VFSMOD-W 모형을 이용한 SWAT 모형의 초생대 유사 저감 효율 모듈 개선

초록

Environment problem has been arising in many countries. Especially, soil erosion has been deemed as one of the biggest issues because sediment causes muddy water and pollutants, such as agricultural chemicals, flow in the stream with this sediment. Many studies, regarding soil loss and non-point source pollution from watershed, has been performed while serious problem has been known. Soil loss occurred in most agricultural area by rainfall and runoff. It makes hydraulic structure unstable, causes environmental economical problems because muddy water destroys ecosystem and causes intake water deterioration. As revealing serious effects of muddy water by sediment, many researches have been doing with various methods. Hydraulic structures establishments such as soil erosion control dams and grit chamber are common. Vegetative filter strip is investigated in this study because vegetative filter strip is designed for reducing sediment from upland areas of the watershed, and it has many functions, not only sediment reduction but also runoff water quality improvement and wildlife habitat. With these positive functions of the vegetative filter strip, the study about vegetative filter strip has been increasing for reducing sediment because it is more effective than hydraulic structures from an environmental perspective. But the sediment trapping efficiency by vegetative filter strip, needs to be investigated and designed first. Therefore the model, VFSMOD-W, was used in this study as it can estimate sediment trapping efficiency of vegetative filter strip under various field, vegetation, weather condition. Sensitive factors to sediment trapping efficiency are studied with VFSMOD-W, and sediment trapping efficiency equation has been derived using two most sensitive factors. It is thought that the equation suggested in this study can be used in Soil and Water Assessment Tool (SWAT), to overcome the limit of SWAT filter strip module, which is based solely on filter strip width.

키워드