STREAM FLOW AND SEDIMENT YIELD MODELING (CASE STUDY: ANGER WATERSHED)

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