Application of the SWAT model for river flow forecasting in Sri Lanka
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Hapuarachchi, H. A. P.; Zhijia, L.; Flugel, Albert Wolfgang. 2003. Application of the SWAT model for river flow forecasting in Sri Lanka. Journal of Lake Sciences, 15:147-154.
Permanent link to cite or share this item: http://hdl.handle.net/10568/41176
In the present study, the SWAT model and the Xinanjiang model have been used for daily flow forecasting of the Kalu River upper catchment in Sri Lanka. Kalu River is the second largest river in Sri Lanka and due to heavy rainfalls over the catchment, steep river slopes with narrow valleys in the upper catchment and mild riverbed slopes with wide and flat plains in the middle and lower catchments, the floods in Kalu River basin have become regular. The SWAT model has been used for daily river flow predictions in the Kalu River, and compared with the results obtained using the Xinanjiang model. In this study, the Xinanjiang model has performed slightly better than the SWAT model for forecasting the daily flow of Kalu River. In fact it might be partly attributable due to the poor quality and inadequate data, since the output of the SWAT (distributed model) strictly depends on the quality of input data. In addition, many people in Sri Lanka use well water for their domestic purposes. When considering a catchment as a whole, normally it is a very large area, and therefore it is not possible to record or count all the individual minor scale water utilizations in detail such as small irrigation, animal husbandry in minor scale and industrial water utilizations in minor scale. The cumulative value of such water utilizations might be large. The absence of these data may specially affect the distributed models in water balancing. But the conceptual watershed models (e.g. Xinanjiang model) are capable of adjusting their parameters while calibrating, according to the situation since most of their parameters have no physical background. As a result conceptual watershed models show better performance than distributed models where the catchment characteristics and model inputs are limited or incomplete.
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