EGU24-4435, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

AI Increases Global Access to Reliable Flood Forecasts

Grey Nearing1, Deborah Cohen1, Vusumuzi Dube1, Martin Gauch1, Oren Gilon1, Shaun Harrigan2, Avinatan Hassidim1, Daniel Klotz3, Frederik Kratzert1, Asher Metzger1, Sella Nevo4, Florian Pappenberger2, Christel Prudhomme2, Guy Shalev1, Shlomo Shenzis1, Tadele Tekalign1, Dana Weitzner1, and Yossi Matias1
Grey Nearing et al.
  • 1Google
  • 2European Centre for Medium-Range Weather Forecasts
  • 3Helmholtz Centre for Environmental Research - UFZ
  • 4RAND Corporation, work was done while at Google

Floods are one of the most common  natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that AI-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System). Additionally, we achieve accuracies over 5-year return period events that are similar to or better than current accuracies over 1-year return period events. This means that AI can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed in this paper 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.

Nearing, Grey, et al. "AI Increases Global Access to Reliable Flood Forecasts." arXiv preprint arXiv:2307.16104 (2023).

How to cite: Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T., Weitzner, D., and Matias, Y.: AI Increases Global Access to Reliable Flood Forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4435,, 2024.