Multiple conceptual hydrological models for simulating streamflow in data-sparse river basins: an application of the Vietnamese Cau river basin

Streamflow plays a critical role in water resources management, requiring precise estimation, especially in data-sparse river basins. In this study, multiple hydrological models (HYMOD, GR4J, and NAM) are presented and employed to simulate streamflow in the data-sparse Vietnamese Cau river basin at the Thac Rieng station, providing an illustrative example. Four indices named Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling–Gupta efficiency (KGE), and root mean square error (RMSE) are implemented to quantitatively assess model performance. Firstly, using the available hydrometeorological data from 1 January 1960 to 31 December 1981, parameters in each model are determined using the Shuffled Complex Evolution University of Arizona optimization method. The results show that three models reproduced acceptably observed streamflow, with NSE ranging from 0.65 to 0.75, r and KGE varying between 0.72 and 0.87, and RMSE is less than 6.5% of the observed streamflow magnitude. Secondly, the NAM model was found as the most suitable for simulating observed streamflow in the studied river basin. Thirdly, three models were applied to simulate streamflow in the period from 1 January 1982 to 31 December 2022, revealing similar magnitudes of four statistical indicators of observed and estimated streamflow. The capability of the three models in simulating streamflow in data-sparse river basins is finally discussed.