Evaluation of hydrological models for streamflow prediction: a case study of the Mille River, Lower Awash Basin, Ethiopia

Abstract

ABSTRACT Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics.