Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/110101
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributorResearch Institute for Land and Spaceen_US
dc.contributorOtto Poon Charitable Foundation Smart Cities Research Instituteen_US
dc.creatorYou, Jen_US
dc.creatorWang, Sen_US
dc.creatorZhang, Ben_US
dc.date.accessioned2024-11-28T01:37:30Z-
dc.date.available2024-11-28T01:37:30Z-
dc.identifier.issn0022-1694en_US
dc.identifier.urihttp://hdl.handle.net/10397/110101-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.subjectCompound flood risken_US
dc.subjectRainfallen_US
dc.subjectSea level riseen_US
dc.subjectStorm surgeen_US
dc.titleSpatially seamless and temporally continuous assessment on compound flood risk in Hong Kongen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume645en_US
dc.identifier.doi10.1016/j.jhydrol.2024.132217en_US
dcterms.abstractCompound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationJournal of hydrology, Dec. 2024, v. 645, pt. A, 132217en_US
dcterms.isPartOfJournal of hydrologyen_US
dcterms.issued2024-12-
dc.identifier.artn132217en_US
dc.description.validate202411 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera3295-
dc.identifier.SubFormID49887-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextEnvironment and Conservation Fund; Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2026-12-31en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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