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Floods are one of the most common natural disasters, with a disproportionateimpact in developing countries that often lack dense streamflow gauge networks.Accurate and timely warnings are critical for mitigating flood risks, buthydrological simulation models typically must be calibrated to long datarecords in each watershed. Using AI, we achieve reliability in predictingextreme riverine events in ungauged watersheds at up to a 5-day lead time thatis similar to or better than the reliability of nowcasts (0-day lead time) froma current state of the art global modeling system (the Copernicus EmergencyManagement Service Global Flood Awareness System). Additionally, we achieveaccuracies over 5-year return period events that are similar to or better thancurrent accuracies over 1-year return period events. This means that AI canprovide flood warnings earlier and over larger and more impactful events inungauged basins. The model developed in this paper was incorporated into anoperational early warning system that produces publicly available (free andopen) forecasts in real time in over 80 countries. This work highlights a needfor increasing the availability of hydrological data to continue to improveglobal access to reliable flood warnings.

AI Increases Global Access to Reliable Flood Forecasts