A better carbon-water flux simulation in multiple vegetation types by data assimilation

Background: The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change. Model simulations and observations have been widely used to research water and carbon cycles of terrestrial ecosystems. Given the advantages and limitations of each method, combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux simulation. However, to the best of our knowledge, few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation types. And little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation types. Methods: In this study, we assimilated leaf area index (LAI) time-series observations into a biogeochemical model (Biome-BGC) using different assimilation algorithms (ensemble Kalman filter algorithm (EnKF) and unscented Kalman filter (UKF)) in different vegetation types (deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF) and grassland (GL)) to simulate carbon-water flux. Results: The validation of the results against the eddy covariance measurements indicated that, overall, compared with the original simulation, assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations (R² increased by 35%, root mean square error decreased by 10%; the sum of the absolute error decreased by 8%) but more significantly, improved the water flux simulations (R² increased by 31%, root mean square error decreased by 18%; the sum of the absolute error decreased by 16%). Among the different forest types, the data assimilation techniques (both EnKF and UKF) achieved the best performance towards carbon-water flux in EBF (R² increased by 44%, root mean square error decreased by 24%; the sum of the absolute error decreased by 28%), and the performances of EnKF and UKF showed slightly different when simulating carbon fluxes. Conclusion: We suggest that to reduce the uncertainty in global carbon-water flux quantification, forthcoming data assimilation treatment should consider the vegetation types where the data assimilation experiments are carried out, the simulated objectives and the assimilation algorithms.