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Title: Stream and spring capture in combined sewer systems
Author: Broadhead, Adam T.
ISNI:       0000 0004 5356 7190
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2014
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Streams and springs have in some cases been connected into the combined sewer system. These “lost watercourses” contribute another source of clean baseflow to sewer networks, in addition to the widely acknowledged and researched infiltration-inflow. Stream and spring capture, as a type of point source inflow to combined sewer networks, has received little specific acknowledgement by the water industry in the UK. The considerable efforts to tackle sewer infiltration-inflow may be confounded by this type of inflow. A literature review demonstrates that stream and spring capture occurs in many cities around the world, and discusses the causes, the costs and consequences, and the potential solutions, with particular reference to Zurich. Evidence that can be used to identify stream and spring capture is reviewed and demonstrated with a case study of Sheffield, UK. It is found that no single source of information can always be relied on to indicate capture. Over half the total original stream length in the search area is now lost, and may be captured into the combined sewers. A novel water typing method is developed that can be used to indicate stream and spring capture sites from the major and minor ion water chemistry. Results of a detailed sampling program of five capture sites in Sheffield are presented, demonstrating that water types can reflect sites of known capture in some circumstances. Finally a Bayesian Belief Network model is developed to predict where both stream and spring capture and infiltration-inflow are likely to occur in a sewer network, providing a useful scoping tool for water companies. Expert beliefs are used to predict the likelihood of stream and spring capture and infiltration-inflow from various sewer characteristics, and proximity to recorded “lost” streams and springs. The model is robustly evaluated using several validation data sources, suggesting that it performs well.
Supervisor: Lerner, David N. ; Horn, Rachel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available