A Tracer-Aided 2D Numerical Framework to Define Fluvial and Pluvial Hazard Mapping

UDC.coleccionInvestigación
UDC.departamentoEnxeñaría Civil
UDC.endPage17
UDC.grupoInvEnxeñaría da Auga e do Medio Ambiente (GEAMA)
UDC.institutoCentroCITEEC - Centro de Innovación Tecnolóxica en Edificación e Enxeñaría Civil
UDC.issue1 (e13039)
UDC.journalTitleJournal of Flood Risk Management
UDC.startPage1
UDC.volume18
dc.contributor.authorPerrini, Pasquale
dc.contributor.authorIacobellis, Vito
dc.contributor.authorGioia, Andrea
dc.contributor.authorCea, Luis
dc.date.accessioned2026-04-06T17:24:45Z
dc.date.available2026-04-06T17:24:45Z
dc.date.issued2025
dc.description.abstract[Abstract]: Flood hazard is a dynamic nonstationary phenomenon, which can be categorized based on the origin of the inundation. Inland flood hazard arises primarily from pluvial and fluvial inundations, typically modeled separately with respect to the pertaining spatial domains of the assessment, namely the urban areas and the riverine floodplains. When modeling is based on the catchment-scale hydrological-hydrodynamic approach, the inundations such as those resulting from pluvial and fluvial processes are usually not discerned, even though disparities in normative flood risk management exist in different countries. This paper establishes a tracer-aided criterion to discretize between pluvial and fluvial flooding at a catchment scale, relying on the advection process of a conservative tracer. Applied to a small urban catchment for multiple probabilistic rainfall scenarios, our physically based methodology shows that the incorporation of a transport equation within a shallow water model can be used to define the inundation sources. We highlight the advantages of the proposed approach compared to commonly employed modeling techniques for mapping fluvial inundations, while emphasizing the significance of mapping and regulating pluvial hazards in urban areas. The study shows the potential role of an abstraction of the tracers' transport toward identifying the hazard sources in a catchment scale 2D numerical framework.
dc.description.sponsorshipThe present study contains part of the results of Ph.D. research by P. Perrini, whose fellowship (CUP H99J21010150001 no. DOT20THYKL-4) is supported by PON Project: “Programma Operativo Nazionale Ricerca e Innovazione 2014-2020, risorse FSE REACT-EU—Azione IV.4—Dottorati di ricerca su tematiche dell'innovazione.” The study was supported by the financial resources of the “National Centre for HPC, Big Data, and Quantum Computing. Area tematica: Simulazioni, calcolo e analisi dei dati ad alte prestazioni PNRR MUR—M4C2—I 1.4”; and of the “Operational Agreement between the Autorità di Bacino Distrettuale dell'Appennino Meridionale (ABDAM) and the Consorzio Interuniversitario per l'Idrologia (CINID)”.
dc.description.sponsorshipItalia. Ministero dell’Università e della Ricerca; CUP H99J21010150001
dc.description.sponsorshipItalia. Ministero dell’Università e della Ricerca; PNRR MUR-M4C2-I1.4
dc.identifier.citationPerrini, P., Iacobellis, V., Gioia, A., & Cea, L. (2025). A tracer-aided 2D numerical framework to define fluvial and pluvial hazard mapping. Journal of Flood Risk Management, 18(1), e13039. https://doi.org/10.1111/jfr3.13039
dc.identifier.doi10.1111/jfr3.13039
dc.identifier.issn1753-318X
dc.identifier.urihttps://hdl.handle.net/2183/47871
dc.language.isoeng
dc.publisherWiley
dc.relation.urihttps://doi.org/10.1111/jfr3.13039
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject2D shallow water equations
dc.subjectCatchment-scale rainfall-runoff models
dc.subjectFluvial flood hazard
dc.subjectPluvial flood hazard
dc.subjectTracer-aided modelling
dc.subjectTracers transport equations
dc.titleA Tracer-Aided 2D Numerical Framework to Define Fluvial and Pluvial Hazard Mapping
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublicationd914d106-6715-40cf-b743-1e240f37dc94
relation.isAuthorOfPublication.latestForDiscoveryd914d106-6715-40cf-b743-1e240f37dc94

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