Statistical–Dynamical Approach for Streamflow Modeling at Malakal, Sudan, on the White Nile River

Abstract

The upper White Nile Basin above Malakal, Sudan, is considered to be one of the most complicated and diverse hydrologic settings on Earth. Accurately depicting and predicting the streamflow at Malakal is essential for water managers considering Nile Basin-wide initiatives and potential large-scale projects. Dynamical, statistical, and combination models are assessed for their ability to predict monthly streamflow at Malakal. The dynamical model represents a lumped parameter, average-monthly water balance, whereas the statistical model incorporates a nonparametric approach based on local polynomial regression, utilizing principal components of precipi- tation and temperature. The combination of dynamical and statistical models through linear regression produces model weights of 0.44 and 0.59, respectively, implying a relatively balanced influence. Evaluation of the combination model demonstrates significant overall skill correlation coefficients equal to 0.83, outperforming either individual model for the validation periods selected. Peak streamflow analyses of timing and quantity also exhibit superior performance by the combination model. An ensemble approach, practical for planning and management from a probabilistic standpoint, is additionally demonstrated.