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

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dc.creatorTedla, H. Z.
dc.creatorBekele, Tilaye Worku
dc.creatorNigussie, Likimyelesh
dc.creatorNegash, E. D.
dc.creatorWalsh, C. L.
dc.creatorO'Donnell, G.
dc.creatorHaile, Alemseged Tamiru
dc.date2024-05
dc.date2024-12-20T07:16:08Z
dc.date2024-12-20T07:16:08Z
dc.date.accessioned2026-06-27T18:43:51Z
dc.descriptionAn 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.
dc.identifierhttps://hdl.handle.net/10568/168115
dc.identifier.urihttp://hdl.handle.net/123456789/163918
dc.languageen
dc.publisherElsevier
dc.rightsLimited Access
dc.sourceTedla, 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]
dc.subjectflood forecasting
dc.subjectearly warning systems
dc.subjectsatellite observation
dc.subjectcitizen science
dc.subjectmonitoring
dc.subjecturbanization
dc.subjecthydrological modelling
dc.subjectdatasets
dc.titleThreshold-based flood early warning in an urbanizing catchment through multi-source data integration: satellite and citizen science contribution
dc.typeJournal Article

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Instituto Internacional para el Manejo del Agua (IWMI)