TY  - JOUR
PY  - 2024//
TI  - Global prediction of extreme floods in ungauged watersheds
JO  - Nature
A1  - Nearing, Grey
A1  - Cohen, Deborah
A1  - Dube, Vusumuzi
A1  - Gauch, Martin
A1  - Gilon, Oren
A1  - Harrigan, Shaun
A1  - Hassidim, Avinatan
A1  - Klotz, Daniel
A1  - Kratzert, Frederik
A1  - Metzger, Asher
A1  - Nevo, Sella
A1  - Pappenberger, Florian
A1  - Prudhomme, Christel
A1  - Shalev, Guy
A1  - Shenzis, Shlomo
A1  - Tekalign, Tadele Yednkachw
A1  - Weitzner, Dana
A1  - Matias, Yossi
SP  - 559
EP  - 563
VL  - 627
IS  - 8004
N2  - Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks(1). Accurate and timely warnings are critical for mitigating flood risks(2), 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0028-0836
UR  - http://dx.doi.org/10.1038/s41586-024-07145-1
ID  - ref1
ER  -