Bewässerungs- und Entwässerungssystemtechnik

Stream Flow and Sediment Yield Modeling: A Case Study of Beles Watershed, Upper Blue Nile Basin

Abstract

Nadew B

Modeling provides important planning tools that can be used in management of land and water resources which can be used in the understanding of dynamic processes and prediction of the existing processes. The evolution of a wide range of hydrologic catchment models employing the physical based and data driven approach introduces the need for objective test benchmark to assess the merits of different models in reconciling alternative approaches. The main objective of this study was to model stream flow and sediment yield by using ANN and SWAT models for the Upper Main Beles gauged catchment. The two models were calibrated and validated at Main Beles gauging station for both stream flow and sediment yield yielding reasonable results in monthly and daily time step. Two days antecedent values were considered during formulation of possible inputs for daily basis and no antecedent value were considered for monthly time step modeling for ANN model. Modeling by SWAT for stream flow yields a mean monthly stream flow of 65.28 m3 /s showing 2.48% deviation whereas the MLP neural model prediction was 67.37 m3 /s showing 5.76% deviation from the observed mean monthly flow. Total mean annual Sediment yield loading from Upper Main Beles simulated by SWAT and ANN model was 4.81 and 5.97 ton/ha/year underestimated by 12.9% and overestimated by 8.1% respectively excluding bed load contribution. The total mean annual sediment yield that was drawn from Upper Main Beles predicted by SWAT and ANN model was found 1,602,845.92 ton and 1,989,395.05 ton respectively. Sediment yield modeling by MLP neural model in both daily and monthly time step predicts better than SWAT including daily stream flow modeling. The calibrated parameter values of the two models can be considered for further hydrologic simulation of the watershed and their application in consideration of their simplicity in data requirement, purpose, prediction accuracy and change in land use dynamics of the watershed.