地表径流
气候变化
环境科学
水文学(农业)
地理
自然地理学
地质学
生态学
岩土工程
海洋学
生物
作者
L. Zhong,Huimin Lei,Bing Gao
出处
期刊:CERN European Organization for Nuclear Research - Zenodo
日期:2023-05-25
被引量:1
标识
DOI:10.5281/zenodo.8041812
摘要
This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication. Please cite the paper as follows: Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118
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