3.2 Projected change in precipitation
Precipitation is a major factor regulating the occurrence of drought from one region to another (Dai 2018). Other factors include mean surface air temperature, wind speed, humidity, and incoming solar radiation that directly or indirectly affect drought occurrence in terms of enhanced evaporative demand through vapor pressure deficit (IPCC 2014). Thus, it is imperative to understand the projected changes in precipitation in order to relate the changes to drought events. Figures 3 and 4 show the future temporal and spatial precipitation change over EA relative to the historical period. Generally, all the scenarios depict increasing patterns with differing magnitudes. The SSP5-8.5 scenario shows intensified changes during the far future (15%) relative to the historical period (5%), while minimal changes are predicted to occur under SSP1-2.6 during the mid (3%) and far future (7%) (Fig. 3). Meanwhile, spatial changes in precipitation over EA during the mid and far future depict a homogeneous spread of precipitation with a 100 – 300% increase under high emission scenarios (Fig. 4d, h). However, negative anomalies are projected to occur over most parts of Kenya and the coastal belt in Tanzania at -40% under SSP1-2.6 during the mid and far future (Fig. 4a, e). Interestingly, a persistent dry anomaly is projected to occur over the coastal belt of Tanzania across all scenarios and timescales, with intense dryness at -60% under SSP1-2.6 and subsequent reduction at -10% under SSP5-8.5 (Fig. 4). Generally, the projected increase in precipitation under a high emission scenario demonstrates the impact of anthropogenic GHG emissions. The results of this study agree with existing literature over EA that demonstrated the pronounced increase in precipitation under high emission scenarios as compared to the low forcing sustainability pathways (Onyutha et al. 2021; Ayugi et al. 2021b; Makula and Zhou 2021). The recently released assessment report of the Inter-governmental Panel on Climate Change Working Group 1 (IPCC, 2021) pointed to unprecedented changes that will affect the climate system due to human influence as a result of GHG emissions. For instance, the report projected an increase or decrease in the frequency and intensity of heavy precipitation over most land areas, leading to more drought or flood events over different regions. It is thus necessary to examine the possible changes in EA’s drought characteristics that exhibit high interannual precipitation variability (Ongoma and Chen 2017; Ongoma et al. 2018; Tan et al. 2020; Ayugi et al. 2021c).
3.3 Projected Changes in dryness/wetness
To quantify the dryness and/or wetness over EA, the estimated probability distribution functions (PDFs) are shown in Fig. 5. The PDFs are calculated from the regional mean CMIP6 SPI for the baseline period, mid and far future under the selected scenarios. Further, spatial changes in drought patterns based on SPI is presented in Fig. 6. In general, relative to the baseline period, the projected drought changes over EA are characterized by both drying and wetting patterns with average negative SPI anomalies of -0.06, -0.03, -0.13, and -0.14 for the mid future and positive SPI of 0.12, 0.20, 0.41, and 0.54 in the far future. Except for SSP1-2.6 (Fig. 5a), the PDFs of SSP2-4.5, SSP3-7.0 and SSP5-8.5 show left-skewness during the mid future, while this phenomenon is expected to lapse into a distinct wetting trend in the far future as portrayed by the right-skewed PDFs (Fig. 5). Likewise, the spatial changes demonstrate noteworthy drought (wetness) events during the mid future (far future) under the SSP3-7.0 and SSP5-8.5 scenarios (Fig. 6). For instance, severe drought events (SPI = -1.5) are projected across the entire domain under SSP3-7.0 and SSP5-8.5 during the mid-century (Fig. 6c, d). Reverse patterns of extreme wetness events of 2.0 to 2.5 are observed towards the end of the twenty-first century (Fig. 6g, h). Obviously, the difference in dryness and wetness is high in the far future and relatively low in the near future.
3.4 Projected Changes in drought event characteristics
The absolute spatial changes of drought characteristics during the mid and far future, relative to the baseline period, are shown in Figs. 7-10. To quantify the changes of drought characteristics, the box plots of regional drought characteristics under the four selected scenarios are shown in Fig. 11. Drought duration is defined as the number of months under drought conditions. The spatial variance and temporal distribution of drought duration are given in Fig. 7a-h and Fig. 11a. Relative to the baseline period, most areas under the four scenarios are projected to experience drought duration in the mid future while wetting patterns are projected in the far future over EA (Fig. 7). The mean drought duration in EA is projected to occur for 3.6, 3.62, 3.8, and 3.85 months under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, respectively, during the mid future (Figs. 11a). Mid future shows the lowest occurrence of DD, except under the high emission scenario that shows the likely occurrence of longer drought duration. The emission effect from SSP1-2.6 on DD is not evident in the mid future (Fig. 7a-d) but distinct in the far future (Fig. 7e-h).
Figure 8 and Fig. 11b present spatially-averaged drought frequency over EA that amounts to 15% during the baseline period, 13% (average value from all scenarios) in the mid future (Fig. 8a-d and 11b) and 9% in the far future (Fig. 8e-h and Fig. 11b). The drought frequency in the mid future will be higher than during the baseline period. This suggests that EA may experience more drought frequency in the mid future. Curiously, during the far future, drought frequency decreases from 14% under the low-emission scenario (SSP1-2.6) to 3% under the high-emission scenario (SSP5-8.5). This implies the possibility of a higher DF during the mid future and lower DF in the far future. This is consistent with the dryness changes shown in Figures 5 and 6. Interestingly, DF in the far future is projected to increase considerably (Fig. 8e-h), particularly under average no policy (SSP3-7.0) and worst-case no policy (SSP5-8.5) (Fig. 8g, h) along the coastal belt of the Tanzanian region. The regions dominated by dry changes (e.g., northern Kenya, the northern parts of Uganda and northern tips of Tanzania, as shown in Fig. 6) tend to experience more drought events in the mid future under the four scenarios (Fig. 8a-d). For example, relative to the baseline period with an average drought frequency, Kenya, Uganda, Rwanda, and Burundi are projected to experience more drought events in the mid future (2%, 4%, and 8%) under SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios (Fig. 8b, c, d), respectively.
The examination of DI, computed as the ratio between DS and DD, is presented in Figures 9 and 11c. DI is used to measure the overall intensity of drought events. For example, drought events of similar severity may have very different intensities if their durations are different. The mean values of DI are calculated for the historical and future periods under the different scenarios. The absolute changes in DI during the mid and far future are shown in Fig. 9a-h, and the regional DI statistics are given in Fig. 11c. Generally, relative to the baseline period, EA’s DI preserves similar or becomes lower in the far future and becomes higher in the mid future under the different scenarios. DI will likely impact the north-eastern parts of Kenya and Uganda during the mid future under SSP3-7.0 and SSP5-8.5 (Fig. 9c, d). In the mid future, relative to the baseline period, EA will experience higher DI than the baseline period at 10, 22, 21 and 25% under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively. Similarly, in the far future, the region is likely to experience DI at 14%, 10%, 8% and 5% under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively.
Lastly, the study assesses the changes in DS, which is defined as the cumulated SPI value during the drought event and is used to measure drought magnitude. The larger the DS value, the more severe the drought. The absolute change in mean drought severity is calculated for each grid in the mid and far future under the four future scenarios (Fig. 10a-h). In addition, the statistics of DS over EA are also shown in Fig. 11d. In general, EA is projected to experience a more severe drought during the mid and far future under the different scenarios. As expected, DS increases much more in the mid future than in the far future, partly contributed to by the great increase in DD as presented in Fig. 7. The coastal region and parts of Tanzania are projected to experience less severe droughts in the far future under SSP5-8.5 scenarios (Fig. 10d). The effect of emission concentration is not apparent in the far future in most regions. In western Kenya and north parts of Tanzania, there is an interesting higher DS for the low emission concentration (Fig. 10e), while in the far future, the emission concentration effects on DS are less significant over the entire region (Fig. 10f, g, h).
The results agree with previous studies conducted over the region based on the Representative Concentration Pathways (RCPs) scenario derived from CMIP5 data and demonstrated an increase in wetness and dryness during the far future (Tan et al. 2020; Spinoni et al. 2020). For instance, related studies (e.g., Nguvava et al. 2019; Haile et al. 2020) showed an increase in drought events, mainly attributed to increased evaporation from higher temperatures. The studies mentioned above were mainly based on the Standardized Precipitation Evapotranspiration Index (SPEI) that accounts for both precipitation and PET. Supportably, Joeng et al. (2014) affirmed that the observed and projected drought extremes are mainly driven by an increase in mean surface temperature and PET. In fact, recent studies based on CMIP6 models (i.e., Almazroui et al. 2021; Ayugi et al. 2021c; Iyakaremye et al. 2021) have all pointed out a steady increase in mean and extreme temperature properties over EA comparative to other sub-regions of the continent. For instance, Almazroui et al. (2020) stated that the increasing trend in temperature over EA under SSP1-2.6, SSP2-4.5, and SSP5-8.5 is projected to be 0.03, 0.22 and 0.49°C decade−1, respectively.
However, other researchers have remarked that the more pronounced drought events projected at a regional level are mainly due to uncoupled modelling approaches that largely overestimate regional drought events due to wrong assumptions under increasing CO2 emissions (Swan et al. 2016; Yang et al. 2019). Overall, the projected drought changes over EA follow changes in emissions and different periods. The mid future shows persistent drought events under medium forcing middle-of-the-road pathways (+7.0 W m−2) and high-end forcing pathways (+8.5 W m−2), reflecting a sustained pattern observed since 1992, following intense warming that occurred along the Western Indian Ocean (Lyon and DeWitt 2012; Liebmann et al. 2014) and SST variations over the Indo-Pacific associated with the Walker circulation (Hua et al., 2016) over EA. In contrast, during the far future, the region will likely experience more wetness under SSP3-7.0 and SSP5-8.5 as compared to the low emission scenario of SSP1-2.6 or under low forcing sustainability pathways of SSP2-4.5. The results reaffirm the conclusions of the IPCC (2021) report that clearly stated the influence of humans in climate change over most parts of the regions around the globe.