In the previous study, Arakida et al. (2017) developed a data assimilation system with the SEIB-DGVM (Spatially Explicit Individual-Based Dynamic Global Vegetation Model), and assimilated satellite-observed Leaf Area Index (LAI) successfully over Siberia. In this study, we extend the experiment to a Japanese deciduous broad-leaved forest at the Takayama flux site to investigate the performance of the DA system with another plant functional type. For the climate forcing data, we used the global reanalysis dataset known as the ERA5. LAI stays at the saturated level for an extended period in Japan, while in Siberia, the LAI starts to drop soon after it reaches to the maximum value. Therefore, we modified the DA system to estimate the additional model parameter which controls the saturated LAI level. In addition, the undergrowth was changed from deciduous to Kumaizasa bamboo (evergreen). The results show that the DA system performs generally well in Japan with the additional parameter estimation and the modification to the undergrowth.

Arakida, H., T. Miyoshi, T. Ise, S.-I. Shima, and S. Kotsuki (2017), Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model, Nonlinear Proc. Geoph., 24, 553-567, doi:10.5194/npg-24-553-2017.