Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-1-2026
https://doi.org/10.5194/gmd-19-1-2026
Development and technical paper
 | 
05 Jan 2026
Development and technical paper |  | 05 Jan 2026

Representing dynamic grassland density in the land surface model ORCHIDEE r9010

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Data sets

MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500 m SIN Grid V061 R. Myneni et al. https://doi.org/10.5067/MODIS/MCD15A3H.061

Global Aridity Index and Potential Evapotranspiration (ET0) Database v3 (Global_AI_PET_v3) R. J. Zomer and A. Trabucco https://doi.org/10.6084/m9.figshare.7504448.v5

Fraction of Green Vegetation Cover 2014-present (raster 300 m), global, 10-daily - version 1 Copernicus Land Monitoring Service https://doi.org/10.2909/09578c73-4f5d-4d2c-90ff-4e17fb7dbf69

Model code and software

ORCHIDEE code for the submitted paper: Representing dynamic grass density in the land surface model ORCHIDEE r9010 Siqing Xu https://doi.org/10.5281/zenodo.15723740

Codes for figures in the submitted paper: Representing dynamic grass density in the land surface model ORCHIDEE r9010 Siqing Xu https://doi.org/10.5281/zenodo.15877635

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Short summary
Prescribing a fixed grassland density in the ORCHIDEE model limits its ability to capture grassland dynamics, leading to unrealistic mortality, especially in semi-arid grasslands. We proposed a dynamic density approach where a positive density-precipitation relationship emerges. This method improves spatial pattern, significantly reduces mortality, sustains productivity, and raises the aridity threshold above which frequent mortality events occur in grasslands.
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