Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO₂ Concentration Data With the Global Carbon Assimilation System (GCAS)

Abstract

The Global Carbon Assimilation System that assimilates ground‐based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C ( Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle‐high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

Evaluation of satellite precipitation products using HEC-HMS model

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Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

“…BEPS is in development for the last years [53]. Although BEPS was initially developed for boreal ecosystems, it was expanded and used for temperate and tropical ecosystems in regional and global scales [48,51,52,[54][55][56][57][58][59] and for the estimation of winter wheat yield in China [18], as C3 plants share the same photosynthesis theory at the leaf level, i.e., Farquhar's leaf-level biochemical model [60], and BEPS has a "two-leaf" approach to upscale the ecosystem GPP to canopy level for various ecosystems including crops [49,61].…”

Distinct Impacts of El Niño-Southern Oscillation and Indian Ocean Dipole on China’s Gross Primary Production

“…The simulation of China's GPP by BEPS is subject to several sources of uncertainty inherent in the model's structure, parameterizations, processes, and input data (Chen et al, 2012;Chen et al, 2017;He et al, 2021a;Liu et al, 2018;Wang et al, 2021a). Leaf Area Index (LAI), a crucial input for the BEPS model, is derived from global remote sensing data that inherently possess uncertainties in spatial distribution and trend changes.…”