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Article

The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors

by
Fernando Maliti Chivangulula
1,2,
Malik Amraoui
1 and
Mário Gonzalez Pereira
1,3,*
1
Centre for Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Inov4Agro, University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal
2
Instituto Politécnico da Huíla (IPH), Universidade Mandume Ya Ndemufayo (UMN), Estrada Principal da Arimba, Lubango 3FJP+27X, Angola
3
Instituto Dom Luiz (IDL), FCUL, Campo Grande Edifício C1, Piso 1, 1749-016 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Climate 2024, 12(12), 221; https://doi.org/10.3390/cli12120221
Submission received: 22 October 2024 / Revised: 6 December 2024 / Accepted: 10 December 2024 / Published: 13 December 2024
Browse Figures
Figure 1
<p>Political map, ecoregions (<b>a</b>), and Köppen–Geiger climate type (<b>b</b>) in Southern Africa. Adapted from Olson et al. [<a href="#B28-climate-12-00221" class="html-bibr">28</a>] and Kottek et al. [<a href="#B24-climate-12-00221" class="html-bibr">24</a>], with the following: equatorial monsoon (Am); equatorial savannah with dry summer (As); equatorial savannah with dry winter (Aw), arid, steppe with hot arid (BSh); arid, steppe with cold arid (BSk); arid, desert with hot arid (BWh); arid, desert with cold arid (BWk); warm temperate, fully humid with hot summer (Cfa); warm temperate, fully humid with warm summer (Cfb); warm temperate, dry summer with hot summer (Csa); warm temperate, dry summer with warm summer (Csb); warm temperate, dry winter with hot summer (Cwa); and warm temperate, dry winter with warm summer (Cwb).</p> "> Figure 2
<p>Sum of the Drought Number (Sum DN, from (<b>a</b>–<b>d</b>)), Sum of the Drought Duration (Sum DD, panels (<b>e</b>–<b>h</b>)), Drought Severity (Sum DS, panels (<b>i</b>–<b>l</b>)) and Drought Intensity (Sum DI, panels (<b>m</b>–<b>p</b>)), and assessed based on the SPI for the 3-, 6-, 9- and 12-month timescales (from left to right), during the 1971–2020 period.</p> "> Figure 3
<p>Sum of the Drought Number (Sum DN) assessed with the SPI, at the 3-, 6-, 9- and 12-month timescales (panels left to right), during the 1971–2020 period for each Drought Class (DC), namely, abnormally dry (DC 1, panels (<b>a</b>–<b>d</b>)), mild drought (DC 2, panels (<b>e</b>–<b>h</b>)), moderate drought (DC 3, panels (<b>i</b>–<b>l</b>)), severe drought (DC 4, panels (<b>m</b>–<b>p</b>)) and extreme drought (DC 5, panels (<b>q</b>–<b>t</b>)).</p> "> Figure 4
<p>Interannual distribution of the Sum of Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with the SPI at timescales of 3, 6, 9 and 12 months (panels (<b>a</b>–<b>d</b>)), for the 1971–2020 period.</p> "> Figure 5
<p>Spatial distribution of the annual Sum of Drought Months (SDM) for 2018 (panels (<b>a</b>–<b>d</b>) and 2019 (panels (<b>e</b>–<b>h</b>)) and the Mean Drought Severity (MDS) also for 2018 (panels (<b>i</b>–<b>l</b>)) and 2019 (panels <b>m</b>–<b>p</b>)), computed with the SPI at timescales of 3, 6, 9 and 12 months (from left to right).</p> "> Figure 6
<p>Anomalies of the NDVI (panels (<b>a</b>–<b>d</b>)), the EVI (panels (<b>e</b>–<b>h</b>)) and the VCI (panels (<b>i</b>–<b>l</b>)) in Southern Africa during the rainy season, from the December 2018 to February 2019 period.</p> "> Figure 7
<p>Drought severity (DS) for November (panels (<b>a</b>–<b>d</b>)) and December (panels (<b>e</b>–<b>h</b>)) 2018, January (panels (<b>i</b>–<b>l</b>)) and February (panels (<b>m</b>–<b>p</b>)) 2019, computed with the SPI, for the timescales of 3, 6, 9 and 12 months (from left to right).</p> "> Figure 8
<p>The difference between the annual MDE evaluated with the SPEI (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>D</mi> <mi>E</mi> </mrow> <mrow> <mi>S</mi> <mi>P</mi> <mi>E</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>) and the SPI (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>D</mi> <mi>E</mi> </mrow> <mrow> <mi>S</mi> <mi>P</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>) at timescales of 3, 6, 9 and 12 months, in SA for the 1971–2020 period.</p> "> Figure 9
<p>Work and results flow diagram of this study.</p> ">
Versions Notes

Abstract

:
Drought consequences depend on its type and class and on the preparedness and resistance of communities, which, in turn, depends on the knowledge and capacity to manage this climate disturbance. Therefore, this study aims to assess the drought regime in Southern Africa based on vegetation and meteorological indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971–2020 period. The results revealed the following: (i) droughts of various classes at different timescales occurred throughout the study period and region; (ii) a greater Sum of Drought Intensity and Number, in all classes, but lower duration and severity of droughts with the SPI than with the SPEI; (iii) drought frequency varies from 1.3 droughts/decade to 4.5 droughts/decade, for the SPI at 12- to 3-month timescales; (iv) the number, duration, severity and intensity of drought present high spatial variability, which tends to decrease with the increasing timescale; (v) the area affected by drought increased, on average, 6.6%/decade with the SPI and 9.1%/decade with the SPEI; and (vi) a high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers and managers in defining legislation and strategies to manage drought and water resources.

1. Introduction

The World Meteorological Organization (WMO) conceptually defines drought as a prolonged dry period [1]. Some researchers refined this definition as a prolonged period of precipitation deficit in terms of the normal climate [2,3] or a recurrent extreme climate event [4,5]. There are also operational definitions based on indicators or indices computed with climate variables or parameters. These indices allow evaluation of the drought regime at different temporal scales and estimate the probability of the occurrence of drought and potential impacts for different severities, intensities, durations or spatial characteristics [6].
The impacts associated with drought can be enormous, especially in Africa. The WMO recently assessed the mortality and economic losses from weather, climate and water extremes for the 1970–2019 [7]. This study revealed that, globally, droughts accounted for 6% of the total number of natural disasters and were responsible for 7% of the total economic damages and 34% of the deaths caused by natural disasters. However, these indicators are much more impressive for the African continent where droughts represent 16% of the total number of disasters and were responsible for 26% of the economic losses and 95% of the total number of deaths from disasters [7].
Drought impacts concern climatologists, hydrologists, ecologists, agroforestry producers, managers and political decision-makers [8]. To face this problem, it is necessary to know the drought regime in each region and detail. The literature review reveals the existence of several comprehensive assessments of the drought regime around the world, i.e., using various meteorological and vegetation drought indices and different drought descriptors (e.g., number/frequency, severity, intensity, duration, start and end dates of drought episodes) to assess the drought regime. For example, Parente et al. [9] used the Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI) and Vegetation Condition Index (VCI) to comprehensively characterise drought in Portugal. Oikonomou et al. [10] evaluated the drought characteristics in Europe over the last 50 years using the SPI and the SPEI at various timescales and different drought descriptors. Fung et al. [11] also used the SPI and the SPEI at multiple scales and several descriptors to assess the spatial and temporal distribution of drought conditions in Peninsular Malaysia. Chen et al. [12] used the SPEI and anomalies of the leaf area index from high-resolution satellite remote sensing to carry out a multimetric and multi-scalar drought assessment and vulnerability to drought of different vegetation types. Oksal [13] used the SPI and SPEI at multiple scales to assess the influence of precipitation and temperature on drought assessment in Marmara, Turkey. Ali et al. [14] used the SPEI at various scales, climate elements, and vegetation indices to identify spatial-temporal patterns of annual changes and the seasonality of dry and wet periods and investigate long-term trends in drought and possible associations with vegetation and climate dynamic factors in five East Asian subregions during 1902–2018. Abiodun et al. [15] employed the SPEI and SPI indices to project drought occurrences across major river basins in Southern Africa under specific global warming levels. Ujeneza et al. evaluated the ability of global climate models to simulate the spatial and temporal structures of drought regimes in Southern Africa using the SPEI [16].
Studies indicate that the drought indices SPI, SPEI, Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and VCI are the most popular, successful and widely used to assess drought [17,18,19,20]. The SPI allows assessing drought on many timescales (from 1 to 48 or more months), is based only on precipitation and cannot consider the effect of other factors, such as temperature [21]. The SPEI is based on precipitation and potential evapotranspiration, makes it possible to overcome the limitations of the SPI, and also allows assessing drought on long timescales. Vegetation indices are widely used to assess drought based on their effects on vegetation [22,23].
Despite all these studies, a very recent literature review study revealed the lack of any complete characterisation studies of the drought regime in Southern Africa (SA), which encompasses the different drought descriptors and analyses their spatial and temporal distribution patterns, including the interannual and intra-annual variability for different Drought Classes (DCs) [20]. Therefore, this study aims to fill this knowledge gap identified in the literature search by answering the following general research question: What is the drought regime in SA under current climatic conditions? In this context, the main objective is to characterise and assess drought, for the whole of SA, in the strict sense, that is, to detect the occurrence of drought and describe the spatial and temporal distribution of its descriptors, without analysing other characteristics such as causes or consequences. To answer the research question, we computed and analysed the spatial and temporal distribution of several drought indices and descriptors, derived from different meteorological datasets. These calculations were performed using high-quality data spanning a sufficiently long period. Additionally, we evaluated and compared these results with anomalies derived from vegetation indices to ensure the robustness and reliability of the findings and conclusions. We strongly believe that a comprehensive assessment of the drought regime is essential for local, regional and national managers to define and initiate management measures to address droughts in a timely and accurate manner and for policymakers to be able to consider the chain of environmental and socioeconomic consequences when defining effective drought policies and strategies to adapt water resource management to current and future climate conditions and to mitigate drought impacts [10,14].

2. Materials and Methods

2.1. Study Area

Usually, SA is considered as the following 10 countries: South Africa, Angola, Botswana, Lesotho, Malawi, Mozambique, Namibia, Eswatini (Swaziland), Zambia and Zimbabwe. In this study, SA is the part of the African continental territory located south of the equator, surrounded by the Atlantic Ocean, to the West, and the Indian Ocean, to the East (Figure 1a), to simplify the data analysis and results’ plots. According to the Köppen–Geiger classification (Figure 1b), SA has three main types of climate: equatorial, arid, and warm temperate [24,25]. The region’s climate is generally characterised by having two distinct seasons (Figure S1), a hot and rainy summer (approximately from November to March) and a cold and dry winter (from April to October) [15]. The highest daytime temperatures can be above 40 °C, namely, in the Kalahari Desert, which extends across south-eastern Namibia, south-western Botswana and north-western South Africa, where minimum air temperatures are also much lower, making the daily temperature range very high (>20 °C) [15]. The lowest daily minimum temperatures (around −13 °C) were recorded in Bethlehem, South Africa [26]. The mean annual precipitation in SA is essentially characterised by a south–north gradient, assuming very low values (<50 mm) in the SW region and higher average values (≈150 mm) in the north (Figure S2). Total summer precipitation in SA (Figure S1) reaches a maximum value of about 1300 mm in central Mozambique, decreasing from north to south and from east to west, with a minimum value of less or equal to 300 mm in the Namib desert [27]. SA has five main terrestrial biomes (Figure 1a): Tropical and Subtropical Moist Broadleaf Forests (hereafter, Tropical moist forests); Tropical and Subtropical Grasslands, Savannas and Shrublands; Deserts and Xeric Shrublands; Montane Grasslands and Shrublands; and Mediterranean Forests, Woodlands and Scrub [28].

2.2. Data

This study used the State-of-the-Art ERA5 global climate reanalysis dataset, extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) portal of the Copernicus Climate Change Service [29]. ERA5 provides high spatial and temporal resolution data on atmospheric, oceanic, and land surface conditions, from a wide range of global observational satellite and ground-based data. ERA5 is used for climate research, weather forecasting, monitoring, and environmental studies, among other meteorological and climate research purposes. Specifically, we use hourly [29] and monthly average [30] data at single levels from 1971 to 2020 for the SA spatial domain defined between 0° and 35° South latitude and 7.5° W to 42.5° E longitude, with the horizontal spatial resolution as follows: 0.25° latitude × 0.25° longitude of the following meteorological fields:
  • Total monthly averages of total precipitation (TP);
  • Total monthly averages of 10 m wind speed (W10m);
  • Total monthly averages of potential evaporation (PEV);
  • Land–sea mask;
  • Geopotential to computed Geopotential height (Z);
  • Daily hourly data of 2 m maximum temperature (TMAX2m);
  • Daily hourly data of 2 m minimum temperature (TMIN2m);
  • Total cloud cover (CC).
The land–sea mask was used to restrict the plot of the other fields over the SA region. To calculate the monthly potential evapotranspiration (PET), we started by computing the monthly 2 m wind speed (W2m), from W10m and Z, and the monthly TMAX2m and TMIN2m from their daily fields. W2m was computed from W10m and Z, using the following equation [31]:
W 2 m = W z 4.87 l n 67.8   z − 5.42
where z is the height of the wind measurement above the ground surface (in this case, 10 m). Then, PET was computed from W2m, TMAX, TMIN, Z, CC and latitude (LAT). The water balance results from PT and PET were calculated as B A L = T P − P E T .
We also used monthly values of vegetation indices NDVI and EVI computed with data from the MODIS radiometer on board the TERRA satellite. The NDVI and EVI data were obtained from NASA ground data MOD13A3 MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V006 for the 2001–2020 period and SA with a spatial resolution of 1 km × 1 km. Monthly values of the VCI were computed from the NDVI for the same region and study period [32].

2.3. Methods

2.3.1. Meteorological Drought Indices

Since drought can occur in any type of climate, and since Southern Africa has a great diversity of climate types, which are essentially defined based on temperature and precipitation, in this sense, for this study, we chose to use meteorological indices that consider both climatic elements as well as vegetation indices to assess the effects of drought on said vegetation.
Although the PDSI has been reasonably successful in quantifying long-term droughts, the monthly PDSI values do not capture droughts on timescales shorter than about 12 months, and its main limitations are as follows: (i) it is not as comparable across regions as the Standardised Precipitation Index (SPI), but this can be mitigated by using the self-calibrating PDSI, (ii) it does not have the multi-timescale capabilities of indices such as the SPI, making it difficult to correlate with specific water resources such as runoff, snow cover and reservoir storage, (iii) it does not consider snow or ice (delayed runoff) and it assumes that precipitation is immediately available [33]. For these reasons, due to the climatic characteristics of the region under study and the specific objectives of this study to also study short-term droughts, the use of the PDSI was ruled out.
The SPI and SPEI were used in the study to assess the characteristics of the spatial-temporal distribution of each of the drought descriptors in SA. Both indices were computed for each grid point of the ERA5 spatial domain and for the 3-, 6-, 9-, and 12-month timescales. These indices were selected because they are standardised and, consequently, have the advantage of being able to be compared when used in different locations and periods [34]. The SPI is a widely used meteorological drought index developed by Mckee et al. [21] and computed using monthly TP [19,21]. The SPI has the advantage of using only precipitation data to characterise drought at different scales and can be applied to any climate type to monitor the characteristics of extreme precipitation and drought events consistently [9,35]. The main limitation of the SPI is that (i) it relies solely on a single input parameter, precipitation, and (ii) it fails to account for the influence of temperature, a critical factor in a region’s overall water balance and water usage. We used the Standardised Drought Analysis Toolbox (SDAT) function by [36,37] to compute the SPI in MATLAB.
The SPEI was computed based on the BAL, also for different timescales [38,39,40], and has been widely used to assess and monitor drought [41,42,43]. The SPEI has the same advantages as the SPI but is considered an improvement on the SPI because, besides precipitation, it also considers the PET, i.e., also considers the impact of temperature in the drought, making this index more suitable to assess the role of air temperature and climate change on drought [1,39]. The SPEI has the disadvantage that, in addition to precipitation, it uses a vast set of data to estimate the PET, which may limit its use due to the insufficient availability of these data, and short drought situations may not be identified. There are several methods to estimate PET, but the Penman–Monteith method is considered the best. We used the Vicente-Serrano SPEI R function to compute the SPEI and estimate the PET with the FAO Penman–Monteith Equation 56 [31,44]:
P E T = 0.48 Δ R n − G + γ 900 T + 273 u 2 e s − e a Δ + γ   1 + 0.34 u 2
where R n is the net radiation at the crop surface, G is the soil heat flux density, T the mean daily air temperature at 2 m height, u 2 is the wind speed at 2 m height, e s is the saturation vapour pressure, e a   is the actual vapour pressure, e s − e a is the saturation vapour pressure deficit, ∆ is the slope of the vapour pressure curve and γ the psychrometric constant. The PET R function requires W2m, TMAX, TMIN, Z, CC and LAT as inputs. We compared this PET with ERA5 PEV, the SPEI computed with the PET and PEV and the drought descriptors calculated with the SPEI estimated with PEV and PET. The obtained results were very similar, as suggested in previous studies [45].
In general, the selected sets of indices for this study have several advantages, including (i) enabling the comparison of results obtained using different indices, those based solely on precipitation (the decline of which triggers drought) as well as those incorporating temperature (to assess the impact of evapotranspiration) and drought effects on vegetation and (ii) allowing for an evaluation of the robustness and reliability of the results and conclusions.

2.3.2. Drought Occurrence and Characteristics

In this study, drought is defined as a consecutive series of months that fulfil the following criteria: (i) the drought index (SPI or SPEI) is always negative; (ii) the index is less or equal to −1 in, at least, one of the months of the drought duration; and (iii) the Drought Intensity (DI) is less or equal to −0.5. The first two criteria are the drought criteria of McKee et al. [21], and the third is based on the concept of DC. McKee et al. [21] arbitrarily defined DI for index values within certain ranges. We defined these boundaries (Table 1) as McKee et al. and many other researchers did, except for mild drought. McKee et al. defined a month of mild drought when − 1 < i n d e x ≤ 0 . However, recent studies [14,38] suggest defining a month of mild drought when − 1 < i n d e x ≤ − 0.5 . As, in general, the index varies throughout the drought, which may last several months, the Drought Class is defined based on the fulfilment of one of the boundary conditions of the DI value (Table 1) [14,21,38]. We defined slightly different boundaries for the SPEI because of the distribution function used to compute this index [46,47].
A large number of drought descriptors were computed based on the SPI and SPEI, including [6,8,14,21,48] the following:
  • The Drought Number ( D N ) is defined as the number of droughts in a given location;
  • The Drought Duration ( D D ) is defined as D D = M e n d − M s t a r t , where M e n d is the end month of the drought (the month in which the index returns to be positive), and M s t a r t is the start month of the drought (the first month of the drought in which the index is negative);
  • The Drought Severity ( D S ) is the Sum of the drought index (e.g., SPI) during the drought, D S = ∑ i = 1 x S P I i ;
  • The Drought Intensity (DI) is the average D S over its duration, D I = D S / D D . We also calculated the Sum and average of these descriptors, for the entire study period, for each month of the year and each year of the study period.
We further calculated the Sum of the number of drought grid points (Sum of the Drought Extent, SDE), the Mean Drought Extent, M D E = S D E / N L G P , where N L G P is the number of land grid points in SA and the Mean Drought Severity, MDS. Finally, we also computed the number of drought months, defined as months when at least 10% of the SA total area was affected by drought, and the Sum of Drought Months (SDM).

2.3.3. Vegetation Index

The NDVIs and EVIs allow consistent spatial and temporal comparisons of global vegetation conditions and serve to monitor terrestrial vegetation activity through radiometric and structural vegetation parameters [23]. The NDVI is an index based on plant reflectance in the visible and near-infrared wavelength electromagnetic spectrum bands that can be used to identify and monitor areas affected by droughts as well as the health status of vegetation [9,49]. The NDVI varies between −1 and 1 as it results from the difference per pixel standardisation between the red and near-infrared (NIR) bands [23,50]. An NDVI value close to −1 indicates that the area has very sparse or no vegetation at all. A value close to 1 indicates that the area has dense to very dense vegetation [23].
The EVI allows the identification of plant water stress associated with drought [23]. The EVI was developed to strengthen the vegetation signal with improved sensitivity in regions of high biomass and vegetation monitoring through a canopy background signal linkage and a reduction in aerosol influences [51]. The EVI is calculated similarly to the NDVI but corrects for some distortions in the light reflected by the vegetation caused by particles in the air (aerosols) and the ground cover below the vegetation. The EVI does not saturate as easily as the NDVI when viewing tropical forests and other surfaces with large amounts of chlorophyll [23,51]. The EVI can map vegetation states on cloudless images, standardise data according to the target sun-sensor position, ensure data quality and consistency and describe and reconstruct phenological variation data [1].
The VCI is a vegetation index fine-tuned to quantitatively and qualitatively determine the impact of drought on vegetation, providing details linked to terrestrial ecological conditions and is widely applied in agriculture [22]. The VCI values range from 0 to 1 and can be distributed into different classes: extremely dry (0 to 0.2), dry (0.2 to 0.4), normal condition (0.4 to 0.6), good condition (0.6 to 0.8) and optimum condition (0.8 to 1.0) [52]. The VCI is widely used for drought monitoring because it can assess changes in vegetation that cannot be easily detected by direct use of NDVIs or EVIs [1]. The monthly VCI was calculated using the following equation:
V C I = 100   ∗   ( N D V I − N D V I m i n ) / ( N D V I m a x − N D V I m i n )
where N D V I m a x   and N D V I m i n are the maximum and minimum values of the NDVI in each month [53].

2.3.4. Other Methods of Applied Statistical Climatology

Statistical methods commonly used in climatological studies, namely, those of the exploratory descriptive, trend and composite analysis, were also used. Several statistics were computed for the meteorological variables and parameters, including arithmetic average, standard deviation, maximum, minimum, range, percentile and interquartile range. Trend analysis was carried out by computing the trend with Sen’s Slope [54], estimator and robust regression analysis and assessing the statistical significance of the trend with the Mann–Kendall (MK) [55] and Theil–Sen tests [56]. The composite analysis comprises the computation of the long-term mean, the composite, which is the average for a specific subsample (one month, in this case), and the anomaly, defined as the difference between the composite and the long-term mean [57,58]. In this study, the composite analysis was performed to compute the non-seasonal monthly anomalies of the vegetation indices. This procedure was adopted because the assessment of the relationship between drought and the state of vegetation can be masked by the annual life cycle of vegetation, as, even in the absence of drought, some plant species can wither or die, in a certain location or period of the year. Therefore, the non-seasonal anomaly of the vegetation index was computed as the departure of the vegetation index composite of a specific month of the study period from the long-term average of the vegetation index for that same month across all years of the study period. For instance, the non-seasonal anomaly of the vegetation index for January 2000 was obtained by subtracting the long-term average vegetation index composite for all Januarys within the study period from the vegetation index composite of January 2000. Therefore, the values of the VCI anomalies can vary between −1 and +1 and have a simple interpretation: a positive anomaly represents above-average climatological conditions (in this study, the vegetation is greener than normal, not affected, including by drought), while a negative anomaly means below-average climatological conditions (vegetation less green than normal, eventually affected due to drought) [58,59,60].

3. Results

3.1. The Drought Regime in SA

This subsection presents and describes the results obtained with the SPI and SPEI. Only figures with SPI results will be displayed. Figures with the results obtained with the SPEI are presented in the Supplementary Materials.

3.1.1. The Spatial Distribution of Drought Descriptors

The Sum of the DN patterns obtained with the SPI (Figure 2) and the SPEI (Figure S4) at different scales are similar in shape and values. For the 3-month timescale, the Sum DN ranges from 5 to 25 droughts in the northern central region of the Tropical moist forests, mostly over the Democratic Republic of the Congo and north and east of Angola, to about 50 droughts at most in the central region of SA, over Zambia, Zimbabwe, Malawi, south and east of Angola and Namibia. When the timescale increases, the pattern is similar, but the values decrease throughout SA up to a maximum of 35 droughts at 6 months, 20 droughts at 9 months, and 10 droughts at 12 months in central SA. The results for the SPI suggest that, in the 50 years of the study period (1971–2020), droughts occurred in SA, on average once every 2.2 years at the 3-month scale, 3.5 years at the 6-month scale, 5.3 years at 9 months and 7.9 years at the 12-month scale. For the SPEI, the periods between drought are slightly longer, namely, one drought every 2.4 years at the 3-month scale, 3.9 years at the 6-month scale, 5.7 years at 9 months and 8.4 years at the 12-month scale.
The Sum DD and Sum DS for the SPI (Figure 2) present similar patterns for each timescale. At the 3-month scale, the pattern of the Sum DD is characterised by low values (between 150 and 200 months) in southern Angola, northern Namibia and western Zambia and higher values (about 260 months) in the surrounding area. The pattern is more uniform for the other timescales but with higher values in north-central SA, more evident in the Sum DS than in the Sum DD, increasing with the timescale, especially in South Africa, Botswana, and Zimbabwe. For the SPEI (Figure S4), the pattern of the Sum DD and the Sum DS are also similar for each timescale but different from those obtained with the SPI at the 3-month timescale. For the SPEI, the region of lower values in central east SA is not apparent. For the other timescales, the distribution patterns are very similar, but the Sum DD and Sum DS are higher for the SPEI than for the SPI. The spatial pattern of the Sum DI for the SPI (Figure 2) and the SPEI (Figure S4) almost resembles the corresponding pattern of the Sum DN for all timescales and reveals higher DI and DN for the SPI than the SPEI.
The patterns of Mean DD and Mean DS obtained with the SPI and the SPEI are very similar to each other, presenting much higher values (of about 250 months at 6-, 9- and 12-month timescales) in the north-central region of SA (of the Tropical moist forests) and relatively uniform lower values in the remaining territory. This region of higher values increases in size with increasing timescales. However, the patterns of Mean DI are very similar for both indices and timescales, presenting very low spatial variability (ranging between −0.75 and −1.25) in the entire SA. In addition, an increase in Mean DI with the increasing timescale in northern and central SA regions is apparent.

3.1.2. The Spatial Distribution of Drought Descriptors by Drought Class

For the SPI, the spatial pattern of the Sum DN for the mild DC (Figure 3) is very similar to the pattern of the Sum DN for all classes of drought (Figure 2), with lower values in north-central SA and higher values elsewhere, but especially in central SA. The patterns are similar for all scales, but the Sum DN significantly decreases when the DC increases from 2 (mild drought) to 4 (severe drought). On average for all timescales, the distribution of the Sum DN across classes is 5.1% for DC 1 (abnormally dry), 58.6% for DC 2 (mild drought), 31.1% for DC 3 (moderate drought), 4.5% for DC 4 (severe drought) and 0.2% for DC 5 (extreme drought). Values and patterns for DC 1 (abnormally dry) are very similar to those for DC 4 (severe drought), with the Sum DN equal to zero in the north-central region and very low in the remaining SA. However, DC 5 (extreme drought) only occurs in a few numbers of small-size locations that, in addition, decrease with the timescale. Mild and moderate droughts can be seen across SA, but severe droughts do not occur in most of the Democratic Republic of the Congo. Only a few extreme droughts were recorded in the SA with the SPI and at lower timescales.
Results obtained with the SPEI (Figure S5) are similar, but it is important to highlight some differences. The Sum DN for all DCs and timescales is generally larger with the SPI (Figure 3) than with the SPEI. On average, the distribution of the Sum of DNs across classes is 4.9% for DC 1, 60.1% for DC 2, 31.1% for DC 3, 4.2% for DC 4 and 0.3% for DC 5. In the SA Tropical moist forest region, the DN is greater with the SPI than with the SPEI for DC 2, but the opposite is true for DC 3. For the SPEI, the Sum DN for DC 5 is very low.

3.1.3. Temporal Distribution of Drought Descriptors: The Intra-Annual Distribution

The monthly MDE, MDS and SDM, evaluated with the SPI (Figure S6) and the SPEI (Figure S6), present similar patterns for all descriptors, characterised by very low intra-annual variability, for the four timescales. This low intra-annual variability of the MDE and MDS computed with the SPEI significantly decreases as the timescale increases. For example, the standard deviation of the MDE at the 3-month scale is 0.012 and decreases to 0.002 at 12 months. The intra-annual distribution of the SDM and MDS was also evaluated at each point in the spatial domain of the study area. In general, the results obtained for both descriptors computed with the SPI and the SPEI are very similar, for all timescales. The patterns change from one month to the following but present relatively low spatial variability. The exception to this behaviour is only apparent in the patterns of both the SDM and MDS computed with the SPI at the 3-month scale, where much lower values occur during the dry season (mainly from June to September) in south Angola, north Namibia, west Zambia and north Botswana. Despite the similarity between results obtained with the SPI and SPEI, it is possible to identify some differences. For example, the slightly lower values of the MDE and MDS from June to September are only observed when calculated with the SPI. In line with previous results (Sum DN), SDM values are lower with the SPEI than with the SPI, on all temporal scales.
The intra-annual distribution of the drought descriptors (MDE, SDM and MDS) computed with the SPI for each DC and timescale (Figure S7) do not present coherent patterns. For DC 1, the SDM is zero; consequently, the MDE is very low (around 1%), and the MDS is the lowest (to the other classes), especially in the months in the middle of the year with more prominent differences when the timescale increases. For DC 2 and 3, the MDS and MDE are approximately constant throughout the year for all timescales and only the SDM presents slightly lower values from June/July to October, for the smallest scales (3 and 6 months). For DC 4 and 5, the MDE and SDM decrease to very low or null, but, as the timescale increases, the MDS increases in a set of increasing numbers of months, centred in April at 6 months, in May at 9 months and in June at 12 months. So, the MDE and SDM decrease with the increasing DC (from DC 2 to DC 5) and timescale. For example, the average MDE is higher (20.24%) for DC 2, decreased to 12.58% for DC 3 and 1.02% for DC 4. The decrease in the drought number does not affect the MDS, which naturally increases with the DC. The changes in the drought descriptors, associated with the number of droughts in each class and timescale, lead to a general increase in intra-annual variability with the DC, except from DC 1 to 2 and in the MDE for DC 5 (extreme drought).
In general, the intra-annual distribution of the drought descriptors computed with the SPI (Figure S7) and SPEI (Figure S8) for each DC are similar. However, in line with previous results (Sum DN, Figure 2 and Figure S4), the SDM is higher with the SPI than with the SPEI, for DC 2. For the other DCs, including DC 3, the SDM values for the SPI and SPEI are very similar. For DC 5, the MDS with the SPEI presents higher variability than with the SPI, at all timescales, and much higher values in months 3 to 6 at the 6-month timescale, in months 4 to 8 at the 9-month scale and 5 to 9, at the 12-month scale.

3.1.4. Temporal Distribution of Drought Descriptors: The Interannual Distribution

The annual MDE, SDM and MDS computed with the SPI (Figure 4) and the SPEI (Figure S9) present high interannual variability, which increases with the timescale. For example, the standard deviation of the MDS computed with the SPI at the 3-month scale is 0.08 and increases on average by 0.03 for the 6-, 9- and 12-month scales. Results for the MDS computed with the SPEI are slightly higher (0.10 at the 3-month scale and increases 0.025 on average). The SDM and the MDE present evident positive trends. The interannual distribution of the MDE, SDM and MDS, evaluated with the SPI and SPEI at all timescales, show similar general patterns from 1990 to 2020, but the MDE is higher when calculated with the SPEI. In the early years of the study period, the SDM presents lower values in some years but is maximum (equal to 12) without interruption from 1990 to 2020. The SDM trends increase with the timescale. The slope of the MDE computed with the SPI increases from 0.63% at the 3-month scale to 0.67% at the 9-month scale, and it is equal to 0.67% and 0.65% for 9- and 12-month timescales, respectively. With the SPEI, the trends are higher, increasing from 3 months (0.89%) to the other scales (0.93%, 0.92% and 0.91%, respectively, for 6-, 9- and 12-month timescales). We also analyse the interannual distribution in each land grid point of the study area. Results reveal high spatial variability in the annual MDS and SDM, at all timescales, as well as decreases with increasing timescales. It is also noted that the Tropical moist forest regions of northern SA had no drought in the first half of the study period (SDM = 0), only from 2001 onwards, but throughout the year (SDM = 12) in the remaining second half of the study period, at all timescales. This result aligns with the Sum DD of 260 months in this region (Figure 2 and Figure S4).
Analysis of the interannual distribution in each land grid point is useful to illustrate the significant drought event that affected SA in 2018 and 2019 (Figure 5 and Figure S10). The pattern of the SDM computed with the SPI (Figure 5) at a 3-month timescale for 2018 reveals higher values in southern and northern SA and relatively lower values in the central belt. This pattern is similar but with increasing contrast for all the timescales. The SDM reaches maximum values (SDM = 12) in the northern and southern regions, with an increasingly larger area, and minimum and decreasing values in central SA, as the timescale increases. The pattern for 2019 is relatively similar, but much more impressive, as the region with the highest values in southern SA is much larger and rapidly connects with the northern region as the timescale increases. In comparison with 2018, in 2019, the area with the maximum SDM is much higher at all timescales. These results reveal that SA was greatly affected by drought in these two years, but especially in 2019, not only in terms of the SDM but also in terms of the MDS. In the MDS maps for 2019, very low values (of about −2.0) stand out in the southwest region (Angola, Namibia and South Africa), observable at all timescales and in increasing areas also with the timescale. The relationship between the SDM and MDS descriptors in 2018 and 2019 is different. In 2018, the regions where droughts were most severe (lowest MDS) roughly coincide with the regions affected for a smaller number of months (lower SDM), at all timescales, except 12 months. In 2019, there was a high spatial correlation between regions with a low MDS (high severity) and a high SDM, at all timescales, which is most evident in the central and southern regions of SA. In the north-central region of Tropical moist forests, the SDM is also maximum (SDM = 12), but the severity is slightly lower (generally greater than −2) than in other regions.
Despite the similarity of patterns obtained with the SPI (Figure 5) and the SPEI (Figure S10) and the differences already described, it is worth noting that, with the SPEI, the SDM is slightly higher, and the drought affects a larger region, while the MDS is higher and in larger areas, than with the SPI. These results, particularly for 2019 at the 9-month scale, suggest the occurrence of some precipitation whose effect is observable only with the SPI, but which must be cancelled out by the effect of temperature (and evapotranspiration).
The interannual distribution patterns of the MDE, MDS and SDM calculated for each class based on the SPI (Figure S11) are similar to those obtained with the SPEI (Figure S12). In general, obtained patterns show high variability, except for DC 1 and the SDM. The SDM is zero for DC 1 and DC 5 for all timescales and for DC 4 at the 3, 6 and 12-month timescales. The results reveal significant increasing trends in the SDM and the MDE for DC 2 and 3 regardless of the timescale. Increasing, but very slight, trends are also observed in the MDS at the 12-month scale for DC 2 and 3. Comparing the results obtained with the SPEI to those obtained with the SPI, a greater variability in the MDS for DC 4 and DC 5 is evident. For DC 5, the MDS with the SPI assumes values for more years, especially for shorter timescales. However, in some years, the MDS calculated with the SPEI is significantly higher than when calculated with the SPI, especially at 3, 6 and 12 months. It is important to highlight that we are comparing averages (MDS), unaffected by fewer droughts with the SPEI.

3.2. Vegetation Conditions During Drought Events

This section presents the results of the analysis of vegetation conditions based on the calculation of NDVI, EVI and VCI anomalies for the 2001–2020 period. The anomaly maps reveal high spatial and temporal variability in vegetation dryness/greenness throughout SA. The patterns obtained make it possible to identify regions where vegetation has been disturbed due to water scarcity and droughts. In general, the spatial patterns of the anomalies of these three indices are similar, and this similarity is more pronounced between the NDVI and EVI. However, the VCI anomalies and the drought-affected regions indicated by this index proved to be the most sensitive and consistent with the results obtained from the meteorological drought indices.
For instance, to emphasise the coherence between the information provided by the anomalies of the vegetation (Figure 6) and drought indices (Figure 7 and Figure S13), we focused on one drought. This case study corresponds to the longest, most severe and least studied droughts that affected SA in the recent years of the study period. For the sake of simplicity, we only present the results for four months of this drought, namely, those that took place during the rainy season, from November 2018 to February 2019. It is worth noting that the DS computed with the SPI (Figure 7) and the SPEI (Figure S13) present very similar patterns, but the DS calculated with the SPEI reveals a larger area affected by drought and with higher severity values, especially in regions where the drought is more severe. In December, almost the entire southern half of SA, including southern Angola and the Democratic Republic of the Congo, Zambia, Botswana, Namibia, Zimbabwe, Mozambique and the eastern part of South Africa, had very negative vegetation index anomalies (Figure 6b,f,j), especially the VCI (Figure 6j). However, it was in November 2018, that is, one month earlier, that the SPI presented the largest area affected by the drought and the highest DS in this region (Figure 7). The observed time lag effect of one month, regarding the maximal area affected by droughts, between the meteorological (SPI) and vegetation indices (VCIs), should also depend on the location and the type of vegetation in the affected region. For example, in the four months of this case study, Namibia was progressively affected by drought (assessed at almost all the timescales except 12 months), from East to West, and almost all of its territory was affected by extreme drought (DS ≤ −2) in January and February 2019. However, negative anomalies in the vegetation indices were only observed along the West Coast during the four months of the drought. Conversely, in this period, and when analysing the anomalies of the vegetation indices, the northern region of South Africa, south-east of Namibia and south-west of Botswana ceased to be affected by drought (assessed at the 3-month scale) in February, and a recovery in the state of vegetation can be observed, apparently in the same month in which the drought ends. It is worth noting that, for the following two months (January and February), there was a reduction in the area of very low negative values of the vegetation index anomalies, remaining only in the southern parts of Botswana and Zimbabwe and the eastern part of South Africa. There is a similar decreasing trend in drought severity and the area affected by drought assessed with the SPI at the 3-, 6- and 9-month timescales. To summarise, in general, and regarding the presented case study, there is good agreement between the maps of vegetation drought index anomalies and the DS maps, which also indicate the regions/areas affected, or not, by drought. For example, regions not affected by drought (e.g., the northwest coast of Angola, the centre and the north of the Democratic Republic of Congo, and north of Zambia, Tanzania and Malawi) assessed at all temporal scales, appear, in general, with positive anomalies (shown in green) in the vegetation index maps.

4. Discussion

Our definition of drought allowed us to identify and characterise droughts for general drought conditions (Figure 2 and Figure S4) and each of the five DCs (Figure 3 and Figure S5) across most of SA, as in many previous studies [9,14,21,38]. The sums of the DN, DD, DS, and DI for the drought evaluated with the SPI (Figure 2) and SPEI (Figure S4) present some characteristics that are important to discuss, namely, the higher values in the central region of SA, approximately between −10° S and −25° S, at all timescales. This feature is compatible with higher precipitation variability in this region disclosed by the standard deviation and interquartile range patterns. In turn, the highest DN values in this region explain the smaller values of the DD Sum and DS Sum. On the one hand, a greater number of droughts implies, on average, droughts of shorter duration. On the other hand, if the duration is shorter, the severity tends to be lower. Finally, proportional decreases in DD and DS may lead to similar DI values throughout the central region.
Unlike the SPEI (Figure S4) and the SPI (Figure 2) at the 3-month scale, the DD Sum pattern is characterised by low values in an almost latitudinal strip of land in southern Angola, northern Namibia and western Botswana. Ongoing studies suggest that these low values are due to the precipitation regime in the Midwest river basins, especially from July to September, which results from a complex set of atmospheric circulation features [61]. This explanation is convincing, as this characteristic only appears on a 3-month scale (see Figures S6 and S7) and does not appear in the Sum of the DN evaluated with the SPEI (Figures S4 and S6) at the same or another timescale. During the study period, droughts occurred in shorter periods when assessed with the SPI on average once every 2.2 years at the 3-month scale, 3.5 years at the 6-month scale, 5.3 years at the 9-month scale and 7.9 years at the 12-month scale, with a difference (SPI-SPEI) of −2.4 months at the 3-month scale to −6 months at the 12-month scale, which suggests that the effect of air temperature can reduce the outcome of precipitation variability.
This feature of the Sum DD explains why the same feature is observed in the Sum DS as shorter duration tends to imply lower severity, defined as the Sum of the indices for the duration of the drought. The absence of this characteristic in the Sum DI pattern is explained by the fact that DI resulting from the division between low values of DS and DD can result in a normal value of DI [48]. The region of low Sum DN values and high Sum DS values but low Sum DI values in the north-central region of Tropical moist forests are easily explained by precipitation showing low variability but decreasing trends, as we will see later [62,63]. These conditions lead to longer and more severe droughts but with low intensity in the second part of the study period (1990–2020). With the increase in the timescale, the following is observed: (i) a decrease in the Sum of the DN, easily justified by the lower probability of encountering dry conditions with the increase in timescale [9]; (ii) an increase in the Sum of the DD, particularly evident in the north-central region, which is justified by the characteristics of precipitation in this region, already discussed [63]; (iii) an increase in severity, which is mainly a consequence of the increase in the Sum of DD; and (iv) a decrease in the Sum of DI, essentially resulting from the greater increase in the Sum of DD than the Sum of DS, as the SPI and the SPEI are limited to values between 3 and −3 [21]. The patterns of the Sums of the DN, DD, DS and DI obtained with the SPEI are very similar to those obtained with the SPI, except in the approximate latitudinal range south of Angola, already discussed, so their interpretation and justification are identical. It is also important to note that the obtained results on drought frequency are in line with previous studies that suggest the occurrence of drought in SA every 3 years, in the 1960–2016 period [64].
Studies carried out for specific regions corroborate the other results obtained. For example, Angola has been repeatedly affected by drought events in the north and centre, particularly in the south of the country, and it is also worth highlighting the occurrence of severe meteorological droughts, lasting several years [65,66]. Droughts frequently affected South Africa, Botswana, Namibia, Zimbabwe, Tanzania Mozambique and Angola [15,16,65]. Since 1981, Lesotho and Swaziland have faced intense and recurring droughts that result in catastrophic socioeconomic situations [67].
To discuss the results obtained for each DC (e.g., Sum DN, Figure 3 and Figure S5), it is important to consider the characteristics of the drought indices used in this study, as some reveal advantages or added values and other restrictions or limitations. The SPI and SPEI are dimensionless and standardised, i.e., with a mean of zero and a standard deviation equal to one [21]. This characteristic has the advantage of allowing comparison results obtained for different regions and periods. Another important characteristic is that the distribution of index values is normal and, therefore, symmetric [21,68]. This means that half of the index values are positive, and the other half are negative, that is, at most, only half of the study period can be, or will be considered dry. This means there will always be the possibility of drought, even in humid climates, resulting from the relative nature of the concept. This also means that 19.1% of the values must lie in the range [0, −0.49], 15.0% in [−0.5, −0.99], 9.2% in [−1.0, −1.49], 4.4% in [−1.5, −1.99] and 2.3% in [−2.0, −∞] [68,69]. This distribution allows us to explain why the number of droughts (Figure 3) decreases significantly when the DC increases (e.g., D N M i l d > D N M o d e r a t e > D N S e v e r e > D N E x t r e m e ), and why it is so difficult to identify extreme droughts. This difficulty results from the combination of two factors: the intensity of the drought resulting from the average severity, that is, the value of the index for the entire drought period and the small number of months with extreme values of the index (<−2.0). In this study, since the temporal dimension of the data is 600 months, there are at most ( 0.023 × 600   m o n t h s = 13.8   m o n t h s ) 14 months in which the index value is less than −2.0. Perhaps for this reason, some researchers especially interested in extreme drought events [70,71] have proposed different drought-type classifications, with different ranges of drought indices (e.g., − 0.5 ≤ S P I < − 0.8 , abnormal dry; − 0.8 ≤ S P I < − 1.3 , moderate drought; − 1.3 ≤ S P I < − 1.6 , severe drought; − 1.6 ≤ S P I < − 2.0 , extreme drought; and S P I < − 2.0 , exceptional drought).
The low intra-annual variability of the distribution of the SDM, MDE and MDS in each month (Figure S6) is justified by the large size of SA. However, it is important to highlight that the effect of the decrease in DN, observed in the southern region of Angola, in the MDE was assessed with the SPI at 3 months. The decrease in value and variability of the SDM and MDE with the DC and temporal scale has to do with the characteristics of the standardised indices that lead to a much smaller number of droughts at higher class and timescales, which tend to be relatively well-distributed in space and time. Of course, if the study region were smaller, greater variability could be observed. This hypothesis motivated the analysis of the distribution of the annual cycle in space, that is, at each point in the study area, which confirmed the hypothesis.
The annual cycle of the MDE, MDS and SDM for each DC (Figures S7 and S8) revealed lower intra-annual variability for DC 2 and 3 than for the remaining classes, which is justified by the characteristics of the standardised drought indices, which lead to a higher number of droughts in these than in other classes and are due to the large size of SA. It is also important to note two facts. The first is that the SDM for DC 1 is zero because of the criterion used to compute this descriptor (the number of grid points with drought is less than 10% of total grid points). The second is that it is “easier” to have drought during the dry period than in the wet period, as a significant decrease in precipitation about normal is much smaller in the dry period than in the wet period, especially on a 3-month scale. For example, according to the literature, arid and semi-arid regions tend to have more droughts [72]. This justifies the higher MDE and lower MDS at 3 months, in the middle months of the year.
The distribution of the annual MDE, MDS and SDM in time (Figure 4 and Figure S9) and space (Figure 5 and Figure S10) present high interannual and spatial variability, in line with the findings of previous studies, which reveal an increase in interdecadal variability in the spatial extent of drought since the early 20th century in many SA countries, including Zimbabwe, Lesotho, South Africa, Eswatini, Mozambique, south Zambia, Botswana, Namibia and part of Angola [16,73]. This high interannual and spatial variability observed in the distribution of the annual MDE, MDS and SDM seems to be a consequence of two main reasons. The first is the high climate variability observed in SA, notably in precipitation and temperature. The second is the possible climate changes that are already observed in some regions of SA. Our results indicate that climatological monthly precipitation in SA can range between less than 200 mm and more than 750 mm. Lower values can be observed in most of SA, especially during the dry season, while higher values are only observed during the rainy southern summer (November to March) in the northern area of SA (e.g., Malawi and Democratic Republic of the Congo) and the centre of Angola, Zambia, Zimbabwe and Mozambique. These results are in very good agreement with previous studies [62]. In addition to the high spatial and temporal variability, these studies also reported long-term precipitation trends in SA, which have been scarce and irregular over the last few years [74].
The presence of statistically significant long-term trends serves as an indicator of climate change and plays a crucial role in assessing the spatial-temporal distributions of drought descriptors. For this reason, we carried out a trend analysis that included determining the slope, with robust and Theil–Sen regression, and its statistical significance, with the Mann–Kendal and Theil–Sen tests. The climatological analysis carried out over 50 years of the study period (Figure S3) revealed the existence of statistically significant increasing and decreasing trends in precipitation.
The region where precipitation has decreased is much larger than the region where precipitation has increased. The precipitation tends to decrease in almost the entire SA, except in coastal and southern Angola, northern Namibia, and western Botswana. However, the regions where the precipitation decreasing trend is statistically significant include most of northern SA (between 0 and −10° S) and small regions of south-eastern SA. The region where precipitation has increased significantly is much smaller and is limited to the northwest coastal region. These results agree with previous findings of precipitation trends in some of SA that documented −0.003 mm/day per year [62] and revealed that the precipitation presents a long-term decreasing trend over broad-leaved evergreen forests, broad-leaved evergreen forests and savannas located in the north and central region [63]. The decreasing trend in precipitation increases the probability of identifying more, longer and more severe droughts at the end of the study period, with the SPI and SPEI, as both depend on precipitation. However, trends in air temperature and other related results have also been reported for SA. For example, projections indicate a precipitation decrease and an increase in air temperature by 2050 [75,76]. CMIP3 climate projections suggest an increasing trend in droughts during the summer season, from December to February [77], along with the increase in global warming levels [73,77]. Similarly, other authors reported a significant increase in droughts in SA due to increasing levels of global warming [15]. Seasonal forecasts for SA also pointed to warmer conditions [78]. Increasing air temperature is expected to lead to an increase in PET and, along with the decreasing trend in precipitation, could increase the number, duration or severity of drought events assessed with the SPEI.
The trends in the annual MDE calculated using the SPEI (Figure S9) are more pronounced than those obtained with the SPI (Figure 4). To further explore these differences, we compared the annual MDE values derived from the SPEI and SPI across all temporal scales (Figure 8). The analysis reveals notable differences between the series, characterised by substantial interannual variability, strong correlations, and significant upward trends. Specifically, the difference between MDE values computed with the SPEI and SPI (〖MDE〗_SPEI-〖MDE〗_SPI) has increased by 15% over the past 50 years across all timescales. The MDE computed with the SPEI at the 3-month increase from 20% in 1971 to 50% in 2020, while the highest MDE values were observed during the 2015–2016 drought. The MDE increased with the timescale, from 70% in 2015 and 2016 at the 3-month scale to 75% at the 12-month scale. These results are in good agreement with the findings of other studies, for example, an increase in drought in Lake Malawi and the Shire River in the 1970–2013 period [79] and an increase in the extent of droughts in the main river basins of Southern Africa, specifically Orange, Limpopo, Zambezi and Okavango, since 1970 [15].
Monitoring vegetation relative to its scarcity of water or in a situation of droughts using vegetation indices, like the NDVI, EVI and VCI, that make use of satellite remote sensing data are much more efficient because of their wide coverage and accessibility, multi-spectral imaging, consistent and continuous monitoring, data integration and analysis and very high spatial and temporal resolutions, compared to in situ measurements or using reanalysis data [80,81]. These indices derived from the MODIS/TERRA or MODIS/AQUA polar-orbiting satellites are very suitable and widely used to monitor the conditions of the vegetation worldwide [82] and to study drought events in the terrestrial ecoregions of Africa [22,23,63].
The good agreement between the spatial patterns of the meteorological indices (Figure S13) and vegetation indices (Figure 6) observed both, in general, and, in the case study, are in line with findings reported in the literature. Low values of the NDVI, EVI and VCI typically occur during drought periods characterised by low or no rain, leading to vegetation dryness and a consequent reduction in near-infrared reflectance [23,49,50]. Furthermore, the robust relationship between drought and vegetation indices has been widely used by researchers to study and monitor the impact of drought’s spatial and temporal characteristics on vegetation at a global scale [12] and across various regions, such as Europe [83] and Africa [63,84,85].
The comparative analysis between drought and vegetation indices (Figure 6, Figure 7 and Figure S13) revealed two other main results: a delay in the influence of drought on the vegetation and a rapid recovery of vegetation after drought, which are also in line with the results of previous studies. For example, several authors identified temporal lags in the influence of drought on vegetation (much more significant in forests); in contrast, pastures and agricultural vegetation were more likely to have no temporal lag or a response time of less than 1 month [86,87,88]. The delay in the effect of drought on vegetation seen in some of the locations in the SA region is justified because the drought response in vegetation is stronger over Southern and Western Africa [63] and weaker in Angola and Malawi [84].
The influence of drought on vegetation is not a simple process but helps explain some discrepancies in the spatial patterns of the drought and vegetation indices (Figure 6 and Figure 7). Drought affects both arid and humid biomes, but some researchers consider the persistence of water deficit (i.e., the timescale of drought) important in assessing the sensitivity (and, therefore, response time) of terrestrial biomes to drought [12]. These authors concluded that arid and humid biomes generally react quickly to drought, although through different physiological mechanisms. In the former, plants have a great capacity to adapt to water stress; in the latter, they do not. On the other hand, semi-arid and sub-humid biomes react to drought over longer timescales, probably because the vegetation is capable of resisting water deficits [12]. In Africa, the results suggest that vegetation is very vulnerable to drought, but the response of vegetation in terrestrial ecoregions varies with vegetation indices, and spatial patterns of the seasonal response vary across timescales [63]. Results from another study also showed that SA vegetation responds differently to drought, depending on the timescales, season and biome, possibly due to the differentiated water needs of vegetation during various growth and phenological phases [84]. Therefore, understanding the precipitation–vegetation interaction is of great importance for implementing adaptation and mitigation measures for terrestrial ecosystems. For example, in some regions of China, vegetation is affected by precipitation anomalies, but the landscape response has a 1 to 2-month delay, and there are also notable seasonal differences in the vegetation response in this region [89].
It is important to highlight that the novelty of our drought regime assessment study is based on two important characteristics: the assessment was carried out comprehensively and for the whole of SA, which, to our knowledge, has never been carried out before. This assessment was comprehensive because it was carried out based on several of the most recommended vegetation and meteorological drought indices, characterised by the spatial and temporal distribution (including intra- and interannual distribution and trend analysis) in a multiscale (at 3, 6, 9 and 12 months), multimetric (several drought descriptors) and multiclass drought approach (Figure 9). Assessing the drought regime across SA using standardised indices allows us to adequately compare results obtained between regions with different and even contrasting climatic characteristics.
It is also important to emphasise that the obtained results (Figure 9) are influenced by the characteristics of the selected methodology, particularly the use of standardised meteorological drought indices. Another key factor is the presence of precipitation and air temperature trends, carefully evaluated and characterised in this study. On the one hand, these trends may influence the results and conclusions about the drought regime if researchers do not account for their implications, especially regarding the methodologies employed. On the other hand, these trends can be of added value, as they allow us to estimate the possible impact of climate change on drought characteristics. We strongly believe that understanding the drought regime in SA will support policymakers in developing legislation, regulations and adaptation strategies for drought and water resources management, creating monitoring programmes, and adapting to changes in the drought regime, as well as mitigating direct or indirect economic, social and environmental impacts, especially in the context of climate change caused by global warming.

5. Conclusions

Selected indices and defined descriptors allowed us to characterise the drought regime in Southern Africa. The main conclusions of this study extracted from the results obtained with the SPI can be summarised as follows: We identified droughts of all classes and all timescales throughout the study period and region. Drought Number, Duration, Severity and Intensity present high spatial variability, which tends to decrease with the increasing timescale. The Sum of the Drought Number and Intensity is greatest in the central region of SA and some regions of South Africa; in contrast, the Sum of Drought Duration and Severity is relatively uniform throughout SA but shows higher values in the Tropical moist forests and significantly lower values in southern Angola, northern Namibia and western Zambia, with the SPI at only 3 months. The average for all timescales, of the number of droughts, by classes is 5.1% for DC 1 (abnormally dry), 58.6% for DC 2 (mild drought), 31.1% for DC 3 (moderate drought), 4.5% for DC 4 (severe drought) and 0.2% for DC 5 (extreme drought). Droughts occurred on average once every 2.2 years at the 3-month scale, 3.5 years at the 6-month scale, 5.3 years at the 9-month scale, and 7.9 years at the 12-month scale. The intra-annual variability of the Sum of Drought Months, Mean Drought Severity and Mean Drought Extension for Southern Africa is very low (very low standard deviation) for all timescales but tends to increase with the Drought Class. On the other hand, the interannual distribution of these drought descriptors presents high variability, at all temporal scales, including very significant increasing trends, namely, in the annual average of the extension of the area affected by drought and in the yearly number of drought months. Specifically, on average, the area affected by drought increased by 6.6%/decade. Results obtained with the SPEI are very similar and lead to similar conclusions to those obtained with the SPI. However, they also present some differences, detailed in Section 3 and Section 4, which allow us to conclude about the added value of considering different indices to characterise the drought regime. For example, based on the SPEI, droughts are fewer and less intense but longer and more severe. On average, the number of droughts is also lower in all classes. However, the trend in the average area affected by drought assessed with the SPEI is more significant (9.1%/decade) than with the SPI.
The spatial patterns of vegetation and meteorological indices are, on the one hand, concordant and, on the other hand, reveal a delay in the influence of meteorological drought on the state of vegetation and a rapid recovery of vegetation after drought, depending on the type of vegetation. This study also allowed us to conclude the usefulness and complementarity of the various vegetation indices used to assess the drought regime, especially concerning its consequences.
We firmly believe that knowledge of the drought regime in Southern Africa will support policymakers in defining legislation/regulations and adaptation strategies for drought and water resource management, creating monitoring programmes and adapting to changes in the drought regime, as well as mitigating direct or indirect economic, social and environmental impacts, especially in the context of climate change caused by global warming.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cli12120221/s1, “The Drought Regime in Southern Africa: additional results of the long-term space-time distribution of main drought descriptors”, which includes the following figures: Figure S1: Average monthly precipitation in Southern Africa, in the 1971–2020 period; Figure S2: Mean annual precipitation in Southern Africa, in the 1971–2020 period; Figure S3: Results of the trend analysis carried out for monthly precipitation in SA during the 1971–2020 period, using the method of Theil-Sen, including (a) the Theil-Sen slope estimator and (b) the statistical significance, assessed with the Theil-Sen H hypothesis test. Regions with statistically significant trends are represented in blue (H = 1); Figure S4: The sum of the drought number (Sum DN, panels a to d), drought duration (Sum DD, panels e to h), drought severity (Sum DS, panels i to l) and drought intensity (Sum DI, panels m to p), assessed with the Standardized Precipitation Evapotranspiration Index (SPEI) at the 3-, 6-, 9- and 12-months timescales (from left to right), during the 1971–2020 period; Figure S5: The sum of the drought number (Sum DN) assessed with Standardized Precipitation Evapotranspiration Index (SPEI), at the 3-, 6-, 9- and 12-month timescales (panels left to right), during the 1971–2020 period for each Drought Class (DC), namely abnormally dry (DC 1, panels a to d), mild drought (DC 2, panels e to h), moderate drought (DC 3, panels i to l), severe drought (DC 4, panels m to p) and extreme drought (DC 5, panels q to t); Figure S6: Intra-annual distribution of the Sum of Drought Months (SDM), Mean Drought Severi-ty (MDS) and Mean Drought Extension (MDE) assessed with Standardized Precipitation Index (SPI) (panels a to d) and Standardized Precipitation Evapotranspiration Index (SPEI) (panels e to h) at timescales of 3-, 6-, 9- and 12-months (panels a to d), for the 1971–2020 period; Figure S7: Intra-annual distribution of Sum Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with SPI at the 3-, 6-, 9- and 12- months timescales (from left to right), for the 1971–2020 period and each Drought Class (DC), namely abnormally dry (DC 1, panels a to d), mild drought (DC 2, panels e to h), moderate drought (DC 3, panels i to l), severe drought (DC 4, panels m to p) and extreme drought (DC 5, panels q to t); Figure S8: Intra-annual distribution of Sum Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with the Standardized Precipitation Evapotranspiration Index (SPEI) at the 3-, 6-, 9- and 12-months timescales (from left to right), for the 1971–2020 period and each Drought Class (DC), namely abnormally dry (DC 1, panels a to d), mild drought (DC 2, panels om e to h), moderate drought (DC 3, panels i to l), severe drought (DC 4, panels m to p) and extreme drought (DC 5, panels q to t); Figure S9: Interannual distribution of the Sum of Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with Standardized Precipitation Evapotranspiration Index (SPEI) at the 3-, 6, 9- and 12-months timescale (panels a to d), for the 1971–2020 period; Figure S10: Spatial distribution of the annual Sum of Drought Months (SDM) for 2018 (panels a to d) and 2019 (panels e to h) and the Mean Drought Severity (MDS) also for 2018 (panels i to l) and 2019 (panels m to p), computed with Standardized Precipitation Evapotranspiration Index (SPEI) at timescales of 3-, 6-, 9- and 12-months (from left to the right); Figure S11: Inter-annual distribution of Sum Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with SPI at 3-, 6-, 9- and 12-months scales (from left to right), during the 1971–2020 period, for each drought class (DC), namely abnormally dry (DC 1, panels a to d), mild drought (DC 2, panels e to h), moderate drought (DC 3, panels i to l), severe drought (DC 4, panels m to p) and extreme drought (DC 5, panels q to t); Figure S12: Inter-annual distribution of Sum Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with Standardized Precipitation Evapotranspiration Index (SPEI), at 3-, 6-, 9- and 12-months timescales (from left to right), during the 1971 – 2020 period, for each drought class (DC), namely abnormally dry (DC 1, panels a to d), mild drought (DC 2, panels e to h), moderate drought (DC 3, panels i to l), severe drought (DC 4, panels m to p) and extreme drought (DC 5, panels q to t); and, Figure S13: Drought severity (DS) for November (panels a to d) and December (panels e to h) 2018, January (panels i to l) and February (panels m to p) 2019, computed with the Standardized Precipitation Evapotranspiration Index (SPEI), at the of 3-, 6-, 9- and 12-months timescales (from left to the right).

Author Contributions

Conceptualization, F.M.C., M.G.P. and M.A.; methodology, F.M.C., M.G.P. and M.A.; software, F.M.C. and M.G.P.; validation, F.M.C., M.G.P. and M.A.; formal analysis, F.M.C. and M.G.P.; investigation, F.M.C., M.G.P. and M.A.; resources, F.M.C., M.G.P. and M.A.; data curation, F.M.C.; writing—original draft preparation, F.M.C.; writing—review and editing, F.M.C., M.G.P. and M.A.; visualisation, F.M.C., M.G.P. and M.A.; supervision, M.G.P. and M.A.; project administration, M.G.P.; funding acquisition, F.M.C. and M.G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by National Funds by FCT—Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020).

Data Availability Statement

All data used in this study are freely accessible on the platforms of data providers, referred to in Section 2. The datasets generated and/or analysed during the current study are available from the corresponding authors upon reasonable request.

Acknowledgments

We want to thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the reanalysis data downloaded from the Climate Data Store, which was essential for carrying out this research. We also want to thank the authors of the SPEI R function and the Standardised Drought Analysis Toolbox (SDAT) function.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

BALWater balance
CCTotal cloud cover
DCDrought Class
DDDrought Duration
DIDrought Intensity
DNDrought Number
DSDrought Severity
ECMWFEuropean Centre for Medium-Range Weather Forecasts
EVIEnhanced Vegetation Index
IQRInterquartile range
MDEMean Drought Extent
MDSMean Drought Severity
MKMann–Kendall
NDVINormalised Difference Vegetation Index
NIRNear-infrared
PETPotential evapotranspiration
PEVPotential evaporation
QQuestion
SASouthern Africa
SDESum Drought Extent
SDMSum of Drought Months
SPEIStandardised Precipitation Evapotranspiration Index
SPIStandardised Precipitation Index
TMAX2mMaximum air temperature at 2 m
TMIN2mMinimum air temperature at 2 m
TPTotal precipitation
VCIVegetation Condition Index
W10mWind speed and directions at 10 m
W2mWind speed and directions at 10 m
ZAltitude (Geopotential height)

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Figure 1. Political map, ecoregions (a), and Köppen–Geiger climate type (b) in Southern Africa. Adapted from Olson et al. [28] and Kottek et al. [24], with the following: equatorial monsoon (Am); equatorial savannah with dry summer (As); equatorial savannah with dry winter (Aw), arid, steppe with hot arid (BSh); arid, steppe with cold arid (BSk); arid, desert with hot arid (BWh); arid, desert with cold arid (BWk); warm temperate, fully humid with hot summer (Cfa); warm temperate, fully humid with warm summer (Cfb); warm temperate, dry summer with hot summer (Csa); warm temperate, dry summer with warm summer (Csb); warm temperate, dry winter with hot summer (Cwa); and warm temperate, dry winter with warm summer (Cwb).
Figure 1. Political map, ecoregions (a), and Köppen–Geiger climate type (b) in Southern Africa. Adapted from Olson et al. [28] and Kottek et al. [24], with the following: equatorial monsoon (Am); equatorial savannah with dry summer (As); equatorial savannah with dry winter (Aw), arid, steppe with hot arid (BSh); arid, steppe with cold arid (BSk); arid, desert with hot arid (BWh); arid, desert with cold arid (BWk); warm temperate, fully humid with hot summer (Cfa); warm temperate, fully humid with warm summer (Cfb); warm temperate, dry summer with hot summer (Csa); warm temperate, dry summer with warm summer (Csb); warm temperate, dry winter with hot summer (Cwa); and warm temperate, dry winter with warm summer (Cwb).
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Figure 2. Sum of the Drought Number (Sum DN, from (a–d)), Sum of the Drought Duration (Sum DD, panels (e–h)), Drought Severity (Sum DS, panels (i–l)) and Drought Intensity (Sum DI, panels (m–p)), and assessed based on the SPI for the 3-, 6-, 9- and 12-month timescales (from left to right), during the 1971–2020 period.
Figure 2. Sum of the Drought Number (Sum DN, from (a–d)), Sum of the Drought Duration (Sum DD, panels (e–h)), Drought Severity (Sum DS, panels (i–l)) and Drought Intensity (Sum DI, panels (m–p)), and assessed based on the SPI for the 3-, 6-, 9- and 12-month timescales (from left to right), during the 1971–2020 period.
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Figure 3. Sum of the Drought Number (Sum DN) assessed with the SPI, at the 3-, 6-, 9- and 12-month timescales (panels left to right), during the 1971–2020 period for each Drought Class (DC), namely, abnormally dry (DC 1, panels (a–d)), mild drought (DC 2, panels (e–h)), moderate drought (DC 3, panels (i–l)), severe drought (DC 4, panels (m–p)) and extreme drought (DC 5, panels (q–t)).
Figure 3. Sum of the Drought Number (Sum DN) assessed with the SPI, at the 3-, 6-, 9- and 12-month timescales (panels left to right), during the 1971–2020 period for each Drought Class (DC), namely, abnormally dry (DC 1, panels (a–d)), mild drought (DC 2, panels (e–h)), moderate drought (DC 3, panels (i–l)), severe drought (DC 4, panels (m–p)) and extreme drought (DC 5, panels (q–t)).
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Figure 4. Interannual distribution of the Sum of Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with the SPI at timescales of 3, 6, 9 and 12 months (panels (a–d)), for the 1971–2020 period.
Figure 4. Interannual distribution of the Sum of Drought Months (SDM), Mean Drought Severity (MDS) and Mean Drought Extension (MDE) assessed with the SPI at timescales of 3, 6, 9 and 12 months (panels (a–d)), for the 1971–2020 period.
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Figure 5. Spatial distribution of the annual Sum of Drought Months (SDM) for 2018 (panels (a–d) and 2019 (panels (e–h)) and the Mean Drought Severity (MDS) also for 2018 (panels (i–l)) and 2019 (panels m–p)), computed with the SPI at timescales of 3, 6, 9 and 12 months (from left to right).
Figure 5. Spatial distribution of the annual Sum of Drought Months (SDM) for 2018 (panels (a–d) and 2019 (panels (e–h)) and the Mean Drought Severity (MDS) also for 2018 (panels (i–l)) and 2019 (panels m–p)), computed with the SPI at timescales of 3, 6, 9 and 12 months (from left to right).
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Figure 6. Anomalies of the NDVI (panels (a–d)), the EVI (panels (e–h)) and the VCI (panels (i–l)) in Southern Africa during the rainy season, from the December 2018 to February 2019 period.
Figure 6. Anomalies of the NDVI (panels (a–d)), the EVI (panels (e–h)) and the VCI (panels (i–l)) in Southern Africa during the rainy season, from the December 2018 to February 2019 period.
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Figure 7. Drought severity (DS) for November (panels (a–d)) and December (panels (e–h)) 2018, January (panels (i–l)) and February (panels (m–p)) 2019, computed with the SPI, for the timescales of 3, 6, 9 and 12 months (from left to right).
Figure 7. Drought severity (DS) for November (panels (a–d)) and December (panels (e–h)) 2018, January (panels (i–l)) and February (panels (m–p)) 2019, computed with the SPI, for the timescales of 3, 6, 9 and 12 months (from left to right).
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Figure 8. The difference between the annual MDE evaluated with the SPEI ( M D E S P E I ) and the SPI ( M D E S P I ) at timescales of 3, 6, 9 and 12 months, in SA for the 1971–2020 period.
Figure 8. The difference between the annual MDE evaluated with the SPEI ( M D E S P E I ) and the SPI ( M D E S P I ) at timescales of 3, 6, 9 and 12 months, in SA for the 1971–2020 period.
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Figure 9. Work and results flow diagram of this study.
Figure 9. Work and results flow diagram of this study.
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Table 1. Classifications of drought using SPIs and SPEIs. Adapted from Ali et al. [14] and Mehr et al. [47].
Table 1. Classifications of drought using SPIs and SPEIs. Adapted from Ali et al. [14] and Mehr et al. [47].
Drought ClassSPI ValuesSPEI Values
Abnormally dry conditions − 0.49   t o   0.0 − 0.49   t o   0.0
Mild drought − 0.99   t o − 0.50 − 0.99   t o − 0.50
Moderate drought − 1.49   t o − 1.0 − 1.42   t o − 1.0
Severe drought − 1.99   t o − 1.5 − 1.82   t o − 1.43
Extreme drought − 2.0 − 1.83
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Chivangulula, F.M.; Amraoui, M.; Pereira, M.G. The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors. Climate 2024, 12, 221. https://doi.org/10.3390/cli12120221

AMA Style

Chivangulula FM, Amraoui M, Pereira MG. The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors. Climate. 2024; 12(12):221. https://doi.org/10.3390/cli12120221

Chicago/Turabian Style

Chivangulula, Fernando Maliti, Malik Amraoui, and Mário Gonzalez Pereira. 2024. "The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors" Climate 12, no. 12: 221. https://doi.org/10.3390/cli12120221

APA Style

Chivangulula, F. M., Amraoui, M., & Pereira, M. G. (2024). The Drought Regime in Southern Africa: Long-Term Space-Time Distribution of Main Drought Descriptors. Climate, 12(12), 221. https://doi.org/10.3390/cli12120221

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