article · ENVIRONMENTAL SYSTEMS RESEARCH
An analysis of monthly rainfall and temperature records from 1983 to 2016 examines climate patterns in the Alwero watershed in western Ethiopia. Based on gridded data at a four-kilometre resolution, the watershed receives mean annual rainfall exceeding 1,600 millimetres, alongside a mean annual temperature of 25 degrees Celsius. Overall annual rainfall, as well as precipitation during the October to February season, demonstrates statistically significant increasing trends. Specific monthly increases occurred in May and November, while March rainfall showed a decreasing trend. Contrary to wider expectations, maximum and minimum temperatures exhibited slight downward trends, with maximum temperatures during the October to February period decreasing significantly. Furthermore, the decade of the 2000s proved cooler than previous decades. These findings highlight the presence of distinct local climatic trends that differ from broader regional patterns.
Understanding fine-scale climate trends is vital for regional planners, hydrologists, and farmers. Because local conditions can diverge significantly from national-level climate trajectories, as shown by increasing rainfall and cooling temperatures in the Alwero watershed, accurate localized data ensures that water resource schemes, flood risk assessments, and agricultural planting schedules reflect actual ground realities rather than broad, coarse-scale assumptions.
This work provides baseline climate intelligence useful for agricultural planning, flood risk assessment, water management, and local climate adaptation programmes. Prospective users include local agricultural planners, water authorities, and environmental project developers. As foundational observational research mapping historical trends, it remains at an early stage and requires translation into operational advisory tools, hydrological models, or decision-support platforms before practical deployment.
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Abstract Climate analysis at relevant time scales is important for water resources management, agricultural planning, flood risk assessment, ecological modeling and climate change adaptation. This study analyses spatiotemporal variability and trends in rainfall and temperature in Alwero watershed, western Ethiopia. Our analysis is focused on describing spatial and temporal variability of rainfall in the study area including detection of trends, with no attempt at providing meteorological explanations to any of the patterns or trends. The study is based on gridded monthly rainfall and maximum and minimum temperature data series at a resolution of 4 × 4 km which were obtained from the National Meteorological Agency of Ethiopia for the period 1983–2016. The study area is represented by 558 points (each point representing 4 × 4 km area). Mean annual rainfall of the watershed is > 1600 mm. Annual, June–September ( Kiremt ), March–May ( Belg ) rainfall totals exhibit low inter-annual variability. Annual and October-February (Bega) rainfalls show statistically significant increasing trends at p = 0.01 level. May and November rainfall show statistically significant increasing trends at p = 0.01 level. March shows statistically significant decreasing trend at p = 0.1 level. The mean annual temperature of the watershed is 25 °C with standard deviation of 0.31 °C and coefficient of variation of 0.01 °C. Mean annual minimum and maximum temperatures show statistically non-significant decreasing trends. Bega season experienced statistically significant deceasing trend in the maximum temperature at p = 0.01 level. The year-to-year variability in the mean annual minimum and maximum temperatures showed that the 2000s is cooler than the preceding decades. Unlike our expectations, annual and seasonal rainfall totals showed increasing trends while maximum and minimum temperatures showed decreasing trends. Our results suggest that local level investigations such as this one are important in developing context-specific climate change adaptation and agricultural planning, instead of coarse-scale national level analysis guiding local level decisions.
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DOI: 10.1186/s40068-020-00184-3
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