Quantitative assessment of sewer overflow performance with climate change in northwest England

Abdellatif, Mawada E., Atherton, William, Alkhaddar, Rafid and Osman, Yassin Z. ORCID: 0000-0003-1121-6598 (2015) Quantitative assessment of sewer overflow performance with climate change in northwest England. Hydrological Sciences Journal, 60 (4). pp. 636-650. ISSN 0262-6667

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Changes in rainfall patterns associated with climate change can affect the operation of a combined sewer system, with the potential increase in rainfall amount. This could lead to excessive spill frequencies and could also introduce hazardous substances into the receiving waters, which, in turn, would have an impact on the quality of shellfish and bathing waters. This paper quantifies the spilling volume, duration and frequency of 19 combined sewer overflows (CSOs) to receiving waters under two climate change scenarios, the high (A1FI), and the low emissions (B1) scenarios, simulated by three global climate models (GCMs), for a study catchment in northwest England. The future rainfall is downscaled, using climatic variables from HadCM3, CSIRO and CGCM2 GCMs, with the use of a hybrid generalized linear–artificial neural network model. The results from the model simulation for the future in 2080 showed an annual increase of 37% in total spill volume, 32% in total spill duration, and 12% in spill frequency for the shellfish water limiting requirements. These results were obtained, under the high emissions scenario, as projected by the HadCM3 as maximum. Nevertheless, the catchment drainage system is projected to cope with the future conditions in 2080 by all three GCMs. The results also indicate that under scenario B1, a significant drop was projected by CSIRO, which in the worst case could reach up to 50% in spill volume, 39% in spill duration and 25% in spill frequency. The results further show that, during the bathing season, a substantial drop is expected in the CSO spill drivers, as predicted by all GCMs under both scenarios.

Item Type: Article
Uncontrolled Keywords: artificial neural network; bathing waters; combined sewer overflows; climate change; generalized linear model; pollution; shellfish waters
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: School of Engineering > Civil Engineering
Depositing User: Sarah Taylor
Date Deposited: 28 Mar 2018 10:18
Last Modified: 28 Mar 2018 10:18
Identification Number: 10.1080/02626667.2014.912755
URI: http://ubir.bolton.ac.uk/id/eprint/1654

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