https://orcid.org/0000-0001-5444-3737
Figures
Abstract
Climate change is one of the foremost hurdles confronting the globe in the 21st century, primarily driven by human activities.. This study aimed to analyze the variability and trend of temperature and rainfall in the Bure Zuria district of northwestern Ethiopia over the period from 1983-2024. To examine the change in rainfall and temperature in Bure Zuria district, coefficients of variation (CV), standard anomaly index (SRA) and precipitation concentration index (PCI) were employed using daily precipitation and temperature data obtained from the national meteorological agency of Ethiopia for the period 1983–2024. Additionally, the time series trends were assessed by using the Mann-Kendall trend test and Sen’s slope estimator. Higher rainfall variability was found in the belg rainfalls than the kiremt and annual totals. Annual rainfall showed insignificant increasing trend at a rate of 1.3mm per year. Similarly, the kiremt and belg rainfall also exhibited insignificant increasing trend at a rate of 2.78 and 1.66mm per year. The mean annual minimum temperature presented a higher variability (CV = 8.5%) than that of the mean annual maximum (CV = 4.2%) and annual mean temperature (CV = 4.1%). Moreover, the Mann-Kendall trend test results for annual mean, minimum, and maximum temperature indicate the existence of a significant warming trend at a rate of 0.033°C, 0.043°C and 0.022°C per year, respectively over the study area at p < 0.05. The results provide crucial empirical evidence to inform policy and adaptation strategies. The study recommends using improved crop varieties, adjusting planting dates, soil and water conservation, afforestation and reforestation practices to mitigate and adapt the existing climate variability and change in the study area. We also recommend future studies to magnify the temporal and geographical scale that includes the entire northwestern region. Studies on the local impact of climate change also need further studies in the area.
Citation: Molla E, Melka Y (2025) Variability and trend analysis of rainfall and temperature in Bure Zuria District, Northwestern Ethiopia. PLOS Clim 4(11): e0000700. https://doi.org/10.1371/journal.pclm.0000700
Editor: Sher Muhammad, ICIMOD: International Centre for Integrated Mountain Development, NEPAL
Received: April 25, 2025; Accepted: October 18, 2025; Published: November 14, 2025
Copyright: © 2025 Molla, Melka. 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: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
1.1. Background
Climate change is among the foremost hurdles confronting the globe in the 21st century, driven primarily by human activities such as fossil fuel combustion, deforestation, and intensive agriculture [1,2]. High temperature extremes, rainfall variability, shrinking glaciers, sea warming and rising are some of the manifestations of climate change attributed to increasing greenhouse gas concentrations [2]. Air temperature and rainfall are the major climate elements that have become a great concern worldwide [3]. The estimated range of global surface temperature rise caused by human activities from 1850 to 2019 is between 0.8°C and 1.3°C, with a central estimate of 1.07°C [4]. The global surface temperature will keep rising until at least the middle of the century, under all emissions scenarios. Seasonal temperatures have also experienced increasing warming trends for almost all seasons [5]. This change in temperature and rainfall has adverse ecological, social and economic impacts generally in the world and especially in Africa [1].
Africa is one of the most vulnerable continents experiencing the adverse effects of climate change. Over the past 50–100 years, land surface temperatures have increased by more than 0.5°C, whereas annual rainfall indicates a declining trend [1,6]. Eastern Africa, in particular, has experienced warming temperatures and a decrease in rainfall trend since the early 1980s [7]. The impacts are severe due to the region’s heavy dependence on rain-fed agriculture and weak adaptive capacity of climate change [8–10].
Ethiopia’s economy and livelihood are mostly based on rain-fed agriculture [11,12]] which is characterized by low productivity [13,14]. Extreme weather events like droughts and floods have impacted water resources, agricultural productivity, and food security. Warmer temperature exacerbate this challenges by increasing crop water demands and reducing yields by shortening growing seasons [1]. Besides climate change, Ethiopian agriculture is challenged by other risks such as low level of technology, poor infrastructure, fragmented agricultural practices, high population, changing land tenure systems and ecological degradation [14].
Analyzing spatiotemporal trends of climate variables is vital to assess climate-induced changes and suggest feasible adaptation strategies [3,15]. [16] analyzed the monthly, seasonal and annual rainfall trends for the Japanese Island of Hokkaido by employing the Mann–Kendall test. Another study examines the most detailed trend in monsoon rainfall across various spatial scales in India, applying a multimethod approach integrating the linear regression model (LRM), Mann–Kendall test (MKT), and innovative trend analysis (ITA) analyzed in a particular and synchronized way [17]. Moreover, many studies have investigated temperature and rainfall trends in Ethiopia [13,15,18–23]. These studies generally indicate warming trends of temperature. Rainfall trends, however, showed a very high level of variability over the years. Some studies, e.g., [24, and 25] reported decreasing rainfall trend in their respective study areas. In contrast, [20] reported an absence of clear rainfall trends in their respective study areas, while studies by [19,26] showed an increasing time series of yearly rainfall. Conducting climate trend and variability studies at local scales is vigorous to understand what has been changing and to develop targeted adaptation strategies [21].
Bure Zuria district, in northwestern Ethiopia, is particularly vulnerable to climate variability due to its reliance on rain-fed agriculture and its rugged topography, which heightens risks of soil erosion, landslides, and climate-induced agricultural impacts. This approach aligns with those [17], who conducted a comprehensive 146-year trend analysis of Indian monsoon rainfall revealing nuanced trend patterns with real-world implications for agronomy, water resources and various associated sectors of the economy in India. The district’s average annual rainfall ranges from 1274 to 1750 mm, but its timing and distribution have become unpredictable over recent decades. The district’s average annual temperature varies between 14°C and 24°C, with a visible warming trend in recent decades. Furthermore, in the district, human activities such as deforestation for firewood and agriculture, overgrazing, and poor land management practices have further intensified land degradation, and ultimately increased the district’s vulnerability.
Regardless of the impact of climate change in Bure Zuria, long-term empirical analyses of rainfall and temperature trends are limited. Previous study in the study area concentrated on climate change adaptation strategies [27], which have a gap in understanding long-term climate trends. To fill this gap, the present study analyzes 42 years of temperature and rainfall data by employing CV, SRA and PCI for examining climate variability and the Mann-Kendall test as well as Sen’s slope estimator for time-series trends. Therefore, the objective of this study is to analyze the variability and trends of the rainfall and temperature of Bure Zuria district.
2. Methodology
2.1. Description of the study area
Bure Zuria district is found between 10◦18′N-10◦49′29″N latitude and 36◦52′1″E − 37◦7′9″E longitude (Fig 1). It is 400 kilometers away from the capital, Addis Ababa. The district’s elevation ranges from 700 to 2350 meters above sea level (m.a.s.l). It includes three agro-ecological zones: 82% of midland, 10% of lowland, and 8% of highland. The district has average maximum, minimum, and mean temperatures of 24°C, 14°C, and 19°C, respectively. The district has a tropical highland climate with marked seasonality. The majority of the annual rainfall, which varies between 1274 and 1750 mm, falls in the kiremt season. The rainy season in the district is influenced by monsoonal winds, which bring moisture from the Indian and Atlantic Ocean through the Inter-tropical Convergence Zone (ITCZ). Wind speed tends to increase during the seasonal transition period. Humidity levels are high in the kiremt (>80%) and this level drops to below 40% in the dry season, leading to moisture stress. Subsistence mixed agriculture (crop production and livestock rearing) is the main source of income in the area. Maize, teff, wheat, finger millet, barley, beans and chili pepper are the most commonly grown crops. Domestic animals include cattle, goats, sheep, equines, donkeys, chickens and bees [28].
2.2. Data collection
Daily rainfall data, maximum and minimum temperature for Bure station, and satellite gridded data for 42 years (1983–2024) were obtained from the National Meteorological Agency of Ethiopia Table 1. The years from 1983-2024 were selected due to the observed variability in rainfall and events in the recent decades and represent a continuous dataset available with reliable and complete records for the study area. The World Meteorological Organization suggests that a minimum of 30 years of data is needed to investigate signs of climate change [29]. The selection of these climatic parameters was made due to their significant impact on the agricultural sector.
To determine if there’s a notable difference between the merged station (from the Ethiopian meteorology agency) and satellite-gridded data (TAMSAT data), the F test was conducted and revealed no substantial variation between the two datasets. As a result, due to the completeness and quality of the data, satellite-gridded data were employed for trend analysis instead of relying on gauge station data. This decision was made to avoid any missing data values identified within these datasets [30].
2.3. Data analysis
Statistical Package for Social Science (SPSS) version 20.0 and XLSTAT 2016 were used to analyze data obtained from the meteorological agency due to their user-friendly interfaces and relative ease of use. CV measures month to month, season to season and year-to-year variation in the climate data series.
CV is used to assess the relative variability of a dataset in relation to its average, making it easier to compare variability between datasets with different units or scales. It is computed as.
Where CV is the coefficient of variation; σ is standard deviation and μ is the mean precipitation.
CV was used to classify the degree of variability as less (CV < 20), moderate (20 < CV < 30), and high (CV > 30) [31].
PCI was used to examine the concentration or unevenness of precipitation during a given time period (seasonal or annual). A higher PCI indicates uneven distribution (high rainfall concentration) of rainfall throughout the year, while a lower PCI suggests more even distribution (low rainfall concentration) [32]: PCI values also help farmers optimize crop planning, manage water resources, and reduce risks associated with rainfall patterns.
PCI were computed as
Where: Pi = the rainfall amount of the ith month.
According to [32], a PCI value below 10 indicates a uniform rainfall distribution, values between 11 and 15 imply to moderate rainfall distribution, while those between 16 and 20 suggest irregular rainfall distribution, and PCI values above 21 indicate a strong erratic rainfall distribution.
Furthermore, standardized anomalies were computed to examine the nature of the trends, enabling the determination of the dry and wet years in the record and used to assess frequency and severity of droughts [33].It was calculated as:
Where, SRA = standardized rainfall anomaly for year t.
= annual rainfall in year t.
= long-term mean annual rainfall.
σ = standard deviation.
The drought severity groups [33] are extreme drought (SRA < -1.65), severe drought (-1.28 > SRA > -1.65), moderate drought (-0.84 > SRA > -1.28), and no drought (SRA > -0.84).
Besides, the Mann-Kendall test and Sen’s slope estimator were used to detect the trend and magnitude of slope for the trend, respectively. Since there are chances of outliers being present in the dataset, the non-parametric MK test is useful because its statistic is basically (+ or -) signs rather than the values of the random variable, and therefore, the trends determined are less affected by the outliers. The Mann-Kendall test statistic S is calculated [34] using the formula
Where xi and xj are the annual values in years j and k, j > k, respectively, and
If the dataset is identically and independently distributed, then the mean of S is zero and the variance of S is given by
Where m is the number of tied groups and is the number of data points in group t. In cases where the sample size n > 10, the test statistic Z(S) is calculated from [34]
A positive (negative) value of Z indicates an upward (downward) trend.
The magnitude of the trend is predicted by [35] slope estimator methods. A positive value of β indicates an ‘upward trend’ (increasing values with time); while a negative value of β indicates a ‘downward trend’. Generally, the slope Ti between any two values of a time series x can be estimated from:
Where and
are considered as data values at time j and i (j > i) correspondingly. And Q is the slope between data points Xj and Xi.
The methodological framework of this study is presented (Fig 2).
3. Results and discussion
3.1. Rainfall trends and variability
3.1.1. Rainfall variability.
The mean, minimum and maximum rainfall during the observation periods are indicated in Table 2.
The mean annual rainfall of the study area during the study period was 1493.03mm, with a standard deviation of 130.6and a CV of 8.8%. The lowest and highest recorded rainfall amounts per year were 1274.7 mm (in 1984, which is the well-known drought year in the country) and 1752.4 mm in 1998, respectively. This showed rainfall in the study area is above the Amhara region, with a mean annual rainfall of 1165.2mm [20]. As reported by agricultural extension experts, in the study area, a shortage of rainfall is not a problem for agriculture; rather a long rainy season that results in sprouting and fungal attacks of widely growing crops (e.g., bread wheat variety) [28]. Therefore, this problem is related with the increased nature of rainfall in the district. Moreover, the increased rainfall in the belg season mostly affects the production of livestock, specifically by spoiling the crop residues that will be used as forage.
Kiremt is the main rainy (growing season) in the study area, which accounts for approximately 72.62% of the overall precipitation. Specifically, a significant portion, nearly 43%, falls during July and August, while June and September contribute 16.11% and 13.51% of the kiremt rainfall, respectively. Additionally, there is a shorter rainy season, known as belg, which contributes an important portion, approximately 15.69%, of the total rainfall in the study area. Farmers in the study area are dependent on belg rainfall for safe transitions to kiremt season, especially for livestock production and land preparation for the cropping season.
CV for annual rainfall (8.8%) revealed less inter-annual rainfall variability Table 2. Similarly, [36,37] resulted in low inter-annual rainfall variability in their studies in southern and central highlands of Ethiopia. However, the CV for the district is significantly lower than the CV observed in most weather stations in north-eastern Ethiopia, showing the region has high inter-annual rainfall variability [38].
Variability of rainfall was higher in the belg (MAM) than kiremt (JJAS) and annual total rainfall (Table 3). High variability in the belg than kiremt rainfall was reported by [18,39]. The variability in rainfall levels across seasons and years may hinder farmers’ capacity to address the adverse effects of climate change and variability [20]. For instance, farmers in the study area rely on belg rainfall to ensure a smooth transition into the kiremt season, particularly for livestock management and land preparation for the upcoming cropping season. Therefore, a high irregularity (variability) in the belg rainfall affects the agricultural activities by complicating the early planting and harvesting decisions.
June, July, August and September month had less rainfall variability (10 < CV < 30). The remaining months had coefficients of variation greater than 30%, indicating higher rainfall variability (Table 4).
The PCI value ranged from the lowest of 12.9 in 2014 to the highest of 21.2 in 1990, indicating no year with uniform rainfall distribution for the whole period of years under observation; precipitation concentrated for a few months rather than being uniformly distributed across the year (Fig 3). High rainfall concentrations in a few months of the year have been reported by [36]. The PCI values indicate rainfall distribution, which has substantial effects on agriculture and water availability. A PCI value less than 10 indicates a uniform distribution of rainfall throughout the year, reduces water demand for irrigation, allowing farmers to adjust planting and harvesting dates more assertively. A PCI value between 10 and 15 reveals moderate rainfall concentration, which implying rainfall is more concentrated in specific months, resulting in dry and rainy seasons. This condition imposes careful selection of crops and planting date depending on the rainfall in the specific months. A PCI value greater than 20, indicating high rainfall concentration as a result of the rainfall occurs during a short period, which can cause flooding and soil erosion.
Ethiopia has successfully used a number of strategies to control rainfall concentration and enhance water supply [40,41]. For instance, household water storage tanks and community-level reservoirs, such as surface reservoirs, underground reservoirs, and roof top rainwater harvesting systems have been widely adopted in the Amhara and Tigray regions. Additionally, multipurpose storage structures like check dams and micro-dams have been constructed for efficient water use in the Tigray Ethiopia [42,43,]. Soil and water conservation measures such as terraces and infiltration pits have also proven effective in reducing soil erosion and improving moisture retention [44,45]. These strategies are particularly relevant to the study area, where high PCI values indicate rainfall concentration over specific season and enhance water efficiency for agriculture and livestock production. Efficient water storage systems, such as rainwater harvesting, are required for areas experiencing rainfall with high PCI values.
The seasonal rainfall distribution results showed that PCI values were 18 and 9 for belg and kiremt rainfall, respectively. Accordingly, kiremt, with a uniform rainfall distribution rainfall was lower than belg, with moderate rainfall distribution in the district.
Further, SRAs were also calculated for the period of 1983–2024 to assess the inter-annual rainfall fluctuations for the study area, which showed a cyclic pattern of variations with alternating drier and wetter years. In general, of the 42 years of observation, twenty years (48%) recorded below the long-term average annual rainfall. Accordingly, there were two years of extreme, three years of severe and five years of moderate drought incidence in the area over the period from 1983 to 2024 (Fig 4). However, the rainfall showed some recovery and slightly progressed above the mean (decreasing from drought years). This is supported by [46] who reported a decreasing incidence of drought years in Indibir, Guragae zone, Ethiopia.
3.1.2. Rainfall trend.
The MK test and sen’s slope estimator showed no significant trend for the long term inter-annual rainfall (Table 5).
Even if, statistically not significant, there is a slight increasing trend (Fig 5) for inter-annual rainfall at a rate of 1.3mm per year (Table 5). The study is consistent with [47], which reported non-significant trends of annual rainfall in north-western Ethiopia. [39], also indicated insignificant increasing trend in annual rainfall. On the other hand, the studies by [19,36] found an increasing trend in annual rainfall at their respective study sites. Similarly, [26] showed the increasing trend of annual rainfall in the studies of selected stations of Amhara regional state, Ethiopia.
Similar to the inter-annual rainfall trend, the results of the Mann-Kendall trend test indicated there is no significant trend for all seasonal rainfall (Table 6).
This finding agrees with the findings of [48] who reported the absence of significant trends in the main (kiremt) and short (belg) rain periods in many parts of Ethiopia. However, it contradicts with the findings [49], who reported a significant decline kiremt in their study on trends and spatial distribution of annual and seasonal rainfall in different parts of Ethiopia. [50,51], also indicated decline and increasing trends of the belg and Kiremt seasonal rainfall, respectively.
However, the results of the Mann-Kendall trend test showed slightly increasing rainfall trend for the belg and kiremt rainfall at a rate of 1.66and 2.78 mm per year, respectively. This is supported by [38,39], reported non-significant increasing trend for the kiremt rainfall in Ethiopia.
The observed increasing trend in both annual and belg rainfall may be driven by a combination of both natural and human-made factors. For instance, variations in the El Niño-Southern Oscillation (ENSO), particularly La Niña events, have a significant influence on the seasonal rainfall pattern in Ethiopia by enhancing monsoonal winds and shifting the position of the Intertropical Convergence Zone (ITCZ). This shift typically brings wetter conditions during the main rainy season (Kiremt), which can extend into Belg (short rainy season) rainfall. Additionally, warming temperatures increase evaporation and the atmosphere’s moisture-holding capacity, potentially enhancing rainfall intensity and duration. Moreover, land use changes, such as deforestation, intensive farming and urbanization can alter local climate by increasing surface temperatures and disrupting moisture recycling, potentially affecting rainfall patterns.
June, July, August, and September are months that have high mean, minimum, and maximum amounts of rainfall. The agricultural populations of the study area are totally dependent on this rainfall for agricultural activities.
The result for the monthly rainfall trend indicated the existence of a significant increasing trend for the months of November and March at a rate of 1.767 and 0.768 mm per year, respectively (Table 7). Increasing rainfall in March and August may have an impact on agriculture by causing elongation of crop harvesting dates that result in sprouting. [52], reported a decreasing rainfall in July and increasing rainfall in August and September in their analysis of the rainfall trend of the West Coast Plain and Hill Agro-Climatic Region for 117 years.
3.2. Temperature trends and variability
Temperature is another important climate variable that influences the climate of an area. The availability of moisture in a given area, even if a normal amount of rainfall exists, is highly influenced by the magnitude of its temperature. The descriptive statistics, trend, and variability of time series maximum, minimum, and mean temperature data for 1983–2024 were analyzed to understand and summarize the long-term temperature of the area.
3.2.1. Temperature variability.
Results showed that April and August were observed as the highest and lowest months in the area, with long-term monthly mean temperatures of 21.5 and 17.8°C, respectively. The results further indicated that the highest variations of mean monthly temperatures were observed in the month of May (CV = 5.4) in the study area (Table 8).
A Belg season was found hotter than a Kiremt season, with mean temperatures of 21.2 and 18.2°C, respectively (Table 9). This temperature difference has significant implications for agricultural activities in the area. During the belg season, higher temperatures can rush soil moisture loss, making it more challenging for land preparation activities. For livestock, high temperatures during the belg season may decrease their productivity and health, impacting the ease of use of animal labor for plowing or other tasks.
The result also showed that the annual mean temperature ranges from 18.2°C in 1989 to 20.2°C in 2011, with a mean value of 19.2°C (Table 10).
Higher variability was observed in the annual minimum temperature (CV = 8.5) than the annual maximum (CV = 4.2) and mean temperature (CV = 4.1%). The reason for higher variability in minimum temperatures is linked to solar radiation, which is relatively consistent in tropical regions like Ethiopia. In addition, in places like Bure Zuria with potential elevation differences, cold air tends to pool in low-lying areas at night, causing higher spatial and temporal variability in minimum temperatures. Minimum temperatures typically occur at night when the earth’s surface loses heat through radiative cooling. This agrees with the results of [53] who analyzed temperature data of Amibara and Gewane districts in Afar region, Ethiopia and reported higher variability in the annual minimum temperature than the annual maximum temperature. A higher CV for minimum temperatures shows greater variability in nighttime temperatures, which can enhance the risk of frost damage to crops, and lead to crop stress or delayed growth. This variability complicates water management, as fluctuating temperatures affect evapotranspiration and soil moisture retention, potentially increasing water needs. Additionally, livestock may experience health issues due to inconsistent night temperatures, and the availability of forage crops may be impacted. The result also indicated the annual mean temperature variability was lower than the seasonal variability.
3.2.2. Temperature trends.
The Mann-Kendall trend test revealed the existence of a significant increasing trend in mean monthly temperature except for April, May, and June months, which showed a non-significant increasing trend (Table 11).
It is observed that significant warming trends in the mean monthly temperature series ranged from 0.02°C per year in the month of March to 0.05°C per year in the months of September (Table 11).
Similarly, the result showed the existence of significant positive trends in maximum temperature for the May, July, August, September and October months, with July having the highest rate of increase at 0.084°C per year. Regarding the monthly minimum temperature, all months except April, June, July and August showed a significant positive trend. For April, June, July and August, the results indicated a non-significant increasing trend.
The Mann-Kendall trend test revealed a significant positive trend in mean temperature during both seasons (Table 12). This finding is supported by [54] that indicated a warming trend exists during all seasons in north eastern Ethiopia.
Kiremt season showed a higher significant rate of change of mean temperature than the Belg season’s rate of changes. The rate of increase for belg and kiremt season mean temperature was found to be 0.022 and 0.032°C per year, respectively. This may have its own consequences on the agricultural communities through increasing evapotranspiration in the study area, as it is happening during the Kiremt season (growing season).
Moreover, the results of the Mann-Kendall trend test for the annual mean, maximum and minimum temperature indicates the existence of a significant warming trend over the study area.(Table 13).
The mean annual temperature showed a positive trend (Fig 8) at a rate of 0.033°C per year (Table 12). The Mann-Kendall test also indicated that a significant increasing trend of annual mean maximum (Fig 7) and mean minimum (Fig 6) temperatures at a rate of 0.022°C and 0.043°C per year, respectively (Table 13). The increasing temperature in the study areas creates water loss due to evaporation, and this affects crop and livestock production. This is caused by increased water demand for crops, particularly during the kiremt season resulted in decreased productivity. On the other hand, livestock is directly affected by the increased temperature, which reduces pasture availability and quality, specifically, in the dry season of the district. This agrees with the result of [Bewuket and Conway, 2006; 46] analyzed temperature data and reported an increasing trend in the mean annual maximum, mean annual minimum, and mean annual temperature. Similarly [36] reported warming trends of the mean annual maximum and minimum temperatures for the period 1981–2013 in the central highlands of Ethiopia. This finding is also consistent with future temperature projections. For example, [55] forecasted increases in maximum and minimum temperatures ranging from 0.03°C to 0.09°C and 0.05°C to 0.2°C, respectively, for the 2020s. Greater variability is anticipated in the extent of warming during the mid- and far-future periods. By the 2050s, maximum temperatures are projected to rise by 0.2°C to 0.8°C, while minimum temperatures could increase by 0.2°C to 1.93°C. Similarly, [56] projected changes in mean temperature over East Africa from 2006 to 2100, with increases of 0.2°C and 0.5°C per decade under RCP4.5 and RCP8.5 scenarios, respectively. Rising temperatures significantly impact agriculture, water demand, and ecosystem services. Warmer conditions can shift and shorten growing seasons, and reduce yields. Increased evapotranspiration as a result of warming can increase moisture loss from soil, raise water demand, which may stress limited water resources. Ecosystem services such as pollination, soil fertility, and pests are also affected, as higher temperatures disrupt pollinator activity, promoting pest explosions.
4. Conclusion and recommendations
This study presents an analysis of temporal variability and trends in rainfall and temperature in Bure Zuria district of northwestern Ethiopia for the period 1983–2024. In the study area, kiremt is the main rainy season (growing season), which provides maximum rainfall, while belg is a short rainy season which provides a substantial amount of rainfall. A significant portion of the kiremt rainfall falls during July and August. The annual mean rainfall in the study area was 1493 mm, with the standard deviation and coefficient of variation of 130.6 and 8.8%, respectively. The coefficient of variation for annual rainfall revealed less inter-annual rainfall variability over the study area. Higher rainfall variability was observed in the belg rainfalls than kiremt. 48% of the observations indicated negative anomalies (recorded below the long-term average annual rainfall). The trend analysis for annual rainfall revealed an insignificant increasing trend at a rate of 1.33 mm per year. Similarly, the belg and kiremt rainfall exhibited insignificant increasing trend at a rate of 1.66 and 2.78 mm per year, respectively, in the area.
The mean annual temperature ranges from 18.2°C to 20.2°C with a mean value of 19.2°C. The results of the Mann-Kendall trend analysis test for the trend of annual minimum, maximum, and mean temperature indicates the existence of a significant warming trend at a rate of 0.043°C, 0.022 and 0.033°C, respectively. The Mann-Kendall trend test also revealed a significant positive trend in mean temperature during all seasons. Kiremt season showed a higher significant rate of change of mean temperature than the Belg season’s rate of changes. The rate of increase for belg and kiremt season mean temperature was found to be 0.022 and 0.032°C per year, respectively.
The observed climatic trends and variability have had a significant impact on agricultural productivity, water resources, and livelihoods in the study area. The increase in kiremt rainfall results in the sprouting of widely growing crops, which is a major constraint in addition to rainfall variability for agriculture. Similarly, increased temperature may have accelerated evapotranspiration and reduced soil moisture availability, leading to a reduction in crop productivity. The increased temperature has also negatively affected livestock production by reducing pasture availability and quality, contributing to lower milk and meat production. Water availability for irrigation and household use has also been impacted by changing rainfall patterns. Livelihoods in the study area have been significantly affected, as the households rely on rain-fed agriculture for their income. Variability in rainfall and increasing temperatures has resulted in frequent crop failures, leading to food insecurity and economic instability.
The study recommends implementation of adaptation and mitigation measures for existing climate variability and climate change in the study area. Since kiremt rainfall is increasing using improved crop varieties, adjusting planting dates and soil and water conservation are climate change adaptation strategies suggested to reduce the impacts of climate variability on crop production. Afforestation and reforestation mitigation activities are recommended to combat the ongoing impacts of climate variability. Effective implementation of soil and water conservation measures in the district can be achieved by giving awareness to the community about the importance of soil and water conservation measures in mitigating climate change impacts. Strengthen land use regulations like reducing deforestation and overgrazing that exacerbate erosion. Indeed, integrating soil and water conservation measures with income-generating activities like agroforestry and ecotourism to improve resilience.
Local communities and other stakeholders play different roles in the implementation of the recommended adaptation and mitigation strategies. For instance, local communities have indigenous knowledge about their environment, and they can provide practical insights into the area’s unique needs. They are also the first and foremost implementers of the strategies. Other stakeholders, particularly government bodies, could play their role in supporting the local communities through finance, awareness creation, and controlling the implementation of policies regarding climate change tackling options.
We also recommend future studies should focus on analyzing the projection of climate variability and trends over large temporal and geographical scales in addition to relying on historical data.
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