Comparison of four light use efficiency models for estimating terrestrial gross primary production

Abstract

a b s t r a c tLight use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPD s ). We suggested that a modified VPM by using minimum method and adding VPD s may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPD s only is optimal.

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

“…For example, we recently compared four widely-used light-use efficiency-based GPP models. The results indicated that most models yielded poorer estimates of GPP in grassland than other ecosystem types [31].…”

“…Uncertainties may also be incurred by the inaccurate estimate of light-use efficiency. Many efforts have been made to solve this issue, such as improving drought constraint [51], accounting for diffusive radiation [57,58] and updating the empirical values in the equations of environmental constraints (e.g., T min , T opt , T max , VPD max ) in the GPP model [31]. Therefore, improving GPP estimation would be anticipated with the outcome of these efforts, which thus will be a benefit for improving ET modeling with the SWH model.…”

“…We compared most schemes and their combinations to yield the solution used in this study, which has a relatively high accuracy and is convenient for use at the regional scale [31].…”

Optimization of forest age-dependent light-use efficiency and its implications on climate-vegetation interactions in china

“…Carnegie-Ames-Stanford Approach (CASA) model (Potter et al, 1993) can estimate monthly NPP with satellite data, monthly temperature, precipitation and soil properties Zhang et al, 2015). The CASA model can simulate the spatial distribution and the variation of NPP on a regional scale and it has been widely used to monitor the NPP for various spatial scales.…”

Estimation of the net primary productivity of forests on age in China

“…Carnegie-Ames-Stanford Approach (CASA) model [11] can estimate monthly NPP with satellite data, monthly temperature, precipitation and soil properties [12].…”