A two-leaf daily GPP model based on a rectangular hyperbolic model adjusted for air temperature and vegetation type

An accurate and easy-to-use gross primary productivity (GPP) model is essential for studying the spatial and temporal dynamics of the terrestrial carbon cycle on a global scale. Light use efficiency (LUE) models and process-based models are the two most commonly used approaches for GPP modeling. While LUE models are simpler and more user-friendly, process-based models often achieve higher accuracy due to their detailed structure. In this study, we introduce a new two-leaf GPP model (TL-RHM) with two expression forms at a daily temporal resolution. The TL-RHM is developed by temporally integrating a modified rectangular hyperbolic model that incorporates the effects of temperature variations on GPP across various vegetation types. The performance of the TL-RHM is evaluated using data from 21 CO₂ eddy-covariance flux sites, covering four vegetation types: evergreen needleleaf forest, deciduous broadleaf forest, grassland, and evergreen broadleaf forest. The results demonstrate that the daily GPP simulated by the TL-RHM agrees well with the measured GPP for both calibration and validation datasets across all four vegetation types. These findings highlight the potential of the TL-RHM to accurately simulate daily GPP with a relatively simple model structure, offering a valuable tool for long time-series GPP simulations at regional or global scales.