构建欧洲5大流域高分辨率气象与地形数据集，支持神经网络精准模拟降雨径流。
Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling
- 将5个流域气象与地理数据统一到9km×9km网格，实现空间精细化。
- 覆盖1981年10月至2011年9月，含土壤、岩石、地表覆盖等多源信息。
- 适合做基于神经网络的分布式水文建模，尤其关注降雨-径流过程研究者。
本文提供一个用于降雨-径流建模的全空间分辨数据集，旨在推动神经网络驱动的水文建模从集中式流域走向分布式建模。数据涵盖中欧五个河域：多瑙河上游、易北河、奥得河、莱茵河和威悉河。包含气象强迫数据及土壤、岩石、土地利用和地形等辅助信息。数据统一为9km×9km规则网格，时间跨度为1981年10月至2011年9月，每日更新。我们还提供代码，可将本数据集与公开河流流量数据结合，实现端到端的降雨-径流建模。
原文摘要 · Abstract (English)
We present a dataset for rainfall streamflow modeling that is fully spatially resolved with the aim of taking neural network-driven hydrological modeling beyond lumped catchments. To this end, we compiled data covering five river basins in central Europe: upper Danube, Elbe, Oder, Rhine, and Weser. The dataset contains meteorological forcings, as well as ancillary information on soil, rock, land cover, and orography. The data is harmonized to a regular 9km times 9km grid and contains daily values that span from October 1981 to September 2011. We also provide code to further combine our dataset with publicly available river discharge data for end-to-end rainfall streamflow modeling.
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