Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: satellite and citizen science contribution

Simple item page
cg.contributor.affiliationAddis Ababa Universityen_US
cg.contributor.affiliationNational University of Lesothoen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.affiliationArba Minch Universityen_US
cg.contributor.affiliationNewcastle Universityen_US
cg.contributor.donorGlobal Challenges Research Funden_US
cg.coverage.countryEthiopiaen_US
cg.coverage.iso3166-alpha2ETen_US
cg.coverage.subregionAddis Ababaen_US
cg.coverage.subregionAkaki Catchmenten_US
cg.creator.identifierLikimyelesh Nigussie: 0000-0002-6380-743Xen_US
cg.creator.identifierAlemseged Tamiru Haile: 0000-0001-8647-2188en_US
cg.identifier.doihttps://doi.org/10.1016/j.jhydrol.2024.131076en_US
cg.identifier.iwmilibraryH053337en_US
cg.isijournalISI Journalen_US
cg.issn0022-1694en_US
cg.journalJournal of Hydrologyen_US
cg.reviewStatusPeer Reviewen_US
cg.volume635en_US
dc.contributor.authorTedla, H. Z.en_US
dc.contributor.authorBekele, Tilaye Workuen_US
dc.contributor.authorNigussie, Likimyeleshen_US
dc.contributor.authorNegash, E. D.en_US
dc.contributor.authorWalsh, C. L.en_US
dc.contributor.authorO'Donnell, G.en_US
dc.contributor.authorHaile, Alemseged Tamiruen_US
dc.date.accessioned2024-12-20T07:16:08Zen_US
dc.date.available2024-12-20T07:16:08Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/168115en_US
dc.titleThreshold-based flood early warning in an urbanizing catchment through multi-source data integration: satellite and citizen science contributionen_US
dcterms.abstractAn effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.en_US
dcterms.accessRightsLimited Accessen_US
dcterms.available2024-03-23en_US
dcterms.bibliographicCitationTedla, H. Z.; Bekele, Tilaye Worku; Nigussie, Likimyelesh; Negash, E. D.; Walsh, C. L.; O'Donnell, G.; Haile, Alemseged Tamiru. 2024. Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: satellite and citizen science contribution. Journal of Hydrology, 635:131076. [doi: https://doi.org/10.1016/j.jhydrol.2024.131076]en_US
dcterms.extent131076en_US
dcterms.issued2024-05en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.publisherElsevieren_US
dcterms.subjectflood forecastingen_US
dcterms.subjectearly warning systemsen_US
dcterms.subjectsatellite observationen_US
dcterms.subjectcitizen scienceen_US
dcterms.subjectmonitoringen_US
dcterms.subjecturbanizationen_US
dcterms.subjecthydrological modellingen_US
dcterms.subjectdatasetsen_US
dcterms.typeJournal Articleen_US

Files

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

IWMI Journal Articles