Abstract:
Proper planning and management of water resources is vital for wise utilization and
sustainable development of the resource. Runoff from the upstream of the watershed and the
subsequent sedimentation in the downstream area is an immense problem threatening the
existing and future water resources development in the watershed. An understanding of the
hydrological response of a river basin would help to resolve potential water resources problems
associated with floods, droughts. The objective of this study was to simulate stream flow and
sediment yield of Anger watershed for proper management of the basin.
The Soil and Water Assessment Tool (SWAT) was used to model the hydrology of the basin with
dataset including soils, land use/cover, digital elevation model, flow and meteorological data
from National meteorological stations. The model was calibrated and validated against
measured flow. The values of model for the annual water yields of Anger watershed at the outlet
are 2032.61mm, with the total annual rainfall of 2726.6mm. Out of this 50% of the water yield
was from surface runoff, 47% of the water yield was from Groundwater, 2% of the water yield
was from lateral flow contribution to the stream flow and 1% of the flow was lost through
transmission. Finally the results show that the average runoff coefficient is 0.24, in Anger
watershed contributes an annual water yields of 3.97 BCM and the model simulation output
annual average suspended sediment yield was53.017T/HA
The study showed that monthly stream flow, sediment yield and other hydrologic components in
Anger watershed was predicted by the Soil and Water Assessment Tool (SWAT) hydrologic
model with very good values of model performance evaluation parameters. The Soil and Water
Assessment Tool (SWAT) model was calibrated from 1987 to 2000 and validated from 2001 to
2004. Both, calibration and validation results, showed a good match between measured and
simulated flow. Both coefficient of determination (R2
) and Nash- Sutcliffe simulation efficiency
(NSE), were 0.75 and 0.71 for both calibration and validation respectively. This shows good
performance of the SWAT model on monthly time step
