Watershed models have gradually been adapted to support both decision and policy making for global environmental pollution control. In this study, two watershed models with different complexity, the Soil and Water Assessment Tool (SWAT) and the Generalized Watershed Loading Function (GWLF), were applied in two catchments in data scarce China, namely the Tunxi and the Hanjiaying basins with contrasting climatic conditions (humid and semi-arid, respectively). The performances of both models were assessed via comparison between simulated and measured monthly streamflow, sediment yield, and total nitrogen. Time series plots as well as four statistical measures (the coefficient of determination (R2
), the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE (root mean square error)—observations standard deviation ratio (RSR)) were used to estimate the performance of both models. The results show that both models were generally able to simulate monthly streamflow, sediment, and total nitrogen loadings during the simulation period. However, SWAT performed better for detailed representations, while GWLF could produce much better average values of the observed data. Thus, GWLF offers a user-friendly prospective alternative watershed model that requires little input data and that is applicable for areas where the input data required for SWAT are not always available. SWAT is more suitable for projects that require high accuracy and offers an advantage when measured data are scarce.
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