Uncertainty analysis of intra-module environmental stress parameter design in light use efficiency-based gross primary productivity estimation models

Terrestrial gross primary productivity is a key part in the carbon cycle, and the light use efficiency model is a widely employed tool for its estimation. However, since the specific designs of environmental stress parameters in light use efficiency models have obvious disparities, even for a single module (e.g., the temperature stress module), the uncertainties related to intra-module environmental stress parameters in light use efficiency models remain unclear. Thus, we gathered mainstream temperature and water stress parameters in light use efficiency models from existing publications, and employed a compatible framework to assess them from three perspectives: (1) identifying similarities and differences of environmental stress parameters; (2) evaluating the error propagation effect of input data under different environmental stress parameter combinations; and (3) assessing the generalization ability of environmental stress parameters. The results showed that the temperature stress parameters exhibited general homogeneity (shared 67.87% variance), while water stress parameters displayed noticeable internal variations (shared variance below 1.0%). Meanwhile, we revealed that the current flexible parameter combination method in light use efficiency model construction should be more cautious, since the flexible environmental stress parameter combinations would influence the error propagation effect from input data to final gross primary productivity estimations. In addition, our analysis found that the variance of environmental stress parameters was closely coupled to model estimation accuracy, and there was a positive relationship between the unique variance of temperature stress parameters and the ability of temperature stress parameters to describe gross primary productivity variation. Overall, the current environmental stress parameters demonstrated acceptable performance across most situations, except for biomes with high temperatures. This study provided a foundation towards the development of future parameter design, which may also inspire the application of empirical environmental factors in other gross primary productivity models.