Advancing regional scale inundation and exposure forecasting to support decision-making, and action in an African context. 

<p>Dissemination of early warning information and effective preparedness are critical components in flood risk management and shape the dynamics of any successful early response. To enable efficient preparedness and an early response to hazards, early warning information should be simple, usable, and deployed through trusted sources.</p><p>Successful management of floods are therefore dependent on clear and systematic communication structures which are, in turn, necessary to enable dissemination of such information. However, in many parts of Africa, flood event response is hampered by a lack of information on the inundation and potential exposure, with most large-scale systems limited to river flow forecasting, while local systems may lack coverage.</p><p>In collaboration with the UK Foreign and Commonwealth Office, we have previously developed a method for fluvial flood inundation and exposure forecasting that combines hydrological forecasts from the Global Flood awareness systems (GLOFAS) with the LISFLOOD-FP global flood model. This was deployed in Mozambique to provide probabilistic inundation maps and exposure estimates to assist humanitarian response for cyclones Idai, Kenneth and Eloise.</p><p>In this work, the flood models built with globally available datasets, and models augmented with local information, were evaluated for a series of uses and cases. Specifically, the 2020 fluvial flood event in the Nzoia basin in Kenya, the 2019 tropical cyclone flooding in Mozambique from cyclone Idai and recent pluvial flooding in the Zambian city of Lusaka.</p><p>We discuss the potential and limitations of such information to inform efficient action and build resilient futures via co-production of forecasts in Kenya and the community learning lab component of the FRACTAL+ initiative in Lusaka.</p>