Abstract
The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the ecophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m2/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales.
Highlights
Gross Primary Productivity (GPP), defined as the total amount of carbon fixed by vegetation through photosynthesis, is one of the most important components of the terrestrial carbon cycle [1,2]
In view of the limitations of the Biome-BGC model when applied to alpine regions, we have prescribed its phenological states based upon the remotely sensed phenology and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP)
We first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model, and we combined the global sensitivity analysis method with the simulated annealing algorithm to calibrate the model parameters
Summary
Gross Primary Productivity (GPP), defined as the total amount of carbon fixed by vegetation through photosynthesis, is one of the most important components of the terrestrial carbon cycle [1,2]. Due to the positive feedbacks associated with the melting of snow and ice, the warming rate on the TP is expected to be much higher than other regions over China [5,6]. Such a unique geographical environment makes the circulation of carbon on the TP different from other regions. Accurate estimation of GPP on the TP and continuous monitoring of its spatial and temporal dynamics are needed to enhance our understanding of terrestrial ecosystems and global carbon cycling
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