| Description |
Modeling leaf gas exchange through the stomata is key to modeling the terrestrial carbon and water fluxes. However, there has been great uncertainty in the land surface models that tried to predict future carbon and water cycles due to the variability in (1) the environment and thus the plants' response to it and (2) plant functional traits both spatially and temporally. Previous attempts that have been devoted to address these issues showed great power in fitting existing data but little potential in predicting the future because the fitted parameters (1) have not been trained at novel environment and (2) lack physiological identities and thus have little potential to track the traits shift with space and time. These deficits, however, can be addressed by the optimality theory. The benefit of gas exchange through the leaf can be quantified by the instantaneous photosynthesis, and the risks associated potentially lie in damage to hydraulic transport and reduction of water supply. Mechanistically modeling the gain-risk optimization with plant functional traits allows for predicting the future stomatal behavior with confidence. Further, the embedded plant traits makes it possible to account for the trait variations. This dissertation consists of works that aim to improve the modeling of leaf gas exchange by incorporating the gain versus risk optimality theory based on plant traits. The chapters include (1) a model that addresses the CO2 andH2Odiffusion in anatomical scale to improve the calculation of carbon gain-photosynthesis, (2) a review that quantifies the sources for carbon risks in leaf gas exchange to guide how to define the risk, (3) an experimental verification of a trait-based gain versus risk model performance by examining whole tree level gas exchange in a growth chamber, and (4) a further development of the trait-based model that provides insights on how to track the trait changes spatially and temporally. Together these works have helped advance the modeling of gas exchange from the leaf level to ecosystem level. |
