Enhancing the water use efficiency model predictions for Platycladus orientalis and Quercus variabilis: Integrating the dynamics of carbon dioxide concentration and soil water availability.

Water use efficiency (WUE) is a tracer for plants on the trade-off exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere; therefore, a dynamic investigation of WUE and its driving factors will be of great significance to optimize water and carbon fitness and predict the plants' response to climate change. In our study, a modified water use efficiency model was proposed to improve the quantification of carbon and water processes by adding a photosynthesis-gs simulation dependent on CO2 concentration and soil moisture to the photosynthetic transpiration model (noted as SMPTSB model). Actual measured water use efficiencies were respectively obtained by the gas exchange measurements (WUEge) and the δ13CWSC that defined as the carbon-heavy isotope of the water-soluble compound in leaves (WUEwsc) of three-year tree saplings of Platycladus orientalis (L.) Franco and Quercus variabilis Blume, which were cultured in an orthogonal treatment consisting of four ambient CO2 concentrations ([CO2]) and five soil volumetric water contents (SWC). Direct comparisons of the modeled and measured stomatal conductance and WUE further indicated that the modified WUE model makes carbon assimilation, stomatal conductance and WUE more sensitive to [CO2] and soil moisture. From this, the enhancement of WUE in P. orientalis and Q. variabilis saplings is expected to occur when the ambient CO2 concentration increases to 600 ppm - 700 ppm and the appropriate SWC reaches 60 % to 80 % of the field capacity for potted soil. In general, the water use efficiency model that accounts for the synergistic effects of environmental CO2 concentration and soil moisture can accurately identify the corresponding thresholds for the optimal efficiency of carbon and water use of vegetation, which is expected to provide a theoretical basis for predicting the corresponding forest management practices to address future climate change.