Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model

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.

Spatiotemporal Dynamics of the Carbon Budget and the Response to Grazing in Qinghai Grasslands

“…The parameters used in this study were based on default values for C 3 grass (Hidy et al, 2016;You et al, 2019). Some parameters were corrected based on data collected from local investigations.…”

“…Observational data can then be used to validate the reliability of model outputs. The Biome-BGC model, which is a process-based model, has been used to study the carbon cycle in many regions such as the Qinghai-Tibet Plateau (Bond-Lamberty et al, 2007;Hidy et al, 2012;Mao et al, 2017;You et al, 2019). Hidy et al (2016) developed the Biome-BGCMuSo model, which is based on the Biome-BGC model.…”

Remote Sensing of Grassland Production and Management—A Review

“…Another LUE-based model used by the reviewed articles is the Vegetation Photosynthesis Model (VPM), e.g., [100], which is relatively similar to the CASA model, but differs in the approach of estimating the LUE [101]. Other process-based models, which were used to estimate productivity of grasslands with remote sensing data are the BIOME-BGC [102], C-Fix [103], DeNitrification-DeComposition (DNDC) [104], Global Production Efficiency Model (GLO-PEM) [105], Temperature and Greenness (TG) model [106], Greenness and Radiation (GR) model [106], Eddy Covariance-Light Use Efficiency (ECLUE) model [106], Vegetation Production and Respiration (VPRM) model [106] and the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model [107]. While comparing some of these models for estimating grassland productivity in China, Jia et al [106] found the LUE-based model ECLUE to perform best.…”

How Does Spring Phenology Respond to Climate Change in Ecologically Fragile Grassland? A Case Study from the Northeast Qinghai-Tibet Plateau

“…Moreover, in semiarid grasslands, precipitation largely controls the direction of phenological changes [67]; however, in meadow grasslands, precipitation is dominated by temperature, which indicates the variability among different geographical regions. To better represent the relationship between climate change and SOS, researchers have also used modeling and other means to estimate phenology [68]. However, due to the complex relationships between phenology and meteorological variables, there are still controversies among the results of various studies, and further research in this area is needed.…”