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Evaluating the performance of HBV, HEC-HMS and ANN models in simulating streamflow for a data scarce high-humid tropical catchment in Tanzania

ABSTRACT This study evaluates the performance of the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, and an artificial neural network (ANN) model in a data-scarce high-humidity tropical catchment. Statistical indices, simulated and observed hydrographs, and flow duration curves were employed for evaluating and comparing their performances. The results indicate that the HEC-HMS model is superior, with Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) values of 0.80 and 0.82 for calibration and 0.84 and 0.79 for validation, respectively. The HBV model is also suitable for simulating catchment hydrology, with NSE and R2 values of 0.73 and 0.74 for calibration and 0.64 and 0.64 for validation, respectively. The ANN model performed satisfactorily, with NSE and R2 values of 0.66 and 0.67 for calibration, and 0.55 and 0.52 for validation, respectively. These findings provide insight into the effectiveness of hydrological models, which is important for effective water resources management in light of climate change impacts in the region.