A land data assimilation system for simultaneous simulation of soil moisture and vegetation dynamics

Despite the importance of the coupling between vegetation dynamics and root-zone soil moisture in land-atmosphere interactions, there is no land data assimilation system (LDAS) that currently addresses this issue, limiting the capacity to positively impact weather and seasonal forecasting. We develop a new LDAS that can improve the skill of an ecohydrological model to simulate simultaneously surface soil moisture, root-zone soil moisture, and vegetation dynamics by assimilating passive microwave observations that are sensitive to both surface soil moisture and terrestrial biomass. This LDAS first calibrates both hydrological and ecological parameters of a land surface model, which explicitly simulates vegetation growth and senescence. Then, it adjusts the model states of soil moisture and leaf area index (LAI) sequentially using a genetic particle filter. We can adjust the subsurface soil moisture, which is not observed directly by satellites, because we simulate the interactions between vegetation dynamics and subsurface water dynamics. From a point-scale evaluation, we succeed in improving the performance of our land surface model and generate ensembles of the model state whose distribution reflects the combined information in the land surface model and satellite observations. We show that the adjustment of the subsurface soil moisture significantly improves the capacity to simulate vegetation dynamics in seasonal forecast timescales. This LDAS can contribute to the generation of ensemble initial conditions of surface and subsurface soil moisture and LAI for a probabilistic framework of weather and seasonal forecasting.