Proceedings of the KSR Conference (한국철도학회:학술대회논문집)
- 2008.11b
- /
- Pages.56-60
- /
- 2008
Evaluation of SWMM Model Adjustment for Rubber-tired Tram Disaster Management System against the Snow-melt during the Winter
겨울철 융설을 대비한 바이모달 트램 재해관리 시스템의 SWMM 모형 적용성 평가
- Kim, Jong-Gun ;
- Park, Young-Kon ;
- Yoon, Hee-Taek ;
- Park, Youn-Shik ;
- Jang, Won-Seok ;
- Yoo, Dong-Seon ;
- Lim, Kyoung-Jae
- 김종건 (한국철도기술연구원, 바이모달수송시스템연구단) ;
- 박영곤 (한국철도기술연구원, 바이모달수송시스템연구단) ;
- 윤희택 (한국철도기술연구원, 바이모달수송시스템연구단) ;
- 박윤식 (강원대학교, 농업공학부) ;
- 장원석 (강원대학교, 농업공학부) ;
- 유동선 (강원대학교, 농업공학부) ;
- 임경재 (강원대학교, 농업공학부)
- Published : 2008.11.13
Abstract
Increasing urban sprawl and climate changes have been causing unexpected high-intensity rainfall events. Thus there are needs to enhance conventional disaster management system for comprehensive actions to secure safety. Therefore long-term and comprehensive flood management plans need to be well established. Recently torrential snowfall are occurring frequently, causing have snow traffic jams on the road. To secure safety and on-time operation of the Bi-modal tram system, well-structured disaster management system capable of analyzing the urban flash flooding and snow pack melt/freezing due to unexpected rainfall event and snowfall are needed. To secure safety of the Bi-modal tram system due to torrential snowfall, the snow melt simulation capability was investigated. The snow accumulation and snow melt were measured to validate the SWMM snow melt component. It showed that there was a good agreement between measured snow melt data and the simulated ones. Therefore, the Bi-modal tram disaster management system will be able to predict snow melt reasonably well to secure safety of the Bi-modal tram system during the winter. The Bi-modal tram disaster management system can be used to identify top priority area for snow removal within the tram route in case of torrential snowfall to secure on-time operation of the tram. Also it can be used for detour route in the tram networks based on the disaster management system predicted data.
Keywords