Forecasting people exposed to tropical cyclone flooding in Southeast Africa: Lessons learned from recent events

Abstract. East Africa has recently experienced a series of devastating tropical cyclone landfalls characterised by hundreds of fatalities, millions of displaced people and substantial economic damage. Forecasting the impact of these tropical cyclones can, in theory, better motivate anticipatory action compared to only forecasting the hazard. This paper describes an approach to forecasting the number of people directly exposed to flooding from tropical cyclones and documents experience gained communicating these forecasts to practitioners via emergency bulletins. Forecasting flood exposure requires a complex cascade of meteorological, hydrological, hydraulic and population models. Interpretation of forecasts was difficult, even for the scientific experts developing the systems, due to uncertainties brought in at each stage of the modelling cascade. Thus, producing interpretable forecast messaging was challenging and often required extensive discussion between forecasters with expertise on different elements of the system. This paper uses practical experience gained from several tropical cyclone events to highlight essential requirements for interpreting and disseminating tropical cyclone flood impact forecasts. We also analyse how forecasts evolved with lead-time and compare them to observed flooding in the case of Tropical Cyclone Freddy. Overall, we aim to synthesise our experience into actionable learning that might inform future use of forecasting in humanitarian response. Exposure estimates were most sensitive to storm track location, even when exposure was aggregated to districts. Uncertainty from track location remained substantial even in the days before landfall, meaning a recipient of these forecasts needs to understand and interpret the distribution of exposure. For the second landfall of Tropical Cyclone Freddy, nationwide exposure estimates were remarkably similar between remotely sensed flood extents and the best estimate from the forecast system. However, this overall similarity results from the averaging of substantial uncertainty at the district scale.