63338.6324.5546.561.7−0.20.328.7310.2366.9
ST0618.0842.651120.31051.31911.6726.10.90.524.21041.21199.4
ST0715.3644.551404.31367.62552.5683.31.15.120.51320.01488.6
ST0816.4543.16736.9711.91257.8383.70.70.824.8683.5790.2
ST0916.2044.031031.81007.41450.2494.30.0−0.320.1971.11092.5
ST1016.9042.76637.7639.1967.9368.00.0−0.423.2594.4681.0
ST1115.2345.261549.51489.72244.11103.20.50.714.71482.81616.1
ST1218.3845.32679.5654.61010.7317.00.20.121.7636.5722.5
ST1314.2745.201574.71565.42105.2991.40.0−0.718.21490.81658.6
ST1416.4343.51803.1767.71203.7486.60.1−0.722.1751.2855.0
ST1516.3846.30849.3858.91310.9481.20.41.418.4803.7895.0
ST1615.1244.07895.9907.11359.7508.80.1−0.220.6841.8949.9
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ST2458.8134.41197.1143.1800.421.11.94.577.3152.6241.6
ST24610.2336.83441.2449.3851.461.70.0−0.137.4393.0489.4
ST24710.1033.88179.0135.6659.431.61.83.969.6142.6215.5
ST2488.8036.48443.3440.4817.661.70.0−0.740.3391.1495.6
ST24910.1035.66283.2278.9587.061.70.50.140.7249.6316.9
ST25011.0836.85485.5468.3847.961.70.0−0.639.2430.0541.1
ST25110.7535.66336.3311.2687.861.70.5−0.547.1290.0382.7
ST2527.8833.8896.581.8330.58.61.72.575.175.4117.7
ST2539.7837.24563.6561.61008.961.7−0.2−0.437.6501.7625.4
ST25410.7234.69199.7190.5499.946.00.91.846.3172.7226.7
ST25511.0933.51236.5211.2572.864.71.10.949.4202.4270.7
ST25635.4137.00684.2645.21078.4311.50.3−0.930.1624.1744.2
ST25730.5338.75422.4437.0680.0270.40.72.019.2398.6446.1
ST25827.8538.91556.6549.1866.0286.40.4−0.222.8519.6593.7
ST25932.0036.551102.51072.01769.0552.80.4−0.528.01012.31192.7
ST26022.4038.90513.6520.4932.9229.70.40.528.2471.2556.0
ST26130.7336.861074.31103.01890.0288.50.20.230.3979.21169.4
ST26227.8537.85609.5634.0948.0134.4−0.40.228.7558.4660.7
ST26327.9139.61566.8552.5911.9323.50.50.321.1531.8601.8
ST26416.8840.43481.8481.0850.1170.00.2−0.532.7435.7527.9
ST26522.0137.06755.1762.01116.0322.1−0.40.421.3708.0802.2
ST26629.0640.18670.2632.01181.1460.01.54.519.1632.8707.6
ST26726.4040.13579.8556.0901.0371.00.6−0.422.7541.4618.2
ST26828.3237.92691.6686.31140.0144.5−0.20.428.2634.7748.4
ST26929.0837.78565.8559.0830.7327.80.30.021.3530.6601.0
ST27040.1837.88487.2475.0768.6258.00.4−0.224.2452.7521.7
ST27126.5641.66593.5566.0963.2388.10.90.522.9553.9633.1
ST27239.2838.60406.4387.0735.4199.10.71.226.8374.7438.2
ST27334.0537.50306.4300.0473.4138.0−0.10.621.8286.8325.9
ST27428.2636.85881.5859.11823.1416.51.23.030.2803.7959.4
ST27526.8839.06626.4608.5926.7372.70.3−0.521.4587.2665.6
ST27630.5639.78388.4380.2630.2196.90.60.724.3360.8416.0
ST27730.1536.30940.0911.01556.0365.00.40.227.3865.01015.1
ST27837.3637.08531.7517.0992.0237.50.50.428.9486.9576.6
ST27936.1636.58741.6753.01010.0492.00.20.016.3706.3776.9
ST28030.5537.75518.9526.0803.0246.9−0.1−0.323.3483.6554.3
ST28128.8140.96662.8615.71214.4351.51.11.131.1602.6723.1
ST28227.0138.51698.4683.01133.1362.00.4−0.327.0643.3753.5
ST28334.5037.60337.9329.2467.0183.00.1−0.521.1317.0358.7
ST28435.4338.81378.1372.0614.0233.60.60.621.2354.8401.5
ST28534.1639.15372.8363.0497.0240.00.0−0.918.5352.7393.0
ST28632.5437.96333.6347.7574.5177.00.30.424.9309.3357.8
ST28729.9639.41552.7538.0851.1328.70.3−0.120.4519.7585.6
ST28838.0838.43345.5345.4676.91.1−0.21.138.2307.0384.1
ST28928.3637.211146.91158.01760.0509.00.00.422.51071.51222.2
ST29034.6837.96343.0341.0475.0205.00.0−0.219.1323.9362.1
ST29133.9336.38560.1561.01007.0299.00.4−0.430.7509.8610.4
ST29229.0840.96697.9676.01047.2432.00.2−0.521.7653.6742.2
ST29327.5540.98590.7580.1879.3248.20.10.422.3552.2629.1
ST29426.7039.30642.3661.1972.1306.00.0−0.424.3596.7687.9
ST29538.7637.13444.1438.1855.0203.00.90.933.0401.2486.9
ST29629.4038.68542.6534.7876.2346.30.61.019.3512.0573.3
ST29736.9337.60743.5718.01416.6443.11.32.525.7687.6799.4
ST29834.8039.81598.3580.0858.0405.50.5−0.218.0566.8629.7
ST29927.4337.03684.8673.31091.4355.40.2−0.928.9626.9742.6
ST30042.0037.93695.2694.01046.4446.00.4−0.321.8650.9739.5
ST30134.5731.67464.7433.1823.9165.70.5−0.235.1417.1512.3
ST30234.5932.27597.4563.21106.9252.10.91.730.4544.4650.5
ST3035.0636.71719.9747.81084.7307.5−0.3−0.425.6665.9773.8
ST3046.9536.88740.1719.11148.9491.60.3−0.421.3694.1786.0
ST30512.2341.80731.9701.31234.9345.50.3−0.430.1667.6796.2
ST30610.6834.71215.4206.2382.094.70.5−0.233.4194.4236.4
ST30727.3038.40709.0706.31088.0361.00.3−0.324.4658.4759.6
ST30834.6036.80631.6590.81023.7280.50.3−0.629.6576.9686.3
ST30934.5338.76422.6417.4589.0287.20.2−0.416.6402.1443.2
ST31038.2837.75648.8602.01171.0350.21.00.629.1593.6704.0
ST31120.8837.75699.6696.01325.00.0−0.61.437.3623.3775.9
ST31214.2837.56604.0563.81631.0269.62.06.140.9531.9676.1
Long: Longitude; Lat: Latitude; Mean: Mean value, Max: Maximum value, Min: Minimum value; SK: Skewness; Ku: Kurtosis; CV%: Coefficient of Variation %; LCL: Lower Control Limit; UCL: Upper Control Limit.
Table A2. Main statistical characteristics of mean annual temperature over the MED during 1975–2019.
Table A2. Main statistical characteristics of mean annual temperature over the MED during 1975–2019.
IDLongLatMeanMedMaxMinSkKuCV%LCLUCL
ST017.4836.5017.717.818.616.6−0.60.32.717.617.8
ST021.2036.1219.219.221.017.80.1−0.34.019.019.4
ST033.2536.6817.517.818.814.5−1.21.94.917.317.8
ST042.1436.1817.017.018.015.5−0.4−0.84.216.817.2
ST05−0.6035.6317.817.918.616.4−0.5−0.83.617.618.0
ST0618.0842.6516.816.618.415.40.3−0.74.416.617.0
ST0715.3644.559.39.211.37.50.1−0.79.99.09.6
ST0816.4543.1616.816.818.315.50.2−0.44.016.617.0
ST0916.2044.0313.313.215.212.00.30.34.913.213.5
ST1016.9042.7616.016.017.514.70.1−0.54.315.816.2
ST1115.2345.2610.510.512.58.7−0.1−0.89.010.310.8
ST1218.3845.3211.511.413.39.70.1−0.57.411.211.7
ST1314.2745.2014.314.316.012.70.1−0.95.814.114.6
ST1416.4343.5116.516.517.915.10.0−0.84.516.316.8
ST1516.3846.3010.810.612.59.0−0.1−0.88.710.511.0
ST1615.1244.0715.415.317.014.10.1−0.74.715.215.6
ST1715.9845.8113.013.115.710.30.1−1.110.612.613.4
ST1814.5944.494.14.15.92.4−0.1−0.619.73.94.4
ST1932.9834.5819.819.721.118.70.20.02.719.619.9
ST2032.9234.9318.218.121.016.51.01.25.217.918.5
ST2133.3634.8819.819.921.418.30.0−0.63.819.620.0
ST2233.0034.7019.719.721.517.70.1−0.44.619.419.9
ST2333.4035.1519.519.221.417.80.4−0.54.419.219.7
ST2432.4834.7119.919.622.917.40.5−0.56.619.520.3
ST2532.4335.0318.718.820.516.6−0.3−0.64.718.418.9
ST2629.9531.2020.720.622.119.50.1−0.63.220.520.9
ST2727.2131.3320.020.021.119.30.4−0.32.519.920.2
ST2832.2931.2621.721.623.420.40.3−0.33.121.521.9
ST298.8041.9115.515.717.113.9−0.2−0.34.615.315.7
ST301.4043.6413.513.514.811.7−0.2−0.75.713.313.7
ST319.4842.5516.116.017.114.8−0.2−0.43.515.916.2
ST321.2045.9011.011.112.99.50.0−0.68.010.811.3
ST33−1.4635.0117.517.618.516.0−0.70.33.117.417.7
ST349.2842.3316.016.016.915.0−0.2−0.12.815.916.2
ST356.4343.5012.612.513.711.20.0−0.95.312.412.8
ST365.5643.0415.515.517.114.00.1−0.65.015.315.8
ST373.1645.7811.711.713.510.00.0−0.87.511.512.0
ST386.5044.5611.111.213.49.40.50.17.710.911.4
ST391.4044.7512.812.714.111.2−0.1−0.85.912.513.0
ST405.3345.3612.412.313.411.30.1−1.05.012.212.5
ST4112.6144.0314.014.015.612.50.0−0.65.113.814.2
ST425.2343.4515.515.616.914.0−0.1−0.84.715.315.8
ST43−0.7044.8313.813.815.112.2−0.2−1.05.913.614.0
ST443.0144.1111.010.912.39.30.0−0.86.910.711.2
ST453.5844.115.35.36.63.8−0.2−0.814.45.15.5
ST46−0.5043.9113.613.514.712.3−0.1−1.05.113.413.8
ST474.7344.5813.913.915.412.3−0.1−0.75.713.614.1
ST483.9643.5815.215.116.513.8−0.2−0.94.714.915.4
ST497.2043.6516.116.117.214.8−0.2−0.83.815.916.2
ST504.4043.8614.914.716.113.70.2−0.94.114.715.1
ST514.8344.1314.214.215.112.9−0.3−0.84.314.014.4
ST522.8942.6915.815.916.914.4−0.2−0.94.115.616.0
ST5314.9137.4117.517.418.816.50.60.23.017.417.7
ST543.6843.4015.115.116.613.6−0.1−0.75.314.915.3
ST551.1043.0012.412.313.411.30.1−1.05.012.212.5
ST565.3543.3115.015.116.413.8−0.3−0.84.614.815.2
ST570.0043.1812.612.513.811.30.1−0.94.912.412.8
ST58−7.6633.5618.018.219.416.90.0−1.13.817.818.2
ST593.3445.7611.711.712.910.1−0.1−0.96.511.411.9
ST607.5147.6010.510.612.39.00.0−0.47.210.310.8
ST612.8742.7715.715.816.714.4−0.3−0.93.815.515.9
ST62−1.2544.6314.114.317.210.2−0.80.412.613.614.6
ST636.6643.2515.715.817.114.40.0−0.74.415.515.9
ST64−0.4143.3813.213.114.811.90.3−0.24.813.013.3
ST6515.2840.0117.017.118.715.60.30.34.016.817.2
ST666.1543.0916.016.016.914.4−0.71.03.115.816.1
ST671.3045.8011.211.012.410.00.2−1.06.111.011.4
ST688.7941.9215.515.616.613.9−0.5−0.34.115.315.7
ST696.6136.2815.415.516.413.3−0.80.34.815.215.6
ST700.3146.5811.911.813.510.40.2−0.66.411.612.1
ST712.3143.2113.814.014.912.3−0.3−0.84.913.614.0
ST72−5.3536.1518.418.419.317.4−0.50.82.218.318.5
ST7320.7738.9217.217.118.616.30.3−0.73.317.117.4
ST7425.9140.8514.814.617.013.40.7−0.36.114.615.1
ST7521.2837.9116.916.818.016.00.1−1.03.316.817.1
ST7623.7038.0018.118.219.615.0−1.12.05.417.818.4
ST7728.0836.4019.319.320.717.4−0.30.93.319.119.5
ST7824.4838.9617.217.118.416.30.4−0.83.517.017.3
ST7923.7037.8918.418.519.814.2−2.58.45.618.118.7
ST8025.1835.3315.215.116.714.30.6−0.34.015.115.4
ST8118.9139.6117.317.118.816.00.3−0.64.117.017.5
ST8222.4139.6111.29.216.37.90.7−1.530.010.212.1
ST8321.7036.8318.018.018.817.30.1−0.62.217.918.1
ST8426.6039.0617.717.519.516.20.4−0.84.817.417.9
ST8525.3837.1018.518.420.517.30.60.23.618.318.7
ST8626.9137.7018.918.820.316.9−0.2−0.44.018.719.1
ST8724.1135.4818.418.419.517.40.2−0.42.718.218.5
ST8822.9640.5115.415.417.014.00.1−1.15.115.215.6
ST8922.4037.5313.713.716.511.90.90.47.713.414.0
ST9034.3931.4920.520.522.319.10.3−0.23.520.220.7
ST9135.5832.6519.219.121.717.70.60.44.718.919.5
ST9234.7831.2320.220.223.418.60.71.14.919.920.5
ST9334.8132.0019.219.121.717.70.60.44.718.919.5
ST9435.0933.0019.319.221.417.90.30.13.819.019.5
ST9534.7031.7020.120.122.118.70.2−0.33.819.920.3
ST9635.2732.8320.120.022.118.70.30.03.619.920.3
ST9735.1531.4717.417.519.515.3−0.3−0.35.417.117.6
ST9835.2131.8618.018.020.416.30.20.74.517.818.2
ST9934.5031.4320.320.322.318.90.3−0.23.820.120.5
ST10034.5331.5620.320.322.518.60.1−1.25.120.020.6
ST10134.8030.6019.219.121.317.90.40.63.619.019.4
ST10234.4632.0420.920.923.118.00.0−0.55.320.621.2
ST10316.1339.3416.015.718.414.20.60.05.815.716.2
ST10416.7841.1316.116.117.515.00.2−0.73.716.016.3
ST1059.7045.6613.213.215.011.50.1−0.76.812.913.4
ST10611.3044.5314.714.817.013.20.0−0.76.214.515.0
ST10717.9540.6517.117.118.115.7−0.3−0.73.816.917.3
ST1089.0539.2517.217.318.415.9−0.4−0.73.817.017.4
ST10914.6541.5612.512.614.210.6−0.50.16.112.312.7
ST1109.7139.9317.317.318.216.0−0.3−0.93.617.117.5
ST1118.1643.9515.615.516.814.1−0.1−0.94.615.415.8
ST11215.0537.4617.417.418.416.2−0.1−0.83.217.317.6
ST11315.1336.6818.718.920.417.20.10.23.818.518.9
ST11411.2043.8015.615.716.814.3−0.4−0.23.715.415.8
ST1158.8544.2516.116.317.414.8−0.3−1.04.415.916.3
ST11611.0642.7515.315.316.913.7−0.1−0.54.815.115.5
ST11715.5538.2018.918.919.717.8−0.80.22.418.719.0
ST1189.1145.2815.514.618.313.10.4−1.510.915.015.9
ST11915.9541.7012.012.113.810.5−0.1−0.16.311.812.3
ST12019.5539.3717.717.818.816.70.0−0.73.117.617.9
ST12113.1038.1818.818.819.617.8−0.4−0.12.118.718.9
ST12211.9636.8118.618.619.917.0−0.2−0.33.818.418.8
ST12314.2042.4315.115.216.113.7−0.4−0.84.414.915.3
ST12410.3843.6815.315.416.913.5−0.2−0.45.115.115.6
ST12512.9540.9116.816.818.215.30.0−0.64.016.617.0
ST12612.5541.7815.715.617.114.2−0.1−0.84.715.515.9
ST12718.3539.8117.117.118.315.8−0.10.13.016.917.2
ST12814.3741.0917.117.218.215.8−0.4−0.23.417.017.3
ST12912.5037.9117.918.018.716.5−0.80.92.617.818.1
ST13025.2539.9315.915.817.314.80.3−0.84.215.716.1
ST13113.7545.6515.115.216.413.4−0.2−0.74.814.915.3
ST13212.3345.5013.613.615.312.10.0−0.65.913.313.8
ST13310.8645.3813.513.415.311.70.1−0.96.813.313.8
ST13414.3040.8516.516.617.914.4−0.4−0.45.316.216.8
ST135−8.6342.2314.114.015.313.00.3−0.94.613.914.3
ST136−0.8037.7816.416.518.114.5−0.1−0.75.816.216.7
ST137−3.7840.3815.015.016.613.5−0.1−0.55.214.715.2
ST138−3.4640.4814.614.615.713.2−0.2−0.74.714.414.8
ST13935.9831.9817.717.720.116.00.30.64.517.518.0
ST14035.8532.5420.019.822.118.40.30.03.819.820.2
ST14138.2032.5619.419.422.016.4−0.22.04.919.119.6
ST14235.4833.8120.620.822.618.7−0.2−0.94.820.320.9
ST14335.8034.4518.518.621.216.50.3−0.66.518.218.9
ST14420.2632.1020.220.221.119.30.0−0.12.220.020.3
ST14515.0532.4120.620.621.819.4−0.1−0.82.920.420.7
ST14610.9831.8620.820.822.019.3−0.3−0.33.120.621.0
ST1478.7536.9518.318.520.016.7−0.1−0.84.818.018.6
ST14816.5831.2021.021.023.319.40.51.13.520.821.3
ST14913.1532.6620.620.621.919.30.0−0.83.420.420.8
ST15014.4835.8518.818.819.717.5−0.5−0.42.918.618.9
ST151−8.0331.6120.020.021.518.1−0.4−0.13.619.820.2
ST152−7.5833.3617.617.718.716.3−0.3−0.83.517.417.7
ST153−1.9334.7817.017.018.615.40.0−0.85.016.717.2
ST154−6.7734.0417.417.418.216.70.2−0.82.517.217.5
ST155−5.9035.7317.917.919.016.9−0.1−0.62.917.818.1
ST156−6.7341.8012.812.814.311.40.0−0.54.912.613.0
ST157−7.9637.0117.817.918.915.8−0.61.23.617.618.0
ST158−9.0938.4317.517.518.516.40.0−1.03.217.317.6
ST15913.5645.5113.613.714.911.2−0.81.85.113.413.8
ST16013.7146.507.47.59.15.5−0.1−1.013.57.27.7
ST161−2.5036.8019.019.120.017.7−0.60.12.818.919.2
ST162−1.8538.9514.314.415.612.7−0.3−0.55.314.114.5
ST163−0.5038.3618.318.419.217.1−0.3−0.93.218.118.4
ST164−2.3836.8519.019.120.017.9−0.3−0.42.718.919.2
ST1652.3841.5714.614.715.913.2−0.1−0.34.214.414.7
ST166−0.5538.2818.218.319.317.0−0.40.02.918.118.4
ST167−4.5140.6511.111.212.99.20.0−0.88.310.811.3
ST168−6.8138.8817.117.018.415.90.0−0.53.716.917.3
ST1692.0741.2515.815.916.714.4−0.4−0.84.015.616.0
ST1702.0641.2816.216.417.814.8−0.1−1.45.915.916.5
ST17125.9631.7117.016.918.915.50.40.24.316.717.2
ST172−6.0343.5510.710.812.19.2−0.2−0.96.910.510.9
ST173−6.3339.4616.416.317.715.00.00.23.516.216.6
ST174−0.0639.9517.617.818.815.9−0.4−0.94.617.317.8
ST175−3.9138.9815.515.717.013.0−0.80.36.315.215.8
ST176−4.8337.8518.218.219.516.9−0.1−0.73.618.018.3
ST177−2.1340.0613.213.215.011.70.2−0.76.613.013.5
ST178−1.4141.1113.013.114.211.6−0.3−0.75.312.813.2
ST1792.5041.5815.616.017.413.2−0.4−0.97.215.315.9
ST1802.7641.9014.814.816.213.2−0.1−0.85.114.615.0
ST181−3.7837.1815.515.417.514.20.5−0.35.015.315.7
ST18235.7830.1617.817.820.115.90.10.84.717.518.0
ST183−6.9137.2818.318.319.217.1−0.30.42.318.218.4
ST1841.3838.8818.218.219.117.1−0.3−0.62.718.018.3
ST185−0.4039.5015.615.616.914.2−0.2−0.54.115.415.8
ST186−6.0636.7518.118.219.316.9−0.2−0.12.918.018.3
ST187−0.3342.0814.114.215.612.90.0−0.75.013.914.3
ST188−5.6542.5811.111.112.210.00.1−0.24.411.011.3
ST1890.6241.6215.115.116.313.90.0−0.84.115.015.3
ST190−2.3342.4514.014.014.912.8−0.1−1.14.413.814.1
ST191−3.5540.4514.614.516.012.90.2−0.45.014.414.8
ST192−3.7140.3014.414.316.212.70.3−0.25.614.214.6
ST193−3.4040.2415.115.116.713.70.1−0.44.914.915.3
ST194−4.4836.6618.618.619.917.3−0.1−1.03.618.418.8
ST195−2.9535.2819.019.019.917.9−0.1−0.92.718.819.1
ST1964.2339.8617.217.318.215.7−0.5−0.73.817.017.4
ST197−1.8840.8510.610.611.79.3−0.1−0.96.210.410.8
ST198−5.6137.1517.918.019.316.4−0.3−0.64.117.718.1
ST199−1.2337.9518.318.319.417.1−0.1−0.43.318.118.5
ST200−4.0140.787.07.08.75.40.0−0.211.36.87.2
ST2012.7339.5516.616.717.915.1−0.4−0.84.516.416.8
ST202−1.3842.4913.013.014.211.70.1−0.95.212.813.2
ST203−6.5842.5613.113.014.512.20.2−1.04.812.913.3
ST2041.1641.1516.216.417.814.5−0.1−0.64.516.016.4
ST205−6.3536.5618.118.219.316.9−0.3−0.23.018.018.3
ST206−5.5040.9512.112.113.310.8−0.1−0.75.211.912.3
ST207−1.3942.4612.912.914.211.4−0.1−0.85.612.713.1
ST208−5.3940.5712.712.716.510.70.70.89.212.413.1
ST209−4.1140.9512.312.313.711.00.0−0.85.812.112.5
ST210−3.6342.3519.119.420.417.8−0.2−1.13.918.919.4
ST211−2.4641.7611.111.112.49.90.0−1.05.910.911.3
ST21213.5045.8313.513.415.012.00.2−0.55.513.313.7
ST213−5.9037.4115.915.817.414.50.1−0.64.715.716.1
ST2140.5040.8117.817.818.916.6−0.1−0.93.517.618.0
ST215−0.4639.5017.617.618.916.3−0.2−0.93.717.417.8
ST216−4.7641.6512.712.714.39.9−0.71.86.412.412.9
ST217−0.4539.5818.318.419.517.0−0.3−0.93.518.118.5
ST21835.0032.8019.819.921.718.30.2−0.94.719.520.1
ST219−5.7341.5113.213.114.712.00.1−0.95.313.013.4
ST220−1.0141.6615.615.517.014.20.0−0.84.715.415.8
ST22140.9134.4120.720.722.219.1−0.2−0.94.120.521.0
ST22237.2136.1817.817.919.516.3−0.10.33.617.718.0
ST22337.4735.2118.218.319.516.5−0.3−0.14.018.018.4
ST22436.5133.4117.217.419.315.2−0.1−0.45.316.917.4
ST22536.1032.6017.717.719.916.30.41.33.917.517.9
ST22640.1535.3120.420.422.318.60.00.63.620.220.6
ST22736.6135.9317.717.919.216.1−0.40.03.817.517.9
ST22836.7535.1118.418.521.016.00.1−0.56.018.018.7
ST22940.7536.5019.018.822.516.60.6−0.37.318.619.4
ST23035.9335.4019.219.220.617.7−0.10.52.819.119.4
ST23136.2532.8517.317.519.815.60.2−0.25.217.117.6
ST23238.0036.8117.817.919.815.9−0.20.04.417.618.1
ST23341.2137.0519.519.421.617.30.3−0.35.219.219.8
ST23436.4733.0516.316.319.114.50.4−0.16.516.016.6
ST23535.7635.5319.819.721.518.10.30.23.519.620.0
ST23637.2036.3117.117.119.615.00.3−0.25.816.817.4
ST23736.7134.0313.713.816.112.20.40.56.213.514.0
ST23838.3034.5519.419.321.217.80.20.23.719.119.6
ST23939.0135.9319.019.021.217.00.1−0.44.918.719.3
ST24036.1334.8118.718.720.217.10.00.33.318.518.9
ST24137.0335.0017.717.420.815.50.6−0.37.117.318.1
ST24236.5832.7016.216.117.614.7−0.30.04.216.016.4
ST24335.8834.8819.919.822.118.40.60.33.919.720.2
ST24438.9536.7017.517.620.915.40.71.36.017.217.9
ST2458.8134.4119.919.921.817.80.00.34.419.720.2
ST24610.2336.8319.019.120.117.2−0.6−0.74.118.819.2
ST24710.1033.8820.320.221.518.70.1−0.23.320.120.5
ST2488.8036.4818.618.721.116.40.00.25.318.318.9
ST24910.1035.6620.220.221.818.1−0.2−1.14.720.020.5
ST25011.0836.8518.818.919.717.1−0.90.03.618.619.0
ST25110.7535.6619.619.620.818.0−0.3−0.73.419.419.8
ST2527.8833.8822.222.323.420.7−0.2−0.22.822.022.4
ST2539.7837.2418.318.419.716.80.1−0.43.918.118.5
ST25410.7234.6919.619.720.718.0−0.5−0.63.319.419.8
ST25511.0933.5120.720.821.819.1−0.3−1.03.620.420.9
ST25635.4137.0018.818.820.317.3−0.1−0.33.518.619.0
ST25730.5338.7511.511.413.99.40.30.67.411.211.7
ST25827.8538.9116.516.319.414.50.80.76.016.216.8
ST25932.0036.5520.019.522.818.30.6−0.96.419.620.3
ST26022.4038.9016.616.718.415.40.4−0.24.216.416.8
ST26130.7336.8618.618.720.317.30.1−1.14.318.418.9
ST26227.8537.8517.918.019.216.40.0−0.94.117.718.1
ST26327.9139.6114.614.715.813.2−0.4−0.34.314.414.8
ST26416.8840.4316.316.319.313.0−0.50.88.015.916.7
ST26522.0137.0617.617.718.816.60.1−0.82.917.517.8
ST26629.0640.1815.115.017.413.60.50.25.514.915.4
ST26726.4040.1315.315.117.014.00.4−0.84.915.015.5
ST26828.3237.9217.617.618.616.60.0−0.42.817.517.8
ST26929.0837.7816.916.918.615.1−0.1−0.85.116.617.1
ST27040.1837.8815.815.917.713.2−0.52.25.015.616.1
ST27126.5641.6614.013.816.812.70.80.86.313.714.2
ST27239.2838.6013.113.315.710.2−0.21.08.212.813.4
ST27334.0537.5012.612.016.79.30.70.013.212.113.0
ST27428.2636.8519.419.321.917.60.61.24.319.219.7
ST27526.8839.0617.116.819.215.61.00.35.616.817.3
ST27630.5639.7810.910.912.79.10.2−0.57.610.711.2
ST27730.1536.3019.719.323.317.91.00.17.119.320.1
ST27837.3637.0815.815.917.414.1−0.3−0.45.115.616.0
ST27936.1636.5820.420.322.118.80.30.13.620.220.6
ST28030.5537.7512.312.313.910.6−0.10.16.212.012.5
ST28128.8140.9615.415.316.814.10.2−1.04.815.215.6
ST28227.0138.5118.018.019.615.5−0.51.04.617.818.2
ST28334.5037.6011.011.014.18.90.2−0.611.410.711.4
ST28435.4338.8110.510.512.97.70.00.79.910.210.8
ST28534.1639.1511.411.413.97.7−0.62.29.811.111.8
ST28632.5437.9611.711.814.29.50.30.87.711.512.0
ST28729.9639.4111.711.215.49.30.90.112.011.312.1
ST28838.0838.4313.613.615.511.5−0.31.15.513.413.9
ST28928.3637.2115.215.216.313.9−0.1−0.53.915.015.3
ST29034.6837.9611.511.513.98.7−0.11.38.111.311.8
ST29133.9336.3820.219.823.318.10.7−0.56.519.820.6
ST29229.0840.9614.814.616.813.40.6−0.76.414.615.1
ST29327.5540.9814.114.015.612.90.1−1.05.213.914.4
ST29426.7039.3017.116.919.215.70.4−0.95.516.917.4
ST29538.7637.1318.718.620.516.7−0.20.14.318.418.9
ST29629.4038.6812.612.514.210.80.2−0.15.812.312.8
ST29736.9337.6017.017.018.915.20.0−0.44.816.817.2
ST29834.8039.819.79.711.87.80.1−0.38.99.49.9
ST29927.4337.0318.618.820.115.8−0.81.34.518.318.8
ST30042.0037.9318.618.820.115.8−0.81.34.518.318.8
ST30134.5731.6720.120.122.118.70.2−0.23.719.920.3
ST30234.5932.2719.919.722.018.60.4−0.13.819.720.1
ST3035.0636.7117.717.818.616.3−0.7−0.43.417.517.9
ST3046.9536.8818.418.619.616.8−0.6−0.53.918.218.7
ST30512.2341.8015.915.916.815.00.1−0.63.015.716.0
ST30610.6834.7119.519.720.617.9−0.6−0.53.619.319.7
ST30727.3038.4017.918.019.614.9−0.91.75.417.618.2
ST30834.6036.8019.619.821.518.00.0−1.04.719.319.9
ST30934.5338.7610.910.814.09.10.81.78.510.711.2
ST31038.2837.7517.117.119.314.3−0.20.85.716.817.4
ST31120.8837.7518.018.119.512.3−2.38.86.917.618.3
ST31214.2837.5614.714.521.611.62.36.214.014.115.3
Long: Longitude; Lat: Latitude; Mean: Mean value, Max: Maximum value, Min: Minimum value; SK: Skewness; Ku: Kurtosis; CV%: Coefficient of Variation %; LCL: Lower Control Limit; UCL: Upper Control Limit.

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Figure 1. Geographical location map of meteorological stations with continuous monthly precipitation and temperature records in the Mediterranean Basin (MED) over the 1975–2019 period.
Figure 1. Geographical location map of meteorological stations with continuous monthly precipitation and temperature records in the Mediterranean Basin (MED) over the 1975–2019 period.
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Figure 2. Spatial patterns of the rotated loadings of the five principal components (PCs) based on the principal component analysis (PCA) on the DXI-12.
Figure 2. Spatial patterns of the rotated loadings of the five principal components (PCs) based on the principal component analysis (PCA) on the DXI-12.
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Figure 3. Subregional drought events in 1975–2019 identified by the DXI-12.
Figure 3. Subregional drought events in 1975–2019 identified by the DXI-12.
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Figure 4. Temporal variations of the degree of dryness (DDIs) over all subregions during 1975–2019.
Figure 4. Temporal variations of the degree of dryness (DDIs) over all subregions during 1975–2019.
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Figure 5. Difference of average severity of drought events (DRS) and average intensity drought events (DRI) between 1975–1996 and 1997–2019 according to the DXI-12.
Figure 5. Difference of average severity of drought events (DRS) and average intensity drought events (DRI) between 1975–1996 and 1997–2019 according to the DXI-12.
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Figure 6. The expressed difference (per decade) of extreme, severe and moderate drought frequency (DRF) between 1975–1996 and 1997–2019 based on DXI-12.
Figure 6. The expressed difference (per decade) of extreme, severe and moderate drought frequency (DRF) between 1975–1996 and 1997–2019 based on DXI-12.
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Figure 7. Drought frequency identified by SPI-12 (upper map), SPEI-12 (middle map) and RDI-12 (lower map) between 1975–1996 and 1997–2019.
Figure 7. Drought frequency identified by SPI-12 (upper map), SPEI-12 (middle map) and RDI-12 (lower map) between 1975–1996 and 1997–2019.
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Figure 8. Drought tendencies from 1975–1996 to 1997–2019 according to the SPI-12 (a), SPEI-12, RDI-12 and DXI-12 (b) at a 12-month timescale and corresponding increases or decreases in precipitation amounts (PP) and evapotranspiration (PET).
Figure 8. Drought tendencies from 1975–1996 to 1997–2019 according to the SPI-12 (a), SPEI-12, RDI-12 and DXI-12 (b) at a 12-month timescale and corresponding increases or decreases in precipitation amounts (PP) and evapotranspiration (PET).
Atmosphere 12 00941 g008aAtmosphere 12 00941 g008b
Figure 9. Values of the matrix correlation of drought severity and intensity identified by SPI-12, SPEI-12, RDI-12 and DXI-12, and the ATPs over the MED. (Values greater than or equal to the absolute value of 0.3 are significant.).
Figure 9. Values of the matrix correlation of drought severity and intensity identified by SPI-12, SPEI-12, RDI-12 and DXI-12, and the ATPs over the MED. (Values greater than or equal to the absolute value of 0.3 are significant.).
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Table 1. Parameters used for a drought event evaluation as listed in the database.
Table 1. Parameters used for a drought event evaluation as listed in the database.
Start event, month (ST)The first month with an index value below a certain threshold
End event, month (EN)The last month with an index value below a certain threshold
Drought duration (DD)Number of consecutive months between drought start and drought end (length of drought event)
Drought severity (DRS)Sum of the absolute values of drought indices during a drought event
Drought intensity (DRI)The mean value of drought indices during a drought event (DRS/DD)
Drought frequency (DRF)Number of drought events during specific interval, usually expressed as number of events every decade.
Areal Extent (AE)Percentage of the region with indicator values below a certain threshold
Peak Area (PA)The largest area exposed to drought during the event (%). Usually the PA is presented with its corresponding date in month and year (AMY)
Peak month and year (PM)The month with the lowest value of the index over the drought event. This parameter is usually presented with severity value of this month.
Table 2. Drought parameters classes allocated to each drought event and their competent grades.
Table 2. Drought parameters classes allocated to each drought event and their competent grades.
GradeDP Status
6DP > 95th percentile over the recorded events at the subregional and regional scale
590th percentile < DP ≤ 95th percentile over the recorded events at the subregional and regional scale
470th percentile < DP ≤ 90th percentile over the recorded events at the subregional and regional scale
350th percentile < DP ≤ 70th percentile over the recorded events at the subregional and regional scale
230th percentile < DP ≤ 50th percentile over the recorded events at the subregional and regional scale
1DP < 30th percentile over the recorded events at the subregional and regional scale
Table 3. The five subregions identified by five PCs for DXI-12 over the MED.
Table 3. The five subregions identified by five PCs for DXI-12 over the MED.
PCsCodeRegionsCountry
PC1IBE–NWAIberian Peninsula–Northwest AfricaSpain–Portugal–South France–Morocco–Gibraltar
PC2EMMEEastern and Middle EastSyria–Lebanon–Israel–Palestine–Jordan–Cyprus–North Egypt–North Libya–South and South-East Turkey
PC3AEGAegean SeaGreece and West Turkey
PC4BLK–FRBalkans–FranceFrance–Croatia–Slovenia–Central and North Italy
PC5NANorth Africa and south ItalyAlgeria–Tunisia–Malta–Tyrrhenian Sea
Table 4. List of the widest drought events detected by the DXI-12 and their corresponding areal extent, the area involved by the peak month, and the Degree of Dryness (DII) of widest area at the subregional scale during 1975–2019.
Table 4. List of the widest drought events detected by the DXI-12 and their corresponding areal extent, the area involved by the peak month, and the Degree of Dryness (DII) of widest area at the subregional scale during 1975–2019.
RegionPeriodDRSDRIPeak EventArea Involved with PM %DDIAE %Widest Area
PM (Mon/Year)DRSPA%Mon/Year
IBE–NWA1981–198320.11.411/19812.457.33.621.157.111/1981
1989–199220.31.404/19922.850.04.820.350.004/1992
1994–199528.72.212/19953.152.111.336.278.110/1995
1999–200027.52.306/19993.250.17.151.058.405/1999
2004–200525.42.101/20053.330.111.952.774.208/2005
201217.61.503/20122.857.08.546.670.007/2012
2014–201620.21.704/20142.833.84.632.058.401/1016
2017–201835.32.011/20173.741.06.233.574.312/2017
201917.01.507/20192.945.55.945.057.111/2019
EMME1999–200130.52.507/19992.870.014.351.080.312/1999
2007–201240.62.205/20083.361.39.833.280.001/2009
2014–201853.62.201/20173.360.010.535.167.912/2017
AGE197714.41.308/19772.322.42.950.051.011/1977
1989–199131.92.301/19903.359.211.352.477.611/1990
1992–199333.01.508/19922.536.76.330.051.012/1992
1999–200137.02.110/20012.965.39.050.065.303/2001
2006–200944.02.106/20083.242.913.051.078.010/2007
2013–201440.11.702/20143.350.04.120.051.002/2014
2015–201843.11.709/20173.830.06.835.055.010/2016
BLK–FR1989–199139.21.810/19893.352.29.448.666.709/1990
2001–200235.41.512/20012.830.07.950.071.604/2002
200315.01.411/20032.446.33.931.050.012/2003
Winter (2003–2004)5.71.601/20042.549.34.043.850.702/2004
2006–200845.41.612/20062.938.87.226.053.710/2007
2011–201233.32.411/20113.139.38.438.671.503/2012
2016–201840.61.510/20173.262.79.237.865.711/2017
201920.01.506/20192.737.34.930.050.103/2019
NA1980–198250.12.108/19812.935.94.930.050.212/1981
198815.01.405/19882.153.85.837.853.805/1988
1999–200251.22.210/19993.464.18.536.064.102/2002
2004–200544.02.112/20043.843.64.928.750.012/2004
201617.11.401/20162.230.82.838.051.311/2016
2017–201826.01.501/20183.961.56.630.361.501/2018
Bold refers to the highest values at the subregional scale.
Table 5. The top drought events (most severe, most intense, longest, widest dry events) and the highest degree of dryness in each subregion according to DXI-12 during 1975–2019.
Table 5. The top drought events (most severe, most intense, longest, widest dry events) and the highest degree of dryness in each subregion according to DXI-12 during 1975–2019.
SubregionMost SevereMost IntenseLongest EventWidest AreaHighest Degree of Dryness
PeriodDRSPeriodDRIPeriodDDAE%PeriodDDIYear
IBE–NWA1989–199057.519992.71989–19904171.32017–201811.92005
EMME2008–201295.42005–20072.82008–20124775.62008–200914.31999
AEG2015–201877.22016–20182.72015–20183667.0200713.02007
BLK–FR2015–201872.72004–20062.72015–20183767.2200210.41990
NA1999–200484.62006–20072.71999–20045162.820028.51999
Bold refers to the highest values at the subregional scale.
Table 6. List of subregional exceptional drought events across the MED detected by DXI-12 at the country level that were identified according the new grade classification, 1975–2019.
Table 6. List of subregional exceptional drought events across the MED detected by DXI-12 at the country level that were identified according the new grade classification, 1975–2019.
CountrySTENDRSDRIAE %Widest AreaGradeMentioned by
PA%AMY
Spain06/199412/199530.22.150.183.310/199515[118]
12/200405/200628.72.750.078.308/200515[62]
04/201708/201835.31.934.171.701/201814[102,119]
Portugal12/200401/200623.72.093.0100.009/200518[107]
Morocco10/200412/200530.32.369.983.310/200516[120]
01/199511/199528.22.348.550.010/199514[105,121]
12/199811/200025.42.055.083.309/199915[121]
Cyprus02/200801/200933.32.257.185.708/200815[122]
12/201312/201420.21.783.5100.012/201315[123]
01/201711/201822.41.957.185.711/201814[124]
Israel11/199812/199929.72.295.1100.03–12/199918[100]
Jordan01/199912/199922.21.988.9100.011/199914[99]
Syria10/200710/201145.01.970.087.502/200916[38,125]
11/199811/200025.62.170.091.712/199914[99,100]
01/201403/201537.02.331.745.804/201414[120]
Turkey01/200701/200943.32.057.791.301/200815[126]
02/201601/201840.82.036.565.210/201614[126]
04/198904/199034.91.939.467.412/198914[127]
05/199911/200137.02.142.669.610/200114[126,127]
Greece02/198901/199142.02.851.177.311/199015[28]
09/199906/200241.11.839.468.207/200014[128]
12/200601/200937.11.834.359.109/200714[28]
Croatia09/201110/201223.01.971.484.612/201116[129]
01/198911/199037.11.670.097.009/199015[111]
08/200304/200415.61.874.0100.012/200314[130]
France09/200109/200223.11.840.065.004/200214[111]
03/198911/199037.02.042.886.510/198914[131]
12/201504/201848.11.841.389.211/201715[101]
Italy07/198812/199144.71.520.040.003/198914[132]
10/200109/200220.01.748.470.304/200214[115]
11/201511/201625.61.935.348.601/201614[101]
02/201705/201825.01.843.865.001/201814[101]
Algeria04/201703/201823.51.950.466.708/201715[133]
08/199911/200267.01.837.589.902/200214[134]
Tunisia04/199907/200028.71.758.292.310/199915[105,114]
02/197903/198250.11.742.769.208/198114[120]
Table 7. A list of exceptional subregional drought events across the MED detected by DXI-12 that were identified according to the new grade classification during 1975–2019.
Table 7. A list of exceptional subregional drought events across the MED detected by DXI-12 that were identified according to the new grade classification during 1975–2019.
RSTENDRSDRIAE %PA %AMYScoreMentioned by
IBE–NWA09/199412/199528.72.236.278.110/199514[87]
01/199903/200027.52.345.058.405/199915[87,100]
11/200412/200525.42..152.774.208/200515[101,111]
01/201212/201220.21.546.468.907/201213[111]
03/201411/201630.61.625.858.401/201613[135]
04/201709/201835.32.033.574.112/201714[136,137]
EMME11/199802/200130.52.351.080.012/199915[100,138]
01/200712/201240.62.233.280.001/200914[38,126]
12/201401/201953.62.135.167.912/201714[120]
AEG11/198901/199131.92.352.477.611/199016[28,57]
11/199904/200137.02.141.765.303/200115[87]
12/200601/200944.02.151.078.010/200716[87,139]
12/201501/201843.11.735.055.010/201614[126,136]
BLK–FR12/198810/199139.21.848.665.709/199014[28]
12/200109/200241.12.650.071.604/200215[28,140]
07/200305/200411.71.937.050.702/200413[137]
01/200512/200520.11.930.038.808/200513[140]
05/200610/200842.71.530.153.704/200713[87]
07/201110/201233.32.538.671.603/201215[129]
11/201504/201838.11.537.865.711/201714[101,135]
NA02/198003/198250.12.127.750.212/198114[135,141]
01/199912/200222.31.536.064.102/200214[45]
02/200410/20054422.128.750.012/200414[120,142]
01/201207/201827.61.923.661.501/201813[133]
Table 8. Subregional drought parameters for two subperiods according to the DXI-12.
Table 8. Subregional drought parameters for two subperiods according to the DXI-12.
SubregionAverage Number of Events (N)DDDRSDRIAE (%)DDI (%)
1975
1996
1997
2019
1975
1996
1997
2019
1975
1996
1997
2019
1975
1996
1997
2019
1975
1996
1997
2019
1975
1996
1997
2019
IBE–NWA5146.16.59.19.81.31.412.418.62.53.6
EMME9114.410.46.016.81.11.55.523.81.14.4
AEG586.88.310.113.11.41.412.118.22.33.5
BLK–FR586.27.39.511.11.31.410.520.22.23.7
NA696.38.08.912.61.31.410.118.31.93.6
Bold refers to the highest value between the two periods.
Table 9. Correlation coefficients between the percent areas in drought identified by SPI-12, SPEI-12 and RDI-12 and ATPs (only coefficients significant at 95% confidence level are shown).
Table 9. Correlation coefficients between the percent areas in drought identified by SPI-12, SPEI-12 and RDI-12 and ATPs (only coefficients significant at 95% confidence level are shown).
SubregionNAOULMOWeMOEATL/WRUSSCAND
SPI-12SPEI-12RDI-12DXI-12SPI-12SPEI-12RDI-12DXI-12SPI-12SPEI-12RDI-12DXI-12SPI-12SPEI-12RDI-12DXI-12SPI-12SPEI-12RDI-12DXI-12
IBE–NWA −0.5−0.3−0.5
EMME −0.3−0.4−0.4−0.5−0.3−0.5−0.4−0.5
AEG −0.3−0.3−0.4
BLK–FR 0.30.30.30.4−0.5−0.5−0.4−0.5 −0.3−0.4−0.3−0.4
NA0.3 0.3 −0.3−0.4−0.5
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