Vegetation models simulate functions of plants such as carbon and water exchanges between the atmosphere and land surface, and play an essential role in climate simulations. However, the vegetation models have large uncertainties. For example, it is very difficult to simulate accurately the dates when the leaves grow and fall. Data assimilation combines the simulation with actual observations, and can reduce those uncertainties.
We developed a data assimilation system with a vegetation model known as the SEIB-DGVM (Spatially Explicit Individual Base Dynamic Global Vegetation Model). As the first step, an experiment was performed at a single location in Eastern Siberia by assimilating satellite-observed leaf area data. The results showed that the growth and decay of the leaves, carbon cycles and the parameters of the tree and grass were successfully estimated. As the second step, the experiment was expanded to multiple locations in Eastern Siberia. The results were promising.