Large scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate.

中文翻译：

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基于气候模型和机器学习构建的全球洪水风险建模框架

大规模洪水风险分析对于许多需要国家或国际洪水风险概览的应用来说都是至关重要的。尽管在这种规模上大规模的气候模式（例如远程连接和气候变化）变得很重要，但以与气候物理一致的方式来表示各个流域的局部水文循环仍然是一个挑战。结果，全球模型往往会缺乏可用的方案和灵活性，这对于计划者，救济组织，监管机构和金融服务行业分析影响暴露的社会经济，人口和气候因素至关重要。在此，我们为不一定需要高分辨率洪水映射的应用程序引入了一个数据驱动的，全球性的，快速的，灵活的，与气候一致的洪水风险建模框架。我们使用统计和机器学习方法来检查历史洪水发生与达特茅斯洪水观测站（1985-2017年）的影响以及全球4,734个HydroSHEDS流域的气候，分水岭和社会经济因素之间的关系。利用NCAR CESM大合唱团（1980-2020）的偏差校正后的输出，以及拟合的统计关系，我们模拟了全世界100万年的事件以及每个事件中流离失所的人口。我们讨论了该模型的潜在应用，并提供了全球洪水灾害和风险图。这种全球洪水模型的主要价值在于它能够以对大型社会经济和金融计划有用的分辨率快速模拟现实洪水事件，