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Accuracy of Local Knowledge in Prediction Seasonal Weather: Empirical Evidence from North eastern Nigeria

Ahmed Abubakar Jajere, Jonah Kunda Joshua, Umar Muhammed Bibi, Yusuf Maina-Bukar
Environment and Pollution , Volume 10; doi:10.5539/ep.v10n2p33

Abstract: Over the years, West African Sahel’s people developed some strategies for predicting the seasonal weather using meteorological indicators to plan for extreme weather events. This study used information on local indicators of seasonal weather prediction and mean monthly rainfall and temperature record (1981-2017) from Nguru weather station located at Latitude 14°N in achieving the aim of the study. Both qualitative and quantitate (descriptive and inferential) statistical tools were employed in analysing the collected data. The study found that the local population of the study area used meteorological indicators in predicting the seasonal weather. The results of the analysis revealed that the variability of the annual rainfall during the study period was large. An increasing trend of 3.1mm annually was observed. While decreasing trend in the cold, dry and hot dry season temperature and an increasing trend in warm moist temperature by 0.025°C, 0.05°C and 0.0004°C respectively, was observed. Annual rainfall amount accounts for 31% and 2% variability in cold dry and warm moist season temperature, respectively. Cold, dry season and warm moist season temperature respond to any 1mm increase in annual rainfall by decreasing by 0.012°C and 0.002°C, respectively. The Hot, dry season temperature also accounts for 4% of the variability in annual rainfall. The model’s result revealed anyone 1°C increase in hot dry season temperature lowers the annual rainfall by 10mm. This study confirmed that the observed relationship between seasons weather conditions by local population exist. Therefore annual rainfall is the major determinant of cold dry seasonal temperature in the study area.
Keywords: rainfall / Sahel / seasonal weather / moist / local population / cold dry / hot dry season / extreme

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