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

Abstract

A B S T R A C TAccurate 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 (E PT ). A maximum light use efficiency (e*) of 1.8 gC MJ À1 and 2.76 gC MJ À1 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 m À2 day À1 , and bias = À0.05 gC m À2 day À1 . For comparison the MODIS GPP products (MOD17A2) had overall statistics of r = 0.73, RMSE = 2.82 gC m À2 day À1 , and bias = À0.31 gC m À2 day À1 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 AE 1.5 Pg C yr À1 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.

Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012

“…A vapor pressure deficit (VPD)‐based stressor was a widely criticized problem in the MOD17 algorithm and previous studies suggested that a soil moisture based water stress should be included in the algorithm [ Kanniah et al ., ; Leuning et al ., ; Mu et al ., ; Pan et al ., ; Yan et al ., ]. However, analyses have shown that models that do not directly consider soil moisture can still capture the impacts of drought on GPP [ He et al ., ; Wagle et al ., ].…”

Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States

“…Modeling evapotranspiration (ET), the combined loss of water from soil through evaporation and from vegetation through transpiration, has many applications in agricultural management [1,2], carbon sequestration [3,4], climate modeling [5], drought monitoring [6], energy security [7], hydrological modeling [8], irrigation scheduling [9], and watershed development [10,11]. Various models and techniques are used for accurately estimating ET at field to global scales.…”

Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China

“…As soil moisture and VPD directly affect photosynthesis, more GPP models explicitly consider the effect of soil moisture as well as temperature. The effect of water stress on ecosystem photosynthesis may be the most uncertain factor in current LUE GPP models (Yan et al, 2015). Also, model validation needs flux measurements from more sites encompassing various ecosystem types to test the applicability and accuracy, which can lay foundation for the carbon flux upscaling.…”

“…In addition to validation against measured GPP, the capacity of the CI-LUE model to simulate GPP was compared with GPP simulated by MOD17 model driven by in situ meteorological measurements (GPP _17 ), MODIS GPP products (GPP _MOD ), the VPM algorithm (Xiao et al, 2004), and the TEC model proposed by Yan et al (2015). The VPM model employed climate data, MODIS Land Surface Water Index (LSWI) and EVI to calculate 8-day GPP.…”