Floods are one of the most common and impactful natural disasters, with a
disproportionate impact in developing countries that often lack dense
streamflow monitoring networks. Accurate and timely warnings are critical for
mitigating flood risks, but accurate hydrological simulation models typically
must be calibrated to long data records in each watershed where they are
applied. We developed an Artificial Intelligence (AI) model to predict extreme
hydrological events at timescales up to 7 days in advance. This model
significantly outperforms current state of the art global hydrology models (the
Copernicus Emergency Management Service Global Flood Awareness System) across
all continents, lead times, and return periods. AI is especially effective at
forecasting in ungauged basins, which is important because only a few percent
of the world's watersheds have stream gauges, with a disproportionate number of
ungauged basins in developing countries that are especially vulnerable to the
human impacts of flooding. We produce forecasts of extreme events in South
America and Africa that achieve reliability approaching the current state of
the art in Europe and North America, and we achieve reliability at between 4
and 6-day lead times that are similar to current state of the art nowcasts
(0-day lead time). Additionally, we achieve accuracies over 10-year return
period events that are similar to current accuracies over 2-year return period
events, meaning that AI can provide warnings earlier and over larger and more
impactful events. The model that we develop in this paper has been incorporated
into an operational early warning system that produces publicly available (free
and open) forecasts in real time in over 80 countries. This work using AI and
open data highlights a need for increasing the availability of hydrological
data to continue to improve global access to reliable flood warnings.

我们开发了一种基于人工智能的模型，能够提前七天预测极端水文事件，该模型在全球各大洲、各种提前期和重现期下均优于现有的全球水文模型，特别适用于没有测量的流域，该研究的成果已被纳入全球超过 80 个国家的运行中的预警系统，提供免费的实时预测。