英国欧洲天气预报中心Nearing Grey团队报道了无资料流域极端洪水的全球预测。相关论文发表在2024年3月20日出版的《自然》杂志上。

研究表明基于人工智能，预测无资料流域的极端河流事件方面的可靠性，其提前期可达5天，与当前最先进的全球建模系统(哥白尼应急管理服务全球洪水预警系统)的临近预报(零日提前期)的可靠性相似或更好。此外，研究团队实现了五年重现期事件的准确性，与当前一年重现期事件的准确性相似或更好。

这意味着人工智能可以在无资料流域更早、更大、更有影响的事件中提供洪水预警。开发的模型被纳入一个可操作的预警系统，该系统在80多个国家提供公开(免费和开放)的实时预报。这项工作强调需要增加水文数据的可用性，以继续改善全球获得可靠的洪水预警的机会。

据了解，洪水是最常见的自然灾害之一。准确和及时的预警对于减轻洪水风险至关重要，但水文模拟模型通常需要根据每个流域的长期数据记录进行校准。

附：英文原文

Title: Global prediction of extreme floods in ungauged watersheds

Author: Nearing, Grey, Cohen, Deborah, Dube, Vusumuzi, Gauch, Martin, Gilon, Oren, Harrigan, Shaun, Hassidim, Avinatan, Klotz, Daniel, Kratzert, Frederik, Metzger, Asher, Nevo, Sella, Pappenberger, Florian, Prudhomme, Christel, Shalev, Guy, Shenzis, Shlomo, Tekalign, Tadele Yednkachw, Weitzner, Dana, Matias, Yossi

Issue&Volume: 2024-03-20

Abstract: 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 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.

DOI: 10.1038/s41586-024-07145-1

Source: https://www.nature.com/articles/s41586-024-07145-1