To support decision-makers in flood disaster management, a real-time flood monitoring system was established in the Niger and Volta basins under the Water disaster platform to enhance climate resilience in Africa (WADiRE-Africa). The system utilized real-time satellite rainfall data to bridge the gap between data scarcity and inaccessibility and a hydrological model to simulate the inundation extents at basin and hotspot scales. Presently, the prototype system is under testing in both basins.

A strong correlation between extreme rainfall and damaged paddy area in the past period was observed and the floods may affect rice paddies when 1-d or 4-d rainfall exceeds 25 mm or 71 mm. Flood inundation and rice crop damage might be more severe in the future due to climate change than in the past period. The average rice crop damage per year is approximately two times higher for the future period than for past period, suggesting more serious rice crop damage in future due to climate change.

The study provides valuable insights into the use of cutting-edge technologies for real-time extreme flow modelling and disaster risk reduction strategies in data-scarce regions. The methodology presented here improved forecasting lead time and accuracy of real-time satellite precipitations. This highlights the practical implications of public domain datasets and models for informed decision-making. The study can complement informed decision-making for flood preparedness in transboundary basins.

There are many global maps of hydrology and water resources, but none on necessary storage to smooth out discharge variability. This paper provides a methodology to create such a map, taking the Ganges–Brahmaputra–Meghna basin as an example. Necessary storage is calculated by a new method, intensity–duration–frequency curves of flood and drought (FDC–DDC). Necessary storage serves as a signature of hydrological variability and its geographical distribution provides new insights for hydrology.