Standardized precipitation index (SPI).

The Standardized Precipitation Index (SPI) is one of the most widely used drought indicators in scientific research due to its simplicity and flexibility, as it relies solely on precipitation data and can be applied across multiple temporal scales [56,67]. The SPI is calculated as follows [67,68].

(19)

Where: x is precipitation value, µ is long-term mean precipitation, and δ is standard deviations of long-term precipitation

Reconnaissance drought index (RDI).

As an extension to the SPI, the Reconnaissance Drought Index (RDI) accounts for both precipitation and atmospheric evaporative demand, making it a multi-scalar index [69,70]. The RDI produces three forms commonly called the initial value of the Reconnaissance Drought Index (α_k), the normalized Reconnaissance Drought Index (RDI_n), and the Standardized Reconnaissance Drought Index (RDI_st) [57]. RDI is computed using precipitation (P) and potential evapotranspiration (PET), which can be estimated using the Hargreaves-Samani method [17], as expressed in Equation (20):

(20)

Where: T _max and T _min represent the maximum and minimum air temperature (^oC), respectively, and Ra (MJ/m²/day) is the extraterrestrial solar radiation, which depends on the latitude of the study area and the time of year.

The initial RDI value (α_k) is calculated as the ratio of cumulative precipitation to cumulative potential evapotranspiration over a selected timescale k, as shown in Equation (21):

(21)

Where: pij and PETij denote precipitation and potential evapotranspiration, respectively, for the j^th month of the i^th year, N is the total number of years, and αk represents the initial value of the RDI value.

The Normalized values of the Reconnaissance Drought Index (RDIn) is calculated by using equation 22: -

(22)

Where: RDIn is normalized Reconnaissance Drought Index, and is mean of the αk value

The RDI_st was initially formulated under the assumption that αk follows a log normal distribution. Accordingly, RDI_st is computed using Equation (23)

(23)

Where: y(i)= ln(αkⁱ), is the arithmetic mean of y(i), and δy is the standard deviation. The αk values generally follow either the log-normal or gamma distribution at most locations and time scales. However, in many cases, the gamma distribution, provides a better fit to the data. Therefore, the estimation of RDI_st is more reliable when the gamma probability density function (pdf) is fitted to the frequency distribution of αk.

Due to a shortage of independent quantitative datasets, a qualitative validation approach was used to test the validity of the suggested exposure risk index in the AC sub-basin, with reference to comparable systems such as the Bilate river watershed. To ensure agreement with known exposure conditions, the index’s spatial patterns were compared with established environmental gradients such as elevation, temperature, drought-prone areas, and proximity to water bodies. High-risk zones were cross-validated against stated drought hotspots in the Wolayta zone and Sidama region, confirming with previous studies. The findings were also interpreted in the context of local environmental information, such as dominating rain-fed agriculture techniques and reported water stress. Furthermore, the index showed its ability to integrate climatic, hydrological, topographic, and socioeconomic characteristics, which providing advantages over existing single-variable indicators. A comparison with established proxies such as rainfall variability, vegetation condition, and soil moisture proved that the index provided better insight into regional exposure risk.

Mean monthly maximum temperature.

Using the Mann-Kendall trend test on the mean monthly maximum temperature, the null hypothesis was rejected for five stations (Fisehagenet, Hageremariam, Arbaminch, Dilla, and Hagereselam) and accepted for Yirgachefe. Sen’s slope estimates were positive for all stations except at Dilla, which exhibited a negative slope, indicating a slight decline in mean monthly maximum temperature at that station. Despite the negative Sen’s slope observed at Dilla, the Mann-Kendall test statistics (S) indicated increasing trends in maximum temperature at most stations, suggesting an overall upward trend in mean monthly maximum temperature during the study period. Furthermore, Kendall’s tau values were positive for the five stations, implying a strong positive correlation between temperature and time, whereas Dilla station showed a negative tau value, indicating a weak or negative correlation (Table 5).

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Table 5. Result of the Mann-Kendall trend test for mean monthly maximum temperature.

https://doi.org/10.1371/journal.pone.0354913.t005

To provide further insight, graphical interpretation, linear regression analysis, and slope estimation were performed, and the results are presented for each station (Fig 3). For example, the graphical analysis of the mean monthly maximum temperature at Fisehagenet showed an increasing trend, with the coefficient of determination (R²) indicating that 65.51% of the variability in temperature was explained by the line of best fit. Similarly, the graphs for Hageremariam, Arbaminch, and Hagereselam also showed increasing trends, with R² values of 36.06%, 42.29%, and 61.74%, respectively. In contrast, Dilla and Yirgachefe exhibited slight decreasing trends in mean monthly maximum temperature, with R² values of 10.93% and 0.0004%, respectively, indicating very weak linear relationships. Overall, the combined results of the Mann-Kendall trend test and Sen’s slope estimator indicated that mean monthly maximum temperatures in the basin increased at five stations and slightly decreased at one station over the selected base period (Table 5).

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Fig 3. Mean monthly maximum temperature trend in the eastern escarpment of the Abaya Chamo sub-basin.

The figure illustrates temporal variability in mean monthly maximum temperature.

https://doi.org/10.1371/journal.pone.0354913.g003

Mean monthly minimum temperature.

The Mann-Kendall trend test was applied to the mean monthly minimum temperature data from six stations, and the results are presented in Table 6. The null hypothesis was accepted for three stations (Arbaminch, Hageremariam, and Dilla) and rejected for the remaining three stations (Fisehagenet, Yirgachefe, and Hagereselam). Overall, the analysis indicated an increase in mean monthly minimum temperature across the study area during the observation period. However, a slight decrease was observed at Hageremariam and Dilla, as evidenced by negative S and Sen’s slope values. Kendall’s tau was negative for three stations (Hageremariam, Hagereselam, and Dilla), indicating weak or negative correlations with time, whereas it was positive for the other three stations (Arbaminch, Fisehagenet, and Yirgachefe), indicating stronger positive correlations (Table 6).

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Table 6. Result of the Mann-Kendall trend test for mean monthly minimum temperature.

https://doi.org/10.1371/journal.pone.0354913.t006

The graphical analysis of the basin revealed variability in the R² across different stations. At Fisehagenet, approximately 44.58% of the variation in mean monthly minimum temperature was explained by the fitted trend line. Similarly, the Hageremariam, Dilla, and Hagereselam showed slight month-to-month fluctuations in temperature, with R² values of 16.62%, 1.59%, and 34.26%, respectively. In contrast, Arbaminch and Yirgachefe exhibited increasing trends, with R² values of 1.59% and 35.1%, respectively, indicating varying degrees of linear fit (Fig 4). Overall, the combined results of the Mann-Kendall trend test and Sen’s slope estimator indicated that mean monthly minimum temperatures across the basin experienced both slight increases and decreases over the selected base period.

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Fig 4. Mean monthly minimum temperature trend in the eastern escarpment of the Abaya-Chamo sub-basin.

The figure illustrates temporal variability in men monthly minimum temperature.

https://doi.org/10.1371/journal.pone.0354913.g004

Comparable studies conducted in several basins worldwide have documented varying degrees of temperature variability. The findings of the present study are consistent with those of studies conducted in the Rift Valley Lake Basin, which reported increases in both maximum and minimum temperatures at varying rates [28,74,75]. Similarly, a study examining temperature and precipitation extremes in the Wolaita Zone of southern Ethiopia found positive trends in annual maximum and minimum temperatures [76]. Moreover, studies on daily temperature and precipitation extremes conducted across different agroecological zones in the Gurage Zone of south-central Ethiopia reported statistically significant increases in the number of warm days and nights, alongside significant decreases in the number of cold days and nights. These findings indicate a persistent warming trend across multiple watersheds in the region [77].

Similar results have been reported in other parts of Ethiopia, where mean annual temperatures showed an increasing trend during the period from 1984 to 2006 [10]. Another study also reported that, due to increasing human pressure, extreme weather events and climatic change have contributed to a continuous rise in temperature [75]. Research conducted in Ethiopia’s Central Rift Valley Basin also identified increasing temperature trends ranging from 0.12 to 0.54 °C per decade across different locations [78]. In the southeastern part of the sub-basin, a study examining the spatiotemporal variability of hydroclimatic variables found that annual minimum temperature increased by 0.05 to 0.5 °C in some areas, while decreases of 0.1 to 0.3 °C were observed in others [47]. Furthermore, the National Adaptation Programme of Action (NAPA) for Ethiopia reported that temperatures across the country have risen in accordance with broader global and African warming trends [79].

Drought analysis for SPI-6.

The SPI-6 index, calculated for the first six-month rolling period (October-March) using 33 years of data from the Arbaminch station, revealed significant drought patterns. Specifically, it identified three moderate drought years (1993–1994, 2007–2008, and 2018–2019) and two severe drought years (1995–1996 and 2010–2011), as shown in Fig 5. Similarly, the SPI-6 calculated for the second rolling period (April-September) identified six moderate dry years (1987–1988, 1992–1993, 1997–1998, 1998−1999, 2001−2002, and 2018−2019), one severe drought year (1989−1990), and one extreme drought year (2008–2009), with most years remaining within near normal. Notably, the years 2008–2009 and 2010–2011 recorded the most severe drought conditions, with SPI values of −1.69 and −2.41, respectively, indicating consecutive years of pronounced drought severity. These findings underscore the variability and intensity of drought events in the region, with certain years exhibiting extreme and persistent dry conditions.

At the Fisehagenet station, the first series of SPI-6 rolling period revealed four moderate drought years (1993–1994, 1997–1998, 1999–2000, and 2018–2019), and two severe drought years (1991–1992 and 2016–2017), with no extreme drought events observed. For the second rolling series, three moderate drought years (1990–1991, 2001–2002, and 2003–2004) and one severe drought year (1989–1990), were identified, again with no extreme drought events. The most intense droughts occurred in 2016–2017 and 2018–2019, with SPI values of −1.65 and −1.60, respectively (Fig 5). At the Yirgachefe station, the first SPI-6 rolling period indicated moderate drought conditions in 1991–1992, a severe drought in 1993–1994, and an extreme drought event in 2010–2011. The second rolling period revealed five moderate drought years (1990–1991, 1996–1997, 1997–1998, 1999–2000, and 2001–2002) and one severe drought year (1989–1990), with no extreme drought events.

The most severe droughts occurred in 1989−1990 and 2010−2011, with SPI values of −1.89 and −3.31, respectively (Fig 5). These findings highlight the variability and severity of drought conditions across different years. At the Hageremariam station, SPI-6 values calculated over 33 years for the first rolling series revealed three severe drought years (1991−1992,1999−2000, and 2014−2015), one extreme drought year (2010−2011), and one moderate drought year (1996−1997). For the second rolling period, three moderate drought years (1989−1990, 2001−2002, and 2005−2006) and three severe drought years (1992−1993, 1994−1995, and 1998−1999) were identified. The highest drought indices were −1.81 and −2.27, respectively, for 1992–1993 and 2010–2011.

For the Dilla station, SPI-6 values calculated over 32 years (1990−2021) for the first rolling series showed no moderate drought events but identified two severe drought years (1996−1997 and 2008−2009), with no extreme drought events observed. Similarly, during the second SPI-6 rolling period, three moderate drought years (1998−1999, 2003−2004, and 2014−2015), two severe drought years (1993−1994 and 2008−2009), and two extreme drought years (2001−2003) were identified. The most intense droughts occurred in 1996−1997 and 2002−2003, with SPI values of −1.97 and −2.17, respectively, representing the highest drought severity recorded indices across the two rolling periods.

At the Hagereselam station, SPI-6 analysis over 32 years revealed four moderate drought years (2005–2006, 2007–2008, 2010–2011, and 2016–2017) and one severe drought year (2003−2004) in the first rolling series, with no extreme drought events and predominantly near-normal conditions. In the second rolling series, eight moderate drought years were identified (1992−1993,1994−1995,1996−1997,2002−2004,2005−2006, and 2008−2009). The most severe droughts occurred in 2007−2008 and 2001−2002, with SPI values of −1.85 and −1.7, respectively.

Overall, the SPI-6 analysis across multiple stations in the Eastern escarpment of the AC sub-basin over the 32–33-year period revealed pronounced spatial and temporal variability in drought frequency and severity. Moderate to extreme drought events were particularly prominent during the early 1990s, late 2000s, and early 2010s, with noticeable seasonal differences between the October-March and April-September periods. The repeated occurrence of droughts of varying intensity across stations highlighted the region’s vulnerability to prolonged dry spells, while the identification of the timing and spatial distribution of the most severe drought events provided valuable insights for targeted seasonal planning and regional water resources management.

Drought analysis for RDI_st-6.

The RDI_st-6 calculated for the first six rolling month series at Arbaminch station, four moderate dry conditions (1993−1994, 2007−2008, 2009−2010, and 2018−2019) and two severe drought years (1991−1992 and 2010−2011) were identified, with no extreme drought years observed. For the second six-month rolling month series, seven moderate drought years (1989−1990, 1992−1993, 1997−1999, 2001−2002, 2009−2010, and 2018−2019) and one extreme drought year (2008−2009) were observed, as shown in Fig 6. The most severe droughts occurred in 2010−2011 and 2008−2009, with RDI values of −1.55 and −2.44, respectively. The RDI_st-6 calculated for the first six rolling months series at Fisehagenet station showed four moderate drought years (1991−1992, 1999−2000, 2010−2011, and 2018−2019) and no severe or extreme drought events. For the second rolling period, three moderate drought years (1989−1990, 2001−2002, and 2007−2008) were identified, again with no severe or extreme drought events were identified. The highest drought indices occurred in 2016−2017 and 2003−2004, with RDI values of −1.45 and −1.17, respectively.

The first six-month rolling series of the RDI_st-6 for Hageremariam station revealed two instances of moderate drought conditions (1996−1997 and 2015−2016), one extreme dry event (2010−2011), and no severe dry conditions. In the second rolling series, three extreme dry events were observed (1989−1990, 1994−1995, and 1987−1988), along with one severe dry year (1992−1993), but no extreme dry conditions. The highest drought indices for these periods were −2.22 for 2010−2011 and −1.73 for 1998–1999, marking the most severe drought conditions in the respective years. For Yirgachefe station, the first six-month rolling series of RDI_st-6 revealed one moderate dry event (1991−1992), two severe dry conditions (1993−1994 and 1996−1997), and one extreme dry event (2010−2011). In the second rolling series, two severe drought years (1989−1990 and 1996−1997) and five moderate dry years (1989−1990, 1992−1993, 1997−1998, 1998−1999, 2001−2002, and 2018−2019) were identified, with no extreme dry events recorded. The most extreme drought indices were −3.27 for 2010–2011 and −1.88 for 1989–1990, as shown in Fig 6. These findings highlight the varying severity and frequency of drought events across different stations, with the most extreme drought conditions observed in 2010–2011 and 1989–1990.

For the Dilla station, the RDI_st-6 values calculated over 32 years for the first six-months rolling revealed three moderate drought years (1991−1992, 1994−1995, 1999−2000) and one extreme drought years (1996−1997). In the second rolling series, three moderate drought years (2003−2004, 2008−2009, and 2014−2015) and one extreme drought years (2001–2003) were observed. The most severe droughts occurred in 2001–2003, with RDI values of −2.1 and −2.33, respectively (Fig 6). At the Hagereselam station, the RDI_st-6 for the first six- month rolling period showed three moderate drought years (2003–2004, 2010–2011, and 2012–2013), two severe drought years (2007–2008, and 2016–2017), and no extreme dry events. In the second rolling series, two moderate drought years (2001–2002, and 2003–2004) and four severe dry conditions (1994–1995, 1996–1997, 2005–2006, and 2008–2009) were identified, with no extreme drought events. The highest drought indices were recorded in 2007−2008 and 1996−1997, with RDI values of −1.65 and −1.89, respectively, across both series (Fig 6).

These results highlighted the spatial and temporal variability in drought intensity and frequency across stations, with some years experiencing extreme and severe drought events. Overall, the 32-to-33-year RDI_st-6 analysis of stations in the Abaya-Chamo sub-basin illustrated substantial regional and seasonal variability in drought intensity. Significant drought events occurred in 2010–2011, 2008–2009, and 1989–1990, with moderate to severe conditions observed across most stations. These findings emphasized the region’s susceptibility to prolonged dry periods and provided valuable insights for targeted drought management and climate resilience planning.

Drought analysis for SPI and RD_Ist-12.

The SPI-12 rolling analysis for the Arbaminch station, calculated over a 12-month period (October–September), revealed one extreme drought year (1998−1999), one moderate dry year (2003−2004), and one severe drought year (2008−2009). At the Fisehagenet station, the SPI-12 analysis showed four moderate drought years (1996−1997, 1999−2000, 2001−2002, and 2016−2017) and two severe drought years (1990−1991, 2003−2004), with no extreme events observed. At the Yirgachefe station, the SPI-12 results revealed five moderate drought years (1994−1995, 1998−1999, 1999−2000, 2010−2011, and 2011−2012), two severe drought years (1988−1989, 1991−1992), and one extreme drought year (1996−1997). The maximum drought index for Fisehagenet was −1.68 in 1990–1991, while Yirgachefe recorded the highest index of −2.31 in 1996–1997.

From 1987 to 2019, the SPI-12 analysis at the Hageremariam station indicated six moderate drought years (1996−1997, 1999−2000, 2001−2002, 2005−2006, 2000−2001, 2007−2008), no severe dry conditions, and one extreme drought year (1994−1995). At the Dilla station, the SPI-12 rolling series revealed five moderate drought years (1998−1999, 1999−2000, 2001−2002, 2003−2004, 2009−2010), and one severe drought year (2002−2003), with no extreme drought events observed. The SPI-12 results for Hagereselam indicated four moderate dry years (1990–1992, 2001–2002, 2007–2008) and three severe drought periods (1994−1996 and 2003–2004), as shown in Fig 7. The most extreme drought indices were recorded in 1994−1995 at Hageremariam (−2.08) and in 2003−2004 at both Hagereselam and Dilla stations (−1.84). These findings underscore the spatial variability in drought intensity and frequency across the stations, with certain periods experiencing severe to extreme sever.

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Fig 7. Comparison of 12-month SPI and RDI_st depicting drought conditions in the Eastern escarpment of the Abaya-Chamo sub-basin.

The figure highlights the similarities and differences between the two indices in capturing drought severity and temporal variability.

https://doi.org/10.1371/journal.pone.0354913.g007

The RDI_st-12 analysis for the Arbaminch station indicated one moderate drought year (2003–2004) and three severe drought years (1998–1999, 2007–2008, and 2017–2018). In contrast, the RDI_st-12 results for the Fisehagenet station revealed four moderate drought years (1996–1997, 1999–2000, 2003–2004, and 2016–2017), with no severe or extreme drought events recorded. At the Yirgachefe station, the RDI_st-12 analysis showed three moderate droughts (1998–2000 and 2010–2011), one severe drought year (1996–1997), and one extreme drought event (1991–1992). For the Hageremariam station, the RDI_st result indicated one severe drought year (1998–1999), while moderate drought conditions occurred in 1996–1997,1999–2000, and 2002–2003. At the Dilla station, three moderate drought years (1999–2000, 2001–2002, and 2003–2004), one severe drought year (2002–2003), and one extreme drought year (2007–2008) were identified over the 32-year period from 1990 to 2021. Similarly, the 32-year RDI_st-12 rolling series for the Hagereselam station showed four moderate drought conditions (1994–1995, 2007–2009, and 2015–2016) and one severe drought year (2003–2004), with no extreme drought events observed. The most severe RDI_st-12 drought index values were recorded as −1.88 at Arbaminch (2007–2008), −1.37 at Fisehagenet (2003–2004), −2.52 at Yirgachefe (2003–2004), −2.52 at Yirgachefe (1996–1997), −1.77 at Hageremariam (1996–1997), −1.93 at Dilla (2002–2003), and −1.77 at Hagereselam (2002–2003).

As evidenced by the results across all stations, drought occurrence was not continuous over time at any single station. The SPI-6 analysis indicated that drought events occurred at all stations, although the magnitude and frequency varied between the two rolling periods. In most cases, the second rolling period exhibited a higher frequency of drought events than the first. Arbaminch and Hageremariam stations experienced the highest frequency of drought events among all stations. Notably, the years 2010−2011 at Arbaminch, Hageremariam, and Yirgachefe, and 2002−2003 at Dilla recorded the most severe drought indices, with SPI-6 values of −2.41, −2.27, −3.31, and −2.17, respectively. Similarly, the RDI_st-6 analysis revealed that all stations experienced drought events during different drought years throughout the study period, with the highest frequency of drought conditions observed at the Arbaminch and Hagereselam stations.

The most extreme RDI_st-6 drought indices were recorded at Yirgachefe (−2.44) in 2008−2009 and at Arbaminch (−3.27) in 2010−2011. Overall, the six-month drought analysis using SPI-6 and RDI_st-6 across multiple stations in the eastern escarpment of the Abaya-Chamo sub-basin demonstrated pronounced spatial and temporal variability in drought frequency and severity throughout the study period. Both indices consistently indicated significant drought events, with 2010−2011, 2008−2009, and 1989−1990 emerging as severe to extreme drought periods. The SPI-6 generally indicated more frequent and intense extreme droughts, such as the extreme drought observed at Yirgachefe in 2010–2011 (−3.31), reflecting its high sensitivity to precipitation deficits.

Conversely, RDI_st-6 index showed fewer extreme drought events but highlighted a broader distribution of moderate to severe droughts, reflecting the integration of rainfall deficit measures that may moderate the classification of extremes. Seasonal patterns were also evident, with the October-March periods exhibiting sharper peaks in drought severity, whereas the April-September periods showed more frequent occurrence of moderate drought across stations such as Arbaminch, Hageremariam, and Dilla. The concurrence of severe drought years identified by both indices reinforces the robustness of the findings, and highlights the region’s vulnerability to prolonged dry spells. These results underscore the need for targeted drought mitigation and improved water resource management strategies.

These results underscore the importance of using complementary drought indices to capture the complexity of drought dynamics and inform effective climate resilience planning in the Abaya-Chamo sub-basin. The SPI-12 analysis across all stations revealed that moderate drought conditions occurred most frequently at each station. However, Hageremariam and Yirgachefe recorded the most extreme drought indices, with values of −2.31 in 1996–1997 and −2.08 in 1994–1995, respectively. Similarly, the RDI_st-12 analysis indicated the most severe conditions at Yirgachefe in 1996–1997 (−2.52) and at Dilla in 2002−2003 (−1.93).

Both SPI-12 and RDI_st-12 analyses revealed substantial spatial variability in drought characteristics among the stations. The SPI-12 index identified more frequent and intense moderate to extreme drought events, whereas the RDI_st-12 index detected fewer extreme events but a greater occurrence of moderate and severe droughts. Overall, SPI-12 demonstrated increased drought frequency and severity, particularly at stations such as Arbaminch and Hageremariam, reinforcing its usefulness for comprehensive drought assessment. Furthermore, the analysis showed that the six-month time scale (SPI-6 and RDI_st-6) captured a higher frequency of drought occurrences than the twelve-month time scales (SPI-12 and RDI_st-12), as illustrated in Fig 8. This highlights the greater sensitivity of shorter time scales in detecting drought conditions.

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Fig 8. Frequency distribution of drought indices for Eastern escarpment of Abaya-Chamo sub-basin.

The figure shows the distribution of drought index values and their frequency of occurrence.

https://doi.org/10.1371/journal.pone.0354913.g008

The usefulness of the identified exposure risk index in describing actual exposure patterns across the Abaya–Chamo sub-basin and similar Ethiopian basins is confirmed by the indirect validation. With good spatial consistency, high index values primarily appeared in lowland, high-temperatures zones, and drought-prone areas near Lake Abaya and Lake Chamo, reflecting persistent climatic stress and limited water availability. Reduced water availability and increased evapotranspiration can limit agricultural productivity and reliable access to water for domestic and livestock use in these densely populated zones [71,84–87]. Local knowledge contributes to these findings, with high-index communities frequently experiencing water shortages and crop failures, affecting irrigation and water availability for household and livestock use, while higher-elevation and vegetated areas show lower exposure levels, indicating consistency with reported socio-environmental conditions. This is consistent agrees with studies from similar Ethiopian basins linking drought exposure to elevation, land use, and rainfall variability. The exposure risk index corresponds with established vulnerability regions in the Wolayta Zone and Sidama Region, which have been identified as ecologically sensitive and drought-prone [88,89].

Compared with conventional single-variable indicators, the index integrates climatic, hydrological, topographic, and socio-economic factors, enabling identification of compound high-risk zones and transitional areas that are not captured by rainfall or vegetation indices alone [90,91]. Additionally, the index detects emerging risk areas driven by land-use change and population pressure, supporting early detection of increasing exposure [87,92]. These findings highlight the need for targeted mitigation measures, including improved irrigation efficiency, rainwater harvesting, watershed management, and enhanced rural water supply systems [93,94].

The exposure risk index also provides actionable guidance for planning in the Abaya-Chamo sub-basin. High-risk zones can be prioritized for irrigation improvements, rainwater harvesting, and rural water supply, while emerging risk areas can inform early warning and land-use planning. By integrating climatic, hydrological, topographic, and socio-economic factors, the index offers more precise, multi-dimensional insight than single-variable indicators, supporting evidence-based decision-making for drought preparedness and climate-resilient development. Overall, the validation confirms that the index reliably reflects environmental and socio-economic vulnerabilities, aligns with existing literature and local observations, and provides added insight beyond traditional metrics, making it a reliable tool for exposure assessment in the Abaya-Chamo sub-basin and similar Ethiopian watersheds.

Significant hydroclimatic changes have been observed in southern Ethiopia, characterized by rising temperatures, altered streamflow patterns, and increasing drought severity. These changes were consistent with the influences of large-scale climate drivers such as ENSO (El Niño–Southern Oscillation), NAO (North Atlantic Oscillation), AMO (Atlantic Multidecadal Oscillation), and IOD (Indian Ocean Dipole), which modulate rainfall and temperature variability in the region [95–97]. ENSO phases can either decrease or increase rainfall by altering moisture transport and seasonal rainfall patterns, whereas positive or negative IOD events often exert a stronger influence on regional precipitation variability. Furthermore, Volcanic aerosols may temporarily cool the Earth’s surface and disrupt atmospheric circulation, potentially inducing El Niño-like conditions that reduce monsoon rainfall and increase the frequency and intensity of drought events [95,97,98]. Volcanic eruptions also influence ENSO dynamics, often causing triggering El Niño like conditions that reduce monsoon rainfall and exacerbate drought severity [95,98,99]. The observed hydroclimatic alteration is likely the result of both natural climatic variability and anthropogenic forcing, underlining the need for integrated climate and water resource management approaches in southern Ethiopia [60–63,97,100].

Previous studies indicate that severe dry conditions have become more frequent in recent decades, reflecting increasing hydroclimatic variability and a shift toward higher water deficit relative to the long-term climatic norm [101,102]. The drought sensitivity results based on RDI in this study showed more pronounced variability compared to the SPI, which is consistent with findings from other catchment-based studies. RDI is considered more sensitive than SPI because it incorporates temperature (via potential evapotranspiration) in addition to precipitation, whereas SPI relies solely on precipitation data [103,104]. Similar studies in northeastern Algeria summarize report that, although SPI and RDI exhibit coherent and comparable behavior, RDI shows smaller differences across climate zones and time scales, which is considered over SPI and is attributed to the inclusion of PET in RDI calculations [105].

Likewise, a study in northwestern Morocco for the period 1971–1972–2010–2011 found that drought frequency varies with time scale (3,6, and 12 months); however, correlation analysis revealed a strong positive relationship between SPI and SDI at the 12 month time scale across different periods [106]. In the present study, the proportion of wet events identified by RDI_st is higher than that detected by SPI at most time scales, which aligns with previous findings. In contrast, SPI identifies a higher proportion of moderate, severe, and extreme drought events compared to wetness events [48,105]. Analysis of the graphical results indicates that the 12-month indices provide more reliable and representative drought characterization than shorter time scales, as illustrated in Fig 6. Therefore, the use of 12-month SPI and RDI indices is recommended for drought assessment and for the formation of effective mitigation and adaptation measures. Furthermore, global sea surface temperature variability, along with seasonal and rainfall characteristics, may have contributed to the observed differences in drought frequency and temporal scale. Drought severity and hydroclimatic variability in the Abaya -Chamo sub-basin threaten rainfed agriculture, water resources, and ecosystem health.

The combined effects of short-term droughts, declining streamflow, and rising temperatures are expected to intensify pressures on food security, water resources, and ecosystem integrity, emphasizing Ethiopia’s acute sensitivity to climate variability [107–109]. To minimize the impacts of drought, an inclusive and integrated approach to adaptation and mitigation is essential to reduce the adverse effects of increasing dryness in the coming decades.