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Validation of General Climate Models (GCMs) over Upper Blue Nile River Basin, Ethiopia

DOI: 10.4236/acs.2017.71006, PP. 65-75

Keywords: Blue Nile, Downscaling, GCM, Validation

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Abstract:

Potential of climate change impact assessment on hydrology and water resources of rivers is increasing from time to time due to its importance for water resources planning and management in the future. In order to carry out climate change impact studies, using General Climate Models (GCM) is a common practice and before using any of these models, it is essential to validate the models for the selected study area. Blue Nile River is one of the most sensitive rivers towards climate change impacts. The main source of Blue Nile River is Lake Tana where the two adjacent tributary rivers, Ribb & Gumera, are located and the main object of this paper is validation of current 15 GCM outputs (IPCC-AR5) over these two rivers using empirical quantile perturbation downscaling technique. The performance of the downscaled outputs of GCMs were evaluated using statistical indicators and graphical techniques for evapotranspiration, rainfall and temperature variables using observed daily meteorological datasets collected from five stations (Addis Zemen, Bahirdar, Debretabor, Woreta and Yifag) for the control period 1971-2000. Analysis results showed that the correlation coefficient of all models for mean monthly (MM) rainfall are 12% - 45%; and the Bias and RMSE -46 mm to +169 mm and 62 mm to 241 mm, respectively. The Bias and RMSE for MM maximum temperature are -2.5°C to +35°C; and 1°C to 35°C whereas for MM minimum temperature -6°C to +22°C and 1.7°C to 23°C, respectively. For the case of MM evapotranspiration, which is estimated using FAO-Penman-Montheith equation, the Bias and RMSE values vary from -35 mm to +10 mm; and +11 mm to +36 mm, respectively. The variation in the performance level of these models indicates that there is high uncertainty in the GCM outputs. Therefore, to use these GCM models for any climate change studies in the basin, careful selection has to be made.

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