尽管地震等自然灾害可通过统一的全球传感器网络进行监测，但洪水等水文气象灾害却缺乏标准化的观测基础设施。长期以来，由于缺乏高质量、全球范围的历史数据用于模型训练与验证，突发性洪水的精准预测受到严重制约。这一‘数据荒漠’构成了关键挑战。尽管1万条记录看似数量可观，但与训练和验证全球规模人工智能所需的数据量相比，仍微不足道。

While some natural disasters, like seismic events, are tracked by unified global sensor networks, hydro-meteorological hazards like floods lack a standardized observation infrastructure. Accurate forecasting for flash floods has long been severely hampered by a lack of high-quality, global historical data for model training and validation. This “data desert” poses a critical challenge. While 10,000 records may seem substantial, they represent a drop in the bucket compared to the data needed to train and verify global-scale AI.