Should seasonal rainfall forecasts be used for flood preparedness?
- 1Red Cross Red Crescent Climate Centre, The Hague, 2521 CV, the Netherlands
- 2School of Archaeology, Geography and Environmental Science, University of Reading, Reading, RG6 6AH, UK
- 3Institute for Environmental Studies, VU University Amsterdam, 1081 HV, the Netherlands
- 4International Research Institute for Climate and Society, Columbia University, New York, 10964, USA
- 5Royal Netherlands Meteorological Institute (KNMI), De Bilt, 3731 GA, the Netherlands
- 6European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.