ASSESSMENT OF SEDIMENT YIELD USING SWAT MODEL: CASE STUDY OF KEBIR WATERSHED, NORTHEAST OF ALGERIA
Journal: Big Data In Water Resources Engineering (BDWRE)
Author: Kamel Khanchoul, Amina Amamra, Bachir Saaidia
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Erosion is identified as one of the most significant threats to land in increasing rates of soil loss and reservoir sedimentation. An integrated approach therefore requires sediment assessment for identification of its sources for efficient watershed management. The present study is aimed to examine the spatial and temporal sediment yield distribution potential and to identify the critical erosion prone zones within Kebir watershed, Algeria using Soil and Water Assessment Tool interfaced in GIS for the period from 1982 to 2014. The model is calibrated by adjusting sensitive parameters and validation is done using observed data from 1982 to 1998. The model performance checked by the coefficient of determination (0.76), Nash–Sutcliffe coefficient (0.75) and relative error (+8.19%) suggests that the model has performed satisfactorily for sediment yield prediction. The simulated outputs of the model show that the 33-year period of sediment load production is estimated to be 19.24×106 tons and a mean annual sediment yield of 856.14 T/km²/yr. Temporally, sixty-four percent (50%) of sediment yield generated in the watershed occurs in five months of the winter and fall seasons. The most erosion vulnerable sub-basins that could have a significant impact on the sediment yield of the reservoirs are identified. Based on this, sub-basin 16, 14, 13, 11 and 8 are found to be the most erosion sensitive areas that could have a significant contribution, of 50%, to the increment of sediment yield. Best management practices are highly recommended for the land sustainability because of the high sediment supply to the dams.