Runoff and sediment yield modelling using soil and water assessment tool for management planning of Mojo Watershed, Ethiopia
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Date
2016-01
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
Physically based Soil and water Assessment Tool (SWAT) model was setup and evaluated to assess runoff and sediment yield from Mojo watershed (2017.21 km2) situated in Central Oromia Regional state, Ethiopia. In this study for stream flow simulation parameters involving surface runoff (CN2.mgt) and ground water (ALPHA_BNK.rte) are found the most sensitive parameter and the parameters representing soil (USLE_K.sol) and surface runoff (SPCON.bsn), were found more sensitive for sediment yield simulation. A good agreement between observed and simulated discharge were observed, which was verified using both graphical technique and quantitative statistics. The value of R2=0.83, NSE=0.82, RSR=0.42 and PBIAS=10.5 obtained during calibration and R2 value 0.77, NSE value 0.75, RSR value 0.50 and PBIAS 9.8 obtained during validation as well as the uniformly scatter points along the 1:1 line during calibration and validation justify that the model is very good in simulating runoff. For sediment yield the computed statistical indicators R2=0.76, NSE=0.75, RSR=0.50 and PBIAS = 8.10 were obtained during calibration and during validation the computed statistical indicators were found 0.79 for R2, 0.71 for NSE, 0.54 for RSR and 35.83 for PBIAS. Based on SWAT model output a multi-objective linear programming model was developed to solve several conflicting objectives and to optimize simultaneously considering minimizing soil erosion and maximizing benefit as an objective function and area under different Land use as a constraint. Accordingly, a reduction of dry land farming by 18.45% and increasing the current rangeland 946.36 ha to 15419.74 ha and 45.96 ha under irrigated agriculture to 25526.69 ha would increase the net income and minimize soil erosion from the watershed by 29.91% and 16.14% respectively without making much difference of the current forest land. Furthermore, a decision support system and methodology was developed for the identification land capability classification of each HRUs and to suggest various watershed management practices based on the identified land capability classification.
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