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dc.contributor.authorWang, X
dc.contributor.authorZhang, H
dc.contributor.authorBertone, E
dc.contributor.authorStewart, RA
dc.contributor.authorHughes, SP
dc.date.accessioned2021-04-21T05:19:20Z
dc.date.available2021-04-21T05:19:20Z
dc.date.issued2021
dc.identifier.issn1364-8152
dc.identifier.doi10.1016/j.envsoft.2021.105053
dc.identifier.urihttp://hdl.handle.net/10072/403879
dc.description.abstractMonitoring and understanding the dissolved organic matter (DOM) cycle in a drinking water reservoir is crucial to water authorities, since most water treatment practices aim to remove DOM to prevent the formation of potentially harmful disinfection by-products. A vertical profiling system (VPS) installed in reservoirs can continuously detect the fluorescent DOM (fDOM) and determine the fDOM transport process. Although the VPS can interprete fDOM concentrations, water treatment operators still collect and rely upon DOM datasets that are manually sampled throughout the year. A long-term historical database provides an opportunity to develop a three-dimensional fDOM prediction model. In the present study, we collected and analysed VPS and sampling data and developed and assessed an innovative coupled data-driven and process-based model. These models were able to forecast future fDOM in both temperate and extreme weather conditions. Modelling scenario analysis concluded that deeper layers of the reservoir as well as areas close to the riverine zone had higher fDOM concentrations than any other zones during storm events. Simulated fDOM can be a proxy for dissolved organic carbon concentration. The model also determined that inflow creeks were predominant fDOM sources during storm events and continuing winds transported the fDOM from bottom to surface water layers. This study has implications for reservoir and water treatment plant operators seeking to gain a better understanding of the DOM cycle in a reservoir and to more efficiently manage DOM removal.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom105053
dc.relation.ispartofjournalEnvironmental Modelling and Software
dc.relation.ispartofvolume141
dc.subject.fieldofresearchEnvironmental sciences
dc.subject.fieldofresearchcode41
dc.titleCoupled data-driven and process-based model for fluorescent dissolved organic matter prediction in a shallow subtropical reservoir
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWang, X; Zhang, H; Bertone, E; Stewart, RA; Hughes, SP, Coupled data-driven and process-based model for fluorescent dissolved organic matter prediction in a shallow subtropical reservoir, Environmental Modelling and Software, 2021, 141, pp. 105053
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-04-21T00:40:36Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorZhang, Hong
gro.griffith.authorBertone, Edoardo
gro.griffith.authorWang, Xinchen
gro.griffith.authorStewart, Rodney A.


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