Turkish Journal of Agriculture and Forestry




As a result of the increased availability of spatial information in watershed modeling, several easy to use and widely accessible spatial datasets have been developed. Yet, it is not easy to decide which source of data is better and how data from different sources affect model outcomes. In this study, the results of simulating the stream flow and sediment yield from the Seyhan River basin in Turkey using 3 different types of land cover datasets through the soil and water assessment tool (SWAT) model are discussed and compared to the observed data. The 3 land cover datasets used include the coordination of information on the environment dataset (CORINE; CLC2006), the global land cover characterization (GLCC) dataset, and the GlobCover dataset. Streamflow and sediment calibration was done at monthly intervals for the period of 2001-2007 at gauge number 1818 (30 km upstream of the Çatalan dam). The model simulation of monthly streamflow resulted in good Nash-Sutcliffe efficiency (NSE) values of 0.73, 0.71, and 0.68 for the GLCC, GlobCover, and CORINE datasets, respectively, for the calibration period. Furthermore, the model simulated the monthly sediment yield with satisfactory NSE values of 0.48, 0.51, and 0.46 for the GLCC, GlobCover, and CORINE land cover datasets, respectively. The results suggest that the sensitivity of the SWAT model to the land cover datasets with different spatial resolutions and from different time periods was very low in the monthly streamflow and sediment simulations from the Seyhan River basin. The study concluded that these datasets can be used successfully in the prediction of streamflow and sediment yield.


CORINE, GLCC, GlobCover, sediment, Seyhan River, SWAT

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