https://orcid.org/0000-0001-8279-8631
Figures
Abstract
The study assessed the frequency and severity of past and future meteorological drought in the Eastern Tigray zone. Global climate model data of Coupled Model Intercomparison Phase 5 (CMIP5), 20 General circulation models (GCM), 2 representative concentration pathways (RCP 4.5 and 8.5), and 2 time series (Mid-term (MT) and End-term (ET)) were used to project the future climate of the study area. Delta scenarios were created from the CMIP5 GCMs, bias corrected and spatially downscaled using the R software. Meteorological drought events were analysed using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI). The result shows the presence of predominant moderate drought severity in the study area, and it is expected to increase in frequency under all time segments and emission scenarios based on SPEI-12 and SPI-12, reaching a peak during the end of the century under RCP 8.5. Nevertheless, Kilte Awulaelo and Atsbi Wenberta experienced the highest frequency of severe drought, with Kilte Awulaelo reaching 50% occurrence. Rising atmospheric carbon dioxide (CO₂) concentrations are projected to exacerbate drought frequency and severity in the study area in the future climate. High drought frequency and severity will negatively influence crop production. There is, therefore, a need to adopt climate-smart agriculture for sustainable crop yield production and water harvesting and management.
Citation: Rubangakene D, Lamaro GP, Girma A, Zenebe A (2026) Assessing projected changes in meteorological drought severity and frequency under future climate scenarios: Insights from CMIP5 Models in Eastern Tigray, Northern Ethiopia. PLOS Clim 5(7): e0000944. https://doi.org/10.1371/journal.pclm.0000944
Editor: Lin Liu, First Institute of Oceanography Ministry of Natural Resources, CHINA
Received: March 25, 2026; Accepted: June 14, 2026; Published: July 14, 2026
Copyright: © 2026 Rubangakene et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The climate datasets used in this study are not publicly redistributable due to institutional access restrictions set by the Institute of Climate and Society, Mekelle University. These datasets are managed under controlled-access policies and cannot be shared directly by the authors. However, the data can be accessed upon reasonable request from the Institute of Climate and Society, Mekelle University, via email (ics@mu.edu.et), subject to their approval and data-sharing policies.
Funding: Damasco Rubangakene (DR) received grants from Transdisciplinary Training Resource Efficiency and Climate Change Adaptation in Africa (TRECCAFRICA), African Climate Change Adaptation Initiative (ACCAI), Vulnerability and Climate Change Adaptation and in Conflict-Affected Regions (VUCCA) (Grant no. 23-09-KU) to facilitate administration, software training and transport. The funders have zero contributions in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Ethiopia is a country inclined to drought with notable disastrous drought events over time [1–5]. Drought is a life-threatening natural peril, especially in the global south, with widespread effects on land, prompting a reduction in rainfall amount and loss of agricultural production and soil moisture, triggering famine, death, and water shortages [6–9]. Drought stress stimulates many biochemical and physiological changes that influence numerous processes that affect crop growth and yield formation [10]. Drought degree may be characterized in the form of intensity, duration, severity, frequency, or spatial or temporal [11]. Drought frequency is the percentage of drought events over a specific period of time and place, stated by its return period or incidence interval, termed as the average time lag between two events of the considered magnitude [12,13]. Meanwhile, drought severity is the withdrawal from normal of an index [14]. It’s a major guide used for depicting meteorological drought [15]. The presence of a meteorological drought is an early sign that other drought types are about to set in [16]. According to [17] and Andargie [18], in the past, the drought return period in Ethiopia used to take long, i.e., more than 10 years, unlike these days, when the return periods are very short, i.e., between 2–3 years [19]. Abrha and Simhadri [20] noted that on many occasions, drought returns before total recovery from the previous drought event, making it very difficult for farmers to cope with drought stress. This was attributed to climate change [21–23]. Many studies show that climate change alters drought occurrences and worsens drought frequencies and severity [24–26]. An increase in global mean temperatures may prompt a radical shift in the annual precipitation and deficit in soil moisture and dry up water reservoirs [27,28]. According to Liu et al. [11], drought occurs on different temporal scales, even though other studies argue that drought occurs at both spatial and temporal scales [26,29,30]. Temporal meteorological drought analysis shows that in the early 21st century, drought events ranged from mild to severe occurrences [31]. On the other hand, Nasir et al. [32] in their study on temporal meteorological drought in the southern zone of Tigray, observed a high severity and frequencies of ‘Kiremt’ season drought from 1983 to 1991, better in 1993–1998, and mild to moderate drought from 2000 to 2016.
Most of the studies conducted on drought cover limited areas of the Southern Tigray zone, leaving behind the Eastern Zone of Tigray, and above all, these studies have not shown well the progressions, spatiotemporal drought frequencies, and severities in food basket zone levels. Several of these studies were conducted on drought impacts on water catchments and yield and yield components of various crops and livestock [2,3,9], leaving out temporal and spatial drought evolutions and drought severity and frequency, especially in the Eastern Tigray zone. Research on drought at a zonal level is important in determining the local level of vulnerabilities. There are a lot of uncertainties about the future climate as well as the drought years, frequencies, severity, and spatial coverage in the Eastern Tigray Zone. The majority of the reviewed documentations show that the meteorological data used were not up-to-date, yet long-term spatially well-distributed recorded data are a prerequisite in drought planning and management [33]. Information on future climate and drought years is very key for timely preparedness and development of an early warning system. This study used the common and robust meteorological drought indices Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) to analyze historical drought and future climate drought in order to generate information that may enhance proactive measures by the policymakers and stakeholders against future drought events. SPI uses precipitation as an input to analyze drought; meanwhile, SPEI gives a better measure of drought severity by incorporating analysis of the difference between precipitation and potential evapotranspiration [1,34,35]. SPEI also integrates temperature variations as a portion of its analyses, making it more outstanding for drought analysis in many parts of the world, including the Tigray region of Ethiopia [1,36]. The livelihood of the study area (Eastern Zone of the Tigray region in Northern Ethiopia) depends on rain-fed agriculture with bimodal rainfall seasons called the ‘Kiremt’ season, which is a long rainy season (June to September), and the ‘Belg’ season, which is a short rainy season (March–May) for both crop and forage production and vegetation growth, respectively. Thus, a detailed study on drought development and trends during ‘Belg’ and ‘Kiremt’ is of paramount importance at the zonal food basket level to capture the exact degrees of vulnerabilities and drought hotspots. Additionally, local historical drought event analysis may be useful in long-term drought planning for a resilient maintenance system for sustainable natural resources management. Thus, this study supports the existing drought monitoring and early warning system and helps in establishing resilience to drought at the household level. This study, therefore, was conducted to assess the frequency and severity of the past and future meteorological drought in the Eastern Tigray zone using CMIP5. According to van Vuuren et al. [37], CMIP5 is useful in assessing future climate change in the milieu of internal climate erraticism, predictability, and situation uncertainty. It is comparatively very efficient in analyzing atmospheric emissions and concentrations and land use, thereby guaranteeing uniformity with past forcing information and between the different forcing groups. The forcings are synchronized across IAMs and scenarios.
2. Materials and methods
2.1. Study area description
The study was conducted in three districts in the Eastern zone of the Tigray region, Northern Ethiopia, with coordinates: latitude = 13.550544 to 14.683731; longitude = 39.195342 to 39.994789. The altitude of the study area ranges from 1982 to 2666 meters above sea level (Table 1, Fig 1).
Available at: https://data.humdata.org/dataset/cod-ab-eth. Licensed under CC BY‑IGO (https://creativecommons.org/licenses/by/4.0/).
The rainfall of this area is bimodal (S1–S6 Figs), with 80% of the total rainfall occurring in the ‘Kiremt’ season (June to September (JJAS)) and the remaining 20% spread over the ‘Belg’ season (March to May). The annual rainfall ranges between 500 and 700 mm [38, NAPA, 2007].
2.2. Data type
2.2.1. Baseline meteorological data preparation and quality checks.
The 30 years (1980–2009) of observed meteorological datasets used in this study were obtained from the Ethiopian National Metrological Agency (NMA). However, the obtained meteorological datasets had some missing values; thus, data gap-filling was required. Data completeness was assessed as the percentage of missing observations relative to the total record length. Data availability exceeded 90% for all variables, with missing rates ranging from 2.3% (temperature) to 2.1% (precipitation). The Statistical Downscaling Model (SDSM), a hybrid statistical tool used to relate large-scale atmospheric predictors to local-scale climate variables, in combination with a bias-correction approach, was applied in this study. Observed meteorological datasets were bias-corrected using a comprehensive set of daily weather observations obtained from the Agricultural Model Intercomparison and Improvement Project (AgMIP), a global initiative that supports the improvement and integration of agricultural models. The resulting gap-filled datasets were subsequently examined for discontinuities and out-of-range values using TAMET (Tool for Assessing Meteorological Time Series), a quality-control software designed to detect inconsistencies and errors in meteorological datasets.
2.2.2. Future climate of the study area.
Historical climate records and future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Models (GCMs) were utilized. Delta change scenarios were derived from CMIP5 outputs, and bias-corrected spatially downscaled datasets were generated using the R script “agmipsimple delta.R”. This procedure followed the guidelines provided for running AgMIP Climate Scenario Generation Tools in R under a Windows environment [39]. For each long-term (30-year) baseline dataset, the above script was used to create 80 delta-adjusted data files (2 time scales * 2 RCPs [4.5 and 8.5] * 20 GCM). The results were organized and presented using R software’s boxplots and probability density functions (PDF). Therefore, the predicted future climate datasets were extracted in R software [40], and afterwards, climate model ensembles were created using the pivot table function of Excel. The use of climate ensembles was intended to overcome uncertainties in prediction that could result from differences in model parameterizations. The use of ensembles enables estimations of the certainty of results [41]. The ensemble future climate datasets for mid-term (2040–2069) and end-term (2070–2099) for each of the stations were then used as inputs for SPI and SPEI computation.
We executed the correlation coefficient (r) test to evaluate the predictive performance of the GCMs, as applied in Adib et al. [42], using equation one (1) below. The correlation coefficient was executed between the six (6) years of observed meteorological datasets [2011–2016] and the predicted meteorological datasets for the same period.
Where and represent predicted and observed values, respectively.
and
represents the average values of the predicted and observed, respectively.
2.3. Data analysis method
2.3.1. Trend analysis of precipitation and temperature.
The R software, version 3.4.0 [40], was used for the computation of both the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI). SPEI and SPI were computed at 4- and 12-month accumulation scales to represent short- and long-term drought conditions, respectively. SPEI-4/SPI-4 capture short-term (agricultural) moisture variability, while SPEI-12/SPI-12 reflect longer-term (hydrological) drought. SPEI accounts for both precipitation and potential evapotranspiration, whereas SPI is based only on precipitation anomalies. The SPI R-scripts developed with the rectangular kernel type, gamma distribution, and “ub-pwm” fit-in Tinn-R software were used for calculating SPI. The SPEI script was developed with log-logistic distribution for standardization and a rectangular kernel function for smoothing noisy SPEI, following the approach by Vicente-Serrano et al. [34].
The station-based metrological drought events in the past and future climate of the study area were analyzed using the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) [38]. SPI is an index based on the probability distribution of precipitation. It depends on the fitted density probability function, the length of the series used to estimate the parameters of the probability function, and the method of estimation. Conceptually, SPI is equivalent to the Z-score used in statistics and is formulated in equation number two (2), as;
Where is the SPI of the month at
time-scale,
is precipitation total for the
month at
time-scale,
and
are long-term mean and standard deviations associated with
month at
the time scale, respectively? The SPI was designed to quantify the precipitation deficit for multiple timescales. These timescales reflect the impact of drought on the availability of different water resources [43].
Since precipitations often do not follow a normal distribution [44], practical applications of the above SPI algorithm reveal some disadvantages [44,45] such as misleadingly large positive or negative SPI values when the index is applied at short time steps to regions of low seasonal precipitation [46]. To counteract this challenge, we considered computing the SPI by fitting the monthly long-term precipitation time series into a gamma parameter distribution to estimate the precipitation probability density functions (PDFs) as suggested by Thom [47]. The gamma distribution is defined by its frequency or probability density function as expressed in equation three (3):
Where:
α > 0 is a shape parameter, β > 0 is a scale parameter, x > 0 is the amount of precipitation and defines the gamma function, as expressed in equation four (4);
To fit the gamma distribution [] to the precipitation data
,
and
are estimated for each time step of interest and each month of the year using the approximation of Thom [47] for maximum likelihood [48], as expressed in equation five (5) and six (6)
Where;
Where;
Integrating the probability density function with respect to
and inserting the estimates of α and β yield an expression for the cumulative probability
of an observed amount of precipitation occurring for a given month and time step is shown using equation seven (7):
Since the gamma distribution is undefined for =0 and
= P (
=0) > 0 where P (
=0) is the probability of zero precipitation, the cumulative probability will become as expressed using equation eight (8):
The cumulative probability distribution was then transformed into the standard normal distribution to yield the SPI using the approximate conversion provided by Abramowitz and Stegun [49] (1) expressed using equation nine (9) and ten (10) respectively;
= 2.515517;
= 0.802853;
= 0.010328;
=1.432788;
= 0.189269;
= 0.001308
Although precipitation is the primary factor controlling the formation and persistence of drought, other variables such as evapotranspiration (negative impact) provide more penalty to the SPI model [50,51]. Standardized Precipitation and Evapotranspiration Index (SPEI) is one of the climatic proxies widely used for drought quantification and monitoring [35]. The index was developed by Vicente-Serrano et al., [35] in attempts to address the Potential Evapotranspiration (PET) issue by including a temperature component in the quantification and monitoring of drought scenarios. SPEI uses the monthly (or weekly) difference between precipitation (P) and PET. This represents a simple climatic water balance, which is calculated at different time scales to obtain the SPEI. In this research, the PET was computed using [52] shown in equation eleven (11):
Where;
With a value for PET, the difference (D) between the precipitation (P) and potential evapotranspiration (PET) for the , will be calculated using equation twelve (12) as:
Standardization of the variables for SPEI computation was done using log-logistic distribution. The probability density function of a three-parameter log-logistic distributed variable is expressed using equation thirteen (13) as:
, where α, β and γ are scale, shape, and origin parameters, respectively, for D values in the range (γ > D < ∞).
Positive SPI/SPEI values indicate greater than median precipitation, and negative values indicate less than median precipitation [35,51]. Because the SPI/SPEI is normalized, wetter and drier climates can be represented in the same way [35,51]; thus, wet periods can also be monitored using the SPI/SPEI. Once standardized, the strength of the SPI and SPEI is given in Table 2.
In this study, a 4-month and 12-month time scale SPI and SPEI were calculated using the historical and predicted meteorological datasets of the future climate for the study area. A 4-month SPI and SPEI ≤ reflect short- and medium-term moisture conditions [38]. In other words, a 4-month SPI and SPEI at the end of September compares the June–July–August–September precipitation total in that particular year with the June–September precipitation totals of all the 30 years on record for the study area [38]. Since the Eastern Tigray zone is a primary agricultural area for the region [3], a 4-month SPI and SPEI shall be more effective in highlighting moisture conditions during cropping cycles. On the other hand, the 12-month SPI and SPEI compare precipitation for 12 consecutive months with that recorded in the same 12 consecutive months in all the 30 years of record for the study area. The 12-month SPI/SPEI highlights hydrological impacts of drought [37].
2.3.2. Quantifying meteorological drought severity and frequency in the past and future climate.
Using the output from SPI and SPEI, the drought frequency in the future climate of the study area was analyzed using equation 14:
Where;
: Number of months with drought events (Negative SPI and SPEI)
: Number of total months
Drought severity classification given by SPI/SPEI was validated on ArcGIS version 10.5 using Vegetation Index Anomaly (Dev-NDVI). The Dev-NDVI was computed following the formula by Thenkabail et al. [54] as:
where, is the NDVI value for the month
and
is the long-term mean NDVI for the same month
. A negative Dev-NDVI is an indicator of below-normal vegetation health, reflecting an agricultural drought situation.
3. Results
3.1. Past drought frequencies
In this study, the drought frequency was explained in terms of severity class frequency over the past 30 years (1980–2009) represented in Fig 2.
Based on SPEI-4 and SPI-4 timescales, moderate drought had the highest frequency, followed by severe drought in all the stations under consideration. SPEI-4 timescale indicated that at least 70 percent (70%) of the total months with drought events experienced moderate drought. Furthermore, Gulomekeda had the highest frequency of moderate drought compared to Kilte Awulaelo and Atsbi Wenberta. SPI-4 timescale, Kilte Awulaelo experienced the highest frequency of moderate drought. In the same timescale, extreme drought (though with the lowest frequencies of occurrences) was registered in all the three stations and was highest in Atsbi Wenberta (12%). Using a 12-month timescale SPEI, no occurrence of extreme drought was registered in Kilte Awulaelo. However, in Atsbi Wenberta and Gulomekeda, drought frequency was only 5.3% and 1.4%, respectively. Based on the SPI-12 timescale, the difference between the frequencies of moderate, severe, and extreme drought was low. The frequency of severe drought in Atsbi Wenberta (38.5%) was higher than moderate (35.4%) and extreme drought (26.2%), respectively. Relatively, Kilte Awulaelo and Atsbi Wenberta experienced the highest frequency of severe drought, with Kilte Awulaelo reaching 50% occurrence.
3.2. Future climate and the meteorological drought events in the study area
The boxplots and probability density plots for the variables under consideration are shown in Figs 3 and 4. It was from the boxplots that contrasting models were identified. The red oval shape indicates the under- and over-predicting GCM models selected for that specific time segment and emission scenario (based on RCP 4.5 and 8.5). Inside the red oval shape is the median, indicating the midpoint of the data, and it is shown by the line that divides the box into two parts.
Figs 3 and 4 together show how minimum temperatures at Fatsi station, for example, are expected to warm over time, with both panels revealing a clear upward shift and substantial differences across climate models. In the near‑term under RCP4.5 (Fig 3), models consistently indicate warmer minimum temperatures than the historical baseline, but they vary widely in their medians, spreads, and distribution shapes. Some models project relatively modest warming, while others show much warmer and more variable nighttime conditions. The density curves reinforce this pattern, clustering around a warmer modal range yet diverging in how broad or skewed the distributions are.
In contrast, by the end of the century under RCP8.5 (Fig 4), the warming signal becomes much stronger, and the spread among models grows even larger. Model medians and interquartile ranges shift far above the baseline, and the density curves converge on a substantially warmer distribution while still differing in width and tail behavior. Some models indicate much warmer temperatures, whereas others retain narrower or slightly cooler distributions.
This persistent variation in model predictions across both scenarios is precisely why an ensemble approach is essential. The use of ensemble allowed us to capture the full range of futures and to assess drought risks in a way that reflects both the consistent warming trend and the uncertainty inherent in individual GCMs.
3.2.1. Predicted change in temperature across the study area.
For each station, time segment, and representative concentration pathway (RCP’s), those models that predict under and over—hereunder referred to as “contrasting models” for the minimum temperature were summarized. The standardized display of the distribution of projected change in minimum temperature and maximum temperature based on the five-number summaries: minimum, first quartile, median, third quartile, and maximum is shown in Fig 5. The figure shows projected changes in minimum and maximum temperatures across the three stations, with each boxplot summarizing outputs from multiple contrast models under RCP4.5 and RCP8.5. All stations exhibit warming, but the magnitude of change differs across locations, with some stations showing stronger increases in both minimum and maximum temperatures. Maximum‑temperature models generally project larger shifts than minimum‑temperature models, and warming intensifies under RCP8.5. The wide spread of the boxplots across scenarios and models highlights substantial inter‑model variability, reflecting differences in how models simulate future warming.
3.3. Projected drought severity frequency and comparison to the baseline
The future drought frequency by severity classes under the future climate is graphically illustrated using Fig 6. The illustrations depict the directional change in the frequency of drought severity classes abbreviated as “MD”, “SD” and “ED” for moderate drought, severe drought, and extreme drought, respectively. The three colors represent drought severity classes, and the different sizes of the points represent the frequency range for that particular drought severity class. The figure shows that all stations experience high drought‑severity frequencies under future climate scenarios, with the magnitude and pattern varying by index (SPEI vs. SPI), timescale (4‑month vs. 12‑month), and emissions pathway. Bubbles above zero indicate higher future occurrence of each severity class, and these increases are most pronounced for moderate and severe droughts across several scenarios. Extreme drought frequencies also rise at specific stations, particularly under RCP8.5 and in the MT‑based projections. The differences among stations highlight spatial variability in drought sensitivity, while the contrasting bubble sizes across scenarios reflect how warming intensifies drought severity in some locations more than others. Overall, the fig indicates a general shift toward more frequent drought conditions in the study area under future climate.
A comparison of baseline and projected drought frequencies was conducted, and the resulting difference plots (Fig 7) show where drought severity classes are expected to increase or decrease in the future. Values at zero indicate no change, values below zero indicate a reduction, and values above zero indicate an increase. Focusing on the positive values, the results reveal distinct spatial and temporal patterns across stations, indices, and emission scenarios.
For short‑term drought (4‑month scale), Atsbi Wenberta shows a consistent increase in severe drought frequency under SPI‑4 across most time periods and scenarios, except under end‑term RCP 8.5, where moderate drought increases instead. SPEI‑4 similarly indicates rising severe drought during mid‑term RCP 4.5 and end‑term RCP 8.5, with moderate drought increasing under mid‑term RCP 8.5.
In Gulomekeda, SPEI‑4 indicates increased extreme drought frequency across nearly all time periods and scenarios, except mid‑term RCP 4.5. Severe drought also increases broadly, except under mid‑term RCP 8.5. SPI‑4 shows an increase in moderate and severe drought during mid‑term RCP 4.5 and RCP 8.5, with moderate drought rising under end‑term RCP 4.5 and severe drought under end‑term RCP 8.5.
For Kilte Awulaelo, SPI‑4 shows a consistent rise in severe drought across all time periods, with extreme drought increasing under mid‑term RCP 4.5 and end‑term RCP 8.5. SPEI‑4 indicates increases in severe drought under mid‑term RCP 4.5, moderate drought under mid‑term RCP 8.5, extreme drought under end‑term RCP 4.5, and both moderate and severe drought under end‑term RCP 8.5.
For long‑term drought (12‑month scale), Kilte Awulaelo shows a general increase in moderate drought frequency across all time periods and scenarios under both SPEI‑12 and SPI‑12, with the strongest increase under end‑term RCP 8.5. Atsbi Wenberta shows rising moderate drought under all periods and scenarios except mid‑term RCP 8.5 in SPEI‑12. SPI‑12 indicates increases in moderate and severe drought during mid‑term and end‑term RCP 4.5, with severe drought also increasing under mid‑term and end‑term RCP 8.5.
Overall, the positive values across stations and indices indicate that future climate conditions are likely to intensify drought occurrence, though the magnitude and severity class vary by location, timescale, and emissions pathway.
4. Discussions
The study revealed that moderate drought was the most frequently occurring drought category across all stations, followed by severe drought. The SPEI-4 analysis shows that at least 70% of all drought-affected months were classified as moderate drought events. Among the study sites, Gulomekeda recorded the highest frequency of moderate drought, compared to Kilte Awulaelo and Atsbi Wenberta. Similarly, results from the SPI-4 timescale indicate that Kilte Awulaelo experienced the highest frequency of moderate drought. Although less frequent, extreme drought events were observed across all three stations under the SPI-4 timescale, with Atsbi Wenberta registering the highest proportion (12%). Over the 12-month SPEI timescale, extreme drought events were largely absent in Kilte Awulaelo, while Atsbi Wenberta and Gulomekeda recorded relatively low occurrences of 5.3% and 1.4%, respectively. In contrast, the SPI-12 timescale revealed a more balanced distribution among drought categories, although slight variations persisted. Notably, Atsbi Wenberta exhibited a higher frequency of severe drought (38.5%) compared to moderate (35.4%) and extreme (26.2%) drought conditions. Overall, Kilte Awulaelo and Atsbi Wenberta showed relatively higher frequencies of severe drought, with Kilte Awulaelo reaching up to 50%. These findings are consistent with earlier studies that highlight the persistent nature of moderate drought conditions in Ethiopia. For instance, Nasir et al. [32] reported that drought severity and frequency in Ethiopia were particularly high between 1983 and 1991, followed by a reduction during 1993–1998, and a shift toward mild to moderate drought conditions from 2000 to 2016. Similarly, Gidey et al. [19] observed recurring moderate drought events with notable frequency and duration in mid- and highland areas over the last 15 years. Tefera et al. [1] further emphasized that districts in Eastern Tigray have experienced comparatively more intense drought conditions, characterized by high spatial variability and frequent occurrences. Zeleke [55] also documented an increasing trend in mild to moderate drought events across both temporal and spatial scales during the study period. Collectively, these studies reinforce the prevalence of moderate drought conditions observed in the present study. Additionally, supporting the present findings, Rubangakene et al. [56], using satellite-based vegetation analysis (NDVI), reported that drought events in 2005, 2006, 2009, 2010, and 2014 simultaneously affected Gulomekeda, Atsbi Wenberta, and Kilte Awulaelo.
Projecting into the future, the results from this study indicate a consistent future increase in drought frequency and severity across the study area under both mid- and end-century scenarios. This trend is associated with projected increases in atmospheric CO₂ concentrations, which drive global warming and subsequently enhance evapotranspiration demand. In the context of the study area, where agriculture is largely rain-fed, this increased atmospheric water demand directly translates into reduced soil moisture availability and prolonged dry spells, thereby intensifying drought conditions. Unlike general global projections, the findings from this study show that moderate drought conditions are likely to become more persistent in the mid-term period. This suggests a transition from episodic drought events to more sustained water deficits, which may not allow sufficient recovery time for soil moisture, crops, and hydrological systems. As a result, agricultural productivity in the study area is expected to become increasingly unstable, with higher vulnerability to seasonal rainfall variability. Our findings are consistent with earlier findings by Nasir et al. [32], and Kamali et al. [57], who reported a steady increase in drought frequency likely attributed to the increase in temperature. Earlier climate prediction studies in the Tigray region of Ethiopia also showed an altered precipitation pattern, accompanied by higher temperatures in the future climate [58]. Compared with historical drought behavior reported by Nasir et al. [32], which showed alternating dry and wet periods, the present study suggests a shift toward more consistently moderate drought conditions in the future. An increase in moderate drought frequency will inevitably expose the study area to increased water stress, crop failure, and food insecurity in the future, provided no actions are taken to positively manage drought events.
The study also observed that drought severity expands spatially and temporally, across all time scales, consistent with findings from other authors such as Kamali et al. [57], who predicted that drought severity shall be felt in a larger span of land coverage, which shall jeopardize food security, putting many lives at high risk of food and nutrition insecurity. The spatial expansion of drought severity observed in this study further indicates that drought impacts will not be confined to traditionally vulnerable zones but are likely to extend across a larger portion of the study area. This spatial intensification is particularly important because it implies that previously less-affected areas may also begin experiencing frequent agricultural stress, reducing the overall resilience of the regional food production system.
The implications of these findings are significant for local water and agricultural management, especially in Kilte Awulaelo and Gulomekeda districts, which are likely to experience a greater upsurge in moderate drought frequency. Increasing drought frequency will likely lead to reduced crop yields, shortened growing seasons, and increased irrigation demand. However, given the limited water storage and irrigation infrastructure in the study area, these increasing demands may not be sustainably met. Consequently, without adaptation strategies such as improved water harvesting, drought-resistant crop varieties, and climate-informed planning, the region is likely to face heightened risks of food insecurity.
5. Conclusions
This study analyzed meteorological drought characteristics using 4-month and 12-month SPI and SPEI indices and found that moderate drought consistently dominated both short- and long-term conditions across all stations. Gulomekeda, Kilte Awulaelo, and Atsbi Wenberta experienced recurrent moderate drought events, while Kilte Awulaelo and Atsbi Wenberta exhibited comparatively higher overall drought frequencies, with severe drought conditions being particularly prominent in these two districts. Future climate projections under the RCP 8.5 scenario indicate an increasing trend in both the frequency and severity of drought events, especially in Kilte Awulaelo and Gulomekeda, likely driven by rising atmospheric CO₂ concentrations and enhanced evapotranspiration demand. This shift is expected to intensify soil moisture deficits, reduce crop productivity, and increase agricultural vulnerability in predominantly rain-fed systems. The findings align with previous studies [32,57,58] and further highlight that drought impacts are likely to expand spatially over time. In addition, in conflict-affected contexts, such as the study area, drought risks are exacerbated by limited adaptive capacity and weakened resource governance [59]. Therefore, strengthening climate-smart agriculture, water harvesting, and drought-resilient livelihood strategies is essential to reduce future vulnerability and enhance resilience to combined climate and fragility pressures.
Supporting information
S1 Fig. Atsbi station rainfall distributional statistics.
https://doi.org/10.1371/journal.pclm.0000944.s001
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S2 Fig. Atsbi station rainfall distribution percentiles.
https://doi.org/10.1371/journal.pclm.0000944.s002
(TIF)
S3 Fig. Fatsi station rainfall distributional statistics.
https://doi.org/10.1371/journal.pclm.0000944.s003
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S4 Fig. Fatsi station rainfall distribution percentiles.
https://doi.org/10.1371/journal.pclm.0000944.s004
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S5 Fig. Wukro station rainfall distributional statistics.
https://doi.org/10.1371/journal.pclm.0000944.s005
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S6 Fig. Wukro station rainfall distribution percentiles.
https://doi.org/10.1371/journal.pclm.0000944.s006
(TIF)
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