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The Blue Nile Basin is confronted by land degradation problems,\udinsufficient agricultural production, and a limited number of developed energy\udsources. Hydrological models provide useful tools to better understand such\udcomplex systems and improve water resources and land management practices. In\udthis study, SWAT was used to model the hydrological processes in the upper\udBlue Nile Basin. Comparisons between a Climate Forecast System Reanalysis (CFSR)\udand a conventional ground weather dataset were done under two\udsub-basin discretization levels (30 and 87 sub-basins) to create an\udintegrated dataset to improve the spatial and temporal limitations of both\uddatasets. A SWAT error index (SEI) was also proposed to compare the\udreliability of the models under different discretization levels and weather\uddatasets. This index offers an assessment of the model quality based on\udprecipitation and evapotranspiration. SEI demonstrates to be a reliable\udadditional and useful method to measure the level of error of SWAT. The\udresults showed the discrepancies of using different weather datasets with\uddifferent sub-basin discretization levels. Datasets under 30 sub-basins\udachieved Nash–Sutcliffe coefficient (NS) values of −0.51, 0.74, and 0.84; p factors of 0.53, 0.66, and 0.70; and r factors of 1.11, 0.83, and 0.67 for the CFSR, ground, and integrated datasets, respectively. Meanwhile, models under 87 sub-basins achieved NS values of −1.54, 0.43, and 0.80; p factors of 0.36, 0.67, and 0.77; r factors of 0.93, 0.68, and 0.54 for the CFSR, ground, and integrated datasets, respectively. Based on the obtained statistical results, the integrated dataset provides a better model of the upper Blue Nile Basin

Improving SWAT model performance in the upper Blue Nile Basin using meteorological data integration and subcatchment discretization