Evaluation of E.coli losses in a tidal river network using a refined 1-D numerical model

Huang, Guoxian, Falconer, Roger A. ORCID: https://orcid.org/0000-0001-5960-2864 and Lin, Binliang ORCID: https://orcid.org/0000-0001-8622-5822 2018. Evaluation of E.coli losses in a tidal river network using a refined 1-D numerical model. Environmental Modelling and Software 108 , pp. 91-101. 10.1016/j.envsoft.2018.07.009

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Abstract

Predicting the rate of Escherischia coli (E.coli) loss in a river network is one of the key conditions required in the management of bathing waters, with well verified numerical models being effective tools used to predict bathing water quality in regions with limited field data. In this study, a unique finite volume method (FVM) one-dimensional model is firstly developed to solve the mass transport process in river networks, with multiple moving stagnation points. The model is then applied to predict the concentration distribution of E.coli in the river Ribble network, UK, where the phenomena of multiple stagnation points and different flow directions appear extensively in a tidal sub-channel network. Validation of the model demonstrates that the proposed method gives reasonably accurate solution. The verification results show that the model predictions generally agree well with measured discharges, water levels and E.coli concentration values, with mass conservation of the solution reaching 99.0% within 12 days for the Ribble case. An analysis of 16 one-year scenario runs for the Ribble network shows that the main reduction in E.coli concentrations occurs in the riverine and estuarine regions due to the relatively large decay rate in the brackish riverine waters and the long retention time, due to the complex river discharge patterns and the tidal flows in the regions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Engineering
Additional Information:	This is an open access article under the CC BY license.
Publisher:	Elsevier
ISSN:	1364-8152
Funders:	NERC
Date of First Compliant Deposit:	24 July 2018
Date of Acceptance:	19 July 2018
Last Modified:	25 May 2023 22:42
URI:	https://orca.cardiff.ac.uk/id/eprint/113423

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