JWSS - Journal of Water and Soil Science

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Improper management of watershed land utilization has many ill effects on the available resources. Land use optimization is one of the proper strategies to achieve sustainable development and to reduce resource dissipation. Focusing on Brimvand watershed in Kermanshah province which comprises an area of 9572 ha, the present study was conducted to find out the most suitable land allocation to different land uses viz. garden, irrigated farming, dry farming and rangeland to achieve soil erosion minimization and benefit maximization. The soil erosion, net benefit and standard land capability maps were supposed as the inputs of the objective functions and to defined constraints. The multi-objective linear problem was then solved using simplex method with the help of ADBASE software package and ultimately the optimal solution was gained. Additionally, the results of the study revealed that the amount of soil erosion could reduce by 7.78% whereas the benefit increases at the rate of 118.62%, in case of implementation of optimal solution. The above mentioned optimization led to dry farming decrease and garden increase over that area. The results of sensitivity analysis also showed that objective functions were strongly susceptible to the variation of maximum constraint of irrigated farming and garden areas.

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Development of precise and simple methods in flood simulation has greatly reduced financial damage and life loss. Various methods and procedures have been implemented based on Saint-Venant's one-dimensional equation governing unsteady flows. To simplify the solution for these flows, analytical and numerical methods have been used. In the present study, a new method that provides the optimal outcome is introduced using non-linear programming. Penalty function has also been used to convert nonlinear programming (NLP) constrained problems into unconstrained optimal issues. To verify the accuracy of decision variables, the study covered 60 cross-sections of Gharasu River and 25-year flood hydrographs. After determining the model correctness, the 50 and 100-year flood hydrograph were routed in 18 Kilometers. The results were statistically compared with hydraulic and Muskingum hydrological methods. To sum up the routed hydrographs introduced by NLP method were very close to the hydrographs produced by dynamic wave method. The R2 of calculated discharge of routed hydrograph by NLP and dynamic wave method were 0.948, 0.990, and 0.989, respectively, with the return period of 25, 50 and 100-year flood being 0.989. It can be concluded that NLP method is more accurate than Muskingum method, especially when predicting the peak discharge of flood hydrograph.