|Title||Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Yan H, Wang S-qiang, Billesbach D, Oechel W, Bohrer G, Meyers T, Martin TA, Matamala R, Phillips RP, Rahman F, Yu Q, Shugart HH|
|Pagination||42 - 59|
Accurate simulation of terrestrial gross primary production (GPP), the largest global carbon flux, benefits our understanding of carbon cycle and its source of variation. This paper presents a novel light use efficiency-based GPP model called the terrestrial ecosystem carbon flux model (TEC) driven by MODIS FPAR and climate data coupled with a precipitation-driven evapotranspiration (E) model (Yan et al., 2012). TEC incorporated a new water stress factor, defined as the ratio of actual E to Priestley and Taylor (1972) potential evaporation (EPT). A maximum light use efficiency (e*) of 1.8 gC MJ1 and 2.76 gC MJ1 was applied to C3 and C4 ecosystems, respectively. An evaluation at 18 eddy covariance flux towers representing various ecosystem types under various climates indicates that the TEC model predicted monthly average GPP for all sites with overall statistics of r = 0.85, RMSE = 2.20 gC m2 day1 , and bias = 0.05 gC m2 day1 . For comparison the MODIS GPP products (MOD17A2) had overall statistics of r = 0.73, RMSE = 2.82 gC m2 day1 , and bias = 0.31 gC m2 day1 for this same set of data. In this case, the TEC model performed better than MOD17A2 products, especially for C4 plants. We obtained an estimate of global mean annual GPP flux at 128.2 1.5 Pg C yr1 from monthly MODIS FPAR and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA reanalysis data at a 1.0 spatial resolution over 11 year period from 2000 to 2010. This falls in the range of published land GPP estimates that consider the effect of C4 and C3 species. The TEC model with its new definition of water stress factor and its parameterization of C4 and C3 plants should help better understand the coupled climate-carbon cycle processes.