Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems

Abstract

AimThe controls of gross radiation use efficiency (RUE), the ratio between gross primary productivity (GPP) and the radiation intercepted by terrestrial vegetation, and its spatial and temporal variation are not yet fully understood. Our objectives were to analyse and synthesize the spatial variability of GPP and the spatial and temporal variability of RUE and its climatic controls for a wide range of vegetation types.Location A global range of sites from tundra to rain forest. MethodsWe analysed a global dataset on photosynthetic uptake and climatic variables from 35 eddy covariance (EC) flux sites spanning between 100 and 2200 mm mean annual rainfall and between -13 and 26°C mean annual temperature. RUE was calculated from the data provided by EC flux sites and remote sensing (MODIS).Results Rainfall and actual evapotranspiration (AET) positively influenced the spatial variation of annual GPP, whereas temperature only influenced the GPP of forests. Annual and maximum RUE were also positively controlled primarily by annual rainfall. The main control parameters of the growth season variation of gross RUE varied for each ecosystem type. Overall, the ratio between actual and potential evapotranspiration and a surrogate for the energy balance explained a greater proportion of the seasonal variation of RUE than the vapour pressure deficit (VPD), AET and precipitation. Temperature was important for determining the intra-annual variability of the RUE at the coldest energy-limited sites.Main conclusions Our analysis supports the idea that the annual functioning of vegetation that is adapted to its local environment is more constrained by water availability than by temperature. The spatial variability of annual and maximum RUE can be largely explained by annual precipitation, more than by vegetation type. The intra-annual variation of RUE was mainly linked to the energy balance and water availability along the climatic gradient. Furthermore, we showed that intra-annual variation of gross RUE is only weakly influenced by VPD and temperature, contrary to what is frequently assumed. Our results provide a better understanding of the spatial and temporal controls of the RUE and thus could lead to a better estimation of ecosystem carbon fixation and better modelling.

Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model

“…For example, the slope coefficient in the Ball-Berry model (Equation (2)) was estimated as 7.5 to 10.3, consistent with the values in Community Land Model(CLM)4.0 [43]. The estimated maximum light-use efficiency values (0.0011 mg·CO 2 ·µmol −1 PPFD for forest and grassland and 0.0022 mg·CO 2 ·µmol −1 PPFD for cropland) are located in the middle range of ε max variations in the meta-analyses of Kergoat et al [44] and Garbulsky et al [45]. Note that we derived ε max using the NDVI data to estimate FPAR (Equation (3)).…”

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

“…In many cases, a simple APAR green -based model based may be insufficient, particularly for evergreen ecosystems where dynamic light-use efficiency can be an important determinant of CO 2 fluxes and may have to be included in the model [35,74,76]. Additionally, the exact definition and parameterization of the LUE model may itself affect observations regarding light-use efficiency.…”

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

“…Bandaruet al (2013) found that modeled NPP in Illinois and Iowa were 2.4 and 1.1 times greater than the MODIS GPP/NPP product for corn and soybean, respectively. However, model evaluation did not identify significant biases in other biomes (Sjöström et al, 2013;Turner et al, 2006), which implies that the differences between field and satellite LUE estimates are the most pronounced in croplands (Garbulsky et al, 2010).…”