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

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cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.donorDigital Innovations for Water Secure Africaen
cg.contributor.donorAccelerating Impact of CGIAR Climate Research for Africaen
cg.contributor.initiativeClimate Resilience
cg.coverage.countryZambia
cg.coverage.iso3166-alpha2ZM
cg.creator.identifierSuman Kumar Padhee: 0000-0002-1949-6830en
cg.creator.identifierNiranga Alahacoon: 0000-0003-0984-5176en
cg.creator.identifierGiriraj Amarnath: 0000-0002-7390-9800en
cg.identifier.iwmilibraryH053390en
cg.identifier.projectIWMI - C-0008en
cg.placeColombo, Sri Lankaen
dc.contributor.authorPadhee, Suman Kumaren
dc.contributor.authorAlahacoon, Nirangaen
dc.contributor.authorAmarnath, Girirajen
dc.date.accessioned2025-01-27T07:56:33Zen
dc.date.available2025-01-27T07:56:33Zen
dc.identifier.urihttps://hdl.handle.net/10568/170045
dc.titleDynamic and scalable framework for flood early warning: Zambia case studyen
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
dcterms.accessRightsOpen Access
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
dcterms.extent19p.en
dcterms.issued2024-01-25en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.subjectflood forecastingen
dcterms.subjectearly warning systemsen
dcterms.subjectframeworksen
dcterms.subjectcommunitiesen
dcterms.subjectindicatorsen
dcterms.subjectrainfallen
dcterms.subjectgovernanceen
dcterms.subjectsatellitesen
dcterms.subjectweather forecastingen
dcterms.subjectresilienceen
dcterms.subjectstakeholdersen
dcterms.subjectcase studiesen
dcterms.typeBrief

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