Introducing a dynamic photosynthetic model of photoinhibition, heat, and water stress in the next-generation land surface model ACASA

Abstract

More frequent, intense, and more extended droughts and heatwaves are challenging agricultural productivity and management. This study introduces a new modeling approach to represent the impact of heat and water stress on energy and mass fluxes from orchards. A photosynthesis model that explicitly accounts for dynamic responses of Rubisco and RuBP limitations to stress was linked to the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). ACASA is a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations to calculate plant-atmosphere exchanges of carbon dioxide, water, and heat. Field measurements of leaf area index, stomatal conductance, and photosynthesis were performed to estimate model parameters. Model results were compared to data from an eddy covariance flux tower deployed at an almond orchard. Overall, this new approach, ACASA-DynPM, is in closer agreement with observations of H, λE, and Fc compared to ACASA. On days of high temperatures and water stress, ACASA-DynPM outperforms ACASA. Thus, we argue that accounting for plant physiological stress might be especially relevant when estimating energy and mass fluxes over intensively irrigated agricultural regions. Such biases can have implications for the modeled degree of crop stress and water demands, and eventually, it can impact climate regional estimates when coupled with an atmospheric model.