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.

翻译：洪水是最常见且影响最严重的自然灾害之一，对发展中国家造成的冲击尤为显著，这些国家通常缺乏密集的径流监测网络。准确及时的预警对于降低洪水风险至关重要，但精准的水文模拟模型通常需要在每个应用流域中基于长期数据记录进行校准。我们开发了一种人工智能（AI）模型，能够提前7天预测极端水文事件。该模型在所有大洲、不同预警时间和重现期下的表现均显著优于当前最先进的全球水文模型（哥白尼应急管理服务全球洪水预警系统）。AI在无测站流域的预测中尤为有效，这之所以重要，是因为全球仅有少数流域设有水文测站，而无测站流域不成比例地集中于发展中国家，这些国家尤其易受洪水对人类生活的影响。我们在南美洲和非洲对极端事件发布的预报，其可靠性已接近当前欧洲和北美洲的先进水平，且我们在4至6天的预警时间内实现的可靠性，与当前最先进的临近预报（0天预警时间）相当。此外，我们对10年重现期事件的预报精度，与当前对2年重现期事件的精度相当，这意味着AI能够更早地发出预警，并覆盖更大范围、更具影响力的洪水事件。本文开发的模型已被整合到一个业务化预警系统中，该系统实时向80多个国家提供公开（免费且开放）的预报。这项工作利用人工智能和开放数据，凸显了增进水文数据可用性的必要性，以持续提升全球获取可靠洪水预警的能力。