AI Increases Global Access to Reliable Flood Forecasts

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 d...

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Bibliographic Details
Published inarXiv.org
Main Authors Nearing, Grey, Cohen, Deborah, Dube, Vusumuzi, Gauch, Martin, Gilon, Oren, Harrigan, Shaun, Hassidim, Avinatan, Klotz, Daniel, Kratzert, Frederik, Metzger, Asher, Sella Nevo, Pappenberger, Florian, Prudhomme, Christel, Shalev, Guy, Shenzis, Shlomo, Tekalign, Tadele, Weitzner, Dana, Yoss Matias
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.11.2023
Subjects
Accuracy
Artificial intelligence
Availability
Basins
Developing countries
Early warning systems
Emergency communications systems
Emergency management
Flood forecasting
Flood management
Flood warnings
Floods
Human influences
Hydrologic data
Hydrologic models
Hydrology
LDCs
Lead time
Natural disasters
Open data
Reliability
Simulation models
Watersheds
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Summary: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. Using AI, we achieve 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.
ISSN:2331-8422