Weekly Streamflow Forecasting Using a Statistical Disaggregation Model for the Upper Blue Nile Basin, Ethiopia

Abstract

Accurately forecasting the weekly seasonal streamflow of the Upper Blue Nile basin (UBNB) in Ethiopia is essential for managing large-scale water projects of Nile basinwide countries. A wavelet-based, artificial neural network calibrated by genetic algorithm (ANN–GA) model and a statistical disaggregation algorithm were integrated to forecast weekly streamflow of the UBNB. The July to October (JASO) streamflow of the El Diem station of UBNB shows strong interannual oscillations prior to the 1920s and after 1990s. Two ANN-GA models were developed to forecast the UBNB JASO streamflow, the first one using the February to May (FMAM) seasonal sea surface temperature (SST) of the global oceans as predictors to directly forecast JASO streamflow, while the second, a hybrid model, is developed to forecast JASO streamflow from two sets of predictors, which consist of FMAM SST and the July to September (JJAS) seasonal rainfall previously forecasted by the wavelet-based, ANN-GA also driven by FMAM SST as predictors. The forecasted JASO streamflow were then disaggregated to weekly total streamflow using the disaggregation model, Valencia and Schaake (VS). Results indicate that seasonal forecasts with up to 4 months lead time only based on SST as predictors achieved reasonable skill (