Evaluation and machine learning improvement of global flood simulations

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ADS bibcode
2019AGUFM.H33L2122Y
year
2019
Listed Authors
Yang, T.
Sun, F.
Gentine, P.
Liu, W.
Wang, H.
Yin, J.
Du, M.
Changming, L.
Listed Institutions
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Department of Earth and Environmental Engineering, Columbia University, New York, United States
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Earth Institute, Columbia University, New York, NY, United StatesAF: Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, ChinaAF: Department of Earth and Environmental Engineering, Columbia University, New York, United States
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

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