Global prediction of extreme floods in ungauged watersheds

Grey Nearing (), Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo, Florian Pappenberger, Christel Prudhomme, Guy Shalev, Shlomo Shenzis, Tadele Yednkachw Tekalign, Dana Weitzner and Yossi Matias
Additional contact information
Grey Nearing: Google
Deborah Cohen: Google
Vusumuzi Dube: Google
Martin Gauch: Google
Oren Gilon: Google
Shaun Harrigan: European Centre for Medium-Range Weather Forecasts
Avinatan Hassidim: Google
Daniel Klotz: Helmholtz Centre for Environmental Research - UFZ
Frederik Kratzert: Google
Asher Metzger: Google
Sella Nevo: RAND Corporation
Florian Pappenberger: European Centre for Medium-Range Weather Forecasts
Christel Prudhomme: European Centre for Medium-Range Weather Forecasts
Guy Shalev: Google
Shlomo Shenzis: Google
Tadele Yednkachw Tekalign: Google
Dana Weitzner: Google
Yossi Matias: Google

Nature, 2024, vol. 627, issue 8004, 559-563

Abstract: Abstract Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Date: 2024
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DOI: 10.1038/s41586-024-07145-1

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