Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-2007-2024
https://doi.org/10.5194/essd-16-2007-2024
Data description paper
 | 
29 Apr 2024
Data description paper |  | 29 Apr 2024

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung

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Cited articles

Baker, I. T., Denning, A. S., Dazlich, D. A., Harper, A. B., Branson, M. D., Randall, D. A., Phillips, M. C., Haynes, K. D., and Gallup, S. M.: Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest, J. Adv. Model. Earth Syst., 11, 2523–2546, https://doi.org/10.1029/2019ms001650, 2019.  
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Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., and Miralles, D. G.: MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles, B. Am. Meteorol. Soc., 103, E710–E732, https://doi.org/10.1175/BAMS-D-21-0145.1, 2022. 
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
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