It is recognised that the blockage of culverts by woody debris can result in an increased risk of infrastructure damage and flooding.To date,debris transport analysis has focused on regional fluvial systems and large ...It is recognised that the blockage of culverts by woody debris can result in an increased risk of infrastructure damage and flooding.To date,debris transport analysis has focused on regional fluvial systems and large woody debris,both in flume and field experiments.Given the social and economic risk associated with urban flooding,and as urban drainage design shifts away from subsurface piped network reliance,there is an increasing need to understand debris movement in urban watercourses.The prediction of urban watercourse small woody debris(SWD)movement,both quantity and risk,has undergone only limited analysis predominantly due to lack of field data.This paper describes the development of a methodology to enable the collection of accurate and meaningful SWD residency and transportation data from watercourses.The presented research examines the limitations and effective function of PIT tag technology to collect SWD transport data in the field appropriate for risk and prediction analysis.Passive integrated transponder(PIT)technology provides a method to collect debris transport data within the urban environment.In this study,the tags are installed within small woody debris and released at known locations into a small urban natural watercourse enabling monitoring of movement and travel time.SWD velocity and detention are collated with solute time of travel,watercourse and point flow characteristics to identify the relationships between these key variables.The work presented tests three hypotheses:firstly,that the potential for unobstructed or un-detained SWD movement increases with flow velocity and water level.Secondly,that SWD travel distance,and the resistance forces along this travel path,influence SWD transport potential.Thirdly,the relationship between SWD and channel dimensions is examined with the aim of advancing representative debris transport prediction modelling.展开更多
基金supported by the Engineering and Physical Sciences Research Council(Grant Nos.EPSRC EP/J501335/1 and EP/K50337X/1)the Heriot-Watt University School of the Built Environment
文摘It is recognised that the blockage of culverts by woody debris can result in an increased risk of infrastructure damage and flooding.To date,debris transport analysis has focused on regional fluvial systems and large woody debris,both in flume and field experiments.Given the social and economic risk associated with urban flooding,and as urban drainage design shifts away from subsurface piped network reliance,there is an increasing need to understand debris movement in urban watercourses.The prediction of urban watercourse small woody debris(SWD)movement,both quantity and risk,has undergone only limited analysis predominantly due to lack of field data.This paper describes the development of a methodology to enable the collection of accurate and meaningful SWD residency and transportation data from watercourses.The presented research examines the limitations and effective function of PIT tag technology to collect SWD transport data in the field appropriate for risk and prediction analysis.Passive integrated transponder(PIT)technology provides a method to collect debris transport data within the urban environment.In this study,the tags are installed within small woody debris and released at known locations into a small urban natural watercourse enabling monitoring of movement and travel time.SWD velocity and detention are collated with solute time of travel,watercourse and point flow characteristics to identify the relationships between these key variables.The work presented tests three hypotheses:firstly,that the potential for unobstructed or un-detained SWD movement increases with flow velocity and water level.Secondly,that SWD travel distance,and the resistance forces along this travel path,influence SWD transport potential.Thirdly,the relationship between SWD and channel dimensions is examined with the aim of advancing representative debris transport prediction modelling.
