| Summary: | 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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