Abstract
Land use/land cover (LULC) change is an important landscape process capable of altering the fluxes of water, sediment, contaminants and energy. In this study, Soil and Water Assessment Tool (SWAT) model was used to examine the effects of LULC on the hydrological process of Gojeb Watershed in the Omo-Gibe basin. The performance of the model was evaluated through sensitivity, uncertainty analysis, calibration and validation. The LULC change analyses for three periods (1989, 2000 and 2013) were performed using ERDAS Imagine 2014 with a maximum likelihood classifier. The time series LULC changes results were used for estimation of stream flow and sediment yield. The result has shown that during the study period most parts of the forest land were converted into cultivated land with an increase of cultivated land by 14.97% over the study periods which resulted in an increase of stream flow and sediment yield by 8.6 m3/s and 41.07ton/km2 respectively. The Nash sutcliff efficiency (NSE), coefficient of determination (R2) and percent of bias (PBIAS) were used for evaluating the model performance. The model results have shown a good agreement with the observed values with NSE > 0.75, R2 > 0.78, and PBIAS < 0.5 values. Based on the validated sediment yield results of 2013 land use, high potential source of spatial variability of sediment was found at middle and downstream of the watershed. Hence, for the critical sub-watersheds, the design and development of three best management practices (BMPs) were performed. These best management scenarios include: S1 (filter strip), S2 (terrace/ bund) and S3 (reforestation). Based on these scenarios, the findings have shown a sediment yield reductions by 22.9%, 29.4% and 32.3% with the implementation of S1, S2 and S3 respectively. Therefore, based on the findings, implementing S3 is the best management strategy as compared to other options and hence, such intervention should be encouraged at a wider scale for efficient sediment reductions of Gojeb watershed.
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More From: Remote Sensing Applications: Society and Environment
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