Considering water-temperature synergistic factors improves simulations of stomatal conductance models under plastic film mulching - Cheng Li; Yunxin Zhang; Jingui Wang; Hao Feng; Renyou Zhang; Wenxin Zhang; Kadambot H.M. Siddique (Agricultural Water Management)
Accurately simulating stomatal behavior is crucial for understanding water, carbon, and energy fluxes between land and atmosphere. Given the significant impact of plastic film mulching on water and temperature, it is essential to incorporate water and temperature modifications into stomatal conductance models under these conditions. In this study, we evaluated three commonly used stomatal conductance models: Ball-Woodrow-Berry (BWB), Ball-Berry-Leuning (BBL), and unified stomatal optimization (USO), to simulate the stomatal conductance of spring maize with or without mulching. We introduced modifications based on air temperature, canopy temperature, and water-temperature synergistic factors. Our results indicate that the USO model performed best, followed by the BBL and BWB models. Introducing temperature response functions improved simulation accuracy, with water-temperature synergistic models (-Tc&T) outperforming others. Models modified by canopy temperature (-Tc) outperformed those modified by air temperature (-Ta). Specifically, for the BWB model, the -Ta, -Tc, and -Tc&T modifications decreased root mean square error (RMSE) by 11.5–33.3 %, 19.2–50.6 %, and 29.5–56.7 %, respectively. For the BBL model, these reductions were 6.0–30.4 %, 20.9–48.1 %, and 25.4–52.9 %, respectively. For the USO model, the reductions were 7.9–55.2 %, 11.1–56.3 %, and 27.8–64.4 %, respectively. By comparing the simulated stomatal conductance curves with the 95 % confidence intervals (CI) of the observed data, we determined that the water-temperature synergistic model is optimal for various temperature conditions, followed by the Tc-modified and Ta-modified models. This study enhances our understanding of stomatal conductance under different temperature conditions and offers a foundation for accurately simulating carbon and water cycles in agricultural ecosystems under diverse water and temperature conditions.