Articles | Volume 22, issue 2
Nat. Hazards Earth Syst. Sci., 22, 461–480, 2022
https://doi.org/10.5194/nhess-22-461-2022
Nat. Hazards Earth Syst. Sci., 22, 461–480, 2022
https://doi.org/10.5194/nhess-22-461-2022
Research article
 | Highlight paper
16 Feb 2022
Research article  | Highlight paper | 16 Feb 2022

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

Agathe Bucherie et al.

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Cited articles

Alam, A., Ahmed, B., and Sammonds, P.: Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh, Quatern. Int., 575–576, 295–307, https://doi.org/10.1016/j.quaint.2020.04.047, 2020. a
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Alfieri, L., Berenguer, M., Knechtl, V., Liechti, K., Sempere-Torres, D., and Zappa, M.: Flash Flood Forecasting Based on Rainfall Thresholds, in: Handbook of Hydrometeorological Ensemble Forecasting, Springer, 1–38, https://doi.org/10.1007/978-3-642-40457-3_49-1, 2015. a
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., Roo, A., Salamon, P., Wyser, K., and Feyen, L.: Global projections of river flood risk in a warmer world, Earth's Future, 5, 171–182, https://doi.org/10.1002/2016EF000485, 2017. a
Alfieri, L., Cohen, S., Galantowicz, J., Schumann, G. J. P., Trigg, M. A., Zsoter, E., Prudhomme, C., Kruczkiewicz, A., Coughlan de Perez, E., Flamig, Z., Rudari, R., Wu, H., Adler, R. F., Brakenridge, R. G., Kettner, A., Weerts, A., Matgen, P., Islam, S. A. K. M., de Groeve, T., and Salamon, P.: A global network for operational flood risk reduction, Environ. Sci. Policy, 84, 149–158, https://doi.org/10.1016/j.envsci.2018.03.014, 2018. a, b
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Short summary
Local communities in northern Malawi have well-developed knowledge of the conditions leading to flash floods, spatially and temporally. Scientific analysis of catchment geomorphology and global reanalysis datasets corroborates this local knowledge, underlining the potential of these large-scale scientific datasets. Combining local knowledge with contemporary scientific datasets provides a common understanding of flash flood events, contributing to a more people-centred warning to flash floods.
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