The Climate Hazards Group Infrared Precipitation Satellite (CHIRPS) dataset, integrating satellite estimates, global climatology, and in situ data, offers high-resolution of 0.05° precipitation records from 1981 to the present, facilitating detailed climatic analyses (Shahid et al., 2021). The CHIRPS precipitation Estimate Data, available through the Google Earth Engine (GEE) software package (Table 1), is a crucial satellite-based source for this drought analysis with a spatial resolution of 5 km by 5 km naturally and reduced to 250 m using the program. However, the GEE provides a cloud data catalog (https://developers.google.com/earth-engine/datasets) website for CHIRPS with a more manageable 250 m resolution, enabling resampling. This high-quality precipitation data is essential for calculating the Standardized Precipitation Index (SPI) and conducting comprehensive drought analysis in the study area.

Daily precipitation data were gathered from the Ethiopian Meteorological Agency (EMA) for the years 2000–2020, sourced from Mekele Obseva, Sekota, Yichila, and Ashere meteorological stations located near and within the study area. Although, a minimum data series of 30 years is typically required for comprehensive climate data analysis, while in regions with scarce historical climate data availability, utilizing shorter time ranges of 20 years and above becomes a viable option to address data constraints and conduct meaningful analyses (Dinku, 2019). These datasets were used to validate the CHIRPS data to ensure accuracy and reliability of the meteorological drought assessment. Random forest regression (RFR) model was applied to evaluate the validation between the variables. It is mainly because, the model is a machine learning algorism, which has a better performance than the other models such as the linear regression (Tesfay, 2024). It has also several advantages, including its ability to handle complex datasets (handle both numerical and categorical features including missing data), and reduces overfitting compared to individual decision trees (Montesinos López et al., 2022).

Socioeconomic survey, and self-observation with field notes in the drought-affected areas were collected for socio-economic drought investigation. Quantitative data were gathered through a questionnaire survey of 198 households (Table 2).

To analyze and organize the socioeconomic data, the research project utilized the SPSS software. Various analysis techniques, including descriptive statistics and qualitative narrations were employed to examine the data collected from different sources. The raw data captured from the various sources were primarily edited and checked for accuracy. The edited data were encoded into the SPSS software for analysis. Following this, the quantitative data collected through questionnaire surveys were analyzed using statistical techniques available in the SPSS software. The software facilitated the generation of tables and charts to present the analyzed data effectively. The integration of statistical, descriptive and narrative analysis allowed for a comprehensive exploration of the research data. The statistical analysis provided quantitative insights and trends, while the narrative analysis delved into the rich narrative aspects of the data. Moreover, python programing options were applied in generating tables and charts from the data.

The identification of drought-prone areas in this study was conducted by combining SPI based meteorological drought frequency maps. This is significant and often applied in various research studies (Senamaw et al., 2021; Bazie and Bantigegn, 2024; WMO, 2016). The study utilized 96 separate agricultural drought images for each summer months and this was aggregated into 24 seasonal images to determine the occurrence of droughts in the study watershed. To represent different severity levels, each yearly binary image was transformed into a Boolean image. A Boolean image refers to a raster image where each pixel has a binary value, typically representing two states such as “true” or “false”, “1” or “0”, or “on” or “off”, used for binary operations in spatial analysis like overlaying multiple layers or defining areas of interest based on specific criteria. It is commonly used for image processing and pattern recognition (Bhowmick et al., 2014). By summing the images from each year, the frequency of drought at each pixel level was calculated. To this end, drought probability zones in an area can be classified as high, moderate, or low risk, when drought occurs over 50%, 30%–50%, or less than 30% of the years, respectively (Gonfa, 1996). Therefore, the frequency map was subsequently classified into five agricultural drought risk categories based on the following criteria: 0–1 (no drought risk); 2–6 (low drought risk); 7–11 (moderate drought risk); 12–16 (high drought risk); and ≥17 (very high drought risk) zones (Senamaw et al., 2021; Gonfa, 1996; Suryabhagavan, 2017; Wassie et al., 2022b).

Temporal trend assessment is important in drought assessment because it helps to monitor drought severity over time, identify drought hotspots, understand climate change impacts on drought patterns, inform water resource planning and management, and contribute to the development of early warning systems. It provides valuable information into long-term changes in drought conditions.

In Figure 3, two charts are presented. The first chart displays SPI-1 values for each summer month (June, July, August, and September), while the second chart shows the SPI-4 values, which represent the combined value for the four summer months of each year. The charts utilize zero as the demarcation line to distinguish between drought and no drought conditions. It is observed that the monthly SPI variations are minimal, indicating slight fluctuations in drought severity throughout the summer months.

Based on the second chart in Figure 3, the results indicate that all the values listed below the demarcation line represent various drought categories. The year 2015 experienced a severe drought condition with a value of −1.72. The years 2000 and 2010 on the other hand fall under the moderate drought category with values of −1.39 and −1.22, respectively. Additionally, the years 2002, 2004, 2005, 2009, 2011, 2013, 2014, 2017, 2021, 2022, and 2023 were classified as mild drought conditions with values of −0.03, −0.46, −0.16, −0.33, −0.66, −0.73, −0.82, −0.11, −0.67, −0.34, and −0.83, respectively. Based on the identified drought years, the drought reoccurrence period is 1.71 which is approximately 2 years. Which means drought can happen in every 2 years for the entire study period in the area.

On the other hand, the value results above the demarcation line of 0.0 indicate no drought categories. The years 2003 and 2007 are classified as extremely wet with values of 2.13 and 2.06, respectively. Additionally, the years 2006 and 2008 fall into the moderately wet category with values of 1.04 and 1.35, respectively. The remaining years, namely 2001, 2012, 2016, 2018, 2019, and 2020, are categorized as near normal.

These results are consistent with findings from various scholars like Birhanu Gedif and Karuturi Venkata Suryabhagavan (2014) focused on agricultural drought in northern Tigray from 1998 to 2005, utilizing NDVI. They identified severe drought conditions in the years 2000 and 2004. Gebre et al. (Gebre et al., 2017) Examined agricultural drought in the North Wollo zone from 2000 to 2015, also using NDVI. Their research identified the drought years as 2005, 2009, 2013, and 2015. Wassie et al. (Wassie et al., 2022b) Analyzed agricultural drought using NDVI anomalies in North Wollo from 2000 to 2019, finding varying levels of drought in the years 2004, 2008, 2009, 2010, 2011, and 2015, which align with the results. Also Enyew and Wassie (Bazie and Bantigegn, 2024) Conducted research on meteorological drought in the Menna watershed from 2000 to 2022 using various indices, also highlighted severe drought conditions were in 2009, 2010, 2011, 2014, and 2015.

The spatial SPI maps in Figure 4, can be classified into various drought categories spatially. The years 2002, 2004, 2009, 2014, 2015, and 2023 are fully categorized under different drought categories. Among them, 2015 stands out as the most notable drought year, with extreme and severe drought conditions covering the entire area.

The years 2000, 2005, 2008, 2011, 2012, 2013, 2016, 2017, 2018, 2019, 2020, and 2021 experienced a mix of moderate and mild droughts, as well as near normal and moderately wet conditions, indicating both drought-affected and drought-free areas. Conversely, the remaining years, namely 2001, 2003, 2006, 2007, 2010, and 2022, are known to be drought-free, with categories ranging from near normal to extremely wet conditions observed in the area. The wettest season, in terms of spatial coverage, occurred in 2001, with the highest percentage of the area classified as extremely wet. Table 3 represents The “Extreme Drought,” “Severe Drought,” “Moderate Drought,” and “Mild Drought” columns representing the total area in ha for each respective drought category. The “Near Normal,” “Moderately Wet,” “Very Wet,” and “Extremely Wet” columns represent the total area in ha for each respective non-drought category. The “% under drought” column indicates the percentage of the area under drought, and the “% no drought” column indicates the percentage of the area without drought.

According to Table 4, several years stand out in terms of drought occurrences and their respective areal coverages. Specifically, the years 2002, 2004, 2005, 2008, 2009, 2013, 2015, 2019, 2020, and 2023 experienced significant drought events with areal coverages of 100, 100, 69, 94, 100, 96, 100, 100, 82, 62, and 100%, respectively. Among these years, 2015 emerged as the most severe drought year, as the entire area was affected by extreme and severe drought conditions. This pattern highlights the recurrent and substantial impact of meteorological drought or precipitation shortages in the watershed. Conversely, the remaining years were considered relatively wet, as 50% or more of their area remained drought-free. Notably, 2001 and 2007 represent the wettest years, with a large portion of their area categorized as very wet and extremely wet. These findings suggest an excessive amount of precipitation during those specific years in the study area.

The findings of this study on meteorological drought align with previous research conducted by various scholars in different regions of the study area and other parts of Ethiopia. For instance, Chere et al. (2022) examined meteorological drought in the highlands of Ethiopia using SPI-1 from 2004 to 2018. His study identified drought conditions in the years 2004, 2005, 2009, 2014, 2015, 2016, 2017, and 2018, which closely correspond to the findings of the current research. Similarly, Nasir et al. (2021) investigated meteorological drought in the northeastern escarpment of the Rift Valley from 1989 to 2016 using SPEI-3 for the summer season. They identified drought years as 2002, 2004, 2008, 2011, and 2015, which aligns with the results of the current study. Senamaw et al. (2021) Conducted a study in the Waghimra Zone using SPI from 2000 to 2016 and identified drought-affected years in 2003, 2004, 2005, 2008, 2009, 2011, 2014, and 2015, which strongly coincide with the current research findings. Likewise, Wassie et al. (2022b) investigated meteorological drought in North Wollo from 2000 to 2019, identifying drought years as 2002, 2004, 2009, 2011, 2014, 2015, and 2019 based on the Z-Score Index. In the upper Tekeze basin, Menna et al. (2022) found drought years to be 1984, 2004, 2006, 2011, and 2015. Lastly, Enyew and Wassie Bazie and Bantigegn (2024) studied meteorological drought in the Mena watershed from 2000 to 2023, identifying drought years as 2002, 2004, 2009, 2014, 2015, and 2019. All of these findings show similarities with the current findings.

Based on data accessed from the Emergency Events Database (EM-DAT) website (www.emdat.be), in the case of Amhara and Tigray regions, the years 2003, 2008, 2009, 2010, 2015, and 2021 were identified as drought years caused by precipitation shortages, La Niña, and El Niño events. These droughts resulted in significant impacts, including crop failure, loss of pasture, food shortages, and famines in 2008 and 2015.

The EM-DAT records also indicated that other disasters such as floods and epidemics exacerbated the drought conditions in the area. Flooding events were documented in most of Amhara and Tigray in 2001, 2005, 2006, 2007, 2010, and 2022, as well as, in the Gondar province in 2020. Additionally, various epidemics, including bacterial diseases (e.g., Meningococcal), infectious diseases (e.g., watery diarrhea), and viral diseases (e.g., Yellow fever and Measles), affected different parts of the study area. Bacterial disease outbreaks were reported in Kobo and Alamata in 2000, while these epidemics have been prevalent in most parts of Tigray and Amhara regions since 2001, 2005, 2006, 2008, 2009, 2018, 2019, and 2022.

Figure 5, presents the severity classes of drought risk based on SPI and their corresponding areas in a percentage. The findings reveal that the analyzed region is characterized by varying levels of drought risks. The class of “Moderate Drought Risk” covers 92.18% of the area. On the other hand, only 7.82% of the area characterized under “High Drought Risk”. This suggests a relatively lower vulnerability to more severe drought conditions. It is important to assess and address the impacts of both moderate and high drought risks, as they can have significant implications for various sectors such as agriculture, water resources, and ecosystems. Proper drought management strategies, including water conservation, land-use planning, and drought-resistant crop cultivation, should be implemented to mitigate the potential impacts of drought in the region.

To validate the results of this study, the researcher compared the findings with those of various other studies. For instance Senamaw et al. (2021) Created a drought risk map for the Waghimra Zone and found that 0.4%, 35%, 56%, and 8.3% of the area fall into the categories of slight, moderate, severe, and very severe drought classes, respectively. According to Enyew (2024) in his study of agro-meteorological drought in the Menna watershed, the combined drought risk map indicated low, moderate, high, and very high drought risks of 3.2%, 21.2%, 47.4%, and 28.1%, respectively.

Table 5 presents the demographic characteristics of the respondents. It provides information about the composition of the respondent’s gender, age, occupation, educational background, and marital status. In Gender composition: among the studied households, 63.1% were male-headed and 36.9% female-headed. This indicates a slightly higher representation of male-headed households in the study.

On the other hand the age distribution of the respondents shows that the majority of household heads fall within the age range of 30–50 years. More than 35% (35.4%) are falling between 30 and 40 years and some 29.8% are 40–50 years of age. The remaining 4, 12.6% and 18.2% of the heads fall within <20, 20 to 30, and above 50 years ages, respectively (Table 5). This indicates that the study has a significant proportion of middle-aged participants.

Furthermore, the occupation of the respondents also reveals that the largest group consists of farmers, accounting for 72.2% of the participants. Business owner’s make-up 10.1% of the households, followed by government employees (6.6%), students (9.6%), and others (1.5%) (Table 5). This majority group of farmers participated in the survey have a great interest. Farmers are directly dependent on precipitation and climatic conditions for their agricultural activities. Therefore, they are particularly vulnerable to the impacts of drought, as it can have severe consequences on their livelihoods, crop yields, and overall economic wellbeing. By including a significant proportion of farmers in the study, the research can gain valuable information about the socioeconomic effects of drought.

In terms of educational background, 54.5% of the respondents had no formal education, while 19.2% had completed primary school education. Additionally, 15.7% had attained a secondary school level, and the other 10.6% had pursued further education at college or university level (Table 5). The distribution of educational backgrounds among the respondents highlights the varying levels of knowledge, skills, and capacities within the population regarding socioeconomic drought. This information is crucial for understanding potential disparities in vulnerability, adaptive capacity, and access to resources and opportunities.

The marital status distribution shows that the majority of the respondents were married (63.1%). Unmarried individuals accounted for 20.2%, while 8.6% were divorced and 8.1% widowed (Table 5). The distribution of marital status among the respondents indicates the diversity of marital situations within the studied population.

Table 6 presents important information about the personal experiences of drought and coping mechanisms of households. Out of the 198 participants, an overwhelming majority of 197 individuals (99.5%) reported personally experienced drought; indicating the significant impact of drought within the study area. This highlights the relevance and importance of their perspectives in understanding the socioeconomic effects of drought. The high percentage of respondents who have encountered drought suggests that they possess valuable firsthand knowledge about the challenges and impacts associated with drought conditions. Their experiences can provide crucial data for assessing the severity of drought, identifying vulnerable groups, and understanding the coping strategies employed during such periods. This result is supported by the results of Olaleye (2010), indicates that 92% of the respondents were experienced drought. Abdela (2024), find that most of the respondents are understanding what a drought is.

The respondents were using various coping mechanisms. The most prevalent coping mechanism reported was reducing food consumption such as feeding once per day (37.4%), indicating the challenges individuals facing in accessing sufficient food during drought periods (Table 5). Some other individuals relied on support, funds, and reliefs (7.6%) provided by governmental or NGOs. Displacement and migration (13.6%) were reported as strategies to search for food, while others sought new job opportunities (9.1%) to mitigate the impact on their livelihoods. Selling animals and non-food items emerged as a means to acquire immediate food by 22.2% of the households. Accessing credits and loans from financial institutions (10.1%) was another coping strategy. These findings highlight the diverse approaches individuals adopt to address the socioeconomic challenges imposed by droughts.

In order to validate the results, it is beneficial to compare them with findings from other researchers. Example, Awoke et al. (2022), identified coping strategies such as planting early maturing crops, changing cropping patterns, purchasing food using cash, and seeking alternative income sources. Bahiru et al. (2023), highlighted coping mechanisms including selling charcoal, firewood, and livestock, migrating in search of employment, and resorting to begging. Additionally, Abate (Abate, 2016), outlined coping strategies such as selling livestock, reducing daily food consumption, and participating in social support networks.

Table 7, represents the results from the respondents that indicate the primary causes or contributing factors to drought occurrence and severity in the local area. The majority of respondents (86.36%) identified climatic variability, including factors such as decreased precipitation, increased temperature, and other changes, as the primary causes of drought. This highlights the significant influence of climate patterns on the occurrence and severity of drought.

Land use land cover changes (LULCCs) and land degradations were mentioned by 36.36% of the respondents, indicating that activities such as deforestation and improper land management play a significant role in exacerbating drought conditions (Table 7). Water mismanagement was reported by 29.80% of respondents, emphasizing the importance of proper water resource management practices to mitigate drought impacts (Table 7). Deforestation, including practices like burning and land clearance for agriculture, was identified as a cause by 45.96% of respondents, suggesting that LULCCs can disrupt the local water balance and contribute to drought events. Other factors such as conflict and desert locust were mentioned by 22.73% of the respondents, indicating their indirect effects on water resources, agricultural practices, and productivity.

The LULCCs in northern Ethiopia, driven by factors such as population growth, agricultural expansion, deforestation, and urbanization, have led to deforestation, soil degradation, water resource depletion, biodiversity loss, and other significant environmental and socioeconomic impacts (Alemu, 2015; Wassie, 2020; Hussein, 2023).

The foregoing causes mentioned by the respondent households are also supported by reports of past researches and scholars. For instance, Bahiru et al. (2023) identified factors such as family size, farm land size, household education levels, pests, diseases, and flooding as contributors to drought. Abate (2016), pointed out causes like inadequate precipitation, population pressure, poor soil conditions, improper harvest management, deforestation, and land scarcity as triggers of drought. Mekonnen and Gökçekuş (2020), highlighted issues such as insufficient precipitation, human activities, and surface water depletion, and climate change, lack of food reserves, high food prices, low cattle prices, and limited knowledge of adaptive farming techniques as common problems leading to drought. Gebremedhin Gebremeskel et al. (2019), attributed the changing pattern of droughts primarily to climate variability and anthropogenic influences.

According to Table 8, the frequency of drought in the local area is perceived differently by the respondents. The majority (75.3%) described drought as “very frequent,” indicating that drought events occur frequently and are a recurring issue in the region. This also validates to the results of the temporal SPI that shows 14 out of the 24 years were facing various drought levels on average indicating that drought is very frequently happening in the Tekeze watershed. A smaller percentage of respondents (15.2%) reported drought as “frequent,” suggesting that while not as persistent as the “very frequent” category, drought events still occur regularly. Some respondents (8.1%) classified drought as “occasional,” indicating that drought is not as common but still occurs periodically. A few respondents (1.5%) described drought as “rarely,” suggesting that drought events are infrequent occurrences in the local area. The tangibility of responses regarding drought frequency stems from a multitude of factors. One significant aspect is the less willingness to respond carefully. Furthermore, when examining the respondents’ profiles, a mere 10.1% comprise business owners, 6.6% are government employees, and 9.6% are students. These figures indicate that individuals whose livelihoods do not primarily depend on agriculture possess a rather superficial comprehension of drought frequency. Consequently, this situation engenders a myriad of divergent and misguided responses.

Table 8 also shows the severity of drought impacts on the local area. The majority of the respondents (79.8%) classified the impact as “very severe,” indicating that drought has a significant and highly detrimental effect on the region. Additionally, 16.2% of the respondents reported the impact as “severe,” suggesting that while not classified as “very severe,” drought still has a substantial negative impact on the local area. A small percentage of respondents (3.5%) perceived the impact of drought as “moderate,” indicating a milder but still noticeable effect. Only 0.5% of respondents described the impact as “mild,” suggesting that for the majority of respondents, the impact of drought is significant and has severe consequences for the local area.

The vast majority of the respondents (99.5%) recall that they were experiencing drought years in the local area since 2000. This suggests a high level of awareness and firsthand experience with drought events in the region. Only a very small percentage of respondents (0.5%) reported not remembering any drought year since 2000.

Based on the researcher’s judgment for the chart in Figure 6, the drought years can be categorized as follows: wet years with a mention of respondents less than 25, mild drought years with a mention of respondents ranging from 25 to 50, moderate drought years with a mention of respondents ranging from 50 to 75, severe and extreme drought years with a mention of respondents above 75. Based on this categorization, the years 2000, 2001, 2002, 2006, 2007, 2010, 2014, 2018, and 2020 had 4, 3, 4, 10, 10, 4, 20, 13, 12, and 15 mentions, respectively, indicating wet years. This result can validate the results of SPI (page 8), mentioned earlier indicating that there are similarities among the satellite estimates and ground observation results.

On the other hand, the years 2004 and 2019, with mentions of 44 and 42 respectively, fall into the category of mild drought years. The years 2008, 2009, 2013, 2016, and 2007, with mentioned values of 55, 60, 51, 50, and 61, respectively, were considered moderate drought years. Finally, the years 2003, 2005, 2012, 2015, 2021, 2022, and 2023, with mentioned values of 96, 123, 96, 138, 78, 76, and 195, respectively, were classified as severe and extreme drought years. More or less, these results also have a great linkage with the foregoing results of the satellite based indices of the SPI.

In Table 9, the survey results indicate that the main impacts of drought in the local area, as reported by the respondents are; crop failure (mentioned by the majority of respondents ≈87.88%), highlighting the vulnerability of agriculture to drought conditions. Water scarcity was reported by 68.18% of respondents, emphasizing the impact on the availability and access to water resources. Income loss was identified by 58.59% of the respondents, indicating the economic consequences of drought on individuals and communities. Food shortage was again mentioned by 32.83% of the respondents, indicating the potential impact on food security. Migration or displacement was reported too by 71.21% of the respondents, suggesting that drought can lead to population movements as people seek better living conditions or struggle to cope with the impacts. Livestock loss was another damage mentioned by 29.29% of the respondents, highlighting the impact on animal husbandry and livelihoods depending on livestock. Other impacts, including the death of humans and animals, were reported by 13.64% of respondents, indicating the severity of the consequences of drought events (see Table 9).

Furthermore, various scholarly works support and reinforce the aforementioned findings. For instance, Bogale and Erena (2022) have highlighted the adverse effects of drought in Ethiopia’s agro-ecological zones, including economic hardships, livestock mortality, water resource depletion, crop failures, increased food prices, losses in livestock productivity, environmental degradation, and health issues. Additionally, Hendrix (Morgan, 2012) has emphasized the consequences of drought in Ethiopia, such as water scarcity, food shortages, famine, water-borne diseases, disrupted education, exacerbation of poverty, and impacts on various aspects of life in the country. Other research conducted by Sinore and Wang (2024) also affirm that the negative impacts of droughts in Ethiopia, caused by insufficient precipitation and prolonged dry spells, extend to agricultural production by disrupting crop suitability, phenology, and productivity, while also significantly affecting water resources, ecosystems, and human wellbeing.

Based on Table 9, LULCCs in the local area have undergone significant transformations. The majority of respondents reported an increase in agricultural land (75.76%), indicating the expansion of agricultural activities. The expansion of urban areas was mentioned by 72.73% of the respondents, highlighting the urbanization process and its associated land use changes. Deforestation, including the clearing of forests, was reported by 79.8% of the respondents, suggesting the loss of forest cover in the local area. These results reflect the dynamic nature of LULCCs, with agricultural expansion, urbanization, and deforestation being prominent factors in the local area. Also out of the total respondents, 192 individuals (representing 97%) believe that LULCCs have an impact on drought occurrence and severity, while only 6 respondents (3%) believe LULCC does not have an impact.

The survey result in Table 9 again highlights the potential impacts of LULCCs on drought occurrence and severity in the local area. A significant proportion of respondents (43.4%) reported that these changes exacerbate drought conditions and severity. This suggests that alterations in Land Use Land Cover (LULC) contribute to worsening drought impacts. A smaller percentage of respondents (3.5%) mentioned a decrease in soil moisture retention and groundwater levels, indicating the potential reduction in water availability due to the LULCCs. Additionally, 6.1% of the respondents reported that LULCCs reduce the land’s ability to absorb and retain water, which can lead to increased runoff and decreased water infiltration. The alteration of the local microclimate and increased temperatures were mentioned by 44.4% of the respondents, suggesting that LULCCs can influence local weather patterns, further affecting drought conditions. A small portion of respondents (1.5%) mentioned increased reliance on surface water or overexploitation of groundwater resources as a potential impact of LULCCs. A few respondents (1%) answered “No,” indicating a perception that LULCCs do not have an impact on drought occurrence and severity.

The outcomes of this research align with findings from various scholars, for example Miheretu and Yimer (2018), observed that shrubland, cultivated and rural settlement areas, grassland, bare land, and urban built-up areas expanded annually at rates of 0.48%, 0.14%, 0.62%, 4.95%, and 28.45%, respectively and this can lead to natural habitat degradation. Tesfaw et al. (2023), investigated LULCCs and noted their potential impacts on livelihoods in the research area. Additionally, Yohannes et al. (2018), highlighted that LULCCs can lead to soil erosion, surface runoff, sediment degradation, and deforestation, underscoring the interconnected environmental consequences of such transformations.

The survey findings in Table 9 reveal that the impact of drought on people in poverty or with low socioeconomic status in the area. The majority of the respondents (86.4%) reported that these vulnerable groups are severely affected by drought. This indicates that individuals and households with limited resources and economic opportunities bear a significant burden when facing drought conditions. A smaller percentage of the respondents (8.1%) mentioned that people in poverty or with low socioeconomic status are moderately affected by drought, suggesting a varying degree of impact across the population. Additionally, 5% of the respondents reported that this group is slightly affected by drought, indicating a relatively milder impact. A very small proportion of respondents (0.5%) expressed uncertainty by selecting “Not sure” regarding the impact on people in poverty or with low socioeconomic status. These results highlight the disproportionate effects of drought on vulnerable communities and underscore the need for targeted interventions and support to mitigate the socioeconomic consequences they face. These findings can be substantiated by the results of (Araya and Stroosnijder, 2011; Bogale and Erena, 2022; Morgan, 2012; Sinore and Wang, 2024).

Based on Table 10, the results from the respondents indicate that the primary concerns regarding future socioeconomic impacts of drought in the local area have increased poverty and inequality, with 79.8% of the respondents mentioning it as a significant concern. This suggests that drought has the potential to exacerbate existing socioeconomic disparities and push more individuals and communities into poverty. This is supported by the results of (Yang et al., 2023; De Silva and Kawasaki, 2018; Nyamwaro et al., 2006). Additionally, 15.15% of the respondents expressed concerns about reduced access to essential resources, highlighting the potential impact on basic necessities such as water, food, and livelihood opportunities. Displacement and migration were identified as concerns by 27.27% of the respondents, indicating that drought may lead to population movements as people seek better living conditions or struggle to cope-with the impacts. A smaller portion of respondents (2.53%) mentioned other concerns related to disputes, conflicts, thefts, and illegal activities, implying that drought can contribute to social tensions and criminal activities in the affected areas (see Table 10).

Furthermore, the results in Table 10 indicated that drought has had a significant impact on the socioeconomic status of communities in the studied watershed. The majority of the respondents (86.9%) reported that drought has significantly worsened the socioeconomic conditions. This suggests that drought has caused substantial negative effects on various aspects of the local economy, including livelihoods, income, and overall wellbeing. A smaller proportion of the respondents (10.6%) mentioned that the impact was slightly worsened, indicating a less severe but still noticeable decline in socioeconomic conditions. Only a few respondents (2%), reported no significant change, suggesting that the majority of the affected communities experienced adverse effects due to drought. Additionally, a small percentage of respondents (0.5%) were unsure about the impact, indicating a need for further examination or information. This result is validated by the results from (Sinore and Wang, 2024; Orievulu et al., 2022; Personal and Archive, 2009; Edwards et al., 2008).

In question three of Table 10, the findings suggest that there have been noticeable long-term trends in the socioeconomic impacts of drought in the local area. The majority of the respondents (90.9%) reported worsening trends, indicating that the socioeconomic conditions have been deteriorating overtime due to the recurrent occurrence or persistent impact of drought. This implies that the negative effects of drought have accumulated, leading to long-lasting and increasingly challenging conditions for the affected communities. This strengthens the results of the various agricultural and meteorological drought indices results used in this research which they reflect that drought status is worsening from time to time. A smaller proportion of respondents (7.6%) mentioned improving trends, suggesting that some communities have managed to mitigate or adapt to the socioeconomic impacts of drought and have witnessed positive changes. However, this positive trend was less prevalent compared to the worsening trends. A very small portion of respondents (1.5%) reported no noticeable trends, indicating that the socioeconomic impacts of drought have remained relatively stable or inconsistent over the years, requiring further investigation or clarification.

Table 11 presents the measures taken by respondents to cope with the impacts of drought. The most common response was diversifying income sources; means engaging in additional income sources such as irrigation, temporary daily labor, merchandise, etc., with 70 individuals (35.4%) indicating this strategy.

Changing cropping patterns or adopting drought-resistant crop varieties (such as teff, Finger millet, Sorghum, etc.) was mentioned by 55 respondents (27.8%), while 34 respondents (17.2%) reported implementing water conservation practices. Seeking off-farm employment was chosen by 29 individuals (14.6%), and reducing household expenses was the least frequently mentioned strategy, with only 9 respondents (4.5%) selecting this option. Only one respondent (0.5%) mentioned seeking support from social networks or community organizations (Table 11).

Table 11 also illustrates the respondents’ awareness of government or NGO programs to support drought-affected communities. The majority of respondents, 197 individuals (99.5%), reported being aware of such programs or initiatives. However, only 1 respondent (0.5%), indicated not being aware of any programs or initiatives in their local area. From the programs applied in the local area, the most frequently mentioned program was temporary or emergency food support programs, with 107 respondents (54%) indicating awareness of such initiatives. Long-term food support programs like safety-net and school feeding were mentioned by 64 individuals (32.3%). Programs focused on providing agricultural commodities were mentioned by 20 individuals (10.1%), whereas programs accessing for credits and loans were mentioned by 6 individuals (3%). Only one respondent (0.5%) did not mention any specific program or initiative.

Furthermore Table 11, displays the respondents’ reported benefit from government or NGO initiatives. Out of the total respondents, 150 individuals (75.8%) reported benefiting from these programs, while 48 respondents (24.2%) indicated not having received any benefits. Among the total respondents, 54 individuals (27.3%) reported benefiting from long-term food support programs like safety-net and school feeding initiatives. These programs have likely provided consistent access to food, particularly for vulnerable individuals and families. Temporary or emergency food support programs were mentioned by 76 individuals (38.4%), indicating that these initiatives have been instrumental in providing immediate relief during periods of acute food scarcity caused by drought. Access to credit, loans, and agricultural commodities support were reported by 33 respondents (16.7%), suggesting that these programs have enabled individuals to invest in agricultural activities and expand their income-generating opportunities. However, it is worth noting that 35 respondents (17.7%) did not specify any particular benefit from the mentioned initiatives. This finding highlights the need to further explore and address the gaps in assistance to ensure comprehensive support for all individuals affected by drought.

Table 12 illustrates the respondents’ awareness of government policies or initiatives aimed at reducing drought impacts in the local area. Out of the total respondents, 126 individuals (63.6%) reported being aware of such policies or initiatives, while 72 (36.4%) indicated not having knowledge of any government policies in place. Among the respondents, the most frequently mentioned policy was providing drought-resistant crop varieties, promoting agroforestry, and offering financial assistance or subsidies, with 39 individuals (19.7%) indicating awareness of these measures. Water conservation, efficient irrigation techniques, and the construction of water storage infrastructure were mentioned by 30 respondents (15.2%), while 26 individuals (13.1%) reported the presence of established emergency response and relief measures. Adopting an integrated approach to water resource management was mentioned by 16 individuals (8.1%), and 15 respondents (7.6%) mentioned the establishment of programs to provide financial assistance, subsidies, or insurance schemes. However, 72 respondents (36.4%) did not specify any particular government policy or initiative in place.

Table 12 additionally presents the measures suggested by respondents that can be taken to mitigate the impacts of drought in the study watershed. The most commonly mentioned measure was reducing water losses through infrastructure improvements and implementing water conservation measures, with 50 individuals (25.3%) advocating for these actions. Implementing sustainable land management practices was suggested by 40 respondents (20.2%), while 46 individuals (23.2%) emphasized the importance of encouraging the adoption of drought-resistant crop varieties. Strengthening climate information and early warning systems to provide timely and accurate information was mentioned by 25 individuals (12.6%), and 21 respondents (10.6%) highlighted the need for fostering community participation in water management initiatives and empowering local communities. Promoting livelihood diversification strategies was suggested by 10 individuals (5.1%). A small number of respondents (3 individuals, or 1.5%) mentioned integrating drought risk reduction measures whilst other 3 respondents did not provide any specific suggestions.