Natural Hazards and Earth System Sciences (Jan 2024)

Impact-based flood forecasting in the Greater Horn of Africa

  • L. Alfieri,
  • A. Libertino,
  • L. Campo,
  • F. Dottori,
  • S. Gabellani,
  • T. Ghizzoni,
  • A. Masoero,
  • L. Rossi,
  • R. Rudari,
  • N. Testa,
  • E. Trasforini,
  • A. Amdihun,
  • J. Ouma,
  • J. Ouma,
  • L. Rossi,
  • Y. Tramblay,
  • Y. Tramblay,
  • H. Wu,
  • H. Wu,
  • M. Massabò

DOI
https://doi.org/10.5194/nhess-24-199-2024
Journal volume & issue
Vol. 24
pp. 199 – 224

Abstract

Read online

Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods, and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup and the methodological approach of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 d. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the Disaster Operation Center of the Intergovernmental Authority on Development (IGAD) in the daily monitoring and early warning from hydro-meteorological disasters in eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River basin in summer 2020. More extensive quantitative evaluation of the system performance is envisaged to provide its users with information on the model reliability in forecasting extreme events and their impacts.