Abdellatif, M, Atherton, W, Alkhaddar, R and Osman, Y (2015) Flood risk assessment for urban water system in a changing climate using artificial neural network. Natural Hazards, 79 (2). pp. 1059-1077. ISSN 0921-030X

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Abstract

Changes in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding.

Item Type:	Article
Additional Information:	The final publication is available at Springer via http://dx.doi.org/10.1007/s11069-015-1892-6
Uncontrolled Keywords:	0401 Atmospheric Sciences, 0406 Physical Geography And Environmental Geoscience, 1701 Psychology
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > TD Environmental technology. Sanitary engineering
Divisions:	Civil Engineering & Built Environment Civil Engineering (merged with Built Env 10 Aug 20)
Publisher:	Springer Verlag
Date Deposited:	19 Oct 2015 12:55
Last Modified:	04 Sep 2021 13:53
DOI or ID number:	10.1007/s11069-015-1892-6
URI:	https://researchonline.ljmu.ac.uk/id/eprint/2201

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