Flash flood warnings in context: combining local knowledge and large-scale hydro-meteorological patterns

Abstract. The small spatial and temporal scales at which flash floods occur make predicting events challenging, particularly in data-poor environments where high-resolution weather models may not be available. Additionally, the uptake of warnings hampered by difficulties translating scientific information to local context experiences. Here we use social science methods characterise knowledge flooding among vulnerable communities along flat Lake Malawi shoreline district Karonga, northern Malawi. This is then used guide a analysis factors that contribute area using contemporary global datasets, including geomorphology, soil land-use characteristics, hydro-meteorological conditions. Our results show interviewed have detailed impacts drivers (deforestation sedimentation), early warning signs (changes clouds, wind direction, rainfall patterns), distinct processes lead flood beginning end wet season. shows data corroborate this combining provides improved understanding within context. We highlight potential linking large-scale datasets with improve usability warnings.