Prediction of base flows from basin characteristics: a case study from Zimbabwe / Prévision de débits de base à partir de caractéristiques du bassin: une étude de cas au Zimbabwe

Abstract

Abstract Base flows make up the flows of most rivers in Zimbabwe during the dry season. Prediction of base flows from basin characteristics is necessary for water resources planning of ungauged basins. Linear regression and artificial neural networks were used to predict the base flow index (BFI) from basin characteristics for 52 basins in Zimbabwe. Base flow index was positively related to mean annual precipitation (r = 0.71), basin slope (r = 0.76), and drainage density (r = 0.29), and negatively related to mean annual evapotranspiration (r = –0.74), and proportion of a basin with grasslands and wooded grasslands (r = –0.53). Differences in lithology did not significantly affect BFI. Linear regression and artificial neural networks were both suitable for predicting BFI values. The predicted BFI was used in turn to derive flow duration curves of the 52 basins and with R ² being 0.89–0.99.

Keywords:

• artificial neural networks
• base flow index
• basin characteristics
• linear regression
• Zimbabwe
• réseaux de neurones artificiels
• indice d’écoulement de base
• caractéristiques de bassin
• régression linéaire
• Zimbabwe

Additional information

Notes on contributors

D. Mazvimavi

[email protected]