Increasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Hydrologic information that is required for understanding flood processes and for developing effective warning procedures is currently lacking in most parts of the world. Procedures that can combine global climate dataset from satellite and reanalysis with fast and low computational cost prediction systems, are attractive solutions for addressing flood predictions in ungauged areas. This work develops and tests a prediction framework that relies on two fundamental components. First, meteorological data from global datasets (IMERG and ERA5-Land) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Catchments in Australia, Brazil, Chile, Switzerland, and Great Britain were used as pseudo-ungauged regions for testing. Results indicate acceptable performance for both IMERG and ERA5-Land forced models with relative difference in flood peak prediction within 30 % and similar overall performance to locally trained ML models. Specific climate regions for which ML models have revealed good performance include Mediterranean climates like the US West Coast, subtropical areas like the Southern Atlantic Gulf, and mild temperate regions like the Mid-Atlantic Basin. This work highlights the potential of combining global precipitation dataset with pre-trained ML models in data-rich areas, for flood prediction in ungauged areas with similar climate.

中文翻译：

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结合全球降水数据和机器学习来预测气候相似的未测量地区的洪峰


城市化和气候变化导致的洪水风险增加，对全球社会构成了重大挑战。目前，世界大部分地区都缺乏了解洪水过程和制定有效预警程序所需的水文信息。可以将来自卫星和再分析的全球气候数据集与快速和低计算成本预测系统相结合的程序，是解决未测量地区洪水预测的有吸引力的解决方案。这项工作开发并测试了一个依赖于两个基本组成部分的预测框架。首先，来自全球数据集（IMERG 和 ERA5-Land）的气象数据提供了关键的输入变量，其次，在数据丰富的美国本土训练的 ML 模型应用于世界其他地区气候相似的地区。澳大利亚、巴西、智利、瑞士和英国的集水区被用作伪无测量区域进行测试。结果表明，IMERG 和 ERA5-Land 强制模型的性能都是可接受的，洪峰预测的相对差异在 30% 以内，并且整体性能与本地训练的 ML 模型相似。ML 模型显示表现良好的特定气候区域包括地中海气候（如美国西海岸）、亚热带地区（如南大西洋湾）和温和气候地区（如中大西洋盆地）。这项工作强调了在数据丰富的地区将全球降水数据集与预先训练的 ML 模型相结合的潜力，以便在具有相似气候的未测量地区进行洪水预测。