Reply on RC1

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 may be hampered by difficulties in translating the scientific information to the local context and experiences. Here we use social science methods to characterise local knowledge of flash flooding among vulnerable communities along the flat Lake Malawi shoreline in the district of Karonga, northern Malawi. This is then used to guide a scientific analysis of the factors that contribute to flash floods in the area using contemporary global datasets; including geomorphology, soil and land-use characteristics, and hydro- meteorological conditions. Our results show that communities interviewed have detailed knowledge of the impacts and drivers of flash floods (deforestation, sedimentation), early warning signs (changes in clouds, wind direction and rainfall patterns), and distinct hydro-meteorological processes that lead to flash flood events at the beginning and end of the wet season. Our analysis shows that the scientific data corroborates this knowledge, and that combining local and scientific knowledge provides improved understanding of flash flood processes within the local context. We highlight the potential in linking large-scale global datasets with local knowledge to improve the usability of flash flood warnings.