Dynamic and scalable framework for flood early warning: Zambia case study - Padhee, Suman Kumar; Alahacoon, Niranga; Amarnath, Giriraj (2024)
Flood 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.