Suspended sediment transportation analysis using soil water assessment tool (SWAT) for Peddavagu tributary in Godavari river basin in India

M. Harini Reddy; N. Manikumari; M. Mohan Raju; Aadhi Naresh; Dinesh. C. S. Bisht; Harish Gupta; M. Gopal Naik

M. Harini Reddy Annamalai University, Annamalai Nagar-608002, India.
N. Manikumari Annamalai University, Annamalai Nagar-608002, India.
M. Mohan Raju Govt. of Telangana State, Hill Colony-508202, India.
Aadhi Naresh Osmania University, Hyderabad-500007, India
Dinesh. C. S. Bisht Jaypee Institute of Information Technology, Noida - 201304, India
Harish Gupta Osmania University, Hyderabad-500007, India
M. Gopal Naik Osmania University, Hyderabad-500007, India

Abstract

The current study is an applied analysis of sediment hydraulic process to estimate and forecast the fluvial scenario of Peddavagu, a tributary of Pranhita Sub-basin of Godavari river basin in India. The peculiarity of the drainage basin is that the catchment area under study falls in a heavy rainfall zone where the average annual rainfall is about 1040 mm and the catchment area receives more than 90 percent of the rain during the South-West Monsoon (June to October) thereby consequently all the inflow is received between June and October only. Further, high floods are generally expected, between August and September, thereafter the flow in the river diminishes down considerably. A comparative performance of the applied methodologies evaluated and presented to understand the response of drainage basin in the study. Artificial Neural Networks (ANNs) and conventional Sediment Rating Curves (SRC) were applied to precisely estimate the sediment transportation at the initial stage later Soil Water Assessment Tool (SWAT) methodology applied to simulate and model the same sediment transport response of the drainage basin by considering widely varied hydrologic parameters. The present study mainly focuses on the precise estimation of the event and to see the other hydrological components influence in a broader sense. Hence, SWAT was adapted to incorporate more hydrological parameters even though the analysis results in lesser values of performance indices for better understanding of the system behavior. Therefore, the performance of the selected two models was carried out for the appropriate prediction of average monthly runoff values during the monsoon and non-monsoon seasons. In view of the above, for the SWAT model, an appropriate set of databases (DEM, landuse / landcover, soil, precipitation, and temperature, etc.) is needed which will entail good performance (NSE) of the model, while ANN is a linear model needs typically dependent and independent parameters. Keeping the objective problem in mind, an attempt has been made to check the performance of both models, when the input data was limited and scarce.