Articles | Volume 16, issue 5
Hydrol. Earth Syst. Sci., 16, 1435–1443, 2012

Special issue: Catchment classification and PUB

Hydrol. Earth Syst. Sci., 16, 1435–1443, 2012

Research article 15 May 2012

Research article | 15 May 2012

Classification and flow prediction in a data-scarce watershed of the equatorial Nile region

J.-M. Kileshye Onema1,2, A. E. Taigbenu1, and J. Ndiritu1 J.-M. Kileshye Onema et al.
  • 1Water Research Group, School of Civil and Environmental Engineering, University of Witwatersrand, Private Bag 3, WITS 2050, South Africa
  • 2WaterNet, Box MP 600 Mount Pleasant, Harare, Zimbabwe

Abstract. Continuous developments and investigations in flow predictions are of interest in watershed hydrology especially where watercourses are poorly gauged and data are scarce like in most parts of Africa. Thus, this paper reports on two approaches to generate local monthly runoff of the data-scarce Semliki watershed. The Semliki River is part of the upper drainage of the Albert Nile. With an average annual local runoff of 4.622 km3/annum, the Semliki watershed contributes up to 20% of the flows of the White Nile. The watershed was sub-divided in 21 sub-catchments (S3 to S23). Using eight physiographic and meteorological variables, generated from remotely sensed acquired datasets and limited catchment data, monthly runoffs were estimated. One ordination technique, the Principal Component Analysis (PCA), and the tree cluster analysis of the landform attributes were performed to study the data structure and spot physiographic similarities between sub-catchments. The PCA revealed the existence of two major groups of sub-catchments – flat (Group I) and hilly (Group II). Linear and nonlinear regression models were used to predict the long-term monthly mean discharges for the two groups of sub-catchments, and their performance evaluated by the Nash-Sutcliffe Efficiency (NSE), Percent bias (PBIAS) and root mean square error to the standard deviation ratio (RSR). The dimensionless indices used for model evaluation indicate that the non-linear model provides better prediction of the flows than the linear one.