An Integrated Model for Assessing Flow Structure and Water Quality in Confluence River Channels

Water resources management is important for today’s society to better utilize the limited water resources. The rapid increase of urban modernization results in an increase of pollutant loads by, at least, one order of magnitude over natural catchments conditions. Typical water pollutants include suspended solids, nutrients, biochemical oxygen demand, pathogenic organisms and trace metals. The water quality in urban river networks has been worsening by intake of surface runoff and sewages in day-to-day consumption cycles. Nowadays, the distribution and restoration of water quality play a key role in sustainable development in modern cities. In addition, the water pollution resulting from sewages is not negligible because of relative high ratio of water in urban river channels. However, the river water quality monitoring is performed mostly by collecting discrete grab samples at single points in the channel but the temporal and spatial variations of water quality in urban river networks could not be measured. In particular, the sudden water pollution caused by unpredictable pollutant divulging due to sudden natural or manmade disasters is beyond routine management. This proposal aims to investigate the real impacts and possible resolution of fluid flow in confluence urban rivers and potential water pollution under sudden natural or manmade disasters. First, the target sites will be chosen by the research team in terms of on-site investigation and surveying of actual river networks in Hong Kong, e.g., Yuen Long Creek marked with bad water quality. Secondly, the morphology and water quality records of selected site will be collected via site surveying and historical database search from Hong Kong government information centre. Thirdly, the computational mock-up will be generated and implemented to the self-written computational code, which contains the integrated hydrodynamic-water quality model developed so far in the Department of Civil and Architectural Engineering, City University of Hong Kong. Fourthly, the scaled, experimental mock-up will be established in the State Key Laboratory of Hydraulics and Mountain Engineering, Sichuan University, PRC and relevant measurement will be carried out. Finally, the measured data will be used to validate the performance of the developed computational model for further improvement, which, in turn, will be used to predict the evolving trends of water quality towards a beneficial application of water quality monitoring in Hong Kong.