Dynamic and scalable framework for flood early warning: Zambia case study

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cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.donorDigital Innovations for Water Secure Africaen_US
cg.contributor.donorAccelerating Impact of CGIAR Climate Research for Africaen_US
cg.contributor.initiativeClimate Resilienceen_US
cg.coverage.countryZambiaen_US
cg.coverage.iso3166-alpha2ZMen_US
cg.creator.identifierSuman Kumar Padhee: 0000-0002-1949-6830en_US
cg.creator.identifierNiranga Alahacoon: 0000-0003-0984-5176en_US
cg.creator.identifierGiriraj Amarnath: 0000-0002-7390-9800en_US
cg.identifier.iwmilibraryH053390en_US
cg.identifier.projectIWMI - C-0008en_US
cg.placeColombo, Sri Lankaen_US
dc.contributor.authorPadhee, Suman Kumaren_US
dc.contributor.authorAlahacoon, Nirangaen_US
dc.contributor.authorAmarnath, Girirajen_US
dc.date.accessioned2025-01-27T07:56:33Zen_US
dc.date.available2025-01-27T07:56:33Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/170045en_US
dc.titleDynamic and scalable framework for flood early warning: Zambia case studyen_US
dcterms.abstractFlood forecasting and early warning systems (FFEWS) are vital for safeguarding vulnerable communities, enabling anticipatory action to mitigate livelihood and income losses before the disaster strikes. This study introduces a dynamic, and scalable framework for flood indicators in Zambia using Global Precipitation Measurement GPM-IMERG (Integrated Multi-satellitE Retrievals for GPM) satellite rainfall data, Global Forecast SystemGFS global weather forecast data, and a GIS-based basin network database (BND). The framework is designed to enhance FFEWS through the CGIAR AWARE platform, strengthening anticipatory action mechanisms. Evaluation of the GPM-IMERG data reveals a strong correlation with ground-based station observations and reliable performance metrics, confirming its suitability for real-time rainfall monitoring. To address limitations in ground data, the framework incorporates intensity duration frequency (IDF) curves across various return periods to generate flood early warning indicators. The BND ensures that flood warnings work for both individual sub-basins and their combined runoff responses. A Comparison of three historical flooding events in Zambia as documented by EM-DAT, demonstrates the reliable performance of GPM-IMERG in identifying floods. A recent flooding event in the Southern province was analysed using GPM-IMERG and GFS data, showing reasonable forecasting of sub-basin level rainfall intensity and accumulation up to 10 days in advance, with accuracy improving closer to the event. This framework aims to bolster resilience and promote proactive disaster management through the AWARE platform, with the potential scalability across Africa and Asia.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationPadhee, Suman Kumar; Alahacoon, Niranga; Amarnath, Giriraj. 2024. Dynamic and scalable framework for flood early warning: Zambia case study. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Climate Resilience. 19p.en_US
dcterms.extent19p.en_US
dcterms.issued2024-01-25en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.subjectflood forecastingen_US
dcterms.subjectearly warning systemsen_US
dcterms.subjectframeworksen_US
dcterms.subjectcommunitiesen_US
dcterms.subjectindicatorsen_US
dcterms.subjectrainfallen_US
dcterms.subjectgovernanceen_US
dcterms.subjectsatellitesen_US
dcterms.subjectweather forecastingen_US
dcterms.subjectresilienceen_US
dcterms.subjectstakeholdersen_US
dcterms.subjectcase studiesen_US
dcterms.typeBriefen_US

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