Generation of monthly precipitation under limited data and climate change: An example from the Upper Blue Nile River Basin

U. Kim
J. J. Kaluarachchi, Utah State University
V. Smakhtin


This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain-fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long-term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100-year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be −7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.